A multi-state switch planning method and device for multi-agent game in a market environment

Abstract

The present application provides a multi-state switch planning method for multi-agent game in a market environment. When executing the method, a planning decision model for multiple objectives is first established respectively; wherein the decision model of the power grid company includes the capacity planning variables of the multi-state switch, and then a dynamic game model is established according to the transmission relationship of the multiple objectives, and then the dynamic game model is solved based on the preset constraints to obtain the capacity planning of the multi-state switch, and a multi-state switch planning scheme that takes into account the market environment and the benefits of the power grid company is obtained in a dynamic solution manner. In this way, by combining the capacity planning variables of the multi-state switch, the DG operator, the power grid company and the prosumer, the effect of improving the utilization rate and cost-effectiveness of the multi-state switch in the market environment is achieved. In this way, a multi-state switch planning can be provided to improve the utilization rate and cost-effectiveness of the multi-state switch in a scenario of multi-agent interactive transactions in a market environment.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a multi-state switch planning method and device for multi-agent game in a market environment. Background Art

[0002] With the rapid development of technology, distributed power sources such as wind turbines and photovoltaics are being connected to the power grid in large numbers. These power sources can be developed in two models: 1. Power users raise funds for development, or development and operation companies sign agreements with power users for joint development, generating their own power for their own use and supplying any surplus electricity to the grid; 2. Distributed power generation companies lease rooftops and connect all generated power to the grid.

[0003] Existing technologies typically employ game theory to address issues such as voltage over-limit, power quality degradation, and increased network losses caused by the introduction of distributed generation (DGs) into power grids. Furthermore, multi-party market transactions and the massive integration of DGs have disrupted the traditional unidirectional power flow model of the power grid, placing new demands on the existing grid structure. Flexible electrical equipment and system operation control technologies, centered around multi-state switches, are particularly important. Multi-state switches (including SNOPs, SOPs, and DC Links) are powerful power distribution devices that provide ample flexibility and can be used to mitigate the adverse impacts of DG integration on the grid. In a market-based power grid, multi-state switches can safeguard the interests of all stakeholders by ensuring the effective implementation of transaction results.

[0004] However, multi-state switches are expensive. How to provide a multi-state switch planning that adapts to multi-agent interactive transaction scenarios in the market environment to improve the utilization rate and cost-effectiveness of multi-state switches is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present application provides a multi-state switch planning method and device for multi-agent game in a market environment, aiming to provide a multi-state switch planning that adapts to the multi-agent interactive transaction scenario in the market environment to improve the utilization rate and cost-effectiveness of the multi-state switch.

[0006] In a first aspect, the present application provides a multi-state switch planning method for multi-agent game in a market environment, comprising:

[0007] Establishing planning decision models for multiple objectives, including DG operators, power grids, and prosumers; wherein the decision model for the power grid company includes capacity planning variables for multi-state switches;

[0008] Establishing a dynamic game model according to the transfer relationship of the multiple goals;

[0009] The dynamic game model is solved based on preset constraints to obtain a capacity plan for the multi-state switch.

[0010] Optionally, establishing a dynamic game model according to the transfer relationship of the multiple objectives includes:

[0011] A dynamic game model is established with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower model.

[0012] Optionally, establishing a dynamic game model with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models includes:

[0013] A decision-making model for power grid companies is established with the goal of maximizing profits. The decision variables are transaction price, transaction power, and multi-state switch capacity.

[0014] Constructing the grid benefits based on the decision variables

[0015] The objective function is

[0016]

[0017] in, x is the grid decision variable vector; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of tThe unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch. , , are prosumers, DG operators, and load node sets respectively;

[0018] A dynamic game model is established with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models.

[0019] Optionally, solving the dynamic game model based on preset constraints includes:

[0020] According to the Kuhn-Tucker method, the KKT conditions corresponding to the DG operator model and the prosumer model are obtained;

[0021] Substitute the KKT conditions corresponding to the DG operator model and the prosumer model into the dynamic game model to determine the Nash equilibrium solution of the dynamic game model.

[0022] Optionally, solving the dynamic game model based on preset constraints includes:

[0023] The duality principle is used to perform dual transformation on the decision model of the power grid company, the DG operator model and the prosumer model.

