A virtual power plant differentiated demand response price calculation method based on multi-agent game

Abstract

This invention discloses a method for calculating differentiated demand response pricing for virtual power plants based on multi-agent game theory, belonging to the field of distribution system optimization and scheduling. The method includes the following steps: S1: Determine the game players; S2: Construct an optimization model for the active distribution network operator; S3: Construct power balance constraints for each virtual power plant, and operational optimization constraints for the various types of adjustable resources aggregated by each virtual power plant; S4: Based on master-slave game theory, construct a multi-agent master-slave game optimization operation model; S5: Solve the operation model to maximize the revenue of the active distribution network operator and each virtual power plant. This invention can effectively mitigate the peak-valley difference in distribution network load, eliminate node voltage exceedance problems, reduce the overall operating cost of the power grid while ensuring the revenue of various virtual power plants, and improve the efficiency of user-side resource utilization, demonstrating high practicality.

Description

Technical Field

[0001] This invention relates to the field of power distribution system optimization scheduling technology, and in particular to a method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory. Background Technology

[0002] With the continuous advancement of the construction of new power systems, the electricity load on the user side in urban distribution networks is characterized by its large quantity, dispersed distribution, and diverse types. However, due to the lack of a unified organization and coordination mechanism, it is difficult to achieve efficient aggregation and coordinated regulation, resulting in increased regulation difficulty and enhanced operational uncertainty in the distribution network under the background of high proportion of flexible load access.

[0003] Virtual power plants, as an important resource aggregation and dispatching mode, can unify the management of distributed power sources, energy storage systems and adjustable loads, and provide ancillary services such as peak shaving and frequency regulation for the power grid. However, different virtual power plants have significant differences in resource composition, response speed and operating characteristics. Traditional single electricity price or unified incentive mechanism is difficult to take into account the participation enthusiasm of multiple types of resources, which can easily lead to insufficient resource utilization or unstable response effect, and fail to fully release the regulation potential on the user side.

[0004] Furthermore, during the operation of the distribution network, there is an inherent difference in objectives between the distribution network operator and the virtual power plant. The distribution network operator focuses on the safe and stable operation of the entire distribution system, while the virtual power plant is more concerned with maximizing its own economic benefits. The two parties have a complex interactive relationship in terms of dispatching strategies, electricity pricing, and response behavior. Traditional centralized optimization methods are difficult to accurately depict this multi-party game process, which often leads to the dispatching plan failing to be implemented or one party's interests being harmed, making it difficult to achieve the optimal synergy of the interests of all parties.

[0005] In view of this, this application proposes a demand response pricing and regulation method that can take into account multi-entity collaboration, differentiated incentives, and coordination of interests among multiple parties, so as to improve the resource utilization efficiency and safe operation level of urban power distribution networks. Summary of the Invention

[0006] The purpose of this invention is to solve the problems existing in the prior art by proposing a method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory includes the following steps: S1: Determine the game players, with the active distribution network operator as the game leader and at least two different types of virtual power plants as game followers. S2: Construct an optimization model for the active distribution network operator; S3: Construct the power balance constraints for each virtual power plant, and the operation optimization constraints for the multiple types of adjustable resources aggregated by each virtual power plant; S4: Based on the theory of master-slave game, construct a multi-agent master-slave game optimization operation model, with the differentiated pricing strategy of the upper-level leader and the electricity response strategy of the lower-level follower as decision variables; S5: Solve the multi-entity master-slave game optimization operation model to obtain the optimal differentiated demand response incentive price for each type of virtual power plant, thereby maximizing the revenue of the active distribution network operator and each virtual power plant.

[0008] Preferably, the game followers include at least two of the following: industrial virtual power plants, commercial virtual power plants, data center virtual power plants, and residential virtual power plants; the differentiated pricing strategy includes electricity purchase prices and demand response incentive prices set separately for different types of virtual power plants, and each type of virtual power plant has its own independent minimum and maximum threshold for electricity purchase prices and demand response incentive prices.

[0009] Furthermore, the optimization model in step S2 aims to maximize the synergistic benefits of multiple stakeholders while taking into account the constraints of safe operation of the distribution network. The objective function of this optimization model is: ; in, Minimize operator; For active distribution network operators, the comprehensive objective function is... This is the line loss weighting coefficient; For line losses in the distribution network; This is the weighting coefficient for voltage over-limit penalty; Penalties for voltage exceeding limits in the distribution network; The cost of the distribution network purchasing electricity from the grid; The revenue generated from the distribution network selling electricity to the grid.

[0010] Preferably, in step S3, the power balance constraint simultaneously considers the output of distributed power sources within the virtual power plant, the power purchased and sold to the distribution network, and the total power consumption of aggregated adjustable resources; the power balance constraint is constructed using the following formula: ; ; In the above formula, for Virtual Power Plant Power purchased from the grid, for Virtual Power Plant Power sold to the grid for Virtual Power Plant Internal photovoltaic output, A collection of resources aggregated for virtual power plants. for Virtual Power Plant The upper limit on the amount of power that can be purchased from the power grid. for Virtual Power Plant The upper limit on the amount of power sold to the grid; for Virtual Power Plant The flag indicating that electricity is purchased from the grid is set to 1, representing a virtual power plant. exist A value of 0 indicates a virtual power plant that constantly purchases electricity from the grid. exist It sells electricity to the grid at all times.

