Park multielement market subject pricing strategy considering resource cooperation characteristics

Abstract

The invention provides a park multi-element market subject pricing strategy considering resource cooperation characteristics, and relates to the field of energy pricing strategies. The method comprises the following steps: step S1, based on the energy flow and interaction relationship of each market main body in a park, constructing a park multi-element market main body frame and a multi-main body collaborative economic optimization model; s2, introducing a logistic regression algorithm based on the park multi-element market main body framework and the multi-main body collaborative economic optimization model, constructing a load fuzzy response mechanism model, and generating a preset total energy demand; and S3, in combination with the preset total energy demand generated in the step S2, constructing and solving a master-slave game model, and realizing a park multi-market subject pricing strategy. According to the invention, the problems of insufficient resource cooperation value mining and inappropriate multi-element market subject benefit balance of the existing park pricing strategy are solved, and the maximization of the overall resource cooperation benefit of the park and the dynamic balance of the profit of each market subject are realized.

Description

Technical Field

[0001] This invention relates to the field of energy pricing strategies, and in particular to a pricing strategy for multiple market players in a park that takes into account the characteristics of resource synergy. Background Technology

[0002] With the accelerated transformation of the energy structure towards low-carbon energy and the widespread application of distributed energy technologies, modern industrial parks are evolving into complex integrated energy systems that combine multiple energy sources, such as photovoltaic power plants, wind power plants, energy storage equipment, combined heat and power units, and adjustable loads.

[0003] However, existing pricing strategies for industrial parks have significant shortcomings, mainly in two aspects: First, they lack a systematic consideration of the synergistic characteristics of multiple energy sources within the system, ignoring the additional system value that can be created through the combined output of new energy and energy storage, and the synergistic optimization of multiple energy flows such as cooling, heating, and electricity. For example, these can reduce network congestion and losses, delay investment in upgrading the transmission and distribution network, increase the local penetration rate of new energy, and enhance the resilience and flexibility of the system. Second, they fail to fully consider the interests of the diverse market players in the industrial park. Traditional methods ignore users' responsiveness to energy pricing, resulting in pricing strategies that cannot effectively guide load adjustments, leading to uneven returns for various market players and making it difficult to achieve overall optimal resource allocation and fair distribution of benefits.

[0004] Therefore, there is an urgent need for a new pricing strategy that can deeply integrate the characteristics of resource synergy and balance the interests of diverse market players, so as to solve the core problems existing in the current energy pricing strategy of the park, and thus improve the overall energy allocation efficiency and market player satisfaction of the park. Summary of the Invention

[0005] The purpose of this invention is to provide a pricing strategy for multiple market entities in a park that takes into account the characteristics of resource synergy, so as to solve the problems of insufficient exploitation of resource synergy value and improper balance of interests among multiple market entities in existing park pricing strategies, so as to maximize the overall resource synergy benefits of the park and achieve dynamic equilibrium of the income of each market entity, thereby improving the scientific nature and fairness of the pricing strategy.

[0006] To achieve the above objectives, this invention provides a pricing strategy for multiple market entities in a park that considers resource synergy characteristics, comprising the following steps: Step S1: Based on the energy flow and interaction relationships among various market entities in the park, construct a multi-market entity framework and a multi-entity collaborative economic optimization model for the park. Step S2: Based on the framework of multiple market entities in the park and the multi-entity collaborative economic optimization model, the logistic regression algorithm is introduced to construct a load fuzzy response mechanism model and generate the preset total energy demand. Step S3: Combining the preset total energy demand generated in Step S2, construct and solve the master-slave game model to realize the pricing strategy of multiple market entities in the park.

[0007] Preferably, step S1 includes: Step S101: Construct a framework for diversified market entities in the park and a multi-entity collaborative economic optimization model; Step S102: Set the constraints of the multi-agent collaborative economic optimization model, including the constraints of the PO model and the constraints of the UC model.

[0008] Preferred multi-agent collaborative economic optimization models include the PO model and the UC model; The specific expression for the objective function of the PO model is: ; ; in, This indicates the net income of the PO within one day; This represents the total number of time periods in a day. ; Represents a collection of energy sources. ; Indicates electrical energy; Indicates thermal energy; Indicates cold energy; Indicates gas energy; Indicates energy; for Time period PO to UC's energy The price; for During the period, PO sold energy to UC. The power; for During the period, PO participates in the energy market established by the park's external energy market. The price; for During the period, PO participates in the sale of energy in the external energy market of the park. The power; This represents the cost of a PO exchanging energy with an external energy market; This represents the set of energy sources for PO. ; This indicates that the electrical energy comes from the power distribution system; This indicates that the electricity comes from the wind turbines in the industrial park; This indicates that the heat energy comes from the heating system. This indicates that the gas energy comes from a natural gas system; Indicates the energy source of PO; express Period PO from energy source The price of obtaining energy; express Time Period PO and Energy Source The power of energy exchange; This indicates the penalty fee for heating interruption.