[0024] In a second aspect, the present application provides a multi-state switch planning device for multi-agent game in a market environment, the device comprising:

[0025] The first building block is used to establish planning decision models for multiple targets, including DG operators, power grids, and prosumers. The decision model for the power grid company includes capacity planning variables for multi-state switches.

[0026] A second building module is used to establish a dynamic game model according to the transfer relationship of the multiple goals;

[0027] The solution module is used to solve the dynamic game model based on preset constraints to obtain the capacity planning of the multi-state switch.

[0028] Optionally, the second building block includes:

[0029] The first construction unit is used to establish a decision model for the power grid company with the goal of maximizing profits, with the decision variables being transaction price, transaction power, and multi-state switch capacity;

[0030] The objective function is

[0031]

[0032] in, x is the decision variable vector of the power grid company; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of t The unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch; , , are prosumers, DG operators, and load node sets respectively;

[0033] The second construction unit is used to establish a dynamic game model with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower model.

[0034] Optionally, the solution module includes:

[0035] A first calculation unit is configured to obtain KKT conditions corresponding to the DG operator model and the prosumer model according to the Kuhn-Tucker method;

[0036] The second calculation unit is used to substitute the KKT conditions corresponding to the DG operator model and the prosumer model into the dynamic game model to determine a Nash equilibrium solution of the dynamic game model.

[0037] Optionally, the solution module includes:

[0038] The third computing unit is used to perform dual transformation on the dynamic game model, the DG operator model and the producer-consumer model by using the duality principle.

[0039] In a third aspect, the present application provides a device comprising a memory and a processor, wherein the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the multi-state switch planning method for multi-agent games in a market environment as described in any one of the first aspects above.

[0040] In a fourth aspect, the present application provides a computer storage medium storing a code. When the code is executed, the device executing the code implements the multi-state switch planning method for multi-agent game in a market environment as described in any one of the first aspects above.

[0041] This application provides a multi-state switch planning method for a multi-agent game in a market environment. When executing the method, a planning decision model is first established for multiple objectives, including DG operators, power grids, and prosumers. The power grid company's decision model includes capacity planning variables for the multi-state switch. A dynamic game model is then established based on the transfer relationship between the multiple objectives. The dynamic game model is then solved based on preset constraints to obtain a capacity plan for the multi-state switch. A multi-state switch planning scheme that takes into account both the market environment and the grid company's benefits is obtained through dynamic solution. Thus, by combining the capacity planning variables for the multi-state switch, the DG operator, the power grid company, and the prosumer, the multi-state switch planning scheme takes into account both the market environment and the grid company's benefits, supports the safe and economic operation of the distribution network in a market environment, and achieves the effect of improving the utilization and cost-effectiveness of multi-state switches in a market environment. In this way, a multi-state switch planning method can be provided to improve the utilization and cost-effectiveness of multi-state switches in a multi-agent interactive transaction scenario in a market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following briefly introduces the drawings required for use in the embodiment or the prior art description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flowchart of a method for multi-state switch planning in a multi-agent game in a market environment provided by an embodiment of the present application;

[0044] Figure 2 A schematic diagram of a single-ring distribution network in a certain area provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a possible SOP daily operation strategy provided in an embodiment of the present application;

[0046] Figure 4 A possible output power curve of a prosumer provided in an embodiment of the present application;

[0047] Figure 5 This is a possible output power curve of a DG operator provided in an embodiment of the present application;

[0048] Figure 6 A structural diagram of a multi-state switch planning device for multi-agent game in a market environment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to solve the many problems such as voltage over-limit, power quality deterioration and network loss increase caused by the introduction of distributed power sources to the power grid, game theory is usually used to simulate the above problems. At the same time, due to the multi-subject market transaction results and the massive access of distributed power sources, the traditional one-way power flow mode of the power grid has been broken, and new requirements have been put forward for the grid structure of the existing power grid. Flexible electrical equipment and system operation control technology with multi-state switches as the core are particularly important. Among them, multi-state switches (including SNOP, SOP, DC Link) as a powerful distribution equipment provide ample flexibility for the power grid and can be used to reduce the adverse effects of the access of distributed power sources on the power grid. In the power grid under the market environment, multi-state switches can safeguard the interests of various entities by ensuring the effective implementation of transaction results. However, multi-state switches are costly, and it is difficult to provide multi-state switch planning that can adapt to the multi-subject interactive transaction scenarios in the market environment.

