A power system optimal dispatch scheme generation method and related device

Abstract

The application relates to the technical field of power systems, and discloses a power system optimization scheduling scheme generation method and related devices, which comprises the following steps: constructing a master-slave game model of a distribution network operator and a multi-comprehensive energy microgrid alliance based on comprehensive constraint conditions, wherein the comprehensive constraint conditions comprise distribution network physical constraints and microgrid power constraints; performing cost and benefit analysis according to Nash negotiation theory to construct a cooperative game model of the multi-comprehensive energy microgrid operator; and based on the cooperative game model, combining a SHADE algorithm and an improved ADMM algorithm to perform double-layer game solving on the master-slave game model to obtain an optimization scheduling scheme. The application can solve the technical problem that the prior art fails to perform targeted analysis and utilization on the operation information and interaction information of the distribution network and the microgrid, and the model is difficult to simultaneously meet the needs of safety and solving privacy protection and other parties, resulting in the lack of accuracy and reliability of the generated optimization scheduling scheme.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and related apparatus for generating power system optimal dispatch schemes. Background Technology

[0002] As the scale of distributed renewable energy and integrated energy microgrids in distribution networks continues to expand, multiple geographically adjacent integrated energy microgrids are gradually forming interconnected and collaborative multi-microgrid systems. Compared to the independent operation of a single microgrid, multiple integrated energy microgrids, through cross-entity power interaction and multi-energy resource complementarity, can effectively improve the renewable energy absorption capacity, smooth load fluctuations, and reduce the overall system operating costs, making it an important technological direction for the low-carbon and efficient transformation of distribution systems.

[0003] Existing research has explored various approaches to the optimal scheduling of multiple microgrids. Some studies have introduced game theory to address the differences in interests among multiple stakeholders, using master-slave game theory to characterize the pricing interaction between distribution network operators and microgrids, or using cooperative game theory to realize power trading and revenue distribution among microgrids. Other studies have constructed a two-level scheduling framework to coordinate the operational goals of stakeholders at different levels.

[0004] However, current optimized scheduling schemes still have some limitations. For example, most two-level scheduling models do not incorporate detailed operational information of the distribution network and ignore the impact of energy interaction among multiple microgrids on distribution network operation information, which can easily lead to safety risks and cannot balance scheduling economy with distribution network operation safety. Moreover, existing microgrid cooperative game theory does not quantify the benefits and costs based on the actual supply and reception contributions of microgrids in energy interaction, making it difficult to fully motivate microgrids to participate in collaboration. Furthermore, existing models cannot simultaneously meet the multiple requirements of distribution network security, multi-stakeholder interest coordination, and distributed solution privacy protection, and the solution efficiency and engineering applicability of scheduling schemes need to be improved. Summary of the Invention

[0005] This application provides a method and related apparatus for generating power system optimal dispatch schemes, which addresses the technical problem that existing technologies do not specifically analyze and utilize the operation and interaction information of distribution networks and microgrids, and that the models are difficult to simultaneously meet multiple requirements such as security and solution privacy protection, resulting in the lack of accuracy and reliability of the generated optimal dispatch schemes.

[0006] In view of this, the first aspect of this application provides a method for generating an optimal power system dispatch scheme, comprising:

[0007] A master-slave game model is constructed based on comprehensive constraints, including distribution network physical constraints and microgrid power constraints.

[0008] Based on Nash negotiation theory, a cost-benefit analysis is conducted to construct a cooperative game model for multiple integrated energy microgrid operators;

[0009] Based on the cooperative game model, the master-slave game model is solved by combining the SHADE algorithm and the improved ADMM algorithm in a two-level game, resulting in an optimized scheduling scheme.

[0010] Preferably, the master-slave game model for constructing a distribution network operator and a multi-integrated energy microgrid alliance based on comprehensive constraints includes:

[0011] A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs.

[0012] Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and an upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints.

[0013] A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance.

[0014] Configure microgrid power constraints for the multi-integrated energy microgrid operator cost model to generate a lower-level microgrid model. The microgrid power constraints include power balance constraints and transmission power constraints.

[0015] By combining the upper-level model of the distribution network and the lower-level model of the microgrid, a two-layer game model is constructed, resulting in a master-slave game model.

[0016] Preferably, the step of constructing a cooperative game model for multiple integrated energy microgrid operators based on Nash negotiation theory through cost and benefit analysis includes:

[0017] Based on the aforementioned microgrid lower-level model, the transaction revenue between multiple integrated energy microgrids is analyzed, and the electricity price constraints within the microgrid are determined.

[0018] Based on Nash negotiation theory and the internal electricity price constraints of the microgrid, the main problem of the lower-level model of the microgrid is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

[0019] Preferably, the step of performing a two-level game solution on the master-slave game model based on the cooperative game model, combining the SHADE algorithm and the improved ADMM algorithm to obtain an optimized scheduling scheme, includes:

[0020] By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid.

[0021] An auxiliary connection power reference value and an auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and the transaction price.

[0022] Based on the improved ADMM algorithm, the first update penalty factor is configured, and the update power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value to determine the optimal trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance.

[0023] The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution.

[0024] Based on the improved ADMM algorithm, a second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight to determine the optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance.

[0025] The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction power solution and the optimal transaction price solution.

