CN115310291A - Intelligent building group energy management method considering dynamic access characteristic of electric vehicle - Google Patents

Intelligent building group energy management method considering dynamic access characteristic of electric vehicle Download PDF

Info

Publication number
CN115310291A
CN115310291A CN202210956562.8A CN202210956562A CN115310291A CN 115310291 A CN115310291 A CN 115310291A CN 202210956562 A CN202210956562 A CN 202210956562A CN 115310291 A CN115310291 A CN 115310291A
Authority
CN
China
Prior art keywords
building
electric
energy
power
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210956562.8A
Other languages
Chinese (zh)
Inventor
袁志毅
靳发文
董崇敏
董睿
赵乐乐
张宏涛
李顺业
侯旭敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Haixi Power Supply Co Of State Grid Qinghai Electric Power Co
Original Assignee
Haixi Power Supply Co Of State Grid Qinghai Electric Power Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Haixi Power Supply Co Of State Grid Qinghai Electric Power Co filed Critical Haixi Power Supply Co Of State Grid Qinghai Electric Power Co
Priority to CN202210956562.8A priority Critical patent/CN115310291A/en
Publication of CN115310291A publication Critical patent/CN115310291A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • 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
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]

Abstract

The invention relates to an intelligent building group energy management method considering dynamic access characteristics of electric vehicles, which belongs to the field of building energy management optimization. The invention fully excavates the schedulable potential of the electric automobile on the load side, and improves the operation economy of the building group and the utilization efficiency of renewable energy.

