Micro-grid scheduling method with independent energy storage cooperating with source load consumption

Abstract

The invention relates to a micro-grid scheduling method for independent energy storage and collaborative source load consumption, and belongs to the technical field of collaborative regulation and interactive transaction of an integrated energy system. The method comprises the following steps: considering the benefit correlation of each subject, and constructing a multi-subject electric energy interaction mechanism; establishing a supply and demand dual-response model based on flexible adjustment of source-storage-load; considering that independent energy storage participates in power transaction, proposing an implementation method of micro-grid electric energy transaction, constructing mathematical models of electric energy transaction of different main bodies, and determining constraint conditions; and constructing a hierarchical nested game model based on a master-slave game to obtain an optimal operation strategy. According to the invention, multi-subject benefits in the micro-grid can be effectively balanced, source-storage-load collaborative optimization operation is realized, and development and application of an energy storage technology and a micro-grid technology are promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of coordinated regulation and interactive trading of integrated energy systems, and specifically relates to a microgrid dispatching method for coordinated source-load consumption of independent energy storage. Background Art

[0002] Driven by the "dual carbon" goal, the development of new energy has become an objective requirement and inevitable choice for the low-carbon transformation of the energy structure. At the same time, under the background of energy Internet technology, the configuration and use of distributed new energy power generation resources on the user side has become a new development trend. As an important carrier of distributed new energy, microgrids can effectively integrate and manage various distributed energy sources, realize energy interconnection, and promote the rational consumption of new energy. However, the uncertainty and volatility of new energy output pose huge challenges to the energy management and dispatch of microgrids. Therefore, how to ensure that microgrids have flexible adjustment capabilities to cope with the impact of continuously growing new energy and meet the needs of balancing power supply and demand is an important direction for future energy development.

[0003] As an important regulating resource for peak load and valley load reduction in microgrids, the dispatching strategy of energy storage systems has an important impact on improving the flexibility of microgrids. With the advancement of electricity market reform, the independent subject status of energy storage in the electricity market has been clarified. It has become an inevitable trend for energy storage to participate in the electricity market as an independent subject, which can solve the problem of low efficiency of the current energy storage business model and is conducive to the efficient use of energy storage resources. Research on energy storage systems as independent subjects participating in microgrid power dispatching will help stimulate the enthusiasm of energy storage to participate in coordinated dispatching. By integrating local distributed power sources and energy storage systems, microgrids can achieve more efficient and environmentally friendly energy utilization methods, providing a reference solution for the transformation of energy structure. Summary of the invention

[0004] The purpose of the present invention is to provide a microgrid dispatching method for independent energy storage and coordinated source-load consumption, which can achieve coordinated optimization operation of source-storage-load and enhance the ability of microgrid electric energy self-sufficiency.

[0005] To achieve the above purpose, the technical solution of the present invention is: a microgrid dispatching method for independent energy storage and coordinated source-load consumption, comprising:

[0006] Consider the interests of each subject and build a multi-subject power interaction mechanism;

[0007] Establish a supply-demand dual response model based on the flexible adjustment of source-storage-load;

[0008] Considering the participation of independent energy storage in power trading, the implementation method of microgrid power trading is proposed, the mathematical model of power trading of different entities is constructed, and the constraints are determined;

[0009] Construct a hierarchical nested game model based on master-slave game to obtain the optimal operation strategy.

[0010] In one embodiment of the present invention, the method further includes:

[0011] Different scenarios are set up to test the effectiveness of the established hierarchical nested game model based on master-slave game.

[0012] In one embodiment of the present invention, the specific implementation method of constructing a multi-agent power interaction mechanism is as follows:

[0013] Step S11: The microgrid operator, as a leader, formulates the initial electricity selling price and charging and discharging strategy according to the power supply and demand situation;

[0014] Step S12: The photovoltaic user optimizes the load demand response according to the electricity selling price set by the microgrid operator, formulates an energy use strategy, and reports the energy use strategy to the microgrid operator; at the same time, the independent energy storage operator formulates an energy storage charging and discharging price strategy to provide services to the microgrid operator;

[0015] Step S13: The microgrid operator optimizes and adjusts the output of the internal energy conversion equipment according to the energy use strategy set reported by the energy storage operator and the photovoltaic user, updates the electricity sales price and the charging and discharging strategy, and guides the independent energy storage operator and the photovoltaic user to make adaptive adjustments based on their own optimal economic benefits until the optimal strategy combination is found.

[0016] In one embodiment of the present invention, the specific implementation method of establishing the supply-demand dual response model based on the flexible adjustment of source-storage-load is as follows:

[0017] Step S21: According to the composition structure of the energy supply of the microgrid, a supply-side response model based on the energy storage usage is established:

[0018]

[0019] Where: It represents the amount of electricity provided by the supply side during period t; and They represent the wind power output and the traditional generator output during period t respectively; It represents the interactive electricity between the microgrid operator and the upper-level grid during period t. When it is greater than 0, it purchases electricity from the upper-level grid, otherwise it sells electricity to the upper-level grid; They represent the charge and discharge amount of energy storage in time period t respectively;

[0020] Step S22: Establishing a demand-side photovoltaic user response model:

[0021]

[0022] Where: They represent the total power load, rigid load and flexible load of users in period t respectively;

