Micro-grid capacity configuration method and device based on cooperative-non-cooperative dynamic game, electronic equipment, storage medium and product

Abstract

The invention discloses a micro-grid capacity configuration method and device based on a cooperation-non-cooperation dynamic game, electronic equipment, a storage medium and a product, and relates to the technical field of power system optimization. The method comprises the following steps: determining a preset capacity, a target output function, a target constraint condition and a revenue function of each participant in the microgrid and a game strategy between the participants; obtaining preset micro-grid operation parameters, and constructing a target capacity optimization function based on the preset capacity, the preset micro-grid operation parameters, the target output function, the target constraint condition, the revenue function and the game strategy; and determining a target optimization result of the target capacity optimization function based on a particle swarm grey wolf optimization algorithm, and adjusting the preset capacity of the participant based on the target optimization result. According to the embodiment of the invention, the capacity is configured in a cooperative-non-cooperative dynamic game mode while the low-carbon property and economical efficiency of the micro-grid are guaranteed, so that the benefit of each participant is maximized as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a microgrid capacity configuration method, device, electronic device, storage medium and product based on cooperative-non-cooperative dynamic game. Background Art

[0002] As a system that integrates advanced information technology, control technology and energy technology, microgrid maximizes energy, economic and environmental benefits, while providing a reliable energy supply that meets the diverse needs of users. Microgrids also have the potential to provide grid support, improve energy efficiency, save energy and reduce consumption of traditional grids. Therefore, microgrids are an indispensable part of building smart grids.

[0003] However, the source, grid, load and storage links in a microgrid often belong to the assets of different participants. Combined with local photovoltaic, wind turbine and other conditions, the optimal capacity configuration for microgrid operation is not necessarily the optimal configuration for each participant. How to ensure the low-carbon and economic performance of the microgrid while configuring capacity through cooperative game to maximize the interests of each participant is an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a microgrid capacity configuration method, device, electronic device, storage medium and product based on cooperative-non-cooperative dynamic game, so as to realize the configuration of capacity through cooperative game while ensuring the low carbon and economic efficiency of the microgrid itself, so as to maximize the interests of each participant.

[0005] According to one aspect of the present invention, a method for configuring microgrid capacity based on cooperative-non-cooperative dynamic game is provided, wherein the method comprises:

[0006] Determine the preset capacity, target output function, target constraint condition, benefit function and game strategy between each participant in the microgrid;

[0007] Acquire preset microgrid operating parameters, and construct a target capacity optimization function based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function, and the game strategy;

[0008] A target optimization result of the target capacity optimization function is determined based on a particle swarm grey wolf optimization algorithm, and the preset capacity of the participant is adjusted based on the target optimization result.

[0009] According to another aspect of the present invention, a microgrid capacity configuration device based on cooperative-non-cooperative dynamic game is provided, wherein the device comprises:

[0010] A parameter configuration module, used to determine the preset capacity, target output function, target constraint condition, benefit function of each participant in the microgrid and the game strategy between the participants;

[0011] A function construction module, used to obtain preset microgrid operating parameters, and construct a target capacity optimization function based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function and the game strategy;

[0012] The capacity configuration module is used to determine the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, and adjust the preset capacity of the participant based on the target optimization result.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game of any embodiment of the present invention.

[0019] The technical solution of the embodiment of the present invention determines the preset capacity, target output function, target constraint conditions, benefit function and game strategy between each participant in the microgrid, obtains preset microgrid operating parameters, constructs a target capacity optimization function based on the preset capacity, preset microgrid operating parameters, target output function, target constraint conditions, benefit function and game strategy, determines the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, adjusts the preset capacity of the participant based on the target optimization result, and facilitates the determination of the benefit of each participant in the microgrid by considering the constraint conditions of each participant and the game relationship between each participant; at the same time, the target optimization result of the target capacity optimization function is determined by the particle swarm grey wolf optimization algorithm, which enhances the rapid convergence ability and global search ability of the target optimization result, improves the accuracy of the target optimization result, and maximizes the benefit of each participant as much as possible.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a flow chart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game provided according to Embodiment 3 of the present invention;

[0025] Figure 4 is a structural schematic diagram of a microgrid capacity configuration device based on cooperative-non-cooperative dynamic game according to a fourth embodiment of the present invention;

[0026] Figure 5 It is a structural schematic diagram of an electronic device for implementing a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flowchart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to the first embodiment of the present invention. This embodiment is applicable to the case of allocating capacity to participants in a microgrid. The method can be executed by a microgrid capacity configuration device based on cooperative-non-cooperative dynamic game. The microgrid capacity configuration device based on cooperative-non-cooperative dynamic game can be implemented in the form of hardware and / or software. The microgrid capacity configuration device based on cooperative-non-cooperative dynamic game can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110, determining the preset capacity, target output function, target constraint condition, benefit function and game strategy among the participants of each participant in the microgrid.

[0032] Among them, microgrid refers to a small power system, and the participants of the microgrid can include at least wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems, which are not limited. The preset capacity refers to the installed capacity and energy storage capacity of each participant set in advance. The target output function can be used to indicate the power output characteristics of various participants in the microgrid, and there can be a corresponding target output function for each participant. The target constraint condition can be used in combination with the target output function to guide the participants to make the best decision. In the actual operation process, the target constraint condition may include an output constraint function and a penalty function power balance constraint function. The output constraint function is used to constrain the energy storage capacity of wind farms and photovoltaic farms, as well as the energy storage capacity and charging and discharging power of the electric-hydrogen hybrid energy storage system; the penalty function power balance constraint function is used to constrain the output power of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems to balance with the demand rate. The benefit function refers to a function used to determine the total benefit of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems. Exemplarily, the profit function is composed of the sum of the net profit of the wind farm, the net profit of the photovoltaic farm, and the net profit of the electric-hydrogen hybrid energy storage system.