[0050] After research, it was found that by combining the capacity planning variables of multi-state switches, DG operators, power grid companies and prosumers, the multi-state switch planning scheme can take into account both the market environment and the benefits of power grid companies, support the safe and economic operation of the distribution network under the market environment, and achieve the effect of improving the utilization rate and cost-effectiveness of multi-state switches under the market environment.

[0051] In view of this, the present application provides a multi-state switch planning method based on multi-agent game-playing in a market environment. When executing the method, planning decision models for multiple objectives are first established; the objectives include DG operators, power grids, and prosumers; the power grid company's decision model includes capacity planning variables for the multi-state switch. A dynamic game model is then established based on the transfer relationship between the multiple objectives. The dynamic game model is then solved based on preset constraints to obtain a capacity plan for the multi-state switch. This dynamic solution method provides a multi-state switch planning solution that takes into account both the market environment and the grid company's benefits. Thus, by combining the capacity planning variables for the multi-state switch, the DG operator, the power grid company, and the prosumer, the multi-state switch planning solution takes into account both the market environment and the grid company's benefits, supports the safe and economic operation of the distribution network in a market environment, and achieves the effect of improving the utilization and cost-effectiveness of multi-state switches in a market environment. In this way, a multi-state switch planning solution can be provided to improve the utilization and cost-effectiveness of multi-state switches in a multi-agent interactive transaction scenario in a market environment.

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] See also Figure 1 , Figure 1 A method flow chart of a multi-state switch planning method for a multi-agent game in a market environment provided by an embodiment of the present application includes:

[0054] S101, establishing planning decision models for multiple objectives respectively.

[0055] In this embodiment, a planning decision model with multiple objectives is first established. An objective is a possible entity that needs to be solved. This embodiment uses a market environment with three entities, namely, DG operators, power grid companies, and prosumers, as an example. A decision model for the power grid company, a DG operator model, and a prosumer model are established, respectively. The power grid company's decision model includes capacity planning variables for multi-state switches.

[0056] The DG operator model mentioned above can be:

[0057] (a) Objective function

[0058] Maximize operating profits, including the revenue from selling electricity to the grid and the charging and discharging costs of energy storage operations.

[0059]

[0060]

[0061] in, The revenue from electricity sales to grid companies for DG operators; Unit power cost for charging source-side energy storage; is the charging power of the source-side energy storage at time t on a typical day m; is the unit power cost of energy storage discharge on the source side; is the discharge power of the source-side energy storage at time t on a typical day m.

[0062] (b) Constraints

[0063] The operating constraints of source-side energy storage are as follows: charging and discharging power limits, energy storage state constraints, capacity balance constraints within the scheduling cycle, and power balance constraints.

[0064]

[0065] in, is the maximum charging power of the source-side energy storage; is the maximum discharge power of the energy storage on the source side; is the amount of energy stored at time t on a typical day m; is the charging efficiency of the source-side energy storage; is the discharge efficiency of the energy storage on the source side; is the unit time period; The minimum amount of electricity allowed for energy storage; The maximum amount of electricity allowed for energy storage; is the power generation capacity of the power source in the DG operator.

[0066] The above prosumer model can be:

[0067] (a) Objective function

[0068] The objective function is to minimize the electricity cost of the prosumer, which mainly includes the comfort loss caused by net load transfer and the cost of exchanging electricity with the distribution network.

[0069]

[0070] in, is the user's comfort function for load transfer; is the net load curve of the prosumer, which is expressed as follows:

[0071]

[0072] Note that the objective function is a nonlinear function, so it is changed by the following linearization method:

[0073]

[0074]

[0075] in, is divided into Piecewise linearized comfort function.

[0076] Therefore, the above objective function is transformed into

[0077]

[0078] (b) Constraints

[0079] The constraints mainly include load transfer constraints. The first formula indicates that the basic load cannot be reduced or exceed the upper limit, and the second formula indicates that the total load after transfer is guaranteed to be no less than the original demand of the user.

[0080]

[0081] Constraints added by piecewise linearizing the comfort function:

[0082]

[0083]

[0084] The decision model of the above-mentioned power grid company can be:

[0085] The objective function is

[0086]

[0087] in, For grid revenue, The construction cost of the multi-state switch; is the operating cost of the multi-state switch.

[0088] It is understandable that the above Can be

[0089] (a) Annual construction cost of multi-state switches

[0090]

[0091] Where d is the discount rate, y is the operating life of the multi-state switch, is the price per unit capacity of the multi-state switch.