[0023] The flexible load can be adaptively adjusted dynamically, which can be expressed as:

[0024]

[0025] Where: It represents the user's electric load adjustment in period t; Represents the flexible load after user demand response; represents the user's power load after adaptive adjustment in period t; λ is the maximum proportion of the power load that can be adjusted in period t; k is the total proportion of the power load that can be adjusted in a day; while the adaptive adjustment of flexible load improves the user's power satisfaction, it is also necessary to ensure that the total amount of flexible load before and after the user's demand response remains unchanged;

[0026] Since the user side is equipped with photovoltaic power generation equipment, the user gives priority to using photovoltaic power generation to meet their own electricity needs. The net power load of photovoltaic users after demand response in period t is:

[0027]

[0028] Where: It represents the output of photovoltaic device during period t;

[0029] Step S23: During operation, the power supply and demand in the microgrid are kept balanced, which can be expressed as:

[0030]

[0031] In one embodiment of the present invention, the constraints of the supply-side response model are as follows:

[0032] In order to cope with the volatility of new energy and improve the feasibility of the dispatch plan, traditional generators must meet the following constraints during operation:

[0033]

[0034] Where: Indicates the maximum output power of a traditional generator set; Respectively represent the minimum and maximum values ​​of the climbing power of the traditional generator set;

[0035] To ensure the continuity of charging and discharging services provided by the energy storage system, the following constraints need to be met during operation:

[0036]

[0037] Where: represents the energy storage capacity during period t; η ES,ch , ηES,dis Respectively represent the energy storage charging and discharging efficiency; Respectively represent the minimum and maximum capacity allowed for energy storage; is a Boolean variable representing the energy storage charging and discharging state; P ch,max , P ch,min , P dis,max , P dis,min They represent the maximum charging power of energy storage, the minimum charging power of energy storage, the maximum discharging power of energy storage, and the minimum discharging power of energy storage respectively;

[0038] The bidirectional power interaction between the microgrid operator and the upper grid should meet the upper and lower limit constraints:

[0039]

[0040] Where: μ net A Boolean variable indicating the state of the microgrid operator purchasing and selling electricity to the superior power grid. When it is 1, the microgrid operator purchases electricity from the superior power grid; P gs,max and P gb,max They respectively represent the maximum power that the microgrid is allowed to transmit to the power purchasing office of the superior power grid and the maximum power that the microgrid is allowed to transmit to the power selling office of the superior power grid.

[0041] In one embodiment of the present invention, the specific implementation method of constructing the mathematical model of electric energy trading between different entities is as follows:

[0042] Step S31: Taking maximum benefit as the optimization goal, formulate the objective function of the microgrid operator

[0043]

[0044] Where: T represents the scheduling period; represents the electricity sales revenue of the microgrid operator during period t; and They represent the electricity transaction costs between microgrid operators and independent energy storage operators and the upper grid respectively; and represent the operating costs of wind turbines and traditional generators, respectively;

[0045] in:

[0046]

[0047] Where: represents the electricity price during period t; represents the net electricity load of photovoltaic users during period t; They represent the charging and discharging prices of energy storage during period t respectively; Respectively represent the grid electricity price and on-grid electricity price in period t; r WT is the wind power unit power operation cost coefficient; ae 、b e 、c e is the cost coefficient of the traditional generator set;

[0048] Constraints:

[0049]

[0050] Where: They represent the upper and lower limits of the dynamic electricity price within the microgrid respectively; represents the upper limit of the average dynamic electricity price during period t;

[0051] Step S32: Taking maximum benefit as the optimization goal, formulate the objective function of the independent energy storage operator

[0052]

[0053] Where: They represent the revenue of independent energy storage operators from electricity trading with microgrid operators and photovoltaic users during period t; they represent the operating cost of energy storage during period t;

[0054] in:

[0055]

[0056] Where: ε is the unit charging and discharging cost coefficient of energy storage;

[0057] Constraints:

[0058]

[0059] Step S33: Taking the maximum consumer surplus as the optimization goal, formulate the objective function of photovoltaic users:

[0060]

[0061] Where: I u It represents the utility function of the user's electricity consumption in period t, and measures the user's satisfaction with electricity purchase and consumption; represents the electricity purchase cost of the user during period t; represents the operating cost of the photovoltaic power generation device during period t;

[0062] in:

[0063]

[0064] Where: is the electricity load of photovoltaic users after demand response in period t; α e , β e Respectively represent the user's preference coefficient for electricity demand; r pvis the unit power operating cost coefficient of the photovoltaic power generation device.

[0065] In one embodiment of the present invention, the hierarchical nested game model based on the master-slave game is specifically expressed as follows:

[0066] G={MGO∪ESO∪user;{δ MGO ,δ ESO ,δ user}; {U MGO ,U ESO ,U user}}

[0067] Where: MGO, ESO and user represent microgrid operators, energy storage operators and photovoltaic users respectively; {δ MGO ,δ ESO ,δ user} represents the strategy set of each participant, among which the strategy set of the microgrid operator δ MGO The electricity price and the amount of energy storage used are set; the strategy set of energy storage operators δ ESO The charging and discharging prices set for each time period; the strategy of photovoltaic usersδ user is the adjusted flexible load; specifically expressed as:

[0068]

[0069] When no participant can obtain better benefits by unilaterally changing strategies, the game reaches Stackelberg equilibrium and obtains the optimal operation strategy, which can be expressed as:

[0070]

[0071] In the formula is the Stackelberg equilibrium solution.