[0011] Furthermore, the operational optimization constraints for multiple types of adjustable resources include operational constraints for at least one of electric vehicles, flexible loads, stationary energy storage systems, temperature-controlled loads, and urban water supply systems; wherein, the flexible loads include transferable loads and loads that can be reduced.

[0012] Preferably, in step S4, the multi-agent master-slave game optimization operation model is a Stackelberg master-slave game model. The upper layer uses the differentiated pricing strategy formulated by the active distribution network operator as the decision variable, and the lower layer uses the electricity consumption response strategy corresponding to each virtual power plant as the decision variable. The upper and lower layer entities achieve bidirectional interaction through price signals. Its master-slave game model is constructed by the following formula: ; ; ; ; ; In the above formula, This represents a multi-agent participation model under master-slave game optimization; Indicates the leader of the game; Indicates a follower in the game. The price strategy of the game leader; This indicates the electricity consumption strategy of each virtual power plant; For distribution network operators Electricity sales price, This indicates the electricity purchase price for each virtual power plant; For distribution network operators The benefits, This represents the benefits of each virtual power plant; Let be the payoff function of the game leader. For the first The payoff function of a follower in a game; , Represent the optimal price strategy and electricity consumption strategy at game equilibrium; This indicates that DSO is in equilibrium electricity price To maximize profits, This indicates that each follower follows a balanced electricity consumption strategy. To obtain the maximum benefit.

[0013] Furthermore, in step S5, a two-level iterative optimization algorithm is used to solve the multi-agent master-slave game optimization model. The specific solution steps include: S51: The upper-level active distribution network operator initializes the differentiated pricing strategy and distributes it to each lower-level virtual power plant; S52: Each lower-level virtual power plant, based on the received pricing strategy, solves the optimal electricity consumption response strategy with the goal of maximizing its own revenue, and feeds back the optimal electricity consumption strategy to the upper-level active distribution network operator. S53: Upper-level active distribution network operators update differentiated pricing strategies based on feedback electricity consumption response data; S54: Iterate through steps S52-S53 until the iterative changes in the upper-level price strategy and the lower-level electricity consumption strategy are both less than the preset convergence threshold. Then, determine that the game has reached equilibrium and output the optimal differentiated incentive price. The preset convergence threshold is a pre-set iteration accuracy control parameter used to ensure that the solution results of the price strategy and electricity consumption strategy meet the accuracy requirements of engineering applications.

[0014] Preferably, the electricity purchase price and demand response incentive price of each type of virtual power plant satisfy the following independent constraints: ; In the formula, This serves as a identifier for the virtual power plant type. For the first Virtual power plants The electricity price at any given time; , The first Minimum and maximum threshold values ​​for electricity purchase prices for virtual power plants; For the first Virtual power plants Incentive pricing based on demand response at any given moment; , The first Minimum and maximum threshold values ​​for the demand response incentive price of virtual power plants.

[0015] Furthermore, the calculation expressions for each component of the objective function of the active distribution network operator optimization model are as follows:

[0016] in, This represents the voltage over-limit penalty coefficient, the voltage over-limit penalty coefficient The dynamic deviation calculation method is used, and the expression is as follows: ; In the formula, For time intervals; for Timetable The current, For line impedance, For the set of distribution network nodes, For load power, For electricity purchase price, For the power purchase capacity, For electricity sales price, This refers to the power output for electricity sales. and These are the upper and lower critical values ​​for the safe operation voltage of the distribution network; For node voltage, and These are the upper and lower limits of the ideal voltage for normal operation of the distribution network.

[0017] Furthermore, in the multi-agent master-slave game optimization model, each virtual power plant aims to maximize its own net profit. The profit function expression for a single type of virtual power plant is as follows: ; In the formula: For the first Net revenue of virtual power plants; For revenue from electricity sales; Incentives for demand response; For electricity purchase costs; For internally adjustable resource control costs; proactive distribution network operators aim to minimize overall economic costs and maximize grid operation security, achieving multi-entity collaborative optimization.

[0018] Compared with existing technologies, this invention provides a method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory, which has the following advantages: 1. This method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory addresses the issues of large number, dispersed distribution, and heterogeneous types of electricity loads in urban power distribution networks by constructing a collaborative control framework for multiple virtual power plants. This enables unified aggregation and flexible scheduling of user-side electricity loads, thereby improving resource integration capabilities and the controllability of the power distribution system.

[0019] 2. This method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory considers the differences in the types of electricity load resources and operating characteristics of different virtual power plants, and designs differentiated electricity price incentive and adjustment strategies to effectively guide various types of electricity load resources to participate in dispatch, thereby improving the flexible utilization efficiency and demand response level of the distribution network.

[0020] 3. This method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory introduces a Stackelberg master-slave game model to characterize the decision-making relationship between the two parties, addressing the issues of inconsistent objectives and complex strategy interactions between distribution network operators and virtual power plants. This enables the coordinated optimization of time-of-use pricing, regulatory response behavior, and economic benefits.

