CN115622096A - Load characteristic-based capacity configuration method and system for grid-connected micro-grid system - Google Patents

Load characteristic-based capacity configuration method and system for grid-connected micro-grid system Download PDF

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CN115622096A
CN115622096A CN202211323374.8A CN202211323374A CN115622096A CN 115622096 A CN115622096 A CN 115622096A CN 202211323374 A CN202211323374 A CN 202211323374A CN 115622096 A CN115622096 A CN 115622096A
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load
grid
energy
determining
microgrid
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冯明灿
万志伟
郑宇光
孙充勃
李敬如
吴志力
杨露露
金强
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
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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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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
    • G06Q10/06313—Resource planning in a project environment
    • 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
    • H02J3/38—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
    • 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

Abstract

The invention relates to a capacity configuration method and a system of a grid-connected micro-grid system based on load characteristics, wherein the capacity configuration method comprises the following steps: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users; determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining an energy scheduling strategy of the microgrid system; and solving the capacity configuration model of the microgrid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load accounts for different proportions of the maximum load according to the energy scheduling result aiming at different types of loads. The invention can promote the consumption of new energy and the efficient allocation of resources, and promote the source-network-load cooperative development of the power distribution network to play an important role in promoting, thereby providing technical support for realizing the double-carbon target and assisting the overall efficient and long-term sustainable development of the power distribution network.

Description

Load characteristic-based capacity configuration method and system for grid-connected micro-grid system
Technical Field
The invention relates to the field of capacity configuration of a grid-connected micro-grid system, in particular to a load characteristic-based capacity configuration method and system of the grid-connected micro-grid system.
Background
The microgrid solves the stability problem of a power system comprising the distributed power supply by effectively regulating and controlling the distributed power supply, the energy storage and users, provides favorable conditions for improving the permeability level of renewable energy in a power distribution network, reasonably utilizes natural resources, and optimally configures the capacities of the wind/light microgrid and the energy storage, which is an important subject in the field of microgrid planning design.
At present, on one hand, more researches on a capacity configuration method of a micro-grid are directed to an independent micro-grid system, and compared with the independent micro-grid, a grid-connected micro-grid can obtain large grid energy support through a power connecting line and is switched to island operation when an external grid fails, so that the micro-grid has good power supply reliability and flexibility. On the other hand, in the actual micro-grid planning, the planning is often performed under the condition determined by the installation of the distributed power supply, which may cause problems such as difficult consumption, insufficient resource utilization, etc., cause excessive renewable energy consumption, and increase the cost of capacity allocation.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a capacity configuration method and system for a grid-connected microgrid system based on load characteristics, which can promote new energy consumption and efficient resource configuration, promote the "source-grid-load" collaborative development of a power distribution network to play an important role in promoting, provide technical support for realizing a dual-carbon target, and assist in the overall efficient and long-lasting sustainable development of the power distribution network.
In order to achieve the purpose, the invention adopts the following technical scheme: a capacity configuration method of a grid-connected micro-grid system based on load characteristics comprises the following steps: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users; determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining an energy scheduling strategy of the microgrid system; and solving the capacity configuration model of the micro-grid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load occupies different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
Further, the determining the networking mode of the microgrid comprises: the method comprises the steps of determining the structure of the micro-grid system according to the conditions of regional new energy resources and spatial distribution, and determining the networking mode of the micro-grid according to different access modes of distributed energy and stored energy in the micro-grid system.
Further, the objective function is:
Figure BDA0003911429620000021
in the formula, C equ For economic purposes, C pv,equ 、C wg,equ Respectively the running unit price of the photovoltaic module and the wind power module; p is a radical of pv,t 、p wg,t Photovoltaic and wind power output at the time t; c j 、N T Respectively the jth charge-discharge life loss cost and the total number of charge-discharge times; c buy,t 、C sell,t Buying and selling electricity prices from the main network in the time period t respectively; p but,t 、P sell,t And respectively purchasing and selling power from the main network in a time period T, wherein T is time.
Further, the constraint conditions include: power balance constraint, energy storage battery constraint, tie line maximum transmission power constraint and rotation standby constraint;
the energy storage battery constraint comprises a charging model constraint and a discharging model constraint; the rotational standby constraints include positive standby and negative standby.