[0072] In one embodiment of the present invention, the specific implementation method of setting different scenarios to test the validity of the established hierarchical nested game model based on the master-slave game is as follows:

[0073] Step S41, setting different operation scenarios;

[0074] Step S42: Programming on the Matlab platform, using a genetic algorithm combined with a CPLEX solver to solve the hierarchical nested game model;

[0075] Step S43: Simulate different scenarios to obtain benefit optimization results and strategy sets of each subject in different scenarios;

[0076] Step S44: By comparing the benefits of various entities in different scenarios and the results of power optimization scheduling, the effectiveness of the proposed method in improving energy storage utilization and promoting win-win cooperation among various entities in the microgrid is verified.

[0077] The present invention also provides a microgrid dispatching system for independent energy storage and coordinated source-load consumption, characterized in that it includes a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0078] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] 1. The present invention introduces an independent pricing and dispatching mechanism for independent energy storage systems. Independent energy storage operators can flexibly adjust the charging and discharging prices according to market conditions, which improves the utilization rate of energy storage resources and provides profit space for independent energy storage operators. The charging and discharging of energy storage serves microgrid operators. Microgrid operators can flexibly adjust the storage and release of electric energy according to supply and demand conditions, reduce dependence on the upper power grid, reduce electricity purchase costs, and improve overall benefits.

[0081] 2. The present invention takes into account the mutual influence of decisions made by different entities, establishes a supply and demand dual-response hierarchical nested game model that takes into account the interests of multiple entities, breaks through the limitation that the traditional single-level game model is difficult to coordinate the interests of multiple entities, and solves the problem of coordinated scheduling and interest balance among multiple entities in the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 2 is a diagram of a microgrid system architecture according to an embodiment of the present invention.

[0083] Figure 2 It is a multi-agent game interaction framework of an embodiment of the present invention.

[0084] Figure 3 The figure is a flow chart of model solving according to an embodiment of the present invention.

[0085] Figure 4 This is a wind-solar power generation and initial load prediction curve according to an embodiment of the present invention.

[0086] Figure 5 This is the optimization result of the dynamic electricity price of the microgrid operator and the independent energy storage operator in the embodiment of the present invention.

[0087] Figure 6This is the user load curve optimization result of the embodiment of the present invention.

[0088] Figure 7 This is the result of the power supply and demand balance game in the embodiment of the present invention.

[0089] Figure 8 These are the power balance results of scenarios 2 and 3 of the embodiments of the present invention.

[0090] Fig. 9 This is a diagram showing the changes in energy storage charging and discharging power and SOC value in scenarios 3 and 4 of an embodiment of the present invention. DETAILED DESCRIPTION

[0091] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0092] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0093] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0094] The present invention provides a microgrid dispatching method for independent energy storage and coordinated source-load consumption, comprising:

[0095] Consider the interests of each subject and build a multi-subject power interaction mechanism;

[0096] Establish a supply-demand dual response model based on the flexible adjustment of source-storage-load;

[0097] Considering the participation of independent energy storage in power trading, the implementation method of microgrid power trading is proposed, the mathematical model of power trading of different entities is constructed, and the constraints are determined;

[0098] Construct a hierarchical nested game model based on master-slave game to obtain the optimal operation strategy.

[0099] Also includes:

[0100] Different scenarios are set up to test the effectiveness of the established hierarchical nested game model based on master-slave game.

[0101] The following is the specific implementation process of the present invention.

[0102] This embodiment provides a microgrid dispatching method for independent energy storage and coordinated source-load consumption, which specifically includes the following steps:

[0103] Step S1: Consider the interests of each subject and build a multi-subject power interaction mechanism;

[0104] Step S2: Establish a supply-demand dual response model based on the flexible adjustment of source-storage-load;

[0105] Step S3: Considering the participation of independent energy storage in power trading, a method for implementing microgrid power trading is proposed, a mathematical model for power trading of different entities is constructed, and constraints are determined;

[0106] Step S4: Construct a hierarchical nested game model based on master-slave game to obtain the optimal operation strategy.

[0107] In this embodiment, the steps are also included:

[0108] Step S5: setting different scenarios to test the effectiveness of the established hierarchical nested game model based on master-slave game.

[0109] In this embodiment, the construction of the electric energy interaction mechanism considering the interests of multiple subjects specifically includes the following steps:

[0110] Step S11: The microgrid operator, as a leader, formulates the initial electricity selling price and charging and discharging strategy according to the power supply and demand situation;

[0111] Step S12: The photovoltaic user optimizes the load demand response according to the electricity selling price set by the microgrid operator, formulates an energy use strategy, and reports the strategy to the microgrid operator; at the same time, the independent energy storage operator formulates an energy storage charging and discharging price strategy to provide services to the microgrid operator;

[0112] Step S13: The microgrid operator optimizes and adjusts the output of the internal energy conversion equipment according to the strategy set reported by the energy storage operator and the user, updates the electricity sales price and the charging and discharging strategy of the energy storage, and guides the independent energy storage operator and the user to make adaptive adjustments based on the optimal economic benefits until the optimal strategy combination is found.