[0021] 4. This virtual power plant differentiated demand response pricing calculation method based on multi-agent game theory significantly improves the safety and efficiency of distribution network operation and dispatch. It can smooth out peak-valley differences through precise load regulation, completely solve the problems of node voltage exceeding limits and line overload, simplify the multi-agent dispatch process, reduce system operation risks, and adapt to the access and absorption needs of high proportion of distributed power sources.

[0022] 5. This method for calculating differentiated demand response electricity prices for virtual power plants based on multi-party game theory can achieve economic benefit equilibrium among multiple parties and universal applicability of the method. It can reduce the overall operating cost of the power grid while maximizing the revenue of each virtual power plant and fully tapping the potential of adjustable resources. Moreover, the model parameters are flexible and adjustable and do not require hardware modification. Attached Figure Description

[0023] Figure 1 This is a topology diagram of the IEEE 33-node power distribution system. Figure 2 This is a comparison diagram of the distribution network node voltages under four different cases in this invention; Figure 3 This is a diagram showing the optimization results of demand response electricity prices for different types of virtual power plants after game equilibrium in this invention. Figure 4 This is an optimized power curve of the data center load after adopting the method of the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0025] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0026] Example 1: Refer to Figures 1-4 A method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory includes the following steps: S1: Determine the game players, with the active distribution network operator as the game leader and at least two different types of virtual power plants as game followers. S2: Construct an optimization model for the active distribution network operator; S3: Construct the power balance constraints for each virtual power plant, and the operation optimization constraints for the multiple types of adjustable resources aggregated by each virtual power plant; S4: Based on the theory of master-slave game, construct a multi-agent master-slave game optimization operation model, with the differentiated pricing strategy of the upper-level leader and the electricity response strategy of the lower-level follower as decision variables; S5: Solve the multi-entity master-slave game optimization operation model to obtain the optimal differentiated demand response incentive price for each type of virtual power plant, thereby maximizing the revenue of the active distribution network operator and each virtual power plant.

[0027] The game followers include at least two of the following: industrial virtual power plants, commercial virtual power plants, data center virtual power plants, and residential virtual power plants; the differentiated pricing strategy includes electricity purchase prices and demand response incentive prices set separately for different types of virtual power plants, and each type of virtual power plant has its own independent minimum and maximum threshold for electricity purchase prices and demand response incentive prices.

[0028] The optimization model in step S2 aims to maximize the synergistic benefits of multiple stakeholders while also considering the constraints of safe operation of the distribution network. The objective function of this optimization model is: ; in, Minimize operator; For active distribution network operators, the comprehensive objective function is... This is the line loss weighting coefficient; For line losses in the distribution network; This is the weighting coefficient for voltage over-limit penalty; Penalties for voltage exceeding limits in the distribution network; The cost of the distribution network purchasing electricity from the grid; The revenue generated from the distribution network selling electricity to the grid.

[0029] The objective function of the game leader operation optimization model is calculated as follows: the line loss cost of the distribution network is calculated based on the square of the current of each line, the line resistance, and the scheduling time interval in each scheduling period; The voltage over-limit penalty cost of the distribution network is calculated based on the voltage over-limit penalty coefficient of each node in each scheduling period and the load power of the corresponding node. The voltage over-limit penalty coefficient adopts a dynamic deviation calculation method. When the node voltage is higher than the upper limit of the ideal operating voltage but lower than the upper limit of the safe operating voltage, the penalty coefficient increases linearly with the deviation between the node voltage and the upper limit of the ideal operating voltage. When the node voltage is lower than the lower limit of the ideal operating voltage but higher than the lower limit of the safe operating voltage, the penalty coefficient increases linearly with the deviation between the node voltage and the lower limit of the ideal operating voltage. The electricity purchase cost of the distribution network is calculated based on the electricity purchase price and corresponding power of each node in each dispatch period; the electricity sales revenue of the distribution network is calculated based on the electricity sales price and corresponding power of each node in each dispatch period.

[0030] Specifically, the calculation methods for each term in the objective function of the above optimization model are as follows:

[0031] In the above formula: For time intervals; for Timetable The current, For the line The resistance value; For the set of distribution network nodes; for Time Node The load penalty factor caused by voltage exceeding the limit. for Time Node The load power, for The power grid is constantly moving to the nodes. The price of electricity purchased for The power grid is constantly moving to the nodes. The power of electricity purchased, for The power grid is constantly moving to the nodes. The price of electricity sold. for The power grid is constantly moving to the nodes. The power output of electricity sold.

[0032] Furthermore, the voltage over-limit penalty coefficient in the above formula The following dynamic calculations are performed based on the deviations between the node voltage and the safe operating voltage and the ideal operating voltage:

[0033] In the above formula, for Time Node The voltage; and These are the upper and lower critical values ​​for the safe operation voltage of the distribution network; and These are the upper and lower limits of the ideal voltage for normal operation of the distribution network.

