CN115395521A - Renewable energy, energy storage and charging pile collaborative planning method and system - Google Patents

Renewable energy, energy storage and charging pile collaborative planning method and system Download PDF

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CN115395521A
CN115395521A CN202211306868.5A CN202211306868A CN115395521A CN 115395521 A CN115395521 A CN 115395521A CN 202211306868 A CN202211306868 A CN 202211306868A CN 115395521 A CN115395521 A CN 115395521A
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electric vehicle
energy storage
representing
planning
transformer
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CN115395521B (en
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李磊
李晓辉
张剑
刘伟东
刘小琛
梁彬
张卫欣
李丹
谢秦
王浩柱
白银明
杨景禄
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Marketing Service Center of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Marketing Service Center of State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Abstract

The embodiment of the invention discloses a renewable energy, energy storage and charging pile collaborative planning method and a system, wherein the method comprises the following steps: establishing a random scene generation framework; predicting the load demand and the load acceleration in a year of a planning cycle of a planning area, and dividing the planning cycle into a plurality of division stages A according to the prediction result; based on the maximum load demand predicted value of each division stage, a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage is constructed by respectively taking the minimum expected total cost of power distribution network planning as a target in each division stage, and the optimal solution of the power distribution network collaborative planning model is solved based on the constraint conditions of the power distribution network collaborative planning model. In the invention, the uncertainty output of new energy and the time-space large-range transfer of the electric automobile are considered, and a random planning model based on a scene is established to describe the uncertainty of energy cost, wind power, photovoltaic output and the charging requirement of the electric automobile.

Description

Renewable energy, energy storage and charging pile collaborative planning method and system
Technical Field
The embodiment of the invention relates to the technical field of urban distribution network collaborative planning, in particular to a renewable energy, energy storage and charging pile collaborative planning method and system.
Background
With the problem of carbon pollution becoming more serious day by day, renewable energy power generation and smart transportation become one of the key elements of urban power distribution network construction and development, electric vehicles are increasingly integrated into a power system through charging facilities, a large amount of uncertain charging demands are generated, and together with uncertainty caused by renewable energy, the electric vehicles form a significant challenge to a new power distribution system; moreover, a large amount of novel power equipment such as distributed power sources, energy storage devices and electric vehicles are connected to the power distribution network side, so that the traditional power distribution network structure is changed greatly, and the original planning technology is difficult to adapt to the requirements of the power distribution network on indexes such as power supply safety and stability. Therefore, based on the large-scale variable load influence, the urban distribution network collaborative planning method and system considering the uncertainty fluctuation of new energy and traditional load and the large-scale time/space transfer of the electric vehicle are provided.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a renewable energy, energy storage and charging pile collaborative planning method and system aiming at the defects of the prior art, an electric vehicle charging demand model under a random scene is established, and the optimal planning of a power distribution system is solved, wherein the optimal planning comprises new and replaced feeder lines, transformer substations, transformers, renewable energy power generation sites, energy storage equipment, electric vehicle charging stations and extra capacity in the sites.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows: a renewable energy, energy storage and charging pile collaborative planning method comprises the following steps:
establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability;
predicting the load demand and the load acceleration in a year of a planning cycle of a planning area, and dividing the planning cycle into a plurality of division stages A according to the prediction result;
constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage based on the maximum load demand predicted value of each division stage and respectively taking the minimum expected total cost of power distribution network planning as a target in each division stage, wherein the expected total cost of power distribution network planning is minimized through the random scene generation framework;
and solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage.
Further, comprising:
calculating the charging demand of the electric vehicle based on the statistical data of the electric vehicle;
the statistical data of the electric vehicle comprises: home data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point.
Further, the calculating the electric vehicle charging demand based on the statistical data of the electric vehicle comprises:
for each electric vehicle, randomly distributing battery capacity, and setting each electric vehicle to be fully charged at the beginning of the first trip;
setting the minimum value of the charging demand of each electric vehicle as R% of the battery capacity of the electric vehicle i;
for one electric vehicle i, a journey T is respectively allocated to a certain month m and a certain day d;
for the electric vehicle i, checking the state of charge SOCi of the battery of the electric vehicle i when the travel T is completed;
when the state of charge SOCi of the battery of the electric vehicle i does not reach the minimum value of the charging requirement, searching the next stroke of the data set, and calculating and updating the charging requirement;
when the electric vehicle i reaches the minimum value of the charging requirement, charging the battery before the travel T and at the time interval when the electric vehicle i stops;
checking the state of charge SOCi of the battery again;
when the electric vehicle i still does not reach the minimum value of the charging requirement, the travel is eliminated and the next travel is searched;
after the last travel, the electric vehicle repeatedly runs by taking the day, the month and the electric vehicle as scales, and the charging requirement of the electric vehicle is calculated
Figure 809902DEST_PATH_IMAGE001
。
Further, the establishment of the random scene generation framework for expressing the load demand, the electric vehicle charging demand, the wind speed, the solar irradiation and the random variability of the substation energy cost comprises the following steps:
acquiring small-scale historical data;
dividing the small-scale historical data, and constructing a scene set of each stage based on a K-CFSFDP algorithm, wherein the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters comprise: load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost;
selecting the number K of the required clusters according to the requirement;
calculating weighted Euclidean distance of scene set data by using entropy method to obtain distance matrix d ij And for said distance matrix d ij The elements are arranged in an ascending order to obtain an ascending sequence, and the distance value of the front 2 percent of the ascending sequence is taken as a truncation distance delta i (ii) a Computing local density rho from a Gaussian kernel function i Establishing a consideration of i And ρ i Is a comprehensive index of i For the said comprehensive index γ i Performing descending order arrangement to obtain a descending order sequence, and selecting the first K data points of the descending order sequence as clustering centroids Ci;
calculating the distance Di (x) from each hour-level historical data to each clustering centroid Ci by using Euclidean distance, wherein D represents the shortest distance from one data point to the nearest particle, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each hour-level historical data;
recalculating the clustering centroid Ci to become the centroids of all the points in the clustering centroid Ci:
and iteratively calculating clustering centroids Ci until cluster composition is unchanged between two continuous iterations, and outputting a matrix consisting of the clustering centroids, working conditions and uncertain parameters, wherein each clustering centroid is represented by numerical values of load requirements, electric vehicle charging requirements, wind speed, solar irradiation and energy cost of a transformer substation, and the numerical values are used for displaying the operating condition of the system.
Further, the dividing the hour-level historical data includes:
dividing the hour-level historical data into 4 seasons of spring, summer, autumn and winter, and dividing the data into day/night blocks, namely a day block and a night block, in each season according to the actual sunrise and sunset time each day;
the K-FSFDP algorithm is performed 8 times per quarter and day/night block, i.e., 4 quarter by 2 day/night blocks;
the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertain parameters, where the 96 operating conditions comprise 12 arrays of each quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observed value within each cluster divided by the total observed value for the respective day/night block b and quarter q.
Furthermore, the load demand and the load acceleration in the planning cycle of the planning area within a year are predicted, and the planning cycle is divided into a plurality of division stages A according to the prediction result:
the planning period a year is less than the service life of the equipment with the shortest service life in the power distribution network;
in the first load acceleration period, the number of the division stages A is Ai, the duration is Ti, in the second load acceleration period, the number of the division stages A is Aj, the duration is Tj, wherein the first load acceleration is smaller than the second load acceleration, ai is smaller than Aj, and Ti is larger than Tj.
Further, the building of the power distribution network collaborative planning model considering the electric vehicle charging station and the renewable energy power generation and energy storage comprises the following steps:
according to the division of the planning period, recording a multi-stage planning sequence of the power distribution network in the planning area as S = [ S ] 1 ，S 2 ，…，S A ]Wherein S is 1 Denotes the first stage, S 2 Denotes the second stage, S A Represents the A stage; the sequence of the construction scheme corresponding to each stage is Eset = [ Eset = [) 1 ，Eset 2 ，…，Eset A ]Of which Eset 1 Denotes S 1 The capacities and installation points, eset, of the feeders, the substations, the additional transformers, the renewable energy power stations, the energy storage systems and the electric vehicle charging stations configured in the planning stage 2 Denotes S 2 The capacities and installation points, eset, of the feeders, the substations, the additional transformers, the renewable energy power stations, the energy storage systems and the electric vehicle charging stations configured in the planning stage A Denotes S A The capacities and installation points of a feeder line, a transformer substation, an additional transformer, a renewable energy power station, an energy storage system and an electric vehicle charging station configured in the planning stage;
minimizing the expected total cost of power distribution network planning includes: the investment costs are the present value of annual amortization and the operational costs over the entire time period of the asset, namely:
Figure 195884DEST_PATH_IMAGE002
；（1）
in the formula (1), t represents a time interval label, I represents an annual investment rate,
Figure 772359DEST_PATH_IMAGE003
in order to reduce the investment cost,
Figure 508847DEST_PATH_IMAGE004
in order to achieve the cost of maintenance,
Figure 792060DEST_PATH_IMAGE005
in order to achieve the aim of reducing the production cost,
Figure 207998DEST_PATH_IMAGE006
for cost of energy loss, E seti Denotes S i And the capacities and installation points of the feeder line, the transformer substation, the additional transformer, the renewable energy power station, the energy storage system and the electric vehicle charging station configured in the planning stage.
Further, the investment cost is as follows:
Figure 209452DEST_PATH_IMAGE007
；（2）
the maintenance cost
Figure 752560DEST_PATH_IMAGE008
Comprises the following steps:
Figure 890280DEST_PATH_IMAGE009
；（3）
said production cost
Figure 477120DEST_PATH_IMAGE010
Comprises the following steps:
Figure 778919DEST_PATH_IMAGE011
；（4）
cost of energy loss
Figure 984772DEST_PATH_IMAGE012
Comprises the following steps:
Figure 101633DEST_PATH_IMAGE013
；（5）
in the formulae (2) to (5), the investment cost
Figure 319DEST_PATH_IMAGE014
Among the system constant parameters considered are: k represents a branch line, a transformer, a Distributed Generation (DG), an Energy Storage System (ESS) and an Electric Vehicle Charging Station (EVCS) option label; t denotes a period index; i, j respectively representDifferent node labels; ch is an EVCS label of the electric vehicle charging station; l represents a feeder type designation; p denotes a distributed power supply DG type index; q represents a quarterly index; tr denotes a transformer number; b represents day/night block number; ω denotes a scene number; b represents a day/night block set; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; k NT ，K l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; l represents a feeder type set, L = { EFF, ERF, NRF, NAF }, EFF, ERF, NRF, and NAF represent an existing fixed feeder, an existing replaceable feeder, a new replacement feeder, and a newly added feeder, respectively; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; q represents a quarterly set; t represents the set of all discrete control periods; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; omega LN ，Ω SS ，Ω p ，Ω ST ，Ω ch Respectively a load node set, a transformer substation node set, a Distributed Generation (DG) candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; II represents a scene set; upsilon-upsilon l Representing a branch set with an l-type feeder;
investment cost
Figure 851731DEST_PATH_IMAGE015
Among the investment and equipment constant parameters considered:
Figure 595696DEST_PATH_IMAGE016
，
Figure 832643DEST_PATH_IMAGE017
，
Figure 636651DEST_PATH_IMAGE018
，
Figure 730288DEST_PATH_IMAGE019
，
Figure 605840DEST_PATH_IMAGE020
，
Figure 572659DEST_PATH_IMAGE021
respectively representing the investment costs of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 688514DEST_PATH_IMAGE022
，
Figure 107994DEST_PATH_IMAGE023
，
Figure 849554DEST_PATH_IMAGE024
，
Figure 670879DEST_PATH_IMAGE025
，
Figure 957635DEST_PATH_IMAGE026
respectively representing the maintenance costs of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 192307DEST_PATH_IMAGE027
，
Figure 81766DEST_PATH_IMAGE028
，
Figure 898543DEST_PATH_IMAGE029
，
Figure 480834DEST_PATH_IMAGE030
respectively representing the energy cost purchased at a transformer substation, the production cost of a distributed power supply DG, the production cost of an energy storage ESS and the storage cost of the energy storage ESS;
Figure 265120DEST_PATH_IMAGE031
，
Figure 958269DEST_PATH_IMAGE032
，
Figure 361044DEST_PATH_IMAGE033
respectively representing the maximum capacity of a p-type generator, the maximum capacity of an energy storage ESS and the maximum capacity of an electric vehicle charging station EVCS; i represents annual investment rate; \8467 ij Representing the feeder length; pf is the system power factor; RR is the asset recovery rate;
Figure 114236DEST_PATH_IMAGE034
for feeder asset recovery;
Figure 323501DEST_PATH_IMAGE035
in order to realize the recovery rate of the assets of the transformer substation,
Figure 695708DEST_PATH_IMAGE036
in order to increase the asset recovery rate of the transformer,
Figure 346132DEST_PATH_IMAGE037
for distributed generation DG asset recovery,
Figure 411171DEST_PATH_IMAGE038
in order to achieve an energy storage ESS asset recovery rate,
Figure 904469DEST_PATH_IMAGE039
for electric vehicle charging station EVCS asset recovery,
Figure 205000DEST_PATH_IMAGE040
、
Figure 850877DEST_PATH_IMAGE041
the single impedance amplitude of the l-shaped feeder line and the impedance amplitude of the transformer are respectively set;
Figure 680292DEST_PATH_IMAGE042
is the scene weight;
Figure 660887DEST_PATH_IMAGE043
the duration of block b is quarterly q days/nights in hours;
investment cost
Figure 499530DEST_PATH_IMAGE044
Among the decision variables considered:
Figure 934666DEST_PATH_IMAGE045
、
Figure 325196DEST_PATH_IMAGE046
respectively representing the current measured by the node i through the alternative k of the feeder type l installed in the branch ij, in the scenario ω of time period t, day/night block b, quarter q, if the node i is a supply point, it is greater than 0, otherwise it is 0;
Figure 402873DEST_PATH_IMAGE047
、
Figure 920573DEST_PATH_IMAGE048
、
Figure 665675DEST_PATH_IMAGE049
、
Figure 227107DEST_PATH_IMAGE050
respectively representing distributed power supply power, transformer power, energy storage ESS power generation power and energy storage ESS power;
Figure 792080DEST_PATH_IMAGE051
，
Figure 51154DEST_PATH_IMAGE052
，
Figure 509818DEST_PATH_IMAGE053
，
Figure 117516DEST_PATH_IMAGE054
，
Figure 45152DEST_PATH_IMAGE055
，
Figure 29289DEST_PATH_IMAGE056
respectively representing the binary variables of the investment transformer substation, the newly-added transformer, the feeder line, the distributed power supply DG, the energy storage ESS and the electric vehicle charging station EVCS;
Figure 608038DEST_PATH_IMAGE057
，
Figure 386638DEST_PATH_IMAGE058
，
Figure 1902DEST_PATH_IMAGE059
，
Figure 648784DEST_PATH_IMAGE060
，
Figure 957406DEST_PATH_IMAGE061
，
Figure 782274DEST_PATH_IMAGE062
binary variables representing the use of transformers, substations, newly added transformers, distributed generators DG, energy storage ESS and electric vehicle charging stations EVCS,
Figure 543556DEST_PATH_IMAGE063
，
Figure 259708DEST_PATH_IMAGE064
binary variables representing feeder type i in branch ij, branch ji, respectively, are used.