Description

Intelligent building group energy management method considering dynamic access characteristic of electric vehicle
Technical Field
The invention relates to the field of building energy management optimization, in particular to an intelligent building group energy management method considering dynamic access characteristics of electric vehicles.
Background
The construction target of the novel power system prompts a large amount of distributed renewable energy sources to be connected into a power distribution network, and the problem of low utilization efficiency of the renewable energy sources becomes the biggest obstacle of development and transformation of the novel power system. In addition, buildings which account for up to 40% of the global energy consumption proportion become main sources of carbon emission, and the building energy management technology for popularizing a zero-carbon emission mode is a new paradigm for current energy development and is also a way for solving the problem of high-proportion renewable energy grid connection and improving the local consumption proportion of renewable energy.
The current technology surrounding building energy management optimization underestimates the schedulable potential of electric vehicles on the load side of buildings, further influences the evaluation of the difference complementation capacity between buildings, and finally reflects the reduction of the renewable energy local power consumption rate.
Disclosure of Invention
The invention aims to provide an intelligent building group energy management method considering dynamic access characteristics of electric automobiles, fully excavates the schedulable potential of the electric automobiles on a load side, and improves the operation economy of a building group and the utilization efficiency of renewable energy.
In order to achieve the purpose, the invention provides the following scheme:
an intelligent building group energy management method considering dynamic access characteristics of electric vehicles, the method comprising:
building an energy model considering that the electric automobile is dynamically accessed to different buildings for multiple times in a dispatching cycle;
building a building group optimization model taking the charging and discharging power of the electric vehicle as an optimization variable according to the building energy model;
building a building energy management optimization model based on electric energy sharing according to the building energy model;
acquiring working parameters of each electric load in a building group to be optimized;
according to the working parameters, the building group optimization model is solved in an optimized mode, and the optimal charge and discharge power of the electric automobile in each time period is obtained;
determining charging energy distribution of the electric automobile in different buildings in a scheduling period according to the optimal charging and discharging power of the electric automobile at each time interval;
and solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
Optionally, the building energy model includes: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
Optionally, the dynamic access model of the electric vehicle is as follows:
Figure BDA0003791591120000021
Figure BDA0003791591120000022
Figure BDA0003791591120000023
Figure BDA0003791591120000024
Figure BDA0003791591120000025
Figure BDA0003791591120000026
Figure BDA0003791591120000027
Figure BDA0003791591120000028
Figure BDA0003791591120000029
Figure BDA00037915911200000210
in the formula, σ b，t Accessing the state value of the building b for the electric automobile in the time period t;
Figure BDA00037915911200000211
and
Figure BDA00037915911200000212
respectively representing first and second control variables;
Figure BDA00037915911200000213
the charging power of the building b is accessed to the electric vehicle j in the time period t under the scene s,
Figure BDA0003791591120000031
for the discharge power of the electric vehicle j at the building b in the time period t in the scene s,
Figure BDA0003791591120000032
and
Figure BDA0003791591120000033
respectively locating the electric automobile j at the charging and discharging power boundary of the building b in the time period t;
Figure BDA0003791591120000034
in a scene s, the electric vehicle j is in the state of charge of the building b in the time period t,
Figure BDA0003791591120000035
and
Figure BDA0003791591120000036
respectively representing the maximum value and the minimum value of the battery allowed state of charge operation of the electric vehicle j in the charging and discharging process of the building b;
Figure BDA0003791591120000037
for the state of charge of the electric vehicle j in travel after leaving b, D j,u The driving distance of the electric vehicle j on the u-th trip,
Figure BDA0003791591120000038
is the unit mileage power consumption t of the electric automobile j a,u And t l,u Are respectively electricityThe length of the network access time interval and the network connection time interval of the automobile j on the u-th trip,
Figure BDA0003791591120000039
the rated capacity of the battery of the electric vehicle j is obtained; at is the time interval at which the time interval,
Figure BDA00037915911200000310
respectively the electric energy conversion efficiency of the electric vehicle j in the charging and discharging mode of the building b;
Figure BDA00037915911200000311
to determine the state of charge of the electric vehicle at the end of the dispatch period,
Figure BDA00037915911200000312
the expected state of charge of the electric vehicle j off the network on the b-th day of the building;
Figure BDA00037915911200000313
the off-grid electric quantity for the preorder trip stage of the electric vehicle, D j And q is the number of travel mileage, q is the building number accessed in the preorder grid-connected stage, and Nb is the total number of the buildings in the building group.
Optionally, the heating, ventilating and air conditioning system model is as follows:
Figure BDA00037915911200000314
Figure BDA00037915911200000315
Figure BDA00037915911200000316
in the formula (I), the compound is shown in the specification,
Figure BDA00037915911200000317
as a third control variable, the control variable,
Figure BDA00037915911200000318
the power of the hvac in building b at time t under scenario s,
Figure BDA00037915911200000319
and
Figure BDA00037915911200000320
respectively the minimum and maximum power of the heating, ventilating and air conditioning in the building b at the moment t;
Figure BDA00037915911200000321
representing the indoor temperature of building b at time t under scene s,
Figure BDA00037915911200000322
and G b Are the thermal mass and thermal conductivity parameters respectively,
Figure BDA00037915911200000323
represents the outdoor temperature of building b at time t;
Figure BDA00037915911200000324
setting a comfortable room temperature range at the time t for a user in the building b;
the intelligent home system model is as follows:
Figure BDA00037915911200000325
Figure BDA00037915911200000326
Figure BDA00037915911200000327
Figure BDA0003791591120000041
Figure BDA0003791591120000042
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000043
the power consumption of the flexible load units in the building b at the moment t under the scene s, nu is the number of the flexible load units in the building b,
Figure BDA0003791591120000044
is a function of the fourth control variable,
Figure BDA0003791591120000045
rated power consumption of a flexible load unit in a building b under a scene s;
Figure BDA0003791591120000046
respectively are the control variables for opening and closing the intelligent home,
Figure BDA0003791591120000047
a range of expected work periods set by a user in building b under scenario s, T is a scheduling period,
Figure BDA0003791591120000048
the minimum continuous working time of the flexible load unit in the building b under the scene s is set;
the building fixed energy storage system model is as follows:
Figure BDA0003791591120000049
Figure BDA00037915911200000410
Figure BDA00037915911200000411
Figure BDA00037915911200000412
Figure BDA00037915911200000413
Figure BDA00037915911200000414
in the formula (I), the compound is shown in the specification,
Figure BDA00037915911200000415
respectively a fifth control variable and a sixth control variable,
Figure BDA00037915911200000416
and
Figure BDA00037915911200000417
respectively the charging and discharging power provided by the fixed energy storage system in the building b at the moment t under the scene s,
Figure BDA00037915911200000418
and
Figure BDA00037915911200000419
respectively representing the maximum value of the charging power and the minimum value of the discharging power of the electric vehicle j at the moment t in the building b;
Figure BDA00037915911200000420
the state of charge of a fixed energy storage system in a building b at a time t under a scene s,
Figure BDA00037915911200000421
and
Figure BDA00037915911200000422
are respectively charged and dischargedThe maximum and minimum allowable state of charge operation of the battery during electrical processes,
Figure BDA00037915911200000423
for the battery rating of the fixed energy storage system within building b,
Figure BDA00037915911200000424
respectively the electric energy conversion efficiency of the fixed energy storage system in the building b under the charging and discharging modes,
Figure BDA00037915911200000425
respectively the charge states of the fixed energy storage system in the building b at the beginning and ending periods of the scheduling cycle;
the uncertainty model of the renewable energy and the building load is as follows:
Figure BDA0003791591120000051
Figure BDA0003791591120000052
Figure BDA0003791591120000053
Figure BDA0003791591120000054
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000055
and with
Figure BDA0003791591120000056
Respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t under the scene s,
Figure BDA0003791591120000057
and
Figure BDA0003791591120000058
respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t,
Figure BDA0003791591120000059
and
Figure BDA00037915911200000510
respectively the prediction errors of the photovoltaic power, the wind power and the base load of the building b under the scene s at the moment t,
Figure BDA00037915911200000511
and the overall output condition of renewable energy sources in the building is shown.