[0092] above Can be

[0093] (b) Annual operating cost of multi-state switches

[0094]

[0095] Where, The annual operation and maintenance cost coefficient of the multi-state switch.

[0096] It should be noted that in this embodiment, the multi-state switch is mainly installed at the traditional tie switch, which can flexibly control the active power transmitted between the two feeders and provide a certain amount of reactive power support. This application mainly uses SOP as an example. Two constraints need to be considered as follows:

[0097] Active power and reactive power constraints for multi-state switches:

[0098]

[0099] Capacity constraints of multi-state switches:

[0100]

[0101] Where i and j are the node numbers of the distribution network to which the multi-state switch is connected; 、 are the active power and reactive power injected by the two converters of the multi-state switch respectively; and is the capacity of the multi-state switch connected between nodes i and j, where A collection of nodes for installing multi-state switches.

[0102] S102: establishing a dynamic game model based on the transfer relationship of multiple goals.

[0103] In this embodiment, the DG operator entity obtains revenue by selling electricity to the power grid, with the goal of maximizing profits. In order to increase the amount of electricity sold and avoid passive power abandonment, it configures energy storage on its own according to the relevant requirements of the power grid and has a certain degree of adjustment capability. The prosumer entity has a demand for electricity exchange and needs to purchase and sell electricity from the power grid entity, hoping to save energy costs as much as possible while ensuring electricity use. At the same time, it has a certain demand response capability based on people's consumption habits. At the same time, the power grid company entity, as an integrated platform, is mainly responsible for the planning and operation of the distribution network and the internal optimization and coordination of the distribution market. On the premise of ensuring the safe and reliable operation of the system, the power grid entity obtains revenue by purchasing and selling electricity between the upper-level power grid, DG operators, prosumers, and ordinary electricity users.

[0104] like Figure 2For example, consider a single-ring grid in a certain region. The left feeder terminates at a DG operator, the right feeder terminates at a responsive prosumer, and the other load points are connected to normal loads. The basic data is as follows: Consider two typical photovoltaic access scenarios: one with a DG operator as the primary operator, selling electricity to the grid company, and the other with a user configuring their own photovoltaic system for self-production and sales. The DG operator has 6 MW of photovoltaic capacity and 2.4 MWh of energy storage, generating 1.2 MW of power. The single-ring grid has a baseload of 9.27 MW, the responsive prosumer has a power of 0.5 MW, and the user's self-configured photovoltaic system has a power of 0.25 MW. The DG operator's price ranges from 0.3 to 0.6 RMB / kWh. The maximum price calculated between the prosumer and the upper-level grid is 0.8 RMB / kWh.

[0105] Optionally, establishing a dynamic game model according to the transfer relationship of the multiple objectives includes establishing a dynamic game model with the decision model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models.

[0106] In this embodiment, the grid entity solves the voltage and current limit problem caused by power exchange during the interaction between source, grid and load in the market environment by configuring and using multi-state switches, and promotes economic operation. It is understandable that if the SOP is not installed, the system will not be able to operate safely under the condition of meeting safety constraints. The role of the SOP is to ensure the normal operation of the market without abandoning power. If the SOP is not configured, the system will not be able to operate, and a large amount of power abandonment will occur, which will cause significant economic losses. Therefore, the configuration of the SOP is necessary. Taking the planned installation of an SOP capacity of 1.97MW as an example, Figure 3 Shown is a possible intraday operation strategy for SOP.

[0107] Therefore, this embodiment establishes a decision-making model for power grid companies that includes multi-state switch planning and trading strategies. This model aims to maximize the average annual revenue of the entity, using the configuration and operation of the multi-state switches, the price of electricity traded with DG operators / prosumers, and the amount of electricity traded with the upper-level power grid as decision variables. An optimized operation model is then established for DG operators and prosumers after they receive the power grid company's electricity trading prices. These models each aim to maximize operational profits, using the amount of electricity traded between DG operators and prosumers and the grid entity as decision variables.

[0108] Optionally, the decision-making model of the power grid company is used as the leader model, and the DG operator model and the prosumer model are used as follower models. The dynamic game model is established including:

[0109] In this embodiment, a typical day in four quarters is considered to establish a benefit optimization model for the power grid company with the goal of maximizing annual revenue. , the power grid company purchases electricity from the higher-level power grid , the power grid company sells electricity to the upper grid , the power grid company purchases electricity from the DG operator and the equivalent annual value of the cost of running the SOP plan.