[0034] In step S3, the power balance constraint simultaneously considers the output of distributed power sources within the virtual power plant, the power purchased and sold to the distribution network, and the total power consumption of aggregated adjustable resources; the power balance constraint is constructed using the following formula: ; ; In the above formula, for Virtual Power Plant Power purchased from the grid, for Virtual Power Plant Power sold to the grid for Virtual Power Plant Internal photovoltaic output, A collection of resources aggregated for virtual power plants. for Virtual Power Plant The upper limit on the amount of power that can be purchased from the power grid. for Virtual Power Plant The upper limit on the amount of power sold to the grid; for Virtual Power Plant The flag indicating that electricity is purchased from the grid is set to 1, representing a virtual power plant. exist A value of 0 indicates a virtual power plant that constantly purchases electricity from the grid. exist It sells electricity to the grid at all times.

[0035] Among them, purchasing electricity refers to the situation where the output of distributed power sources within the virtual power plant is insufficient to meet all the load demand it aggregates, and electricity needs to be purchased from the grid to make up the gap. This usually occurs during peak electricity consumption periods, at night when photovoltaic output is insufficient, or on cloudy or rainy days. Selling electricity refers to the situation where the output of distributed power sources within the virtual power plant exceeds the load demand it aggregates, and the surplus electricity is sold to the grid to generate revenue. This usually occurs during midday when photovoltaic power generation is high, and during off-peak periods when energy storage is discharging.

[0036] In step S3, the operational optimization constraints for multiple types of adjustable resources include operational constraints for at least one of electric vehicles, flexible loads, stationary energy storage systems, temperature-controlled loads, and urban water supply systems. When electric vehicle operating constraints are included, the constraints are constructed using the following formula: ; ; ; ; ; In the above formula, , A collection of electric vehicles; The total number of electric vehicles; For the first electric vehicles The on / off state during a certain period of time. Indicates disconnection from the network. Indicates grid connection; For the first electric vehicles Maximum charging and discharging power during the period; For the first electric vehicles Charge and discharge power during the period A positive value indicates charging. A negative value indicates discharge; For the first Total battery capacity of the electric vehicle; For the first electric vehicles Battery level during the period; For the first electric vehicles Battery level during the period; , The first The upper and lower limits of the state of charge of the battery of an electric vehicle; For the first The minimum required state of charge of the battery when an electric vehicle leaves a charging station; For the first The initial state of charge of the battery of an electric vehicle; For the first The charging and discharging coefficient of an electric vehicle; For the first electric vehicles Discharge power during the period of operation.

[0037] When flexible load operating constraints are included, flexible loads include transferable loads and loads that can be reduced: Transferable load constraints are constructed using the following formula: ; ; ; In the above formula, For nodes The connected load is Transferable power over time period; , They are nodes The connected load is The upper and lower limits of the adjustable transferable power for a given time period; , They are nodes Upward and downward adjustment rate constraints of the transferable power of the connected load; For nodes The connected load is Transferable power over time period; For nodes The connected load is The original power when the time period is not subject to regulation.

[0038] Reduceable load constraints are constructed using the following formula: ; ; ; In the above formula, For nodes The connected load is Power after time period reduction; For nodes The connected load is Reduce state quantities during the time period. When, it indicates that it is not in a reduction state. When this time, it indicates that the load is being reduced. For nodes The connected load is Reduce state quantities during the time period. for Load reduction factor for a given time period; For nodes The connected load is Power level before time period reduction; For nodes The maximum number of load shedding operations on the connection. The maximum number of times the time can be continuously reduced; This indicates the period in which load shedding begins.

[0039] When operational constraints for stationary energy storage systems are included, these constraints are constructed using the following formula: ; ; ; ; In the above formula, For nodes Connected energy storage systems Charging power during the period For nodes Connected energy storage systems Discharge power during the period and Represents a node The maximum charging and discharging power of the connected energy storage system; For nodes Connected energy storage systems Charging indicator for different time periods When, it indicates the charging status. At 0, it indicates the discharge state. For nodes Connected energy storage systems State of charge during a period of time For nodes Connected energy storage systems State of charge during a period of time and Represents a node The charging and discharging efficiency of the connected energy storage system; The rated battery capacity of the energy storage system. Represents a node The maximum state of charge allowed by the connected energy storage system; Represents a node The minimum state of charge allowed by the connected energy storage system.

[0040] When temperature-controlled load operation constraints are included, the constraints are constructed using the following formula: ; ; ; ; In the above formula, for Time-of-use temperature control load The power; , They are respectively Time-of-use temperature control load The upper and lower limits of power adjustment; , Temperature control load Power upward and downward adjustment rate constraints; for Time-of-use temperature control load The indoor temperature; , Temperature control load The upper and lower limits of the ideal indoor temperature; For the thermal resistance of the house; The specific heat of air; for Time-of-use temperature control load The ambient temperature; When constraints related to the operation of the urban water supply system are included, these constraints are constructed using the following formula: ; ; ; ; In the above formula: , They are respectively The equivalent amount of water stored and released in the upper reservoir of a pumped storage power station during a given period; , These refer to the pumping efficiency and power generation efficiency of the pumped storage unit, respectively. The density of water is 1000 kg / m³. 3 ; This is the acceleration due to gravity, with a value of 9.8 m / s². 2 ; Average water level; for Pumping power of a pumped storage power station during a specific time period; for Power generation capacity of pumped storage power stations during specific time periods; , They represent The start-up and shutdown status of the pumped storage unit during pumping and power generation is a 0-1 variable. , These are the maximum and minimum operating power of pumped storage, respectively. for The reservoir water level at any time, For time intervals, , These are the lower and upper limits of the reservoir's capacity, respectively. The storage capacity coefficient is adjustable. , These represent the starting and target reservoir capacities, respectively.

