Micro-grid alliance and shared energy storage based double-layer game scheduling method, system and device

Abstract

The application provides a kind of based on microgrid alliance and shared energy storage double-layer game scheduling method, system and equipment, comprising: establishing double-layer cooperative game model;According to microgrid scheduling optimization objective function and shared energy storage scheduling optimization objective function, establish comprehensive objective function;Determine the constraint condition of microgrid scheduling optimization and shared energy storage scheduling optimization;The interaction variable in constraint condition is subjected to consistency constraint;According to consistency constraint and comprehensive objective function, construct ADMM augmented Lagrangian function;The ADMM augmented Lagrangian function is decoupled by ADMM algorithm, and the corresponding interaction function and aggregation optimization function are constructed;The optimization variable in each interaction function and aggregation optimization function is optimized by constraint condition, and optimization result is obtained;According to optimization result, the double-layer cooperative game model is scheduled. It aims to realize power interaction and energy storage sharing under multiple microgrid subjects.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a two-layer game scheduling method, system and equipment based on microgrid alliance and shared energy storage. Background Technology

[0002] Currently, a power grid structure where centralized and distributed generation coexist is beginning to take shape. Microgrids are an effective means to promote the consumption of distributed renewable energy and reduce the regulation pressure on the distribution network. With the increasing penetration rate of distributed generation, the joint operation of multiple microgrids can not only coordinate and complement each other through source-load differences, promoting further consumption of distributed energy, but also utilize the complementarity of their charging and discharging behaviors to share energy storage and improve its utilization rate. However, currently, the main entities participating in microgrid dispatch are singular, with energy storage participating as a passive dispatch unit. Furthermore, the diversification of dispatch entities increases the requirements for privacy and security. Therefore, under the new circumstances, the participation of energy storage in the operation and dispatch of multiple microgrids in a shared model is a topic worthy of in-depth research. Summary of the Invention

[0003] In view of this, the present invention provides a two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage. It aims to achieve power interaction and energy storage sharing among multiple microgrid entities.

[0004] In a first aspect of the present invention, a two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage is provided, the method comprising:

[0005] A two-layer cooperative game model is established, which includes an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance.

[0006] A comprehensive objective function is established based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function.

[0007] Determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization;

[0008] Apply consistency constraints to the interactive variables in the constraints;

[0009] Based on the consistency constraints and the synthetic objective function, the ADMM-enhanced Lagrangian function of the synthetic objective function is constructed as follows:

[0010]

[0011] The ADMM augmented Lagrangian function of the comprehensive objective function is decoupled by the ADMM algorithm to construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction.

[0012] The first optimization variable in the power interaction function of the microgrid alliance is optimized by the constraints of the microgrid scheduling optimization, and the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance is optimized by the constraints of the shared energy storage scheduling optimization, and the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction is optimized by the consistency constraints, so as to obtain the final optimization result.

[0013] The scheduling of each entity in the two-layer cooperative game model is controlled based on the final optimization result.

[0014] In a second aspect of the present invention, a two-layer game-theoretic scheduling system based on microgrid alliances and shared energy storage is provided, the system comprising:

[0015] A two-layer cooperative game model construction module is used to establish a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance.

[0016] The integrated objective function determination module is used to establish an integrated objective function based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function;

[0017] The constraint determination module is used to determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization.

[0018] The consistency constraint determination module is used to apply consistency constraints to the interactive variables in the constraint conditions.

[0019] The augmented Lagrangian function determination module is used to construct the ADMM augmented Lagrangian function of the comprehensive objective function based on the consistency constraints and the comprehensive objective function. The ADMM augmented Lagrangian function of the comprehensive objective function is as follows:

[0020]

[0021] Where, σ i,t , θ i,t This indicates that the dual variables (Lagrange multipliers) ρ1, ρ2, and ρ3 are penalty parameters. For the operational efficiency of microgrid i, Iseso In order to share the benefits of energy storage operators, Let i be the power traded within the microgrid consortium during time period t. for The corresponding consistency constraint parameter, p ch,t For the net charging power of the Microgrid Consortium, For p ch,t The corresponding consistency constraint parameter, p dis,t The net discharge power of the microgrid consortium, For p dis,t The corresponding consistency constraint parameters;

[0022] The interaction function determination module is used to decouple the ADMM augmented Lagrangian function of the comprehensive objective function through the ADMM algorithm, and construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction.

[0023] The variable optimization module is used to optimize the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, optimize the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and optimize the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints, so as to obtain the final optimization result.

[0024] The scheduling module is used to control the scheduling of each entity in the two-layer cooperative game model according to the final optimization result.

[0025] In a third aspect of the present invention, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0026] Memory, used to store computer programs;

[0027] When a processor executes a program stored in a memory, it implements the steps of the two-layer game scheduling method based on microgrid alliance and shared energy storage as described in the first aspect of the present invention.

[0028] Compared with prior art, the present invention has the following advantages:

[0029] This invention provides a two-layer cooperative game scheduling method based on microgrid consortia and shared energy storage. A two-layer cooperative game model is established, comprising an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid consortium, while the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid consortium. A comprehensive objective function is established based on the microgrid scheduling optimization objective function for the microgrid and the shared energy storage scheduling optimization objective function for the shared energy storage. Constraints for microgrid scheduling optimization and shared energy storage scheduling optimization are determined for use in the scheduling optimization process. To decouple the two-layer cooperative game model during scheduling optimization, consistency constraints are applied to the interactive variables in the constraints. Based on the consistency constraints and the comprehensive objective function, an ADMM augmented Lagrangian function of the comprehensive objective function is constructed. Based on the constructed ADMM augmented Lagrangian function with a comprehensive objective function, the ADMM (Alternating Direction Method of Multipliers) algorithm is used to decouple the ADMM augmented Lagrangian function with the comprehensive objective function, thereby obtaining the decoupled power interaction function of the microgrid consortium, the charging and discharging power interaction function between shared energy storage and the microgrid consortium, and the aggregated optimization function of inner and outer layer power interaction. The first optimization variable in the power interaction function of the microgrid consortium is optimized by the constraints of the microgrid scheduling optimization; the second optimization variable in the charging and discharging power interaction function between shared energy storage and the microgrid consortium is optimized by the constraints of the shared energy storage scheduling optimization; and the third optimization variable in the aggregated optimization function of inner and outer layer power interaction is optimized by the consistency constraints, obtaining the final optimization result. The scheduling of each entity in the two-layer cooperative game model is controlled according to the obtained final optimization result to achieve power interaction and energy storage sharing among multiple microgrid entities.

[0030] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0032] Figure 1 A flowchart illustrating a two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage, provided for an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the structure of a two-layer cooperative game model in a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the structure of a multi-microgrid and shared energy storage system in a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention.

[0035] Figure 4 A schematic diagram of the supply-demand ratio and transaction price within a microgrid alliance in a two-layer game scheduling method based on microgrid alliance and shared energy storage, provided for an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the operation and scheduling of the first, second, and third types of microgrids in a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention.