[0110] (a) Objective function

[0111]

[0112] in, x is the decision variable vector of the power grid company; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of t The unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch; , , They are prosumers, DG operators and load node sets respectively.

[0113] (b) Constraints

[0114] Electricity price constraints:

[0115]

[0116] Power balance constraints of power grid companies:

[0117]

[0118] Distribution network flow constraints:

[0119] For nodes j and branch roads ij of t At this moment, there are the following constraints, where N T The number of hours for each day of the year.

[0120]

[0121]

[0122]

[0123] Branch voltage constraints:

[0124]

[0125] Node voltage constraints:

[0126]

[0127] Branch capacity constraints:

[0128]

[0129] (c) Uncertainty in the electricity price settled between the power grid company and the upper-level power grid

[0130] Considering the uncertainty of the electricity price settled between the power grid company and the upper-level power grid, the price uncertainty interval is modeled as follows:

[0131]

[0132]

[0133]

[0134] in, is the lower bound of the unit price of electricity purchased by the power grid company from the upper grid at time t on a typical day m; is the upper bound of the price per unit of electricity that the power grid company purchases from the upper grid at time t on a typical day m; is the lower bound of the unit price of electricity sold by the power grid company to the upper grid at time t on typical day m; is the upper bound of the price per unit of electricity sold by the power grid company to the upper grid at time t on a typical day m.

[0135] Considering the uncertainty of the above electricity price, the above objective function is transformed into:

[0136]

[0137]

[0138] S103: Solve the dynamic game model based on preset constraints to obtain a capacity plan for the multi-state switch.

[0139] In this embodiment, the dynamic game model is a master-slave model, with a leader model serving as the dynamic game model. As the leader, its objectives are prioritized over the decision-making objectives of the other two layers. A two-layer model cannot be solved independently for each layer. This embodiment utilizes KKT conditions to transform the DG operator model and the prosumer model into the KKT conditions of the dynamic game model for solution.

[0140] Optionally, solving the dynamic game model based on preset constraints includes:

[0141] The KKT conditions corresponding to the DG operator model and the prosumer model are obtained according to the Kuhn-Tucker method.

[0142] In this example, the KKT condition is used to equivalently transform the problem to be solved by the lower-level model. Specifically, a single-level nonlinear programming problem with multiple constraints is used to equivalently replace the original two-level programming problem. The KKT condition is used to replace the strategy space of the lower-level followers, while the upper-level leader solves its own planning problem while considering the optimal decision of the lower-level model.

[0143] Among them, the KKT conditions of the DG operator model and the prosumer model obtained by KKT transformation according to the Kuhn-Tucker method are:

[0144]

[0145]

[0146] in, is the Lagrangian function; is the set of Lagrange multiplier vectors of the inequality; Constrain the left side of all equality constraints. is the left side of the inequality; is a vector set of independent variables, including the transaction price between the grid company and the prosumers, DG operators, and the transmission power between multiple DG operators and multiple prosumers and the grid company.

[0147] Substitute the KKT conditions corresponding to the above DG operator model and prosumer model into the dynamic game model to determine the Nash equilibrium solution of the dynamic game model.

[0148] Optionally, solving the dynamic game model based on preset constraints includes:

[0149] In this embodiment, in order to solve the problem, the nonlinear programming problem solved by the dynamic game model needs to be converted into a linear programming problem. Therefore, it is necessary to use the duality principle to perform a dual transformation on the decision model of the power grid company, the DG operator model and the prosumer model.

[0150] The specific process is as follows:

[0151]

[0152] Optionally, when solving the dynamic game model based on the preset constraints, the decision model of the power grid company has a bilinear term. and ,Therefore, the dual problem is transformed to eliminate the bilinear terms in the objective function.

[0153] The specific process is as follows:

[0154]

[0155]

[0156] is a nonlinear term, and the big M method is used for linearization to obtain:

[0157]

[0158]

[0159] in, It is a vector of binary variables.

[0160] It should be noted that the Big M method, described above, is a method for finding an initial feasible solution to a linear programming problem using artificial variables, when the constraints are either equal (=) or greater than (≥). Adding artificial variables to the constraints of the linear programming problem requires adding the corresponding M or terms with M as coefficients to the objective function. In maximization problems, the artificial variable is assigned an M as its coefficient; in minimization problems, the artificial variable is assigned an M as its coefficient, where M is an arbitrarily large (but not infinite) positive number. In other words, M is treated as an algebraic symbol and the solution is solved using the simplex method.

