Price strategy parameter adjustment method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a price strategy parameter adjustment method and device, electronic equipment and a storage medium. The problem that income distribution of all subjects in an IES is not considered can be solved. The method comprises the following steps: solving a first objective function of an electric vehicle cluster decision model through an initial price strategy parameter to obtain a first initial surplus value and a first initial decision variable; solving a second objective function of the user decision model through the initial price strategy parameter to obtain a second initial surplus value and a second initial decision variable; solving a third objective function of the integrated energy system operator decision model through the first initial decision variable and the second initial decision variable to obtain an initial fitness value and a third initial decision variable; and updating the initial price strategy parameter through the updated convergence factor and the differential evolution strategy, and repeating the above steps to determine a target price strategy parameter and a target decision variable.

Description

Technical Field

[0001] This application relates to the field of energy optimization technology, and in particular to a method, apparatus, electronic device, and storage medium for adjusting price strategy parameters. Background Technology

[0002] Electric vehicles (EVs) have become a rapidly growing new type of load in recent years, possessing the dual characteristics of both electrical load and energy storage devices. Integrated Energy Systems (IES), as a key platform for promoting energy transformation and improving energy efficiency, can effectively guide the charging and discharging behavior of EV cluster users through rational planning and optimized operation and management, fully leveraging the synergistic and complementary advantages of various energy sources, thereby improving the economy and stability of power grid operation. Besides being constrained by user demand, the charging and discharging behavior of EVs is also significantly guided by economic incentive mechanisms such as electricity prices. In actual energy trading environments, there are clear interactive relationships and hierarchical decision-making characteristics among Integrated Energy System Operators (IESOs), EV clusters, and users. Their decision-making process clearly conforms to the characteristics of a master-slave game, where each entity pursues its own profit maximization while also influencing and constraining each other. However, related technologies have not fully considered the distribution of benefits among the various entities within the IES, and lack in-depth analysis of the economic interest relationships among different stakeholders among the IES, EV clusters, and user loads. Summary of the Invention

[0003] To address the aforementioned technical problems, or at least partially address them, embodiments of this application provide a method, apparatus, electronic device, and storage medium for adjusting price strategy parameters, thereby resolving the issue of not considering the revenue distribution among entities within an IES.

[0004] To achieve the above objectives, the technical solutions provided in this application are as follows:

[0005] In a first aspect, embodiments of this application provide a method for adjusting price strategy parameters, the method comprising: obtaining initial price strategy parameters;

[0006] The first objective function of the electric vehicle cluster decision model is solved using the initial price strategy parameters to obtain the first initial residual value and the first initial decision variable. The first initial decision variable is used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster in each time period.

[0007] The second objective function of the user decision model is solved using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variable. The second initial decision variable is used to represent the user's energy consumption in each time period.

[0008] The third objective function of the integrated energy system operator decision model is solved using the first and second initial decision variables to obtain the initial fitness value and the third initial decision variable. The initial fitness value is used to represent the net revenue of the integrated energy system operator decision model, and the third initial decision variable is used to represent the power output of each device within the integrated energy system operator.

[0009] The initial price strategy parameters are updated by updating the convergence factor and differential evolution strategy, and the above steps are repeated to determine the target price strategy parameters and target decision variables. The first target residual value and the second target residual value corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters.

[0010] Secondly, embodiments of this application provide a price strategy parameter adjustment device, the price strategy parameter adjustment device comprising: an acquisition module, used to acquire initial price strategy parameters;

[0011] The processing module is used to solve the first objective function of the electric vehicle cluster decision model through the initial price strategy parameters to obtain the first initial residual value and the first initial decision variable. The first initial decision variable is used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster in each time period.

[0012] The processing module is further configured to solve the second objective function of the user decision model using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variable, wherein the second initial decision variable is used to represent the user's energy consumption in each time period.

[0013] The processing module is further configured to solve the third objective function of the integrated energy system operator decision model using the first initial decision variable and the second initial decision variable, to obtain an initial fitness value and a third initial decision variable; the initial fitness value is used to represent the net revenue of the integrated energy system operator decision model, and the third initial decision variable is used to represent the power output of each device within the integrated energy system operator.

[0014] The processing module is further configured to update the initial price strategy parameters by updating the convergence factor and differential evolution strategy, and repeat the above steps to determine the target price strategy parameters and target decision variables. The first target residual value and the second target residual value corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters.

[0015] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising:

[0016] Memory containing executable program code;

[0017] A processor coupled to the memory;

[0018] The processor calls the executable program code stored in the memory to execute the price strategy parameter adjustment method in the first aspect of the embodiments of this application.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that causes a computer to execute the price strategy parameter adjustment method of the first aspect of embodiments of this application. The computer-readable storage medium includes ROM / RAM, a magnetic disk, or an optical disk, etc.

[0020] Fifthly, embodiments of this application provide a computer program product that, when run on a computer, causes the computer to perform some or all of the steps of any of the methods of the first aspect.

[0021] Sixthly, embodiments of this application provide an application publishing platform for publishing computer program products, wherein when the computer program product is run on a computer, the computer performs some or all of the steps of any of the methods of the first aspect.

[0022] Compared with the prior art, the embodiments of this application have the following beneficial effects:

[0023] This application provides a method, apparatus, electronic device, and storage medium for adjusting pricing strategy parameters. The method involves obtaining initial pricing strategy parameters; solving a first objective function of an electric vehicle cluster decision model using these parameters to obtain a first initial residual value and a first initial decision variable, where the first initial decision variable represents the charging / discharging amount and battery capacity of the electric vehicle cluster at different time periods; solving a second objective function of a user decision model using these parameters to obtain a second initial residual value and a second initial decision variable, where the second initial decision variable represents the user's energy consumption at different time periods; and finally, using the first and second initial decision variables, adjusting the integrated energy system... The third objective function of the operator decision-making model is solved to obtain the initial fitness value and the third initial decision variable. The initial fitness value represents the net profit of the integrated energy system operator decision-making model, and the third initial decision variable represents the output of each device within the integrated energy system operator. The initial price strategy parameters are updated using an updated convergence factor and a differential evolution strategy. The above steps are repeated to determine the target price strategy parameters and target decision variables. The first and second target residual values ​​corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters. In this scheme, through dynamic game theory among the three stakeholders—the electric vehicle cluster decision-making model, the user decision-making model, and the integrated energy system operator decision-making model—the final price strategy parameters that maximize the residual value of the electric vehicle cluster and users, and maximize the profit of the integrated energy system operator, are obtained through continuous iteration. This achieves collaborative optimization among all stakeholders, demonstrating the effectiveness and rationality of the strategy. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of an overall IES operating architecture provided in an embodiment of this application;

[0027] Figure 2 This is a flowchart illustrating a method for adjusting price strategy parameters provided in an embodiment of this application. Figure 2 ;

[0028] Figure 3 This is a schematic diagram of an interface for a pricing strategy parameter adjustment method provided in an embodiment of this application. Figure 1 ;

[0029] Figure 4 This is a comparative schematic diagram of a convergence factor provided in an embodiment of this application;

[0030] Figure 5 This is a schematic diagram of a fitness iteration convergence curve provided in an embodiment of this application;

[0031] Figure 6 This is a schematic diagram of the structure of a price strategy parameter adjustment device provided in an embodiment of this application;

[0032] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0033] To better understand the above-mentioned objectives, features, and advantages of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features of this application can be combined with each other. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0034] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, rather than to describe a specific order of objects.