Further, the determining the energy scheduling strategy of the microgrid system comprises the following steps:
when the output of the new energy is greater than the load, the energy storage equipment is charged, and if the surplus electric quantity exists, the surplus electric quantity is transmitted to the main network or is abandoned;
and when the output of the new energy is smaller than the load, the energy storage device preferentially discharges, if the sum of the output of the energy storage device and the output of the new energy generator set can meet the load requirement, the main network is not purchased with electricity, otherwise, the main network supplies power to the load.
Further, the solving of the capacity configuration model of the microgrid system comprises the following steps:
solving the model by adopting a self-adaptive particle swarm algorithm;
calculating the fitness value of each particle in the population, comparing the fitness values, and recording the individual optimal value and the global optimal value;
updating the speed and the position of the particles, and judging whether the particles perform self-adaptive variation according to preset conditions;
if the particles need to be mutated, the speed and the position of the particles are initialized again, otherwise, the particle fitness value is recalculated, and the individual optimal value and the global optimal value of the particles are found out; and meeting the preset maximum iteration times to obtain the optimal solution.
Further, the energy storage configuration condition of the distributed power supply when the installed loads account for different proportions of the maximum loads includes:
and (3) giving curves of the distributed power supplies with different proportions of the installed maximum load, and respectively calculating the generated energy of the distributed power supplies, the generated energy of the external grid, the energy storage configuration capacity and the investment cost under different installed capacity proportions of the distributed power supplies based on a capacity configuration model and an energy scheduling result of the micro-grid system to obtain the energy storage configuration condition.
A capacity configuration system of a grid-connected micro-grid system based on load characteristics comprises: the first processing module is used for collecting typical daily load data of different types of users as samples and determining typical daily load characteristic curves of the different types of users; the second processing module is used for determining a networking mode of the microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis and determining an energy scheduling strategy of the microgrid system; and the capacity configuration analysis module is used for solving the capacity configuration model of the micro-grid system and obtaining the energy storage configuration conditions of the distributed power supply when the installed power supply occupies different proportions of the maximum load according to different types of loads of the energy scheduling result.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the above methods.
A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described above.
Due to the adoption of the technical scheme, the invention has the following advantages:
the method can analyze the energy storage configuration condition of the distributed power supply when the installed power accounts for different proportions of the maximum load according to different types of loads, plays an important role in promoting new energy consumption and efficient resource configuration and promoting the source-network-load collaborative development of the power distribution network, provides technical support for realizing double-carbon targets, and assists the overall efficient long-term sustainable development of the power distribution network.
Drawings
FIG. 1 is a flow chart of a capacity allocation method for a grid-connected microgrid system based on load characteristics according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a grid-connected microgrid operation strategy according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the hierarchical clustering effect according to an embodiment of the present invention;
FIG. 4 is a networking mode of a grid-connected micro-grid according to an embodiment of the present invention;
FIG. 5 is a graph of the installed load versus the maximum load for a distributed power supply (urban business office type load) in accordance with an embodiment of the present invention;
FIG. 6 is a graph of the distributed power generation installed at different rates of maximum load (urban occupancy loads) in accordance with an embodiment of the present invention;
fig. 7 is a graph of the installed maximum load of the distributed power supply in different proportions (industrial type load) in accordance with an embodiment of 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 of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention provides a load characteristic-based capacity configuration method and system for a grid-connected micro-grid system, which comprises the steps of collecting typical daily load data of different types of users as samples, and giving typical daily load characteristic curves of the different types of users based on a hierarchical clustering method; determining a networking mode of the microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system; establishing a micro-grid system capacity configuration model based on load characteristic analysis, determining a micro-grid system energy scheduling strategy, introducing self-adaptive variation on the basis of a basic Particle Swarm Optimization (PSO), and compiling a particle swarm algorithm program by adopting MATLAB software to perform simulation analysis on an example; and analyzing the energy storage configuration condition of the distributed power supply when the distributed power supply is installed in different proportions of the maximum load aiming at different types of loads. The method plays an important role in promoting new energy consumption and efficient resource allocation and promoting the source-network-load collaborative development of the power distribution network, provides technical support for realizing a double-carbon target, and is used for assisting the overall efficient and long-term sustainable development of the power distribution network.