[0161] It is understandable that in the process of market competition, the power grid influences the prosumers through electricity prices, and the prosumers, considering their own comfort, maximize their own profits by adjusting the utilization time of their own transferable loads. Figure 4 As shown, the two curves represent the baseload of a prosumer with demand response capabilities and the power exchanged between the prosumer and the grid company after participating in demand response. The adjusted energy usage reflects the multi-agent game equilibrium solution, where prosumers reduce their electricity consumption during periods of high electricity prices. This is the result of a compromise between the grid and prosumers, effectively influencing the energy consumption of prosumers through market means, thus validating the effectiveness of the proposed method.

[0162] On the other hand, in order to increase profits, DG operators tend to release their output power at higher prices after participating in the market game process, such as Figure 5 As shown, the two curves are the exchange power between the DG operator and the upper power grid with and without energy storage, respectively. At noon, when the DG operator's output power is at its peak, the grid company tends to purchase more electricity from the DG operator and sell it to the upper power grid. If the DG operator does not make adjustments, the excess electricity will be discarded. Now it is stored to avoid waste of resources. It is understandable that due to network constraints, the power exceeding 5.5MW is unacceptable. In the specific game process, the DG operator can maintain the safe operation of the system by reducing the output power peak. In the power market environment of multi-agent game, the grid company does not need to directly control the DG operator to achieve the above effect, thereby verifying the effectiveness of the method proposed in this application.

[0163] In this embodiment, by combining the capacity planning variables of multi-state switches with DG operators, power grid companies, and prosumers, a multi-state switch planning scheme takes into account both the market environment and the grid company's profitability, supporting the safe and economical operation of the distribution network in this market environment and improving the utilization and cost-effectiveness of multi-state switches in this market environment. This provides a multi-state switch planning method that improves the utilization and cost-effectiveness of multi-state switches in a multi-agent interactive transaction scenario within a market environment.

[0164] The above is some specific implementation of a multi-state switch planning method for multi-agent game in a market environment provided by the embodiment of this application. Based on this, the application also provides a corresponding device. The device provided by the embodiment of this application will be introduced from the perspective of functional modularization.

[0165] See also Figure 6The structure diagram of a multi-state switch planning device 600 for multi-agent game in a market environment is shown. The device 600 includes a first construction module 601, a second construction module 602 and a solution module 603.

[0166] The first construction module 601 is used to establish planning decision models for multiple targets respectively; the targets include DG operators, power grids, and prosumers; wherein the decision model of the power grid company includes capacity planning variables for multi-state switches;

[0167] A second building module 602 is configured to establish a dynamic game model based on the transfer relationship of the multiple objectives;

[0168] The solving module 603 is configured to solve the dynamic game model based on preset constraints to obtain a capacity plan for the multi-state switch.

[0169] Optionally, the second building block includes:

[0170] The first construction unit is used to establish a decision model for the power grid company with the goal of maximizing profits, with the decision variables being transaction price, transaction power, and multi-state switch capacity;

[0171] The objective function is

[0172]

[0173] in, x is the decision variable vector of the power grid company; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of t The unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch; , , are prosumers, DG operators, and load node sets respectively;

[0174] The second construction unit is used to establish a dynamic game model with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models.

[0175] Optionally, the solution module includes:

[0176] A first calculation unit is configured to obtain KKT conditions corresponding to the DG operator model and the prosumer model according to the Kuhn-Tucker method;

[0177] The second calculation unit is used to substitute the KKT conditions corresponding to the DG operator model and the prosumer model into the dynamic game model to determine a Nash equilibrium solution of the dynamic game model.

[0178] Optionally, the solution module includes:

[0179] The third computing unit is used to perform dual transformation on the dynamic game model, the DG operator model and the producer-consumer model by using the duality principle.

[0180] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0181] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the multi-state switch planning method for multi-agent game in a market environment described in any embodiment of the present application.

[0182] The computer storage medium stores codes. When the codes are executed, the device executing the codes implements the multi-state switch planning method for multi-agent game in a market environment as described in any embodiment of the present application.