[0037] Figure 6 A schematic diagram of the power interaction of each microgrid in a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention;

[0038] Figure 7 This is a schematic diagram illustrating the operation and scheduling of shared energy storage in a two-layer game-theoretic scheduling method based on microgrid alliance and shared energy storage, provided in an embodiment of the present invention.

[0039] Figure 8 This is a schematic diagram of the residual convergence curve of the ADMM method in a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention. Detailed Implementation

[0040] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings.

[0041] Before describing this invention, the background of its proposal should be explained. Currently, a power grid structure with both centralized and distributed generation is taking shape. Microgrids are an effective means to promote the consumption of distributed renewable energy and reduce the regulation pressure on the distribution network. With the increasing penetration rate of distributed generation, the joint operation of multiple microgrids can not only coordinate and complement each other through source-load differences, promoting further consumption of distributed energy, but also utilize the complementarity of their charging and discharging behaviors to share energy storage and improve its utilization rate. However, currently, the main entities participating in microgrid dispatch are singular, with energy storage participating as a passive dispatch unit. At the same time, the diversification of dispatch entities increases the requirements for privacy and security. Therefore, under the new circumstances, the participation of energy storage in the operation and dispatch of multiple microgrids in a shared model is a topic worthy of in-depth research.

[0042] However, in existing research, cooperative game theory among multiple microgrids mainly involves the distribution network and multiple microgrids determining interactive electricity prices and internal power interactions through master-slave game theory. Each microgrid acts as a passive dispatching unit, neglecting its autonomous decision-making capabilities and lacking protection for its internal privacy, thus reducing its enthusiasm for participating in electricity market transactions. Furthermore, existing research, which focuses on autonomous microgrids and their distributed energy storage, proposes distributed energy storage control strategies with economic objectives, comprehensively considering charging and discharging losses and line losses. While these studies treat microgrids as independent decision-making entities participating in optimal dispatch and satisfying their privacy requirements, energy storage participates in the operation and dispatch of microgrids in an independent and passive manner, without sharing, resulting in low utilization. In addition, existing research has proposed a two-stage joint optimal dispatching model involving the distribution network, multiple microgrids, and shared energy storage. The first stage aims to optimize the application of shared energy storage to mitigate microgrid power fluctuations, while the second stage aims to maximize the alliance benefit by optimizing the power interactions within each microgrid. However, in this study, the microgrids and shared energy storage belong to different stakeholders and have different operation and scheduling methods. Therefore, it is currently a problem to be solved to balance the interests of all parties while meeting the requirements of privacy and security, and to formulate a reasonable optimization scheduling strategy. In view of this, this invention proposes a two-level game scheduling method based on microgrid alliances and shared energy storage.

[0043] Figure 1 A flowchart of a two-layer game scheduling method based on microgrid alliance and shared energy storage provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0044] Step S101: Establish a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance.

[0045] Step S102: Establish a comprehensive objective function based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function;

[0046] Step S103: Determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization;

[0047] Step S104: Apply consistency constraints to the interaction variables in the constraints.

[0048] Step S105: Based on the consistency constraints and the synthetic objective function, construct the ADMM augmented Lagrangian function of the synthetic objective function, wherein the ADMM augmented Lagrangian function of the synthetic objective function is:

[0049]

[0050] Step S106: Decouple the ADMM augmented Lagrangian function of the comprehensive objective function using the ADMM algorithm to construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction.

[0051] Step S107: Optimize the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, optimize the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and optimize the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints to obtain the final optimization result;

[0052] Step S108: Based on the final optimization result, control the scheduling of each subject in the two-layer cooperative game model.

[0053] In an embodiment of the present invention, step S101 first constructs a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance. Figure 2 As shown, the two-layer cooperative game model includes an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game optimizes the operation and scheduling of each microgrid within the microgrid alliance with the goal of maximizing the alliance's profits. Each microgrid optimizes the interaction power between microgrids and the demand for shared energy storage resources based on day-ahead load forecasts and renewable energy output forecasts. The cooperative game platform aggregates the charging and discharging power demands of each microgrid to obtain the net charging and discharging demand of the microgrid alliance, and interacts with the shared energy storage. The outer-layer cooperative game involves the shared energy storage optimizing its charging and discharging response based on the microgrid alliance's net charging and discharging demand and actual operating constraints. The cooperative game platform then negotiates with each microgrid within the alliance based on the actual charging and discharging response of the shared energy storage, feeding back the energy storage supply to each microgrid. This process is repeated multiple times to achieve power interaction balance between microgrids in the inner-layer cooperative game and charging and discharging power interaction balance between shared energy storage and the microgrid alliance in the outer-layer cooperative game.

[0054] In an embodiment of the present invention, after establishing a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game, step S102 constructs a microgrid scheduling optimization objective function with the goal of maximizing the microgrid alliance's profits. Simultaneously, shared energy storage, while meeting the charging and discharging power demands of multiple microgrids, utilizes its remaining capacity to respond to the time-of-use pricing of the distribution network, profiting through low-storage-high-discharge. This establishes a shared energy storage scheduling optimization objective function. Since the main components of the two-layer cooperative game model include the various microgrids within the power grid alliance and the shared energy storage, a comprehensive objective function corresponding to the two-layer cooperative game model is established based on the established microgrid scheduling optimization objective function and the shared energy storage scheduling optimization objective function for scheduling optimization.

[0055] In this invention, establishing a comprehensive objective function based on the microgrid scheduling optimization objective function and the shared energy storage scheduling optimization objective function includes: determining the microgrid scheduling optimization objective function as follows: The objective function for shared energy storage scheduling optimization is determined as follows: Based on the determined scheduling optimization objective function of the microgrid and the shared energy storage scheduling optimization objective function, a comprehensive objective function is established, which is:

[0056] In an embodiment of the present invention, one implementation of step S102 is as follows: A microgrid scheduling optimization objective function is constructed with the goal of maximizing the benefits of the microgrid consortium. The microgrid scheduling optimization objective function is:

[0057]

[0058] in, For the operational efficiency of microgrid i, For power trading between microgrid i and the external power grid, For electricity trading between microgrids, The generation cost of conventional units within microgrid i, The maintenance costs for conventional units within the microgrid i. Service fees for shared energy storage.

[0059] In embodiments of the present invention, the operating efficiency of the microgrid, the power trading between the microgrid and the external power grid, the power trading between microgrids, the power generation cost of conventional units within the microgrid, the maintenance cost of conventional units within the microgrid, and the service fee for shared energy storage are calculated using the following expressions:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] in, Let λ represent the power purchased by microgrid i from the distribution network during time period t and the power sold by microgrid i to the distribution network during time period t, respectively. t buy , λ t sell These refer to the electricity purchase and sale prices between the microgrid and the distribution network. The power traded within the microgrid consortium by microgrid i during time period t. This indicates that electricity is purchased from other microgrids. (meaning selling electricity to other microgrids), a i GB b i GB c i GB These are the cost coefficients for conventional generating units (such as thermal power units), p om λ is the maintenance cost coefficient per unit power of conventional generating units. sess The service fee for charging and discharging shared energy storage units. The electricity price traded within the microgrid consortium. The purchase price of electricity for transactions within the microgrid consortium. For the output of conventional units within the microgrid i, These represent the charging and discharging power of the shared energy storage used by microgrid i during time period t.