[0009] Preferably, the expression for the user optimization objective function of the UC model is: ; ; in, express Cost of UC power comfort during specific time periods; express Cost of cooling comfort during UC time period; express Cost of comfort during UC gas usage during specific time periods; express Cost of thermal comfort during UC use; express The cost of UC purchasing energy from PO during the specified period; express The electrical load used for heating in electric-to-heat conversion equipment during certain time periods; express Fixed electrical load during specific time periods; express The electrical load can be shifted during certain time periods; This represents the preference coefficient for consuming electricity; This represents a coefficient that affects electricity consumption; This represents the preference coefficient for using cold; This represents the total cooling load of UC during time period t; This represents the coefficient that affects cooling capacity; This represents the gas preference coefficient; This represents the total gas load of UC during time period t; This represents a coefficient that affects gas consumption. This represents the heat preference coefficient; This represents the total heat load of UC during time period t; This represents the coefficient that affects heat consumption; This represents UC's revenue.

[0010] Preferably, the constraints of the PO model include energy source constraints, power balance constraints, energy coupling device output constraints, energy storage constraints, and pricing constraints.

[0011] Preferably, the constraints of the UC model include electrical load constraints, gas load constraints, heat load constraints, and cooling load constraints.

[0012] Preferably, step S2 includes: Step S201: Based on multi-source historical data, generate the predicted output data of preset energy sources using the user energy load prediction method; Step S202: Based on the logistic regression algorithm, construct a load fuzzy response mechanism model to generate the preset total energy demand.

[0013] Preferably, the specific expression for the logistic regression algorithm is as follows: ; in, Indicates the load transfer rate; Indicates the electricity price difference; , , This represents the known quantities in the logistic regression algorithm; Indicates a variable parameter; Represents the numerical value of the natural constant.

[0014] Preferably, step S3 includes: Step S301: Based on the balanced interests of various market entities in the park, construct a master-slave game model; Step S302: Based on the Hessian matrix, prove the existence and uniqueness of the game equilibrium solution of the master-slave game model, and then realize the pricing strategy of multiple market entities in the park.

[0015] Preferably, the conditions for determining the existence and uniqueness of the equilibrium solution in the master-slave game model include: Condition 1: The set of decision variables of each game participant within their range of values ​​is a non-empty, compact convex subset. Condition 2: The payoff function of the follower is a continuous convex function within the scope of its policy set.

[0016] Therefore, the present invention adopts the above-mentioned pricing strategy for multiple market entities in the park that considers resource synergy characteristics, and the beneficial technical effects are as follows: First, by constructing a multi-entity collaborative economic optimization model for park operators and user clusters, the additional system value created by resource synergy characteristics such as joint output of new energy and energy storage, and collaborative optimization of multiple energy flows such as cooling, heating and power (e.g., reducing network congestion and loss, delaying investment in power transmission and distribution network upgrades, increasing the penetration rate of new energy and system flexibility) is systematically quantified and integrated into the pricing mechanism for the first time, overcoming the limitation of traditional pricing models that ignore the value of resource synergy. Secondly, an innovative load fuzzy response mechanism model based on logistic regression algorithm is introduced. By combining users' fuzzy response behavior and uncertainty to energy pricing, a more realistic load transfer rate is generated and the predicted output data of the scheduled energy is corrected. This avoids pricing deviations caused by ignoring user response needs and makes the pricing strategy more in line with actual energy demand. Finally, through the game equilibrium of the master-slave game model, while ensuring reasonable profits for operators, the needs of users to reduce energy costs and maintain energy stability are met, thereby achieving a synergistic improvement in the overall energy allocation efficiency of the park and the satisfaction of market entities, and significantly enhancing the scientific nature and fairness of the pricing strategy. Attached Figure Description

[0017] Figure 1 A schematic diagram of the framework for diverse market entities in the park; Figure 2 Pre-set the predicted energy output data diagram for the park's integrated energy system; Figure 3 The total energy demand for the park's integrated energy system is pre-set; Figure 4 A schematic diagram of a master-servant game mechanism; Figure 5 Flowchart for solving the master-slave game model; Figure 6 The price for energy sold by PO. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example 1 A pricing strategy for multiple market players in a park that considers resource synergy includes the following steps: Step S1: Based on the energy flow and interaction relationships among various market entities in the park, construct a framework for diversified market entities and a multi-entity collaborative economic optimization model for the park.