Optionally, the building group optimization model includes: a first objective function and a power balance constraint of the building group;
the first objective function is:
Figure BDA00037915911200000512
Figure BDA00037915911200000513
Figure BDA00037915911200000514
wherein C is the total operation cost of the building group when the dynamic access characteristic of the electric automobile is considered, and pi s Is the scene probability, ns is the number of scene sets,
Figure BDA00037915911200000515
in order to dynamically switch in the charging and discharging cost of the electric automobile,
Figure BDA00037915911200000516
for building groups and upper levelsThe interaction cost of the power grid, nev is the total number of electric vehicles in the region,
Figure BDA00037915911200000517
respectively the purchase and sale prices of the external power grid at the moment t,
Figure BDA00037915911200000518
respectively representing the charging power and the discharging power of the electric automobile j in the time period t under the scene s,
Figure BDA00037915911200000519
respectively expecting the electricity sale and purchase quantity of the building b and an external power grid in the time period t through an agent in the scene s;
the power balance constraint of the building group comprises an electric energy balance constraint and a peak load constraint of trading between an agent and an external power grid;
the electric energy balance constraint is as follows:
Figure BDA0003791591120000061
the peak load constraint of the agent trading with the external power grid is as follows:
Figure BDA0003791591120000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000063
for the peak load of a building b transacting with the external power grid through an agent during a time period t under a scene s,
Figure BDA0003791591120000064
and (4) carrying out transaction with the external power grid for the peak load maximum value of the building b in the time period t through the agent.
Optionally, the building energy management optimization model based on electric energy sharing includes: a second objective function and a power balance constraint inside the building;
the second objective function is:
Figure BDA0003791591120000065
Figure BDA0003791591120000066
Figure BDA0003791591120000067
Figure BDA0003791591120000068
in the formula, C BEMSs For the total operating cost of the building complex based on power sharing,
Figure BDA0003791591120000069
respectively the dispatching cost of the electric automobile and the fixed energy storage,
Figure BDA00037915911200000610
for the interaction cost of the building with the external grid, gamma s，b，t Cost of loss, α, for shared transactions p In order to share the loss factor of the transmission line,
Figure BDA00037915911200000611
the electric quantity shared by the electric energy between the buildings, J is the total quantity of the energy storage resources configured in the buildings,
Figure BDA00037915911200000612
for the investment cost of batteries of electric vehicles and energy storage systems,
Figure BDA00037915911200000613
and
Figure BDA00037915911200000614
respectively the total charge-discharge cycle number, the discharge depth and the degradation factor of the battery of the electric automobile j in the building b;
Figure BDA00037915911200000615
respectively buying and selling electric quantity of the building b to the external power grid in the time period t under the scene s;
the power balance constraints inside the building include:
power balance constraints inside the building:
Figure BDA00037915911200000616
Figure BDA0003791591120000071
Figure BDA0003791591120000072
Figure BDA0003791591120000073
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000074
and with
Figure BDA0003791591120000075
Respectively an electric energy gap and surplus after the building b shares the time interval t under the scene s,
Figure BDA0003791591120000076
respectively sharing electric energy between the intelligent buildings at the time t under the scene s to purchase and sell electric energy;
electric energy sharing power balance constraint between intelligent buildings:
Figure BDA0003791591120000077
and (3) power capacity constraint of connecting lines among buildings:
Figure BDA0003791591120000078
Figure BDA0003791591120000079
Figure BDA00037915911200000710
Figure BDA00037915911200000711
in the formula (I), the compound is shown in the specification,
Figure BDA00037915911200000712
respectively a seventh control variable and an eighth control variable,
Figure BDA00037915911200000713
m is an arbitrarily large positive number for the limit of the line transmission power.
Optionally, according to the building energy model, a building energy management optimization model based on electric energy sharing is established, and then the method further includes:
introducing a Lagrange multiplier vector lambda, and converting the building energy management optimization model into a Lagrange form:
Figure BDA00037915911200000714
in the formula (I), the compound is shown in the specification,
Figure BDA00037915911200000715
representing the clear share electricity price for Lagrange multiplier variable;
decomposing Lagrange-form building energy management optimization model into NbThe sub-problem is solved by each building independently, and the sub-optimization model with the lowest total operation cost is the target; the objective function of the sub-optimization model of each building is
Figure BDA00037915911200000716
In the formula (I), the compound is shown in the specification,
Figure BDA00037915911200000717
the total operating cost of the building b based on electric energy sharing.
Optionally, based on the charging energy distribution of the electric vehicle in different buildings in the scheduling period, the building energy management optimization model is solved by using a secondary gradient method, and the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each power load in the buildings are determined, which specifically includes:
determining a pilot signal as
Figure BDA0003791591120000081
And
Figure BDA0003791591120000082
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000083
is the shared electricity price at the current iteration, omega is the number of interactive iterations,
Figure BDA0003791591120000084
for iteration step size, epsilon pri In order to be the criterion of convergence, the system,
Figure BDA0003791591120000085
the electric quantity shared by the electric energy between the buildings when the omega-th interactive iteration times;
based on charging energy distribution and guide signals of the electric automobile in different buildings in the scheduling period, a sub-optimization model of each building is solved by adopting a dual-order gradient method, and the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings are determined.
An intelligent building group energy management system considering dynamic access characteristics of electric vehicles, the system comprising:
the building energy model building module is used for building a building energy model considering that the electric automobile is dynamically accessed to different buildings for multiple times in a dispatching cycle;
the building group optimization model establishing module is used for establishing a building group optimization model taking the charge and discharge power of the electric automobile as an optimization variable according to the building energy model;
the building energy management optimization model establishing module is used for establishing a building energy management optimization model based on electric energy sharing according to the building energy model;
the parameter acquisition module is used for acquiring working parameters of each electric load in the building group to be optimized;
the power optimization module is used for optimizing and solving the building group optimization model according to the working parameters to obtain the optimal charging and discharging power of the electric automobile at each time interval;
the charging energy distribution module is used for determining charging energy distribution of the electric automobile in different buildings in a scheduling cycle according to the optimal charging and discharging power of the electric automobile at each time interval;
and the building energy optimization module is used for solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
Optionally, the building energy model includes: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses an intelligent building group energy management method considering dynamic access characteristics of electric vehicles. The invention fully excavates the schedulable potential of the electric automobile on the load side, and improves the operation economy of the building group and the utilization efficiency of renewable energy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of an intelligent building group energy management method considering dynamic access characteristics of electric vehicles according to an embodiment of the present invention;
fig. 2 is a framework diagram of coordinated operation of an energy management system of an intelligent building group according to an embodiment of the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide an intelligent building group energy management method considering dynamic access characteristics of electric automobiles, so that the schedulable potential of the electric automobiles on the load side is fully excavated, and the running economy of a building group and the utilization efficiency of renewable energy are improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The embodiment of the invention provides an intelligent building group energy management method considering dynamic access characteristics of electric automobiles, and as shown in figure 1, the method comprises the following steps:
s1, building energy models considering that the electric vehicles are dynamically accessed to different buildings for multiple times in a scheduling period are built.
The building energy model includes: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
1) Dynamic access model of electric automobile
A set of control variables δ is introduced into the model to represent the necessary logical constraints.
Figure BDA0003791591120000101
Figure BDA0003791591120000102
Figure BDA0003791591120000103
Figure BDA0003791591120000104
Figure BDA0003791591120000105
In the formula: sigma b，t Accessing the state value of the building b for the electric automobile in the time period t;