[0113] In this embodiment, the establishment of a supply-demand dual response model based on the flexible adjustment of source-storage-load specifically includes the following steps:

[0114] Step S21: According to the composition structure of the energy supply of the microgrid, a supply-side response model based on energy storage usage is established:

[0115]

[0116] Where: P G It represents the amount of electricity provided by the supply side during period t; and They represent the wind power output and the traditional generator output during period t respectively; It represents the interactive electricity between the microgrid operator and the upper-level grid during period t. When it is greater than 0, it purchases electricity from the upper-level grid, otherwise it sells electricity to the upper-level grid; They respectively represent the charge and discharge amount of energy storage in time period t.

[0117] in:

[0118] In order to cope with the volatility of new energy and improve the feasibility of the dispatch plan, traditional generators must meet the following constraints during operation:

[0119]

[0120] Where: Indicates the maximum output power of a traditional generator set; They respectively represent the minimum and maximum values ​​of the climbing power of the traditional generator set.

[0121] Secondly, to ensure the continuity of charging and discharging services provided by the energy storage system, it needs to meet the corresponding constraints during operation:

[0122]

[0123] Where: represents the energy storage capacity during period t; η ES,ch , η ES,dis Respectively represent the energy storage charging and discharging efficiency; Respectively represent the minimum and maximum capacity allowed for energy storage; is a Boolean variable representing the energy storage charging and discharging state; P ch,max (P ch,min ), P dis,max (P dis,min ) represent the maximum (minimum) charging and discharging power of energy storage respectively.

[0124] In addition, the bidirectional electric energy interaction power between MGO and the upper power grid should also meet the upper and lower limit constraints:

[0125]

[0126] Where: μ net A Boolean variable indicating the state of the microgrid operator purchasing and selling electricity to the superior power grid. When it is 1, the microgrid operator purchases electricity from the superior power grid; P gs,max and P gb,max They respectively represent the maximum power that the microgrid is allowed to transmit to the power purchasing office of the superior power grid and the maximum power that the microgrid is allowed to transmit to the power selling office of the superior power grid.

[0127] Step S22: Establishing a demand-side photovoltaic user response model:

[0128]

[0129] Where: They represent the total electricity load, rigid load and flexible load of users in period t respectively.

[0130] The flexible load can be adaptively adjusted dynamically, which can be expressed as:

[0131]

[0132] Where: It represents the user's electric load adjustment in period t; Represents the flexible load after user demand response; represents the user's power load after adaptive adjustment in period t; λ is the maximum proportion of the power load that can be adjusted in period t; k is the total proportion of the power load that can be adjusted in a day. While the adaptive adjustment of flexible load improves the user's power satisfaction, it is also necessary to ensure that the total amount of flexible load before and after the user's demand response remains unchanged.

[0133] Since the user side is equipped with photovoltaic power generation equipment, the user gives priority to using photovoltaic power generation to meet their own electricity needs. The net power load of photovoltaic users after demand response in period t is:

[0134]

[0135] Where: Represents the output of the photovoltaic device during period t.

[0136] Step S23: During operation, the power supply and demand in the microgrid are kept balanced, which can be expressed as:

[0137]

[0138] In this embodiment, the method for implementing microgrid power trading considering independent energy storage participating in power trading, constructing a mathematical model of power trading of different entities specifically includes the following steps:

[0139] Step S31: Taking maximum benefit as the optimization goal, formulate the objective function of the microgrid operator

[0140]

[0141] Where: T represents the scheduling period, and in this paper, T = 24h; represents the electricity sales revenue of the microgrid operator during period t; and They represent the electricity transaction costs between microgrid operators and independent energy storage operators and the upper grid respectively; and Represent the operating costs of wind turbines and traditional generators respectively.

[0142] in:

[0143]

[0144] Where: represents the electricity price during period t; represents the net electricity load of photovoltaic users during period t; They represent the charging and discharging prices of energy storage during period t respectively; Respectively represent the grid electricity price and on-grid electricity price in period t; r WT is the wind power unit power operation cost coefficient; a e 、b e 、c e is the cost coefficient of traditional generator sets.

[0145] Constraints:

[0146]

[0147] Where: They represent the upper and lower limits of the dynamic electricity price within the microgrid respectively; represents the average dynamic electricity price cap for period t

[0148] Step S32: Taking maximum benefit as the optimization goal, formulate the objective function of the independent energy storage operator

[0149]

[0150] Where: They represent the revenue of independent energy storage operators from electricity trading with microgrid operators and photovoltaic users during period t; they represent the operating cost of energy storage during period t;

[0151] in:

[0152]

[0153] Where: ε is the unit charging and discharging cost coefficient of energy storage.

[0154] Constraints:

[0155]

[0156] Step S33: Taking the maximum consumer surplus as the optimization goal, formulate the objective function of photovoltaic users:

[0157]

[0158] Where: I uIt represents the utility function of the user's electricity consumption in period t, and measures the user's satisfaction with electricity purchase and consumption; represents the electricity purchase cost of the user during period t; represents the operating cost of the photovoltaic power generation device during period t;

[0159] in:

[0160]

[0161] Where: is the electricity load of photovoltaic users after demand response in period t; α e , β e Respectively represent the user's preference coefficient for electricity demand; r pv is the unit power operating cost coefficient of the photovoltaic power generation device.