In one embodiment of the invention, a capacity configuration method of a grid-connected micro-grid system based on load characteristics is provided. In this embodiment, as shown in fig. 1 and fig. 2, the method includes the following steps:
1) Collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users;
2) Determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining an energy scheduling strategy of the microgrid system;
3) And solving the capacity configuration model of the micro-grid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load occupies different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
In the step 1) above, the load data of different types of load users are collected as samples, for example, typical daily load data of 50 samples of regional residential users, 30 samples of business office users and 20 samples of industrial users are collected.
In this embodiment, different types of load characteristic curves are given by using a hierarchical clustering method.
And selecting a microgrid, and performing similarity evaluation on the predicted objects based on a hierarchical clustering method, wherein the hierarchical clustering method can display the clustering condition of the data sets on different clustering cluster distance scales. The hierarchical clustering algorithm is divided into a top-down hierarchical clustering algorithm and a bottom-up hierarchical clustering algorithm according to the hierarchical decomposition sequence, namely a split hierarchical clustering algorithm and an agglomeration hierarchical clustering algorithm. Wherein the agglomerative hierarchical clustering algorithm treats all data points as independent clusters and then successively merges the clusters to generate clusters containing more data points until all data points are aggregated into one cluster. The process of the split-level clustering algorithm is reversed, treating all data points as the same cluster, and then filtering the data points for successive partitioning until each data point is an independent cluster. According to the prediction object and according to various types of load characteristics of the hierarchy, samples with high similarity can be grouped into one type, and similarity evaluation of load characteristics is carried out.
Taking the clustering algorithm of the agglomerative hierarchy using the average euclidean distance as an example, as shown in fig. 3, the specific process includes:
(1) Each sample is taken as a class cluster, and the average Euclidean distance between every two class clusters is calculated;
(2) Merging the two cluster classes with the minimum distance into a cluster class;
(3) Recalculating the average Euclidean distance between the new clusters;
(4) And (4) repeating the step (2) and the step (3) until all the class clusters are finally combined into one class.
In the step 2), determining a networking mode of the microgrid specifically includes: the method comprises the steps of determining the structure of the micro-grid system according to the conditions of regional new energy resources and spatial distribution, and determining the networking mode of the micro-grid according to different access modes of distributed energy and stored energy in the micro-grid system.
The wind power generation, the photovoltaic power generation, the energy storage system, the inverter and the load form a grid-connected wind/storage, light/storage or wind/light/storage micro-grid system structure. And determining a proper micro-grid system structure according to the conditions of the regional new energy resources and the spatial distribution, and determining a networking mode of the micro-grid according to different access modes of distributed energy and stored energy in the micro-grid system.
In this embodiment, a multi-section moderate-connection power distribution network connection mode is selected, in this connection mode, the distributed power supplies and the stored energy in the microgrid are distributed in a group of standard connections, and when the upper-level power grid fails, the energy management system calculates the disjunction of the power supply range control switch of the microgrid according to the output power and load conditions of the distributed power supplies, and flexibly supports the load in the area, as shown in fig. 4.
In this embodiment, the power supply subareas of the areas are analyzed, and the connection form of the power distribution network adapting to the regional development is selected.
In the step 2), the objective function is:
Figure BDA0003911429620000051
in the formula, C equ Is an economic target; c pv，equ 、C wg，eq u is the operating unit price of the photovoltaic module and the wind power module respectively; p is a radical of pv，t 、p wg，t Photovoltaic and wind power output at the time t; c j And NT is the jth charge-discharge life loss cost and the total number of charge-discharge times respectively; c buy，t 、C sell，t Buying and selling electricity prices from the main network in the time period t respectively; p but，t 、P sell，t And respectively purchasing and selling power from the main network in a time period T, wherein T is time.
Wherein the constraint condition comprises: power balance constraints, energy storage battery constraints, tie-line maximum transmission power constraints, and spinning reserve constraints. Specifically, the method comprises the following steps:
(1) Constraint of power balance
P pv，t +P wg，t +P B，t +P but，t -P sell，t ＝P D，t
In the formula, P pv，t 、P wg，t Predicting values of photovoltaic power and wind power at t time interval; p B，t The charging and discharging power (positive when discharging and negative when charging) of the energy storage battery is in a period of t; p D，t Load prediction value for t time interval; p but，t 、P sell，t And purchasing and selling power from the main network respectively for the time period t.