Figure BDA0003791591120000106
and
Figure BDA0003791591120000107
respectively representing first and second control variables;
Figure BDA0003791591120000108
the charging power of the building b is accessed to the electric vehicle j in the time period t under the scene s,
Figure BDA0003791591120000109
the discharge power of the electric vehicle j at the building b in the time period t under the scene s,
Figure BDA00037915911200001010
and with
Figure BDA00037915911200001011
Respectively enabling the electric vehicle j to be positioned on the charging and discharging power boundaries of the building b in the time period t;
Figure BDA00037915911200001012
in a scene s, the electric vehicle j is in the state of charge of the building b in a time period t,
Figure BDA00037915911200001013
and
Figure BDA00037915911200001014
the maximum value and the minimum value of the state of charge operation allowed by the battery when the electric vehicle j is in the charging and discharging process of the building b are respectively.
Figure BDA00037915911200001015
In the formula:
Figure BDA00037915911200001016
for the state of charge of the electric vehicle j in travel after leaving b, D j,u The driving distance of the electric vehicle j on the u-th trip,
Figure BDA00037915911200001017
is the unit mileage power consumption of the electric automobile j, t a,u And t l,u The lengths of the network access time interval and the network connection time interval of the electric vehicle j on the u-th trip are respectively,
Figure BDA00037915911200001018
the rated capacity of the battery of the electric vehicle j. The above equation describes the discharge process in the EV moving state.
Figure BDA0003791591120000111
Figure BDA0003791591120000112
Figure BDA0003791591120000113
In the formula: at is the time interval at which the time interval,
Figure BDA0003791591120000114
respectively the electric energy conversion efficiency of the electric vehicle j in the charging and discharging mode of the building b;
Figure BDA0003791591120000115
to determine the state of charge of the electric vehicle at the end of the dispatch period,
Figure BDA0003791591120000116
and (4) the expected state of charge of the electric vehicle j off the network on the b-th day of the building. The vehicle-mounted battery energy constraint under the EV dynamic access characteristic is realized by the following formula:
Figure BDA0003791591120000117
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000118
off-grid power for the preorder travel stage of an electric vehicle, D j And q is the number of travel mileage, q is the building number accessed in the preorder grid-connected stage, and Nb is the total number of the buildings in the building group.
2) Heating, ventilating and air conditioning system model:
Figure BDA0003791591120000119
Figure BDA00037915911200001110
Figure BDA00037915911200001111
in the formula:
Figure BDA00037915911200001112
as a third control variable, the control variable,
Figure BDA00037915911200001113
for the power of the hvac in building b at time t under scenario s,
Figure BDA00037915911200001114
and
Figure BDA00037915911200001115
respectively the minimum and maximum power of the heating, ventilating and air conditioning in the building b at the moment t;
Figure BDA00037915911200001116
representing the indoor temperature of building b at time t under scene s,
Figure BDA00037915911200001117
and G b Are the thermal mass and thermal conductivity parameters respectively,
Figure BDA00037915911200001118
represents the outdoor temperature of building b at time t;
Figure BDA00037915911200001119
a comfortable room temperature range at time t set for a user within the building b.
3) The intelligent home system model:
household loads such as washing machines and induction cookers in buildings can be used as power transfer flexible loads to participate in energy management optimization:
Figure BDA00037915911200001120
Figure BDA0003791591120000121
Figure BDA0003791591120000122
Figure BDA0003791591120000123
Figure BDA0003791591120000124
in the formula:
Figure BDA0003791591120000125
the power consumption of the flexible load units in the building b at the moment t under the scene s, nu is the number of the flexible load units in the building b,
Figure BDA0003791591120000126
is the fourth control variable and is the fourth control variable,
Figure BDA0003791591120000127
interior gentle of building b for scene sRated power consumption of the sexual load unit;
Figure BDA0003791591120000128
respectively are the control variables for opening and closing the intelligent home,
Figure BDA0003791591120000129
describing a time flexibility space for power transfer for a desired working period range set by a user in a building b under a scene s; t is the scheduling period of the time sequence,
Figure BDA00037915911200001210
the minimum continuous working time of the flexible load unit in the building b under the scene s. The constraints of the operation start interval and the minimum continuous operation time are expressed by the formulas (3 b-3 d).
4) Building fixed energy storage system model:
Figure BDA00037915911200001211
Figure BDA00037915911200001212
Figure BDA00037915911200001213
Figure BDA00037915911200001214
Figure BDA00037915911200001215
Figure BDA00037915911200001216
in the formula:
Figure BDA00037915911200001217
respectively a fifth control variable and a sixth control variable,
Figure BDA00037915911200001218
and
Figure BDA00037915911200001219
respectively the charging and discharging power provided by the fixed energy storage system in the building b at the moment t under the scene s,
Figure BDA00037915911200001220
and
Figure BDA00037915911200001221
the charging power maximum value and the discharging power minimum value of the electric automobile j at the moment t in the building b are respectively;
Figure BDA00037915911200001222
the state of charge of a fixed energy storage system in a building b at a time t under a scene s,
Figure BDA00037915911200001223
and
Figure BDA00037915911200001224
respectively the maximum value and the minimum value of the state of charge operation allowed by the battery in the charging and discharging process,
Figure BDA0003791591120000131
for the battery rating of the fixed energy storage system within building b,
Figure BDA0003791591120000132
the electric energy conversion efficiency of the fixed energy storage system in the building b in the charging and discharging modes is respectively,
Figure BDA0003791591120000133
respectively charging the fixed energy storage system in the building b at the starting and ending periods of the scheduling cycleStatus. Equation (4 f) ensures energy balance of the fixed energy storage system at the beginning and ending periods of the scheduling cycle.
5) Uncertainty model of renewable energy and building load:
Figure BDA0003791591120000134
Figure BDA0003791591120000135
Figure BDA0003791591120000136
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000137
and with
Figure BDA0003791591120000138
Respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t under the scene s,
Figure BDA0003791591120000139
and with
Figure BDA00037915911200001310
Respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t,
Figure BDA00037915911200001311
and with
Figure BDA00037915911200001312
Respectively the prediction errors of the photovoltaic power, the wind power and the base load of the building b under the scene s at the moment t,
Figure BDA00037915911200001313
and the overall output condition of renewable energy sources in the building is shown. The invention will utilize Monte Carlo sampling andk-means clustering generates a typical scene set and scene occurrence probability to characterize uncertainty.
Figure BDA00037915911200001314
And S2, establishing a building group optimization model taking the charging and discharging power of the electric automobile as an optimization variable according to the building energy model.
The building group optimization model comprises: a first objective function and a power balance constraint for the building group.
1) Establishing a first objective function:
Figure BDA00037915911200001315
Figure BDA00037915911200001316
Figure BDA00037915911200001317
in the formula: c is the total operation cost of the building group, pi, when the dynamic access characteristic of the electric automobile is considered s Is the scene probability, ns is the number of scene sets,
Figure BDA00037915911200001318
in order to dynamically switch in the charging and discharging cost of the electric automobile,
Figure BDA00037915911200001319
the interaction cost between a building group and a superior power grid is shown, nev is the total number of electric vehicles in the area,
Figure BDA0003791591120000141
respectively the purchase and sale prices of the external power grid at the moment t,
Figure BDA0003791591120000142
respectively the charging power and the discharging power of the electric automobile j in the time period t under the scene s,
Figure BDA0003791591120000143
and respectively expecting the electricity selling and purchasing quantity of the building b in the scene s and the external power grid in the time period t through the agent. Equation (7 c) represents charge/discharge optimization cost of EV.
2) Power balance constraints for building groups:
the electric energy balance constraint of each intelligent building in each time period t in the scene s is as follows, and the real-time balance of the internal resource power of the building is represented:
Figure BDA0003791591120000144
the peak load constraints for agent trading with the external grid are as follows:
Figure BDA0003791591120000145
in the formula (I), the compound is shown in the specification,
Figure BDA0003791591120000146
for the peak load of building b trading with the external power grid at time t by the agent under scenario s,
Figure BDA0003791591120000147
and (4) carrying out transaction with the external power grid for the peak load maximum value of the building b in the time period t through the agent.
And S3, building a building energy management optimization model based on electric energy sharing according to the building energy model.
The building energy management optimization model based on electric energy sharing comprises the following steps: a second objective function and a power balance constraint inside the building.
1) Establishing an objective function:
Figure BDA0003791591120000148
Figure BDA0003791591120000149
Figure BDA00037915911200001410