[0033] Game strategy refers to the action plan taken by participants in the game process in order to maximize their own interests. In the actual operation process, the game strategy may include but is not limited to a fully cooperative game in which wind farms and photovoltaic farms make joint decisions; a partially cooperative game in which wind farms and photovoltaic farms form an alliance, and the electric-hydrogen hybrid energy storage system makes independent decisions; a partially cooperative game in which wind farms and electric-hydrogen hybrid energy storage systems form an alliance, and photovoltaic farms make independent decisions; a partially cooperative game in which photovoltaic farms and electric-hydrogen hybrid energy storage systems form an alliance, and wind farms make independent decisions; and a non-cooperative game in which wind farms, photovoltaic farms, and electric-hydrogen hybrid energy storage systems all make independent decisions.

[0034] In an embodiment, the preset capacity, target output function, target constraint conditions, benefit function and game strategy between participants of each participant in the microgrid can be preconfigured. In the actual operation process, the target output function of the wind farm, the target output function of the photovoltaic power field and the target output function of the electric hydrogen hybrid energy storage system can be configured as the target output function. Exemplarily, the target output function of the wind farm can be constructed by the cut-in wind speed, cut-out wind speed, rated wind speed, rated power of the wind turbine and natural wind speed of the wind power generation group in the wind farm; the target output function of the photovoltaic power field can be constructed by the preset illumination amplitude probability density function, the photovoltaic output power and the maximum photovoltaic output power in the preset time period; the target output function of the electric hydrogen hybrid energy storage system can be constructed by the storage energy of the battery in the preset time period, the power shortage, the total power of the fuel cell, the total power of the electrolyzer, the preset charge and discharge coefficient of the battery, the maximum power generation power of the fuel cell, the maximum storage capacity of the battery and the maximum power consumption of the electrolyzer.

[0035] For different participants, the capacity can be affected by different factors. For example, the target constraints can be the carbon saving of the microgrid, the wind and solar curtailment of the microgrid, the load interruption of the microgrid, the wind farm installed capacity constraint, the photovoltaic power station installed capacity constraint, the power balance constraint, the electrolyzer power constraint, the fuel cell power constraint, the battery capacity constraint and the hydrogen storage tank constraint.

[0036] S120, obtaining preset microgrid operating parameters, and constructing a target capacity optimization function based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint conditions, the benefit function, and the game strategy.

[0037] The preset microgrid operating parameters refer to the parameters that are preset to determine the microgrid revenue results. For example, the preset microgrid operating parameters may include but are not limited to preset scenario information and preset time-of-use electricity prices. Generally speaking, different scenarios will have an impact on the decision-making of participants. It is possible to obtain scenarios within a typical month and make decisions for each scenario separately to improve the adaptability of the decision. The target capacity optimization function refers to the pre-set function rules for optimizing the capacity of each participant.

[0038] In an embodiment, a historical scene set can be extracted, the scene information in the historical scene set can be clustered, the scene information within a typical month can be determined, and the time-of-use electricity price at the time corresponding to the scene information can be determined, and the scene information and the time-of-use electricity price can be used as preset microgrid operating parameters. After obtaining the preset microgrid operating parameters, the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function, and the game strategy can be used as the target capacity optimization function to determine the capacity of the participant according to the target capacity optimization function.

[0039] S130. Determine a target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, and adjust the preset capacity of the participant based on the target optimization result.

[0040] Among them, the Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO) is a hybrid optimization algorithm that combines the Particle Swarm Optimization Algorithm and the Grey Wolf Optimization Algorithm. By combining the advantages of the Particle Swarm Optimization Algorithm and the Grey Wolf Optimization Algorithm, it can have better global search capabilities and local search capabilities, and can find better target optimization results in a shorter time. The target optimization result refers to the optimal solution that can be understood as the target capacity optimization function, which is the optimal capacity combination of participants in the microgrid.

[0041] In an embodiment, a game strategy can be selected from several modes such as a fully cooperative game, a partially cooperative game, and a completely non-cooperative game for the preset capacity of each participant. The order in which the participants make decisions is randomly generated, and the arrangement of the decision order is changed once a day to ensure that each participant takes turns to make decisions first, and the target output function and the target constraint conditions are superimposed to make decisions. In the actual operation process, the decision granularity of each participant can be 1 hour by mixed integer optimization solution, and the output data for 24 hours a day can be calculated. After each participant makes an output decision, the penalty function power balance constraint function is used for iteration, and the penalty factor is increased in each iteration until the optimal output of each participant under the current capacity configuration and game strategy is solved, the participant's income is determined by the optimal output and the income function, and the income of each participant is determined based on the Shapley value of cooperative game theory. The income is used as a fitness vector, and the preset capacity of each participant is updated according to the particle swarm gray wolf optimization algorithm through the fitness vector until the preset capacity converges, and the updated preset capacity is used as the target optimization result, that is, the Nash equilibrium solution. Configure the capacity of each participant according to the target optimization result.

[0042] In the actual operation process, the preset capacity can be used as a particle, and the optimal output of the day under each game strategy can be determined based on the preset microgrid operating parameters, target output function, and target constraints. The preset order is randomly updated until the output data corresponding to each scenario information in the preset scenario set is traversed, and the optimal output of the day corresponding to each scenario information is determined. Then, the sum of the optimal outputs of each day is determined as the total output, and the participant's profit is determined according to the total output and the profit function. The profit of each participant is determined based on the Shapley value of cooperative game theory. The profit is used as the fitness vector, and the particle position and speed are updated according to the update rule of the particle swarm grey wolf optimization algorithm. The Nash equilibrium solution is analyzed when the particle converges, and the Nash equilibrium solution is used as the target optimization result to configure the capacity of each participant.