[0026] The second aspect of this application provides a power system optimal dispatch scheme generation device, comprising:

[0027] A two-layer model construction unit is used to construct a master-slave game model between distribution network operators and multi-integrated energy microgrid alliances based on comprehensive constraints, including distribution network physical constraints and microgrid power constraints.

[0028] The microgrid model optimization unit is used to conduct cost and benefit analysis based on Nash negotiation theory and to construct a cooperative game model for multi-integrated energy microgrid operators.

[0029] The two-layer game solving unit is used to solve the master-slave game model based on the cooperative game model, combining the SHADE algorithm and the improved ADMM algorithm to obtain an optimized scheduling scheme.

[0030] Preferably, the two-layer model building unit is specifically used for:

[0031] A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs.

[0032] Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and an upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints.

[0033] A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance.

[0034] Configure microgrid power constraints for the multi-integrated energy microgrid operator cost model to generate a lower-level microgrid model. The microgrid power constraints include power balance constraints and transmission power constraints.

[0035] By combining the upper-level model of the distribution network and the lower-level model of the microgrid, a two-layer game model is constructed, resulting in a master-slave game model.

[0036] Preferably, the microgrid model optimization unit is specifically used for:

[0037] Based on the aforementioned microgrid lower-level model, the transaction revenue between multiple integrated energy microgrids is analyzed, and the electricity price constraints within the microgrid are determined.

[0038] Based on Nash negotiation theory and the internal electricity price constraints of the microgrid, the main problem of the lower-level model of the microgrid is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

[0039] Preferably, the two-layer game solving unit is specifically used for:

[0040] By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid.

[0041] An auxiliary connection power reference value and an auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and the transaction price.

[0042] Based on the improved ADMM algorithm, the first update penalty factor is configured, and the update power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value to determine the optimal trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance.

[0043] The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution.

[0044] Based on the improved ADMM algorithm, a second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight to determine the optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance.

[0045] The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction power solution and the optimal transaction price solution.

[0046] A third aspect of this application provides a power system optimization dispatch scheme generation device, the device including a processor and a memory;

[0047] The memory is used to store program code and transmit the program code to the processor;

[0048] The processor is used to execute the power system optimal scheduling scheme generation method described in the first aspect according to the instructions in the program code.

[0049] The fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the power system optimal dispatch scheme generation method of the first aspect.

[0050] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0051] This application provides a method for generating optimal power system dispatch schemes, comprising: constructing a master-slave game model between a distribution network operator and a multi-integrated energy microgrid alliance based on comprehensive constraints, the comprehensive constraints including distribution network physical constraints and microgrid power constraints; conducting cost and benefit analysis based on Nash negotiation theory to construct a cooperative game model for the multi-integrated energy microgrid operator; and, based on the cooperative game model, performing a two-level game solution on the master-slave game model by combining the SHADE algorithm and the improved ADMM algorithm to obtain the optimal dispatch scheme.

[0052] The power system optimal dispatch scheme generation method provided in this application incorporates distribution network physical constraints and microgrid power constraints into the master-slave game model. It embeds various physical security indicators in the power system into the upper-level pricing optimization and lower-level operation response processes, allowing for simultaneous constraints on distribution network operation status during dispatch optimization, achieving a balance between dispatch economy and distribution network operation security. Furthermore, it constructs a cooperative game model based on Nash negotiation theory, allocating cooperative benefits through a cost-benefit negotiation and distribution mechanism based on the actual contributions of microgrids during interaction. This ensures the incremental benefits for each participant in the collaboration, fully stimulating the willingness of microgrids to participate in interconnection and collaboration. In addition, it employs the SHADE algorithm to adapt to the upper-level global optimization with physical constraints, and combines it with an improved ADMM algorithm to achieve distributed solution of the lower-level cooperative game. This not only adapts to the complexity of the two-layer, multi-constraint model but also protects the operation information of each microgrid through distributed computing, improving the engineering applicability of the scheme. Therefore, this application can solve the technical problems that the existing technology does not specifically analyze and utilize the operation information and interaction information of the distribution network and microgrid, and the model is difficult to meet multiple requirements such as security and solution privacy protection at the same time, resulting in the lack of accuracy and reliability of the generated optimized scheduling scheme. Attached Figure Description

[0053] Figure 1 A flowchart illustrating a method for generating an optimized power system dispatching scheme, provided in an embodiment of this application;

[0054] Figure 2 A schematic diagram of a power system optimization dispatch scheme generation device provided in this application embodiment;

[0055] Figure 3 This is a schematic diagram of a two-layer game model provided in an embodiment of this application;

[0056] Figure 4 A three-dimensional comparison diagram of voltage amplitude provided for an application example of this application;

[0057] Figure 5 Example diagram of pricing results provided for application of this application;

[0058] Figure 6 Power histogram for optimized scheduling provided as an application example of this application;

[0059] Figure 7 The thermal power histogram provided for the application example of this application. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0061] For easier understanding, please refer to Figure 1 An embodiment of a power system optimal dispatch scheme generation method provided in this application includes:

[0062] Step 101: Construct a master-slave game model between the distribution network operator and the multi-integrated energy microgrid alliance based on comprehensive constraints. The comprehensive constraints include distribution network physical constraints and microgrid power constraints.

[0063] Further, step 101 includes:

[0064] A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs.