[0162] In this embodiment, the hierarchical nested game model that considers the interests of various entities can be specifically expressed as follows:

[0163] G={MGO∪ESO∪user;{δ MGO ,δ ESO ,δ user}; {U MGO ,U ESO ,U user}} (25)

[0164] Where: MGO, ESO and user represent the participants: microgrid operator, energy storage operator and photovoltaic user. MGO ,δ ESO ,δ user} represents the strategy set of each participant, among which the strategy set of the microgrid operator δ MGO The electricity price and the amount of energy storage used are set; the strategy set of energy storage operators δ ESO The charging and discharging prices set for each time period; the strategy of photovoltaic usersδ user is the adjusted flexible load. Specifically expressed as:

[0165]

[0166] When no participant can obtain better benefits by unilaterally changing strategies, the game reaches Stackelberg equilibrium and obtains the optimal operation strategy, which can be expressed as:

[0167]

[0168] In the formula is the Stackelberg equilibrium solution.

[0169] In this embodiment, setting different scenarios to test the effectiveness of the established hierarchical nested game model based on master-slave game specifically includes the following steps:

[0170] Step S41: setting different operation scenarios;

[0171] Step S42: Programming on the Matlab platform, solving the game model by combining the genetic algorithm with the CPLEX solver;

[0172] Step S43: Simulate different scenarios to obtain the benefit optimization results and strategy sets of each subject in different scenarios.

[0173] Step S44: By comparing the benefits of various entities in different scenarios and the results of power optimization scheduling, the effectiveness of the proposed method in improving the utilization rate of energy storage and promoting win-win cooperation among various entities in the microgrid is verified.

[0174] Next, this embodiment is further described in combination with specific parameters.

[0175] According to step S1, based on Figure 1 The microgrid structure analyzes the electric energy interaction behavior of multiple subjects. As the manager of the microgrid, the microgrid operator (MGO) combines wind turbines and traditional generators to meet the electricity demand of photovoltaic users based on the principle of maximizing the consumption of new energy. According to the output of wind turbines and the electricity demand of photovoltaic users, the internal electricity price of the microgrid is optimized, the internal controllable units are reasonably dispatched, the energy storage charging and discharging services are used, and the electric energy is interacted with the upper power grid to ensure the power balance within the microgrid. Photovoltaic users refer to the aggregation of small and medium-sized users equipped with photovoltaic power generation devices. By comprehensively considering the electricity satisfaction and electricity cost, the adjustable load is reasonably dispatched to maximize the consumer surplus. When photovoltaic users are in operation, they give priority to the photovoltaic output to support the load. The power surplus is the net power load of photovoltaic users. When the photovoltaic output cannot meet their own electricity demand, they purchase electricity from the microgrid operator to smooth the net power load fluctuation and meet the electricity demand. The energy storage system is configured in the microgrid and works closely with the microgrid operator to provide the microgrid operator with energy storage charging and discharging services to smooth power fluctuations, coordinate to meet the supply and demand balance needs in the microgrid, and obtain profits through the "low charging and high discharging" strategy.

[0176] According to step S2, based on Figure 2The game interaction framework of the paper constructs a dual-response model of supply and demand, in which the first-level game is that photovoltaic users respond to demand based on the dynamic electricity price set by the microgrid operator. As the leader, the microgrid operator formulates a dynamic electricity price strategy based on the supply and demand of electricity in the microgrid and market information with the goal of maximizing operational benefits. As a follower, the photovoltaic user adaptively adjusts the electricity load in each period according to the electricity price set by the microgrid operator; the second-level game is that the independent energy storage operator and the microgrid operator coordinate and optimize the charging and discharging strategy of energy storage. As the leader, the independent energy storage operator (IESO) formulates the charging and discharging price with the goal of maximizing operational benefits. As a follower, the microgrid operator optimizes the charging and discharging of energy storage according to the charging and discharging price of the energy storage operator, the output of the wind turbine and the flexible load of the photovoltaic user. The result of the first-level game (i.e., the electricity consumption strategy of the photovoltaic user) is used as one of the input conditions of the second-level game (i.e., the supply and demand of electricity in the microgrid), which affects the decision of the microgrid operator in the second-level game. The result of the second-level game (i.e., the charging and discharging strategy of the microgrid operator using energy storage) affects the energy supply structure and electricity price strategy of the microgrid operator in the first-level game, thus forming a closed-loop nested game structure. Through hierarchical nested games, the decision-making of different entities in the microgrid is optimized to promote the efficient use of energy.

[0177] According to step S3, each participant conducts electricity trading with the goal of maximizing its own interests. The objective function of each participant is equation (9), (16), and (21). During the operation, the electricity must meet the constraints of equations (2), (3), (4), and (6), and the internal transaction electricity price must meet the constraints of equations (15) and (20).

[0178] According to step S4, the present invention uses a genetic algorithm combined with a distributed equilibrium solution method of mixed integer programming to iteratively solve the hierarchical nested game model. The solution process is as follows: Figure 3 As shown. In the present invention, a photovoltaic power generation device is installed on the user side of the microgrid, and a traditional generator set with a capacity of 600kW is installed on the microgrid operator side to ensure the reliable supply of electricity for the microgrid. The relevant parameters of the energy storage system and the microgrid are shown in Table 1. The unit power operation and maintenance coefficients of the photovoltaic unit and the wind turbine unit are 0.0096 and 0.0296 respectively, and the preference coefficient α of the power consumption on the user side is e , β e The maximum ratio of flexible load adjustment is about 20%. The wind and solar power output forecast and user load forecast curves are as follows: Figure 4 shown.