[0021] Step S101: Construct a framework for diversified market entities in the park and a multi-entity collaborative economic optimization model.

[0022] like Figure 1As shown, this embodiment divides the park's integrated energy system into Park Operators (POs) and User Clusters (UCs) based on the functional attributes of each market entity. POs include combined heat and power (CHP) units, photovoltaic power plants, natural gas stations, energy storage devices, wind power plants, and other equipment. On the one hand, POs participate in external energy market transactions, interacting bidirectionally with the upstream energy grid to purchase and sell energy; on the other hand, they supply various energy sources, including electricity, heat, cooling, and gas, to UCs. UCs, comprising 5 commercial buildings and 10 residential communities, purchase energy by issuing integrated demand responses to POs. In terms of market interaction, POs set energy prices and regulate supply through flexible pricing, energy management, grid interaction, and supply-demand balancing. UCs, based on integrated demand responses, comprehensively consider factors such as energy purchase costs, user comfort, and supply reliability, forming a market interaction relationship with POs linked by price and power, ultimately achieving efficient operation of the park's integrated energy system and a balance of interests among all parties.

[0023] This embodiment integrates the interests of POs (Power Providers) and UCs (Users) to establish a multi-stakeholder collaborative economic optimization model. POs generate revenue by participating in energy trading in the external energy market and energy demand trading within the UCs (Users); UCs need to meet requirements such as reducing energy costs, ensuring continuous and stable power supply, and minimizing power outages.

[0024] Multi-agent collaborative economic optimization models include the PO model and the UC model.

[0025] The Property Owner (PO) manages energy by acquiring electricity, heat, and gas, and controls various devices in the park's integrated energy system for energy conversion. It also participates in external energy market transactions and supplies four types of energy (electricity, heat, cooling, and gas) to the Universal Energy Controller (UC), ultimately setting energy prices. The PO's optimization objective for energy management, market transactions, and energy price setting within a single day is to maximize net profit. Therefore, the specific expression of the objective function of the constructed PO model is: (1); (2); in, This indicates the net income of the PO within one day; This represents the total number of time periods in a day. ; Represents a collection of energy sources. ; Indicates electrical energy; Indicates thermal energy; Indicates cold energy; Indicates gas energy; Indicates energy; for Time period PO to UC's energy The price; for During the period, PO sold energy to UC. The power; for During the period, PO participates in the energy market established by the park's external energy market. The price; for During the period, PO participates in the sale of energy in the external energy market of the park. The power; This represents the cost of a PO exchanging energy with an external energy market; This represents the set of energy sources for PO. ; This indicates that the electrical energy comes from the power distribution system; This indicates that the electricity comes from the wind turbines in the industrial park; This indicates that the heat energy comes from the heating system; This indicates that the gas energy comes from a natural gas system; Indicates the energy source of PO; express Period PO from energy source The price of obtaining energy; express Time Period PO and Energy Source The power of energy exchange; This indicates the penalty fee for heating interruption.

[0026] The Consumer Utility (UC) is the service recipient of the Product Owner (PO) and is subject to indirect energy management by the PO. It adjusts its initial load according to the various energy prices set by the PO to optimize its overall utility. In integrated demand response, different energy sources can coordinate and complement each other. Considering energy substitution can alleviate the operational pressure on the integrated energy system while increasing the flexibility of resource allocation. Users have installed power-to-heat (PTH) and power-to-cool (HRT) equipment. There are two methods to obtain heat and cold energy: the first is to directly purchase the required heat and cold energy from the PO; the second is to purchase electricity, which is then converted into heat and cold energy through the PTH and HRT equipment to meet energy needs. The user optimization objective of the UC model is to maximize consumer surplus, i.e., the difference between the UC's energy comfort cost and its energy cost. Therefore, the expression for the user optimization objective function of the UC model is: (3); (4); in, express Cost of UC power comfort during specific time periods; express Cost of cooling comfort during UC time period; express Cost of comfort during UC gas usage during specific time periods; express Cost of thermal comfort during UC use; express The cost of UC purchasing energy from PO during the specified period; express The electrical load used for heating in electric-to-heat conversion equipment during certain time periods; express Fixed electrical load during specific time periods; express The electrical load can be shifted during certain time periods; This represents the preference coefficient for consuming electricity; This represents a coefficient that affects electricity consumption; This represents the preference coefficient for using cold; This represents the total cooling load of UC during time period t; This represents the coefficient that affects cooling capacity; This represents the gas preference coefficient; This represents the total gas load of UC during time period t; This represents a coefficient that affects gas consumption. This represents the heat preference coefficient; This represents the total heat load of UC during time period t; This represents the coefficient that affects heat consumption; This represents UC's revenue.