[0065] Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and the upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints.

[0066] A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance.

[0067] Configure microgrid power constraints for the cost model of multi-integrated energy microgrid operators, and generate the lower-level model of the microgrid. The microgrid power constraints include power balance constraints and transmission power constraints.

[0068] A two-layer game model is constructed by combining the upper-level model of the distribution network and the lower-level model of the microgrid, resulting in a master-slave game model.

[0069] It should be noted that you should refer to [link / reference]. Figure 3The master-slave game model constructed in this embodiment is a two-layer interactive game model based on the distribution network operator (DSO) and the multi-integrated energy microgrid (IEM) alliance. It can be divided into an upper-layer distribution network model and a lower-layer microgrid model. The upper-layer distribution network model fully considers the impact of electricity trading prices and electrical physical parameters, configuring corresponding grid physical constraints for the model. Electricity price constraints are configured based on electricity trading prices, while power flow constraints, security constraints, and reactive power compensation constraints can be configured based on electrical physical parameters. The lower-layer microgrid model fully considers the impact of specific microgrid operating information, carbon trading information, basic physical parameters, and power, constructing a cost model for the multi-integrated energy microgrid alliance, while also satisfying power balance constraints and transmission power constraints.

[0070] Specifically, the objective function of the upper-level model of the distribution network is to maximize the revenue of the distribution network operator (DSO). The total revenue of the DSO is defined as the sum of the transaction revenue between the distribution network and microgrids and the transaction revenue between the distribution network and the upper-level power grid, minus the network loss cost. Among them, the transaction revenue with microgrids is calculated by time period, that is, the electricity sales price multiplied by the electricity sales volume, minus the product of the electricity purchase price and the electricity purchase volume; the transaction revenue with the upper-level power grid is also calculated by time period, corresponding to the electricity purchase cost minus the electricity sales revenue for the corresponding time period; and the network loss cost calculated by time period refers to the sum of the unit network loss cost multiplied by the product of all branches.

[0071] Therefore, the upper-level model of the distribution network can be expressed as:

[0072]

[0073]

[0074]

[0075]

[0076] in, This indicates the revenue of the DSO; This represents the transaction revenue of DSOs and microgrids; This represents the trading revenue between the DSO and the upstream power grid; Indicates network loss cost; and They represent DSO to microgrid during time period Electricity price and electricity volume; and express Time period DSO from microgrid Electricity purchase price and electricity volume; and They represent The electricity price and volume sold by the upstream power grid to the DSO during the specified time period; and They represent The electricity price and volume purchased by the upstream power grid from the DSO during the specified time period; Indicates DSO in Unit network loss cost for a given time period; Represents the set of branches; Representing branches The resistance; express Time flow through branch road The current is T, and the total duration is T.

[0077] Several power grid physical constraints were configured for the upper-level model of the distribution network. Since DSOs dominate the pricing process, to prevent them from exploiting their price advantage to harm the interests of the microgrid side, the DSO purchase and sale prices need to meet the following price constraints:

[0078]

[0079]

[0080]

[0081]

[0082] in, and These represent the upper and lower limits of the electricity price that a DSO sells to a microgrid; and These represent the upper and lower limits of the electricity price that a DSO purchases from a microgrid; and This represents the average price paid by the DSO for electricity sold to and purchased from the microgrid; price constraints can prevent the DSO from using its dominant position to harm the interests of the microgrid.

[0083] Considering that the distribution network actually connected to the microgrid has a certain reactive power regulation capability, this embodiment introduces a static var compensator (SVC) during the distribution network modeling process. Based on this, corresponding power flow constraints can be configured for the model:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] in, , Representing the nodes respectively The set of nodes for the first and last segments of connected branches; Represents a set of nodes; and Representing branches Resistance and reactance; , , , express The contribution and inefficiency of the branch paths during a given time period; express Node voltage during a given time period; express Time-of-day branch The current; , They represent Time period nodes The consumed active and reactive power; , They represent Time period nodes Electricity purchased and sold from microgrids; , They represent The time period of the upper-level power grid from the node Electricity sold and electricity purchased; express Time period nodes Active load; , , , They represent Time period nodes The reactive load, the reactive compensation amount of the static var compensator, the reactive power purchased by the upstream power grid, and the reactive power sold by the upstream power grid.

[0091] The second-order cone relaxation method can be used to handle nonlinear problems, allowing... , Then we have:

[0092]

[0093] By using the second-order cone relaxation method to handle nonlinear power flow, the square of voltage and the square of current can be defined as new variables, thus achieving the convex solution of nonconvex problems.

[0094] The security constraints in this embodiment are expressed as follows:

[0095]

[0096]

[0097] in, and Representing nodes respectively The upper and lower limits of the voltage; and Representing branches The upper and lower limits of the current; safety constraints mean that the voltage amplitude of all nodes and the current of all branches must meet the rated upper and lower limits.

[0098] The reactive power compensation constraint in this embodiment is the reactive power compensation device constraint, which can be expressed as:

[0099]

[0100] in, , Representing nodes respectively The upper and lower limits of the compensation amount of the static var compensator; this constraint means that the reactive power compensation amount of the static var compensator at each node needs to be within the upper and lower limits of the equipment capacity.