[0066] Energy storage operators formulate charging and discharging strategies for shared energy storage. Specifically, while meeting the charging and discharging power demands of multiple microgrids, the shared energy storage utilizes its surplus capacity to respond to the time-of-use pricing of the distribution network, profiting through low-storage, high-discharge strategies. Based on this, a shared energy storage scheduling optimization objective function is established, which is as follows:

[0067]

[0068] Among them, I seso To share the benefits of energy storage operators, I dist To generate revenue from energy arbitrage using surplus energy storage capacity, I serve To share the revenue from energy storage leasing services provided by energy storage operators in microgrids, This refers to the maintenance costs for energy storage operators.

[0069] In embodiments of the present invention, the benefits of the shared energy storage operator, the energy arbitrage income from the remaining capacity of the shared energy storage, the revenue from the shared energy storage operator's participation in the energy storage leasing service of the microgrid, and the maintenance cost of the energy storage operator are calculated using the following expressions:

[0070]

[0071]

[0072]

[0073] Where, p ch,t p dis,t These represent the net charging power and net discharging power of the microgrid consortium, respectively. These represent the electricity purchased, electricity sold, purchase price, and sales price for a shared energy storage operator during time period t, respectively, and λ. sess The service fees for charging and discharging shared energy storage units, ε, These are the shared energy storage maintenance cost coefficient, the unit discharge power of shared energy storage during time period t, and the unit charging power of shared energy storage during time period t, respectively.

[0074] Based on the established microgrid scheduling optimization objective function and shared energy storage scheduling optimization objective function, a comprehensive objective function corresponding to the two-layer cooperative game model is established, which is:

[0075]

[0076] In an embodiment of the present invention, after establishing the comprehensive objective function corresponding to the two-layer cooperative game model, step S103 establishes constraints for the scheduling optimization process of the comprehensive objective function. Specifically, constraints for microgrid scheduling optimization are established for each microgrid in the microgrid alliance, and constraints for shared energy storage scheduling optimization are established for shared energy storage.

[0077] In this invention, the constraints for determining microgrid scheduling optimization include:

[0078] Construct the power balance constraints of the microgrid, the output constraints of the gas turbine, the internal electricity trading constraints, and the interaction constraints between the microgrid and shared energy storage;

[0079] The constructed microgrid power balance constraints, gas turbine output constraints, internal electricity trading constraints, and interaction constraints between the microgrid and shared energy storage are determined as constraints for microgrid scheduling optimization.

[0080] The power balance constraint condition of the microgrid is as follows:

[0081]

[0082] The power output constraint condition of the gas turbine is:

[0083]

[0084] The internal electricity trading constraints are as follows:

[0085]

[0086] The interaction constraints between the microgrid and the shared energy storage are as follows:

[0087]

[0088] The constraints for optimizing shared energy storage scheduling are determined, including:

[0089] Construct shared energy storage power balance constraints and shared energy storage charge and discharge constraints;

[0090] The constructed shared energy storage power balance constraints and shared energy storage charge and discharge constraints are determined as the constraints for shared energy storage scheduling optimization.

[0091] The shared energy storage power balance constraint condition is as follows:

[0092]

[0093] The shared energy storage charging and discharging constraints are as follows:

[0094]

[0095] In an embodiment of the present invention, one implementation of step S103 is as follows: constructing microgrid power balance constraints, gas turbine output constraints, internal electricity trading constraints, and interaction constraints between the microgrid and shared energy storage. The constructed microgrid power balance constraints, gas turbine output constraints, internal electricity trading constraints, and interaction constraints between the microgrid and shared energy storage are then determined as constraints for microgrid scheduling optimization.

[0096] The power balance constraint condition of the microgrid is as follows:

[0097]

[0098] The power output constraint condition of the gas turbine is:

[0099]

[0100] The internal electricity trading constraints are as follows:

[0101]

[0102] The interaction constraints between the microgrid and the shared energy storage are as follows:

[0103]

[0104] Among them, u i,t This refers to the charging and discharging state of microgrid i, i.e., charging or discharging; e i,t The energy state of the energy storage is leased to the microgrid i, which is the capacity of the energy storage device; To provide power to new energy sources within the microgrid i; For the load demand of microgrid i; These represent the upper and lower limits of the output of conventional units within microgrid i; These represent the maximum charging power and maximum discharging power of the shared energy storage leased by the microgrid i; η c η d These are the charging efficiency and discharging efficiency of shared energy storage, respectively; E max To share the maximum energy storage capacity.

[0105] Construct shared energy storage power balance constraints and shared energy storage charge / discharge constraints, and determine the constructed shared energy storage power balance constraints and shared energy storage charge / discharge constraints as constraints for shared energy storage scheduling optimization.

[0106] The shared energy storage power balance constraint condition is as follows:

[0107]

[0108] The shared energy storage charging and discharging constraints are as follows:

[0109]

[0110] in, These represent the unit charging power and discharging power of the shared energy storage system during time period t; i,t Let E be the status flag of the i-th energy storage device in the shared energy storage during time period t, and it can be either 0 or 1. t E max These represent the shared energy storage capacity and the maximum shared energy storage capacity during time period t, respectively.

[0111] In an embodiment of the present invention, after establishing the constraints for scheduling optimization of the comprehensive objective function, step S104 decouples the interactive subjects in the two-layer cooperative game model by applying consistency constraints to the interaction variables in the constraints, thereby facilitating scheduling optimization of the two-layer cooperative game model. Specifically, within the microgrid consortium, each microgrid engages in a game regarding power trading volume. Therefore, decoupling is performed on the microgrid consortium in the inner-layer cooperative game, with power interaction between microgrids used as interaction variables and corresponding consistency constraints added, thus facilitating scheduling optimization of the inner-layer cooperative game. The microgrid consortium and shared energy storage engage in a game regarding charging and discharging power. Decoupling is performed on the cooperative alliance between the outer-layer microgrid consortium and shared energy storage. Based on the energy storage charging and discharging power requirements of each inner-layer microgrid, the net energy storage charging and discharging power requirement of the microgrid consortium is calculated. Shared energy storage interacts with the microgrid consortium in terms of charging and discharging power, with the net charging and discharging power requirement of the microgrid consortium used as an interaction variable and corresponding consistency constraints added, thus facilitating scheduling optimization of the outer-layer cooperative game.