[0043] The embodiment of the present invention obtains preset microgrid operating parameters by determining the preset capacity, target output function, target constraint conditions, benefit function and game strategy between each participant in the microgrid, constructs a target capacity optimization function based on the preset capacity, preset microgrid operating parameters, target output function, target constraint conditions, benefit function and game strategy, determines the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, adjusts the preset capacity of the participant based on the target optimization result, and facilitates determining the benefit of each participant in the microgrid by considering the constraint conditions of each participant and the game relationship between each participant; at the same time, the target optimization result of the target capacity optimization function is determined by the particle swarm grey wolf optimization algorithm, which enhances the rapid convergence ability and global search ability of the target optimization result, improves the accuracy of the target optimization result, and maximizes the benefit of each participant as much as possible.

[0044] Embodiment 2

[0045] Figure 2 1 is a flow chart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to the second embodiment of the present invention. This embodiment is a further description of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0046] S201, presetting the preset capacity of each participant in the microgrid and the game strategy between the participants.

[0047] In an embodiment, the preset capacity of participants and the game strategy between participants can be configured based on user needs.

[0048] S202: construct a target output function of the wind farm based on the cut-in wind speed, cut-out wind speed, rated wind speed, wind turbine rated power and natural wind speed of the wind power generation groups in the wind farm within a preset time period.

[0049] Among them, the cut-in wind speed refers to the minimum wind speed when the wind turbine starts to generate electricity; the cut-out wind speed refers to the wind speed at which the wind turbine stops operating in order to protect the safety of its own equipment; the rated wind speed refers to the wind speed when the wind turbine reaches the rated power output; the rated power of the wind turbine is the maximum power that the wind turbine can continuously output under the design conditions; the natural wind speed refers to the ambient wind speed.

[0050] In an embodiment, the cut-in wind speed, cut-out wind speed, rated wind speed, wind turbine rated power and natural wind speed of the wind power generation groups in the wind farm within a preset time period can be determined, and the target output function of the wind farm can be constructed by the cut-in wind speed, cut-out wind speed, rated wind speed, wind turbine rated power and natural wind speed of the wind power generation groups in the wind farm. In the actual operation process, the target output function of the wind farm can be:

[0051]

[0052] m / s、v ci is the cut-off wind speed, m / s, v co is the cut-out wind speed, m / s, v r is the rated wind speed, m / s; P WT (v) is the output power of the wind farm.

[0053] S203: construct a target output function of the photovoltaic electric field based on a preset illumination amplitude probability density function, the photovoltaic output power within a preset time period, and the maximum photovoltaic output power.

[0054] The preset illumination amplitude probability density function refers to a preset function that describes the photovoltaic distribution.

[0055] In the embodiment, the solar irradiance in a certain period of time is considered to obey the Beta distribution, and its probability density function is: Where f(r) represents the probability density when the solar irradiance is r; Γ(·), α, and β are gamma functions and shape parameters of the Beta distribution; r is the solar irradiance, W / m 2 ; r max is the maximum solar irradiance, W / m 2 . From this, the probability model of photovoltaic output power can be deduced as: Where: is the photovoltaic output power within the preset time period, It is the maximum photovoltaic output power in the preset time period. The photovoltaic power plant output power can be determined through the probability model of photovoltaic output power, and the target output function of the photovoltaic power field can be constructed.

[0056] S204. Construct a target output function of the electric-hydrogen hybrid energy storage system based on the battery's stored energy, power shortage, total fuel cell power, total electrolyzer power, preset battery charge and discharge coefficient, maximum power generation of the fuel cell, maximum storage capacity of the battery, and maximum power consumption of the electrolyzer within a preset time period.

[0057] Among them, the storage energy of the battery refers to the total amount of electrical energy that the battery can store; the power shortage refers to the difference between the electrical energy required by the microgrid within a preset time period and the actual electrical energy that can be provided; the total power of the fuel cell is the total power that the fuel cell can output or consume when working; the total power of the electrolyzer refers to the total power that the electrolyzer can output or consume when working; the preset charge and discharge coefficient of the battery refers to the ratio of the pre-set charge and discharge current to the battery capacity in order to ensure the life and performance of the battery when charging and discharging the battery; the maximum power generation power of the fuel cell refers to the maximum power that the fuel cell can output under ideal working conditions; the maximum storage capacity of the battery refers to the maximum value of the electrical energy that the battery can store; the maximum power consumption of the electrolyzer refers to the maximum power that the electrolyzer can consume when working.

[0058] In an embodiment, the energy of the electric-hydrogen hybrid energy storage system within a preset time period can be determined based on the storage energy of the battery within a preset time period, the power shortage, the total power of the fuel cell, the total power of the electrolyzer, the preset charge and discharge coefficient of the battery, the maximum power generation power of the fuel cell, the maximum storage capacity of the battery, and the maximum power consumption of the electrolyzer, and the ratio of the energy to the preset time period is used as the target output function of the electric-hydrogen hybrid energy storage system. In actual operation, the energy of the electric-hydrogen hybrid energy storage system within the preset time period can be:

[0059] Among them, E bat (t) is the energy of the electric-hydrogen hybrid energy storage system at time t, kWh; ΔP is the power shortage, kW; P fc (t-1) is the total power of the fuel cell at time t-1, P ec (t-1) is the total power of the electrolyzer at time t-1, kW; η ch is the charge and discharge coefficient of the battery; Δt is the time interval; P fc_all is the maximum power generation of the fuel cell; E bat_all is the maximum storage capacity of the battery; P ec_all is the maximum power consumption of the electrolytic cell.

[0060] S205. Taking the target output function of the wind farm, the target output function of the photovoltaic farm, and the target output function of the electric-hydrogen hybrid energy storage system as the target output function.