(2) Energy storage battery restraint
Figure BDA0003911429620000052
Charging model constraints
Figure BDA0003911429620000061
Figure BDA0003911429620000062
Discharge model constraint
Figure BDA0003911429620000063
In the formula: p is bat (t) and E bat (t) represents the charging and discharging power and the stored energy of the energy storage system at the tth hour respectively; p ch-max And P dch-max Respectively representing the maximum charging power and the maximum discharging power of the energy storage system; e min And E max Representing the minimum and maximum capacity limits of the energy storage system, respectively.
(3) Tie line maximum transmit power constraint
0≤P but,t ≤P line,max
0≤P sell,t ≤P line,max
In the formula, P line,max The maximum transmission power of the transmission line between the main network and the micro-grid.
(4) Rotational back-up constraint
The standby in the grid-connected microgrid is provided by the main network in addition to the energy storage battery in operation.
a. Is ready for use
In the grid-connected microgrid, the positive backup provided by the main network is limited by the maximum transmission power of the transmission line connected with the microgrid, and the positive backup provided by the main network in the period t is as follows:
Figure BDA0003911429620000064
the maximum positive standby provided by an energy storage battery in the grid-connected microgrid is as follows:
Figure BDA0003911429620000065
the positive reserve capacity provided by the energy storage battery during the period t is limited by the 2 constraints of the minimum capacity and the maximum discharge power of the energy storage battery. If the energy storage battery is in a charging state in the period of t, in order to balance the output and load fluctuation of the wind turbine generator, the energy storage battery needs to reduce the charging power and even transits from the charging state to the discharging state, and the maximum positive standby state which can be provided by the lead-acid storage battery in the period of time can also be obtained through the formula.
b. Negative reserve
The positive standby provided by the main network in the grid-connected microgrid is limited by the maximum transmission power of a transmission line connected with the microgrid, and the negative standby provided by the main network in the period t is as follows:
Figure BDA0003911429620000071
the maximum negative standby provided by the energy storage battery in the grid-connected microgrid is as follows:
Figure BDA0003911429620000072
when the grid-connected operation is carried out, the main grid provides rotation reserve for the micro grid through PCC, and the rotation reserve capacity is determined by adopting probability constraint, wherein the formula is as follows:
Figure BDA0003911429620000073
in the formula, R t The rotating spare capacity required by the system in the period t; p { } denotes the probability: a is the rotational standby confidence level.
In the step 2), determining an energy scheduling strategy of the microgrid system specifically comprises:
when the output of the new energy is greater than the load, the energy storage equipment is charged, and if the surplus electric quantity exists, the surplus electric quantity is transmitted to the main network or is abandoned;
and when the output of the new energy is smaller than the load, the energy storage device preferentially discharges, if the sum of the output of the energy storage device and the output of the new energy generator set can meet the load requirement, the main network is not purchased with electricity, otherwise, the main network supplies power to the load.
In the step 3), self-adaptive variation is introduced on the basis of a basic particle swarm algorithm, and the PSO algorithm seeks an optimal solution from an initial solution through continuous iteration until the maximum iteration times are reached or the obtained global optimal solution is within a preset precision range, and then the cycle is stopped. In particle swarm optimization, each particle represents a possible solution of the problem to be solved existing in the search space range. First, the algorithm initializes each particle within the population to have a corresponding initial velocity and initial position. Then, each time the particle is updated, the particle can find the optimal position by tracking itself
Figure BDA0003911429620000074
(Individual optimal solution) and optimal position obtained by all particles
Figure BDA0003911429620000075
(global optimal solution) to readjust its speed and position, ready for the next particle update. In addition, each particle has a corresponding fitness function value, the degree of the position of the particle is judged by comparing the fitness function values, and finally, the position of the generation corresponding to the particle with the optimal fitness function value in all the generations is the target to be searched and is also the optimal solution of the optimization problem.
The position and velocity of all particles can be represented by a d-dimensional vector, i.e. x i ＝[x i1 ,x i2 ,…,x id ]And v i ＝[v i1 ,v i2 ,…,v id ]. At each iteration, the particle velocity and position update formula is as follows:
Figure BDA0003911429620000076
Figure BDA0003911429620000077
wherein j =1,2, \8230;, d; t represents the current evolution algebra; c. C 1 ，c 2 The value is (0, 2) for learning factor; r is 1 ，r 2 Are random numbers independent of each other (0, 1). In the particle swarm optimization, the larger inertia weight value is beneficial to the optimization of the algorithm in a wider search space, the smaller inertia weight value can improve the accurate local search capability of the algorithm, and the value of the inertia weight w is generally between (0, 4).