[0112] In this invention, the step of applying consistency constraints to the interaction variables in the constraints includes: adjusting the power of transactions within the microgrid consortium in the constraints. And the net charging power p of the Microgrid Consortium ch,t The net discharge power p of the microgrid alliance dis,t The interaction variable is identified as an interaction variable; a consistency constraint is applied to the interaction variable, the expression of which is:

[0113]

[0114] In an embodiment of the invention, the power traded within the microgrid consortium... As an interaction variable, consistency constraints are applied, and the expression for the consistency constraint is:

[0115]

[0116] The net charging power p of the microgrid consortium ch,t The net discharge power p of the microgrid alliance dis,t The interaction variable is determined to be subject to consistency constraints, and the consistency constraint expression is as follows:

[0117]

[0118] In an embodiment of the present invention, after applying consistency constraints to the interaction variables, step S105 constructs the ADMM augmented Lagrangian function of the comprehensive objective function based on the determined consistency constraints and the comprehensive objective function, for use in subsequent scheduling optimization solutions for the two-layer cooperative game model. The ADMM augmented Lagrangian function of the comprehensive objective function is as follows:

[0119]

[0120] Where, σ i,t , θ i,t Let ρ1, ρ2, and ρ3 represent the dual variables (Lagrange multipliers), and ρ1, ρ2, and ρ3 are penalty parameters.

[0121] In an embodiment of the present invention, after constructing the ADMM augmented Lagrangian function of the comprehensive objective function, in order to protect the information privacy and decision-making autonomy of each participating entity in the two-layer cooperative game model, step S106 decouples the ADMM augmented Lagrangian function of the comprehensive objective function using the ADMM algorithm, constructs a corresponding power interaction function for the microgrid alliance to schedule and optimize the power interaction of the inner-layer cooperative game composed of the microgrid alliance, a charging and discharging power interaction function between shared energy storage and the microgrid alliance to optimize the power interaction of the outer-layer cooperative game composed of shared energy storage and the microgrid alliance, and an aggregation optimization function for the inner and outer-layer power interactions to perform aggregation optimization between the inner and outer-layer power interactions.

[0122] In this invention, the step of decoupling the ADMM augmented Lagrangian function of the comprehensive objective function using the ADMM algorithm to construct the corresponding power interaction function of the microgrid consortium, the charging and discharging power interaction function of shared energy storage and the microgrid consortium, and the aggregated optimization function of inner and outer layer power interaction includes:

[0123] The ADMM-augmented Lagrangian function of the integrated objective function is decoupled using the ADMM algorithm to construct a power interaction function characterizing the power interaction within the microgrid consortium itself. The power interaction function of the microgrid consortium is as follows:

[0124]

[0125] The ADMM-augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct a charge-discharge power interaction function characterizing the charge-discharge power interaction between shared energy storage and the microgrid consortium. This charge-discharge power interaction function between shared energy storage and the microgrid consortium is as follows:

[0126]

[0127] The ADMM augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct an aggregated optimization function for power interaction between the microgrid consortium (inner layer) and the shared energy storage (outer layer). The aggregated optimization function for power interaction between the inner and outer layers is as follows:

[0128]

[0129] In one embodiment of the present invention, step S106 is implemented as follows: the ADMM augmented Lagrangian function of the integrated objective function is decoupled using the ADMM algorithm to construct a power interaction function characterizing the power interaction within the microgrid consortium itself. The power interaction function of the microgrid consortium is as follows:

[0130]

[0131] Subsequently, the power interaction of the inner-layer cooperative game of the microgrid alliance is scheduled and optimized through the power interaction function of the microgrid alliance.

[0132] The ADMM-augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct a charge-discharge power interaction function characterizing the charge-discharge power interaction between shared energy storage and the microgrid consortium. This charge-discharge power interaction function between shared energy storage and the microgrid consortium is as follows:

[0133]

[0134] Subsequently, the power interaction optimization of the outer-layer cooperative game formed by shared energy storage and microgrid alliance is carried out through the charging and discharging power interaction function of shared energy storage and microgrid alliance.

[0135] The ADMM augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct an aggregated optimization function for power interaction between the microgrid consortium (inner layer) and the shared energy storage (outer layer). The aggregated optimization function for power interaction between the inner and outer layers is as follows:

[0136]

[0137] Subsequently, the aggregation optimization of the power interaction between the inner and outer layers is performed through the aggregation optimization function of the inner and outer layer power interaction.

[0138] In an embodiment of the present invention, after decoupling the ADMM augmented Lagrangian function of the comprehensive objective function using the ADMM algorithm, and constructing the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction, step S107 optimizes the first optimization variable in the power interaction function of the microgrid alliance by using the constraints of the microgrid scheduling optimization. Then, it optimizes the second optimization variable in the charging and discharging power interaction function of shared energy storage and the microgrid alliance by using the constraints of the shared energy storage scheduling optimization. Finally, it optimizes the third optimization variable in the aggregated optimization function of inner and outer layer power interaction by using the consistency constraints. After completing these three optimizations, the final optimization result is obtained. Based on this final optimization result, power interaction scheduling among the various entities in the two-layer cooperative game model is realized.

[0139] In this invention, the optimization of the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, the optimization of the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and the optimization of the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints, to obtain the final optimization result, includes: determining the power traded within the microgrid. Power sold by microgrids to the distribution network Microgrids purchase power from the distribution network Output of conventional units within the microgrid The microgrid uses the charging power of shared energy storage during time period t. The microgrid uses the charging power of shared energy storage during time period t. The first optimization variable is used as the first optimization variable; the first optimization variable in the determined power interaction function of the microgrid alliance is optimized according to the constraints of the microgrid scheduling optimization; the net charging power p of the microgrid alliance is determined. ch,t The net discharge power p of the microgrid consortium dis, t, the electricity purchased by the shared energy storage operator during time period t Electricity sold by shared energy storage operators during time period t Charging power of shared energy storage Discharge power of shared energy storage The second optimization variable is used as the constraint condition for the shared energy storage scheduling optimization. This second optimization variable is then optimized within the determined charging and discharging power interaction function between the shared energy storage and the microgrid alliance. Finally, the coordination variable is determined after applying consistency constraints to the power parameters of the microgrid's internal transactions. Coordination variables of net charging power parameters of microgrid consortium after consistency constraints Coordination variables for net discharge power parameters of microgrid consortia after consistency constraints The third optimization variable is determined by the consistency constraint and optimized using the consistency constraint.

[0140] In an embodiment of the present invention, the power traded within the microgrid is... Power sold by microgrids to the distribution network Microgrids purchase power from the distribution network Output of conventional units within the microgrid The microgrid uses the charging power of shared energy storage during time period t. The microgrid uses the charging power of shared energy storage during time period t. All of these were determined as the first optimization variables. The first optimization variables in the power interaction function of the microgrid alliance were then optimized using the constructed constraints of the microgrid scheduling optimization and the power interaction function of the microgrid alliance.

[0141] In an embodiment of the invention, the net charging power p of the microgrid consortium is... ch,t The net discharge power p of the microgrid consortium dis,t Power purchased by shared energy storage operators during time period t Electricity sold by shared energy storage operators during time period t Charging power of shared energy storage Discharge power of shared energy storage All of these were identified as the second optimization variables. The second optimization variables in the charge-discharge power interaction function of the shared energy storage and microgrid alliance were optimized by constructing constraints for shared energy storage scheduling optimization and the charge-discharge power interaction function of the shared energy storage and microgrid alliance.