[0101] The objective function of the microgrid lower-level model is to minimize the total operating cost of the multi-energy integrated microgrid alliance. This total operating cost includes six categories of cost items: fuel cost, energy storage operation and maintenance cost, demand response cost, equipment operation and maintenance cost, carbon trading cost, and grid access fee. These can be summarized according to each microgrid and each time period, and are specifically expressed as follows:

[0102]

[0103]

[0104] in, This indicates the operating cost of a multi-energy microgrid system; , , , , , , , They represent microgrids respectively. Total operating costs, transaction costs with DSO, fuel costs, energy storage operation and maintenance costs, demand response costs, CCS-P2G equipment operation and maintenance costs, carbon trading costs, and grid access fees; This indicates the unit price of natural gas; , , They represent in Time-based microgrid Natural gas consumption of CHP, natural gas consumption of GB, and gas production of P2G; and These represent the unit operation and maintenance costs of electrical and thermal energy storage, respectively. , , These represent the compensation unit prices for electricity load reduction, electricity compensation load, and heat load reduction, respectively. and These represent the unit operation and maintenance costs of CCS and P2G equipment, respectively. Indicates the unit carbon trading cost; This indicates the fee charged per unit of electricity used for network access. and They represent in Time-based microgrid Actual carbon emissions and carbon emission quotas; , , , , , , , , They represent in Time-based microgrid The charging power of the energy storage device, the discharging power of the energy storage device, the charging power of the thermal storage device, the releasing power of the thermal storage device, the power that can reduce electrical load, the power that can transfer electrical load, the power that can reduce thermal load, the power consumed by CCS, and the power consumed by P2G. Indicates time period microgrid microgrid The trading power.

[0105] The microgrid power constraints configured for the lower-level model of the microgrid include power balance constraints and transmission power constraints. Among them, the power balance constraint means that each microgrid needs to meet the real-time balance of electrical power and thermal power at each time period, specifically expressed as follows:

[0106]

[0107]

[0108] in, , They represent in Time-based microgrid The electrical and thermal loads; , , , These represent CHP power generation, renewable energy power generation, CHP heat generation, and GB heat generation, respectively.

[0109] Transmission power constraints are mainly divided into the microgrid-distribution network interaction stage and the microgrid-to-microgrid interaction stage. The power transmission constraint between the microgrid and distribution network can be expressed as:

[0110]

[0111] in, This represents a 0-1 variable; a value of 1 indicates that the DSO sells electricity to the microgrid, and a value of 0 indicates that the DSO purchases electricity from the microgrid. The maximum power transmitted between the distribution network and the microgrid n.

[0112] To ensure the rationality of power exchange between microgrids, the trading power in this embodiment needs to meet the corresponding capacity constraints, and the trading power between any two microgrids should be consistent. That is, the transmission power constraint satisfied by the interaction between microgrids is expressed as:

[0113]

[0114] in, This represents the maximum amount of electrical energy exchanged between microgrids. , They represent microgrids respectively. microgrid Transaction power and microgrid microgrid The trading power.

[0115] Step 102: Based on Nash negotiation theory, conduct cost and benefit analysis and construct a cooperative game model for multi-integrated energy microgrid operators.

[0116] Further, step 102 includes:

[0117] The transaction revenue between multiple integrated energy microgrids is analyzed based on the microgrid lower-level model, and the electricity price constraints within the microgrid are determined.

[0118] Based on Nash negotiation theory and the internal electricity price constraints of microgrids, the main problem of the lower-level microgrid model is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

[0119] It should be noted that the microgrid lower layer model in this embodiment is the overall cost optimization of the microgrid alliance. Based on this, a cooperative game can be constructed based on asymmetric Nash negotiation theory to decompose the original main problem of the microgrid lower layer model into two sub-problems that can be solved step by step, thereby achieving optimization of transaction electricity and reasonable distribution of revenue.

[0120] Before breaking down the problem, this embodiment can analyze the transaction revenue between multiple integrated energy microgrids based on the microgrid lower-level model, that is, the transaction revenue of a single microgrid is expressed as:

[0121]

[0122] in, This represents the transaction revenue of microgrid n. The time period t represents the microgrid. microgrid Electricity trading price and microgrid microgrid The transaction price of electricity, This represents the P2P transaction power between microgrid n and microgrid m, i.e., the amount of electricity traded between microgrids.

[0123] Microgrid internal electricity trading price satisfy:

[0124]

[0125] To ensure priority for transactions within the microgrid consortium and prevent microgrids from bypassing their cooperative relationships to transact directly with DSOs, the internal transaction price for electricity within the microgrid should be lower than the DSO's electricity sales price and lower than the DSO's electricity purchase price. Specifically, this can be expressed as follows:

[0126]

[0127] in, and These represent the electricity prices sold by the DSO to the microgrid and the electricity purchased from it, respectively, typically expressed as averages. This constitutes the internal electricity price constraint within the microgrid, ensuring priority in transactions within the alliance and preventing microgrids from leaving the alliance to trade directly with the DSO. This internal electricity price constraint is updated in subsequent solutions as the electricity purchase and sale prices are updated. In the integrated energy microgrid system, each microgrid is an independent stakeholder. Microgrids are only willing to participate in negotiation and cooperation when cooperation brings greater benefits to each stakeholder without harming their own interests. Based on the internal electricity price constraint, a negotiation mechanism can be established to promote electricity trading between microgrids, thereby improving overall profitability.