Further, the constraint conditions of the power grid collaborative planning model comprise system operation constraint, ESS cell operation constraint, investment and equipment use constraint and electric vehicle demand constraint.
Further, the system operation constraints comprise kirchhoff voltage, current conservation constraints and upper and lower limits of voltage, current and power;
kirchhoff voltage and current conservation constraints are as follows:
Figure 422836DEST_PATH_IMAGE065
；（6）
Figure 418605DEST_PATH_IMAGE066
；（7）
in the formulae (6) to (7),
Figure 932763DEST_PATH_IMAGE045
、
Figure 124710DEST_PATH_IMAGE046
respectively representing the current measured by node i through alternative k of feeder type i installed in branch ij, branch ji in scenario ω of time period t, day/night block b, quarter q;
Figure 17711DEST_PATH_IMAGE067
、
Figure 574594DEST_PATH_IMAGE068
、
Figure 435103DEST_PATH_IMAGE069
、
Figure 102845DEST_PATH_IMAGE070
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 113002DEST_PATH_IMAGE071
representing node load;
Figure 840786DEST_PATH_IMAGE072
representing the charging requirement of the node electric vehicle;
Figure 486793DEST_PATH_IMAGE073
a binary variable representing the feeder;
Figure 692647DEST_PATH_IMAGE074
representing a single impedance magnitude of the l-type feeder;ℓ ij representing the feeder length;
Figure 75087DEST_PATH_IMAGE075
、
Figure 973772DEST_PATH_IMAGE076
representing the voltages at nodes i, j, respectively;
l represents a feeder type designation; l represents a feeder type set; k represents a branch line, a transformer, a Distributed Generation (DG), an Energy Storage System (ESS) and an Electric Vehicle Charging Station (EVCS) option label; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; tr denotes a transformer number; TR represents a set of transformer types; omega N Representing a set of system nodes; t represents a set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; k l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 291097DEST_PATH_IMAGE077
representing a set of nodes connected to feeder l; p denotes a distributed generator DG type designation; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
the upper and lower limits of voltage, current and power are:
Figure 300641DEST_PATH_IMAGE078
；（8）
Figure 740850DEST_PATH_IMAGE079
；（9）
Figure 420224DEST_PATH_IMAGE080
；（10）
Figure 617987DEST_PATH_IMAGE081
；（11）
Figure 290277DEST_PATH_IMAGE082
；（12）
in the formulae (8) to (12),
Figure 522675DEST_PATH_IMAGE083
represents the node voltage;
Figure 372950DEST_PATH_IMAGE084
represents the current measured by node i through alternative k of feeder type i installed in branch ij in scenario ω of time period t, day/night block b, quarter q;
Figure 58010DEST_PATH_IMAGE067
、
Figure 533990DEST_PATH_IMAGE085
、
Figure 433944DEST_PATH_IMAGE069
、
Figure 845334DEST_PATH_IMAGE086
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 876744DEST_PATH_IMAGE087
，
Figure 766203DEST_PATH_IMAGE088
，
Figure 580050DEST_PATH_IMAGE089
binary variables representing the use of feeders, transformers, DG;
Figure 162341DEST_PATH_IMAGE090
representing the upper current limit of the feeder;
Figure 681047DEST_PATH_IMAGE091
representing the upper power limit of the transformer;
Figure 374197DEST_PATH_IMAGE092
representing the maximum power utilization of the p-type generator;εrepresents the power generation permeability;
Figure 45481DEST_PATH_IMAGE093
representing node load;
Figure 126569DEST_PATH_IMAGE094
representing the charging requirement of the node electric vehicle; omega N ，Ω SS ，Ω p ，Ω ST Respectively representing a system node set, a transformer substation node set, a distributed power supply DG candidate node set and an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; t represents the set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; k p Represents a set of alternatives for the distributed power supply DG;
Figure 7938DEST_PATH_IMAGE095
、
Figure 114565DEST_PATH_IMAGE096
respectively representing a lower limit and an upper limit of the node voltage;
Figure 764989DEST_PATH_IMAGE097
representing a set of nodes connected to feeder l; k NT Representing a set of alternatives for newly adding transformers; l represents a feeder type designation; l represents a feeder type set; tr denotes a transformer number; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
ESS cell constraints include:
Figure 813717DEST_PATH_IMAGE098
（13）
Figure 447960DEST_PATH_IMAGE099
（14）
Figure 358279DEST_PATH_IMAGE100
； （15）
in the formulae (13) to (15),
Figure 863209DEST_PATH_IMAGE101
、
Figure 286101DEST_PATH_IMAGE102
respectively representing the upper limit and the lower limit of the power of the energy storage ESS;
Figure 14498DEST_PATH_IMAGE103
、
Figure 587561DEST_PATH_IMAGE104
、
Figure 337212DEST_PATH_IMAGE105
binary variables representing energy storage ESS usage, production and storage, respectively;
Figure 603108DEST_PATH_IMAGE106
、
Figure 821731DEST_PATH_IMAGE107
respectively representing ESS power generation power and ESS energy storage power; i represents a node number; omega ST Representing an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k ST Representing a set of energy storage ESS alternatives; t denotes a period index; t represents the set of all discrete control periods; q represents a quarterly index; q represents a quarterly set; b represents day/night block number; b represents a day/night block set; ω denotes a scene number; II represents a scene set;
equipment investment and usage constraints include:
Figure 464065DEST_PATH_IMAGE108
；（16）
Figure 68221DEST_PATH_IMAGE109
；（17）
Figure 505019DEST_PATH_IMAGE110
；（18）
Figure 148621DEST_PATH_IMAGE111
；（19）
Figure 188121DEST_PATH_IMAGE112
；（20）
Figure 787730DEST_PATH_IMAGE113
；（21）
Figure 536374DEST_PATH_IMAGE114
；（22）
Figure 588644DEST_PATH_IMAGE115
；（23）
in the formulae (16) to (23),
Figure 431835DEST_PATH_IMAGE116
，
Figure 885950DEST_PATH_IMAGE117
，
Figure 763686DEST_PATH_IMAGE118
，
Figure 427886DEST_PATH_IMAGE119
，
Figure 950134DEST_PATH_IMAGE120
，
Figure 134122DEST_PATH_IMAGE121
respectively representing binary variables of an investment substation, a newly-added transformer, a feeder line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; t denotes a period index; t represents a set of all discrete control periods; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; i and j respectively represent different node labels;
Figure 83623DEST_PATH_IMAGE122
representing different sets of nodes; l represents a feeder type designation; NRF and NAF represent the existing new replacement feeder and the newly added feeder, respectively; omega SS ，Ω p ，Ω ST ，Ω ch Respectively a transformer substation node set, a distributed power supply DG candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
Figure 235119DEST_PATH_IMAGE123
a binary variable representing an electric vehicle charging station EVCS with extra capacity,
Figure 561058DEST_PATH_IMAGE124
a binary variable representing the electric vehicle charging station EVCS with the newly added capacity,
Figure 599552DEST_PATH_IMAGE125
representing a set of electric vehicle charging station EVCS candidate nodes with additional capacity,
Figure 719955DEST_PATH_IMAGE126
representing a set of alternatives, K, for an electric vehicle charging station EVCS with extra capacity NT ，K l ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; ch is an EVCS mark of the electric vehicle charging station; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
the electric vehicle demand constraints include:
Figure 358747DEST_PATH_IMAGE127
（24）
Figure 301426DEST_PATH_IMAGE128
；（25）
in the formulae (24) to (25),
Figure 319061DEST_PATH_IMAGE072
representing the charging requirement of the node electric vehicle;
Figure 578DEST_PATH_IMAGE129
represents the upper power limit of the electric vehicle charging station EVCS,
Figure 736453DEST_PATH_IMAGE062
a binary variable representing the use of the electric vehicle charging station EVCS;
Figure 276631DEST_PATH_IMAGE130
representing the total electric vehicle charging requirement of the system; omega ch Representing a set of EVCS candidate nodes of an electric vehicle charging station; ch is an EVCS mark of the electric vehicle charging station; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; i represents a node number; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k ch Represents a set of alternatives for an electric vehicle charging station EVCS; t denotes a period index; t represents a set of all discrete control periods; b represents day/night block number; b represents a day/night block set; q represents a quarterly index; q represents a quarterly set; ω denotes a scene number; Π represents a set of scenes.
The invention also provides a system for collaborative planning of renewable energy, energy storage and charging pile, comprising:
the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability;
the planning system comprises a dividing unit, a planning unit and a planning unit, wherein the dividing unit is used for predicting the load demand and the load acceleration in a planning cycle a year of a planning area and dividing the planning cycle into a plurality of dividing stages A according to the prediction result;
the construction unit is used for constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage based on the maximum load demand predicted value of each division stage and respectively aiming at the minimum expected total cost of power distribution network planning in each division stage, wherein the expected total cost of power distribution network planning is minimized through the random scene generation framework;
and the solving unit is used for solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage.
Further, the establishing unit includes:
the calculating module is used for calculating the charging requirement of the electric vehicle based on the statistical data of the electric vehicle;
the statistical data of the electric vehicle comprises: home data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point.