[0061] In an embodiment, after determining the target output function of the wind farm, the target output function of the photovoltaic field, and the target output function of the electric-hydrogen hybrid energy storage system, the target output function of the wind farm, the target output function of the photovoltaic field, and the target output function of the electric-hydrogen hybrid energy storage system can be used as the target output function.

[0062] S206: construct an output constraint function, a penalty function power balance constraint function and a benefit function, and use the output constraint function and the penalty function power balance constraint function as target constraint conditions.

[0063] Among them, the output constraint function is used to constrain the energy storage capacity of wind farms and photovoltaic farms, as well as the energy storage capacity and charging and discharging power of the electric-hydrogen hybrid energy storage system; the penalty function power balance constraint function is used to constrain the output power of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems to balance the demand rate; the profit function is used to determine the total profit of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems.

[0064] In an embodiment, for different participants, the capacity may be affected by different factors, and output constraint functions may be set for different participants. Exemplarily, the output constraint function may include the total carbon savings of the microgrid, the total wind and solar power abandonment of the microgrid, the total load interruption of the microgrid, the wind farm installed capacity constraint, the photovoltaic farm installed capacity constraint, the electrolyzer power constraint, the fuel cell power constraint, the battery capacity constraint, the hydrogen storage tank constraint, etc. In general, the total carbon savings of the microgrid can be: in, is the total carbon savings of the microgrid; is the carbon emission factor of power grid electricity in Northwest China, P fc (t) is the power of the fuel cell at time t, P bat (t) is the power of the battery at time t. The total amount of wind and solar power abandoned in the microgrid can be: Among them, P break,total is the total amount of wind and solar power abandoned in the microgrid, kW; P break (t) is the amount of wind and solar power abandoned by the microgrid at each moment. The total load interruption of the microgrid can be: Among them, P waste,total is the total load interruption of the microgrid, kW; WT is the wind farm, PV is the photovoltaic farm, EH is the electric-hydrogen hybrid energy storage system; P x,waste (t) is the amount of microgrid load interruption at each moment.

[0065] In one embodiment, the wind farm installed capacity constraint and the photovoltaic power station installed capacity constraint may be: Where: P WT,max is the maximum installed capacity of the wind farm, kW, P PV,max is the maximum installed capacity of the photovoltaic power station, kW. The electrolyzer power constraint can be 0≤P ec (t)≤P ec,max ; Among them, P ec,max is the maximum power of the electrolyzer, kW. The fuel cell power constraint can be: 0≤P fc (t)≤P fc,max ; Among them, Pfc,max is the maximum operating power of the fuel cell, kW. The battery capacity constraint can be: 0≤E bat ≤E bat,max ; Among them, E bat,max is the maximum capacity of the battery, in kWh. The hydrogen tank constraint can be:

[0066] in, is the amount of hydrogen stored in the hydrogen storage tank at time t, m 3 , is the amount of hydrogen consumed by the hydrogen storage tank at time t, m 3 ; It is a binary number, which is to avoid the hydrogen storage and consumption processes to occur simultaneously; is the upper limit coefficient of hydrogen storage tank at time t, is the upper limit coefficient of hydrogen consumption of the hydrogen storage tank at time t; It is the upper limit of hydrogen storage tank capacity. It is the lower limit coefficient of the hydrogen storage tank capacity.

[0067] The penalty function power balance constraint function can be: P load (t) = P WT (t)+P PV (t)+P EH (t)+P grid (t); where P EH (t) = P fc (t)+P bat (t)+P ec (t).

[0068] Among them, P grid (t) is positive, indicating that electricity is purchased from the main grid; P grid (t) is negative, which means selling electricity to the main grid; P EH (t) is the real-time power of the electric-hydrogen hybrid energy storage system, kW. fc (t) is the power of the fuel cell at time t; P bat (t) is the power of the battery at time t; P ec (t) is the power of the electrolytic cell at time t.

[0069] The profit function can be determined by the investor's electricity sales income, subsidy income, gas sales income, investor's equipment investment costs, equipment operation and maintenance costs, wind and solar power abandonment penalty costs, load interruption penalty costs, electricity purchase costs, and equipment investment costs. For example, the profit function can be:

[0070]

[0071] Among them, F xis the monthly net income of investors (wind farms, photovoltaic power plants, and electric-hydrogen hybrid energy storage systems); R x , C x is the income and cost of investors; R x,sell , R x,sub , R x,gas The income from electricity sales, subsidies and gas sales of investors; C x,inv , C x,rum , C x,waste , C x,break , C x,buy It includes investors' equipment investment costs, equipment operation and maintenance costs, penalty fees for curtailment of wind and solar power, penalty fees for load interruption, and electricity purchase costs.

[0072] The investment cost is:

[0073] in,

[0074] Among them, C wT,inv , C PV,inv , C EH,inv is the annual investment cost of wind farms, photovoltaic power plants, and electric-hydrogen hybrid energy storage systems; P WT , P pv is the installed capacity of wind farms and photovoltaic power stations, kW; N ec 、N hst 、N fc 、N bat , is the number of electrolyzers, gas tanks, fuel cells, and batteries; U WT , U pv is the investment cost per installed capacity of wind farms and photovoltaic power stations; r is the depreciation rate; m is the useful life; C ec,inv , C hst,inv , C fc,inv , C bat,inv is the annual investment cost of the electrolyzer, gas tank, fuel cell, and battery; U ec , U hst , U fc , U bat It is the unit price of electrolyzer, hydrogen storage tank, fuel cell and battery.

[0075] After the output constraint function and the penalty function power balance constraint function are determined, the output constraint function and the penalty function power balance constraint function can be used as target constraint conditions.