[0035] The terms “comprising” and “having”, and any variations thereof, in this application are intended to cover non-exclusive inclusion. 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 explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0036] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] With the proposal of my country's strategic goal of "carbon peaking and carbon neutrality" and the continuous advancement of power market reform, the efficient integration and full utilization of high-proportion distributed energy resources (DERs) has become an important development trend driving energy transformation. Meanwhile, electric vehicles (EVs) have become a rapidly growing new type of load in recent years, possessing the dual characteristics of both electricity load and energy storage devices. While effectively improving the efficiency of power resource utilization, they have also significantly accelerated the clean and low-carbon transformation of the energy system. Against this backdrop, integrated energy systems, as a key platform for promoting energy transformation and improving energy efficiency, can effectively guide the charging and discharging behavior of EV cluster users through rational planning and optimized operation and management, fully leveraging the synergistic and complementary advantages of various energy sources, thereby improving the economy and stability of grid operation. However, with a large number of DERs being decentralized and operating independently, and EV clusters being randomly connected to the grid and charging without order, problems such as unreasonable resource allocation and the untapped dispatchable potential of EV clusters have become key challenges restricting the coordinated development of the energy system.

[0038] In recent years, vehicle-energy interaction has gradually become an important research direction for promoting the high-quality development of new energy vehicles. Specifically, the charging and discharging behavior of EVs is not only constrained by user demand, but also significantly guided by economic incentive mechanisms such as electricity prices. The charging and discharging power response characteristics formed under this economic drive, and the resulting dynamic energy regulation capabilities, can be regarded as the dispatchable potential of electric vehicles. This study analyzes the coupling characteristics of electricity, natural gas, and transportation systems around the synergistic benefits of EV interaction with the energy system, and proposes a collaborative planning scheme for urban power grids, natural gas pipelines, and transportation facilities to explore the interaction potential between the energy system and the transportation system. A data-driven clustering model is used to accurately assess the dispatchability of large-scale EV clusters participating in grid ancillary services. However, the above research has not fully considered the revenue distribution among various stakeholders within the IES (Environmental Engineering System), and lacks in-depth analysis of the economic interest relationships among different stakeholders in the IES, EV clusters, and user loads.

[0039] Current research on the optimized operation of integrated energy systems (IES) mainly focuses on low-carbon economy, multi-energy coupling, and demand response. However, in actual energy trading environments, there are clear interactive relationships and hierarchical decision-making characteristics between integrated energy system operators, EV clusters, and users. Their decision-making process clearly conforms to the characteristics of a master-slave game, where each entity, while pursuing its own profit maximization, also influences and constrains each other. In recent years, some studies have applied master-slave game theory to analyze energy trading strategies. Related technologies have proposed a master-slave game model based on vehicle-to-grid (V2G) technology, optimizing electricity pricing strategies and EV charging and discharging behavior, effectively alleviating grid overload problems. However, they have not explored the synergistic effects of large-scale user load participation in demand response. Related technologies have also constructed a master-slave game model integrating electricity and heat load regulation and shared energy storage, optimizing the pricing mechanism between operators and users, but without specific analysis of EV load characteristics and their charging and discharging behavior. Related technologies have also fully considered the impact of road network structure and user satisfaction, effectively improving the economic benefits of load aggregators and the consumer surplus of electric vehicle users; however, they have not fully considered issues such as multi-energy coupling in the context of integrated energy systems. Furthermore, since the master-slave game model involves a large number of decision variables in practical applications and belongs to a high-dimensional mixed-integer nonlinear programming problem, intelligent optimization algorithms can be used to solve the game model. The GreyWolf Optimizer (GWO), as a novel swarm intelligence optimization algorithm, has advantages such as simple structure, low parameter dependence, and strong global optimization ability, and has been widely used in the field of power system dispatch optimization. However, the traditional GWO algorithm still has shortcomings such as low convergence accuracy and susceptibility to getting trapped in local optima.

[0040] To address some or all of the aforementioned technical problems, embodiments of this application provide a method, apparatus, electronic device, and storage medium for adjusting pricing strategy parameters. The method involves obtaining initial pricing strategy parameters; solving a first objective function of an electric vehicle cluster decision model using these initial pricing strategy parameters to obtain a first initial residual value and a first initial decision variable, where the first initial decision variable represents the charging / discharging amount and battery capacity of the electric vehicle cluster at different time periods; solving a second objective function of a user decision model using these initial pricing strategy parameters to obtain a second initial residual value and a second initial decision variable, where the second initial decision variable represents the user's energy consumption at different time periods; and finally, adjusting the first initial decision variable and the second initial decision variable... The variables are used to solve the third objective function of the integrated energy system operator decision model, obtaining the initial fitness value and the third initial decision variable. The initial fitness value represents the net profit of the integrated energy system operator decision model, and the third initial decision variable represents the output of each device within the integrated energy system operator. The initial price strategy parameters are updated using an updated convergence factor and a differential evolution strategy. The above steps are repeated to determine the target price strategy parameters and target decision variables. The first and second target residual values ​​corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters. In this scheme, through dynamic game theory of price strategies among the three stakeholders—the electric vehicle cluster decision model, the user decision model, and the integrated energy system operator decision model—and continuous iteration, the final price strategy parameters that maximize the residual value of the electric vehicle cluster and users, and maximize the profit of the integrated energy system operator, are obtained. This achieves collaborative optimization among all stakeholders, demonstrating the effectiveness and rationality of the strategy.

[0041] like Figure 1 As shown, Figure 1This is a schematic diagram of the overall IES operating architecture provided in this application embodiment. The overall IES operating architecture mainly consists of three stakeholders: IESO (Integrated Energy System Operator), EV (Electric Vehicle) clusters, and users. IESO represents the interests of the energy supply side and multi-energy coupled devices, including photovoltaic (PV), wind turbines (WT), combined heating and power units (CHP), gas boilers (GB), and heat storage (HS). The CHP system consists of gas turbines (GT) and waste heat boilers (WHB). User base load mainly consists of electrical and thermal loads. Furthermore, the EV cluster, as an independent stakeholder, participates in the game process, possessing characteristics of both load and mobile energy storage, and can proactively optimize charging and discharging strategies to cooperate with the IESO in demand response.