In the embodiment, the particle swarm optimization is adopted, and the self-adaptive variation is added on the basis of the basic particle swarm optimization, and the self-adaptive variation is derived from the variation thought in the genetic algorithm. Mutation operation is introduced in the particle swarm algorithm, namely, some particles can be reinitialized with a certain probability. The variation operation jumps away from a continuously reduced population search space in iteration, so that the particles can jump out of the optimal value position searched previously, the search is developed in a larger space, the diversity of the population is ensured, and the possibility of searching a better value by an algorithm is improved. Meanwhile, the idea of linearly decreasing the inertia weight is introduced into the used particle swarm algorithm, and optimization can be carried out in a wide search space in the early stage of algorithm search, so that the value of the inertia weight is larger, the inertia weight is gradually reduced along with the search, the local search capability of the algorithm is stronger, and the optimal solution in the range can be more accurately found in a certain search range.
Specifically, the method for solving the capacity configuration model of the micro-grid system by adopting the self-adaptive particle swarm optimization comprises the following steps:
3.1 Setting algorithm-related parameters: a particle population size N; learning factors c1, c2; an inertia weight w; maximum iteration times T, and the like, and the velocity and the position of the particle are initialized.
3.2 Calculating the fitness value of each particle in the population, comparing the fitness values, and recording the individual optimal value and the global optimal value;
3.3 Updating the speed and position of the particles according to the formula (4-1) and the formula (4-2), and judging whether the particles are subjected to self-adaptive variation according to preset conditions;
3.4 If the particle needs to be varied, the speed and the position of the particle are reinitialized, otherwise, the particle fitness value is recalculated, and the individual optimal value and the global optimal value of the particle are found;
3.5 ) satisfies a preset maximum number of iterations to obtain an optimal solution.
In the step 3), the obtaining of the energy storage configuration condition when the distributed power supply installed accounts for different proportions of the maximum load specifically includes: and (3) giving curves of the distributed power supplies with different proportions of the installed maximum load, and respectively calculating the generated energy of the distributed power supplies, the generated energy of the external grid, the energy storage configuration capacity and the investment cost under different installed capacity proportions of the distributed power supplies based on a capacity configuration model of the microgrid system and an energy scheduling result to obtain the energy storage configuration condition.
In this embodiment, analyzing the energy storage configuration conditions of the distributed power supply when the installed loads account for different proportions of the maximum loads includes:
(1) And analyzing the energy storage configuration condition of the distributed power supply when the installed distributed power supply occupies different proportions of the maximum load aiming at the urban business type load.
The commercial office load mainly refers to the electric loads of illumination, air conditioning, power and the like of commercial departments, the coverage area is large, and the increase of the electricity consumption is stable. Commercial offices generally focus on daytime hours, loads with large diurnal load variation amplitude are divided into two obvious time periods, namely a load peak period from 9 a.m. to 18 a.m. Based on the load characteristics, the study on the micro-grid source-load storage configuration scheme is carried out when the maximum load of the light storage micro-grid system is 1M.
In order to reflect the source load configuration condition of the microgrid, a graph is given when the distributed power supply accounts for different proportions of the maximum load, as shown in fig. 5. The distributed photovoltaic is not available at night, so the night load electricity demand is mainly provided by an external network or stored energy, and the electricity of the stored energy is derived from the external network and the distributed photovoltaic. And respectively calculating the generated energy of the distributed power supply, the generated energy of the external network and the energy storage configuration capacity under different installed capacity ratios of the distributed power supply, as shown in table 1.
Table 1 considers the commercial office microgrid capacity configuration under typical daily load conditions
Figure BDA0003911429620000091
According to the consideration of the investment cost of photovoltaic and energy storage, when the maximum load of commercial office is 1M, the installed photovoltaic capacity in the micro-grid is 1.2MW, and the capacity of an energy storage system is 2.43MWh.
(2) And analyzing the energy storage configuration condition of the distributed power supply when the installed machine occupies different proportions of the maximum load aiming at the urban residential load.