[0142] In an embodiment of the present invention, the coordination variable after applying consistency constraints to the power parameters of the microgrid internal transactions is... Coordination variables of net charging power parameters of microgrid consortium after consistency constraints Coordination variables for net discharge power parameters of microgrid consortia after consistency constraints The variable is identified as the third optimization variable. The third optimization variable in the aggregation optimization function of the inner and outer layer power interaction is optimized through the aggregation optimization function of the inner and outer layer power interaction and the consistency constraint.

[0143] In this invention, the method further includes:

[0144] Construct a pricing model for microgrid cluster alliances;

[0145] The pricing model of the microgrid consortium is used to price the power interaction between the individual microgrids in the consortium.

[0146] The pricing model for the microgrid group alliance is expressed as follows:

[0147]

[0148]

[0149]

[0150]

[0151] In embodiments of this invention, cooperation among multiple microgrids essentially involves power trading within the consortium. Compared to direct trading between microgrids and the distribution network, a suitable pricing mechanism is needed to ensure that each microgrid receives better returns after participating in power trading within the consortium, thereby maintaining the stability of the microgrid consortium. Therefore, this invention establishes a pricing model for power trading within a microgrid consortium, the expression of which is:

[0152]

[0153]

[0154]

[0155]

[0156] Among them, SDR t For the supply and demand ratio within the microgrid consortium, To contribute to the renewable energy sources within the microgrid i For the output of conventional units within the microgrid i, These represent the charging and discharging power of the shared energy storage used by microgrid i during time period t. For the load demand of microgrid i, The electricity price traded within the microgrid consortium. β represents the purchase price of electricity traded within the microgrid consortium. t This is a price compensation factor.

[0157] When SDR t When SDR = 0, it indicates that there is no electricity trading within the microgrid consortium, meaning each microgrid has no surplus output, and all power deficits must be purchased from the grid. In this case, the purchase and sale price of electricity equals the grid's purchase and sale price. When SDR = 0, it means there is no electricity trading within the microgrid consortium, i.e., each microgrid has no surplus output, and all power deficits must be purchased from the grid. tWhen = 1, it indicates that the microgrid alliance can achieve supply and demand balance, and the purchase and sale prices of electricity within the alliance are equal, all equal to the price sold to the grid after considering the compensation factor; 0 <SDR t <1 or 1 <SDR t Within a microgrid consortium, electricity trading prices fluctuate in response to changes in the supply-demand ratio.

[0158] In this invention, the method further includes: allocating the total revenue obtained corresponding to the final scheduling result to each entity in the two-layer cooperative game model through the Shapley value method, wherein each entity includes each microgrid in the microgrid alliance and shared energy storage.

[0159] In an embodiment of the present invention, based on the proportional coalition Shapley value method, the total revenue obtained corresponding to the final power interaction scheduling result of the two-layer cooperative game model is allocated to each entity in the two-layer cooperative game model, wherein each entity includes each microgrid in the microgrid coalition and shared energy storage.

[0160] For a coalition-structured game [v,P]∈G N , i∈P k The proportional Shapley value π is a coalition value given by the first expression below, which is defined as follows:

[0161]

[0162]

[0163] Where, [v,P]∈G N , i∈P k G N For all individuals in a monotonically occurring game with a coalition structure, P k Let i be any alliance structure, and let i be a participant in the alliance structure. Here, S is the number of elements in the alliance S, n is the number of elements in the larger alliance N, i is an element in alliance S, V(S) is the characteristic function of alliance S, and V(S\{i}) represents the characteristic function of the alliance formed by the remaining elements after removing i. For example, in one alliance structure, microgrid 1 is a monotonically playing individual, microgrid 2 is a monotonically playing individual, and microgrid 3 does not participate in the alliance structure; in another alliance structure, microgrid 1 is a monotonically playing individual, microgrid 3 is a monotonically playing individual, and microgrid 2 does not participate in the alliance structure. Shared energy storage is an element in the microgrid. An alliance is an alliance structure, and all alliance structures constitute a larger alliance. The proportional alliance Shapley value adopted in this invention reflects the idea that after the microgrid alliance and shared energy storage obtain benefits through cooperation and sharing, all participants in the alliance will fairly obtain the benefits of joining the alliance, that is, the payment ratio of the participants is given by the Shapley value in the original game, thus maintaining the relative importance of each participant in the original game.

[0164] In the embodiments of this invention, a two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage is provided. In this two-layer cooperative game model, each microgrid participates in scheduling optimization as a principal in the model. Each microgrid has autonomous decision-making capabilities, effectively protecting the privacy within the microgrid and thus significantly increasing its participation in the electricity market. Furthermore, in this two-layer cooperative game model, both the individual microgrids in the microgrid alliance and the shared energy storage entity are autonomous decision-makers participating in power interaction. Shared energy storage has a higher utilization rate, meaning it acts as a power interaction entity, participating in power interaction with the various microgrids in the microgrid alliance, resulting in high utilization. In the constructed two-layer cooperative game model, both shared energy storage and the individual microgrids in the microgrid alliance participate in the power interaction process as autonomous decision-makers. Since shared energy storage and the microgrid alliance are the same stakeholders, they share the same operation and scheduling method for the two-layer cooperative model. This method balances the interests of all parties while meeting privacy and security requirements and allows for the formulation of reasonable optimization scheduling strategies.

[0165] In the embodiments of this invention, the model is solved by calling CPLEX using Yalmip in the MATLAB 2020a compilation environment. Numerical examples are analyzed to verify the effectiveness and correctness of the proposed two-layer cooperative game model. The analysis and verification include benefit analysis under different cooperative game modes, microgrid scheduling strategy analysis, shared energy storage scheduling strategy analysis, convergence analysis of the ADMM algorithm, and fairness analysis of the distribution of benefits under cooperative modes.

[0166] by Figure 3 The proposed system constructs multiple microgrids and a shared energy storage system for testing. Three types of microgrids—P1 (e.g., industrial microgrid), P2 (e.g., commercial microgrid), and P3 (residential microgrid)—were selected as test microgrids. Coordination and complementarity were achieved through the source-load differences between the different microgrids. A 24-hour scheduling cycle and a 1-hour scheduling interval were selected to verify the effectiveness and correctness of the proposed two-layer cooperative game model. Ps represents the shared energy storage. The model was solved using CPLEX via Yalmip in the MATLAB 2020a compilation environment.

[0167] The results of the benefit analysis under different cooperative game modes were obtained by solving the model. The operating benefits under different alliance combinations are shown in Table 1 below:

[0168]

[0169] As shown in Table 1, compared with the non-cooperative mode, the operational efficiency of different alliance combinations all increased accordingly. Among them, the mode in which each microgrid forms a large microgrid alliance and cooperates with shared energy storage resulted in the largest increase in total operational efficiency, at 31.32%. This indicates that the joint dispatch strategy of microgrid alliance and shared energy storage proposed in this invention can effectively improve the operational efficiency of each entity and has the characteristics of a win-win situation for all parties.