[0027] Step S102: Set the constraints of the multi-agent collaborative economic optimization model, including the constraints of the PO model and the constraints of the UC model.

[0028] To ensure the scientific validity and practicality of the multi-agent collaborative economic optimization model in the industrial park, the following constraints define clear operational boundaries for the model, preventing imbalances in the integrated energy system or extreme imbalances in interests due to unconstrained strategy adjustments. The constraints are divided into two categories: PO model constraints and UC model constraints, as detailed below: The constraints of the PO model include energy source constraints, power balance constraints, output constraints of energy coupling devices, energy storage constraints, and pricing constraints, as detailed below: The specific expression for the energy source constraint is: (5); in, express Time Period PO and Energy Source The lower limit of the power for energy exchange; express Time Period PO and Energy Source The upper limit of the power for energy exchange.

[0029] Power balance constraints include electrical power balance constraints, gas power balance constraints, thermal power balance constraints, and cold power balance constraints.

[0030] The specific expression for the power balance constraint is: (6); in, express The power generation capacity of wind turbines during a given time period; express The exchange power of the electrical energy storage during a given period, a positive value indicates that the electrical energy storage is in the energy release state, and a negative value indicates that the electrical energy storage is in the energy storage state; express The electrical power output of the combined heat and power unit during a given period; This indicates the electrical power consumed by the air conditioner; This indicates the energy efficiency ratio of the air conditioner; for The amount of electricity sold by PO to UC during the specified time period.

[0031] The specific expression for the gas power balance constraint is: (7); in, express Gas production capacity of the gas tank during a given time period; express The exchange power of gas energy storage during a given period, a positive value indicates that the gas energy storage is in the energy release state, and a negative value indicates that the gas energy storage is in the energy storage state; This indicates the conversion efficiency of a combined heat and power (CHP) unit from gas to electricity. express The thermal power output of the gas-fired boiler during a given period; This indicates the conversion efficiency of the gas-fired boiler; for The power of gas sold by PO to UC during the specified time period.

[0032] The specific expression for the thermal power balance constraint is: (8); in, express The exchange power of thermal energy storage during a given period, a positive value indicates that the thermal energy storage is in the energy release state, and a negative value indicates that the thermal energy storage is in the energy storage state; express The thermal power output of the combined heat and power unit during the specified time period; express The cooling power output of the lithium bromide absorption chiller during the specified time period; This indicates the coefficient of performance (COP) of a lithium bromide absorption chiller. for The power of heat energy sold by PO to UC during the specified time period.

[0033] The specific expression for the cold power balance constraint is: (9); in, express The exchange power of cold energy storage during a given period, a positive value indicates that the cold energy storage is in the energy release state, and a negative value indicates that the cold energy storage is in the energy storage state; express The cooling power output of the air conditioner during different time periods; for The power of cooling energy sold by PO to UC during the specified time period.

[0034] The specific expression for the output constraint of the energy coupling device is: (10); in, This represents a collection of energy coupling devices within the park; This refers to the power generation section of a combined heat and power (CHP) unit; This refers to the heat-generating section of a combined heat and power (CHP) unit. Indicates air conditioner; This refers to a lithium bromide absorption chiller; Indicates a gas-fired boiler; express The lower limit of the output power of each energy coupling device during the time period; express The upper limit of the output power of each energy coupling device during the time period; express Output power of each energy coupling device during the time period.

[0035] Assuming that the energy storage device can only be in one of two states—energy storage or energy release—within a certain time period, and that the power remains constant during that time period, then the specific expression for the energy storage constraint is: (11); (12); (13); in, express Energy storage capacity for time-of-use electrical energy storage; express Energy storage capacity for time-of-use electrical energy storage; This represents the self-loss coefficient of electrical energy storage; This indicates the energy release efficiency of electrical energy storage; express The lower limit of energy storage capacity for time-of-use energy storage; express The upper limit of energy storage capacity for time-of-use electrical energy storage; express The lower limit of the exchange power of time-limited electrical energy storage; express The upper limit of the exchange power of time-limited energy storage.

[0036] When setting energy prices for the universal market (UC), producers (POs) are subject to pricing constraints to prevent them from excessively inflating prices to obtain high profits. These pricing constraints include period-specific price constraints and average price constraints, specifically: The specific expression for the price constraint for each time period is: (14); in, express Time-of-use energy The lower limit of the price; express Time-of-use energy The upper limit of the price.