Figure BDA00037915911200001411
in the formula: c BEMSs For the total operating cost of the building complex based on power sharing,
Figure BDA00037915911200001412
the dispatching costs of the electric automobile and the fixed energy storage are respectively,
Figure BDA00037915911200001413
for the interaction cost of the building with the external grid, gamma s，b，t Cost of loss, α, for shared transactions p J is the total number of energy storage resources configured in the building for the loss factor of the shared transmission line,
Figure BDA0003791591120000151
for the investment cost of batteries of electric vehicles and energy storage systems,
Figure BDA0003791591120000152
and
Figure BDA0003791591120000153
respectively the total charge-discharge cycle number, the discharge depth and the degradation factor of the battery of the electric automobile j in the building b;
Figure BDA0003791591120000154
the electricity purchasing and selling of the building b to the external power grid in the time period t under the scene s are respectively carried out.
Figure BDA0003791591120000155
The electric power shared by the electric energy between buildings is represented, and the formula (12 c) is specifically described.
2) Power balance constraints inside the building:
Figure BDA0003791591120000156
description of the amount of electricity between the building and the external grid and other buildings:
Figure BDA0003791591120000157
Figure BDA0003791591120000158
in the formula:
Figure BDA0003791591120000159
and with
Figure BDA00037915911200001510
Respectively the electric energy gap and surplus after the building b shares in the time period t under the scene s,
Figure BDA00037915911200001511
electric energy is respectively shared between the intelligent buildings at the time t under the scene s to buy and sell electric energy. The following equation (12 c) describes the final exchange of power shared by the intelligent building and other buildings.
Figure BDA00037915911200001512
Finally, the electric energy sharing power balance among the intelligent buildings is expressed by the following formula:
Figure BDA00037915911200001513
the power capacity constraints of the connected lines between buildings are as follows:
Figure BDA00037915911200001514
Figure BDA00037915911200001515
in addition to the constraint of equation (9), the following constraint needs to be added to ensure the direction of the current, and the present invention adopts the large M method.
Figure BDA00037915911200001516
Figure BDA00037915911200001517
In the formula (I), the compound is shown in the specification,
Figure BDA00037915911200001518
respectively a seventh control variable and an eighth control variable,
Figure BDA00037915911200001519
m is an arbitrary, large positive number for the limit value of the line transmission power.
And S4, acquiring working parameters of each electric load in the building group to be optimized.
And S5, according to the working parameters, optimizing and solving the building group optimization model to obtain the optimal charge and discharge power of the electric automobile at each time interval.
And S6, determining charging energy distribution of the electric automobile in different buildings in the scheduling period according to the optimal charging and discharging power of the electric automobile at each time interval.
And S7, solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
Firstly, introducing a Lagrange multiplier vector lambda to process a coupling constraint formula (13) between buildings, and converting an optimization model into a Lagrange form:
Figure BDA0003791591120000161
in the formula, L () is a mapping symbol,
Figure BDA0003791591120000162
is a lagrange multiplier variable representing a clear share price of electricity. The dual optimization model (16) can be disassembled into Nb sub-problems, and sub-optimization models with the lowest total operation cost of the building as the target are independently solved by each building. Wherein the total operation cost optimization model objective function of each building can be described by the following formula:
Figure BDA0003791591120000163
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003791591120000164
the total operating cost of the building b based on electric energy sharing.
The invention adopts dual gradient algorithm (DSG) calculation, and the guide signals in the iteration process are as follows:
Figure BDA0003791591120000165
in the formula: ω denotes the number of interaction iterations,
Figure BDA0003791591120000166
respectively representing an optimal solution of the optimization model (16),
Figure BDA0003791591120000167
representing iterationsThe step size is such that,
Figure BDA0003791591120000168
the shared electricity price at the current iteration. In the present invention, the original residual of each scene is used as the convergence criterion of the DSG algorithm to ensure that equation (13) is satisfied:
Figure BDA0003791591120000169
wherein epsilon pri In order to be the criterion of convergence, the system,
Figure BDA00037915911200001610
the number of the power shared by the electric energy between the buildings when the number of the interaction iterations is omega.
The optimization result of the invention comprises the following steps: optimal electric quantity shared by electric energy between buildings in each time interval
Figure BDA0003791591120000171
Charging power of electric vehicle j in time period t under scene s
Figure BDA0003791591120000172
And discharge power
Figure BDA0003791591120000173
Charging power of electric vehicle j connected to building b at time t under scene s
Figure BDA0003791591120000174
Electric vehicle j is in discharge power of building b in time period t under scene s
Figure BDA0003791591120000175
Power of heating, ventilating and air conditioning at time t in building b under scene s
Figure BDA0003791591120000176
Power consumption power of flexible load unit in building b at time t under scene s
Figure BDA0003791591120000177
Charging power provided by fixed energy storage system in building b at moment t under scene s
Figure BDA0003791591120000178
And discharge power
Figure BDA0003791591120000179
The scheduling period is one day, the potential that the electric automobile is connected to different buildings and receives charging and discharging power regulation within one day is considered according to the moving and parking characteristics of the electric automobile, and the charging energy requirement of the electric automobile within one day is fixed on the assumption that the power consumption of the electric automobile in the distance from the electric automobile to the different buildings tends to be constant. Due to the space-time difference between the moving and parking of the electric vehicle, the electric vehicle charging energy demand is distributed among different buildings, so that the electric vehicle charging energy demand has optimization potential. At present, a specific electric vehicle is supposed to run to a building b from the building a in a full-power state, the initial vehicle-mounted electric quantity is 60% when the building b is parked, the vehicle-mounted electric quantity is supposed to be charged to x% when the building b is connected to participate in charging and discharging adjustment, the vehicle-mounted electric quantity is supposed to be charged to x% when the building b is off the network, the building b returns to the building a, and the building a is parked to be full until the building a is re-traveled the next day. Wherein x is an optimization variable, the feasible space of the optimization solution of x% is any number between 60% and 100%, and charging energy distribution of the electric vehicle in different buildings is determined through the steps S5 and S6, namely the charging state of the electric vehicle when the electric vehicle leaves from the building b is determined, so that the size of flexible resources releasable by the electric vehicle in different buildings is optimized. Further, based on the charging state of the electric vehicle when the electric vehicle leaves the building b, the optimal electric quantity shared by the electric energy between the buildings and the optimal power of each electric load inside the building can be obtained optimally by using the step S7.
The invention has the following advantages:
(1) And based on the schedulable resource modeling of the dynamic access characteristic of the electric automobile, the schedulable potential of the building load side is mined.
(2) The difference of the complementary building group energy in operation can be optimized, and the autonomy and privacy of building optimization are ensured.
(3) The dual decomposition gradient method adopted by the invention can effectively solve the problem of balanced supply and demand of distributed electric energy sharing, reduce the dependence of building groups on the electric energy of the power distribution network and improve the utilization efficiency of distributed renewable energy.
The embodiment of the invention also provides an intelligent building group energy management system considering the dynamic access characteristic of the electric automobile, which comprises:
the building energy model building module is used for building a building energy model considering that the electric automobile is dynamically accessed to different buildings for multiple times in a dispatching cycle;
the building group optimization model establishing module is used for establishing a building group optimization model taking the charge and discharge power of the electric vehicle as an optimization variable according to the building energy model;
the building energy management optimization model establishing module is used for establishing a building energy management optimization model based on electric energy sharing according to the building energy model;
the parameter acquisition module is used for acquiring working parameters of each electric load in the building group to be optimized;
the power optimization module is used for solving the building group optimization model in an optimization mode according to the working parameters to obtain the optimal charging and discharging power of the electric automobile at each time interval;
the charging energy distribution module is used for determining charging energy distribution of the electric automobile in different buildings in a scheduling cycle according to the optimal charging and discharging power of the electric automobile at each time interval;