Further, the calculation module includes:
a first allocation sub-module for randomly allocating, for each electric vehicle, a battery capacity assuming that all electric vehicles are fully charged at the start of a first trip;
a second allocating submodule, configured to allocate a trip T to one of the electric vehicles i, a month m, and a day d, respectively;
the checking submodule is used for checking the charging state SOCi of the battery of the electric vehicle i when the travel T is finished for the electric vehicle i;
the setting submodule is used for setting the minimum value of the charging requirement to be R% of the battery capacity of the electric vehicle i;
the searching submodule is used for searching the next stroke of the data set when the electric vehicle i does not reach the minimum value of the charging requirement, and calculating and updating the charging requirement;
the charging submodule is used for charging the battery at the time interval when the electric vehicle i stops before the travel T when the electric vehicle i reaches the minimum value of the charging requirement;
the checking submodule is also used for checking the state of charge SOCi of the battery again;
the searching submodule is also used for excluding the travel and searching the next travel when the electric vehicle i still does not reach the minimum value of the charging requirement;
the electric vehicle demand calculation submodule is used for repeatedly operating on the scale of days, months and vehicles after the last trip, and calculating to obtain the electric vehicle demand
Figure 414351DEST_PATH_IMAGE131
。
Further, the establishing unit includes:
the acquisition module is used for acquiring the small-scale historical data;
the data dividing module is used for dividing the small-level historical data;
the scene set constructing module is used for constructing a scene set of each stage based on a K-CFSFDP algorithm, the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters comprise: load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost;
the selecting module is used for selecting the number K of the required clusters according to the requirement;
a clustering centroid calculation module, which calculates weighted Euclidean distances of scene set data by using an entropy method to obtain a distance matrix d ij And for the distance matrix d ij The elements are arranged in an ascending order to obtain an ascending sequence, and the distance value of the front 2 percent of the ascending sequence is taken as a truncation distance delta i (ii) a Computing local density rho from a Gaussian kernel function i Establishing a consideration of i And ρ i Comprehensive index gamma of i For the said comprehensive index γ i Performing descending order arrangement to obtain a descending order sequence, and selecting the first K data points of the descending order sequence as clustering centroids Ci;
the distance calculation module is used for calculating the distance Di (x) from each hour-level historical data to each clustering centroid Ci by using Euclidean distance, D represents the shortest distance from one data point to the nearest particle, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each hour-level historical data;
the cluster centroid calculation module is further configured to recalculate the cluster centroids Ci to be centroids of all the points in the cluster centroids Ci:
the iteration calculation module is used for iteratively calculating the clustering mass center Ci until the cluster composition is not changed between two continuous iterations;
and the output module is used for outputting clustering mass centers and a matrix formed by working conditions and uncertain parameters, wherein each clustering mass center is represented by numerical values of load requirements, electric vehicle charging requirements, wind speed, solar irradiation and transformer substation energy cost, and the numerical values are used for displaying the operating condition of the system.
Further, the data partitioning module includes:
the data division submodule is used for dividing the hour-level historical data into 4 seasons of spring, summer, autumn and winter, and in each season, the hour-level historical data are divided into day/night blocks, namely a day block and a night block, according to the actual sunrise and sunset time every day;
the scene set building module comprises:
the execution submodule carries out the K-FSFDP algorithm and executes 8 times in each quarter and day/night block, namely 4 quarters multiplied by 2 day/night blocks;
the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertainty parameters, where the 96 operating conditions include 12 arrays per quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observations within each cluster divided by the total observations for the respective day/night block b and quarter q.
Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of considering the coupling relation between an urban traffic network and an urban power distribution network, constructing an extended plan of a power distribution system, introducing power flow constraints, voltage constraints and construction economic cost constraints of the power distribution network, and solving an optimal investment combination decision under the influence of new energy and electric vehicles, wherein the extended plan comprises new and replaced feeder lines, transformer substations, additional transformers, renewable energy power stations, energy storage systems, electric vehicle charging stations and additional capacities in the stations; and in addition, the uncertainty output of new energy and the time-space large-range transfer of the electric automobile are considered, a scene-based random planning model is established to describe the uncertainty of energy cost, wind power, photovoltaic output and the charging requirement of the electric automobile, the integration of energy storage into an investment planning model under the condition that the permeability of renewable energy is continuously improved is prospectively emphasized, and the influence of the charging requirement of the electric automobile on the planning of a power distribution network under the uncertainty is further emphasized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
Fig. 1 is a flow chart of a method for collaborative planning of renewable energy, energy storage, and charging piles according to the present invention;
FIG. 2 is a flowchart of electric vehicle charging demand calculation in the renewable energy, energy storage and charging pile collaborative planning method of the present invention;
FIG. 3 is a schematic diagram of one of the investment planning scenarios of the 54-node network of the present invention;
fig. 4 is a schematic structural diagram of a renewable energy, energy storage and charging pile collaborative planning system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, the invention provides a renewable energy, energy storage and charging pile collaborative planning method, which includes:
modeling the charging requirement of the electric automobile; specifically, calculating the total charging demand of the electric vehicle based on the statistical data of the electric vehicle; wherein, the statistical data of the electric vehicle includes: family data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point; illustratively, the family data includes the number of vehicles owned by each family, electric vehicle proportion, vehicle usage frequency; the vehicle types refer to types of electric vehicles, including a plug-in electric vehicle BEV, a gasoline-electric hybrid electric vehicle HEV, a plug-in hybrid electric vehicle PHEV, an extended range electric vehicle EREV, and a fuel electric vehicle FCV.
As shown in fig. 2, specifically, for each electric vehicle, the battery capacity is randomly allocated, assuming that all electric vehicles are fully charged at the start of the first trip; illustratively, the randomly assigned battery capacities are 24kWh,30kWh,36kWh for each vehicle.
For one electric vehicle i, allocating a journey T for a certain month m and a certain day d respectively;
for electric vehicle i, checking the state of charge SOCi of the battery of electric vehicle i upon completion of journey T;
setting the minimum value of the charging demand as R% of the battery capacity of the electric vehicle i; illustratively, if R% is 20%, setting the minimum value of the SOC to be charged as 20% of the battery capacity, and if a certain electric automobile does not reach the minimum value of the SOC to be charged, searching the next journey of the data set by the algorithm, and calculating and updating the charging requirement;
when the electric vehicle i does not reach the minimum value of the charging requirement, searching the next stroke of the data set, and calculating and updating the charging requirement;
when the electric vehicle i reaches the minimum value of the charging requirement, the battery is charged at the time interval when the electric vehicle i stops before the travel T;
checking the state of charge SOCi of the battery again;
when the electric vehicle i still does not reach the minimum value of the charging requirement, the travel is eliminated and the next travel is searched; specifically, if the SOC reaches a minimum value, it is assumed that the vehicle is ahead of the trip T, at the vehicleiThe time interval between stops (between strokes T-1 and T) charges the battery. Then, the SOCi of the battery was checked again. If the minimum value is still reached, the trip is excluded, and the algorithm searches for the next trip;
after the last journey, repeatedly operating on the scale of days, months and vehicles, and calculating to obtain the requirement of the electric vehicle
Figure 266770DEST_PATH_IMAGE132
。
Establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability; specifically, a random scene generation framework based on K-CFSFDP is established: establishing a random scene generation framework considering the relevance among initial uncertain data according to the hour-level historical data, wherein the random scene generation framework is used for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability, reducing clustering errors caused by the difference degree among the data by applying a K-CFSFDP fusion clustering algorithm based on information entropy, and ensuring the relevance among the initial uncertain data, wherein the load requirements express the load requirements of all substations except the electric vehicle charging requirements;
further, acquiring small-scale historical data;
dividing the small-scale historical data, and constructing a scene set of each stage based on a K-CFSFDP algorithm, wherein the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters comprise: load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost; specifically, the hour-level historical data is divided into 4 seasons of spring, summer, autumn and winter, and in each season, the data is divided into day/night blocks, namely a day block and a night block, according to the actual sunrise and sunset time each day; the K-FSFDP algorithm is performed 8 times per quarter and day/night block, i.e., 4 quarter by 2 day/night blocks;
the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertainty parameters, where the 96 operating conditions include 12 arrays per quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observations within each cluster divided by the total observations for the respective day/night block b and quarter q. Illustratively, the historical data is divided into four seasons of spring, summer, autumn, and winter, and each season is divided into daytime blocks in general according to the actual sunrise and sunset times of each day [6:00 to 18:00] and black night block [18: 00-day 6:00], then the K-CFSFDP algorithm is executed 8 times per quarterly and day/night data block, so there are 4 quarterly and 2 day/night data blocks.
Selecting the number K of the required clusters according to the requirement;
selecting clustersHeart: calculating weighted Euclidean distance of scene set data by using entropy method to obtain distance matrix d ij And the elements are arranged in ascending order, and the distance value of the first 2% position of the sequence is taken as the truncation distance delta i (ii) a Calculating local density rho according to Gaussian kernel function i Establishing a consideration of i And ρ i Is a comprehensive index of i Selecting gamma i Taking the first K data points of the descending sequence as a clustering mass center Ci;
calculating the distance Di (x) from each sample (hour-level historical data) to each clustering centroid Ci by using Euclidean distance, wherein D represents the shortest distance from one data point to the nearest mass point, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each other;
recalculating the clustered centroid Ci to become the centroid of all points in the clustered centroid Ci, which is equal to
Figure 224361DEST_PATH_IMAGE133
；
And iteratively calculating clustering centroids Ci until cluster composition is unchanged between two continuous iterations, and outputting a matrix consisting of the clustering centroids, working conditions and uncertain parameters, wherein each clustering centroid is represented by numerical values of load requirements, electric vehicle charging requirements, wind speed, solar irradiation and energy cost of a transformer substation, and the numerical values are used for displaying the operating condition of the system. Illustratively, the cluster center point is output along with an array of 96 values x 5 parameters, i.e., the number of historical data observations assigned to each cluster. Each cluster centroid is represented by values for energy cost, wind speed, solar radiation, load and electric vehicle charging demand purchased at the substation, which show the operating conditions of the system. For each day/night block b and quarter q, the probability for each case is determined by the observations within each cluster divided by the total observations for the respective block b and quarter q.
Predicting the load demand and the load acceleration in a year of a planning cycle of a planning area, and dividing the planning cycle into a plurality of division stages A according to the prediction result; specifically, the future total load and acceleration of the planned area are predicted based on the economic development level and the infrastructure construction progress of the planned area, and the whole planning cycle is divided into a plurality of stages according to the prediction result. Specifically, the planning period a year is less than the service life of the equipment with the shortest service life in the power distribution network; in the first load acceleration period, the number of the division stages A is Ai, the duration is Ti, in the second load acceleration period, the number of the division stages A is Aj, the duration is Tj, wherein the first load acceleration is smaller than the second load acceleration, ai is smaller than Aj, and Ti is larger than Tj. It should be noted that the planning cycle division principle considering the dynamic change of the load is as follows: supposing that the planning total cycle of the urban distribution network to be built is a years, and in order to avoid the situation that the equipment needs to be replaced due to insufficient service life, a is less than the service life of the equipment with the shortest service life in the distribution network. And predicting the total load and the acceleration according to the infrastructure construction progress of a year a in the future of the planning area, wherein the number of the divided stages A is small and the duration is long in the period of small load increase according to the load increase amplitude, and the number of the divided stages A is large and the duration is short in the period of large load increase.
On the basis of the maximum load demand predicted value of each division stage, constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage by taking the minimum planned total cost of the power distribution network as a target in each division stage, wherein the planned total cost of the power distribution network is minimized through the random scene generation framework; specifically, based on the maximum load demand predicted value of each stage, the urban distribution network collaborative planning optimization model considering renewable energy power generation, energy storage and electric vehicle charging stations is constructed by taking the minimum expected total cost of distribution network planning as a target in each stage, and the expected total cost of the distribution network is minimized by using scene-based stochastic planning. Specifically, the urban distribution network collaborative planning optimization model considering renewable energy power generation, energy storage and electric vehicle charging stations is as follows: according to the division of the planning period, recording a multi-stage planning sequence of the power distribution network in the planning area as S = [ S ] 1 ，S 2 ，…，S A ]Wherein S is 1 Denotes the first stage, S 2 Denotes the second stage, S A Represents the A stage; the construction scheme sequence corresponding to each stage is E set =[E set1 ，E set2 ，…，E setA ]Wherein E is set1 Denotes S 1 Capacity and installation point of feeder line, transformer substation, additional transformer, renewable energy power station, energy storage system and electric vehicle charging station configured in planning stage, E set2 Denotes S 2 Capacities and installation points of feeders, substations, additional transformers, renewable energy power stations, energy storage systems and electric vehicle charging stations configured in the planning stage, E setA Denotes S A The capacities and installation points of a feeder line, a transformer substation, an additional transformer, a renewable energy power station, an energy storage system and an electric vehicle charging station configured in the planning stage; minimizing the expected total cost of power distribution network planning includes: the investment costs are the present value of annual amortization and the operational costs over the entire time period of the asset, namely:
Figure 774422DEST_PATH_IMAGE134
；（1）
in the formula (1), t represents a time interval label, I represents an annual investment rate,
Figure 891283DEST_PATH_IMAGE135
in order to achieve the purpose of investment cost,
Figure 524390DEST_PATH_IMAGE136
in order to achieve the cost of maintenance,
Figure 110223DEST_PATH_IMAGE137
in order to achieve the aim of reducing the production cost,
Figure 119767DEST_PATH_IMAGE138
for cost of energy loss, E seti Denotes S i And the capacities and installation points of the feeder line, the transformer substation, the additional transformer, the renewable energy power station, the energy storage system and the electric vehicle charging station configured in the planning stage.