[0170] The microgrid dispatch strategy analysis results are obtained by solving the model, including: the analysis results of the internal transaction price of the microgrid alliance and the analysis results of the microgrid alliance dispatch strategy.

[0171] Regarding the analysis of electricity trading prices within the microgrid consortium, under the cooperative consortium structure, each microgrid coordinates and complements itself through source-load differences, and conducts power interaction within the consortium based on a supply-demand ratio pricing mechanism. Internal pricing based on the supply-demand ratio is as follows: Figure 4 As shown. Figure 4 As shown, the transaction price within a microgrid consortium varies with the supply-demand ratio within the consortium. During periods 11 and 12, the supply-demand ratio is greater than 1, indicating a more abundant energy market supply within the consortium and lower internal transaction prices. In other periods, the supply-demand ratio is less than 1, indicating a power deficit within the consortium, resulting in higher internal transaction prices. Simultaneously, influenced by the external grid, the internal transaction price fluctuates between external purchase and sale prices, allowing both buyers and sellers within the consortium to obtain more favorable returns than from the external grid. Therefore, under the cooperative model, each microgrid reduces power interaction with the external grid through low-cost, high-yield internal transactions, promoting local consumption of distributed energy resources and improving operational efficiency.

[0172] Based on the analysis of microgrid alliance dispatch strategies, when microgrids operate independently, they cannot complement each other's power during periods of power imbalance. Only through cooperation, forming an alliance, and changing the original power dispatch method can coordinated complementarity among microgrids be achieved, reaching the goal of optimal operational efficiency. The power interaction function of the microgrid alliance and the aggregation optimization function of the inner and outer layer power interactions determine that the optimal power dispatch efficiency of the microgrid (microgrid transaction revenue and operating costs) can be viewed as an iterative optimization problem of electricity trading between microgrids. Through iteration, a win-win situation is achieved among the participating entities within the microgrid alliance. The dispatch strategies of each microgrid within the microgrid alliance and their internal power interactions are as follows: Figure 5 As shown.

[0173] like Figure 5 As shown in the diagram, for the operation and dispatching of microgrid P1, the dispatching strategies of each microgrid are as follows: From 22:00 to 5:00, wind power output is relatively high, and after meeting the load demand of microgrid P1, there is still surplus power. At this time, power balance in microgrid P1 is achieved through leasing shared energy storage for charging and power interaction with other microgrids. From 6:00 to 8:00, photovoltaic power begins to participate in the power dispatching of the microgrid. This period is a low-price period, and the load on microgrid P1 is high, so the output of new energy sources cannot meet the load demand. Meanwhile, other microgrids... There is no surplus power for mutual assistance, so the load demand is mainly met by purchasing electricity from the distribution network; during the period from 9:00 to 12:00, which is the peak electricity price period, the load deficit is mainly made up by shared energy storage discharge; during the period from 13:00 to 16:00, which is the flat electricity price period, the load deficit is mainly made up by purchasing electricity from the grid and the output of the gas turbine; during the period from 17:00 to 21:00, which is the peak electricity price period, the load deficit is first met by energy storage discharge, and the remaining load deficit is made up by purchasing electricity from the grid and the output of the gas turbine.

[0174] like Figure 5 The operation and scheduling diagrams of microgrid P2 and microgrid P3 shown can be used to analyze and derive the internal power scheduling strategies of microgrids P2 and P3 using a method similar to that used for microgrid P1.

[0175] like Figure 6The power interaction diagram of each microgrid shown illustrates the power interaction among the microgrids within the alliance. From 1:00 to 5:00, wind power output is high, while photovoltaic power output is low. Microgrid P1 has a significant surplus of renewable energy. After meeting the charging and discharging needs of shared energy storage, it interacts with microgrids P2 and P3. From 9:00 to 14:00, microgrid P3 has a significant photovoltaic output. After meeting the charging and discharging needs of shared energy storage, it interacts with microgrids P1 and P2. Throughout the entire dispatch period, microgrid P2 has no surplus power and does not interact with other microgrids. Its main function is to assist other microgrids in absorbing excess renewable energy output to reduce their electricity costs.

[0176] By sharing power and leasing energy storage for charging and discharging among microgrids, renewable energy can be shared and utilized at different times, promoting the absorption of renewable energy within the microgrid alliance and maximizing the benefits of multiple microgrids.

[0177] The analysis results of the shared energy storage scheduling strategy were obtained by solving the model. In the case of a cooperative alliance, the complementary charging and discharging behaviors of different types of microgrids (such as industrial microgrids, commercial microgrids, and residential microgrids) can be used to lease shared energy storage to meet the charging and discharging needs of each microgrid. This can improve the utilization rate of energy storage resources and reduce the investment pressure of microgrids. As represented by the charging and discharging power interaction function of shared energy storage and the microgrid alliance and the aggregation optimization function of the inner and outer layer power interaction, the optimal scheduling problem of shared energy storage power can be regarded as an iterative optimization problem of the net charging and discharging demand of the microgrid alliance and the charging and discharging response of shared energy storage. Through iteration, a win-win situation can be achieved for the microgrid alliance and shared energy storage.

[0178] Figure 7 The operation and scheduling of shared energy storage is shown below. For the shared energy storage scheduling strategy, from 23:00 to 5:00, which is a low-price period, the shared energy storage meets the charging needs of microgrid P1 and purchases electricity from the grid using the remaining space. From 9:00 to 14:00, the shared energy storage utilizes the complementarity of the charging and discharging behaviors of each microgrid to meet the discharging needs of microgrids P1 and P2, and the charging needs of microgrid P3, and uses the remaining space for power interaction with the distribution network. From 17:00 to 22:00, which is a peak-price period, the shared energy storage meets the discharging needs of the distribution network and each microgrid.

[0179] The convergence analysis results of the ADMM algorithm were obtained through model solving. This invention uses the ADMM algorithm to optimize the joint scheduling problem of multiple microgrids and shared energy storage. The convergence of the algorithm iteration is as follows: Figure 8 As shown. Figure 8As shown, in the initial stage of the iteration, the dual residual increases rapidly while the original residual gradually decreases. As the iteration progresses, both the dual residual and the original residual gradually decrease and tend to stabilize, reaching convergence accuracy after 60 iterations.

[0180] The fairness analysis results of the profit distribution in the cooperation model were obtained by solving the model. After the microgrid alliance and shared energy storage obtain profits through cooperation and sharing, all participants in the alliance will fairly receive the profits they received for joining the alliance. That is, the payment ratio of the participants is given by the Shapley value in the original game, thus maintaining the relative importance of each participant in the original game. The proportional alliance Shapley value method used in this invention distributes the additional profits of the cooperative alliance, as shown in Table 2:

[0181]

[0182] As shown in Table 2, under the cooperative model, the cooperative surplus generated by the cooperative game brings varying degrees of benefit improvement to each microgrid and the shared energy storage. For the microgrid alliance, the allocation ratio of each microgrid is recalculated within the alliance while keeping the original Shapley value unchanged. The allocation results are as follows: microgrid P3 has the largest benefit improvement at 54.86%, followed by microgrid P1 at 21.29%, and microgrid P2 has the smallest improvement at 15.86%. This is because microgrids P1 and P3 have significant renewable energy output, complementing each other within the alliance by utilizing the source-load differences at different times. Microgrid P2 has a smaller renewable energy output and does not have surplus power to complement other microgrids during the entire dispatch period. Its main role in the alliance is to help absorb the excess renewable energy of microgrids P1 and P3. For shared energy storage, its allocation ratio is directly calculated according to the original Shapley value. Therefore, when allocating benefits according to the proportional alliance Shapley value, the importance of each participant remains unchanged, promoting fairness in allocation and increasing the stability of the alliance.