[0037] The specific expression for the average price constraint is: (15); in, express Time-of-use energy The upper limit of the average price; express Time-of-use energy The price.

[0038] The constraints of the UC model include electrical load constraints, gas load constraints, heat load constraints, and cooling load constraints, as detailed below: Electrical load is divided into mobile load and stationary load. Mobile load is sensitive to electricity prices and can be moved from high-price periods to low-price periods according to the needs of the UC (Unified Energy Controller). Stationary load is the load that meets the basic needs of the UC in each period and cannot be moved between periods. Due to the influence of PO (Power Output) indirect energy management, the UC must meet load constraints when adjusting its own load. The specific expression of the load constraints is as follows: (16); (17); (18); in, express The mobile electrical load for time period UC, when the value is positive, indicates that the mobile electrical load has been transferred from other time periods to [the specified time period]. The time period, when the value is negative, indicates that the movable electrical load is from The time slot is transferred to another time slot; express Fixed electrical load during time period UC; express Lower limit of mobile power load for the time period UC; express The maximum allowable mobile electrical load for the time period UC.

[0039] The specific expression for the gas load constraint is: (19); (20); (twenty one); in, express The mobile gas load for time period UC, with a positive value indicating that the mobile gas load has been transferred from other time periods to [the specified time period]. During a given time period, a negative value indicates that the movable gas load has shifted from... The time slot is transferred to another time slot; express Fixed gas load of UC during the time period; express Lower limit of mobile gas load for the UC period; express The maximum movable gas load for the time period UC.

[0040] In the UC model constructed in this embodiment, the heat load is mainly considered as the hot water load. UC has explicit comfort requirements for water temperature, manifested as upper and lower limits of the acceptable water temperature range. Furthermore, the constraint on water temperature can be represented by the hot water load power required to maintain a certain water temperature, i.e., the heat load constraint, as specifically expressed below: (twenty two); (twenty three); (twenty four); in, , These represent the specific heat capacity and density of water, respectively. Indicates in The volume of cold water added during the specified time period; Indicates the lowest acceptable water temperature for UC; This indicates the highest acceptable water temperature for UC. This indicates the initial water temperature of the UC. express The lower limit of acceptable hot water load power for the UC period; express The maximum acceptable hot water load power for the UC period; express The actual hot water load power of UC during the time period.

[0041] Similarly, UC also has a comfortable range for cooling temperature, which can be constrained by upper and lower limits. Therefore, the cooling load power required to maintain a certain cooling temperature must meet the cooling load constraint, specifically expressed as: (25); (26); (27); in, Indicates the thermal resistance of building materials; This indicates the lowest acceptable room temperature for UC. This indicates the highest acceptable room temperature for UC; express Outdoor temperature during the time period; express The lower limit of acceptable cooling load power for the UC period; express The maximum acceptable cooling load power for the UC period; express The actual cooling load power of UC during the time period.

[0042] Step S2: Based on the framework of multiple market entities in the park and the multi-entity collaborative economic optimization model, the logistic regression algorithm is introduced to construct a load fuzzy response mechanism model and generate the preset total energy demand.

[0043] Step S201: Based on multi-source historical data, generate the predicted output data of preset energy through the user energy load prediction method.

[0044] Before participating in external energy market transactions and internal UC energy sales, the PO needs to obtain the projected output data of preset energy sources for each time period based on multi-source historical data. This multi-source historical data includes historical electricity price data, historical heat price data, historical cooling price data, historical gas price data, historical UC energy load data, and time-of-use energy price information. Preset energy refers to the electricity, heat, cooling, and gas energy that the PO plans to supply in advance to meet UC energy demand. The projected output data of preset energy sources includes projected electricity output data, projected heat output data, projected cooling output data, and projected gas output data. Simultaneously, using user energy load forecasting methods, such as the Autoregressive Integral Moving Average (ARIMA) model, recurrent neural network models, and exponential smoothing, the output of preset energy sources for different time periods is predicted, thus obtaining the projected output data for each time period, such as... Figure 2 As shown.

[0045] Step S202: Based on the logistic regression algorithm, construct a load fuzzy response mechanism model to generate the preset total energy demand.

[0046] Based on the predicted output data of the preset energy in step S201, and considering the UC load response demand, the predicted output data of the preset energy needs to be corrected using a load fuzzy response mechanism model. The load fuzzy response mechanism model uses a logistic regression algorithm to simulate the load response behavior of UC under different energy prices, and obtains the preset total energy demand in each time period.