The daily load curve shows that the load characteristics of residents are in a shape of two peaks and two valleys, and the noon peak is smaller than the evening peak. Based on the load characteristics, the study on the micro-grid source-load storage configuration scheme is carried out when the maximum load of the light storage micro-grid system is 1M.
In order to reflect the source load configuration condition of the microgrid, a graph is given when the distributed power supply accounts for different proportions of the maximum load, as shown in fig. 6. The distributed photovoltaic is not available at night, so the night load electricity demand is mainly provided by an external network or stored energy, and the electricity of the stored energy is derived from the external network and the distributed photovoltaic. And respectively calculating the generated energy of the distributed power supply, the generated energy of the external network, the energy storage configuration capacity and the investment cost under different installed capacity ratios of the distributed power supply, as shown in table 2.
Table 2 considers residential microgrid capacity configuration under typical daily load conditions
Figure BDA0003911429620000101
According to the consideration of the investment cost of photovoltaic and energy storage, when the maximum load of residence is 1M, the installed photovoltaic capacity in the micro-grid is 0.9MW, and the capacity of the energy storage system is 4.33MWh.
(3) And analyzing the energy storage configuration condition of the distributed power supply when the installed distributed power supply accounts for different proportions of the maximum load aiming at the industrial load.
Industrial loads are divided into heavy industry and light industry, the load of heavy industry enterprises is large generally, meanwhile, many enterprises are in three-shift continuous production operation, the daily load characteristic of the industry changes flatly, the minimum load is about the maximum load in one day, and the peak valley difference is not large. The light industrial industry has more production operations for one work, the daily load characteristic change range of enterprises is larger, the load is higher in the daytime, many enterprises stop production and rest at night, and the load is lower.
The micro-grid power supply system is characterized in that a large-scale industrial enterprise and the industrial park are combined, distributed power supplies are configured in commercial districts and worker residential areas matched with the industrial enterprise to supply power on occasions where micro-grids are more likely to be applied in the industrial park, the power supply mode is approximately the same as the urban residential load micro-grid mode, but the micro-grid power supply is selected to be more inclined to consider that the enterprise has clean energy which is easy to obtain, enterprise resources are effectively utilized in the plant area to construct the micro-grid, the industrial park is generally wide, and distributed photovoltaic and small wind power can be considered.
Firstly, in order to reflect the source load configuration condition of the microgrid, a graph is given when the distributed power supply accounts for different proportions of the maximum load, as shown in fig. 7. Based on the above requirements for reliability satisfaction of the microgrid and the constraint conditions of energy storage configuration, the generated energy of the distributed power supply, the generated energy of the external grid, the configured energy storage capacity and the investment cost under different installed capacity ratios of the distributed power supply are calculated respectively, as shown in table 3.
Table 3 considers the capacity configuration of an industrial microgrid under typical daily load conditions
Figure BDA0003911429620000111
According to the investment cost of photovoltaic, wind power and energy storage, when the industrial maximum load is 1M, the installed photovoltaic capacity in the micro-grid is 1.1MW, the wind power is 0.7MW, and the capacity of an energy storage system is 2.43MWh.
In one embodiment of the present invention, there is provided a capacity configuration system for a grid-connected microgrid system based on load characteristics, including:
the first processing module is used for collecting typical daily load data of different types of users as samples and determining typical daily load characteristic curves of the different types of users;
the second processing module is used for determining a networking mode of the microgrid according to different access modes of distributed energy resources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis and determining an energy scheduling strategy of the microgrid system;
and the capacity configuration analysis module is used for solving the capacity configuration model of the micro-grid system and obtaining the energy storage configuration conditions of the distributed power supply when the installed power supply occupies different proportions of the maximum load according to different types of loads of the energy scheduling result.
In the above embodiment, in the second processing module, determining the networking mode of the microgrid includes: the method comprises the steps of determining the structure of a micro-grid system according to the conditions of regional new energy resources and spatial distribution, and determining the networking mode of the micro-grid according to different access modes of distributed energy and stored energy in the micro-grid system.