[0076] S207, extracting a preset scene set and a preset time-of-use electricity price, and using the preset scene set and the preset time-of-use electricity price as preset microgrid operating parameters; wherein the preset scene set includes at least two scene information, and the scene information includes lighting information and wind information.

[0077] The light information can be understood as light condition information; the wind information can be understood as wind speed information. The preset time-of-use electricity price refers to different electricity prices for different time periods that are preset.

[0078] In an embodiment, a pre-stored preset scene set and a preset time-of-use electricity price may be extracted and used as preset microgrid operation parameters. Generally speaking, to avoid contingency, the preset scene set may include typical scene information for one month.

[0079] S208. Using the preset capacity, preset microgrid operating parameters, target output function, target constraint conditions, benefit function and game strategy as the target capacity optimization function.

[0080] In an embodiment, after determining the preset capacity, preset microgrid operating parameters, target output function, target constraints, profit function and game strategy, the preset capacity, preset microgrid operating parameters, target output function, target constraints, profit function and game strategy can be used as the target capacity optimization function.

[0081] S209: Determine the scene information in the preset scene set, and determine the output data of each participant for each time interval according to the scene information and the target output function.

[0082] The output data refers to the output power of the participant for each time interval. The time interval refers to the time range for determining the output data, and for example, the time interval may be 1 hour.

[0083] In an embodiment, after the scene information in the preset scene set is determined, the scene information can be input into the target output function of each participant to calculate the output data of each participant for the time interval.

[0084] S210. For the preset capacity, each participant makes a decision on each game strategy according to the output data and the target constraint conditions in a preset order, and determines the optimal output for the day based on the penalty function power balance constraint function in the target constraint conditions.

[0085] In an embodiment, the preset capacity can be used as the initial particle position of each participant, and the fully cooperative game in which the wind farm and the photovoltaic farm make joint decisions is selected in turn; the partial cooperative game in which the wind farm and the photovoltaic farm form an alliance, and the electric-hydrogen hybrid energy storage system makes independent decisions; the partial cooperative game in which the wind farm and the electric-hydrogen hybrid energy storage system form an alliance, and the photovoltaic farm makes independent decisions; the partial cooperative game in which the photovoltaic farm and the electric-hydrogen hybrid energy storage system form an alliance, and the wind farm makes independent decisions; and the game strategy in the non-cooperative game in which the wind farm, the photovoltaic farm, and the electric-hydrogen hybrid energy storage system all make independent decisions. Each participant makes a decision according to the output data and the target constraints in a preset order, and determines the optimal output for the day through the penalty function power balance constraint function in the target constraints.

[0086] In one embodiment, for a preset capacity, each participant makes a decision on each game strategy according to the output data and the target constraint condition in a preset order, and determines the optimal output of the day based on the penalty function power balance constraint function in the target constraint condition, including:

[0087] Arrange the participants in a preset order, and make decisions for each game strategy in turn according to the output data and target constraints to determine the output situation corresponding to each game strategy;

[0088] The maximum value of the output corresponding to each game strategy is taken as the candidate optimal output;

[0089] Determine whether the candidate optimal output satisfies the power balance of each time interval based on the penalty function power balance constraint function;

[0090] If satisfied, determine the candidate best output as the best output for the day;

[0091] If it is not satisfied, the preset penalty factor of the penalty function power balance constraint function is increased, and the decision is made again according to the output data and the target constraint conditions until the power balance of each time interval is satisfied.

[0092] In an embodiment, each participant can be arranged in a preset order, and a decision can be made for each participant according to the preset order through the output data and the target constraint conditions to determine the output situation corresponding to each game strategy. The maximum value of the output situation corresponding to each game strategy in the same scene information is taken as the candidate optimal output. Then, the penalty function power balance constraint function is used to determine whether the candidate optimal output satisfies the power balance of each time interval. When the candidate optimal output satisfies the power balance of each time interval, the candidate optimal output can be used as the optimal output of the day; when the candidate optimal output does not satisfy the power balance of each time interval, the preset penalty factor of the penalty function power balance constraint function can be increased, and the decision can be made again according to the output data and the target constraint conditions until the power balance of each time interval is met.

[0093] S211. Randomly update the preset order until the output data corresponding to each scene information in the preset scene set is traversed to determine the optimal output of the day corresponding to each scene information.

[0094] In an embodiment, after a decision is made for a scene information, the preset order can be randomly changed, and a decision can be made again for the next scene information in the preset scene set to determine the corresponding output data, and the optimal output of the day corresponding to each scene information can be determined according to the above steps.

[0095] In one embodiment, the preset order is randomly updated until the output data corresponding to each scene information in the preset scene set is traversed to determine the best output of the day corresponding to each scene information, including:

[0096] Updating the preset order through a preset random algorithm;

[0097] For each game strategy, each participant makes decisions in turn according to the output data and target constraints in the updated preset order to determine the output situation corresponding to each game strategy;

[0098] The maximum value of the output conditions belonging to the same scene information is taken as the best output of the day.

[0099] The preset random algorithm may be understood as a preset algorithm for updating a preset order.

[0100] In an embodiment, the preset order can be rearranged according to a preset random algorithm to ensure that each participant takes turns to make decisions first, and each participant in each game strategy makes decisions in turn according to the output data and target constraints through the updated preset order to determine the output situation corresponding to each game strategy, and determine the maximum output situation of the same scenario information as the best output for the day.

[0101] S212, determining the sum of the best outputs of each day as the total output, determining the participant's benefits according to the total output and the benefit function, and determining the benefit of each participant based on the Shapley value of cooperative game theory, and using the benefit as the fitness vector.

[0102] The Shapley value represents the average contribution of the participant to the total revenue in all possible cooperation combinations.