[0128] Based on this, a cooperative game model for multi-energy microgrid alliances can be constructed using Nash negotiation theory. This involves transforming the main problem of the microgrid's lower-level model into two sub-problems: cost minimization and revenue maximization for the multi-energy microgrid alliance. This yields a cooperative game model for multi-energy microgrid operators. The cost minimization problem aims to minimize the overall operating cost of the microgrid alliance by optimizing the equipment output, energy storage charging and discharging strategies, demand response, and inter-microgrid trading power of each microgrid, thus determining the globally optimal inter-microgrid trading volume. Therefore, the cost minimization problem for multi-energy microgrids is expressed as:

[0129]

[0130] The problem of maximizing revenue distribution in multi-integrated energy microgrid alliances involves two aspects: quantifying bargaining weights and solving the bargaining model. Quantifying bargaining weights requires first calculating the electricity supply and demand of each microgrid. Then, based on the supply or demand contribution in the electricity interaction between microgrids, a nonlinear energy mapping function is used to calculate the bargaining weight; the greater the supply or demand contribution, the higher the corresponding bargaining weight.

[0131] The solution to the negotiation model requires the establishment of an asymmetric Nash negotiation revenue distribution model. By utilizing the monotonicity of the natural logarithm, the original maximization problem is transformed into a minimization problem that is easier to solve, thereby obtaining the optimal transaction price within the microgrid. The negotiation weight is used as the distribution coefficient to realize the distribution of cooperative revenue among the microgrids according to their contributions.

[0132] Specifically, we can first calculate the electricity trading volume for each microgrid:

[0133]

[0134]

[0135] in, For micro-network The amount of electrical energy supplied; For micro-network The amount of electrical energy obtained.

[0136] Then, based on the contribution of each microgrid to power interaction, the bargaining power of each microgrid is quantified, and a nonlinear energy mapping function is designed, which can be expressed as:

[0137]

[0138] in, microgrid Bargaining weight; This represents the maximum electrical energy supplied to each microgrid. This represents the maximum electrical energy received in each microgrid.

[0139] Next, we establish a profit distribution model based on asymmetric bargaining:

[0140]

[0141]

[0142] In the formula, This indicates the point of breakdown in negotiations, namely the microgrid. Costs incurred before cooperation; This represents the transaction revenue of micronet n.

[0143] By utilizing the monotonicity of the natural logarithm function and taking the negative logarithm of the above equation, the original maximization problem is transformed into a minimization problem. In other words, the problem of maximizing the revenue distribution of multi-integrated energy microgrids can be expressed as a minimization problem:

[0144]

[0145] Solving the subproblems using the above formula yields the optimal electricity trading price. and the corresponding electricity trading costs .

[0146] Step 103: Based on the cooperative game model, the master-slave game model is solved by combining the SHADE algorithm and the improved ADMM algorithm in a two-level game to obtain the optimized scheduling scheme.

[0147] Further, step 103 includes:

[0148] By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid.

[0149] Auxiliary communication power reference value and auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and transaction price.

[0150] Based on the improved ADMM algorithm, the first update penalty factor is configured, and the updated power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value. The optimal power trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance is determined.

[0151] The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution.

[0152] Based on the improved ADMM algorithm, the second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight. The optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance is then determined.

[0153] The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction volume solution and the optimal transaction price solution.

[0154] It should be noted that this embodiment designs a hybrid algorithm architecture to meet the needs of upper-level global optimization and lower-level distributed solution in the master-slave game model, balancing solution efficiency and the privacy of subject information. Specifically, the upper-level distribution network model is solved by nesting the SHADE algorithm and the Gurobi solver, ensuring efficient solution for complex nonlinear distribution network power flow and mixed-integer optimization. The lower-level microgrid model, which is the decomposed and transformed cooperative game model, mainly addresses the problem of power trading and revenue distribution among multiple integrated energy microgrids. Therefore, an improved ADMM algorithm is used for distributed solution to protect the information privacy of each microgrid.

[0155] Specifically, the solution process for the upper-level distribution network model requires first initializing the SHADE algorithm parameters to generate an initial population of DSO (Distributed Energy Solicitation Organization) purchase and sale prices. Then, in each iteration, the current electricity price is input into the lower-level Integrated Energy Microgrid (IEM) alliance model, i.e., a cooperative game model, and the alliance's purchase and sale power response is obtained. The DSO calculates its own revenue based on the response results and updates the electricity price population. In other words, the solution output of the upper-level model is the globally optimal DSO purchase and sale price. The obtained optimal purchase and sale price can update the microgrid's internal electricity price constraints in the lower-level microgrid model, constraining the solution direction of the lower-level model.

[0156] The solution to the lower-level cooperative game model can also be divided into two sub-problems: minimizing the cost of the multi-integrated energy microgrid alliance and maximizing the distribution of benefits. In this embodiment, to facilitate the solution, an auxiliary interconnection power reference value and an auxiliary price reference value are introduced by coupling the average consistency value of the distribution network and microgrid transaction information. The transaction information includes the transaction volume and the transaction price, so the average consistency value of the transaction volume or transaction price between the two parties in the previous round is actually taken as the reference for the local optimization of the microgrid in this round. The auxiliary interconnection power reference value is expressed as:

[0157]

[0158] Where k is the iteration number; this reference value is constructed from the electricity consumption information of the previous round of inter-microgrid P2P transactions and is used for local optimization in the (k+1)th iteration.