[0183] This invention proposes a two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage. This method effectively alleviates the limitations of traditional independent microgrid scheduling methods in addressing power interaction and energy storage sharing issues across multiple microgrid entities. It satisfies privacy and security requirements while balancing the interests of all stakeholders and formulating reasonable optimization scheduling strategies. Ultimately, it ensures the correctness and effectiveness of the joint scheduling mode of multiple microgrids and shared energy storage. Specifically, the supply-demand ratio-based pricing mechanism effectively promotes mutual cooperation among microgrids and improves their operational efficiency. The greater the difference in source-load curves among the microgrids, the more significant the economic advantage. The proposed two-layer cooperative game model integrates microgrids with shared energy storage through an alliance, leveraging the differences in source-load and the complementarity of charging and discharging behaviors among the microgrids to achieve win-win cooperation among multiple stakeholders. Based on the privacy and security requirements of each stakeholder, a joint scheduling model of microgrids and shared energy storage is established using the distributed ADMM algorithm, ensuring the correctness of the optimized scheduling results. For multi-microgrid shared energy storage scenarios with an alliance structure, the cooperative benefit allocation model based on the Shapley value of the proportional alliance has practical application value.

[0184] A second aspect of the present invention provides a two-layer game-theoretic scheduling system based on microgrid alliances and shared energy storage, the system comprising:

[0185] A two-layer cooperative game model construction module is used to establish a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance.

[0186] The integrated objective function determination module is used to establish an integrated objective function based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function;

[0187] The constraint determination module is used to determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization.

[0188] The consistency constraint determination module is used to apply consistency constraints to the interactive variables in the constraint conditions.

[0189] The augmented Lagrangian function determination module is used to construct the ADMM augmented Lagrangian function of the comprehensive objective function based on the consistency constraints and the comprehensive objective function. The ADMM augmented Lagrangian function of the comprehensive objective function is as follows:

[0190]

[0191] Where, σ i,t , θ i,tLet ρ1, ρ2, and ρ3 represent the dual variables (Lagrange multipliers), and ρ1, ρ2, and ρ3 be the penalty parameters. For the operational efficiency of microgrid i, I seso In order to share the benefits of energy storage operators, Let i be the power traded within the microgrid consortium during time period t. for The corresponding consistency constraint parameter, p ch,t For the net charging power of the Microgrid Consortium, For p ch,t The corresponding consistency constraint parameter, p dis,t The net discharge power of the microgrid consortium, For p dis,t The corresponding consistency constraint parameters;

[0192] The interaction function determination module is used to decouple the ADMM augmented Lagrangian function of the comprehensive objective function through the ADMM algorithm, and construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction.

[0193] The variable optimization module is used to optimize the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, optimize the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and optimize the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints, so as to obtain the final optimization result.

[0194] The scheduling module is used to control the scheduling of each entity in the two-layer cooperative game model according to the final optimization result.

[0195] In a third aspect of the present invention, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0196] Memory, used to store computer programs;

[0197] When a processor executes a program stored in a memory, it implements the steps of the two-layer game scheduling method based on microgrid alliance and shared energy storage as described in the first aspect of the present invention.

[0198] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0199] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage, characterized in that, The method includes: A two-layer cooperative game model is established, which includes an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance. A comprehensive objective function is established based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function. Determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization; Apply consistency constraints to the interactive variables in the constraints; Based on the consistency constraints and the synthetic objective function, the ADMM-enhanced Lagrangian function of the synthetic objective function is constructed as follows: ； in, , , Represents dual variables (Lagrange multipliers). , , For penalty parameters, For microgrids operational efficiency In order to share the benefits of energy storage operators, For microgrids The power traded within the microgrid consortium during time period t. for The corresponding consistency constraint parameters, For the net charging power of the Microgrid Consortium, for The corresponding consistency constraint parameters, The net discharge power of the microgrid consortium, for The corresponding consistency constraint parameters; The ADMM augmented Lagrangian function of the comprehensive objective function is decoupled by the ADMM algorithm to construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction. The first optimization variable in the power interaction function of the microgrid alliance is optimized by the constraints of the microgrid scheduling optimization, and the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance is optimized by the constraints of the shared energy storage scheduling optimization, and the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction is optimized by the consistency constraints, so as to obtain the final optimization result. The scheduling of each entity in the two-layer cooperative game model is controlled based on the final optimization result. Construct a pricing model for microgrid cluster alliances; The pricing model of the microgrid consortium is used to price the power interaction between the individual microgrids in the consortium. The pricing model for the microgrid group alliance is expressed as follows: ， ， ， ； in, For the supply and demand ratio within the microgrid consortium, For microgrids Internal renewable energy output, For microgrids Output of internal conventional units, microgrids exist The charging and discharging power of shared energy storage is used during certain time periods. For microgrids The load demand, The electricity price traded within the microgrid consortium. The purchase price of electricity for transactions within the microgrid consortium. This is a price compensation factor.

2. The two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in claim 1, characterized in that, The establishment of a comprehensive objective function based on the microgrid scheduling optimization objective function and the shared energy storage scheduling optimization objective function includes: The objective function for microgrid scheduling optimization is determined as follows: ; The objective function for shared energy storage scheduling optimization is determined as follows: ; Based on the determined scheduling optimization objective function of the microgrid and the shared energy storage scheduling optimization objective function, a comprehensive objective function is established, which is: ； in, For microgrids Electricity trading with external power grids For electricity trading between microgrids, For microgrids The power generation cost of conventional generating units in China For microgrids Maintenance costs of internal conventional units, For shared energy storage service fees, To generate revenue from energy arbitrage using surplus energy storage capacity. To share the revenue from energy storage leasing services provided by energy storage operators in microgrids, This refers to the maintenance costs for energy storage operators.