In the above embodiment, in the second processing module, the objective function is:
Figure BDA0003911429620000112
in the formula, C equ For economic purposes, C pv,equ 、C wg,equ The operation unit price of the photovoltaic module and the operation unit price of the wind power module are respectively set; p is a radical of formula pv,t 、p wg,t Photovoltaic and wind power output at the moment t; c j 、N T Respectively the jth charge-discharge life loss cost and the total number of charge-discharge times; c buy,t 、C sell,t Buying and selling electricity prices from the main network for t time period respectively; p is but,t 、P sell,t And respectively purchasing and selling power from the main network in a time period T, wherein T is time.
In the foregoing embodiment, in the second processing module, the constraint condition includes: power balance constraint, energy storage battery constraint, tie line maximum transmission power constraint and rotation standby constraint; the energy storage battery constraint comprises a charging model constraint and a discharging model constraint; the spinning reserve constraints include positive reserve and negative reserve.
In the above embodiment, in the second processing module, determining the energy scheduling policy of the microgrid system includes:
when the output of the new energy is greater than the load, the energy storage equipment is charged, and if the surplus electric quantity exists, the surplus electric quantity is transmitted to the main network or is abandoned;
and when the output of the new energy is smaller than the load, the energy storage device preferentially discharges, if the sum of the output of the energy storage device and the output of the new energy generator set can meet the load requirement, the main network is not purchased with electricity, otherwise, the main network supplies power to the load.
In the above embodiment, in the capacity configuration analysis module, solving the capacity configuration model of the microgrid system includes:
solving the model by adopting a self-adaptive particle swarm algorithm;
calculating the fitness value of each particle in the population, comparing the fitness values, and recording the individual optimal value and the global optimal value;
updating the speed and the position of the particles, and judging whether the particles perform self-adaptive variation according to preset conditions;
if the particles need to be mutated, the speed and the position of the particles are initialized again, otherwise, the particle fitness value is recalculated, and the individual optimal value and the global optimal value of the particles are found out; and satisfying the preset maximum iteration times to obtain the optimal solution.
In the above embodiment, in the capacity configuration analysis module, the energy storage configuration conditions when the installed distributed power supplies occupy different proportions of the maximum load include:
and (3) giving curves of the distributed power supplies with different proportions of the installed maximum load, and respectively calculating the generated energy of the distributed power supplies, the generated energy of the external grid, the energy storage configuration capacity and the investment cost under different installed capacity proportions of the distributed power supplies based on a capacity configuration model and an energy scheduling result of the micro-grid system to obtain the energy storage configuration condition.
The system provided in this embodiment is used for executing the above method embodiments, and for specific flows and details, reference is made to the above embodiments, which are not described herein again.
In an embodiment of the present invention, a schematic structural diagram of a computing device is provided, where the computing device may be a terminal, and the computing device may include: a processor (processor), a communication Interface (communication Interface), a memory (memory), a display screen and an input device. The processor, the communication interface and the memory are communicated with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory comprises a nonvolatile storage medium and an internal memory, wherein the nonvolatile storage medium stores an operating system and a computer program, and the computer program is executed by a processor to realize a capacity configuration method of a grid-connected type micro-grid system based on load characteristics; the internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computing equipment, an external keyboard, a touch pad or a mouse and the like. The processor may call logic instructions in memory to perform the following method: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users; determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining a microgrid system energy scheduling strategy; and solving the capacity configuration model of the micro-grid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load occupies different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that the above-described configurations of computing devices are merely some of the configurations associated with the present application, and do not constitute a limitation on the computing devices to which the present application may be applied, and that a particular computing device may include more or fewer components, or some components may be combined, or have a different arrangement of components.
In one embodiment of the invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments, for example, comprising: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users; determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining an energy scheduling strategy of the microgrid system; and solving the capacity configuration model of the micro-grid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load occupies different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
In one embodiment of the invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided by the above embodiments, for example, including: collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users; determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining a microgrid system energy scheduling strategy; and solving the capacity configuration model of the micro-grid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load occupies different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A capacity configuration method of a grid-connected micro-grid system based on load characteristics is characterized by comprising the following steps:
collecting typical daily load data of different types of users as samples, and determining typical daily load characteristic curves of the different types of users;
determining a networking mode of a microgrid according to different access modes of distributed energy sources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis, and determining a microgrid system energy scheduling strategy;
and solving the capacity configuration model of the microgrid system, and obtaining the energy storage configuration condition of the distributed power supply when the installed load accounts for different proportions of the maximum load according to the energy scheduling result aiming at different types of loads.