[0159] The auxiliary price reference value is expressed as follows:

[0160]

[0161] Where k is the iteration number; this reference value is constructed from the P2P transaction price information between micro-networks in the previous round and is used for local optimization in the (k+1)th iteration.

[0162] To solve the cost minimization problem of multi-integrated energy microgrid alliances, each microgrid can update its electricity trading volume locally based on auxiliary interconnection power reference values. The update process can be expressed as follows:

[0163]

[0164] in, , Let F represent the first update penalty factor and the Lagrange multiplier, respectively, for the sub-cost minimization problem. After each microgrid completes its local optimization update for this round, it recalculates the auxiliary interconnection power reference value based on the latest electricity trading volume.

[0165]

[0166] Update the Lagrange multipliers:

[0167]

[0168] The update calculation process controls the optimization direction through a penalty factor. The first update penalty factor is obtained based on the improved ADMM algorithm configuration, and the penalty factor can also be continuously updated iteratively.

[0169]

[0170] in, , Represents a constant greater than 1; , Represents a constant greater than 0; and These represent the original residual and the dual residual of the traded electricity, respectively.

[0171] The iterative update process can determine whether the algorithm has converged by calculating the original residual and the dual residual:

[0172]

[0173]

[0174] in, and These represent the original residual and the dual residual of the traded electricity, respectively; This represents the penalty factor for subproblem 1. If the residual satisfies a given threshold, the iteration stops, and the comparison and judgment process is as follows:

[0175]

[0176]

[0177] in, , Let represent the convergence thresholds corresponding to the original residual and the dual residual of subproblem 1, respectively.

[0178] By performing the above iterative update method until convergence, the solution to the cost minimization problem of the multi-integrated energy microgrid alliance, i.e. the optimal electricity trading solution, can be obtained.

[0179] The optimal power purchase and sale price of the distribution network obtained from the solution of the upper-level model can update the internal power price constraints of the microgrid. The bargaining weight of each microgrid after participating in P2P power trading can be calculated based on the optimal transaction volume solution. Specifically, the optimal electricity trading solution is the optimal electricity trading volume between microgrids, which is also the optimal trading power. Based on the optimal electricity trading solution, the electricity provided by each microgrid can be calculated separately. With obtaining power Since the optimal transaction volume solution represents which two microgrids trade under the optimal condition, and what the transaction volume is in each time period, that is, the optimal P2P power trading strategy, the transaction volume of each microgrid can be counted. The transaction volume is divided into the electricity sold (provided) and the electricity bought (acquired). Based on these two parameters and the bargaining weight calculation formula provided above, the corresponding bargaining weight can be calculated.

[0180] Similarly, to solve the problem of maximizing revenue distribution in a multi-energy microgrid consortium, the updated electricity trading price of the microgrid can be calculated iteratively based on the auxiliary price reference value and the bargaining weight. The update process is as follows:

[0181]

[0182]

[0183] Once each microgrid completes its local optimization and update, the ancillary price reference value can be recalculated based on the latest electricity trading price.

[0184]

[0185]

[0186] In the formula, , Let represent the second update penalty factor and the Lagrange multiplier, respectively, for the sub-profit maximization problem.

[0187] The update process of the configured second update penalty factor is expressed as follows:

[0188]

[0189] in, , Represents a constant greater than 1; , Represents a constant greater than 0; and The original residual and dual residual of the transaction price.

[0190] The iterative solution process can determine whether the algorithm has converged and calculate the original residual and the dual residual:

[0191]

[0192]

[0193] in, and These represent the original residual and the dual residual of the transaction price, respectively. This represents the penalty factor for subproblem 2. If the residual satisfies a given threshold, the iteration stops, and the comparison and judgment process is as follows:

[0194]

[0195]

[0196] in, , Let represent the convergence thresholds corresponding to the original residual and the dual residual of subproblem 2, respectively.

[0197] By performing the above iterative update method until convergence, the solution to the problem of maximizing the revenue distribution of the multi-integrated energy microgrid alliance, i.e., the optimal transaction price solution, can be obtained. Based on the optimal transaction electricity solution and the optimal transaction price solution output by the solution model, the corresponding scheduling scheme for the distribution network and microgrids at this point can be determined, thus obtaining the optimized scheduling scheme. Similarly, the transaction revenue distribution results for each microgrid can be determined based on the optimal transaction electricity solution and the optimal transaction price solution.

[0198] To facilitate understanding, this application provides an application example for generating power system optimal dispatch schemes. The example system uses an IEEE 33-node distribution network, with three microgrids connected at nodes 10, 21, and 32. Under the optimized dispatching method provided in this application, the voltage of all nodes remains within the safe range of [0.95, 1.05] pu throughout all time periods, and no voltage exceedance occurs. The node voltage amplitude diagram of the method in this application is shown below. Figure 4As shown, the model of this application, which fully considers dynamic power flow constraints, can effectively constrain the power flow distribution of each branch in the distribution network, which is beneficial to improving the safety and economy of system operation.