[0041] In step S4, the multi-agent master-slave game optimization model is a Stackelberg master-slave game model. The upper layer uses the differentiated pricing strategy formulated by the active distribution network operator as the decision variable, and the lower layer uses the electricity consumption response strategy corresponding to each virtual power plant as the decision variable. The upper and lower layer agents achieve bidirectional interaction through price signals. Its master-slave game model is constructed through the following formula: ; ; ; ; In the above formula, This represents a multi-agent participation model under master-slave game optimization; Indicates the leader of the game; Indicates a follower in the game. The price strategy of the game leader; This indicates the electricity consumption strategy of each virtual power plant, specifically... Electricity consumption strategy for industrial loads, Electricity consumption strategies for commercial loads, Power consumption strategy for data center load, Electricity consumption strategies for residential loads; This indicates that DSO is in equilibrium electricity price To maximize profits, This indicates that each follower follows a balanced electricity consumption strategy. To obtain the maximum benefit.

[0042] For distribution network operators Electricity sales price, This indicates the electricity purchase price for each virtual power plant, specifically... The electricity purchase price for industrial loads, The electricity purchase price for commercial load, The electricity purchase price for data center load, The electricity purchase price for residential load; For distribution network operators The benefits, This represents the specific benefits of each virtual power plant. For the benefit of industrial load, For the benefit of commercial load, For the benefit of data center load, Benefits for residents' workload.

[0043] In the multi-agent master-slave game optimization model, each virtual power plant aims to maximize its own net profit. The profit function expression for a single type of virtual power plant is as follows: ; In the formula: For the first Net revenue of virtual power plants; For revenue from electricity sales; Incentives for demand response; For electricity purchase costs; For internally adjustable resource control costs; proactive distribution network operators aim to minimize overall economic costs and maximize grid operation security, achieving multi-entity collaborative optimization.

[0044] In step S5, a two-level iterative optimization algorithm is used to solve the multi-agent master-slave game optimization model. The specific solution steps include: S51: The upper-level active distribution network operator initializes the differentiated pricing strategy and distributes it to each lower-level virtual power plant; S52: Each lower-level virtual power plant, based on the received pricing strategy, solves the optimal electricity consumption response strategy with the goal of maximizing its own revenue, and feeds back the optimal electricity consumption strategy to the upper-level active distribution network operator. S53: Upper-level active distribution network operators update differentiated pricing strategies based on feedback electricity consumption response data; S54: Iterate through steps S52-S53 until the iterative changes in the upper-level price strategy and the lower-level electricity consumption strategy are both less than the preset convergence threshold. Then, determine that the game has reached equilibrium and output the optimal differentiated incentive price. The preset convergence threshold is a pre-set iteration accuracy control parameter used to ensure that the solution results of the price strategy and electricity consumption strategy meet the accuracy requirements of engineering applications.

[0045] The electricity purchase price and demand response incentive price for each type of virtual power plant meet the following independent upper and lower bound constraints: ; In the formula, This serves as a identifier for the virtual power plant type. For the first Virtual power plants The electricity price at any given time; , The first Minimum and maximum threshold values ​​for electricity purchase prices for virtual power plants; For the first Virtual power plants Incentive pricing based on demand response at any given moment; , The first Minimum and maximum threshold values ​​for the demand response incentive price of virtual power plants.

[0046] No. The minimum and maximum thresholds for the purchase price of electricity for virtual power plants, as well as the minimum and maximum thresholds for the demand response incentive price, are set differently based on the unit adjustment cost, maximum response capacity, electricity response characteristics, and power supply adequacy of the distribution network at different times for the corresponding type of virtual power plant.

[0047] Reference Figure 1 To verify the effectiveness of the virtual power plant differentiated demand response pricing method based on multi-agent game theory provided in this embodiment, this embodiment uses an improved IEEE 33-node distribution system for simulation analysis. The system topology diagram is as follows: Figure 1 As shown, the power distribution system structure used in the simulation of this invention is illustrated. The system contains 33 nodes and is divided into four functional zones: industrial zone, commercial zone, data center zone, and residential zone. Each node is connected to corresponding types of loads, distributed photovoltaics, energy storage, and charging / swapping stations.