[0047] The load fuzzy response mechanism model is based on the fuzzy response behavior of UC to time-of-use electricity prices, time-of-use heat prices, time-of-use cooling prices, and time-of-use gas prices. It quantifies the psychological reaction and uncertainty of UC to changes in time-of-use electricity prices, time-of-use heat prices, time-of-use cooling prices, and time-of-use gas prices. It is modeled and analyzed through fuzzy logic to guide UC to perform load transfer, thereby optimizing PO's pricing strategy.

[0048] This embodiment takes time-of-use electricity pricing as an example to construct a load fuzzy response mechanism model for time-of-use electricity pricing. The construction method of this model is also applicable to scenarios such as time-of-use heat pricing, time-of-use cold pricing, and time-of-use gas pricing. Its principle and implementation process are consistent with the load fuzzy response mechanism model for time-of-use electricity pricing described here, and will not be repeated here.

[0049] A fuzzy load response mechanism model for time-of-use (TOU) electricity pricing is constructed using a logistic regression algorithm to guide UC (Unified Consumer) load transfer based on TOU pricing. The resulting total electricity demand will serve as the pricing basis for PO (Unified Producer) and will be used to predict pricing strategies for both external energy market transactions and internal UC electricity sales within the park. Specifically, the fuzzy load response mechanism model for TOU electricity pricing first calculates the load transfer rate for each time period, and then uses this rate to correct the predicted power output data, thus obtaining the total electricity demand for each time period.

[0050] For example, a certain industrial park's UC (Universal Utility Unit) includes both community and commercial users, whose electricity demand dynamically changes with electricity price fluctuations at different times. A load fuzzy response mechanism model for time-of-use pricing simulates user load response behavior using a fuzzy response mechanism and adjusts the predicted power output data accordingly.

[0051] Assuming that during the T1-T2 period, multi-source historical data indicates that UC's electricity consumption will decrease by 20% to 40% during periods of high electricity prices, 20% is set as the pessimistic response prediction value, and 40% as the optimistic response prediction value. The load transfer rate refers to the ratio of load transferred from one time period to another. For each time period, optimistic response transfer rates based on optimistic response prediction values ​​and pessimistic response prediction values ​​are calculated. The load transfer rate value falls between the optimistic and pessimistic response transfer rates. The fuzzy response mechanism model for time-of-use electricity pricing uses the load transfer rate to correct the predicted power output data.

[0052] If the predicted power output data for the T1~T2 period is 200MW and the load transfer rate for the T1~T2 period is 10%, the corrected predicted power output data is calculated, that is, the preset total power demand is reduced by 10% and adjusted to 180MW.

[0053] The specific expression for the logistic regression algorithm involved in this embodiment is as follows: (28); in, Indicates the load transfer rate; Indicates the electricity price difference; , , This represents the known quantities in the logistic regression algorithm; Indicates a variable parameter; Represents the numerical value of the natural constant.

[0054] The actual load transfer rate for each time period is calculated using the following expression: (29); (30); in, , This indicates the dividing point for different regions of electricity price differences; Indicates the actual load transfer rate; Indicates the membership degree of an optimistic response; This represents the minimum load transfer rate predicted by the pessimistic response. This represents the maximum load transfer rate predicted by the optimistic response.

[0055] After calculating the actual load transfer rates corresponding to the "peak-to-flat" and "flat-to-valley" periods for time-of-use pricing, the load transfer amount and load value after UC (Unified Utility Capacity) participates in demand response can be further calculated. The specific expression is as follows: (31); (32); in, Indicates peak electricity price periods; Indicates the off-peak electricity price period; Indicates the normal electricity price period; This represents the average load during the peak period before the implementation of time-of-use pricing; This represents the average load during normal periods before the implementation of time-of-use pricing; These represent the average load during off-peak hours before the implementation of time-of-use pricing; express The amount of load transfer during the period that takes into account demand response; express Load values ​​before the implementation of time-of-use pricing; express Load values ​​after time-of-use pricing is implemented during certain time periods; This indicates the actual load shift rate during the "peak-to-flat" period of electricity pricing; This indicates the actual load shift rate during the "flat-to-valley" period of electricity price.

[0056] After calculating the load transfer rate for each time period based on the time-of-use electricity price, the predicted power output data is corrected to obtain the preset total power demand. The construction and calculation method of the above-mentioned fuzzy load response mechanism model based on time-of-use electricity prices is also applicable to heat, cooling, and gas energy. The corresponding load transfer rate needs to be calculated based on their respective time-of-use prices, and the predicted power output data of the corresponding preset energy sources needs to be corrected accordingly. The final result is as follows: Figure 3 The total energy demand of the park's integrated energy system, which includes electricity, heat, cooling, and gas, is shown in the figure.