Illustratively, the investment cost is:
Figure 356713DEST_PATH_IMAGE139
；（2）
said maintenance cost
Figure 426301DEST_PATH_IMAGE008
Comprises the following steps:
Figure 437113DEST_PATH_IMAGE009
；（3）
said production cost
Figure 374982DEST_PATH_IMAGE010
Comprises the following steps:
Figure 341801DEST_PATH_IMAGE140
；（4）
cost of energy loss
Figure 454726DEST_PATH_IMAGE012
Comprises the following steps:
Figure 139785DEST_PATH_IMAGE013
；（5）
in the formulae (2) to (5), the investment cost
Figure 615766DEST_PATH_IMAGE141
Among the system constant parameters considered are: k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; t denotes a period index; i and j respectively represent different node labels; ch is an EVCS label of the electric vehicle charging station; l represents a feeder type designation; p denotes a distributed generator DG type designation; q represents a quarterly index; tr denotes a transformer number; b represents a day/night block number; ω denotes a scene number; b represents a day/night block set; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; k is NT ，K l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; l represents a feeder type set, L = { EFF, ERF, NRF, NAF }, EFF, ERF, NRF, and NAF represent an existing fixed feeder, an existing replaceable feeder, a new replacement feeder, and a newly added feeder, respectively; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; q represents a quarterly set; t represents the set of all discrete control periods; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; omega LN ，Ω SS ，Ω p ，Ω ST ，Ω ch Respectively a load node set, a transformer substation node set, a Distributed Generation (DG) candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; II represents a scene set; gamma ray l Representing a branch set with an l-type feeder line;
investment cost
Figure 437092DEST_PATH_IMAGE015
Among the investment and equipment constant parameters considered:
Figure 458268DEST_PATH_IMAGE016
，
Figure 692941DEST_PATH_IMAGE017
，
Figure 847978DEST_PATH_IMAGE018
，
Figure 399177DEST_PATH_IMAGE019
，
Figure 247047DEST_PATH_IMAGE020
，
Figure 765753DEST_PATH_IMAGE021
respectively represents feeder line, transformer substation, newly-added transformer, distributed Generation (DG), energy Storage System (ESS) and electric vehicle chargingInvestment cost of station EVCS;
Figure 458902DEST_PATH_IMAGE022
，
Figure 130186DEST_PATH_IMAGE023
，
Figure 211275DEST_PATH_IMAGE024
，
Figure 827064DEST_PATH_IMAGE025
，
Figure 196341DEST_PATH_IMAGE026
respectively representing the maintenance costs of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 846765DEST_PATH_IMAGE027
，
Figure 161072DEST_PATH_IMAGE028
，
Figure 529736DEST_PATH_IMAGE029
，
Figure 440054DEST_PATH_IMAGE030
respectively representing the cost of energy purchased at a transformer substation, the production cost of a distributed generation DG, the production cost of an energy storage ESS and the storage cost of the energy storage ESS;
Figure 272881DEST_PATH_IMAGE031
，
Figure 367876DEST_PATH_IMAGE032
，
Figure 833624DEST_PATH_IMAGE033
respectively representing the maximum capacity of a p-type generator, the maximum capacity of an energy storage ESS and the maximum capacity of an electric vehicle charging station EVCS; i represents annual investment rate; \8467 ij Representing the feeder length; pf is the system power factor; RR is the asset recovery rate;
Figure 672267DEST_PATH_IMAGE034
the feed line asset recovery rate is obtained;
Figure 421917DEST_PATH_IMAGE035
in order to realize the recovery rate of the assets of the transformer substation,
Figure 953392DEST_PATH_IMAGE036
in order to increase the asset recovery rate of the transformer,
Figure 906436DEST_PATH_IMAGE037
for distributed generation DG asset recovery,
Figure 283191DEST_PATH_IMAGE038
in order to provide an energy storage ESS asset recovery rate,
Figure 887347DEST_PATH_IMAGE039
for electric vehicle charging station EVCS asset recovery,
Figure 324145DEST_PATH_IMAGE040
、
Figure 964817DEST_PATH_IMAGE041
the single impedance amplitude of the l-shaped feeder line and the impedance amplitude of the transformer are respectively set;
Figure 269897DEST_PATH_IMAGE042
is the scene weight;
Figure 869505DEST_PATH_IMAGE043
duration of block b, quarterly q days/nights in hours;
investment cost
Figure 352570DEST_PATH_IMAGE044
Among the decision variables considered: :
Figure 139261DEST_PATH_IMAGE045
、
Figure 513610DEST_PATH_IMAGE046
respectively representing the current measured by the node i through an alternative k of the feeder type l installed in the branch ij, the branch ji in a scenario ω of time period t, day/night block b, quarter q; if node i is a supply point, it is greater than 0, otherwise it is 0;
Figure 967725DEST_PATH_IMAGE047
、
Figure 621692DEST_PATH_IMAGE048
、
Figure 895678DEST_PATH_IMAGE049
、
Figure 745823DEST_PATH_IMAGE050
respectively representing distributed power supply power, transformer power, energy storage ESS power generation power and energy storage ESS power;
Figure 929810DEST_PATH_IMAGE051
，
Figure 879312DEST_PATH_IMAGE052
，
Figure 30808DEST_PATH_IMAGE053
，
Figure 356747DEST_PATH_IMAGE054
，
Figure 657890DEST_PATH_IMAGE055
，
Figure 778293DEST_PATH_IMAGE056
respectively representing the binary variables of the investment transformer substation, the newly-added transformer, the feeder line, the distributed power supply DG, the energy storage ESS and the electric vehicle charging station EVCS;
Figure 354768DEST_PATH_IMAGE057
，
Figure 94185DEST_PATH_IMAGE058
，
Figure 377399DEST_PATH_IMAGE059
，
Figure 403124DEST_PATH_IMAGE060
，
Figure 14365DEST_PATH_IMAGE061
，
Figure 682106DEST_PATH_IMAGE062
binary variables representing the use of transformers, substations, newly added transformers, distributed Generators (DG), energy storage ESS and electric vehicle charging stations EVCS,
Figure 944461DEST_PATH_IMAGE063
，
Figure 406666DEST_PATH_IMAGE064
binary variables representing feeder type i in branch ij, branch ji, respectively, are used.
And solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage. Specifically, a constraint condition set of the urban distribution network collaborative planning optimization model is constructed, constraint conditions including kirchhoff's law, operation limits and the like related to actual operation of the system are formulated, the optimal solution of the urban distribution network collaborative planning optimization model is solved under the constraint conditions, the optimal line topology, equipment combination, capacity configuration and site selection scheme of each stage are obtained, and the optimal planning scheme is evaluated and analyzed. To evaluate the behavior of the proposed model, planning is performed for a period of 15 years, one phase every three years. Consider a 54-node network containing 4 substation nodes, 50 load nodes, 8 wind power plants, 9 energy storage systems, 8 photovoltaic power plants, 5 electric vehicle charging stations, and 63 branch lines. By utilizing the urban distribution network collaborative planning optimization model, the optimal investment scheme of feeder lines, transformer substations, transformers, energy storage systems and renewable energy power generation (wind energy and photovoltaic) is solved, as shown in fig. 3. The optimal investment planning construction scheme within 15 years is given in table 1, and the investment operation cost within 15 years is given in table 2.
TABLE 1 investment planning construction plan (distribution node position) in total time interval
Figure 705536DEST_PATH_IMAGE142
TABLE 2 investment planning investment and operating costs over the total period of time (10) 6 USD）
Figure 301602DEST_PATH_IMAGE144
The constraint conditions of the power grid collaborative planning model comprise system operation constraint, ESS cell operation constraint, investment and equipment use constraint and electric vehicle demand constraint. The system operation constraints comprise kirchhoff voltage and current conservation constraints and upper and lower limits of voltage, current and power;
kirchhoff voltage and current conservation constraints are as follows:
Figure 293829DEST_PATH_IMAGE065
；（6）
Figure 802302DEST_PATH_IMAGE066
；（7）
in the formulae (6) to (7),
Figure 778348DEST_PATH_IMAGE045
、
Figure 912526DEST_PATH_IMAGE046
respectively, in time t, day/night block b, seasonUnder the scene omega of the degree q, the current measured by the node i passes through an alternative scheme k of a feeder type l installed in a branch ij and a branch ji;
Figure 759259DEST_PATH_IMAGE067
、
Figure 704213DEST_PATH_IMAGE068
、
Figure 964293DEST_PATH_IMAGE069
、
Figure 777528DEST_PATH_IMAGE070
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 354134DEST_PATH_IMAGE071
representing node load;
Figure 594622DEST_PATH_IMAGE072
representing the charging requirement of the node electric vehicle;
Figure 404316DEST_PATH_IMAGE073
a binary variable representing the feeder;
Figure 431413DEST_PATH_IMAGE074
representing a single impedance magnitude for a type i feeder;ℓ ij representing the feeder length;
Figure 125175DEST_PATH_IMAGE075
、
Figure 661199DEST_PATH_IMAGE076
respectively representing the voltages at nodes i, j;
l represents a feeder type designation; l represents a feeder type set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; bA day/night block number; ω denotes a scene number; tr denotes a transformer number; TR represents a set of transformer types; omega N Representing a set of system nodes; t represents a set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; k l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 567975DEST_PATH_IMAGE145
representing a set of nodes connected to feeder l; p denotes a distributed generator DG type designation; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
the upper and lower limits of voltage, current and power are:
Figure 598379DEST_PATH_IMAGE078
；（8）
Figure 602107DEST_PATH_IMAGE079
；（9）
Figure 184398DEST_PATH_IMAGE080
；（10）
Figure 453837DEST_PATH_IMAGE081
；（11）
Figure 412565DEST_PATH_IMAGE082
；（12）
in the formulae (8) to (12),
Figure 333117DEST_PATH_IMAGE083
represents the node voltage;
Figure 820730DEST_PATH_IMAGE084
represents the current measured by node i through alternative k of feeder type i installed in branch ij in scenario ω of time period t, day/night block b, quarter q;
Figure 574535DEST_PATH_IMAGE067
、
Figure 399271DEST_PATH_IMAGE085
、
Figure 49695DEST_PATH_IMAGE069
、
Figure 849155DEST_PATH_IMAGE086
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 952241DEST_PATH_IMAGE087
，
Figure 111826DEST_PATH_IMAGE088
，
Figure 147916DEST_PATH_IMAGE089
binary variables representing the use of feeders, transformers, DG;
Figure 118277DEST_PATH_IMAGE090
represents the upper current limit of the feeder;
Figure 708658DEST_PATH_IMAGE091
representing the upper power limit of the transformer;
Figure 875197DEST_PATH_IMAGE092
representing the maximum power utilization of the p-type generator;εrepresents the power generation permeability;
Figure 375580DEST_PATH_IMAGE093
representing node load;
Figure 641476DEST_PATH_IMAGE094
representing the charging requirement of the node electric vehicle; omega N ，Ω SS ，Ω p ，Ω ST Respectively representing a system node set, a transformer substation node set, a Distributed Generation (DG) candidate node set and an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; t represents a set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; k p Represents a set of alternatives for the distributed power supply DG;
Figure 843787DEST_PATH_IMAGE095
、
Figure 220542DEST_PATH_IMAGE096
respectively representing a lower limit and an upper limit of the node voltage;
Figure 838081DEST_PATH_IMAGE146
representing a set of nodes connected to feeder l; k NT Representing a set of alternatives for newly adding transformers; l represents a feeder type designation; l represents a feeder type set; tr denotes a transformer number; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
ESS cell constraints include:
Figure 274878DEST_PATH_IMAGE098
（13）
Figure 964485DEST_PATH_IMAGE099
（14）
Figure 144931DEST_PATH_IMAGE100
； （15）
in the formulae (13) to (15),
Figure 354327DEST_PATH_IMAGE101
、
Figure 289922DEST_PATH_IMAGE102
respectively representing the upper limit and the lower limit of the power of the energy storage ESS;
Figure 342191DEST_PATH_IMAGE103
、
Figure 201694DEST_PATH_IMAGE104
、
Figure 390230DEST_PATH_IMAGE105
binary variables representing energy storage ESS usage, production and storage, respectively;
Figure 293464DEST_PATH_IMAGE106
、
Figure 98609DEST_PATH_IMAGE107
respectively representing ESS power generation power and ESS energy storage power; i represents a node number; omega ST Representing an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k ST Represents a set of energy storage ESS alternatives; t denotes a period index; t represents the set of all discrete control periods; q represents a quarterly index; q represents a quarterly set; b represents a day/night block number; b represents a day/night block set; ω denotes a scene number; II represents a scene set;
equipment investment and usage constraints include:
Figure 496223DEST_PATH_IMAGE108
；（16）
Figure 804845DEST_PATH_IMAGE109
；（17）
Figure 82242DEST_PATH_IMAGE147
；（18）
Figure 981541DEST_PATH_IMAGE148
；（19）
Figure 41901DEST_PATH_IMAGE149
；（20）
Figure 329663DEST_PATH_IMAGE150
；（21）
Figure 981224DEST_PATH_IMAGE151
；（22）
Figure 105169DEST_PATH_IMAGE152
；（23）
in the formulae (16) to (23),
Figure 969219DEST_PATH_IMAGE153
，
Figure 377067DEST_PATH_IMAGE154
，
Figure 668371DEST_PATH_IMAGE155
，
Figure 482874DEST_PATH_IMAGE156
，
Figure 275250DEST_PATH_IMAGE157
，
Figure 412970DEST_PATH_IMAGE158
respectively representing the binary variables of the investment transformer substation, the newly-added transformer, the feeder line, the distributed power supply DG, the energy storage ESS and the electric vehicle charging station EVCS; t denotes a period index; t represents the set of all discrete control periods; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; i and j respectively represent different node labels;
Figure 16121DEST_PATH_IMAGE159
representing different sets of nodes; l represents a feeder type designation; NRF and NAF represent the existing new replacement feeder and the newly added feeder, respectively; omega SS ，Ω p ，Ω ST ，Ω ch Respectively a transformer substation node set, a distributed power supply DG candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
Figure 239292DEST_PATH_IMAGE160
a binary variable representing the electric vehicle charging station EVCS with extra capacity,
Figure 569779DEST_PATH_IMAGE161
a binary variable representing the electric vehicle charging station EVCS with the newly added capacity,
Figure 562006DEST_PATH_IMAGE162
representing a set of electric vehicle charging station EVCS candidate nodes with additional capacity,
Figure 536391DEST_PATH_IMAGE163
representing a set of alternatives, K, for an electric vehicle charging station EVCS with extra capacity NT ，K l ，K p ，K ST ，K ch Are respectively a newly-added transformer,A branch line, a distributed power supply DG, an energy storage ESS and an alternative scheme set of an electric vehicle charging station EVCS; p denotes a distributed generator DG type designation; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; ch is an EVCS mark of the electric vehicle charging station; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
the electric vehicle demand constraints include:
Figure 371491DEST_PATH_IMAGE164
（24）
Figure 115457DEST_PATH_IMAGE165
；（25）
in the formulae (24) to (25),
Figure 103135DEST_PATH_IMAGE166
representing the charging requirement of the node electric vehicle;
Figure 907143DEST_PATH_IMAGE167
represents the upper power limit of the electric vehicle charging station EVCS;
Figure 495119DEST_PATH_IMAGE168
a binary variable representing the use of the electric vehicle charging station EVCS;
Figure 42775DEST_PATH_IMAGE169
representing the total charging requirement of the system; omega ch Representing a set of electric vehicle charging station EVCS candidate nodes; ch is an EVCS mark of the electric vehicle charging station; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; i represents a node number; k represents a branch line, a transformer, a Distributed Generation (DG), an Energy Storage System (ESS) and an Electric Vehicle Charging Station (EVCS) option label; k ch Represents a set of alternatives for an electric vehicle charging station EVCS; t denotes a period index; t represents all discrete controlsA set of time-keeping periods; b represents day/night block number; b represents a day/night block set; q represents a quarterly index; q represents a quarterly set; ω denotes a scene number; Π represents the set of scenes.