[0179] Table 1 Model related parameters

[0180]

[0181] Based on the method proposed in the present invention, the microgrid is optimized and dispatched, and the dynamic electricity price strategy optimized by the microgrid operator and the energy storage operator is obtained as follows: Figure 5 The user demand response optimization results are shown in Figure 6 shown.

[0182] analyze Figure 5 It can be seen that the dynamic electricity prices set by microgrid operators and energy storage operators are between the on-grid electricity price and the grid time-of-use electricity price. Compared with the grid, it provides a better price strategy for both sides of the supply, which encourages microgrid operators in the microgrid to use the charging and discharging services of the energy storage system, reduce the power interaction with the upper grid, and thus enhance the ability of microgrids to operate autonomously and adjust internal supply and demand. Figure 5 It can be seen from (b) that the charging and discharging prices of independent energy storage operators are affected by real-time market price fluctuations and changes in supply and demand. In most periods of time, the discharge price set by the energy storage operator is greater than the charging price, and the energy storage operator makes a profit through low charging and high discharging.

[0183] analyze Figure 6 It can be seen that the load curve before user demand response has peaks in the two periods of 11:00-13:00 and 18:00-20:00, when the electricity price is relatively high. After optimization, the user adaptively adjusts the electricity load, increases the electricity load in the periods of 00:00-10:00 and 23:00-24:00, and reduces the electricity load in the period of 11:00-22:00, that is, the user responds to the changing trend of market electricity prices and tends to transfer the flexible load to the period with lower electricity prices, and reduces the electricity load in the period of higher market electricity prices. The user load curve before and after demand response shows the characteristics of "peak shaving and valley filling", which shows that the optimization scheduling method proposed in the present invention can effectively smooth load fluctuations, reduce electricity purchase costs while ensuring electricity satisfaction, and increase consumer surplus.

[0184] According to step S5, different scenarios are set to test the effectiveness of the established multi-agent collaborative optimization scheduling model.

[0185] Scenario 1: The energy storage system does not participate in scheduling, and the microgrid operator and PV users do not engage in master-slave game;

[0186] Scenario 2: The energy storage system does not participate in the dispatch, and the microgrid operator and the PV user each consider their own interests and engage in a master-slave game;

[0187] Scenario 3: The energy storage system participates in the dispatch according to the electricity sales price set by the microgrid operator, that is, the microgrid operator is the leader, and the energy storage system and users are followers.

[0188] Scenario 4: The energy storage system participates in scheduling as an independent entity, and multiple entities hierarchically nest a master-slave game to respond to both supply and demand, which is the operating strategy proposed in this article.

[0189] The benefit optimization results of each subject under different scenarios are shown in Table 2. The power optimization dispatch results are shown in Figure 7 shown.

[0190] Table 2 Benefit optimization results of each subject in different scenarios

[0191]

[0192] From the analysis in Table 2, it can be seen that the introduction of game strategy (scenario 2) and the participation of energy storage system in scheduling (scenario 3) significantly improve the benefits of photovoltaic users. Among them, compared with scenario 1, the consumer surplus of photovoltaic users in scenario 2 increased by 890 yuan. This is because photovoltaic users have improved the flexibility of load demand through the master-slave game with MGO. In scenario 3, the energy storage system participates in the optimized operation. MGO can dispatch the charging and discharging power of energy storage to meet the balance of power supply and demand and reduce the dependence of microgrids on the upper power grid. Compared with scenario 2, the income of photovoltaic users increased by 390 yuan, and IESO made a profit of 699.8 yuan. This is because the discharge price set by IESO is lower than the power grid price. Compared with purchasing electricity from the upper power grid, the power transaction between MGO and IESO reduces the electricity cost of photovoltaic users.

[0193] In scenario 4, IESO autonomous pricing is combined with the supply-demand dual response game strategy, which takes into account the benefits of energy storage while achieving the optimal operation of the microgrid, so that the benefits of each subject are at the highest level. The revenue of the energy storage system increased to 713.8 yuan. Compared with scenario 3, the revenue of MGO increased by 582 yuan, and the revenue of photovoltaic users increased by 80 yuan. This is because the autonomous pricing strategy of IESO has improved its flexibility in participating in scheduling. At the same time, the hierarchical nested game allows each independent subject to play according to its own interests and maximize its benefits through two-way interaction and multi-level scheduling. From scenario 1 to scenario 4, the benefits of MGO, IESO and photovoltaic users have all improved, indicating that through the introduction of the hierarchical nested mechanism and the optimization of the game strategy, the coordinated optimization of both supply and demand sides can be achieved, and the overall economic benefits can be improved.

[0194] Figure 7 The results of the power supply and demand balance game of the proposed strategy (scenario 4) are given. Figure 7 It can be seen that the independent energy storage system interacts with MGO in most periods of the day. MGO also purchases electricity from the energy storage system during the periods of 6:00-9:00 and 17:00-18:00 when the grid electricity price is low, and stores it in the energy storage system. It also releases electricity during the period of 10:00-12:00 when the market electricity price is high to reduce the electricity cost of the microgrid, effectively utilizing the time and space shifting capability of energy storage to meet the microgrid's power supply and demand balance needs.