[0103] In the embodiment, the sum of the best outputs of each day can be determined as the total output, the participant benefits can be determined by the benefit function, and the benefit of each participant can be determined according to the Shapley value of cooperative game theory. In the actual operation process, the benefit of each participant can be determined based on the Shapley value of cooperative game theory: Among them, X iis the benefit of member i in the alliance; Z is the set of N members in the alliance; W(S) is the corresponding average contribution factor; |S| is the number of elements in all sets containing member i and its subset S; V(S) is the benefit of alliance cooperation containing element i; V(S i ) is the cooperative benefit of the alliance that does not contain element i. After determining the benefit of each participant, the benefit can be used as a fitness vector.

[0104] S213. Based on the fitness vector, the preset capacity of each participant is updated according to the particle swarm grey wolf optimization algorithm until the preset maximum number of iterations is reached, the updated preset capacity is used as the target optimization result, and the capacity of each participant is configured according to the target optimization result.

[0105] In an embodiment, after the fitness vector is determined, the preset capacity of each participant can be updated by the particle swarm grey wolf optimization algorithm until the preset maximum number of iterations is reached. In the actual operation process, the position of the preset capacity can be updated according to the particle speed particle and particle optimal position set by the particle swarm grey wolf optimization algorithm, the preset capacity corresponding to the final fitness vector is used as the updated preset capacity, the updated preset capacity is used as the target optimization result, and the capacity of each participant is configured according to the target optimization result.

[0106] In the embodiment of the present invention, by presetting the preset capacity of each participant in the microgrid and the game strategy between the participants, the target output function of the wind farm, the target output function of the photovoltaic farm and the target output function of the electric-hydrogen hybrid energy storage system are used as the target output function, the output constraint function, the penalty function power balance constraint function and the benefit function are constructed, the output constraint function and the penalty function power balance constraint function are used as the target constraint conditions, the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint conditions, the benefit function and the game strategy are used as the target capacity optimization function, the target capacity optimization function is solved to obtain the target optimization result, and the capacity of each participant is configured according to the target optimization result, so as to achieve the configuration of capacity through cooperative game while ensuring the low-carbon and economic performance of the microgrid itself, so as to maximize the interests of each participant as much as possible.

[0107] Embodiment 3

[0108] Figure 3 1 is a flowchart of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to Embodiment 3 of the present invention. This embodiment takes a preset scenario set including 30 days of scenario information as an example to illustrate a specific embodiment of a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game. Figure 3 As shown, the method includes:

[0109] Step 1: To avoid contingency, select typical scenario information with a time scale of one month to build a preset scenario set, obtain the load demand curve, and predict wind power and photovoltaic output based on historical data of wind speed, light and temperature. Obtain the load, photovoltaic, and wind power output curves on a monthly time scale, input the time-of-use electricity price, and the initial value of the preset penalty factor. The participants are wind farms and electric-hydrogen hybrid energy storage systems.

[0110] Step 2: Each participant first initializes the population strategy and generates particle positions, that is, randomly initializes the capacity configuration (preset capacity) within the allowed capacity range.

[0111] Step 3: After the capacity configuration of each participant is generated, select from the game strategies of fully cooperative game, partially cooperative game, and fully non-cooperative game. Randomly generate the preset order for participants to make decisions, and change the arrangement of the preset order of decisions once a day to ensure that each participant takes turns to make decisions. Among them, the participants who form the alliance under the cooperative game strategy make decisions as a whole, superimposing the objective function and constraints.

[0112] Step 4. The target output function and target constraints of each participant are input into according to the standard form of convex optimization. Mixed integer optimization is solved in the preset order. The decision granularity of each participant is 1 hour, and the output curve for 24 hours a day is calculated. After each participant makes an output decision, the penalty function power balance constraint function is iterated, and the penalty factor is increased in each iteration until the optimal output of each participant under the current capacity configuration and game strategy is solved. Then return to step 3 and reselect the game strategy to repeat the above steps. The optimal output curve and game strategy for 24 hours on the day under each game strategy are calculated, and the benefits of the participants under all game strategies are compared. The game strategy with the highest benefit is selected as the final solution for the day. The benefits of the participants who form the alliance in the cooperative game strategy are distributed using the Shapley value. Then the solution for the next day is carried out, and the optimal output curve for a typical month and the optimal game strategy for each day are solved 30 times in a cycle. Then the monthly income of each participant is summed up and returned to the PSO-GWO algorithm as a fitness vector. The elements in the fitness vector contain the income of each participant and the global carbon savings.

[0113] Step 5. Update the speed and position of the particles according to the PSO-GWO algorithm process, change the original capacity configuration, repeat steps 3 and 4, record the monthly income of each participant, eliminate the inferior solution, and finally find the Pareto optimal solution, which can be regarded as the Nash equilibrium solution, that is, the capacity configuration result in which all participants can obtain the optimal income.

[0114] Finally, the equilibrium solution can be output, which is the optimal capacity configuration that takes into account the economic efficiency of the microgrid, the interests of each participating user, and the carbon emissions of the microgrid.

[0115] Embodiment 4

[0116] Figure 4 Schematic diagram of a microgrid capacity configuration device based on cooperative-non-cooperative dynamic game according to Embodiment 4 of the present invention. Figure 4 As shown, the device includes: a parameter configuration module 41, a function construction module 42 and a capacity configuration module 43.

[0117] Among them, the parameter configuration module 41 is used to determine the preset capacity, target output function, target constraint conditions, profit function and game strategy between participants of each participant in the microgrid;

[0118] A function construction module 42 is used to obtain preset microgrid operating parameters, and to construct a target capacity optimization function based on preset capacity, preset microgrid operating parameters, target output function, target constraint conditions, benefit function and game strategy;

[0119] The capacity configuration module 43 is used to determine the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, and adjust the preset capacity of the participant based on the target optimization result.