As shown in fig. 4, the present invention further provides a system for collaborative planning of renewable energy, energy storage, and charging pile, including: the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability; the dividing unit is used for predicting the load demand and the load acceleration in a year of a planning cycle of the planning area and dividing the planning cycle into a plurality of dividing stages A according to the prediction result; the construction unit is used for constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage based on the maximum load demand predicted value of each division stage and respectively aiming at the minimum expected total cost of power distribution network planning in each division stage, wherein the expected total cost of power distribution network planning is minimized through the random scene generation framework; and the solving unit is used for solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage.
Specifically, the establishing unit includes: the calculating module is used for calculating the total charging requirement of the electric vehicle based on the statistical data of the electric vehicle; the statistical data of the electric vehicle comprises: home data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point.
Specifically, the calculation module includes: a first allocation sub-module for randomly allocating, for each electric vehicle, a battery capacity assuming that all electric vehicles are fully charged at the start of a first trip; a second allocating submodule, configured to allocate a trip T to one of the electric vehicles i, a month m, and a day d, respectively; the checking submodule is used for checking the charging state SOCi of the battery of the electric vehicle i when the travel T is finished for the electric vehicle i; the setting submodule is used for setting the minimum value of the charging requirement to be R% of the battery capacity of the electric vehicle i; the searching submodule is used for searching the electric vehicle i when the electric vehicle i does not meet the charging requirementWhen the value is small, searching the next stroke of the data set, and calculating and updating the charging requirement; the charging submodule is used for charging the battery at a time interval when the electric vehicle i stops before the travel T if the electric vehicle i reaches the minimum value of the charging requirement; the checking submodule is also used for checking the state of charge SOCi of the battery again; the searching submodule is also used for eliminating the travel and searching the next travel when the electric vehicle i still does not reach the minimum value of the charging requirement; the electric vehicle demand calculation submodule is used for repeatedly running on the scale of days, months and vehicles after the last trip, and calculating to obtain the electric vehicle demand
Figure 884961DEST_PATH_IMAGE170
。
Specifically, the establishing unit includes: the acquisition module is used for acquiring the small-level historical data; the data dividing module is used for dividing the small-level historical data; a scene set constructing module, configured to construct a scene set of each stage based on a K-CFSFDP algorithm, where the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters include: load requirements, electric vehicle charging requirements, wind speed, solar energy irradiation and substation energy cost; the selection module is used for selecting the number K of the required clusters according to the requirement; a clustering centroid calculation module for calculating weighted Euclidean distance of scene set data by using entropy method to obtain distance matrix d ij And the elements are arranged in ascending order, and the distance value of the first 2% position of the sequence is taken as the truncation distance delta i (ii) a Calculating local density rho according to Gaussian kernel function i Establishing a consideration of i And ρ i Comprehensive index gamma of i Selecting gamma i Taking the first K data points of the descending sequence as a clustering mass center Ci; the distance calculation module is used for calculating the distance Di (x) from each sample (hour-level historical data) to each clustering centroid Ci by using Euclidean distance, D represents the shortest distance from one data point to the nearest particle, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each other; the clustering centroid calculating module is also used for recalculating the clustering centroid Ci to be the clustering centroidCentroid of all points in Ci: the iteration calculation module is used for iteratively calculating the clustering mass center Ci until the cluster composition is not changed between two continuous iterations; and the output module is used for outputting clustering mass centers and a matrix formed by working conditions and uncertain parameters, wherein each clustering mass center is represented by numerical values of load requirements, electric vehicle charging requirements, wind speed, solar irradiation and transformer substation energy cost, and the numerical values are used for displaying the operating condition of the system.
Specifically, the data partitioning module includes: the data dividing submodule is used for dividing the hour-level historical data into 4 seasons of spring, summer, autumn and winter, and in each season, the data are divided into day/night blocks, namely a day block and a night block, according to the actual sunrise and sunset time each day; the scene set building module comprises: the execution submodule carries out the K-FSFDP algorithm and executes 8 times in each quarter and day/night block, namely 4 quarters multiplied by 2 day/night blocks; the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertain parameters, where the 96 operating conditions comprise 12 arrays of each quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observed value within each cluster divided by the total observed value for the respective day/night block b and quarter q.
Specifically, in the dividing unit: the planning period a year is less than the service life of the equipment with the shortest service life in the power distribution network; in the first load acceleration period, the number of the division stages A is Ai, the duration is Ti, in the second load acceleration period, the number of the division stages A is Aj, the duration is Tj, wherein the first load acceleration is smaller than the second load acceleration, ai is smaller than Aj, and Ti is larger than Tj.
Specifically, in the building unit: according to the division of a planning period, a multi-stage planning sequence of the power distribution network in a planning region is marked as S = [ S1, S2, \8230; SA ], and a construction scheme corresponding to each stage is Eset = [ Eset1, eset2, \8230; eset A ]; minimizing the expected total cost of power distribution network planning includes: investment costs are the present value of annual amortization and operational costs over the entire time period of an asset over its entire life cycle, namely:
Figure 125449DEST_PATH_IMAGE171
； （1）
in the formula (1), t represents a time interval label, I represents an annual investment rate,
Figure 872825DEST_PATH_IMAGE172
in order to achieve the purpose of investment cost,
Figure 99538DEST_PATH_IMAGE173
in order to achieve the cost of maintenance,
Figure 920864DEST_PATH_IMAGE137
in order to achieve the aim of reducing the production cost,
Figure 456887DEST_PATH_IMAGE174
for the cost of energy loss, E seti Denotes S i And the capacities and installation points of the feeder line, the transformer substation, the additional transformer, the renewable energy power station, the energy storage system and the electric vehicle charging station configured in the planning stage.
In particular, the investment costs
Figure 629243DEST_PATH_IMAGE175
Comprises the following steps:
Figure 391138DEST_PATH_IMAGE176
；（2）
the maintenance cost
Figure 332549DEST_PATH_IMAGE008
Comprises the following steps:
Figure 39474DEST_PATH_IMAGE009
；（3）
said production cost
Figure 512175DEST_PATH_IMAGE010
Comprises the following steps:
Figure 205324DEST_PATH_IMAGE177
；（4）
cost of said energy loss
Figure 860296DEST_PATH_IMAGE012
Comprises the following steps:
Figure 613489DEST_PATH_IMAGE013
；（5）
in the formulae (2) to (5), the investment cost
Figure 635803DEST_PATH_IMAGE141
Among the system constant parameters considered are: k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; t denotes a period index; i. j respectively represent different node labels; ch is an EVCS mark of the electric vehicle charging station; l represents a feeder type designation; p denotes a distributed power supply DG type index; q represents a quarterly index; tr denotes a transformer number; b represents a day/night block number; ω denotes a scene number; b represents a day/night block set; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; k NT ，K l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; l represents a feeder type set, L = { EFF, ERF, NRF, NAF }, EFF, ERF, NRF, and NAF represent an existing fixed feeder, an existing replaceable feeder, a new replacement feeder, and a newly added feeder, respectively; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; q represents a quarterly set; t represents the set of all discrete control periods; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; omega LN ，Ω SS ，Ω p ，Ω ST ，Ω ch Respectively load node set and power transformationThe method comprises the steps of collecting station nodes, collecting Distributed Generation (DG) candidate nodes, collecting Energy Storage System (ESS) candidate nodes and collecting Electric Vehicle Charging Station (EVCS) candidate nodes; II represents a scene set; gamma ray l Representing a branch set with an l-type feeder line;
investment cost
Figure 132643DEST_PATH_IMAGE015
Among the investment and equipment constant parameters considered:
Figure 907701DEST_PATH_IMAGE016
，
Figure 566215DEST_PATH_IMAGE017
，
Figure 13508DEST_PATH_IMAGE018
，
Figure 173094DEST_PATH_IMAGE019
，
Figure 943604DEST_PATH_IMAGE020
，
Figure 668894DEST_PATH_IMAGE021
respectively representing the investment costs of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 790434DEST_PATH_IMAGE022
，
Figure 753711DEST_PATH_IMAGE023
，
Figure 113148DEST_PATH_IMAGE024
，
Figure 254410DEST_PATH_IMAGE025
，
Figure 659984DEST_PATH_IMAGE026
are respectively provided withThe maintenance cost of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS is represented;
Figure 302318DEST_PATH_IMAGE027
，
Figure 657207DEST_PATH_IMAGE028
，
Figure 94004DEST_PATH_IMAGE029
，
Figure 783612DEST_PATH_IMAGE030
respectively representing the energy cost purchased at a transformer substation, the production cost of a distributed power supply DG, the production cost of an energy storage ESS and the storage cost of the energy storage ESS;
Figure 229636DEST_PATH_IMAGE031
，
Figure 439032DEST_PATH_IMAGE032
，
Figure 46731DEST_PATH_IMAGE033
respectively representing the maximum capacity of a p-type generator, the maximum capacity of an energy storage ESS and the maximum capacity of an electric vehicle charging station EVCS; i represents annual investment rate; \8467 ij Representing the feeder length; pf is the system power factor; RR is the asset recovery rate;
Figure 426897DEST_PATH_IMAGE034
for feeder asset recovery;
Figure 17890DEST_PATH_IMAGE035
in order to realize the recovery rate of the assets of the transformer substation,
Figure 472006DEST_PATH_IMAGE036
in order to increase the asset recovery rate of the transformer,
Figure 375239DEST_PATH_IMAGE037
for distributed generation DG asset recovery,
Figure 914805DEST_PATH_IMAGE038
in order to achieve an energy storage ESS asset recovery rate,
Figure 577999DEST_PATH_IMAGE039
for electric vehicle charging station EVCS asset recovery,
Figure 886620DEST_PATH_IMAGE040
、
Figure 960756DEST_PATH_IMAGE041
the single impedance amplitude of the l-shaped feeder line and the impedance amplitude of the transformer are respectively set;
Figure 722038DEST_PATH_IMAGE042
is the scene weight;
Figure 126606DEST_PATH_IMAGE043
duration of block b, quarterly q days/nights in hours;
investment cost
Figure 414368DEST_PATH_IMAGE044
Among the decision variables considered:
Figure 534771DEST_PATH_IMAGE045
、
Figure 189874DEST_PATH_IMAGE046
respectively representing the current measured by node i through alternative k of feeder type i installed in branch ij, branch ji, in the scenario ω of time period t, day/night block b, quarter q, if node i is the supply point, it is greater than 0, otherwise it is 0;
Figure 53925DEST_PATH_IMAGE047
、
Figure 196193DEST_PATH_IMAGE048
、
Figure 753076DEST_PATH_IMAGE049
、
Figure 456328DEST_PATH_IMAGE050
respectively representing distributed power supply power, transformer power, energy storage ESS power generation power and energy storage ESS power;
Figure 104828DEST_PATH_IMAGE051
，
Figure 242549DEST_PATH_IMAGE052
，
Figure 845699DEST_PATH_IMAGE178
，
Figure 68870DEST_PATH_IMAGE054
，
Figure 399358DEST_PATH_IMAGE179
，
Figure 391584DEST_PATH_IMAGE056
respectively representing binary variables of an investment substation, a newly-added transformer, a feeder line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 365969DEST_PATH_IMAGE057
，
Figure 935491DEST_PATH_IMAGE058
，
Figure 945035DEST_PATH_IMAGE059
，
Figure 932714DEST_PATH_IMAGE060
，
Figure 2301DEST_PATH_IMAGE180
，
Figure 324698DEST_PATH_IMAGE062
binary variables representing the use of transformers, substations, newly added transformers, distributed generators DG, energy storage ESS and electric vehicle charging stations EVCS,
Figure 872354DEST_PATH_IMAGE063
，
Figure 917801DEST_PATH_IMAGE064
a binary variable representing feeder type i in branch ji, using branch ij.