[0048] In this embodiment, the line loss weighting coefficient The voltage over-limit penalty weighting coefficient is 0.833. The time interval is 0.167. The time limit is 1 hour, and the dispatch cycle is 24 hours. The nominal voltage of the distribution network is 12.66kV, the feasible bus voltage range is 1±5%pu, and the ideal operating voltage range is 1±3%pu. Among them, the load aggregated by industrial virtual power plants, commercial virtual power plants, and residential virtual power plants all include 30% adjustable flexible load and a corresponding proportion of temperature-controlled load. The load aggregated by data center virtual power plants includes energy storage systems and temperature-controlled loads. pu is an abbreviation for per-unit value, which is the ratio of the actual physical quantity to the selected reference value. It is a standard dimensionless unit commonly used in the field of power system engineering. The initial operating parameters of various virtual power plants are shown in Table 1 below. Table 1 Initial Operating Parameters

[0049] To fully verify the effectiveness of the virtual power plant differentiated demand response pricing method based on multi-agent game theory provided in this embodiment of the invention, this example sets up the following four sets of progressive comparative simulation cases: Case 1 serves as the baseline case, where users do not participate in the demand response market, all virtual power plants operate according to the original electricity consumption curve, do not accept any price incentives, and do not perform any load regulation.

[0050] Case 2 is a single fixed incentive case, in which a single decentralized user directly participates in electricity market regulation. It adopts a uniform fixed demand response incentive price across the entire system, without virtual power plant aggregation or consideration of the load characteristics differences of different types of users.

[0051] Case 3 adopts a unified incentive case for VPP clusters, where users are aggregated into four types of virtual power plant clusters: industrial, commercial, data center, and residential, to participate in market regulation. All virtual power plants adopt a unified demand response incentive price, which realizes the aggregation of user-side resources, but does not achieve differentiated pricing.

[0052] Case 4 is an example of an embodiment of the present invention. It adopts the differentiated demand response price calculation method based on multi-entity master-slave game proposed in the present invention. The master-slave game model is constructed with the active distribution network operator as the leader and four types of virtual power plants as followers. Independent electricity purchase price and demand response incentive price are formulated for different types of virtual power plants.

[0053] Through comparative analysis of the above four cases, the effects of demand response mechanism, virtual power plant aggregation mechanism, and multi-stakeholder game-based differentiated pricing mechanism on improving the economic efficiency and security of distribution network operation can be verified.

[0054] Table 2. Cost Comparison of Four Case Studies

[0055] As can be seen from Table 2, with the gradual improvement of the demand response mechanism, the operating costs and total system costs of various virtual power plants show a gradual decreasing trend, verifying the progressive optimization effect of the technical solution of this invention.

[0056] Compared to Case 1, Case 2 shows a 17.35% reduction in total system cost, with commercial virtual power plants decreasing by 32.76%, data center virtual power plants by 19.95%, industrial virtual power plants by 12.29%, and residential virtual power plants by 13.88%. This indicates that even with a single fixed incentive price, the demand response mechanism can still bring direct economic benefits to users and is an effective means of reducing electricity costs. However, due to the dispersed participation of individual users in the market, their bargaining power is weak, and the differences in load characteristics among different users are not considered, thus failing to achieve global optimization.

[0057] Compared to Case 2, Case 3 further reduced the total system cost by 9.81%; by introducing a virtual power plant (VPP) cluster, the dispersed user-side resources were aggregated and managed in a unified manner, significantly enhancing the user-side regulation capabilities and market bargaining power, while lowering the threshold for users to participate in demand response; by increasing the overall incentive price, the distribution network effectively promoted more users to participate in regulation, forming a positive interaction between the power grid and users.

[0058] Case 4, which illustrates the solution of this invention, shows that compared to Case 3, the total system cost is reduced by 5.35%, with commercial virtual power plants experiencing a cost reduction of 7.52% and industrial virtual power plants a reduction of 6.50%, demonstrating the most significant optimization effect. This is because the invention introduces a multi-entity master-slave game mechanism, fully considering the differences in adjustment costs, response capacity, and electricity consumption characteristics among different types of virtual power plants, and formulating independent differentiated pricing strategies for them. This overcomes the limitations of unilateral decision-making and uniform pricing, achieving the coordination of interests between the distribution network operator and each virtual power plant, and optimizing overall benefits.

[0059] Reference Figure 2 The voltage quality comparison analysis of the distribution network is as follows: The voltage distribution at distribution network nodes in the four case studies are as follows: Figure 2 As shown; it should be noted that, Figure 2 The 33 node numbers in Figure 1 The 33 nodes described in the diagram are numbered one-to-one and are unique digital identifiers for electrical connection points in the distribution network. They are used for topology modeling, resource location, application of security constraints, and analysis of simulation results. Case 1 serves as a baseline reference scenario. Without any demand response adjustments, due to the limited transmission power of the distribution network, some end nodes exhibited slight voltage exceedance behavior during the morning and evening peak electricity consumption periods, i.e., the voltage was below 0.95 pu. The morning peak was from 8:00 to 12:00, and the evening peak was from 18:00 to 22:00. Voltage exceedance not only affects the normal operation of electrical equipment and shortens its service life, but also reduces the power supply reliability of the distribution network.

[0060] Compared to Case 1, Case 2, under the incentive of demand response, saw some adjustable loads shift from peak to off-peak periods, resulting in a significant reduction in voltage during the off-peak period from 0:00 to 8:00. This alleviated the voltage over-limit problem during peak periods to some extent. However, due to the single incentive mechanism, it was difficult to effectively mobilize more different types of loads to participate in the response. Therefore, the voltage improvement effect was significantly limited, and some node voltages were still close to the safe lower limit during peak periods.