[0195] Compared with the one-way demand-side response in Scenario 2, the energy supply side in Scenario 4 can also dynamically adjust the power supply strategy according to the load fluctuations on the demand side, thereby achieving two-way collaborative optimization. Figure 7 , Figure 8 (a) It can be seen that compared with scenario 2, scenario 4 replaces the output of internal traditional generators with energy storage discharge during the low energy storage discharge price period from 1:00 to 5:00; and significantly reduces the amount of purchased electricity during the peak load periods from 10:00 to 14:00 and 18:00 to 22:00. The above results show that the global optimization strategy of dual response to supply and demand improves the flexibility of microgrid power supply, and enhances the microgrid's autonomous regulation level and ability to cope with fluctuations in power supply and demand.

[0196] Comparative Analysis Figure 7 and Figure 8 (b) It can be found that in the one-master-multiple-slave game strategy of scenario 3, the energy storage system cannot effectively balance the peak load demand, resulting in frequent activation of traditional generator sets and low system scheduling efficiency. In scenario 4, through the active scheduling of the independent energy storage system, the output of traditional generator sets during peak load periods is significantly reduced. They no longer bear the main power supply load, but play an auxiliary regulation role during peak hours. The total output fluctuation is reduced and the scheduling is more stable. The above results show that the strategy proposed in this paper can improve the energy interaction level of microgrids, optimize the composition structure of energy supply while balancing supply and demand fluctuations, and verify the superiority of the proposed game strategy in the supply-side energy structure scheduling.

[0197] analyze Fig. 9 It can be seen that compared with scenario 3, the total number of energy storage charge and discharge times in scenario 4 increased by 14 times, the total discharge power increased by 985.3kW, and the total charging power increased by 1091.39kW, which greatly improved the utilization rate of energy storage resources. This is because in scenario 3, the energy storage system simply relied on the passive response of the decision made by MGO, and the scheduling initiative was poor. In scenario 4, the energy storage system, as an independent subject, independently prices and participates in the game scheduling according to its own interest maximization goal, which can effectively improve the flexibility and enthusiasm of the energy storage system to participate in the regulation, and fully tap the regulation potential of the microgrid power supply. Through the two-level nested game, MGO can dispatch energy more flexibly, and cooperate with IESO to more economically and reasonably meet the electricity needs of users in the microgrid.

[0198] The present invention also provides a microgrid dispatching system for independent energy storage and coordinated source-load consumption, characterized in that it includes a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0199] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0200] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0201] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0202] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0203] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0204] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A microgrid dispatching method for independent energy storage and coordinated source-load consumption, characterized in that: include: Consider the interests of each subject and build a multi-subject power interaction mechanism; Establish a supply-demand dual response model based on the flexible adjustment of source-storage-load; Considering the participation of independent energy storage in power trading, the implementation method of microgrid power trading is proposed, the mathematical model of power trading of different entities is constructed, and the constraints are determined; Construct a hierarchical nested game model based on master-slave game to obtain the optimal operation strategy.

2. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: Also includes: Different scenarios are set up to test the effectiveness of the established hierarchical nested game model based on master-slave game.

3. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: The specific implementation method of constructing the multi-agent power interaction mechanism is as follows: Step S11: The microgrid operator, as a leader, formulates the initial electricity selling price and charging and discharging strategy according to the power supply and demand situation; Step S12: The photovoltaic user optimizes the load demand response according to the electricity selling price set by the microgrid operator, formulates an energy use strategy, and reports the energy use strategy to the microgrid operator; at the same time, the independent energy storage operator formulates an energy storage charging and discharging price strategy to provide services to the microgrid operator; Step S13: The microgrid operator optimizes and adjusts the output of the internal energy conversion equipment according to the energy use strategy set reported by the energy storage operator and the photovoltaic user, updates the electricity sales price and the charging and discharging strategy, and guides the independent energy storage operator and the photovoltaic user to make adaptive adjustments based on their own optimal economic benefits until the optimal strategy combination is found.

4. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: The specific implementation method of establishing the supply-demand dual response model based on the flexible adjustment of source-storage-load is as follows: Step S21: According to the composition structure of the energy supply of the microgrid, a supply-side response model based on the energy storage usage is established: Where: It represents the amount of electricity provided by the supply side during period t; and They represent the wind power output and the traditional generator output during period t respectively; It represents the interactive electricity between the microgrid operator and the upper-level grid during period t. When it is greater than 0, it purchases electricity from the upper-level grid, otherwise it sells electricity to the upper-level grid; They represent the charge and discharge amount of energy storage in time period t respectively; Step S22: Establishing a demand-side photovoltaic user response model: Where: They represent the total power load, rigid load and flexible load of users in period t respectively; The flexible load can be adaptively adjusted dynamically, which can be expressed as: Where: It represents the user's electric load adjustment in period t; Represents the flexible load after user demand response; represents the user's power load after adaptive adjustment in period t; λ is the maximum proportion of the power load that can be adjusted in period t; k is the total proportion of the power load that can be adjusted in a day; while the adaptive adjustment of flexible load improves the user's power satisfaction, it is also necessary to ensure that the total amount of flexible load before and after the user's demand response remains unchanged; Since the user side is equipped with photovoltaic power generation equipment, the user gives priority to using photovoltaic power generation to meet their own electricity needs. The net power load of photovoltaic users after demand response in period t is: Where: It represents the output of photovoltaic device during period t; Step S23: During operation, the power supply and demand in the microgrid are kept balanced, which can be expressed as:

5. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 4, characterized in that: The constraints of the supply-side response model are as follows: In order to cope with the volatility of new energy and improve the feasibility of the dispatch plan, traditional generators must meet the following constraints during operation: Where: Indicates the maximum output power of a traditional generator set; Respectively represent the minimum and maximum values ​​of the climbing power of the traditional generator set; To ensure the continuity of charging and discharging services provided by the energy storage system, the following constraints need to be met during operation: Where: represents the energy storage capacity during period t; η ES,ch , η ES,dis Respectively represent the energy storage charging and discharging efficiency; Respectively represent the minimum and maximum capacity allowed for energy storage; is a Boolean variable representing the energy storage charging and discharging state; P ch,max , P ch,min , P dis,max , P dis,min They represent the maximum charging power of energy storage, the minimum charging power of energy storage, the maximum discharging power of energy storage, and the minimum discharging power of energy storage respectively; The bidirectional power interaction between the microgrid operator and the upper grid should meet the upper and lower limit constraints: Where: μ net A Boolean variable indicating the state of the microgrid operator purchasing and selling electricity to the superior power grid. When it is 1, the microgrid operator purchases electricity from the superior power grid; P gs,max and P gb,max They respectively represent the maximum power that the microgrid is allowed to transmit to the power purchasing office of the superior power grid and the maximum power that the microgrid is allowed to transmit to the power selling office of the superior power grid.

6. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: The specific implementation method of constructing the mathematical model of electric energy transactions between different entities is as follows: Step S31: Taking maximum benefit as the optimization goal, formulate the objective function of the microgrid operator Where: T represents the scheduling period; represents the electricity sales revenue of the microgrid operator during period t; and They represent the electricity transaction costs between microgrid operators and independent energy storage operators and the upper grid respectively; and represent the operating costs of wind turbines and traditional generators, respectively; in: Where: represents the electricity price during period t; represents the net electricity load of photovoltaic users during period t; They represent the charging and discharging prices of energy storage during period t respectively; Respectively represent the grid electricity price and on-grid electricity price in period t; r WT is the wind power unit power operation cost coefficient; a e 、b e 、c e is the cost coefficient of the traditional generator set; Constraints: Where: They represent the upper and lower limits of the dynamic electricity price within the microgrid respectively; represents the upper limit of the average dynamic electricity price during period t; Step S32: Taking maximum benefit as the optimization goal, formulate the objective function of the independent energy storage operator Where: They represent the revenue of independent energy storage operators from electricity trading with microgrid operators and photovoltaic users during period t; they represent the operating cost of energy storage during period t; in: Where: ε is the unit charging and discharging cost coefficient of energy storage; Constraints: Step S33: Taking the maximum consumer surplus as the optimization goal, formulate the objective function of photovoltaic users: Where: I u It represents the utility function of the user's electricity consumption in period t, and measures the user's satisfaction with electricity purchase and consumption; represents the electricity purchase cost of the user during period t; represents the operating cost of the photovoltaic power generation device during period t; in: Where: is the electricity load of photovoltaic users after demand response in period t; α e , β e Respectively represent the user's preference coefficient for electricity demand; r pv is the unit power operating cost coefficient of the photovoltaic power generation device.

7. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: The hierarchical nested game model based on master-slave game is specifically expressed as follows: G={MGO∪ESO∪user;{δ MGO ,d ESO ,d user };{U MGO ,U ESO ,U user }} Where: MGO, ESO and user represent microgrid operators, energy storage operators and photovoltaic users respectively; {δ MGO ,δ ESO ,δ user } represents the strategy set of each participant, among which the strategy set of the microgrid operator δ MGO The electricity price and the amount of energy storage used are set; the strategy set of energy storage operators δ ESO The charging and discharging prices set for each time period; the strategy of photovoltaic usersδ user is the adjusted flexible load; specifically expressed as: When no participant can obtain better benefits by unilaterally changing strategies, the game reaches Stackelberg equilibrium and obtains the optimal operation strategy, which can be expressed as: In the formula is the Stackelberg equilibrium solution.

8. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that: The specific implementation method of setting different scenarios to test the effectiveness of the established hierarchical nested game model based on master-slave game is as follows: Step S41, setting different operation scenarios; Step S42: Programming on the Matlab platform, using a genetic algorithm combined with a CPLEX solver to solve the hierarchical nested game model; Step S43: Simulate different scenarios to obtain benefit optimization results and strategy sets of each subject in different scenarios; Step S44: By comparing the benefits of various entities in different scenarios and the results of power optimization scheduling, the effectiveness of the proposed method in improving energy storage utilization and promoting win-win cooperation among various entities in the microgrid is verified.

9. A microgrid dispatching system with independent energy storage and coordinated source and load consumption, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps as claimed in any one of claims 1 to 8 can be implemented.

10. A computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, and when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 8 can be implemented.

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