3. The two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in claim 2, characterized in that, The constraints for microgrid dispatch optimization are determined, including: Construct the power balance constraints of the microgrid, the output constraints of the gas turbine, the internal electricity trading constraints, and the interaction constraints between the microgrid and shared energy storage; The constructed microgrid power balance constraints, gas turbine output constraints, internal electricity trading constraints, and interaction constraints between the microgrid and shared energy storage are determined as constraints for microgrid scheduling optimization. The power balance constraint condition of the microgrid is as follows: ； The power output constraint condition of the gas turbine is: ； The internal electricity trading constraints are as follows: ； The interaction constraints between the microgrid and the shared energy storage are as follows: ； in, , microgrids During time period t, the power purchased from the distribution network and the microgrid The amount of electricity sold to the distribution network during time period t. For microgrids Output of internal conventional units, For microgrids Internal new energy output, , microgrids exist The charging and discharging power of shared energy storage is used during certain time periods. For microgrids The load demand, , microgrids The upper and lower limits of the output of the internal conventional units. For microgrids During the charging and discharging state at time t, For microgrids The energy state of leased energy storage during time period t. , microgrids The maximum charging power and maximum discharging power of leased shared energy storage. , These refer to the charging efficiency and discharging efficiency of shared energy storage, respectively. To share the maximum energy storage capacity, , microgrids The energy state of the leased energy storage during the initial period and the energy state of the leased energy storage during period t at the end of the dispatch; The constraints for optimizing shared energy storage scheduling are determined, including: Construct shared energy storage power balance constraints and shared energy storage charge and discharge constraints; The constructed shared energy storage power balance constraints and shared energy storage charge and discharge constraints are determined as the constraints for shared energy storage scheduling optimization. The shared energy storage power balance constraint condition is as follows: ； The shared energy storage charging and discharging constraints are as follows: ； in, , These represent the electricity purchased and sold by the shared energy storage operator during time period t, respectively. For the charging power of shared energy storage, To share the discharge power of the energy storage, This is the status flag of the i-th energy storage device in the shared energy storage during time period t, and it can be either 0 or 1. These represent the shared energy storage capacity and the maximum shared energy storage capacity during time period t, respectively.

4. The two-layer game scheduling method based on microgrid alliance and shared energy storage according to claim 3, characterized in that, The process of applying consistency constraints to the interaction variables in the constraints includes: The power traded within the microgrid alliance under the constraints and the net charging power of the Microgrid Consortium Net discharge power of the microgrid alliance It was determined to be an interactive variable; The interaction variables are subject to consistency constraints, and the consistency constraint expression is as follows: ， 。 5. The two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in claim 4, characterized in that, The process of decoupling the ADMM-augmented Lagrangian function of the comprehensive objective function using the ADMM algorithm to construct the corresponding power interaction function of the microgrid consortium, the charging and discharging power interaction function of shared energy storage and the microgrid consortium, and the aggregated optimization function of inner and outer layer power interaction includes: The ADMM-augmented Lagrangian function of the integrated objective function is decoupled using the ADMM algorithm to construct a power interaction function characterizing the power interaction within the microgrid consortium itself. The power interaction function of the microgrid consortium is as follows: ； The ADMM-augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct a charge-discharge power interaction function characterizing the charge-discharge power interaction between shared energy storage and the microgrid consortium. This charge-discharge power interaction function between shared energy storage and the microgrid consortium is as follows: ； The ADMM augmented Lagrangian function of the comprehensive objective function is decoupled using the ADMM algorithm to construct an aggregated optimization function for power interaction between the microgrid consortium (inner layer) and the shared energy storage (outer layer). The aggregated optimization function for power interaction between the inner and outer layers is as follows: 。 6. The two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in claim 5, characterized in that, The optimization of the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, the optimization of the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and the optimization of the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints, to obtain the final optimization result, includes: Determine the power of transactions within the microgrid The power sold by the microgrid to the distribution network Microgrids purchase power from the distribution network Output of conventional units within the microgrid The charging power of the shared energy storage used by the microgrid during time period t. The charging power of the shared energy storage used by the microgrid during time period t. It is the first optimization variable; The first optimization variable in the determined power interaction function of the microgrid alliance is optimized by means of the constraints of the microgrid scheduling optimization. Determine the net charging power of the microgrid consortium Net discharge power of the microgrid consortium Power purchased by shared energy storage operators during time period t The electricity sold by shared energy storage operators during time period t Charging power of shared energy storage Discharge power of shared energy storage This is the second optimization variable; The second optimization variable in the determined charging and discharging power interaction function of the shared energy storage and microgrid alliance is optimized by means of the constraints of the shared energy storage scheduling optimization. Coordination variables for determining power parameters in microgrid transactions after applying consistency constraints Coordination variables of net charging power parameters of microgrid consortia after consistency constraints Coordination variables for the net discharge power parameters of the microgrid consortium after consistency constraints As the third optimization variable; The third optimization variable in the aggregate optimization function, which is determined to be the inner and outer layer power interaction, is optimized by the consistency constraint.

7. The two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in claim 1, characterized in that, The method further includes: The Shapley value method is used to allocate the total revenue obtained from the final scheduling result to the various entities in the two-layer cooperative game model. These entities include the microgrids and shared energy storage in the microgrid alliance.

8. A two-layer game-theoretic scheduling system based on microgrid alliances and shared energy storage, characterized in that, The system applied to the two-layer game-theoretic scheduling method based on microgrid alliances and shared energy storage as described in any one of claims 1-7, the system comprising: A two-layer cooperative game model construction module is used to establish a two-layer cooperative game model including an inner-layer cooperative game and an outer-layer cooperative game. The inner-layer cooperative game includes the interactive game between microgrids within the microgrid alliance, and the outer-layer cooperative game includes the interactive game between shared energy storage and the microgrid alliance. The integrated objective function determination module is used to establish an integrated objective function based on the microgrid dispatch optimization objective function and the shared energy storage dispatch optimization objective function; The constraint determination module is used to determine the constraints for microgrid dispatch optimization and shared energy storage dispatch optimization. The consistency constraint determination module is used to apply consistency constraints to the interactive variables in the constraint conditions. The augmented Lagrangian function determination module is used to construct the ADMM augmented Lagrangian function of the comprehensive objective function based on the consistency constraints and the comprehensive objective function. The ADMM augmented Lagrangian function of the comprehensive objective function is as follows: ； in, , , Represents dual variables (Lagrange multipliers). , , For penalty parameters, For microgrids operational efficiency In order to share the benefits of energy storage operators, For microgrids The power traded within the microgrid consortium during time period t. for The corresponding consistency constraint parameters, For the net charging power of the Microgrid Consortium, for The corresponding consistency constraint parameters, The net discharge power of the microgrid consortium, for The corresponding consistency constraint parameters; The interaction function determination module is used to decouple the ADMM augmented Lagrangian function of the comprehensive objective function through the ADMM algorithm, and construct the corresponding power interaction function of the microgrid alliance, the charging and discharging power interaction function of shared energy storage and the microgrid alliance, and the aggregated optimization function of inner and outer layer power interaction. The variable optimization module is used to optimize the first optimization variable in the power interaction function of the microgrid alliance through the constraints of the microgrid scheduling optimization, optimize the second optimization variable in the charging and discharging power interaction function of the shared energy storage and the microgrid alliance through the constraints of the shared energy storage scheduling optimization, and optimize the third optimization variable in the aggregation optimization function of the inner and outer layer power interaction through the consistency constraints, so as to obtain the final optimization result. The scheduling module is used to control the scheduling of each entity in the two-layer cooperative game model according to the final optimization result.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the steps of a two-layer game scheduling method based on microgrid alliance and shared energy storage as described in any one of claims 1-7.

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