Specifically, the constraints of the grid collaborative planning model include system operation constraints, ESS cell operation constraints, investment and equipment use constraints and electric vehicle demand constraints.
Specifically, the system operation constraints include kirchhoff voltage, current conservation constraints, and upper and lower voltage, current, and power limits;
specifically, kirchhoff voltage and current conservation constraints are as follows:
Figure 17344DEST_PATH_IMAGE065
；（6）
Figure 702404DEST_PATH_IMAGE066
；（7）
in the formulae (6) to (7),
Figure 929117DEST_PATH_IMAGE045
、
Figure 16021DEST_PATH_IMAGE046
respectively representing the current measured by node i through alternative k of feeder type i installed in branch ij, branch ji in scenario ω of time period t, day/night block b, quarter q;
Figure 552045DEST_PATH_IMAGE067
、
Figure 458821DEST_PATH_IMAGE068
、
Figure 423979DEST_PATH_IMAGE069
、
Figure 490024DEST_PATH_IMAGE070
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 806735DEST_PATH_IMAGE071
representing node load;
Figure 76174DEST_PATH_IMAGE072
representing the charging requirement of the node electric vehicle;
Figure 769323DEST_PATH_IMAGE073
a binary variable representing the feeder;
Figure 955454DEST_PATH_IMAGE074
representing a single impedance magnitude for a type i feeder;ℓ ij representing the feeder length;
Figure 708646DEST_PATH_IMAGE075
、
Figure 465381DEST_PATH_IMAGE076
respectively representing the voltages at nodes i, j;
l represents a feeder type designation; l represents a feeder type set; k represents a branch line, a transformer, a Distributed Generation (DG), an Energy Storage System (ESS) and an Electric Vehicle Charging Station (EVCS) option label; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; tr denotes a transformer number; TR represents a set of transformer types; omega N Representing a set of system nodes; t represents a set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; k is l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 696642DEST_PATH_IMAGE181
representing a set of nodes connected to feeder l; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
the upper and lower limits of voltage, current and power are:
Figure 674962DEST_PATH_IMAGE078
；（8）
Figure 474422DEST_PATH_IMAGE079
；（9）
Figure 843087DEST_PATH_IMAGE080
；（10）
Figure 2673DEST_PATH_IMAGE081
；（11）
Figure 773183DEST_PATH_IMAGE082
；（12）
in the formulae (8) to (12),
Figure 740614DEST_PATH_IMAGE083
represents the node voltage;
Figure 596575DEST_PATH_IMAGE084
represents the current measured by node i through alternative k of feeder type i installed in branch ij in scenario ω of time period t, day/night block b, quarter q;
Figure 497535DEST_PATH_IMAGE067
、
Figure 997917DEST_PATH_IMAGE085
、
Figure 263813DEST_PATH_IMAGE069
、
Figure 466125DEST_PATH_IMAGE086
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 108459DEST_PATH_IMAGE087
，
Figure 728927DEST_PATH_IMAGE088
，
Figure 165724DEST_PATH_IMAGE089
binary variables representing the use of feeders, transformers, DG;
Figure 855332DEST_PATH_IMAGE090
representing the upper current limit of the feeder;
Figure 848827DEST_PATH_IMAGE091
representing the upper power limit of the transformer;
Figure 448435DEST_PATH_IMAGE092
representing the maximum power utilization of the p-type generator;εrepresents the power generation permeability;
Figure 180768DEST_PATH_IMAGE093
representing node load;
Figure 233038DEST_PATH_IMAGE094
representing the charging requirement of the node electric vehicle; omega N ，Ω SS ，Ω p ，Ω ST Respectively representing a system node set, a transformer station node set, a Distributed Generation (DG) candidate node set and an Energy Storage System (ESS) candidate node setCombining; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; t represents the set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; k p Represents a set of alternatives for the distributed power supply DG;
Figure 113048DEST_PATH_IMAGE095
、
Figure 567163DEST_PATH_IMAGE096
respectively representing a lower limit and an upper limit of the node voltage;
Figure 470397DEST_PATH_IMAGE182
representing a set of nodes connected to feeder l; k is NT Representing a set of alternatives for newly adding transformers; l represents a feeder type designation; l represents a feeder type set; tr denotes a transformer number; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
ESS cell constraints include:
Figure 9963DEST_PATH_IMAGE098
（13）
Figure 407577DEST_PATH_IMAGE099
（14）
Figure 44095DEST_PATH_IMAGE100
； （15）
in the formulae (13) to (15),
Figure 993596DEST_PATH_IMAGE101
、
Figure 630245DEST_PATH_IMAGE102
respectively representing the upper limit and the lower limit of the power of the energy storage ESS;
Figure 221764DEST_PATH_IMAGE103
、
Figure 509525DEST_PATH_IMAGE104
、
Figure 629928DEST_PATH_IMAGE105
binary variables representing energy storage ESS usage, production and storage, respectively;
Figure 19452DEST_PATH_IMAGE106
、
Figure 211399DEST_PATH_IMAGE107
respectively representing ESS power generation power and ESS energy storage power; i represents a node number; omega ST Representing an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k ST Representing a set of energy storage ESS alternatives; t denotes a period index; t represents a set of all discrete control periods; q represents a quarterly index; q represents a quarterly set; b represents day/night block number; b represents a day/night block set; ω denotes a scene number; II represents a scene set;
equipment investment and usage constraints include:
Figure 229034DEST_PATH_IMAGE108
；（16）
Figure 658354DEST_PATH_IMAGE109
；（17）
Figure 394229DEST_PATH_IMAGE183
；（18）
Figure 186604DEST_PATH_IMAGE148
；（19）
Figure 324324DEST_PATH_IMAGE149
；（20）
Figure 927475DEST_PATH_IMAGE150
；（21）
Figure 212963DEST_PATH_IMAGE184
；（22）
Figure 684396DEST_PATH_IMAGE152
；（23）
in the formulae (16) to (23),
Figure 551989DEST_PATH_IMAGE153
，
Figure 185095DEST_PATH_IMAGE154
，
Figure 20196DEST_PATH_IMAGE155
，
Figure 295320DEST_PATH_IMAGE156
，
Figure 17419DEST_PATH_IMAGE157
，
Figure 87006DEST_PATH_IMAGE158
respectively representing the binary variables of the investment transformer substation, the newly-added transformer, the feeder line, the distributed power supply DG, the energy storage ESS and the electric vehicle charging station EVCS; t denotes a period index; t denotes all discrete control periodsA set of (a); k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a set of spur alternatives; i and j respectively represent different node labels;
Figure 143824DEST_PATH_IMAGE159
represent different sets of nodes; l represents a feeder type designation; NRF and NAF represent the existing new replacement feeder and the newly added feeder, respectively; omega SS ，Ω p ，Ω ST ，Ω ch The method comprises the steps of respectively obtaining a substation node set, a Distributed Generation (DG) candidate node set, an Energy Storage System (ESS) candidate node set and an Electric Vehicle Charging Station (EVCS) candidate node set; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
Figure 32758DEST_PATH_IMAGE185
a binary variable representing an electric vehicle charging station EVCS with extra capacity,
Figure 999577DEST_PATH_IMAGE186
a binary variable representing the electric vehicle charging station EVCS with the newly added capacity,
Figure 99120DEST_PATH_IMAGE162
representing a set of electric vehicle charging station EVCS candidate nodes with additional capacity,
Figure 49758DEST_PATH_IMAGE163
representing a set of alternatives, K, for an electric vehicle charging station EVCS with extra capacity NT ，K l ，K p ，K ST ，K ch The alternative scheme sets of a newly-added transformer, a branch line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS are respectively set; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; ch is an EVCS mark of the electric vehicle charging station; CH is a type set of an electric vehicle charging station EVCS, CH = { NCH, ACH }, wherein NCH and ACH respectively representNew and extra capacity of electric vehicle charging stations;
the electric vehicle demand constraints include:
Figure 276472DEST_PATH_IMAGE164
（24）
Figure 97797DEST_PATH_IMAGE165
；（25）
in the formulae (24) to (25),
Figure 368241DEST_PATH_IMAGE166
representing the charging requirement of the node electric vehicle;
Figure 353646DEST_PATH_IMAGE167
represents the upper power limit of the electric vehicle charging station EVCS,
Figure 508684DEST_PATH_IMAGE168
a binary variable representing the use of the electric vehicle charging station EVCS;
Figure 309150DEST_PATH_IMAGE169
representing the total charging requirement of the system; omega ch Representing a set of electric vehicle charging station EVCS candidate nodes; ch is an EVCS mark of the electric vehicle charging station; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; i represents a node number; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k is ch Represents a set of alternatives for an electric vehicle charging station EVCS; t denotes a period index; t represents a set of all discrete control periods; b represents a day/night block number; b represents a day/night block set; q represents a quarterly index; q represents a quarterly set; ω denotes a scene number; Π represents a set of scenes.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention may be apparent to those skilled in the relevant art and are intended to be within the scope of the present invention.

Claims (15)

1. A renewable energy, energy storage and charging pile collaborative planning method is characterized by comprising the following steps:
establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability;
predicting the load demand and the load acceleration in a year of a planning period of a planning region, and dividing the planning period into a plurality of division stages A according to the prediction result;
constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage based on the maximum load demand predicted value of each division stage and respectively taking the minimum expected total cost of power distribution network planning as a target in each division stage, wherein the expected total cost of power distribution network planning is minimized through the random scene generation framework;
and solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage.
2. The renewable energy, energy storage and charging pile co-planning method of claim 1, comprising:
calculating the charging demand of the electric vehicle based on the statistical data of the electric vehicle;
the statistical data of the electric vehicle comprises: home data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point.