[0183] The “first” and “second” in the names such as “first” and “second” (if any) mentioned in the embodiments of this application are only used as name identifiers and do not mean the first or second in order.

[0184] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment methods can be implemented by means of software plus a general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or certain parts of the embodiments of the present application.

[0185] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0186] The above description is merely an exemplary embodiment of the present application and is not intended to limit the scope of protection of the present application.

Claims

1. A multi-state switch planning method for multi-agent game in a market environment, characterized by: include: Establishing planning and decision-making models for multiple objectives respectively; the objectives include DG operators, power grids, and prosumers; The decision model of the power grid company includes capacity planning variables of multi-state switches; Establishing a dynamic game model according to the transfer relationship of the multiple goals; Solving the dynamic game model based on preset constraints to obtain a capacity plan for the multi-state switch; The establishing of a dynamic game model according to the transfer relationship of the multiple objectives includes: A decision-making model for the power grid with the goal of maximizing revenue is established, with the decision variables being transaction price, transaction power, and multi-state switch capacity. Constructing the grid benefits based on the decision variables The objective function is ； in, x is the decision variable vector of the power grid company; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of t The unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch; , , are prosumers, DG operators, and load node sets respectively; A dynamic game model is established with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models.

2. The method according to claim 1, characterized in that Solving the dynamic game model based on preset constraints includes: According to the Kuhn-Tucker method, the KKT conditions corresponding to the DG operator model and the prosumer model are obtained; Substitute the KKT conditions corresponding to the DG operator model and the prosumer model into the dynamic game model to determine the Nash equilibrium solution of the dynamic game model.

3. The method according to claim 1, characterized in that Solving the dynamic game model based on preset constraints includes: The duality principle is used to perform dual transformation on the decision model of the power grid company, the DG operator model and the prosumer model.

4. A multi-state switch planning device for multi-agent game in a market environment, characterized in that: The device comprises: The first building block is used to establish planning decision models for multiple targets, including DG operators, power grids, and prosumers. The decision model for the power grid company includes capacity planning variables for multi-state switches. A second building module is used to establish a dynamic game model according to the transfer relationship of the multiple goals; A solution module, configured to solve the dynamic game model based on preset constraints to obtain a capacity plan for the multi-state switch; The second building block includes: The first construction unit is used to establish a decision model for the power grid company with the goal of maximizing profits, with the decision variables being transaction price, transaction power, and multi-state switch capacity; The objective function is ； in, x is the decision variable vector of the power grid company; For the m Number of days in a quarter; T is the number of time intervals in a day; For the power grid company on a typical day m of t The unit price of electricity sold to the load at any given moment; For the power grid company on a typical day m of t Always on the i The electricity sales of each load; For a typical day m of t The unit electricity price that the power grid company purchases from the upper-level power grid at that moment; For the power grid company on a typical day m of t The amount of electricity purchased from the upper power grid at all times; For a typical day m of t The unit price of electricity sold by the power grid company to the upper grid at that moment; For the power grid company on a typical day m of t The amount of electricity sold to the upper power grid at all times; For the power grid company on a typical day m of t The unit price of electricity purchased from the DG operator at any given time; For the power grid company on a typical day m of t The amount of electricity purchased from DG operators at all times; The construction cost of the multi-state switch; is the operating cost of the multi-state switch; , , are prosumers, DG operators, and load node sets respectively; The second construction unit is used to establish a dynamic game model with the decision-making model of the power grid company as the leader model, the DG operator model and the prosumer model as the follower models.

5. The device according to claim 4, characterized in that The solution module includes: The first calculation unit is used to obtain KKT conditions corresponding to the DG operator model and the prosumer model according to the Kuhn-Tucker method; The second calculation unit is used to substitute the KKT conditions corresponding to the DG operator model and the prosumer model into the dynamic game model to determine a Nash equilibrium solution of the dynamic game model.

6. The device according to claim 4, characterized in that The solution module includes: The third computing unit is used to perform dual transformation on the decision model of the power grid company, the DG operator model and the prosumer model by using the duality principle.

7. An electronic device, characterized in that: The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes, so that the device executes the multi-state switch planning method for multi-agent games in a market environment as described in any one of claims 1 to 3.

8. A computer storage medium, characterized in that The computer storage medium stores codes. When the codes are executed, the device executing the codes implements the multi-state switch planning method for multi-agent game in a market environment according to any one of claims 1 to 3.

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