[0057] Step S3: Combining the preset total energy demand generated in Step S2, construct and solve the master-slave game model to realize the pricing strategy of multiple market entities in the park.

[0058] Step S301: Based on the balanced interests of various market entities in the park, and combined with the preset total energy demand generated in step S2, construct a master-slave game model.

[0059] The Power Provider (PO) and the Universal Utility (UC) establish a relationship of mutual benefit through a master-slave game mechanism. The PO provides various energy supply services to the UC and sets energy prices, while the UC provides load adjustment feedback to the PO. The master-slave game mechanism is as follows: Figure 4 As shown.

[0060] The master-slave game model proposed in this embodiment is applicable to various energy types, including electrical, thermal, cold, and gas energy, in terms of both structure and solution method. Although Figure 5 This example uses electricity as an example to illustrate the specific process of solving the master-slave game model, but the decision iteration and game equilibrium determination mechanism shown are also applicable to thermal energy, cold energy, and gas energy. In practical applications, it is necessary to construct corresponding master-slave game models for each type of energy and solve them collaboratively.

[0061] In each phase of the master-follower game, the Producer (PO) acts as the leader, aiming to maximize net profit. This is achieved through energy management, exchanging energy with external entities, adjusting the operating status of its own equipment, and setting energy prices for the User (UC). The User (UC) acts as the follower, adjusting its energy load based on the energy price offered by the PO. This adjusted load, in turn, influences the PO's strategy. The two sides continuously interact in this game. When the game reaches equilibrium, any unilateral change in the strategy of one market participant cannot alter the strategies of the other participants; that is, no market participant intends to deviate from the equilibrium state.

[0062] Based on the preset total energy demand (including electricity, heat, cooling, and gas) obtained in step S202, a suitable initial energy management and pricing strategy for the Public Provider (PO) is provided to enable the PO to make decisions with the goal of maximizing net revenue. The decision results are then provided to the Utility Controller (UC), which makes decisions with the goal of maximizing the overall utility function. The PO's net revenue and UC's overall utility are calculated, and it is determined whether a game equilibrium solution can be reached. If a game equilibrium solution is reached, the equilibrium result and related data are output. Otherwise, if a game equilibrium solution is not reached, the PO's energy management and pricing strategy is readjusted, and the cycle of "PO decision → UC decision → calculation of PO net revenue and UC overall utility" is repeated until the game equilibrium solution condition is met. The flowchart for solving the master-slave game model is as follows: Figure 5 As shown.

[0063] Step S302: Based on the Hessian matrix, prove the existence and uniqueness of the game equilibrium solution of the master-slave game model, and then realize the pricing strategy of multiple market entities in the park.

[0064] The equilibrium solution of the master-slave game model exists and is unique when the following conditions are met: Condition 1: The set of decision variables of each game participant within their range of values ​​is a non-empty, compact convex subset. Condition 2: The follower's payoff function (in this example, maximizing consumer surplus) is a continuous convex function within its policy set.

[0065] For condition 1, the decision variables of PO and UC are constrained by formulas (5) to (15) and (16) to (27) respectively. Their value ranges are all bounded, non-empty, continuous, and closed convex subsets. Therefore, the master-slave game model satisfies condition 1.

[0066] For condition 2, considering maximizing consumer surplus for UC, let UC be... Perform second derivative. Because... It contains multiple types of energy, making If the object to be differentiated in the second step is a known derivative, then the specific expression for the second derivative is: (33); in, Represents partial derivatives; This indicates UC's revenue; express During the period, PO sold energy to UC. The power; Indicates an index variable; , This indicates a positive parameter.

[0067] for Due to , Since it is a positive value, Hengxiao 0; For In this situation, It is always equal to 0. And... Hessian matrix The specific expression is: (34); therefore, Hessian matrix Negative fixed, There exists a unique maximum point, which is the optimal energy consumption point with respect to the PO energy sales price. From this, we can obtain the function... Since it is a continuous convex function, the master-slave game model satisfies condition 2.

[0068] In summary, the master-slave game model constructed in this embodiment satisfies all the conditions for the existence and uniqueness of the game equilibrium solution. By solving the master-slave game model, the demands for balancing the interests of various market players in the park are met, the energy sales price of PO is obtained, and the pricing strategy of multiple market players in the park is realized, such as... Figure 6 As shown.