3. The collaborative planning method for renewable energy, energy storage and charging pile according to claim 2, wherein the calculating the charging requirement of the electric vehicle based on the statistical data of the electric vehicle comprises:
for each electric vehicle, randomly distributing battery capacity, and setting each electric vehicle to be fully charged at the beginning of the first trip;
setting the minimum value of the charging demand of each electric vehicle as R% of the battery capacity of the electric vehicle i;
for one electric vehicle i, a journey T is respectively allocated to a certain month m and a certain day d;
for the electric vehicle i, checking the state of charge SOCi of the battery of the electric vehicle i when the travel T is completed;
when the state of charge SOCi of the battery of the electric vehicle i does not reach the minimum value of the charging requirement, searching the next stroke of the data set, and calculating and updating the charging requirement;
when the electric vehicle i reaches the minimum value of the charging requirement, charging the battery before the travel T and at the time interval when the electric vehicle i stops;
checking the state of charge SOCi of the battery again;
when the electric vehicle i still does not reach the minimum value of the charging requirement, the travel is eliminated and the next travel is searched;
after the last travel, the electric vehicle repeatedly runs by taking the day, the month and the electric vehicle as scales, and the charging requirement of the electric vehicle is calculated
Figure 626126DEST_PATH_IMAGE001
。
4. The collaborative planning method for renewable energy, energy storage and charging piles according to claim 1, wherein the establishment of the stochastic scenario generation framework for expressing the stochastic variability of load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost comprises:
acquiring small-level historical data;
dividing the small-scale historical data, and constructing a scene set of each stage based on a K-CFSFDP algorithm, wherein the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters comprise: load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost;
selecting the number K of the required clusters according to the requirement;
calculating weighted Euclidean distance of scene set data by using entropy method to obtain distance matrix d ij And for said distance matrix d ij The elements are arranged in an ascending order to obtain an ascending sequence, and the distance value of the front 2 percent of the ascending sequence is taken as a truncation distance delta i (ii) a Calculating local density rho according to Gaussian kernel function i Establishing a consideration of i And ρ i Is a comprehensive index of i For the said comprehensive index γ i Performing descending order arrangement to obtain a descending order sequence, and selecting the first K data points of the descending order sequence as clustering centroids Ci;
calculating the distance Di (x) from each hour-level historical data to each clustering centroid Ci by using Euclidean distance, wherein D represents the shortest distance from one data point to the nearest mass point, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each hour-level historical data;
recalculating the clustering centroid Ci to become the centroids of all the points in the clustering centroid Ci:
and iteratively calculating the clustering mass centers Ci until the cluster composition is not changed between two continuous iterations, and outputting a matrix consisting of the clustering mass centers, the working conditions and uncertain parameters, wherein each clustering mass center is represented by numerical values of load requirements, electric vehicle charging requirements, wind speeds, solar irradiation and energy cost of a transformer substation, and the numerical values are used for displaying the operating condition of the system.
5. The collaborative planning method for renewable energy, energy storage and charging pile according to claim 4, wherein the dividing of the hourly historical data includes:
dividing the hour-level historical data into 4 seasons of spring, summer, autumn and winter, and dividing the data into day/night blocks, namely a day block and a night block, in each season according to the actual sunrise and sunset time each day;
the K-FSFDP algorithm is performed 8 times per quarter and day/night block, i.e., 4 quarter by 2 day/night blocks;
the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertainty parameters, where the 96 operating conditions include 12 arrays per quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observations within each cluster divided by the total observations for the respective day/night block b and quarter q.
6. The collaborative planning method for renewable energy, energy storage and charging piles according to any one of claims 1 to 5, wherein in the forecasting of the load demand and the load acceleration within a year of the planning cycle of the planning area, the planning cycle is divided into a plurality of divided phases A according to the forecasting result:
the planning period a year is less than the service life of the equipment with the shortest service life in the power distribution network;
in the first load acceleration period, the number of the division stages A is Ai, the duration is Ti, in the second load acceleration period, the number of the division stages A is Aj, the duration is Tj, wherein the first load acceleration is smaller than the second load acceleration, ai is smaller than Aj, and Ti is larger than Tj.
7. The collaborative planning method for renewable energy, energy storage and charging pile according to any one of claims 1-5, wherein the constructing of the collaborative planning model for the power distribution network considering electric vehicle charging stations and renewable energy generation and energy storage comprises:
according to the division of the planning period, recording a multi-stage planning sequence of the power distribution network in the planning area as S = [ S ] 1 ，S 2 ，…，S A ]Wherein S is 1 Denotes the first stage, S 2 Denotes the second stage, S A Represents the A stage; the sequence of the construction scheme corresponding to each stage is Eset = [ Eset = [) 1 ，Eset 2 ，…，Eset A ]Wherein, eset 1 Denotes S 1 Capacities and installation points, eset, of feeders, substations, additional transformers, renewable energy power stations, energy storage systems and electric vehicle charging stations configured in the planning stage 2 Denotes S 2 The capacities and installation points, eset, of the feeders, the substations, the additional transformers, the renewable energy power stations, the energy storage systems and the electric vehicle charging stations configured in the planning stage A Denotes S A The capacities and installation points of a feeder line, a transformer substation, an additional transformer, a renewable energy power station, an energy storage system and an electric vehicle charging station configured in the planning stage;
minimizing the expected total cost of power distribution network planning includes: the investment costs are the present value of annual amortization and the operational costs over the entire time period of the asset, namely:
Figure 12107DEST_PATH_IMAGE002
；（1）
in the formula (1), t represents a time interval label, I represents an annual investment rate,
Figure 260686DEST_PATH_IMAGE003
in order to reduce the investment cost,
Figure 859158DEST_PATH_IMAGE004
in order to achieve the cost of maintenance,
Figure 391639DEST_PATH_IMAGE005
in order to achieve the aim of reducing the production cost,
Figure 682943DEST_PATH_IMAGE006
for cost of energy loss, E seti Denotes S i And the capacities and installation points of the feeder line, the transformer substation, the additional transformer, the renewable energy power station, the energy storage system and the electric vehicle charging station configured in the planning stage.
8. The collaborative planning method for renewable energy, energy storage and charging pile according to claim 7, wherein the investment cost is
Figure 369883DEST_PATH_IMAGE007
Comprises the following steps:
Figure 37625DEST_PATH_IMAGE008
；（2）
said maintenance cost
Figure 175345DEST_PATH_IMAGE009
Comprises the following steps:
Figure 371971DEST_PATH_IMAGE010
；（3）
said production cost
Figure 844410DEST_PATH_IMAGE011
Comprises the following steps:
Figure 50263DEST_PATH_IMAGE012
；（4）
cost of said energy loss
Figure 776911DEST_PATH_IMAGE013
Comprises the following steps:
Figure 426329DEST_PATH_IMAGE014
；（5）
in the formulae (2) to (5), the investment cost
Figure 580140DEST_PATH_IMAGE015
Among the system constant parameters considered are: k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; t denotes a period index; i. j represents different node numbers respectively; ch is an EVCS mark of the electric vehicle charging station; l represents a feeder type designation; p denotes a distributed generator DG type designation; q represents a quarterly index; tr denotes a transformer number; b represents a day/night block number; omega denotes a scene number(ii) a B represents a day/night block set; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; k NT ，K l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; l represents a feeder type set, L = { EFF, ERF, NRF, NAF }, EFF, ERF, NRF, and NAF represent an existing fixed feeder, an existing replaceable feeder, a new replacement feeder, and a newly added feeder, respectively; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; q represents a quarterly set; t represents the set of all discrete control periods; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; omega LN ，Ω SS ，Ω p ，Ω ST ，Ω ch Respectively a load node set, a transformer substation node set, a Distributed Generation (DG) candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; II represents a scene set; upsilon-upsilon l Representing a branch set with an l-type feeder line;
investment cost
Figure 573373DEST_PATH_IMAGE015
Among the investment and equipment constant parameters considered:
Figure 420106DEST_PATH_IMAGE016
，
Figure 224114DEST_PATH_IMAGE017
，
Figure 172610DEST_PATH_IMAGE018
respectively representing the investment costs of a feeder line, a transformer substation, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 454686DEST_PATH_IMAGE019
，
Figure 421505DEST_PATH_IMAGE020
respectively representing the maintenance costs of a feeder line, a newly-added transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 661994DEST_PATH_IMAGE021
，
Figure 330741DEST_PATH_IMAGE022
respectively representing the energy cost purchased at a transformer substation, the production cost of a distributed power supply DG, the production cost of an energy storage ESS and the storage cost of the energy storage ESS;
Figure 39678DEST_PATH_IMAGE023
，
Figure 861004DEST_PATH_IMAGE024
，
Figure 521661DEST_PATH_IMAGE025
respectively representing the maximum capacity of a p-type generator, the maximum capacity of an energy storage ESS and the maximum capacity of an electric vehicle charging station EVCS; i represents annual investment rate; \8467 ij Representing the feeder length; pf is the system power factor; RR is the asset recovery rate;
Figure 162858DEST_PATH_IMAGE026
for feeder asset recovery;
Figure 521158DEST_PATH_IMAGE027
in order to realize the recovery rate of the assets of the transformer substation,
Figure 213302DEST_PATH_IMAGE028
in order to increase the asset recovery rate of the transformer,
Figure 795593DEST_PATH_IMAGE029
for distributed generation DG asset recovery,
Figure 924086DEST_PATH_IMAGE030
in order to provide an energy storage ESS asset recovery rate,
Figure 866503DEST_PATH_IMAGE031
for an electric vehicle charging station EVCS asset recovery rate,
Figure 396841DEST_PATH_IMAGE032
、
Figure 618875DEST_PATH_IMAGE033
the single impedance amplitude of the l-shaped feeder line and the impedance amplitude of the transformer are respectively set;
Figure 982467DEST_PATH_IMAGE034
is the scene weight;
Figure 479307DEST_PATH_IMAGE035
duration of block b, quarterly q days/nights in hours;
investment cost
Figure 129732DEST_PATH_IMAGE036
Among the decision variables considered:
Figure 771934DEST_PATH_IMAGE037
、
Figure 140599DEST_PATH_IMAGE038
respectively representing the current measured by the node i through an alternative k of the feeder type l installed in the branch ij, the branch ji in a scenario ω of time period t, day/night block b, quarter q; if node i is a supply point, it is greater than 0, otherwise it is 0;
Figure 175551DEST_PATH_IMAGE039
、
Figure 680482DEST_PATH_IMAGE040
、
Figure 260630DEST_PATH_IMAGE041
、
Figure 116590DEST_PATH_IMAGE042
respectively representing distributed power supply power, transformer power, energy storage ESS power generation power and energy storage ESS power;
Figure 689654DEST_PATH_IMAGE043
，
Figure 298359DEST_PATH_IMAGE044
，
Figure 564255DEST_PATH_IMAGE045
，
Figure 641933DEST_PATH_IMAGE046
，
Figure 18687DEST_PATH_IMAGE047
，
Figure 246013DEST_PATH_IMAGE048
respectively representing binary variables of an investment substation, a newly-added transformer, a feeder line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 417231DEST_PATH_IMAGE049
，
Figure 982205DEST_PATH_IMAGE050
，
Figure 411918DEST_PATH_IMAGE051
，
Figure 745947DEST_PATH_IMAGE052
，
Figure 88067DEST_PATH_IMAGE053
，
Figure 140337DEST_PATH_IMAGE054
binary variables representing the use of transformers, substations, newly added transformers, distributed generators DG, energy storage ESS and electric vehicle charging stations EVCS,
Figure 609626DEST_PATH_IMAGE055
，
Figure 798162DEST_PATH_IMAGE056
binary variables representing feeder type i in branch ij, branch ji, respectively, are used.
9. The method for collaborative planning of renewable energy, energy storage and charging piles according to any one of claims 1 to 5, wherein the constraints of the grid collaborative planning model include system operation constraints, ESS cell operation constraints, investment and equipment use constraints and electric vehicle demand constraints.