[0199] The transaction pricing result of this application is as follows: Figure 5 As shown, the trend of the purchase and sale price of electricity set by the distribution network operator for the multi-energy microgrid system is basically consistent with the trend of the purchase and sale price of electricity from the upper-level power grid. The transaction price between the multi-energy microgrids is within the range of the distribution network operator's purchase and sale price, indicating that the pricing mechanism has good economic rationality.

[0200] The optimized scheduling results of this application are as follows: Figure 6 and Figure 7 As shown, from the optimized scheduling results, during peak load periods, IEMs can purchase low-priced surplus electricity from other alliance members through a P2P model, thereby reducing their dependence on high-priced electricity purchases from the large power grid. Simultaneously, multi-energy equipment complementarity, energy storage regulation, and demand response jointly alleviate system peak-shaving pressure and improve overall operational flexibility. It can be determined that the method provided in this application embodiment can achieve reasonable energy coordination and revenue distribution among multiple integrated energy microgrids while meeting distribution network power flow constraints and network operation requirements.

[0201] The power system optimization dispatch scheme generation method provided in this application incorporates distribution network physical constraints and microgrid power constraints into the master-slave game model. It embeds various physical security indicators in the power system into the upper-level pricing optimization and lower-level operation response processes, allowing for simultaneous constraints on distribution network operation status during dispatch optimization, achieving a balance between dispatch economy and distribution network operation security. Furthermore, it constructs a cooperative game model based on Nash negotiation theory, allocating cooperative benefits according to the actual contribution of microgrids in the interaction process through a cost-benefit negotiation and allocation mechanism. This ensures the incremental benefits for each entity participating in collaboration, fully stimulating the willingness of microgrids to participate in interconnection and collaboration. In addition, it uses the SHADE algorithm to adapt to the upper-level global optimization with physical constraints, and combines it with the improved ADMM algorithm to achieve distributed solution of the lower-level cooperative game. This not only adapts to the complexity of the two-layer, multi-constraint model but also protects the operation information of each microgrid through distributed computing, improving the engineering applicability of the scheme. Therefore, the embodiments of this application can solve the technical problems that the prior art does not specifically analyze and utilize the operation information and interaction information of the distribution network and microgrid, and the model is difficult to meet multiple requirements such as security and solution privacy protection at the same time, resulting in the lack of accuracy and reliability of the generated optimized scheduling scheme.

[0202] For easier understanding, please refer to Figure 2 This application provides an embodiment of a power system optimal dispatch scheme generation device, comprising:

[0203] The two-layer model construction unit 201 is used to construct a master-slave game model between the distribution network operator and the multi-integrated energy microgrid alliance based on comprehensive constraints. The comprehensive constraints include distribution network physical constraints and microgrid power constraints.

[0204] The microgrid model optimization unit 202 is used to conduct cost and benefit analysis based on Nash negotiation theory and to construct a cooperative game model for multi-integrated energy microgrid operators.

[0205] The two-layer game solving unit 203 is used to solve the master-slave game model based on the cooperative game model, combining the SHADE algorithm and the improved ADMM algorithm to obtain an optimized scheduling scheme.

[0206] Furthermore, the two-layer model building unit 201 is specifically used for:

[0207] A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs.

[0208] Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and the upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints.

[0209] A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance.

[0210] Configure microgrid power constraints for the cost model of multi-integrated energy microgrid operators, and generate the lower-level model of the microgrid. The microgrid power constraints include power balance constraints and transmission power constraints.

[0211] A two-layer game model is constructed by combining the upper-level model of the distribution network and the lower-level model of the microgrid, resulting in a master-slave game model.

[0212] Furthermore, the microgrid model optimization unit 202 is specifically used for:

[0213] The transaction revenue between multiple integrated energy microgrids is analyzed based on the microgrid lower-level model, and the electricity price constraints within the microgrid are determined.

[0214] Based on Nash negotiation theory and the internal electricity price constraints of microgrids, the main problem of the lower-level microgrid model is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

[0215] Furthermore, the two-layer game solving unit 203 is specifically used for:

[0216] By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid.

[0217] Auxiliary communication power reference value and auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and transaction price.

[0218] Based on the improved ADMM algorithm, the first update penalty factor is configured, and the updated power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value. The optimal power trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance is determined.

[0219] The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution.

[0220] Based on the improved ADMM algorithm, the second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight. The optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance is then determined.

[0221] The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction volume solution and the optimal transaction price solution.

[0222] This application also provides a power system optimization dispatch scheme generation device, the device including a processor and a memory;

[0223] The memory is used to store program code and transfer the program code to the processor;

[0224] The processor is used to execute the power system optimal scheduling scheme generation method in the above method embodiment according to the instructions in the program code.

[0225] This application also provides a computer-readable storage medium for storing program code for executing the power system optimal dispatch scheme generation method in the above method embodiments.

[0226] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0227] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0228] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0229] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0230] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for generating optimal power system dispatch schemes, characterized in that, include: A master-slave game model is constructed based on comprehensive constraints, including distribution network physical constraints and microgrid power constraints. Based on Nash negotiation theory, a cost-benefit analysis is conducted to construct a cooperative game model for multiple integrated energy microgrid operators; Based on the cooperative game model, the master-slave game model is solved by combining the SHADE algorithm and the improved ADMM algorithm in a two-level game, resulting in an optimized scheduling scheme.