[0069] Therefore, the present invention adopts the above-mentioned pricing strategy for multiple market entities in the park that takes into account the characteristics of resource synergy, which solves the problems of insufficient mining of resource synergy value and improper balance of interests of multiple market entities in the existing park pricing strategy. It realizes the maximization of the overall resource synergy benefits of the park and the dynamic balance of the income of each market entity, and improves the scientificity and fairness of the pricing strategy.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, characterized in that, Includes the following steps: Step S1: Based on the energy flow and interaction relationships among various market entities in the park, construct a multi-market entity framework and a multi-entity collaborative economic optimization model for the park. Step S2: Based on the framework of multiple market entities in the park and the multi-entity collaborative economic optimization model, the logistic regression algorithm is introduced to construct a load fuzzy response mechanism model and generate the preset total energy demand. Step S3: Combining the preset total energy demand generated in Step S2, construct and solve the master-slave game model to realize the pricing strategy of multiple market entities in the park.

2. The pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 1, is characterized in that... Step S1 includes: Step S101: Construct a framework for diversified market entities in the park and a multi-entity collaborative economic optimization model; Step S102: Set the constraints of the multi-agent collaborative economic optimization model, including the constraints of the PO model and the constraints of the UC model.

3. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 2, is characterized in that... Multi-agent collaborative economic optimization models include the PO model and the UC model; The specific expression for the objective function of the PO model is: ； ； in, This indicates the net income of the PO within one day; This represents the total number of time periods in a day. ; Represents a collection of energy sources. ; Indicates electrical energy; Indicates thermal energy; Indicates cold energy; Indicates gas energy; Indicates energy; for Time period PO to UC's energy The price; for During the period, PO sold energy to UC. The power; for During the period, PO participates in the energy market established by the park's external energy market. The price; for During the period, PO participates in the sale of energy in the external energy market of the park. The power; This represents the cost of a PO exchanging energy with an external energy market; This represents the set of energy sources for PO. ; This indicates that the electrical energy comes from the power distribution system; This indicates that the electricity comes from the wind turbines in the industrial park; This indicates that the heat energy comes from the heating system. This indicates that the gas energy comes from a natural gas system; Indicates the energy source of PO; express Period PO from energy source The price of obtaining energy; express Time Period PO and Energy Source The power of energy exchange; This indicates the penalty fee for heating interruption.

4. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 3, is characterized in that... The expression for the user optimization objective function of the UC model is: ； ； ； ； ； in, express Cost of UC power comfort during specific time periods; express Cost of cooling comfort during UC time period; express Cost of comfort during UC gas usage during specific time periods; express Cost of thermal comfort during UC use; express The cost of UC purchasing energy from PO during the specified period; express The electrical load used for heating in electric-to-heat conversion equipment during certain time periods; express Fixed electrical load during specific time periods; express The electrical load can be shifted during certain time periods; This represents the preference coefficient for consuming electricity; This represents a coefficient that affects electricity consumption; This represents the preference coefficient for using cold; This represents the total cooling load of UC during time period t; This represents the coefficient that affects cooling capacity; This represents the gas preference coefficient; This represents the total gas load of UC during time period t; This represents a coefficient that affects gas consumption. This represents the heat preference coefficient; This represents the total heat load of UC during time period t; This represents the coefficient that affects heat consumption; This represents UC's revenue.

5. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 2, is characterized in that... The constraints of the PO model include energy source constraints, power balance constraints, output constraints of energy coupling devices, energy storage constraints, and pricing constraints.

6. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 2, is characterized in that... The constraints of the UC model include electrical load constraints, gas load constraints, heat load constraints, and cooling load constraints.

7. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 1, is characterized in that... Step S2 includes: Step S201: Based on multi-source historical data, generate the predicted output data of preset energy sources using the user energy load prediction method; Step S202: Based on the logistic regression algorithm, construct a load fuzzy response mechanism model to generate the preset total energy demand.

8. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 7, is characterized in that... The specific expression for the logistic regression algorithm is: ； in, Indicates the load transfer rate; Indicates the electricity price difference; , , This represents the known quantities in the logistic regression algorithm; Indicates a variable parameter; Represents the numerical value of the natural constant.

9. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 1, is characterized in that... Step S3 includes: Step S301: Based on the balanced interests of various market entities in the park, construct a master-slave game model; Step S302: Based on the Hessian matrix, prove the existence and uniqueness of the game equilibrium solution of the master-slave game model, and then realize the pricing strategy of multiple market entities in the park.

10. A pricing strategy for multiple market entities in a park that considers resource synergy characteristics, as described in claim 9, is characterized in that... The conditions for determining the existence and uniqueness of the equilibrium solution in a master-slave game model include: Condition 1: The set of decision variables of each game participant within their range of values ​​is a non-empty, compact convex subset. Condition 2: The payoff function of the follower is a continuous convex function within the scope of its policy set.

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