10. The renewable energy, energy storage and charging pile co-planning method of claim 9, wherein system operation constraints comprise kirchhoff voltage, current conservation constraints and upper and lower voltage, current and power limits;
kirchhoff voltage and current conservation constraints are as follows:
Figure 576762DEST_PATH_IMAGE057
；（6）
Figure 365596DEST_PATH_IMAGE058
；（7）
in the formulae (6) to (7),
Figure 622265DEST_PATH_IMAGE059
、
Figure 930886DEST_PATH_IMAGE060
respectively representing the current measured by node i through alternative k of feeder type i installed in branch ij, branch ji in scenario ω of time period t, day/night block b, quarter q;
Figure 880388DEST_PATH_IMAGE061
、
Figure 982948DEST_PATH_IMAGE062
、
Figure 308887DEST_PATH_IMAGE063
、
Figure 957169DEST_PATH_IMAGE064
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 77572DEST_PATH_IMAGE065
representing node load;
Figure 591729DEST_PATH_IMAGE066
representing the charging requirement of the node electric vehicle;
Figure 190201DEST_PATH_IMAGE067
a binary variable representing the feeder;
Figure 457103DEST_PATH_IMAGE068
representing a single impedance magnitude for a type i feeder;ℓ ij representing the feeder length;
Figure 13986DEST_PATH_IMAGE069
、
Figure 749861DEST_PATH_IMAGE070
respectively representing the voltages at nodes i, j;
l represents a feeder type designation; l represents a feeder type set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents day/night block number; ω denotes a scene number; tr denotes a transformer number; TR represents a set of transformer types; omega N Representing a set of system nodes; t represents the set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; k is l ，K tr ，K p ，K ST ，K ch The alternative schemes are respectively a branch line, a transformer, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS;
Figure 899827DEST_PATH_IMAGE071
representing a set of nodes connected to feeder l; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
the upper and lower limits of voltage, current and power are:
Figure 771968DEST_PATH_IMAGE072
；（8）
Figure 499752DEST_PATH_IMAGE073
；（9）
Figure 457344DEST_PATH_IMAGE074
；（10）
Figure 912465DEST_PATH_IMAGE075
；（11）
Figure 904692DEST_PATH_IMAGE076
；（12）
in the formulae (8) to (12),
Figure 537798DEST_PATH_IMAGE077
represents the node voltage;
Figure 733419DEST_PATH_IMAGE078
represents the current measured by node i through alternative k of feeder type i installed in branch ij in scenario ω of time period t, day/night block b, quarter q;
Figure 742963DEST_PATH_IMAGE061
、
Figure 855275DEST_PATH_IMAGE062
、
Figure 393704DEST_PATH_IMAGE063
、
Figure 840735DEST_PATH_IMAGE064
respectively representing distributed power supply power, transformer power, ESS power generation power and ESS energy storage power;
Figure 388391DEST_PATH_IMAGE079
，
Figure 978379DEST_PATH_IMAGE080
，
Figure 936976DEST_PATH_IMAGE081
binary variables representing the use of feeders, transformers, DG;
Figure 622036DEST_PATH_IMAGE082
representing the upper current limit of the feeder;
Figure 707803DEST_PATH_IMAGE083
representing the upper power limit of the transformer;
Figure 14282DEST_PATH_IMAGE084
representing the maximum power utilization of the p-type generator;εrepresents the power generation permeability;
Figure 425672DEST_PATH_IMAGE085
representing node load;
Figure 332448DEST_PATH_IMAGE086
representing the charging requirement of the node electric vehicle; omega N ，Ω SS ，Ω p ，Ω ST Respectively representing a system node set, a transformer substation node set, a Distributed Generation (DG) candidate node set and an energy storage ESS candidate node set; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k l Representing a branch option set; t represents the set of all discrete control periods; q represents a quarterly set; b represents a day/night block set; II represents a scene set; t denotes a period index; i and j respectively represent different node labels; q represents a quarterly index; b represents a day/night block number; ω denotes a scene number; k is p Represents a set of alternatives for the distributed power supply DG;
Figure 956327DEST_PATH_IMAGE087
、
Figure 147006DEST_PATH_IMAGE088
respectively representing a lower limit and an upper limit of the node voltage;
Figure 463718DEST_PATH_IMAGE071
representing a set of nodes connected to feeder l; k NT Represents a set of alternatives for newly adding transformers; l represents a feeder type designation; l represents a feeder type set; tr denotes a transformer number; TR represents a transformer type set, TR = { ET, NT }, and ET and NT represent an existing transformer and a newly added transformer respectively; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation;
ESS cell constraints include:
Figure 857790DEST_PATH_IMAGE089
（13）
Figure 33163DEST_PATH_IMAGE090
（14）
Figure 829081DEST_PATH_IMAGE091
； （15）
in the formulae (13) to (15),
Figure 582273DEST_PATH_IMAGE092
、
Figure 198062DEST_PATH_IMAGE093
respectively representing the upper limit and the lower limit of the power of the energy storage ESS;
Figure 678591DEST_PATH_IMAGE094
、
Figure 63436DEST_PATH_IMAGE095
、
Figure 987530DEST_PATH_IMAGE096
binary variables representing energy storage ESS usage, production and storage, respectively;
Figure 106927DEST_PATH_IMAGE097
、
Figure 141879DEST_PATH_IMAGE098
respectively representing ESS power generation power and ESS energy storage power; i represents a node number; omega ST Representing an energy storage ESS candidate node set; k represents a branch line, a transformer, a Distributed Generation (DG), an Energy Storage System (ESS) and an Electric Vehicle Charging Station (EVCS) option label; k is ST Represents a set of energy storage ESS alternatives; t denotes a period index; t represents the set of all discrete control periods; q represents a quarterly index; q represents a quarterly set; b represents a day/night block number; b represents a day/night block set; ω denotes a scene number; II represents a scene set;
equipment investment and use constraints include:
Figure 381230DEST_PATH_IMAGE099
；（16）
Figure 476225DEST_PATH_IMAGE100
；（17）
Figure 581453DEST_PATH_IMAGE101
；（18）
Figure 154517DEST_PATH_IMAGE102
；（19）
Figure 513954DEST_PATH_IMAGE103
；（20）
Figure 551091DEST_PATH_IMAGE104
；（21）
Figure 363189DEST_PATH_IMAGE105
；（22）
Figure 5523DEST_PATH_IMAGE106
；（23）
in the formulae (16) to (23),
Figure 485046DEST_PATH_IMAGE107
，
Figure 171111DEST_PATH_IMAGE108
，
Figure 627762DEST_PATH_IMAGE109
，
Figure 542629DEST_PATH_IMAGE110
，
Figure 876658DEST_PATH_IMAGE111
，
Figure 733624DEST_PATH_IMAGE112
respectively representing binary variables of an investment substation, a newly-added transformer, a feeder line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; t denotes a period index; t represents the set of all discrete control periods; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; i and j respectively represent different node labels;
Figure 785894DEST_PATH_IMAGE113
represent different sets of nodes; l represents a feeder type designation; NRF and NAF represent existing new replacement feeders, respectivelyAnd a newly added feeder line; omega SS ，Ω p ，Ω ST ，Ω ch Respectively a transformer substation node set, a distributed power supply DG candidate node set, an energy storage ESS candidate node set and an electric vehicle charging station EVCS candidate node set; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
Figure 455516DEST_PATH_IMAGE114
a binary variable representing an electric vehicle charging station EVCS with extra capacity,
Figure 909631DEST_PATH_IMAGE115
a binary variable representing the electric vehicle charging station EVCS with the newly added capacity,
Figure 688232DEST_PATH_IMAGE116
representing a set of electric vehicle charging station EVCS candidate nodes with additional capacity,
Figure 227797DEST_PATH_IMAGE117
representing a set of alternatives, K, for an electric vehicle charging station EVCS with extra capacity NT ，K l ，K p ，K ST ，K ch The alternative schemes are respectively a newly added transformer, a branch line, a distributed power supply DG, an energy storage ESS and an electric vehicle charging station EVCS; p denotes a distributed power supply DG type index; p is a Distributed Generator (DG) type, P = { W, theta }, and W, theta respectively represent wind power generation and photovoltaic power generation; ch is an EVCS mark of the electric vehicle charging station; CH is a set of electric vehicle charging station EVCS types, CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively;
the electric vehicle demand constraints include:
Figure 733734DEST_PATH_IMAGE118
（24）
Figure 42355DEST_PATH_IMAGE119
；（25）
in the formulae (24) to (25),
Figure 991857DEST_PATH_IMAGE066
representing the charging requirement of the node electric vehicle;
Figure 238293DEST_PATH_IMAGE120
represents the upper power limit of the electric vehicle charging station EVCS,
Figure 564232DEST_PATH_IMAGE054
a binary variable representing the use of the electric vehicle charging station EVCS;
Figure 727360DEST_PATH_IMAGE121
representing the total electric vehicle charging requirement of the system; omega ch Representing a set of electric vehicle charging station EVCS candidate nodes; ch is an EVCS mark of the electric vehicle charging station; CH = { NCH, ACH }, where NCH and ACH represent new and additional capacity of the electric vehicle charging station, respectively; i represents a node number; k represents the labels of branch lines, transformers, distributed generation DGs, energy storage ESS and EVCS alternatives of the electric vehicle charging station; k ch Represents a set of alternatives for an electric vehicle charging station EVCS; t denotes a period index; t represents a set of all discrete control periods; b represents a day/night block number; b represents a day/night block set; q represents a quarterly index; q represents a quarterly set; ω denotes a scene number; Π represents a set of scenes.
11. A renewable energy, energy storage and charging pile collaborative planning system is characterized by comprising:
the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing a random scene generation framework for expressing load requirements, electric vehicle charging requirements, wind speed, solar irradiation and substation energy cost random variability;
the dividing unit is used for predicting the load demand and the load acceleration in a year of a planning cycle of the planning area and dividing the planning cycle into a plurality of dividing stages A according to the prediction result;
the construction unit is used for constructing a power distribution network collaborative planning model considering electric vehicle charging stations and renewable energy power generation and energy storage based on the maximum load demand predicted value of each division stage and respectively aiming at the minimum expected total cost of power distribution network planning in each division stage, wherein the expected total cost of power distribution network planning is minimized through the random scene generation framework;
and the solving unit is used for solving the optimal solution of the power distribution network collaborative planning model based on the constraint conditions of the power grid collaborative planning model to obtain the optimal planning scheme of each stage.
12. The renewable energy, energy storage and charging pile collaborative planning system according to claim 11, wherein the establishing unit includes:
the calculating module is used for calculating the charging requirement of the electric vehicle based on the statistical data of the electric vehicle;
the statistical data of the electric vehicle comprises: home data, vehicle type, travel distance, travel start time, travel end time, travel departure point, and travel end point.
13. The renewable energy, energy storage and charging pile co-planning system of claim 12, wherein the computing module comprises:
the first distribution submodule is used for randomly distributing the battery capacity of each electric vehicle and setting each electric vehicle to be fully charged when the first trip is started;
the setting submodule is used for setting the minimum value of the charging demand of each electric vehicle to be R% of the battery capacity of the electric vehicle i;
a second allocating submodule, configured to allocate a trip T to one of the electric vehicles i, a month m, and a day d, respectively;
the checking submodule is used for checking the charging state SOCi of the battery of the electric vehicle i when the travel T is finished for the electric vehicle i;
the searching submodule is used for searching the next stroke of the data set when the charging state SOCi of the battery of the electric vehicle i does not reach the minimum value of the charging requirement, and calculating and updating the charging requirement;
the charging submodule is used for charging the battery before the travel T and at the time interval when the electric vehicle i stops when the electric vehicle i reaches the minimum value of the charging requirement;
the checking submodule is also used for checking the state of charge SOCi of the battery again;
the searching submodule is also used for eliminating the travel and searching the next travel when the electric vehicle i still does not reach the minimum value of the charging requirement;
the electric vehicle demand calculation submodule is used for repeatedly running on the scale of days, months and vehicles after the last trip, and calculating to obtain the charging demand of the electric vehicle
Figure 565872DEST_PATH_IMAGE122
。
14. The renewable energy, energy storage and charging pile collaborative planning system according to claim 11, wherein the establishing unit includes:
the acquisition module is used for acquiring the small-level historical data;
the data dividing module is used for dividing the small-level historical data;
a scene set constructing module, configured to construct a scene set of each stage based on a K-CFSFDP algorithm, where the scene set of each stage is represented by a matrix formed by working conditions and uncertain parameters, and in the matrix, the uncertain parameters include: load demand, electric vehicle charging demand, wind speed, solar irradiation and substation energy cost;
the selection module is used for selecting the number K of the required clusters according to the requirement;
a clustering centroid calculation module for calculating weighted Euclidean distance of scene set data by using entropy method to obtain distance matrix d ij And for said distance matrix d ij The elements are arranged in an ascending order to obtain an ascending order sequence, and the ascending order sequence is takenThe distance value of the first 2% position of the ascending sequence is used as the truncation distance delta i (ii) a Calculating local density rho according to Gaussian kernel function i Establishing a consideration of i And ρ i Is a comprehensive index of i For the said comprehensive index γ i Performing descending order arrangement to obtain a descending order sequence, and selecting the first K data points of the descending order sequence as clustering centroids Ci;
the distance calculation module is used for calculating the distance Di (x) from each hour-level historical data to each clustering centroid Ci by using Euclidean distance, D represents the shortest distance from one data point to the nearest particle, and Di (x) represents that x is divided into clusters corresponding to the clustering centroids closest to each hour-level historical data;
the cluster centroid calculation module is further configured to recalculate the cluster centroids Ci to be centroids of all the points in the cluster centroids Ci:
the iteration calculation module is used for iteratively calculating the clustering mass center Ci until the cluster composition is not changed between two continuous iterations;
and the output module is used for outputting clustering mass centers and a matrix formed by working conditions and uncertain parameters, wherein each clustering mass center is represented by numerical values of load requirements, electric vehicle charging requirements, wind speed, solar irradiation and transformer substation energy cost, and the numerical values are used for displaying the operating condition of the system.
15. The renewable energy, energy storage, and charging pile collaborative planning system of claim 14, wherein the data partitioning module comprises:
the data dividing submodule is used for dividing the hour-level historical data into 4 seasons of spring, summer, autumn and winter, and in each season, the data are divided into day/night blocks, namely a day block and a night block, according to the actual sunrise and sunset time each day;
the scene set building module comprises:
the execution submodule carries a K-FSFDP algorithm to execute 8 times in each quarter and day/night block, namely 4 quarter multiplied by 2 day/night blocks;
the set of scenes for each stage is represented by a matrix of 96 operating conditions x 5 uncertain parameters, where the 96 operating conditions comprise 12 arrays of each quarter or day/night block x 4 quarter x 2 blocks, where for each day/night block b and quarter q, the probability for each case is determined by the observed value within each cluster divided by the total observed value for the respective day/night block b and quarter q.
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