2. The capacity configuration method for the grid-connected microgrid system based on load characteristics as claimed in claim 1, wherein the step of determining the networking mode of the microgrid comprises the following steps: the method comprises the steps of determining the structure of the micro-grid system according to the conditions of regional new energy resources and spatial distribution, and determining the networking mode of the micro-grid according to different access modes of distributed energy and stored energy in the micro-grid system.
3. The load characteristic-based capacity configuration method for the grid-connected microgrid system as claimed in claim 1, characterized in that the objective function is:
Figure FDA0003911429610000011
in the formula, C equ For economic purposes, C pv,equ 、C wg,equ Respectively the running unit price of the photovoltaic module and the wind power module; p is a radical of formula pv,t 、p wg,t Photovoltaic and wind power output at the moment t; c j 、N T Respectively the jth charge-discharge life loss cost and the total charge-discharge times; c buy,t 、C sell,t Buying and selling electricity prices from the main network for t time period respectively; p but,t 、P sell,t And respectively purchasing and selling power from the main network in a time period T, wherein T is time.
4. The capacity configuration method for the grid-connected microgrid system based on load characteristics as claimed in claim 1, wherein the constraint conditions comprise: power balance constraint, energy storage battery constraint, tie line maximum transmission power constraint and rotation standby constraint;
the energy storage battery constraint comprises a charging model constraint and a discharging model constraint; the rotational standby constraints include positive standby and negative standby.
5. The load characteristic-based capacity allocation method for the grid-connected microgrid system according to claim 1, wherein the determining of the energy scheduling strategy of the microgrid system comprises:
when the output of the new energy is greater than the load, the energy storage equipment is charged, and if the surplus electric quantity exists, the surplus electric quantity is transmitted to the main network or is abandoned;
and when the output of the new energy is smaller than the load, the energy storage device preferentially discharges, if the sum of the output of the energy storage device and the output of the new energy generator set can meet the load requirement, the main network is not purchased with electricity, otherwise, the main network supplies power to the load.
6. The load characteristic-based capacity allocation method for the grid-connected microgrid system, as claimed in claim 1, wherein the solving of the capacity allocation model of the microgrid system comprises:
solving the model by adopting a self-adaptive particle swarm algorithm;
calculating the fitness value of each particle in the population, comparing the fitness values, and recording the individual optimal value and the global optimal value;
updating the speed and the position of the particles, and judging whether the particles perform self-adaptive variation according to preset conditions;
if the particles need to be varied, the speed and the positions of the particles are reinitialized, otherwise, the particle fitness value is recalculated, and the individual optimal value and the global optimal value of the particles are found out; and meeting the preset maximum iteration times to obtain the optimal solution.
7. The capacity configuration method of the grid-connected micro-grid system based on the load characteristics as claimed in claim 1, wherein the energy storage configuration condition of the distributed power supply when the installed loads account for different proportions of the maximum loads comprises:
and (3) giving a curve graph of the distributed power supplies with different maximum load occupation ratios, and respectively calculating the generated energy of the distributed power supplies, the generated energy of the external grid, the energy storage configuration capacity and the investment cost under different installed capacity occupation ratios of the distributed power supplies based on a capacity configuration model of the microgrid system and an energy scheduling result to obtain the energy storage configuration condition.
8. A grid-connected micro-grid system capacity configuration system based on load characteristics is characterized by comprising:
the first processing module is used for collecting typical daily load data of different types of users as samples and determining typical daily load characteristic curves of the different types of users;
the second processing module is used for determining a networking mode of the microgrid according to different access modes of distributed energy resources and stored energy in the microgrid system, determining a target function and constraint conditions, establishing a microgrid system capacity configuration model based on load characteristic curve analysis and determining an energy scheduling strategy of the microgrid system;
and the capacity configuration analysis module is used for solving the capacity configuration model of the micro-grid system and obtaining the energy storage configuration conditions of the distributed power supply when the installed power supply occupies different proportions of the maximum load according to different types of loads of the energy scheduling result.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115796393A (en) * 2023-01-31 2023-03-14 深圳市三和电力科技有限公司 Energy network management optimization method, system and storage medium based on multi-energy interaction
CN115796393B (en) * 2023-01-31 2023-05-05 深圳市三和电力科技有限公司 Energy management optimization method, system and storage medium based on multi-energy interaction

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