2. The method for generating power system optimal dispatch schemes according to claim 1, characterized in that, The master-slave game model for the distribution network operator and the multi-integrated energy microgrid alliance, based on comprehensive constraints, includes: A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs. Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and an upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints. A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance. Configure microgrid power constraints for the multi-integrated energy microgrid operator cost model to generate a lower-level microgrid model. The microgrid power constraints include power balance constraints and transmission power constraints. By combining the upper-level model of the distribution network and the lower-level model of the microgrid, a two-layer game model is constructed, resulting in a master-slave game model.

3. The method for generating power system optimal dispatch schemes according to claim 2, characterized in that, The aforementioned cost-benefit analysis based on Nash negotiation theory constructs a cooperative game model for multiple integrated energy microgrid operators, including: Based on the aforementioned microgrid lower-level model, the transaction revenue between multiple integrated energy microgrids is analyzed, and the electricity price constraints within the microgrid are determined. Based on Nash negotiation theory and the internal electricity price constraints of the microgrid, the main problem of the lower-level model of the microgrid is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

4. The method for generating power system optimal dispatch schemes according to claim 3, characterized in that, The optimized scheduling scheme is obtained by combining the SHADE algorithm and the improved ADMM algorithm to solve the master-slave game model using a two-level game theory approach, based on the cooperative game model, including: By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid. An auxiliary connection power reference value and an auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and the transaction price. Based on the improved ADMM algorithm, the first update penalty factor is configured, and the update power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value to determine the optimal trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance. The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution. Based on the improved ADMM algorithm, a second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight to determine the optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance. The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction power solution and the optimal transaction price solution.

5. A device for generating optimized dispatch schemes for power systems, characterized in that, include: A two-layer model construction unit is used to construct a master-slave game model between distribution network operators and multi-integrated energy microgrid alliances based on comprehensive constraints, including distribution network physical constraints and microgrid power constraints. The microgrid model optimization unit is used to conduct cost and benefit analysis based on Nash negotiation theory and to construct a cooperative game model for multi-integrated energy microgrid operators. The two-layer game solving unit is used to solve the master-slave game model based on the cooperative game model, combining the SHADE algorithm and the improved ADMM algorithm to obtain an optimized scheduling scheme.

6. The power system optimized dispatch scheme generation device according to claim 5, characterized in that, The two-layer model construction unit is specifically used for: A cost model for distribution network operators is constructed based on distribution network transaction information, electricity sales information, and network loss costs. Based on the transaction price and electrical physical parameters of the distribution network, the power grid physical constraints are configured for the cost model of the distribution network operator, and an upper-level model of the distribution network is generated. The power grid physical constraints include price constraints, power flow constraints, security constraints and reactive power compensation constraints. A cost model for multi-energy microgrid operators is constructed based on the operation information, carbon trading information, and basic physical parameters of the multi-energy microgrid alliance. Configure microgrid power constraints for the multi-integrated energy microgrid operator cost model to generate a lower-level microgrid model. The microgrid power constraints include power balance constraints and transmission power constraints. By combining the upper-level model of the distribution network and the lower-level model of the microgrid, a two-layer game model is constructed, resulting in a master-slave game model.

7. The power system optimized dispatch scheme generation device according to claim 6, characterized in that, The microgrid model optimization unit is specifically used for: Based on the aforementioned microgrid lower-level model, the transaction revenue between multiple integrated energy microgrids is analyzed, and the electricity price constraints within the microgrid are determined. Based on Nash negotiation theory and the internal electricity price constraints of the microgrid, the main problem of the lower-level model of the microgrid is transformed into two sub-problems, generating a cooperative game model for multi-energy integrated microgrid operators. The sub-problems include cost minimization and revenue maximization of the multi-energy integrated microgrid alliance.

8. The power system optimized dispatch scheme generation device according to claim 7, characterized in that, The two-layer game solving unit is specifically used for: By combining the Gurobi solver and the SHADE algorithm, the upper-level model of the distribution network in the master-slave game model is solved nonlinearly to obtain the optimal purchase and sale price of electricity in the distribution network, which is used to update the internal electricity price constraints of the microgrid. An auxiliary connection power reference value and an auxiliary price reference value are introduced by coupling the average consistency value of P2P internal transaction information between microgrids. The internal transaction information includes the transaction electricity and the transaction price. Based on the improved ADMM algorithm, the first update penalty factor is configured, and the update power trading volume of each microgrid is iteratively calculated according to the auxiliary interconnection power reference value to determine the optimal trading volume solution that minimizes the cost of the multi-integrated energy microgrid alliance. The bargaining weight of each microgrid after participating in P2P electricity trading is calculated based on the optimal transaction volume solution. Based on the improved ADMM algorithm, a second update penalty factor is configured, and the update electricity trading price of each microgrid is iteratively calculated according to the auxiliary price reference value and the bargaining weight to determine the optimal trading price solution that maximizes the revenue distribution of the multi-integrated energy microgrid alliance. The optimal scheduling scheme for the distribution network and microgrid is determined based on the optimal transaction power solution and the optimal transaction price solution.

9. A device for generating power system optimal dispatch schemes, characterized in that, The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power system optimal scheduling scheme generation method according to any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code, which is used to execute the power system optimal dispatch scheme generation method according to any one of claims 1-4.

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