and the building energy optimization module is used for solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
The building energy model includes: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
FIG. 2 illustrates an energy management system coordinated operation framework of an intelligent building complex. The intelligent building group is connected with the communication network through electric power, and the intelligent building group agent serves as a third party for interaction of the building and power grid energy, coordinates electric energy sharing transactions among the buildings and supervises sharing electric quantity balance among the buildings. The intelligent building firstly determines the electric energy interaction with the electric automobile: the buildings which are movably connected to form a coupling relation are reported to the agent according to the running conditions of the buildings, and the agent coordinates the charging energy requirements of the electric vehicles connected to different buildings according to the running characteristics of the building group, so that the capacity of the building group for consuming renewable energy is maximized. After charging energy distribution of the electric automobile is completed, the intelligent building makes an optimal power distribution plan according to the cost of internal resource scheduling and reports the power distribution plan to a building group agent, wherein the optimal power distribution plan comprises expected shared electric quantity and electric quantity interacted with a power distribution network. The agent receives the shared electric quantity of each building and sends a guide signal to enable the electric power of the building in the shared network to be balanced. Finally, the electric energy gap or surplus part after the two-stage electric energy sharing is finished is cleared by an external power grid according to market price, and the final electric energy balance between the intelligent building group and the power grid is realized.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
The principle and the implementation mode of the invention are explained by applying a specific example, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. An intelligent building group energy management method considering dynamic access characteristics of electric vehicles is characterized by comprising the following steps:
building an energy model considering that the electric automobile is dynamically accessed to different buildings for multiple times in a dispatching cycle;
building a building group optimization model taking the charging and discharging power of the electric vehicle as an optimization variable according to the building energy model;
building a building energy management optimization model based on electric energy sharing according to the building energy model;
acquiring working parameters of each electric load in a building group to be optimized;
according to the working parameters, the building group optimization model is optimized and solved, and the optimal charging and discharging power of the electric automobile at each time interval is obtained;
determining charging energy distribution of the electric automobile in different buildings in a scheduling period according to the optimal charging and discharging power of the electric automobile at each time interval;
and solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
2. The method of claim 1, wherein the building energy model comprises: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
3. The method of claim 2, wherein the dynamic access model of the electric vehicle is:
Figure FDA0003791591110000011
Figure FDA0003791591110000012
Figure FDA0003791591110000013
Figure FDA0003791591110000014
Figure FDA0003791591110000015
Figure FDA0003791591110000016
Figure FDA0003791591110000021
Figure FDA0003791591110000022
Figure FDA0003791591110000023
Figure FDA0003791591110000024
in the formula, σ b，t Accessing the state value of the building b for the electric automobile in the time period t;
Figure FDA0003791591110000025
and
Figure FDA0003791591110000026
respectively representing first and second control variables;
Figure FDA0003791591110000027
the charging power of the building b is accessed to the electric vehicle j in the time period t under the scene s,
Figure FDA0003791591110000028
the discharge power of the electric vehicle j at the building b in the time period t under the scene s,
Figure FDA0003791591110000029
and
Figure FDA00037915911100000210
respectively enabling the electric vehicle j to be positioned on the charging and discharging power boundaries of the building b in the time period t;
Figure FDA00037915911100000211
in a scene s, the electric vehicle j is in the state of charge of the building b in a time period t,
Figure FDA00037915911100000212
and with
Figure FDA00037915911100000213
Respectively representing the maximum value and the minimum value of the battery allowed state of charge operation of the electric vehicle j in the charging and discharging process of the building b;
Figure FDA00037915911100000214
for the state of charge of the electric vehicle in driving after j leaves b, D j,u The driving distance of the electric vehicle j on the u-th trip,
Figure FDA00037915911100000215
is the unit mileage power consumption t of the electric automobile j a,u And t l,u The lengths of the network access time interval and the network connection time interval of the electric vehicle j on the u-th trip are respectively,
Figure FDA00037915911100000216
the rated capacity of the battery of the electric automobile j is obtained; at is the time interval at which the time interval,
Figure FDA00037915911100000217
respectively the electric energy conversion efficiency of the electric vehicle j in the charging and discharging mode of the building b;
Figure FDA00037915911100000218
to determine the state of charge of the electric vehicle at the end of the dispatch period,
Figure FDA00037915911100000219
the expected state of charge of the electric vehicle j off the network on the b-th day of the building;
Figure FDA00037915911100000220
the off-grid electric quantity for the preorder trip stage of the electric vehicle, D j And q is the number of travel mileage, q is the building number accessed in the preorder grid-connected stage, and Nb is the total number of the buildings in the building group.
4. The method of claim 3, wherein the HVAC system model is:
Figure FDA00037915911100000221
Figure FDA00037915911100000222
Figure FDA00037915911100000223
in the formula (I), the compound is shown in the specification,
Figure FDA00037915911100000224
as a third control variable, the control variable,
Figure FDA00037915911100000225
for the power of the hvac in building b at time t under scenario s,
Figure FDA00037915911100000226
and
Figure FDA00037915911100000227
respectively the minimum and maximum power of the heating, ventilating and air conditioning in the building b at the moment t;
Figure FDA00037915911100000228
representing the indoor temperature of building b at time t under scene s,
Figure FDA00037915911100000229
and G b Are the thermal mass and thermal conductivity parameters respectively,
Figure FDA0003791591110000031
represents the outdoor temperature of building b at time t;
Figure FDA0003791591110000032
setting a comfortable room temperature range at the time t for a user in the building b;
the intelligent home system model is as follows:
Figure FDA0003791591110000033
Figure FDA0003791591110000034
Figure FDA0003791591110000035
Figure FDA0003791591110000036
Figure FDA0003791591110000037
in the formula (I), the compound is shown in the specification,
Figure FDA0003791591110000038
the power consumption of the flexible load units in the building b at the moment t under the scene s, nu is the number of the flexible load units in the building b,
Figure FDA0003791591110000039
is a function of the fourth control variable,
Figure FDA00037915911100000310
rated power consumption of a flexible load unit in a building b under a scene s;
Figure FDA00037915911100000311
respectively are the control variables for opening and closing the intelligent home,
Figure FDA00037915911100000312
a range of expected working periods set for users in building b under scene s, T is a scheduling period,
Figure FDA00037915911100000313
the minimum continuous working time of the flexible load unit in the building b under the scene s;
the building fixed energy storage system model is as follows:
Figure FDA00037915911100000314
Figure FDA00037915911100000315
Figure FDA00037915911100000316
Figure FDA00037915911100000317
Figure FDA00037915911100000318
Figure FDA00037915911100000319
in the formula (I), the compound is shown in the specification,
Figure FDA00037915911100000320
respectively a fifth control variable and a sixth control variable,
Figure FDA00037915911100000321
and
Figure FDA00037915911100000322
respectively the charging and discharging power provided by the fixed energy storage system in the building b at the moment t under the scene s,
Figure FDA0003791591110000041
and with
Figure FDA0003791591110000042
Are respectively electricityThe maximum value of the charging power and the minimum value of the discharging power of the electric vehicle j at the moment t in the building b;
Figure FDA0003791591110000043
the state of charge of a fixed energy storage system in a building b at a time t under a scene s,
Figure FDA0003791591110000044
and with
Figure FDA0003791591110000045
Respectively the maximum value and the minimum value of the state of charge operation allowed by the battery in the charging and discharging process,
Figure FDA0003791591110000046
for the battery rating of the fixed energy storage system within building b,
Figure FDA0003791591110000047
respectively the electric energy conversion efficiency of the fixed energy storage system in the building b under the charging and discharging modes,
Figure FDA0003791591110000048
respectively the charge states of the fixed energy storage system in the building b at the beginning and ending periods of the scheduling cycle;
the uncertainty model of the renewable energy and the building load is as follows:
Figure FDA0003791591110000049
Figure FDA00037915911100000410
Figure FDA00037915911100000411
Figure FDA00037915911100000412
in the formula (I), the compound is shown in the specification,
Figure FDA00037915911100000413
and with
Figure FDA00037915911100000414
Respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t under the scene s,
Figure FDA00037915911100000415
and
Figure FDA00037915911100000416
respectively are the predicted values of the photovoltaic power, the wind power and the base load of the building b at the moment t,
Figure FDA00037915911100000417
and with
Figure FDA00037915911100000418
Respectively the prediction errors of the photovoltaic power, the wind power and the base load of the building b under the scene s at the moment t,
Figure FDA00037915911100000419
and the overall output condition of renewable energy sources in the building is shown.
5. The method of claim 4, wherein the building group optimization model comprises: a first objective function and a power balance constraint of the building group;
the first objective function is:
Figure FDA00037915911100000420
Figure FDA00037915911100000421
Figure FDA00037915911100000422
wherein C is the total operation cost of the building group when the dynamic access characteristic of the electric automobile is considered, and pi s Is the scene probability, ns is the number of scene sets,
Figure FDA00037915911100000423
in order to dynamically switch in the charge-discharge cost of the electric automobile,
Figure FDA00037915911100000424
the interaction cost between a building group and a superior power grid is shown, nev is the total number of electric vehicles in the area,
Figure FDA0003791591110000051
respectively the purchase and sale prices of the external power grid at the moment t,
Figure FDA0003791591110000052
respectively representing the charging power and the discharging power of the electric automobile j in the time period t under the scene s,
Figure FDA0003791591110000053
respectively expecting the electricity selling and purchasing quantity of the building b and the external power grid in the time period t through the agent in the scene s;
the power balance constraint of the building group comprises an electric energy balance constraint and a peak load constraint of trading between an agent and an external power grid;
the electric energy balance constraint is as follows:
Figure FDA0003791591110000054
the peak load constraint of the agent trading with the external power grid is as follows:
Figure FDA0003791591110000055
in the formula (I), the compound is shown in the specification,
Figure FDA0003791591110000056
for the peak load of a building b transacting with the external power grid through an agent during a time period t under a scene s,
Figure FDA0003791591110000057
and (4) carrying out transaction with the external power grid for the peak load maximum value of the building b in the time period t through the agent.
6. The method of claim 5, wherein the power-sharing based building energy management optimization model comprises: a second objective function and a power balance constraint inside the building;
the second objective function is:
Figure FDA0003791591110000058
Figure FDA0003791591110000059
Figure FDA00037915911100000510
Figure FDA00037915911100000511
in the formula, C BEMSs For the total operating cost of the building complex based on power sharing,
Figure FDA00037915911100000512
respectively the dispatching cost of the electric automobile and the fixed energy storage,
Figure FDA00037915911100000513
for the interaction cost of the building with the external grid, gamma s，b，t Cost of loss, α, for shared transactions p In order to share the loss factor of the transmission line,
Figure FDA00037915911100000514
is the electric quantity shared by the electric energy between the buildings, J is the total number of the energy storage resources configured in the buildings,
Figure FDA0003791591110000061
for the investment cost of batteries of electric vehicles and energy storage systems,
Figure FDA0003791591110000062
and with
Figure FDA0003791591110000063
Respectively the total charge-discharge cycle number, the discharge depth and the degradation factor of the battery of the electric automobile j in the building b;
Figure FDA0003791591110000064
respectively buying and selling electric quantity of the building b to the external power grid in the time period t under the scene s;
the power balance constraints inside the building include:
power balance constraints inside the building:
Figure FDA0003791591110000065
Figure FDA0003791591110000066
Figure FDA0003791591110000067
Figure FDA0003791591110000068
in the formula (I), the compound is shown in the specification,
Figure FDA0003791591110000069
and
Figure FDA00037915911100000610
respectively an electric energy gap and surplus after the building b shares the time interval t under the scene s,
Figure FDA00037915911100000611
respectively sharing electric energy between the intelligent buildings at the time t under the scene s to purchase and sell electric energy;
electric energy sharing power balance constraint between intelligent buildings:
Figure FDA00037915911100000612
and (3) power capacity constraint of connecting lines among buildings:
Figure FDA00037915911100000613
Figure FDA00037915911100000614
Figure FDA00037915911100000615
Figure FDA00037915911100000616
in the formula (I), the compound is shown in the specification,
Figure FDA00037915911100000617
respectively a seventh control variable and an eighth control variable,
Figure FDA00037915911100000618
m is an arbitrarily large positive number for the limit of the line transmission power.
7. The method of claim 6, wherein building energy management optimization model based on power sharing is established based on the building energy model, and thereafter further comprising:
introducing a Lagrange multiplier vector lambda, and converting the building energy management optimization model into a Lagrange form:
Figure FDA00037915911100000619
in the formula (I), the compound is shown in the specification,
Figure FDA00037915911100000620
representing the clear share electricity price for Lagrange multiplier variable;
decomposing a Lagrange-form building energy management optimization model into Nb sub-problems, and independently solving the sub-optimization model with the lowest total operation cost as a target by each building; the objective function of the sub-optimization model of each building is
Figure FDA0003791591110000071
In the formula (I), the compound is shown in the specification,
Figure FDA0003791591110000072
the total operating cost of the building b based on electric energy sharing.
8. The method as claimed in claim 7, wherein the determining the optimal electric quantity shared by the electric energy among the buildings and the optimal power of each electric load in the buildings according to the charging energy distribution of the electric vehicle in different buildings in the scheduling period by solving the building energy management optimization model by using a secondary gradient method comprises:
determining a pilot signal as
Figure FDA0003791591110000073
And
Figure FDA0003791591110000074
in the formula (I), the compound is shown in the specification,
Figure FDA0003791591110000075
is the shared electricity price at the current iteration, omega is the number of interactive iterations,
Figure FDA0003791591110000076
for iteration step size, epsilon pri In order to be the criterion of convergence, the system,
Figure FDA0003791591110000077
the electric quantity shared by the electric energy between the buildings when the omega-th interactive iteration times;
and based on the charging energy distribution and guide signals of the electric automobile in different buildings in the scheduling period, solving a sub-optimization model of each building by adopting a dual-order gradient method, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each power load in the buildings.
9. An intelligent building group energy management system considering dynamic access characteristics of electric vehicles, the system comprising:
the building energy model building module is used for building a building energy model considering that the electric automobile is dynamically accessed to different buildings for multiple times in a dispatching cycle;
the building group optimization model establishing module is used for establishing a building group optimization model taking the charge and discharge power of the electric vehicle as an optimization variable according to the building energy model;
the building energy management optimization model establishing module is used for establishing a building energy management optimization model based on electric energy sharing according to the building energy model;
the parameter acquisition module is used for acquiring working parameters of each electric load in the building group to be optimized;
the power optimization module is used for solving the building group optimization model in an optimization mode according to the working parameters to obtain the optimal charging and discharging power of the electric automobile at each time interval;
the charging energy distribution module is used for determining charging energy distribution of the electric automobile in different buildings in a scheduling cycle according to the optimal charging and discharging power of the electric automobile at each time interval;
and the building energy optimization module is used for solving the building energy management optimization model by adopting a secondary gradient method based on the charging energy distribution of the electric automobile in different buildings in the scheduling period, and determining the optimal electric quantity shared by the electric energy between the buildings at each time interval and the optimal power of each electric load in the buildings.
10. The system of claim 9, wherein the building energy model comprises: the system comprises an electric automobile dynamic access model, a heating ventilation air conditioning system model, an intelligent home system model, a building fixed energy storage system model and an uncertainty model of renewable energy and building load.
CN202210956562.8A 2022-08-10 2022-08-10 Intelligent building group energy management method considering dynamic access characteristic of electric vehicle Pending CN115310291A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210956562.8A CN115310291A (en) 2022-08-10 2022-08-10 Intelligent building group energy management method considering dynamic access characteristic of electric vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210956562.8A CN115310291A (en) 2022-08-10 2022-08-10 Intelligent building group energy management method considering dynamic access characteristic of electric vehicle