[0120] In the embodiment of the present invention, the preset capacity, target output function, target constraint condition, benefit function and game strategy between participants of each participant in the microgrid are determined through the parameter configuration module, the function construction module obtains the preset microgrid operating parameters, and constructs the target capacity optimization function based on the preset capacity, preset microgrid operating parameters, target output function, target constraint condition, benefit function and game strategy. The capacity configuration module determines the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, and adjusts the preset capacity of the participant based on the target optimization result. By considering the constraint conditions of each participant and the game relationship between each participant, it is convenient to determine the benefit of each participant in the microgrid; at the same time, the target optimization result of the target capacity optimization function is determined through the particle swarm grey wolf optimization algorithm, which enhances the rapid convergence ability and global search ability of the target optimization result, improves the accuracy of the target optimization result, and maximizes the benefit of each participant as much as possible.

[0121] In one embodiment, the parameter configuration module 41 includes:

[0122] A strategy configuration unit, used to pre-set the preset capacity of each participant in the microgrid and the game strategy between the participants;

[0123] A first function construction unit is used to construct a target output function of the wind farm based on the cut-in wind speed, cut-out wind speed, rated wind speed, wind turbine rated power and natural wind speed of the wind power generation groups in the wind farm within a preset time period;

[0124] A second function construction unit is used to construct a target output function of the photovoltaic electric field based on a preset illumination amplitude probability density function, the photovoltaic output power in a preset time period, and the maximum photovoltaic output power;

[0125] A third function construction unit is used to construct a target output function of the electric-hydrogen hybrid energy storage system based on the storage energy of the battery within a preset time period, the power shortage, the total power of the fuel cell, the total power of the electrolyzer, the preset charge and discharge coefficient of the battery, the maximum power generation power of the fuel cell, the maximum storage capacity of the battery, and the maximum power consumption of the electrolyzer;

[0126] An output function construction unit, used to use the target output function of the wind farm, the target output function of the photovoltaic farm, and the target output function of the electric-hydrogen hybrid energy storage system as the target output function;

[0127] Construct a constraint construction unit, which is used to construct an output constraint function, a penalty function power balance constraint function and a benefit function, and use the output constraint function and the penalty function power balance constraint function as target constraint conditions;

[0128] Among them, the output constraint function is used to constrain the energy storage capacity of wind farms and photovoltaic farms, as well as the energy storage capacity and charging and discharging power of the electric-hydrogen hybrid energy storage system; the penalty function power balance constraint function is used to constrain the output power of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems to balance the demand rate; the profit function is used to determine the total profit of wind farms, photovoltaic farms and electric-hydrogen hybrid energy storage systems.

[0129] In one embodiment, the function building module 42 includes:

[0130] A scene determination unit, used to extract a preset scene set and a preset time-of-use electricity price, and use the preset scene set and the preset time-of-use electricity price as preset microgrid operation parameters; wherein the preset scene set includes at least two scene information, and the scene information includes light information and wind information;

[0131] The optimization function determination unit is used to use the preset capacity, preset microgrid operating parameters, target output function, target constraint conditions, profit function and game strategy as the target capacity optimization function.

[0132] In one embodiment, the capacity configuration module 43 includes:

[0133] An output data determination unit, used to determine scene information in a preset scene set, and determine the output data of each participant for each time interval according to the scene information and the target output function;

[0134] The output determination unit is used to make decisions on each game strategy for each participant according to the output data and the target constraint conditions in a preset order according to the preset capacity, and determine the optimal output of the day based on the penalty function power balance constraint function in the target constraint conditions;

[0135] The best output determination unit is used to randomly update the preset order until the output data corresponding to each scene information in the preset scene set is traversed to determine the best output of the day corresponding to each scene information;

[0136] The profit determination unit is used to determine the sum of the best outputs of each day as the total output, determine the participant's profit according to the total output and the profit function, and determine the profit of each participant based on the Shapley value of cooperative game theory, and use the profit as the fitness vector;

[0137] The capacity configuration unit is used to update the preset capacity of each participant according to the particle swarm grey wolf optimization algorithm based on the fitness vector until the preset maximum number of iterations is reached, take the updated preset capacity as the target optimization result, and configure the capacity of each participant according to the target optimization result.

[0138] In one embodiment, the output determination unit is specifically configured to:

[0139] Arrange the participants in a preset order, and make decisions for each game strategy in turn according to the output data and target constraints to determine the output situation corresponding to each game strategy;

[0140] The maximum value of the output corresponding to each game strategy is taken as the candidate optimal output;

[0141] Determine whether the candidate optimal output satisfies the power balance of each time interval based on the penalty function power balance constraint function;

[0142] If satisfied, determine the candidate best output as the best output for the day;

[0143] If it is not satisfied, the preset penalty factor of the penalty function power balance constraint function is increased, and the decision is made again according to the output data and the target constraint conditions until the power balance of each time interval is satisfied.

[0144] In one embodiment, the optimal output determination unit is specifically configured to:

[0145] Updating the preset order through a preset random algorithm;

[0146] For each game strategy, each participant makes decisions in turn according to the output data and target constraints in the updated preset order to determine the output situation corresponding to each game strategy;

[0147] The maximum value of the output conditions belonging to the same scene information is taken as the best output of the day.

[0148] The microgrid capacity configuration device based on cooperative-non-cooperative dynamic game provided in an embodiment of the present invention can execute the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0149] Embodiment 5

[0150] Figure 5 It is a structural schematic diagram of an electronic device for implementing a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0151] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0152] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0153] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a microgrid capacity configuration method based on cooperative-non-cooperative dynamic game.

[0154] In some embodiments, the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game by any other appropriate means (for example, by means of firmware).

[0155] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0157] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0158] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0159] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0160] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0161] In one embodiment, the embodiment of the present invention also includes a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game of any embodiment of the present invention.

[0162] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0163] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0164] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A microgrid capacity configuration method based on cooperative-non-cooperative dynamic game, characterized in that: include: Determine the preset capacity, target output function, target constraint condition, benefit function and game strategy between each participant in the microgrid; Acquire preset microgrid operating parameters, and construct a target capacity optimization function based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function, and the game strategy; A target optimization result of the target capacity optimization function is determined based on a particle swarm grey wolf optimization algorithm, and the preset capacity of the participant is adjusted based on the target optimization result.

2. The method according to claim 1, characterized in that The method of determining the preset capacity, target output function, target constraint condition, benefit function and game strategy between the participants of each participant in the microgrid includes: Presetting the preset capacity of each participant in the microgrid and the game strategy between the participants; Constructing a target output function of the wind farm based on the cut-in wind speed, cut-out wind speed, rated wind speed, wind turbine rated power and natural wind speed of the wind power generation units in the wind farm within a preset time period; Constructing a target output function of the photovoltaic electric field based on a preset illumination amplitude probability density function, the photovoltaic output power within a preset time period, and the maximum photovoltaic output power; Constructing a target output function of the electric-hydrogen hybrid energy storage system based on the storage energy of the battery, the power shortage, the total power of the fuel cell, the total power of the electrolyzer, the preset charge and discharge coefficient of the battery, the maximum power generation power of the fuel cell, the maximum storage capacity of the battery and the maximum power consumption of the electrolyzer within a preset time period; The target output function of the wind farm, the target output function of the photovoltaic farm and the target output function of the electric-hydrogen hybrid energy storage system are used as target output functions; Constructing an output constraint function, a penalty function power balance constraint function and a benefit function, and using the output constraint function and the penalty function power balance constraint function as target constraint conditions; Among them, the output constraint function is used to constrain the energy storage capacity of the wind farm and the photovoltaic farm, as well as the energy storage capacity and charging and discharging power of the electric-hydrogen hybrid energy storage system; the penalty function power balance constraint function is used to constrain the output power of the wind farm, the photovoltaic farm and the electric-hydrogen hybrid energy storage system to be balanced with the demand rate; the profit function is used to determine the total profit of the wind farm, the photovoltaic farm and the electric-hydrogen hybrid energy storage system.

3. The method according to claim 1, characterized in that The target capacity optimization function is constructed based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function and the game strategy, including: Extracting a preset scene set and a preset time-of-use electricity price, and using the preset scene set and the preset time-of-use electricity price as preset microgrid operation parameters; wherein the preset scene set includes at least two scene information, and the scene information includes light information and wind power information; The preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the profit function and the game strategy are used as a target capacity optimization function.

4. The method according to claim 1, characterized in that: The step of determining a target optimization result of the target capacity optimization function based on a particle swarm grey wolf optimization algorithm, and adjusting the preset capacity of the participant based on the target optimization result, comprises: Determine the scene information in the preset scene set, and determine the output data of each participant for each time interval according to the scene information and the target output function; For the preset capacity, each of the participants makes a decision on each of the game strategies according to the output data and the target constraint conditions in a preset order, and determines the optimal output for the day based on the penalty function power balance constraint function in the target constraint conditions; Randomly updating the preset order until the output data corresponding to each scene information in the preset scene set is traversed to determine the optimal output of the day corresponding to each scene information; Determine the sum of the best outputs of the day as the total output, determine the participant's benefit according to the total output and the benefit function, and determine the benefit of each participant based on the Shapley value of cooperative game theory, and use the benefit as the fitness vector; Based on the fitness vector, the preset capacity of each participant is updated according to the particle swarm grey wolf optimization algorithm until a preset maximum number of iterations is reached, the updated preset capacity is used as the target optimization result, and the capacity of each participant is configured according to the target optimization result.

5. The method according to claim 4, characterized in that For the preset capacity, each participant makes a decision on each of the game strategies according to the output data and the target constraint condition in a preset order, and determines the optimal output of the day based on the penalty function power balance constraint function in the target constraint condition, including: Arrange the participants in a preset order, and make decisions for each game strategy in accordance with the output data and the target constraint conditions, to determine the output situation corresponding to each game strategy; The maximum value of the output conditions corresponding to each of the game strategies is taken as the candidate optimal output; Determining whether the candidate optimal output satisfies the power balance of each time interval based on the penalty function power balance constraint function; If satisfied, determine the candidate best output as the best output for the day; If not, the preset penalty factor of the penalty function power balance constraint function is increased, and the decision is made again according to the output data and the target constraint condition until the power balance of each time interval is met.

6. The method according to claim 4, characterized in that The randomly updating the preset order until the output data corresponding to each scene information in the preset scene set is traversed to determine the best output of the day corresponding to each scene information includes: Updating the preset order by a preset random algorithm; For each game strategy, each participant makes a decision in turn according to the output data and the target constraint conditions in an updated preset order to determine the output situation corresponding to each game strategy; The maximum value of the output conditions belonging to the same scene information is taken as the best output for the day.

7. A microgrid capacity configuration device based on cooperative-non-cooperative dynamic game, characterized in that: include: A parameter configuration module, used to determine the preset capacity, target output function, target constraint condition, benefit function of each participant in the microgrid and the game strategy between the participants; A function construction module, used to obtain preset microgrid operating parameters, and construct a target capacity optimization function based on the preset capacity, the preset microgrid operating parameters, the target output function, the target constraint condition, the benefit function and the game strategy; The capacity configuration module is used to determine the target optimization result of the target capacity optimization function based on the particle swarm grey wolf optimization algorithm, and adjust the preset capacity of the participant based on the target optimization result.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game as described in any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the microgrid capacity configuration method based on cooperative-non-cooperative dynamic game according to any one of claims 1 to 6.

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