[0042] like Figure 2 As shown, Figure 2 This application provides a flowchart of a method for adjusting price strategy parameters, which may include the following steps:

[0043] 201. Obtain the initial price strategy parameters.

[0044] In this embodiment of the application, the initial pricing strategy parameter can be understood as the initial pricing of the EV cluster and users by the IESO. The initial pricing strategy parameter can be the parameter obtained by mapping the IESO, EV cluster and users after initialization, and it needs to be solved by the objective function and iterated in the future.

[0045] 202. Solve the first objective function of the electric vehicle cluster decision model using the initial price strategy parameters to obtain the first initial residual value and the first initial decision variables.

[0046] It should be noted that the electric vehicle cluster decision-making model can be a model pre-created based on the charging and discharging behavior of EVs. Individual EV charging and discharging models can be constructed first. Since individual EVs charge at maximum power and are uncontrollable in fast charging mode, while participating in the IES scheduling plan through V2G technology in slow charging mode, this application only models EVs in slow charging mode. The individual EV charging and discharging model can be expressed by the following formula:

[0047]

[0048] in, and Let represent the charging and discharging power of the i-th electric vehicle at time t; and Let represent the upper limits of charging and discharging power of the i-th electric vehicle, respectively; Let represent the set of grid connection times for the i-th electric vehicle; and Let μ represent the battery capacity of the i-th electric vehicle at time t and the previous time, respectively; ch and μ dis These represent the charge / discharge efficiency, μ and μ, respectively. ref Δt represents the discharge compensation coefficient, which is determined by the discharge loss; Δt represents the time step. and This represents the safety boundary of the battery capacity of the i-th electric vehicle.

[0049] Modeling the charging and discharging behavior of individual EVs involves numerous parameters and high dimensionality, and EV travel behavior exhibits significant randomness, making it difficult to model each EV individually. To reduce model complexity, the Minkowski method is used to map the multidimensional decision variables of all EVs to a single dimension, thus forming a generalized energy storage model. This model can predict the dispatchable potential of EV clusters while preserving variable constraints, and can be expressed by the following formula:

[0050]

[0051] Where t∈T, and Let represent the charging and discharging power of the EV cluster at time t; and These represent the upper limits of the charging and discharging power of the EV cluster at time t; This represents the battery capacity of the EV cluster at time t. and Indicates its upper and lower limits; μ ch and μ dis These represent the charge / discharge efficiency, μ and μ, respectively. ref Indicates the discharge compensation coefficient; This represents the change in the power status of the EV cluster at time t, indicating the change in its power consumption between the grid and the off-grid.

[0052] It should be noted that the EV cluster participates in the master-slave game based on the dynamic electricity price set by the IESO. However, EV cluster users need to consider both economic efficiency and user satisfaction during the game. Therefore, the optimization objective of the EV cluster is to maximize the difference between the EV cluster utility function and the charging and discharging cost, that is, to maximize the consumer surplus value. The first objective function of this electric vehicle cluster decision model can be expressed by the following formula:

[0053]

[0054] Among them, F EV This represents the consumer surplus value of the EV cluster; Represents the utility function of an EV cluster; C EV For the transaction revenue between IESO and EV clusters.

[0055] In this embodiment of the application, by solving the first objective function of the above-mentioned electric vehicle cluster decision model through the initial price strategy parameters, the first initial residual value and the first initial decision variable corresponding to the electric vehicle cluster decision model can be obtained. The first initial decision variable can be used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster at each time period.

[0056] 203. Solve the second objective function of the user decision model using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variables.

[0057] It should be noted that user electrical load includes fixed electrical load and movable electrical load. Fixed electrical load has a fixed electricity consumption time and requires a high degree of power supply stability; movable electrical load, on the other hand, can be adjusted autonomously according to real-time electricity prices and can be expressed by the following formula:

[0058]

[0059] in, and These represent the fixed electrical load and the movable electrical load of the user at time t, respectively.

[0060] User heat load includes basic heat load and reduceable heat load. The basic heat load is the minimum requirement for user heating, while the reduceable heat load can be reduced proportionally based on user comfort requirements and energy supply conditions, and can be expressed by the following formula:

[0061]

[0062] in, and Let t represent the fixed heat load and the reducible heat load of the user at time t, respectively.

[0063] It should be noted that users optimize their movable electrical load and reduceable heat load based on the dynamic energy sales price set by the upper-level IESO. This also incorporates a user satisfaction utility function. Therefore, the lower-level user's objective function is the one with the largest difference between the utility function and the cost, which can be expressed by the following formula:

[0064]

[0065] Among them, F U This represents the consumer surplus value of the user; Represents the user's utility function; C U This represents the transaction revenue between IESO and the user.

[0066] In this embodiment of the application, by solving the second objective function of the above-mentioned user decision model through the initial price strategy parameters, the second initial residual value and the second initial decision variable corresponding to the user decision model can be obtained. The second initial decision variable can be used to represent the user's energy consumption in each time period.

[0067] 204. Solve the third objective function of the integrated energy system operator decision model using the first and second initial decision variables to obtain the initial fitness value and the third initial decision variable.

[0068] It should be noted that integrated energy system operators include many devices, such as gas turbines, waste heat boilers, gas boilers, and thermal energy storage devices, which can be modeled separately.

[0069] CHP (Gas Power Plant) includes GT (Gas Turbine) equipment and WHB (Waste Heat Boiler) equipment. The heat power output by the GT is supplied to the heat load by the WHB. The mathematical model of CHP equipment can be expressed by the following formula:

[0070]

[0071] in, and Let GT be the power generated by electricity and heat at time t; and For the electrical and thermal efficiency of GT; Let GT be the power corresponding to the natural gas consumed by GT at time t; The thermal power output of WHB at time t; For WHB's heat loss; and These represent the upper and lower limits of the input power of the GT, respectively; and These represent the upper and lower limits of GT power ramp-up, respectively.

[0072] The GB mathematical model can be expressed by the following formula:

[0073]

[0074] in, Let GB be the heat generation power at time t; The thermal conversion efficiency of a gas-fired boiler; This represents the power consumption of hydrogen and natural gas by the gas boiler at time t. and These represent the upper and lower limits of the input power of GB, respectively; and These represent the upper and lower limits of the GB power ramp, respectively.

[0075] The mathematical model of a thermal energy storage device can be expressed by the following formula:

[0076]

[0077] Where t∈T, and These represent the charging and discharging power of the energy storage device at time t; and These represent the maximum charging and discharging power of the energy storage device at time t; It is a Boolean variable; Let be the capacity of the energy storage device at time t; and These are the charging and discharging efficiencies of the energy storage device, respectively. and These represent the maximum and minimum values ​​of the energy storage device capacity, respectively.

[0078] It should be noted that IESO formulates the optimal pricing strategy for EV clusters and users based on constraints. The optimization objective is to maximize the net benefit between various revenues and costs. The third objective function of the integrated energy system operator decision model can be expressed by the following formula:

[0079]

[0080] Where T represents the total runtime segment; C IESO For IESO's net income; C D For the transaction revenue between IESO and the upstream distribution network; C EV For the transaction revenue between IESO and EV clusters; C U For the transaction revenue between IESO and its users; This represents the total natural gas cost for IESO; C represents the total carbon trading cost of IESO. M This represents the total operating and maintenance cost of IESO; and Let t be the purchase and sale price of electricity to the distribution network; and The electricity sold to and purchased from the distribution network at time t; and Let t be the electricity purchase and sale price of IEO to the EV cluster; and The electricity sold or purchased to the EV cluster at time t; and The electricity and heat prices set for time t; and Let t be the electrical and thermal energy sold to users. The natural gas price set at time t; Let k be the amount of natural gas sold to users at time t; i Let P be the maintenance coefficient of device i. i,t Let be the output power of device i at time t.

[0081] in addition, The carbon trading cost at time t can be expressed by the following formula:

[0082]

[0083] in, Let t be the difference between the actual carbon emissions of IES and the carbon quota, i.e., the carbon trading volume; λ is the carbon trading base price; l is the length of the carbon emission range; and θ is the price increase rate.

[0084] In this embodiment of the application, by solving the first initial decision variable obtained by solving the first objective function and the second initial decision variable obtained by solving the second objective function, the third objective function of the above-mentioned integrated energy system operator decision model can be solved to obtain the initial fitness and the third initial decision variable corresponding to the integrated energy system operator decision model. The initial fitness value can be used to represent the net benefit of the integrated energy system operator decision model, and the third initial decision variable can be used to represent the power output of each device in the integrated energy system operator.

[0085] 205. Update the initial price strategy parameters by updating the convergence factor and differential evolution strategy, and repeat the above steps to determine the target price strategy parameters and target decision variables.

[0086] In this embodiment, after the initial fitness value is obtained by the preliminary calculation of the initial price strategy parameters, since the initial price strategy parameters can be considered to be randomly priced, it is necessary to optimize and update the initial price strategy parameters. Therefore, the initial price strategy parameters can be updated again by updating the convergence factor and differential evolution strategy. The convergence factor is also updated with the number of iterations. That is, the convergence factor is updated once for each iteration. Based on the updated price strategy parameters, the objective function is solved again for the integrated energy system operator decision model, the electric vehicle cluster decision model and the user decision model. The above steps are repeated until the final target price strategy parameters and target decision variables are obtained.

[0087] Since the ultimate goal is to maximize the residual value of the electric vehicle cluster and users, and maximize the revenue of the integrated energy system operator, the first and second target residual values ​​corresponding to this target price strategy parameter are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameter is greater than the fitness value corresponding to other price strategy parameters.

[0088] Specifically, by solving the objective function of the electric vehicle cluster decision model using the target price strategy parameters, the first objective residual value and the first objective decision variable can be obtained; by solving the objective function of the user decision model using the target price strategy parameters, the second objective residual value and the second objective decision variable can be obtained; by solving the objective function of the integrated energy system operator decision model using the first and second decision variables, the objective fitness value and the third objective decision variable can be obtained. The objective decision variable can include the first objective decision variable, the second objective decision variable, and the third objective decision variable.

[0089] This application provides a method for adjusting price strategy parameters. Through dynamic game theory among three stakeholders—an electric vehicle cluster decision-making model, a user decision-making model, and an integrated energy system operator decision-making model—the method iterates continuously to find the final price strategy parameters that maximize the residual value of the electric vehicle cluster and users, as well as the revenue of the integrated energy system operator. This achieves collaborative optimization among all stakeholders and demonstrates the effectiveness and rationality of the strategy.

[0090] like Figure 3 As shown, Figure 3 This application provides a flowchart of a method for adjusting price strategy parameters, which may include the following steps:

[0091] 301. Initialize the pricing strategy formulated by the integrated energy system operator through a preset mapping method to generate initial pricing strategy parameters.

[0092] It should be noted that the traditional Grey Wolf Algorithm (GWO) is built on biomimetic principles. Its iterative process mainly includes several stages: ranking, surrounding prey, attacking prey, and searching for prey. It has the advantages of few initial parameters, ease of implementation, and flexibility and efficiency. However, when the model is more complex, GWO suffers from slow convergence speed and a tendency to get trapped in local optima. In the Grey Wolf Algorithm, the more uniform the distribution of the initial population during initialization, the higher the algorithm's solution accuracy and convergence efficiency.

[0093] Compared to other chaotic mapping methods, the Tent mapping possesses both good randomness and regularity, which helps to generate a more uniform initial population. Therefore, in this embodiment, the Tent mapping is used to initialize the population (i.e., the pricing strategy formulated by the integrated energy system operator), which can be achieved through the following formula:

[0094]

[0095] in, For chaotic parameters; Let be the i-th dimension of the j-th decision variable.

[0096] 302. Obtain the electric vehicle cluster consumer satisfaction utility function, charging and discharging power constraints, battery capacity constraints, and electricity purchase and sale price constraints of the electric vehicle cluster decision model.

[0097] It should be noted that in demand response research, utility functions are typically used to quantify user satisfaction with energy consumption, using numerical values ​​to more intuitively compare the effects of different strategies. The utility function representing user satisfaction with consumption costs is usually a non-decreasing convex function. In this application, a quadratic EV cluster utility function is constructed to quantify charging cost satisfaction, which can be achieved using the following formula:

[0098]

[0099] Among them, u ev and v ev This is the electricity preference coefficient. Let be the utility function for consumer satisfaction of electric vehicle clusters. The larger the value, the higher the satisfaction of the EV cluster with charging costs. Let t be the amount of electricity sold to the EV cluster.

[0100] Because EV clusters can act as aggregated energy storage resources and their charging and discharging can be influenced by electricity prices, it is necessary to avoid simultaneous charging and discharging of EV clusters at the same time while meeting the expected electricity demand of EV users. Therefore, the constraint can be achieved through the following formula:

[0101]

[0102] Where t∈T, and Let represent the charging and discharging power of the EV cluster at time t; and These represent the upper limits of the charging and discharging power of the EV cluster at time t; This represents the battery capacity of the EV cluster at time t. and Indicates its upper and lower limits; μ represents the change in the power state of the EV cluster at time t, indicating the change in its power consumption between the grid and the off-grid. ch and μ dis These represent the charge / discharge efficiency, μ and μ, respectively. ref Indicates the discharge compensation coefficient; The expected power consumption of the EV cluster; Use 0-1 variables to avoid simultaneous charging and discharging of EV clusters.

[0103] 303. Based on the initial price strategy parameters, the utility function of electric vehicle cluster consumer satisfaction, charging and discharging power constraints, battery capacity constraints, and electricity purchase and sale price constraints, solve the first objective function of the electric vehicle cluster decision model to obtain the first initial residual value and the first initial decision variables.

[0104] In this embodiment of the application, when solving the first objective function, it is also necessary to consider the utility function of electric vehicle cluster consumer satisfaction, charging and discharging power constraints, battery capacity constraints, and electricity purchase and sale price constraints, so as to ensure that the solution of the objective function is more in line with the actual application scenario.

[0105] 304. Obtain the user consumption satisfaction utility function, demand response constraint, power balance constraint, and purchase price constraint of the user decision model.

[0106] It should be noted that in demand response research, utility functions are typically used to quantify user satisfaction with energy consumption, and numerical values ​​are used to more intuitively compare the effects of different strategies. The user consumption satisfaction utility function can be expressed by the following formula:

[0107]

[0108] in, Let the utility function be user consumption satisfaction. and Let t be the electrical and thermal energy sold to users; u e v e u h v h These are the user's preference coefficients for electricity and heat consumption, respectively, which can influence the user's energy demand preferences.

[0109] The constraints on user load can be achieved using the following formula:

[0110]

[0111] in, Let t be the user's movable electrical load. E represents the maximum movable electrical load of the user at time t. sel This represents the total amount of electrical load that can be shifted within T time periods; The user's heat load that can be reduced at time t. This represents the maximum amount of heat load that can be reduced for the user at time t.

[0112] 305. Based on the initial price strategy parameters, user consumption satisfaction utility function, demand response constraints, power balance constraints, and purchase and sale price constraints, solve the second objective function of the user decision model to obtain the second initial residual value and the second initial decision variables.

[0113] In this embodiment of the application, when solving for the second objective function, it is also necessary to consider the user consumption satisfaction utility function, demand response constraints, power balance constraints, and purchase and sale price constraints to ensure that the solution of the objective function is more in line with the actual application scenario.

[0114] 306. Obtain the power balance constraints, energy pricing constraints, and equipment constraints of the integrated energy system operator's decision-making model.

[0115] It should be noted that the power balance constraint can be achieved using the following formula:

[0116]

[0117] in, Let t be the amount of electricity purchased from the distribution network at time t; and Let represent the charging and discharging power of the EV cluster at time t; Let be the power generated by GT at time t; The thermal power output of WHB at time t; Let GB be the heat generation power at time t; and Let t be the electrical and thermal energy sold to users.

[0118] Energy prices set by the IESO should be within a reasonable range and should meet the following constraints:

[0119]

[0120] in, and Let t be the electricity purchase and sale price of IEO to the EV cluster; and The electricity and heat prices set for time t; These represent the maximum and minimum electricity sales and purchase prices from IEO to the EV cluster, respectively. These are the upper and lower limits of the electricity and heat sales prices that IESO sets for its users.

[0121] 307. Based on the first initial decision variable, the second initial decision variable, the power balance constraint, the energy price constraint, and the equipment constraint, solve the third objective function of the integrated energy system operator decision model to obtain the initial fitness value and the third initial decision variable.

[0122] In this embodiment of the application, when solving the third objective function, it is also necessary to consider power balance constraints, energy price constraints and equipment constraints to ensure that the solution of the objective function is more in line with the actual application scenario.

[0123] 308. Update the initial price strategy parameters based on the updated convergence factor, stochastic scaling factor, and crossover probability.

[0124] It should be noted that, to address the problem of easily getting trapped in local optima during population updates, α, β, and γ wolves are used to perform mutation and crossover operations on other individuals in the population. Simultaneously, an adaptive mechanism is incorporated to dynamically adjust the scaling factor τ and the crossover probability r. This allows for strong exploratory capabilities in the early stages of iteration, gradually improving search accuracy in later stages. This can be achieved through the following formula:

[0125] V i =X α +τ(X β -X γ )

[0126]

[0127] Among them, V i U is the position vector of the i-th mutated individual; τ is the random scaling factor; U i,j V is the position vector of the i-th intersecting individual; i,j X is the value of the j-th dimension of the location vector of the i-th mutated individual; i,j Let r be the value of the j-th dimension of the position vector of the i-th offspring gray wolf; r is the crossover probability; rand is a random number between [0,1]; j rand D represents the random dimension of an individual gray wolf. max This represents the maximum number of iterations.

[0128] The elite retention strategy can effectively promote the continuous preservation and rapid evolution of high-quality individuals in the population, thereby improving the convergence efficiency and solution quality of the algorithm. To prevent the loss of excellent individuals during the evolution process, this paper retains the top three individuals in the parent population, namely α, β, and γ wolves, as elite individuals during the iteration process. These individuals, together with the offspring gray wolf population generated through differential evolution, constitute the next generation population.

[0129] In some embodiments, the traditional linearly decreasing convergence factor 'a' of GWO is insufficient to satisfy the requirement of gray wolf individuals to obtain a larger search range in the early stages and to perform a finer local search in the later stages. To address this issue, embodiments of this application propose a nonlinear convergence factor strategy, which can be implemented using the following formula:

[0130]

[0131] Where d and D max These represent the current iteration number and the maximum iteration number, respectively; a st and a end These are the initial and final values ​​of the convergence factor, respectively.

[0132] like Figure 4 The figure shows a comparison between the convergence factor in this application, the traditional linear convergence factor, and other types of convergence factors. In the early stage of the algorithm, the improved convergence factor decreases more slowly, which is conducive to the gray wolf population to achieve a more thorough global search. In the middle stage, the convergence factor decays rapidly to improve the convergence efficiency of the algorithm. In the later stage, the rate of decrease of the convergence factor slows down again, which helps to achieve local fine-grained search and thus improve the optimization accuracy of the algorithm.

[0133] 309. Continuously obtain the first residual value, second residual value, and fitness value corresponding to the price strategy parameters after each iteration.

[0134] 310. When it is detected that the difference between the first residual values ​​obtained by a consecutive preset number of iterations is less than the first preset difference, and the difference between the second residual values ​​obtained by a consecutive preset number of iterations is less than the second preset difference, and the difference between the fitness values ​​obtained by a consecutive preset number of iterations is less than the third preset difference, then the current price strategy parameter is determined as the target price strategy parameter, and the current decision variable is determined as the target decision variable.

[0135] In this embodiment, during the continuous iterative process, the first residual value, second residual value, and fitness value change with each iteration. For example, in the second iteration, the first objective function of the electric vehicle cluster decision model and the second objective function of the user decision model are solved using the price strategy parameters updated after the first iteration, thereby obtaining the first residual value, first decision variable, second residual value, and second decision variable. Then, the third objective function of the integrated energy system operator decision model is solved based on the first and second decision variables to obtain the fitness value and third decision variable. The fitness value at this time can be compared with the fitness value after the first iteration. If the fitness value at this time is greater than the fitness value after the first iteration... If the fitness value is less than or equal to the fitness value after the first iteration, it means that the price strategy parameters in the second iteration are better than those in the first iteration. Therefore, based on the fitness parameters from the second iteration, the price strategy parameters can be updated using an updated convergence factor and a differential evolution strategy, and the first, second, and third objective functions can be solved again in a loop. If the fitness value at this point is less than or equal to the fitness value after the first iteration, it means that the price strategy parameters in the second iteration are not better than those after the first iteration. Therefore, based on the fitness parameters from the first iteration, the price strategy parameters can be updated using an updated convergence factor and a differential evolution strategy, and the first, second, and third objective functions can be solved again in a loop.

[0136] During the iteration process, the changes in the first residual value, the second residual value, and the fitness value will gradually level off and eventually remain constant as the number of iterations increases. When the first residual value, the second residual value, and the fitness value remain constant, the current price strategy parameters can be considered to be optimal. Therefore, the differences between the first residual value, the second residual value, and the fitness value obtained from two consecutive iterations can be calculated, and thresholds can be set for each. If it is detected that the differences between the first residual value, the second residual value, and the fitness value obtained after a preset number of iterations are all less than their respective thresholds, then it can be considered that the current first residual value, the second residual value, and the fitness value have changed very little, and the curve has converged smoothly. Therefore, the price strategy parameters at this time can be determined as the target price strategy parameters, and the decision variables at this time can be determined as the target decision variables.

[0137] In this embodiment, a game model with IEO as the leader and EV clusters and users as followers is first constructed, and an improved gray wolf optimization algorithm is proposed. The pricing strategy parameters are initialized using Tent mapping, and the first objective function of the EV cluster decision model is solved to obtain the first maximum residual value and decision variables. The second objective function of the user decision model is solved using the initial pricing strategy parameters to obtain the second maximum residual value and decision variables. The third objective function of the integrated energy system operator decision model is solved using the decision variables and decision variables to obtain the initial fitness value and decision variables. The first initial residual value, the second initial residual value, and the initial fitness value are recorded. The convergence factor is updated, and the initial pricing strategy parameters are updated using a differential evolution strategy. The above steps are repeated; if the fitness value of the second iteration is greater than the initial fitness value, the result of the second iteration is considered the optimal strategy, and vice versa. The above steps are repeated and iterated to determine the final target pricing strategy parameters and decision variables.

[0138] In some embodiments, taking a region in Inner Mongolia as an example, assuming a day is divided into 24 time periods, the power output of wind and solar power within the IES, as well as the user's base electricity, heat load curves, and parameters of other equipment can be collected. The charging behavior of vehicles aggregated in the EV cluster is mainly divided into three types: Class I vehicles mainly charge from 19:00 to 8:00 the next day; Class II vehicles mainly charge from 22:00 to 7:00 the next day; and Class III vehicles mainly charge from 9:00 to 17:00.

[0139] To demonstrate the superior performance of the IGW algorithm proposed in this application, the upper-level objective function was solved using both the traditional Genetic Algorithm (GA) and the GOW algorithm. The results of each algorithm are shown in Table 1 below.

[0140] Table 1 Comparison of Algorithm Solution Results

[0141] algorithm Number of iterations fitness value Residual value of EV clusters User residual value IGOW 200 1071010.91 319480.11 623423.83 GOW 209 988376.72 321302.05 648914.36 GA 221 965515.15 321715.12 676426.34

[0142] Fitness iteration convergence curve as shown Figure 5 As shown, the fitness value represents the net benefit of the ISO.

[0143] Analysis of the solution results shows that the IGOW algorithm has the fewest convergence iterations and achieves a higher final fitness than the other two algorithms. Although the residual value of the EV cluster and the residual value of the users are slightly lower than those of the other algorithms, this result aligns with the IESO's position as the upper-level leader in multi-agent game theory, effectively protecting its economic interests. Figure 5As shown, IGOW converges significantly faster than the other two algorithms, reaching 90% fitness by generation 26, while GOW and GA reach this level at generations 33 and 62 respectively, with slightly lower final results than IGOW. This fully demonstrates that the population initialization based on the Tent map in this application can effectively improve the uniformity and diversity of the initial population; at the same time, the improved nonlinear convergence factor enhances the global search capability in the early stages of iteration and accelerates convergence in the later stages to strengthen local exploration. In addition, the introduction of differential evolution further enhances population diversity and prevents the algorithm from getting trapped in local optima. Therefore, the IGOW algorithm proposed in this application can more effectively balance global search and local exploration when solving multi-agent master-slave game problems, showing significant advantages.

[0144] With the increasing market penetration of EVs and the rapid growth in urban ownership, the role of EVs as a broad energy storage resource in energy interaction should also be emphasized when analyzing their participation in demand response. Especially when EV clusters are large-scale, it is necessary not only to meet actual charging and discharging needs but also to ensure the economic benefits of the cluster. To further investigate the impact of EV clusters on IES operation and demand response under different scenarios, this application designs three scenarios for comparative analysis: Scenario 1, EV clusters charge randomly according to their own needs and participate in a master-slave game with users as a single stakeholder; Scenario 2, EV clusters consider their charging and discharging characteristics and dispatchable potential, and participate in a master-slave game with users as a unified stakeholder; Scenario 3, EV clusters consider their charging and discharging characteristics and dispatchable potential, becoming independent stakeholders from users, and participating in a master-slave game as two followers alongside users. The optimized operation results of each stakeholder under the three scenarios are shown in Table 2 below:

[0145] Table 2 Comparison of Optimization Results

[0146]

[0147] Analysis of the data in Table 2 shows that both Scenario 1 and Scenario 2 treat the EV cluster and the user as the same stakeholders in the game, the difference being that Scenario 2 allows the EV cluster to generate revenue through discharge. From an economic perspective, the revenue obtained by the IESO in Scenario 1 is significantly higher than that in Scenario 2, indicating that the disordered charging mode of the EV cluster is beneficial for the IESO to obtain more economic benefits. However, the operating cost of the IESO in Scenario 2 is significantly lower than that in Scenario 1, while generating an additional revenue of approximately 34,700 yuan for the user through EV discharge, resulting in a lower total cost for the user compared to Scenario 1. In Scenario 3, the IESO achieves the highest net revenue and the lowest operating cost, with the total cost for the user and the EV cluster approaching that of Scenario 2, but the EV cluster's own revenue is higher than in other scenarios. Therefore, fully utilizing the flexibility of the EV cluster not only reduces user costs but also significantly reduces the overall operating cost of the system, fully leveraging the advantages of the EV cluster as a broad energy storage resource.

[0148] During the early morning period (0:00-5:00), electrical load is at its lowest while thermal load is at its highest. Electricity demand is primarily met by wind power and CHP units, with surplus electricity absorbed by the EV cluster or sold to the grid. Thermal load is shared by the CHP units, GB units, and HS units. During the morning peak period (6:00-9:00), electrical load increases rapidly. Due to lower photovoltaic output, the IESO (Enhanced Environmentally Friendly Unit) supplements the power gap through EV cluster discharge and sells excess electricity to the grid. At this time, thermal load gradually decreases, and HS units begin to store heat. During the afternoon low period (15:00-16:00), thermal load is at a low level, and CHP unit output decreases accordingly. Coupled with relatively low time-of-use pricing on the grid, electrical load is mainly met through grid purchases and EV cluster discharge. During the nighttime peak period (18:00-21:00), thermal load gradually increases, photovoltaic output drops to zero, and the system relies primarily on wind power, CHP units, and EV cluster discharge to ensure power supply. Overall, the optimized electric heating load curve tends to be more stable, and the load fluctuation is significantly reduced, achieving a significant "peak shaving and valley filling" effect.

[0149] In Scenario 1, the EV cluster adopts a disordered charging approach, with charging behavior dependent on user habits and preferences, showing little response to electricity price signals. Category I and II EV users concentrate on charging at night, while Category III EV users concentrate on charging during daytime working hours. In Scenarios 2 and 3, IESO increases electricity prices during peak load periods to incentivize EV clusters to discharge, and lowers electricity prices during off-peak periods to guide EV clusters to charge. Notably, in Scenario 2, the EV cluster does not actively respond to low-price charging signals between 10:00 and 17:00, but instead charges during the higher-price period between 19:00 and 20:00. This is mainly because EVs and users, as a collective interest group, charge more at night, thus reducing daytime charging demand. In contrast, Scenario 3 treats the EV cluster as an independent entity, allowing EV charging and discharging behavior to flexibly adapt to electricity price changes. This more effectively coordinates their own economic interests with charging demand, thereby reducing overall system operating costs and achieving collaborative optimization among all stakeholders, demonstrating the effectiveness and rationality of the strategy.

[0150] To address the optimization and operation of IES under the current large-scale DER and EV cluster network conditions, as well as the balance of interests among IESO, EV clusters, and user loads, this application establishes a game theory model with IESO as the upper-level leader and EV clusters and users as followers, and solves the model using an improved Grey Wolf algorithm.

[0151] The master-slave game optimization strategy in this embodiment can simultaneously consider the economic interests of the upper-level leader and the lower-level followers, achieving optimized scheduling of various DERs and related equipment within the IES, and reducing the overall system operating cost. Furthermore, by implementing a dynamic pricing strategy, it effectively incentivizes EV clusters and user loads to respond to demand, ensuring that both the transferred electrical load and reduced heat load are around 10%.

[0152] By comparing and analyzing the charging and discharging behavior of EV clusters in three typical scenarios, it was found that treating EV clusters as independent stakeholders and participating in the game through V2G technology can give full play to the flexible scheduling capabilities of EV clusters. This not only reduces the charging cost of EVs and increases the discharging benefits, but also effectively reduces the peak-valley difference of the grid load by about 17.56%, demonstrating the advantages of EV clusters as a generalized energy storage device.

[0153] The improved gray wolf optimization algorithm in this application uses Tent chaotic mapping for population initialization, which effectively improves the uniformity and diversity of the initial population. By improving the convergence factor, the algorithm has stronger global exploration capabilities in the early stage, and accelerates convergence and improves solution accuracy in the middle and late stages. In addition, the introduction of differential evolution operation further increases population diversity and effectively reduces the risk of the algorithm getting trapped in local optima.

[0154] like Figure 6 As shown in the figure, this application embodiment provides a price strategy parameter adjustment device, which may include:

[0155] Module 601 is used to obtain the initial price strategy parameters;

[0156] The processing module 602 is used to solve the first objective function of the electric vehicle cluster decision model through the initial price strategy parameters to obtain the first initial residual value and the first initial decision variable. The first initial decision variable is used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster in each time period.

[0157] The processing module 602 is also used to solve the second objective function of the user decision model through the initial price strategy parameters to obtain the second initial residual value and the second initial decision variable, which is used to represent the user's energy consumption in each time period;

[0158] The processing module 602 is also used to solve the third objective function of the integrated energy system operator decision model through the first initial decision variable and the second initial decision variable to obtain the initial fitness value and the third initial decision variable; the initial fitness value is used to represent the net benefit of the integrated energy system operator decision model, and the third initial decision variable is used to represent the power output of each device in the integrated energy system operator.

[0159] The processing module 602 is further configured to update the initial price strategy parameters by updating the convergence factor and differential evolution strategy, and repeat the above steps to determine the target price strategy parameters and target decision variables. The first target residual value and the second target residual value corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters.

[0160] In some embodiments, the processing module 602 is specifically used to initialize the pricing strategy formulated by the integrated energy system operator through a preset mapping method, and generate initial pricing strategy parameters.

[0161] In some embodiments, the processing module 602 is specifically used to update the initial price strategy parameters based on the convergence factor, the stochastic scaling factor, and the crossover probability.

[0162] In some embodiments, the acquisition module 601 is specifically used to acquire the electric vehicle cluster consumer satisfaction utility function, charging and discharging power constraints, battery capacity constraints, and electricity purchase and sale price constraints of the electric vehicle cluster decision model.

[0163] The processing module 602 is specifically used to solve the first objective function of the electric vehicle cluster decision model based on the initial price strategy parameters, the utility function of electric vehicle cluster consumer satisfaction, the charging and discharging power constraints, the battery capacity constraints, and the electricity purchase and sale price constraints, so as to obtain the first initial residual value and the first initial decision variables.

[0164] In some embodiments, the acquisition module 601 is specifically used to acquire the user consumption satisfaction utility function, demand response constraint, power balance constraint and purchase price constraint of the user decision model;

[0165] The processing module 602 is specifically used to solve the second objective function of the user decision model based on the initial price strategy parameters, the user consumption satisfaction utility function, the demand response constraint, the power balance constraint, and the purchase and sale price constraint, so as to obtain the second initial residual value and the second initial decision variable.

[0166] In some embodiments, the acquisition module 601 is specifically used to acquire the power balance constraints, energy pricing constraints, and equipment constraints of the integrated energy system operator decision model;

[0167] The processing module 602 is specifically used to solve the third objective function of the integrated energy system operator decision model based on the first initial decision variable, the second initial decision variable, the power balance constraint, the energy price constraint, and the equipment constraint, so as to obtain the initial fitness value and the third initial decision variable.

[0168] In some embodiments, the acquisition module 601 is specifically used to update the initial price strategy parameters by updating the convergence factor and differential evolution strategy, and repeat the above steps to continuously acquire the first residual value, the second residual value and the fitness value corresponding to the price strategy parameters after each iteration.

[0169] The processing module 602 is specifically used to determine the current price strategy parameter as the target price strategy parameter and the current decision variable as the target decision variable when it detects that the difference between the first residual values ​​obtained by a consecutive preset number of iterations is less than the first preset difference, the difference between the second residual values ​​obtained by a consecutive preset number of iterations is less than the second preset difference, and the difference between the fitness values ​​obtained by a consecutive preset number of iterations is less than the third preset difference.

[0170] In this embodiment, each module can implement the price strategy parameter adjustment method provided in the above method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0171] like Figure 7 As shown in the embodiments of this application, an electronic device is also provided, which may include:

[0172] Memory 701 storing executable program code;

[0173] Processor 702 coupled to memory 701;

[0174] Specifically, the processor 702 calls the executable program code stored in the memory 701 to execute the price strategy parameter adjustment method executed by the electronic device in the above method embodiments.

[0175] This application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the price strategy parameter adjustment method described in the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0176] This application also provides a computer program product, which stores a computer program. When the computer program is executed by a processor, it implements each process of the price strategy parameter adjustment method in the above method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0177] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0178] It should be understood, in the several embodiments provided in this application, that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0179] In this application, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0180] In this application, memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0181] In this application, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including permanent and non-permanent, removable and non-removable storage media. The storage medium can implement information storage by any method or technology, and the information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), other types of random access memory (RAM), read-only memory (ROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device. As defined in this document, computer-readable media do not include transient media, such as modulated data signals and carrier waves.

[0182] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0183] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application. The above-mentioned multiple embodiments are not necessarily multiple independent embodiments; they are divided into multiple embodiments only to highlight different technical features in different embodiments. Those skilled in the art should understand that the above-mentioned multiple embodiments can also be combined arbitrarily.

[0184] In the various embodiments of this application, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0185] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-accessible memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of this application.

[0188] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adjusting price strategy parameters, characterized in that, The method includes: Obtain the initial pricing strategy parameters; The first objective function of the electric vehicle cluster decision model is solved using the initial price strategy parameters to obtain the first initial residual value and the first initial decision variable. The first initial decision variable is used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster in each time period. The second objective function of the user decision model is solved using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variable. The second initial decision variable is used to represent the user's energy consumption in each time period. The third objective function of the integrated energy system operator decision model is solved using the first and second initial decision variables to obtain the initial fitness value and the third initial decision variable. The initial fitness value is used to represent the net revenue of the integrated energy system operator decision model, and the third initial decision variable is used to represent the power output of each device within the integrated energy system operator. The initial price strategy parameters are updated by updating the convergence factor and differential evolution strategy, and the above steps are repeated to determine the target price strategy parameters and target decision variables. The first target residual value and the second target residual value corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters.

2. The method according to claim 1, characterized in that, The process of obtaining the initial price strategy parameters includes: The pricing strategy formulated by the integrated energy system operator is initialized using a preset mapping method, thereby generating the initial pricing strategy parameters.

3. The method according to claim 1, characterized in that, The step of updating the initial price strategy parameters through the updated convergence factor and differential evolution strategy includes: The initial price strategy parameters are updated based on the updated convergence factor, stochastic scaling factor, and crossover probability.

4. The method according to claim 1, characterized in that, The first objective function of the electric vehicle cluster decision model is solved using the initial pricing strategy parameters to obtain the first initial residual value and the first initial decision variables, including: Obtain the electric vehicle cluster consumer satisfaction utility function, charging and discharging power constraints, battery capacity constraints, and electricity purchase and sale price constraints of the electric vehicle cluster decision model; Based on the initial pricing strategy parameters, the electric vehicle cluster consumer satisfaction utility function, the charging and discharging power constraints, the battery capacity constraints, and the electricity purchase and sale price constraints, the first objective function of the electric vehicle cluster decision model is solved to obtain the first initial residual value and the first initial decision variable.

5. The method according to claim 1, characterized in that, The step of solving the second objective function of the user decision model using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variables includes: Obtain the user consumption satisfaction utility function, demand response constraint, power balance constraint, and purchase price constraint of the user decision-making model; Based on the initial pricing strategy parameters, the user consumption satisfaction utility function, the demand response constraint, the power balance constraint, and the purchase and sale price constraint, the second objective function of the user decision-making model is solved to obtain the second initial residual value and the second initial decision variable.

6. The method according to claim 1, characterized in that, The process of solving the third objective function of the integrated energy system operator decision model using the first and second initial decision variables to obtain the initial fitness value and the third initial decision variable includes: Obtain the power balance constraints, energy pricing constraints, and equipment constraints of the integrated energy system operator decision model; Based on the first initial decision variable, the second initial decision variable, the power balance constraint, the energy price constraint, and the equipment constraint, the third objective function of the integrated energy system operator decision model is solved to obtain the initial fitness value and the third initial decision variable.

7. The method according to claim 1, characterized in that, The initial price strategy parameters are updated by updating the convergence factor and differential evolution strategy, and the above steps are repeated to determine the target price strategy parameters and target decision variables, including: The initial price strategy parameters are updated using the updated convergence factor and the differential evolution strategy, and the above steps are repeated to continuously obtain the first residual value, the second residual value, and the fitness value corresponding to the price strategy parameters after each iteration. When it is detected that the difference between the first residual values ​​obtained after a consecutive preset number of iterations is less than the first preset difference, and the difference between the second residual values ​​obtained after a consecutive preset number of iterations is less than the second preset difference, and the difference between the fitness values ​​obtained after a consecutive preset number of iterations is less than the third preset difference, then the current price strategy parameter is determined to be the target price strategy parameter, and the current decision variable is determined to be the target decision variable.

8. A pricing strategy parameter adjustment device, characterized in that, include: The acquisition module is used to obtain the initial price strategy parameters; The processing module is used to solve the first objective function of the electric vehicle cluster decision model through the initial price strategy parameters to obtain the first initial residual value and the first initial decision variable. The first initial decision variable is used to represent the charging and discharging amount and battery capacity of the electric vehicle cluster in each time period. The processing module is further configured to solve the second objective function of the user decision model using the initial price strategy parameters to obtain the second initial residual value and the second initial decision variable, wherein the second initial decision variable is used to represent the user's energy consumption in each time period. The processing module is further configured to solve the third objective function of the integrated energy system operator decision model using the first initial decision variable and the second initial decision variable, to obtain the initial fitness value and the third initial decision variable; The initial fitness value is used to represent the net benefit of the integrated energy system operator decision model, and the third initial decision variable is used to represent the power output of each device within the integrated energy system operator. The processing module is further configured to update the initial price strategy parameters by updating the convergence factor and differential evolution strategy, and repeat the above steps to determine the target price strategy parameters and target decision variables. The first target residual value and the second target residual value corresponding to the target price strategy parameters are both greater than the residual values ​​corresponding to other price strategy parameters, and the target fitness value corresponding to the target price strategy parameters is greater than the fitness values ​​corresponding to other price strategy parameters.

9. An electronic device, characterized in that, include: Memory containing executable program code; and the processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the price strategy parameter adjustment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, include: The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the price strategy parameter adjustment method as described in any one of claims 1 to 7.

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