[0061] The voltage trends in Case 3 and Case 4 are generally consistent. The voltage quality in both cases is significantly improved compared to Case 1 and Case 2. The voltage of all nodes is stable within the safe operating range of 0.95pu-1.05pu, completely eliminating the problem of voltage exceeding the limit. Among them, the node voltage distribution in Case 4 of this invention is more uniform and the voltage fluctuation amplitude is smaller. This is due to the differentiated pricing mechanism, which can more accurately guide the adjustment of different types of loads and achieve a better spatiotemporal distribution of loads.

[0062] Reference Figure 3 The price analysis for differentiated demand response incentives is as follows: After game equilibrium, the optimization results of demand response incentive prices for different types of virtual power plants are as follows: Figure 3 As shown, the incentive price and the daily load curve show a high degree of consistency in the overall trend. During peak load periods, the incentive price is at its peak level; during off-peak load periods, the incentive price is zero or at a low level. This price signal can effectively guide virtual power plants to reduce electricity consumption or sell electricity to the grid during peak load periods and increase electricity consumption or charge during off-peak load periods, thereby smoothing load fluctuations and alleviating peak power supply pressure on the distribution network.

[0063] From the perspective of differentiation characteristics, there are significant differences in the incentive prices of different types of virtual power plants. The incentive price of industrial virtual power plants is the highest because industrial loads have higher regulation costs and larger regulation capacities, resulting in the most significant impact on the distribution network. The incentive prices of commercial virtual power plants and data center virtual power plants are the next highest, with their regulation costs and response speeds falling between those of industrial and residential loads. The incentive price of residential virtual power plants is the lowest because residential loads have small individual regulation capacities but large total volumes, resulting in relatively low regulation costs.

[0064] By setting such differentiated incentive prices, this invention not only ensures reasonable returns for different types of virtual power plants participating in demand response, but also avoids the problem of "insufficient returns for users with strong adjustment capabilities and excessive returns for users with weak adjustment capabilities" caused by uniform pricing, thereby maximizing the adjustment potential of various user-side resources.

[0065] Reference Figure 4 The load optimization effect analysis is as follows: After adopting the method of this invention, the load optimization power curve of the data center virtual power plant is as follows: Figure 4As shown, during peak load periods, time-of-use electricity prices and demand response incentive prices are both at their peak, leading to a significant increase in the electricity costs of data centers. During off-peak periods, data centers can simultaneously receive triple economic incentives: low electricity prices, demand response subsidies, and revenue from energy storage charging and discharging. Guided by these price signals, the adjustable temperature-controlled load and energy storage load of data centers tend to shift from peak to off-peak periods, thereby effectively mitigating their load fluctuations.

[0066] Besides data center virtual power plants, the transferable production load of industrial virtual power plants, the air conditioning temperature control load of commercial virtual power plants, and the electric vehicle charging load of residential virtual power plants have also achieved a reasonable transfer from peak hours to off-peak hours under the guidance of differentiated pricing signals. Simulation results show that after adopting the method of this invention, the overall peak-to-valley load difference of the distribution network is reduced by about 15%, effectively alleviating the peak power supply pressure of the distribution network and providing a strong guarantee for the safe and stable operation of the distribution network.

[0067] Example 2: This application also discloses an electronic device, including a memory and a processor. The memory is used to store a program that supports the processor in executing a virtual power plant differentiated demand response electricity price calculation method based on multi-agent game theory, and the processor is configured to execute the program stored in the memory.

[0068] Example 3: This application also discloses a computer-readable storage medium storing a computer program. When the computer program is run by a processor, it executes the steps of the method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory in this application.

[0069] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory, characterized in that, Includes the following steps: S1: Determine the game players, with the active distribution network operator as the game leader and at least two different types of virtual power plants as game followers. S2: Construct an optimization model for the active distribution network operator; S3: Construct the power balance constraints for each virtual power plant, and the operation optimization constraints for the multiple types of adjustable resources aggregated by each virtual power plant; S4: Based on the theory of master-slave game, construct a multi-agent master-slave game optimization operation model, with the differentiated pricing strategy of the upper-level leader and the electricity response strategy of the lower-level follower as decision variables; S5: Solve the multi-entity master-slave game optimization operation model to obtain the optimal differentiated demand response incentive price for each type of virtual power plant, thereby maximizing the revenue of the active distribution network operator and each virtual power plant.

2. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 1, characterized in that, The game followers include at least two of the following: industrial virtual power plants, commercial virtual power plants, data center virtual power plants, and residential virtual power plants; the differentiated pricing strategy includes electricity purchase prices and demand response incentive prices set separately for different types of virtual power plants, and each type of virtual power plant has its own independent minimum and maximum threshold for electricity purchase prices and demand response incentive prices.

3. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 2, characterized in that, The optimization model in step S2 aims to maximize the synergistic benefits of multiple stakeholders while taking into account the constraints of safe operation of the distribution network. The objective function of this optimization model is: ； in, Minimize operator; For active distribution network operators, the comprehensive objective function is... This is the line loss weighting coefficient; For line losses in the distribution network; This is the weighting coefficient for voltage over-limit penalty; Penalties for voltage exceeding limits in the distribution network; The cost of the distribution network purchasing electricity from the grid; The revenue generated from the distribution network selling electricity to the grid.

4. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 1, characterized in that, In step S3, the power balance constraint simultaneously considers the output of distributed power sources within the virtual power plant, the power purchased and sold to the distribution network, and the total power consumption of aggregated adjustable resources; the power balance constraint is constructed using the following formula: ； ； In the above formula, for Virtual Power Plant Power purchased from the grid, for Virtual Power Plant Power sold to the grid for Virtual Power Plant Internal photovoltaic output, A collection of resources aggregated for virtual power plants. for Virtual Power Plant The upper limit on the amount of power that can be purchased from the power grid. for Virtual Power Plant The upper limit on the amount of power sold to the grid; for Virtual Power Plant The flag indicating that electricity is purchased from the grid is set to 1, representing a virtual power plant. exist A value of 0 indicates a virtual power plant that constantly purchases electricity from the grid. exist It sells electricity to the grid at all times.

5. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 4, characterized in that, The operational optimization constraints for multiple types of adjustable resources include operational constraints for at least one of electric vehicles, flexible loads, stationary energy storage systems, temperature-controlled loads, and urban water supply systems; wherein, the flexible loads include transferable loads and loads that can be reduced.

6. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 2, characterized in that, In step S4, the multi-agent master-slave game optimization model is a Stackelberg master-slave game model. The upper layer uses the differentiated pricing strategy formulated by the active distribution network operator as the decision variable, and the lower layer uses the electricity consumption response strategy corresponding to each virtual power plant as the decision variable. The upper and lower layer agents achieve bidirectional interaction through price signals. Its master-slave game model is constructed through the following formula: ； ； ； ； ； In the above formula, This represents a multi-agent participation model under master-slave game optimization; Indicates the leader of the game; Indicates a follower in the game. The price strategy of the game leader; This indicates the electricity consumption strategy of each virtual power plant; This indicates that DSO is in equilibrium electricity price To maximize profits, This indicates that each follower follows a balanced electricity consumption strategy. To obtain the maximum benefit; For distribution network operators Electricity sales price, This indicates the electricity purchase price for each virtual power plant; For distribution network operators The benefits, This represents the benefits of each virtual power plant; Let be the payoff function of the game leader. For the first The payoff function of a follower in a game; , This represents the optimal price strategy and electricity consumption strategy at game equilibrium.

7. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 6, characterized in that, In step S5, a two-level iterative optimization algorithm is used to solve the multi-agent master-slave game optimization model. The specific solution steps include: S51: The upper-level active distribution network operator initializes the differentiated pricing strategy and distributes it to each lower-level virtual power plant; S52: Each lower-level virtual power plant, based on the received pricing strategy, solves the optimal electricity consumption response strategy with the goal of maximizing its own revenue, and feeds back the optimal electricity consumption strategy to the upper-level active distribution network operator. S53: Upper-level active distribution network operators update differentiated pricing strategies based on feedback electricity consumption response data; S54: Iterate through steps S52-S53 until the iterative changes in the upper-level price strategy and the lower-level electricity consumption strategy are both less than the preset convergence threshold. Then, determine that the game has reached equilibrium and output the optimal differentiated incentive price. The preset convergence threshold is a pre-set iteration accuracy control parameter used to ensure that the solution results of the price strategy and electricity consumption strategy meet the accuracy requirements of engineering applications.

8. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 2, characterized in that, The electricity purchase price and demand response incentive price for each type of virtual power plant satisfy the following independent constraints: ； In the formula, This serves as a identifier for the virtual power plant type. For the first Virtual power plants The electricity price at any given time; , The first Minimum and maximum threshold values ​​for electricity purchase prices for virtual power plants; For the first Virtual power plants Incentive pricing based on demand response at any given moment; , The first Minimum and maximum threshold values ​​for the demand response incentive price of virtual power plants.

9. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 3, characterized in that, The calculation expressions for each component of the objective function of the active distribution network operator optimization model are as follows: ； in, This represents the voltage over-limit penalty coefficient, the voltage over-limit penalty coefficient The dynamic deviation calculation method is used, and the expression is as follows: ； In the formula, For time intervals; for Timetable The current, For line impedance, For the set of distribution network nodes, For load power, For electricity purchase price, For the power purchase capacity, For electricity sales price, This refers to the power output for electricity sales. and These are the upper and lower critical values ​​for the safe operation voltage of the distribution network; For node voltage, and These are the upper and lower limits of the ideal voltage for normal operation of the distribution network.

10. The method for calculating differentiated demand response electricity prices for virtual power plants based on multi-agent game theory as described in claim 6, characterized in that, In the multi-agent master-slave game optimization model, each virtual power plant aims to maximize its own net profit. The profit function expression for a single type of virtual power plant is as follows: ； In the formula: For the first Net revenue of virtual power plants; For revenue from electricity sales; Incentives for demand response; For electricity purchase costs; For internally adjustable resource control costs; proactive distribution network operators aim to minimize overall economic costs and maximize grid operation security, achieving multi-entity collaborative optimization.

Images