Publications (1)

Publication Number Publication Date
CN115310291A true CN115310291A (en) 2022-11-08

Family

ID=83861317

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210956562.8A Pending CN115310291A (en) 2022-08-10 2022-08-10 Intelligent building group energy management method considering dynamic access characteristic of electric vehicle

Country Status (1)

Country Link
CN (1) CN115310291A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116227892A (en) * 2023-05-05 2023-06-06 南方科技大学 Intelligent building group energy scheduling method based on electric vehicle charging uncertainty
CN116227891A (en) * 2023-05-05 2023-06-06 南方科技大学 Intelligent building group scheduling method and system participating in multiple markets

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116227892A (en) * 2023-05-05 2023-06-06 南方科技大学 Intelligent building group energy scheduling method based on electric vehicle charging uncertainty
CN116227891A (en) * 2023-05-05 2023-06-06 南方科技大学 Intelligent building group scheduling method and system participating in multiple markets
CN116227891B (en) * 2023-05-05 2023-08-25 南方科技大学 Intelligent building group scheduling method and system participating in multiple markets
CN116227892B (en) * 2023-05-05 2023-08-25 南方科技大学 Intelligent building group energy scheduling method based on electric vehicle charging uncertainty

Similar Documents

Publication Publication Date Title
Huang et al. A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered
CN109599856B (en) Electric vehicle charging and discharging management optimization method and device in micro-grid multi-building
CN115310291A (en) Intelligent building group energy management method considering dynamic access characteristic of electric vehicle
CN107104454A (en) Meter and the optimal load flow node electricity price computational methods in electric automobile power adjustable control domain
Wang et al. Load curve smoothing strategy based on unified state model of different demand side resources
CN110866627A (en) Multi-region electricity-gas coupling comprehensive energy system optimal scheduling method considering step gas price
Tostado-Véliz et al. A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations
CN102708425A (en) Coordination control system and method for electric vehicle service network based on Multi-Agent system
CN109636056A (en) A kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology
CN107069773A (en) Unify the load smooth control method of state model based on Demand-side resource
CN112365021A (en) Regional comprehensive energy system planning optimization method based on hybrid energy storage
CN110245810B (en) Regional energy Internet coordinated optimization operation method
CN115587668A (en) Distributed collaborative optimization scheduling method for multi-park integrated energy system
CN115360804A (en) Ordered charging system and ordered charging method
CN109693573B (en) Electric automobile cluster charging power optimization method for promoting consumption of renewable energy
Zheng et al. Coordinated energy management for commercial prosumers integrated with distributed stationary storages and EV fleets
CN113326467B (en) Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties
Li et al. Optimal dispatch for PV-assisted charging station of electric vehicles
Fu et al. Research on the stackelberg game method of building micro-grid with electric vehicles
Qureshi et al. Real-time control for charging discharging of electric vehicles in a charging station with renewable generation and battery storage
CN110992206B (en) Optimal scheduling method and system for multi-source electric field
CN113222227B (en) Building comprehensive energy system scheduling method based on building characteristics and virtual power plant
CN111768311B (en) Micro-grid energy management system based on two-stage optimal charging strategy
CN115149540A (en) Multi-region virtual power plant collaborative optimization operation method based on partition autonomy
CN112109580B (en) Micro-grid electric automobile charge and discharge control system with electric quantity self-distribution function

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination