Multi-agent multi-energy scheduling system and method for integrated energy system

Abstract

The invention discloses a multi-agent multi-energy scheduling system and method for an integrated energy system. An operator formulates a 24-hour energy purchase and sale initial plan based on electricity price, heat price, gas price and energy storage parameters; suppliers independently bid according to wind, light, garbage amount and equipment capability; the producer responds to the energy selling price in an alliance form, and the overall energy purchasing demand and the internal P2P transaction volume are optimized; the operator iteratively updates the energy purchasing amount and the energy selling amount, and boundaries are tightened through a bisection method for rapid convergence; and finally, distributing internal settlement prices according to'multi-supply and multi-benefit 'through asymmetric Nash negotiation, and outputting energy storage, energy purchase and sale and P2P transaction plans of all parties to realize multi-agent and multi-energy closed-loop coordination. The method aims at solving the energy scheduling problem of the comprehensive energy system involving multi-energy coupling, multi-subject benefit conflict and renewable energy power generation uncertainty, and reasonable allocation of resources among different subjects of the comprehensive energy system is achieved.

Description

Technical Field

[0001] This invention relates to energy dispatching and control methods, and more particularly to a multi-entity, multi-energy dispatching system and method for integrated energy systems. Background Technology

[0002] Against the backdrop of the current energy and environmental crisis, research on integrated energy systems that primarily utilize renewable energy and couple multiple energy sources is receiving increasing attention. Integrated energy systems can achieve complementarity among electricity, heat, and gas, and are characterized by high energy utilization efficiency and the promotion of renewable energy consumption. While renewable energy sources, such as wind and solar power, are becoming increasingly widespread, their randomness and uncertainty pose significant challenges to the safe operation of integrated energy systems. Furthermore, a large amount of biomass waste resources, such as garbage and sewage, remain underdeveloped and unutilized. Simultaneously, as more and more stakeholders are involved in integrated energy systems, these stakeholders have complex interests in both horizontal and vertical dimensions. Therefore, research on the distribution of interests among multiple stakeholders in integrated energy systems that consider the uncertainties of renewable energy and biomass waste resources is of great significance.

[0003] Existing research on integrated energy system dispatching systems and methods has several main shortcomings: 1) Research on biomass waste resources has limitations. Current research on the role of biomass waste resources in integrated energy system scheduling mainly considers the overall operating cost of the system. However, with the increase in participating entities, considering only the overall operating cost cannot reflect the energy interaction behavior of internal entities, which has certain limitations.

[0004] 2) Research on the energy interaction behavior of participating entities has limitations. Current research on multi-entity energy system dispatch only considers the competitive relationship on the supply side or the cooperative relationship on the demand side, failing to comprehensively consider the energy interaction relationships within each entity, and failing to consider interaction relationships from both horizontal and vertical dimensions. Horizontal interaction refers to the relationships between multiple energy suppliers and multiple prosumers. Vertical interaction refers to the relationships between upper-level operators and lower-level energy suppliers and lower-level prosumers. This has certain limitations.

[0005] 3) Limitations in addressing uncertainties in renewable energy generation. Currently, robust optimization and stochastic optimization are mainly used to address uncertainties in renewable energy generation within multi-entity, multi-energy dispatch systems. However, both methods have limitations, which sub-Browser optimization overcomes. Furthermore, using sub-Browser optimization to solve the uncertainty problem in renewable energy generation within multi-entity, multi-energy dispatch systems only considers the supply side and neglects the demand side, thus limiting its effectiveness.

[0006] With the increasing number of participants in integrated energy system dispatching, existing dispatching systems and trading methods cannot adequately address the conflicts of interest and resource allocation issues among these stakeholders. This invention proposes a multi-stakeholder, multi-energy dispatching system for integrated energy systems, incorporating a biomass waste conversion system and considering the economic benefits of each stakeholder. It also proposes a multi-stakeholder, multi-energy trading method for integrated energy systems, considering both competitive relationships on the supply side and cooperative relationships on the demand side, as well as the competitive relationship between third-party operators and supply and demand, fully considering different trading methods among different stakeholders from both horizontal and vertical dimensions. Furthermore, it utilizes distributed bar optimization to address the uncertainty issue in renewable energy generation, comprehensively considering uncertainties on both the supply and demand sides, making it more realistic. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a multi-entity, multi-energy dispatching system and method for an integrated energy system, which aims to reasonably balance the conflicting interests of energy interactions among various entities, solve the uncertainty of renewable energy power generation on both the supply and demand sides, and realize the rational allocation of resources among different entities in the integrated energy system.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A multi-entity, multi-energy coordinated control method for an integrated energy system, wherein, Operators formulate 24-hour initial energy purchase and sale plans based on electricity prices, heat prices, gas prices, and energy storage parameters; Energy suppliers bid independently based on wind and solar power capacity, waste volume, and equipment capacity. Producers and consumers respond to energy sales prices in the form of alliances, optimizing overall energy purchase demand and internal P2P transaction volume; Operators iteratively update energy purchase and sales prices, and the boundary is tightened and converged rapidly through a binary search method; Ultimately, the internal settlement price is allocated through asymmetric Nash negotiations based on the principle of "more supply, more benefit," and the energy storage, energy purchase and sale, and P2P transaction plans of all parties are output to achieve closed-loop coordination among multiple entities and multiple energy sources.

[0009] The above technical solution includes step S1: reading the time-of-use list of external electricity price, heat price, and gas price for the 24 hours of the day, and simultaneously reading the energy storage limitation parameters of the integrated energy system operator's energy storage equipment: initial power, charge and discharge efficiency, and upper and lower capacity limits; the energy storage equipment includes at least an electrical energy storage unit, a thermal energy storage unit, and a gas energy storage unit.

[0010] The above technical solution includes step S2: reading the photovoltaic and wind power output power of each energy supplier in typical scenarios, as well as the probability value of each scenario, the availability of dry and wet waste, the flow rate of domestic sewage, and the unit price for maintenance of each piece of equipment. The above technical solution also includes step S3: reading the fixed electrical load, heat load, and gas load curves, the proportion of transferable / reducible loads, and the predicted photovoltaic and wind power generation curves for each producer and consumer.

[0011] The above technical solution includes step S4: The operator first makes a "top-level decision": Under the goal of maximizing its own profits, it calculates the energy to be purchased from various energy suppliers and the energy to be sold to producers and consumers in the next 24 hours, based on the external electricity price, heat price, gas price and the energy storage limitation parameters, and forms the first version of the plan.

[0012] The above technical solution includes step S5: the first version of the planned energy purchase is distributed to each energy supplier, and each energy supplier independently runs the "bidding module" locally: based on the amount of waste, wind and solar forecasts, and equipment capacity, each supplier calculates its own optimal energy sales price and sends it back to the operator.

[0013] The above technical solution includes step S6: distributing the first version of the planned energy sales price to each producer and consumer, and each producer and consumer receiving the price in the form of an "alliance". Under the objective of minimizing total cost, the optimal energy purchase demand of the alliance as a whole and the operator as a whole, as well as the internal P2P shared transaction volume, are calculated, and the total optimal energy purchase demand is sent back to the operator. The "alliance" form is set so that at least two producers and consumers obtain the same price.

[0014] The above technical solution includes step S7: The operator updates the energy purchase amount to each energy supplier and the energy sales price to the producer and consumer based on the optimal energy sales price of each energy supplier and the total optimal energy purchase demand of the producer and consumer, thus obtaining the second version of the plan.

[0015] The above technical solution includes step S8: the second version of the planned energy purchase is distributed to each energy supplier, and each energy supplier updates its optimal energy sales price based on maximizing its own profit and sends it back to the operator.

[0016] The above technical solution includes step S9: distributing the second version of the planned energy purchase price to each producer and consumer, and each producer and consumer, in the form of an "alliance", updating the total optimal energy purchase demand and internal P2P shared transaction volume under the goal of minimizing total cost, and sending the total optimal energy purchase demand back to the operator.

[0017] The above technical solution includes step S10: The operator updates the energy purchase price from each energy supplier and the energy sales price to the prosumers based on the energy sales price of each energy supplier and the total purchase demand of the prosumers, thus obtaining the third version of the plan; S11: The boundary of the next round of the plan is tightened by using a binary search method, taking the minimum value of the energy purchase price and the energy sales price of the first two rounds as the lower limit of the new round, and the maximum value as the upper limit of the new round, thereby quickly narrowing the search range and reducing the number of iterations; S12: Each producer-consumer receives the internal P2P transaction volume and the operator's energy sales price, and then uses asymmetric Nash negotiation to allocate the internal settlement price according to the "more supply, more benefit" mechanism, forming a 24-hour transaction price between each pair of producers-consumers; the "more supply, more benefit" mechanism is set so that the more electricity a producer-consumer provides to other producers-consumers throughout the "alliance" process, the more benefits it will receive. S13: Output the final scheduling package: The operator receives the energy storage charging and discharging plan, the external energy purchase plan, the energy sales price to producers and consumers, and the energy purchase plan from each energy supplier; each energy supplier receives the output power plan and energy sales price for each device; each producer and consumer receives the energy purchase plan from the operator and the internal P2P transaction volume and settlement price, for closed-loop execution on the next day. In the above technical solution, in addition to photovoltaic and wind power generation devices, the energy supplier also includes at least one of the following: a pyrolysis gasification unit, a wastewater treatment device, and an electric boiler; wherein, the pyrolysis gasification unit model is set to include: a waste treatment unit that generates combustible gas by consuming dry waste, an electrical energy output unit for burning combustible gas to generate electricity, and a thermal energy output unit for recovering and outputting the waste heat from the combustion of combustible gas; The wastewater treatment equipment is configured to include: a biogas output unit that converts domestic sewage and wet waste into biogas, and a biogas-to-natural gas conversion device that purifies the biogas to meet the standards for natural gas output; the electric boiler is configured to generate heat energy by consuming electrical energy. In the above technical solution, the executable sub-steps of the "bidding module" mentioned in step S5 include: Sub-step 1: Sending the daily dry waste weight into the pyrolysis gasification unit to obtain an electrical energy and heat energy production curve; Sub-step 2: Mixing wet waste and sewage and sending it into the anaerobic digester to obtain a biogas production curve; Sub-step 3: Purifying the biogas into substitute natural gas and recording the hourly available gas power; Sub-step 4: Combining gas, photovoltaic, and wind power, determining the external electricity, heat, and gas prices according to the operator's energy purchase needs, and finally transmitting the electricity price-heat price-gas price quotation curve. In the above technical solution, the total optimal energy purchase demand and internal P2P shared transaction volume in step S6 are obtained through a two-stage debunking method: Stage 1: First, calculate the energy purchase demand when the total cost of the alliance is minimized based on the output power values ​​of photovoltaic and wind power generation in typical scenarios and the initial probability values ​​corresponding to each scenario; Stage 2: Within the allowable fluctuation range of probability, find the "worst-case scenario demand" that results in the highest demand response cost, and then fine-tune the energy purchase demand obtained in Stage 1 to ensure that even if the scenario worsens, the alliance cost remains the lowest. In the above technical solution, the asymmetric Nash negotiation sub-step in step S12 includes: Sub-step A: Calculate the total electricity sent and received by each prosumer during the shared period, and calculate the "supply ratio" and "receiver ratio"; Sub-step B: Solve the internal electricity price by using the method of obtaining higher bargaining weight for prosumer members with "high supply ratio and low receiver ratio", so that the operating cost of each person after cooperation is lower than when purchasing electricity individually. In the above technical solution, the binary search sub-step S11 includes: comparing whether the third version of the plan is equal to the first version of the plan; if they are not equal, the energy purchase and sales prices in the next round are based on the second and third round plans as upper and lower limits, and the process returns to step four for recalculation; if they are equal, the energy purchase and sales prices in the next round are equal to the midpoint of the first two rounds of plans, and the process proceeds to step S5. In the above technical solution, a prediction correction step is added before executing step S1 daily: the latest predicted wind and solar power output power values ​​are read, and through sampling and scenario clustering, the output power of typical scenarios and the corresponding scenario probabilities are obtained. The weighted sum of the output power and the corresponding probabilities yields a corrected scheduling package to address the uncertainties of wind and solar power generation. In the above technical solution, the "upper-level decision" in step S4 simultaneously considers the "low-charge, high-discharge" principle of energy storage during calculation: when the price of external energy supply companies is lower than the price quoted by energy suppliers, operators prioritize purchasing external electricity-heat-gas; when the price of external energy supply companies is higher than the price quoted by energy suppliers, operators prioritize purchasing electricity-heat-gas from energy suppliers, rationally selecting energy sources. Operators purchase more energy for storage during periods of low energy prices and release energy during periods of high energy prices to reduce the amount of energy they need to purchase during those periods, thereby lowering their total energy purchase costs.

[0018] In the above technical solution, the instructions output to the energy supplier are issued in the form of "one report per hour": within each hour, the pyrolysis gasification unit, electric boiler, and biogas to natural gas conversion device operate according to the target values ​​of received electricity, heat, and gas volume. If the equipment fails, it immediately reduces the load locally and uploads the new available capacity to the operator, triggering incremental recalculation.

[0019] In the above technical solution, producers and consumers consider the "elastic load" principle of adjusting load demand based on price guidance: based on the price information provided by operators, producers and consumers adjust the load that can be reduced or transferred during peak periods of electricity, heat and gas prices, thereby adjusting the load demand at each moment and further reducing energy purchase costs.

[0020] In the above technical solution, the final output scheduling package is uploaded to the cloud interface in the form of a JSON file, which can be downloaded and directly parsed and executed by three types of terminals: operator EMS, supplier local SCADA, and consumer home energy gateway, to achieve closed-loop control.

[0021] The pyrolysis gasification unit is specifically as follows: Electrical output: The pyrolysis gasification unit consumes dry waste to produce combustible gas, which is then burned to generate electricity. The electrical output is directly proportional to the volume of combustible gas burned and is limited by the amount of dry waste processed and the minimum and maximum power generation capacity of the equipment. Thermal output: The pyrolysis gasification unit consumes dry waste to produce combustible gas, which is then burned to recover waste heat and generate heat. The thermal output is directly proportional to the power generation capacity and is related to the flue gas recovery rate. The wastewater treatment equipment model is set as follows: Biogas output: Converts domestic sewage and wet waste into biogas, which is limited by the volume of wastewater to be treated and the power consumption of the wastewater treatment equipment; The biogas to natural gas conversion device model is set as follows: Natural gas output: Purifies biogas to standard natural gas, and its natural gas output depends on the amount of biogas and the conversion efficiency of biogas to natural gas; The electric boiler model is set as follows: Heat output: The electric boiler generates heat energy by consuming electrical energy, and the heat energy output is proportional to the electrical energy input, which is limited by the power range of the electric boiler equipment.

[0022] Based on the above method, the present invention also provides: A scheduling system includes a processor, a memory, and a program stored in the memory and executable on the processor, characterized in that: when the program is executed by the processor, it implements the steps of any of the methods described above.

[0023] A computer-readable storage medium, characterized in that the storage medium stores a program, characterized in that: when the program is executed by a processor, it implements the steps of any of the methods described above.

[0024] In summary, this invention proposes a multi-entity, multi-energy dispatching system and method for an integrated energy system. The system consists of an integrated energy system operator (IESO), multiple energy suppliers (ES), and multiple producers and consumers. Furthermore, it incorporates a biomass waste conversion system into energy dispatching, aiming to achieve a rational allocation of benefits among multiple stakeholders in both horizontal and vertical dimensions, and to realize the rational utilization of biomass waste resources.

[0025] Compared with the prior art, the beneficial effects of this invention are: This invention takes into account the diversity of participating entities, the diversity of load demand, and the uncertainty of renewable energy power generation, and proposes a new integrated energy system multi-entity multi-energy dispatching system. It effectively solves the problem of conflict of interests among multiple entities from both horizontal and vertical dimensions, ensures stable energy supply, meets users' load demand, and achieves rational allocation of resources.

[0026] This invention proposes a multi-entity, multi-energy trading method for integrated energy systems, and designs a two-layer hybrid game model. Vertically, a Stackelberg game with one integrated energy system operator as the leader and multiple energy suppliers and multiple prosumers as followers is established. Horizontally, a non-cooperative game is established among the multiple energy suppliers, and a cooperative game is established among the multiple prosumers. Due to the different trading methods, different methods are chosen to address the uncertainty of renewable energy generation for both supply and demand sides.

[0027] This approach utilizes a Stackelberg game theory model with one master and multiple slaves to achieve dynamic interactions among integrated energy system operators, energy suppliers, and prosumers. Cooperative game theory facilitates dynamic interactions among multiple prosumers, while non-cooperative game theory enables dynamic interactions among multiple energy suppliers. By fully considering the dynamic interactions among different stakeholders, this method optimizes the allocation of integrated energy system resources and improves economic efficiency. Furthermore, stochastic optimization and distributed stackelberg optimization are employed to address the uncertainties surrounding renewable energy for energy suppliers and prosumers, respectively, and are nested within the aforementioned game model to achieve a balance of interests among multiple stakeholders under uncertain scenarios. This trading method can improve the economic efficiency of participating entities while meeting load demand and promote the local consumption of renewable energy.

[0028] This invention designs a fully distributed iterative algorithm. It utilizes a bisection method to solve the oscillation problem in a Stackelberg game with one master and multiple slaves through a two-level iterative solution. It couples two solution methods (alternating direction multiplier method and column and constraint generation method) to solve a two-stage distributed Stackelberg cooperative game model, achieving a fully distributed iterative solution and thus protecting the privacy of each master.

[0029] The algorithm employs a bisection method to obtain the equilibrium solution of a Stackelberg game with one master and multiple slaves, thus obtaining the optimal price for the energy supplier, the optimal purchase quantity and optimal selling price for the integrated energy operator, and the optimal purchase quantity for the prosumers, achieving the objectives of maximizing the profits of the energy supplier, maximizing the profits of the integrated energy operator, and minimizing the costs of the prosumer alliance. A two-stage distributed Stackelberg cooperative game model is solved using the alternating direction multiplier method coupled with a constraint generation method to obtain the optimal electricity trading quantity within the prosumers, minimizing the costs of individual prosumers. Then, based on asymmetric Nash negotiation, the optimal electricity trading price within the prosumers is obtained, achieving a fair distribution of the prosumer alliance's benefits. Compared to traditional centralized optimization algorithms, the algorithm proposed in this invention can effectively protect the privacy of each agent and can solve the energy dispatch problem of a comprehensive energy system involving multi-energy coupling, multi-agent interest conflicts, and uncertainties in renewable energy generation. Attached Figure Description

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a structural diagram of the integrated energy system multi-entity multi-energy dispatching system of the present invention.

[0031] Figure 2 This is an internal structural diagram of the energy supplier of the present invention.

[0032] Figure 3 This is a flowchart of the multi-entity, multi-energy trading method for the integrated energy system of the present invention.

[0033] Figure 4 The IESO electricity, heat and gas prices are as described in Embodiment 2 of the present invention.

[0034] Figure 5 This is the IESO power balance diagram (thermal energy, natural gas, and electrical energy balance diagram) of Embodiment 2 of the present invention.

[0035] Figure 6 This is a producer-consumer demand response diagram for Embodiment 2 of the present invention (the electricity, heat, and gas demand response of producer-consumer 1).

[0036] Figure 7 This is a producer-consumer demand response diagram for Embodiment 2 of the present invention (the electricity, heat, and gas demand response of producer-consumer 2).

[0037] Figure 8 This is a producer-consumer demand response diagram for Embodiment 2 of the present invention (the electricity, heat, and gas demand response of producer-consumer 3).

[0038] Figure 9 This is a graph showing the producer-consumer electricity trading volume and price in Embodiment 2 of the present invention.

[0039] Figure 10 This is a price chart of energy sales (electricity price, heat price, and gas price) for ES in Embodiment 2 of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] Example 1 like Figure 1-3 As shown in the figure, this invention proposes a multi-entity, multi-energy dispatching system for an integrated energy system, comprising an integrated energy system operator, multiple energy suppliers, multiple prosumers, a power supply company, a heating company, and a gas supply company. The multiple energy suppliers and multiple prosumers are respectively represented by sets... and express, This represents the total number of energy suppliers in the system. This represents the total number of prosumers in the system. The power supply company, connected to the integrated energy system operator, sells electricity to the operator using a time-of-use pricing mechanism; the heating company, connected to the operator, sells heat to the operator using a time-of-use heating pricing mechanism; and the gas company, connected to the operator, sells gas to the operator using a time-of-use gas pricing mechanism. The integrated energy system operator acts as a link between supply and demand. Internally, it has invested in and constructed energy storage equipment (batteries, thermal storage tanks, and gas storage tanks). It can purchase energy from energy suppliers and then sell it to prosumers to generate profits. During energy trading, it bears the risk of supply-demand imbalances. If energy supply is insufficient, it needs to purchase energy from external power supply companies, gas supply companies, and heating companies. Each energy supplier is equipped with photovoltaic, wind power, biomass waste conversion systems, and electric boilers, such as... Figure 2 As shown. The biomass waste conversion system can treat biomass waste (dry and wet waste) and domestic sewage, and is divided into three parts: a pyrolysis gasification power generation unit, an anaerobic biogas-sewage treatment unit, and a biogas-to-natural gas unit. The main difference between the various energy suppliers lies in the inconsistent maximum output power of their internal investment equipment. Each prosumer is equipped with renewable energy power generation and has multiple energy needs (electricity, heat, and natural gas), and multiple prosumers share electricity point-to-point (P2P). When the electricity produced by a prosumer meets its demand and there is still surplus, priority is given to trading electricity with other prosumers. If renewable energy cannot be fully absorbed, the surplus electricity can be sold to an integrated energy system operator. Using a collection... This represents a scheduling period. If we set a scheduling period to 24 hours, with each time interval being 1 hour, then... .

[0042] like Figure 3 As shown, another aspect of the present invention provides a multi-entity, multi-energy trading method based on the above system, the specific steps of which are as follows: The first phase includes the optimal energy trading strategy between the integrated energy system operator and the multiple energy suppliers and multiple prosumers, as well as the optimal P2P electricity sharing trading strategy among multiple prosumers: Step 1: Based on the energy purchase demand of multiple prosumers and the energy sales prices of various energy suppliers, the integrated energy system operator combines price constraints with external energy supply companies (including power supply companies, heating companies and gas supply companies), transaction volume constraints with energy suppliers, and comprehensively considers multi-energy balance constraints and energy storage equipment operation constraints. Finally, the optimal strategy for maximizing the interests of the integrated energy system operator is calculated, and the optimized decision is used as one of the inputs to multiple prosumers and multiple energy suppliers. The specific process is as follows: Step 1.1: First, input the price parameters of the external energy supply company and the parameters of the energy storage equipment. You also need to input the fixed load data of multiple producers and consumers as the initial energy purchase demand, and randomly generate a set of prices within the price constraints of the external energy supply company as the initial energy sales price of each energy supplier. Complete the parameter input. Step 1.2: Next, establish the objective function for the economic benefits of the integrated energy system operator and the dynamic model of the energy storage equipment; Specifically, the profit statement for integrated energy system operators is as follows: ; in, For the profits of integrated energy system operators; and These respectively indicate that energy will be sold to the first The profit of individual consumers and from the first The cost of purchasing energy from an energy supplier; The cost for integrated energy system operators to purchase energy from external energy suppliers; The investment cost of energy storage equipment; Index representing energy It mainly includes electricity, heat and natural gas.

[0043] Furthermore, the specific descriptions of each part are as follows: ; ; ; ; ; in, and They represent from the first The amount and price of energy purchased by an energy supplier; Indicates the first Individual consumers purchase energy from integrated energy system operators; This indicates the price at which producers and consumers purchase energy from integrated energy system operators; Indicates the first Electricity sold by individual consumers to integrated energy system operators; This indicates the price of electricity sold by producers and consumers to integrated energy system operators; This indicates the amount of energy that an integrated energy system operator purchases from external energy suppliers; This indicates the price at which integrated energy system operators purchase energy from external energy suppliers; , and These represent the investment costs of the storage battery, thermal storage tank, and gas storage tank, respectively. This represents the cost coefficient of energy storage equipment; and These respectively represent the energy storage devices in The charging power and discharging power at any given moment; Index representing energy storage devices It mainly includes storage batteries, thermal storage tanks and gas storage tanks.

[0044] The dynamic equations for the energy storage equipment of the integrated energy system operator are as follows: ; Step 1.3: Then establish the constraint models for the integrated energy system operator; The constraints on the energy storage equipment of the integrated energy system operator are as follows: ; ; ; ; in, Indicates that energy storage devices are in Energy storage at all times; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. and These represent the lower and upper limits of energy storage, respectively. and These represent the upper limits of the charging power and discharging power of the energy storage device, respectively. and These represent the stored energy at the final moment and the initial moment of the entire cycle, respectively.

[0045] The energy power balance statement of the integrated energy system operator is as follows: ; ; ; The energy price constraints of the integrated energy operator are described as follows: ; ; ; in, Is Lowest energy price at any time; Is Purchase prices from external energy suppliers at all times; It is the highest average selling price of energy.

[0046] The energy purchase constraints for the integrated energy system operator from energy suppliers are as follows: ; in, Indicates the first One energy supplier The maximum energy output at any given moment.

[0047] Step 1.4: Finally, use the solver to solve the optimization problem to obtain the optimal energy purchase and sales price of electricity, the purchase price of heat and natural gas, and the maximum profit for the integrated energy system operator.

[0048] Step 2: Each of the multiple energy suppliers solves its own optimal scheduling strategy. Each energy supplier, based on the energy purchase quota set by the integrated energy system operator, and considering its own equipment operation constraints (equipment output limits), multi-energy balance constraints, and the price constraints for selling energy to the integrated energy system operator, calculates its optimal strategy to maximize profit. Specifically, vertically, each energy supplier and the integrated energy system operator constitute a Stackelberg game, while horizontally, after receiving different energy purchase information from the integrated energy system operator, each energy supplier optimizes its own energy sales price and equipment output power. Since the equipment output power of each energy supplier is different, there is a competitive relationship between multiple energy suppliers horizontally, thus constructing a non-cooperative game.

[0049] The specific process is as follows: Step 2.1: First, input the internal equipment parameters of each energy supplier. Then, process the photovoltaic and wind power prediction data to obtain the typical scenario power and corresponding scenario probability as one of the input parameters. The processing involves sampling and generation. Historical data on renewable energy were collected, and then the generated scenarios were reduced using the K-means algorithm to obtain... Given a set of typical scenarios and their corresponding probabilities, the output power of photovoltaic and wind power can be expressed as: ; = ; in, and Represented as the first The probability of photovoltaic and wind power scenarios for each energy supplier; and Represented as the first Power output corresponding to typical photovoltaic and wind power scenarios from an energy supplier.

[0050] Step 2.2: Next, establish the objective function for the economic benefits of each of the multiple energy suppliers and the energy conversion model for internal equipment; The energy supplier's profits are described as follows: ; in, Indicates the first The profits that an energy supplier makes from selling energy to integrated energy system operators; Indicates equipment maintenance costs; Indicates the first The cost of curtailment penalties for individual energy suppliers; Furthermore, the specific descriptions of each part are as follows: ; ; in, , , and This represents the operation and maintenance cost coefficient for biogas-to-natural-gas conversion, pyrolysis gasification power generation, anaerobic biogas, and wastewater treatment equipment. As a penalty factor; For the first The amount of wind and solar power curtailed by individual energy suppliers; For the first One energy supplier The power of natural gas obtained after biogas is purified; For the first One energy supplier Electricity generation at any given moment; For the first One energy supplier Real-time biogas production; For the first One energy supplier The power consumption of the sewage treatment system at any given time.

[0051] The internal equipment model of the energy supplier is described as follows: 1) The pyrolysis gasification power generation unit converts organic waste into combustible gas under high temperature, oxygen-deficient or anaerobic conditions. The combustible gas is burned to drive an internal combustion engine to generate electricity. The high-temperature waste heat flue gas is used for heating through a waste heat boiler. The specific model is shown below: ; ; ; in, For the first One energy supplier The volume of combustible gas produced by pyrolysis and gasification at any given time; For the first One energy supplier The amount of dry waste processed at any given time; The coefficient for gasifiable dry waste; The gasification coefficient; For the efficiency of the pyrolysis furnace; and The first One energy supplier The amount of electricity and heat generated at any given moment; The power generation efficiency of the gas turbine; The calorific value of the fuel used for gasification; This refers to the heat loss rate; This refers to the flue gas recovery rate.

[0052] 2) The anaerobic biogas-sewage treatment unit uses anaerobic fermentation technology to convert organic matter in domestic sewage and wet waste into biogas. The specific model is as follows: ; ; ; in, For the first One energy supplier The volume of wastewater processed by the wastewater treatment system at any given time; For the first One energy supplier The power consumption of the wastewater treatment system at any given time; The volume factor representing the amount of wastewater treated per unit of electrical energy; The coefficient of fermentable organic matter; These are the sludge coefficient and average density after wastewater has been left to stand and settled, respectively. and These are the mass of sludge and the mass of wet waste mixed with sludge, respectively. The biogas production coefficient of the mixture of sludge and wet waste; For the first One energy supplier The amount of biogas produced at any given time.

[0053] 3) The biogas-to-natural-gas unit desulfurizes and decarbonizes the biogas (methane content approximately 60%) to meet the requirements of standard natural gas (methane content >95%) for user consumption. The model formula for biogas-to-natural-gas conversion is shown below: ; in, For the first One energy supplier The power of natural gas obtained after biogas is purified; This is the conversion coefficient for biogas to natural gas.

[0054] 4) The model of the electric boiler is shown below: ; in, and The first One energy supplier The power consumption and heat output of the electric boiler at all times; This refers to the conversion efficiency of the electric boiler.

[0055] Step 2.3: Establish constraints for each of the multiple energy suppliers, as the equipment output limits differ for different energy suppliers; The equipment output constraints for each energy supplier are described below: ; ; ; ; ; in, and The first The lower and upper limits of electricity generation from a pyrolysis gasification power generation unit of an energy supplier; and The first The lower and upper limits of power consumption for each energy supplier's wastewater treatment unit; For the first The upper limit on the electricity consumption of electric boilers of an energy supplier; and The first One energy supplier The upper limit of the daily average amount of waste processed by the pyrolysis power generation equipment and the daily average amount of sewage processed by the sewage treatment equipment.

[0056] The power balance constraints for each energy supplier (electricity, heat, and gas) are described below: ; ; ; in, and The first One energy supplier Real-time solar and wind power generation; Since the parameters of the internal equipment of the energy suppliers are different, it is not appropriate to adopt a fixed and uniform energy price. Therefore, the relationship between energy price and energy sold is as follows: ; in, For the first One energy supplier The slope of the price curve at any given time, i.e., the adjustment factor for energy prices; For the first One energy supplier The intercept of the price curve at any given time is the energy benchmark price. Since multiple energy suppliers have different adjustment coefficients for their energy prices, after receiving varying energy purchase information from the integrated energy system operator, the energy benchmark price is optimized to obtain the optimized energy sales price.

[0057] The price constraints on energy sales by various energy suppliers to integrated energy system operators are described as follows: ; ; ; in, , and These represent the highest average prices for electricity, heat, and gas, respectively.

[0058] Step 2.4: Use the solver to solve for the optimal energy sales price and maximum profit for each energy supplier.

[0059] Step 3: Based on the electricity purchase and sale price, heat and natural gas purchase and sale price set by the integrated energy system operator, the prosumer calculates the optimal strategy that minimizes the overall cost of the prosumer by combining the demand response constraints of each prosumer, the multi-energy balance constraints within each prosumer, and the P2P sharing transaction constraints of electricity within each prosumer. Specifically, vertically, considering the multi-prosumer population as a whole, in a Stackelberg game with the integrated energy system operator, the multi-prosumer population solves the total cost minimization problem. The multi-prosumer population cost minimization problem (i.e., the constructed two-stage split-Bluerberg model) is a three-level optimization problem with uncertainty. It is solved using a combination of alternating direction multiplier method coupled columns and constraint generation method. During the solution process, the optimization problem of the multi-prosumer population is further decomposed into... The block consists of subproblems, each solved using a column and constraint generation method to achieve a fully distributed solution. This represents the number of prosumers in a multi-prosumer system. In the process of treating the multi-prosumer system as a whole, a cooperative game model is established horizontally among the various prosumers to create a fully distributed peer-to-peer (P2P) energy sharing mechanism.

[0060] The specific process is as follows: Step 3.1: First, input the predicted fixed load demand and demand response parameters for multiple prosumers. Then, process the renewable energy generation output power of each prosumer to obtain the initial scenario probability as one of the input parameters. The specific process mainly uses Latin hypercube sampling to generate... Historical data on renewable energy were collected, and then the generated scenarios were reduced using the K-means algorithm to obtain... A set of typical scenarios and their corresponding initial scenario probability distributions; Step 3.2: Establish a multi-producer-consumer overall optimization model that considers the uncertainties of renewable energy power generation; The producer-consumer operating costs, without considering the uncertainties of renewable energy power generation, are expressed as follows: ; ; in, The total cost for multiple producers and consumers; For the first Operating costs for individual consumers; For the first The demand response cost for individual consumers; For the first The cost of electricity sharing for individual consumers.

[0061] Furthermore, the specific descriptions of each part are as follows: ; ; in, , , and These represent the compensation unit prices for electricity load transfer, and reductions in electricity, heat, and gas loads, respectively. , and They represent the first Individual consumers It can reduce electrical, heat, and gas loads at any time; Indicates the first Individual consumers Electrical load can be transferred at any time; Indicates the first Individual consumers and the first Individual consumers The amount of electricity traded at any given moment, if , indicating the first Individual consumers from the first Individual consumers obtain electricity, and conversely, they turn to the first consumer for electricity. Provide electricity to individual consumers; Indicates the first Individual consumers and the first The price of electricity traded by individual producers and consumers.

[0062] The total cost of multi-producer-consumer consumption, taking into account the uncertainty of renewable energy, is expressed as follows: 1) Constructing a data-driven uncertainty set for renewable energy To ensure that the probability distribution values ​​fluctuate within a reasonable range, a discrete probability distribution is constructed. The feasible region of the probability distribution centered at a given point and constrained by the sets of 1-norm and ∞-norm. As shown in the following formula: ; in, , These represent the actual and initial probability distribution values ​​for a typical scenario, respectively. , These are the probability error values ​​under the constraints of 1-norm and ∞-norm, respectively, and are all known parameters.

[0063] Since the absolute value constraint is a nonlinear constraint, it needs to be decomposed into a linear equivalent. By performing an equivalent transformation on the absolute value constraint, we can obtain: ; in, and These represent the positive and negative offsets of the actual probability relative to the initial probability, respectively. and The indicators representing the positive and negative offsets of the probability are binary variables.

[0064] 2) Construct a two-stage split-Brow model The objective of minimizing the overall cost for multiple prosumers is incorporated into the probability of uncertain scenarios, where scenario probability is also an optimization variable, thereby constructing a two-stage sub-Bruker model. The first stage aims to minimize the energy transaction costs between prosumers and integrated energy operators, while the second stage aims to minimize the uncertainties of renewable energy sets. Find the probability distribution of the worst-case scenario, and minimize the demand response cost under the worst-case scenario, given that the decision variables in the first stage are determined. The specific description is as follows: ; Step 3.3: Then establish the constraint model for the multi-prosumer; The constraints on producer-consumer participation in electricity trading are described as follows: ; ; The electricity, heat, and gas loads after the demand response of each producer and consumer are described below: ; ; ; in, , and They represent the first Individual consumers Fixed electrical, heat, and gas loads at any given time.

[0065] The electricity-heat-gas load constraints for the demand response of each producer and consumer are described as follows: ; ; ; ; ; in, These are respectively represented as the transferable electrical load proportion coefficients; , and These are respectively represented as the coefficients for reducing the proportion of electricity, heat, and gas loads.

[0066] The electricity-heat-gas load balance constraints for each producer and consumer are described as follows: ; ; ; in, For the first Electricity generated by renewable energy sources by individual producers and consumers.

[0067] Step 3.4: Solve the optimization problem to obtain the optimal total electricity purchase volume, total heat energy and total natural gas purchase volume, and the minimum total cost for multiple producers and consumers.

[0068] Step 4: Based on the total electricity purchase volume, heat and natural gas purchase volume reported by multiple prosumers and the energy sales price reported by each energy supplier, the integrated energy operator updates and calculates the optimal values ​​for the transaction volume of electricity purchased from external energy supply companies, the energy sales price set for prosumers, and the energy purchase volume set for each energy supplier, which maximizes the benefits of the integrated energy system operator. Step 5: Each of the multiple energy suppliers updates and calculates the optimal values ​​of internal equipment output power and the price at which energy is sold to the integrated energy system operator, based on the energy purchase volume set by the integrated energy system operator, to maximize its own profit. Step 6: Based on the electricity purchase and sale price, heat and natural gas purchase price set by the integrated energy system operator, the multi-prosumer updates and calculates the optimal values ​​of the total energy transaction volume between the multi-prosumer and the integrated energy operator, and the P2P shared electricity transaction volume between the multi-prosumer and the multi-prosumer, which minimizes the overall cost of the multi-prosumer. Step 7: Repeat steps 4-6 and save the results of each solution. The integrated energy system operator compares the optimal energy purchase and sales prices obtained from the previous two solutions, uses the bisection method to continuously narrow the range of values ​​for the decision variables, and modifies the variable constraints when executing step 4 in the next solution, until the game equilibrium strategy that maximizes the profits of the integrated energy system operator, maximizes the profits of each energy supplier, and minimizes the overall cost of producers and consumers is finally obtained, then the loop stops.

[0069] The second phase includes the optimal electricity trading price among the aforementioned producers and consumers: Step 1: Based on the energy trading price with the integrated energy system operator and the optimal P2P electricity sharing transaction volume obtained in the first stage, each prosumer in the multi-prosumer group quantifies its energy contribution in P2P electricity sharing using a nonlinear energy mapping method. ; ; ; in, and They represent the first The energy provided and received by individual consumers when they participate in electricity sharing; The maximum amount of electricity supplied to each producer and consumer; This represents the maximum electrical energy received by each producer and consumer.

[0070] Step 2: Each prosumer in the multi-prosumer group allocates benefits based on asymmetric Nash negotiation, and the preliminary calculation yields the electricity P2P sharing transaction price that maximizes the benefit of each prosumer: ; in, Indicates the first Individual consumers do not incur operational costs associated with participating in peer-to-peer (P2P) electricity sharing. Indicates the first The optimal operating cost for individual consumers to participate in P2P electricity sharing in the first phase.

[0071] Step 3: Each prosumer updates and calculates its own electricity transaction price according to the electricity P2P sharing transaction price fed back by other prosumers, and finally obtains the electricity P2P sharing transaction price that maximizes the interests of each prosumer, and calculates the cost of each prosumer.

[0072] After the two-stage transaction is completed, the optimal energy trading strategy for the integrated energy system operator, the multiple energy suppliers, and the multiple prosumers is finally obtained, along with the maximum profit or minimum cost. All output data are plotted as curves to more intuitively observe the feasibility and effectiveness of the system and trading method of this invention in practical applications. The profit or cost data also reflect the economic and environmental benefits of the system and trading method of this invention, addressing the issue of conflicting interests among multiple stakeholders more deeply from both horizontal and vertical dimensions, and achieving on-site consumption of renewable energy.

[0073] Example 2 like Figure 4-10 A simulation experiment was conducted using two energy suppliers (ES) and three prosumers as examples.

[0074] (1) Results analysis and discussion of integrated energy system operators Integrated Energy System Operator (IESO) energy sales prices such as Figure 4 and Figure 5 As shown, taking electricity prices as an example, Figure 4 and Figure 5 This indicates that the electricity price set by the IESO consistently falls between the time-of-use price and the feed-in price, and its price fluctuation trend is consistent with that of the power supply company. While meeting price constraints, the IESO maximizes its profits by raising electricity prices during periods of high demand.

[0075] Figure 5 The power balance diagram of IESO, combined with Figure 5It can be observed that between 6:00 and 17:00, the electricity purchased by prosumers from the IESO is essentially zero, and the electricity price during this period is significantly lower than the time-of-use price. Because the prosumer alliance is self-sufficient in electricity during this time, and even has surplus electricity that can be sold to the IESO for storage, it can then be sold back to prosumers when demand is high. When the energy supply from the ES is sufficient and the price is relatively low, the IESO will store a portion of the energy to avoid purchasing it from external sources at high prices when the ES supply is insufficient. Therefore, the strategy proposed in this invention can maximize profits and reduce dependence on external energy suppliers for integrated energy system operators.

[0076] (2) Simulation results and analysis of prosumers Prosumers consider demand response and adjust their energy demand according to the IESO's energy sales price. The demand response results for each prosumer are as follows: Figures 6-8 As shown. Taking the electricity demand response results of producer-consumer 1 as an example, Figure 6 This indicates that electricity demand shifts from 18:00-22:00 to a portion of the time between 3:00-16:00. Load shifting and reduction during periods of high prices alleviate peak-hour power supply pressure and fully utilize low-price periods to reduce electricity purchase costs. Heat and natural gas loads only consider situations where reduction is possible. Due to reduction constraints, producers and consumers can both meet necessary load demands and improve energy flexibility. Producers' energy demand response is correlated with IESO's energy sales prices, allowing them to flexibly adjust their energy demand according to IESO's energy sales strategy and effectively adapt to price fluctuations.

[0077] Three prosumers form a cooperative alliance, which can conduct peer-to-peer (P2P) electricity trading. The trading volume and price of electricity among the prosumers are as follows: Figures 9-10 As shown. Between 1:00-4:00, 7:00-9:00, and 18:00-24:00, Prosumer 1 generates more renewable energy, and even after meeting its own electricity load demand, it still has surplus electricity to sell to Prosumers 2 and 3. Between 5:00-6:00 and 10:00-17:00, Prosumer 1 generates less renewable energy while its electricity load demand is high, leading to a shortage of its own power supply. During these times, Prosumers 2 and 3 generate more renewable energy, and the surplus electricity is sold to Prosumer 1. According to... Figure 9 As shown in the diagram on the right, the electricity transaction price among prosumers is lower than the sales price of the IEO, thereby reducing the energy purchase cost for prosumers and allowing them to obtain certain profits, thus encouraging prosumers to participate in cooperative game theory.

[0078] This embodiment uses an asymmetric Nash negotiation method to allocate benefits based on the contribution factors of producers and consumers in the energy sharing process. The specific allocation results are shown in Table 1.

[0079] Table 1. Benefit Distribution Results Based on Asymmetric Nash Negotiation

[0080] As shown in Table 1, through P2P electricity sharing, the costs for each participating prosumer are lower after cooperation than before, and a fairer distribution of benefits is achieved based on the amount of electricity contributed by each entity. Prosumer 2 has a larger contribution factor, thus possessing greater bargaining power, and therefore tends to receive a larger share of the cooperation benefits. Conversely, prosumers 1 and 3 have smaller renewable energy generation and receive more electricity, resulting in smaller contribution factors and lower bargaining power, thus receiving relatively less cooperation benefits.

[0081] Therefore, the strategy proposed in this invention can not only reduce energy purchase costs and achieve fair distribution of benefits for producers and consumers, but also enable the local consumption of renewable energy.

[0082] (3) Simulation results and analysis of energy suppliers The retail price of ES is as follows Figure 10 As shown, the price curve trend set by ES (Energy Providers) is the same as the energy purchase trend of IESO (Engineering Isolation Organizations). Since the selling price of ES is linearly related to the sales volume, the price set by ES is also higher when the amount of energy purchased by IESO is higher. At the same time, due to competition among ES, there will always be one with a lower price in each period, seeking to sell more energy to IESO to gain greater profits.

[0083] Combination Figure 5 , Figure 6 Analysis suggests that in order to reduce energy purchase costs, IESO will rationally allocate the amount of energy purchased from various energy sources. Figure 6 As shown, the periods from 1:00 to 6:00 and from 21:00 to 24:00 are low-price periods for purchasing heat from external heat networks. During these periods, the IESO will select to purchase a portion of its heat energy from the heat network to reduce its purchase volume from the ES, thereby lowering the purchase price from the ES and reducing the overall heat purchase cost. Therefore, the strategy proposed in this invention reduces the IESO's energy purchase cost and avoids a monopoly by any single party.

[0084] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A multi-entity, multi-energy coordinated control method for an integrated energy system, characterized in that, Includes the following sequentially executable steps: Operators formulate 24-hour initial energy purchase and sale plans based on electricity prices, heat prices, gas prices, and energy storage parameters; The initial energy purchase plan is distributed to each energy supplier, and each energy supplier operates a "bidding module" independently in its local area. The bidding module is set to make independent bids based on wind and solar power, waste volume, and equipment capacity. Producers and consumers respond to energy sales prices in the form of alliances, optimizing overall energy purchase demand and internal P2P transaction volume; Operators iteratively update energy purchase and sales prices, and the boundary is tightened and converged rapidly through a binary search method; Ultimately, the internal settlement price is allocated through asymmetric Nash negotiations based on the principle of "more supply, more benefit," and the energy storage, energy purchase and sale, and P2P transaction plans of all parties are output to achieve closed-loop coordination among multiple entities and multiple energy sources.

2. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... Read the time-of-use list of external electricity price, heat price, and gas price for the 24 hours of the day, and simultaneously read the energy storage limitation parameters of the integrated energy system operator's energy storage equipment: initial power, charge and discharge efficiency, and upper and lower capacity limits; the energy storage equipment includes at least an electrical energy storage unit, a thermal energy storage unit, and a gas energy storage unit; Read the output power of photovoltaic and wind power generation in typical scenarios for each energy supplier, as well as the probability value of each scenario, the availability of dry and wet waste, the flow rate of domestic sewage, and the unit price of maintenance for each piece of equipment; Read the fixed electricity load, heat load, and gas load curves for each producer and consumer, the proportion of load that can be transferred / reduced, and the predicted power generation curves for photovoltaic and wind power.

3. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... The operator first makes a "top-level decision": under the goal of maximizing its own profits, it calculates the energy to be purchased from various energy suppliers and the energy to be sold to producers and consumers in the next 24 hours, based on external electricity prices, heat prices, gas prices and the aforementioned energy storage limitation parameters, thus forming an initial energy sales plan or the first version of the plan.

4. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that: The first version of the planned energy purchase is distributed to each energy supplier. Each energy supplier independently runs a "bidding module" locally: based on the amount of waste, wind and solar forecasts, and equipment capacity, each supplier calculates its own optimal energy sales price and sends it back to the operator; the first version of the planned energy sales price is distributed to each producer and consumer. Each producer and consumer receives the distributed price in the form of an "alliance". Under the goal of minimizing total cost, they calculate the optimal energy purchase demand of the alliance as a whole and the operator as a whole, as well as the internal P2P shared transaction volume, and send the total optimal energy purchase demand back to the operator. The "alliance" format is set so that at least two prosumers obtain the same price; the operator updates the energy purchase amount from each energy supplier and the energy sales price to the prosumers based on the best energy sales price of each energy supplier and the total best energy purchase demand of the prosumers, thus obtaining the second version of the plan. The final plan is obtained through iterative processing.

5. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... By using a binary search method to tighten the boundaries of the next round of planning, the minimum value of the energy purchase and sales prices in the previous two rounds is taken as the lower limit of the new round, and the maximum value is taken as the upper limit of the new round, thereby quickly narrowing the search range and reducing the number of iterations.

6. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... Each producer-consumer receives the internal P2P transaction volume and the energy sales price from the operator, and then uses asymmetric Nash negotiation to allocate the internal settlement price according to the "more supply, more benefit" mechanism, forming a 24-hour transaction price between each pair of producers-consumers; the "more supply, more benefit" mechanism is set so that the more electricity a producer-consumer provides to other producers-consumers throughout the "alliance" process, the more benefits it will receive.

7. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... The executable sub-steps of the "bidding module" include: Sub-step 1: Send the daily dry waste weight into the pyrolysis gasification unit to obtain an electricity and heat production curve; Sub-step 2: Mix wet waste and sewage and send them into the anaerobic digester to obtain a biogas production curve; Sub-step 3: Purify the biogas into a substitute natural gas and record the hourly available gas power; Sub-step 4: After merging gas, photovoltaic, and wind power, determine the external electricity, heat, and gas prices according to the operator's energy purchase needs, and finally send back the electricity price-heat price-gas price quotation curve.

8. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... Step S6: The total optimal energy purchase demand and internal P2P shared transaction volume are obtained through a two-stage decentralization method: Stage 1: First, calculate the energy purchase demand when the total cost of the alliance is minimized based on the output power values ​​of photovoltaic and wind power generation in typical scenarios and the initial probability values ​​corresponding to each scenario; Stage 2: Within the allowable fluctuation range of probability, find the "worst-case demand" that results in the highest demand response cost, and then fine-tune the energy purchase demand obtained in Stage 1 to ensure that even if the scenario worsens, the alliance cost remains the lowest.

9. The multi-entity, multi-energy coordinated control method for an integrated energy system according to claim 1, characterized in that... The asymmetric Nash negotiation sub-steps described in step S12 include: Sub-step A: Calculate the total electricity sent and received by each producer and consumer during the shared time period, and calculate the "supply ratio" and "receiver ratio"; Sub-step B: Solve the internal electricity price by using the method of obtaining higher bargaining weight for producer and consumer members with "high supply ratio and low receiver ratio", so that the operating cost of each person after cooperation is lower than when purchasing electricity individually.

10. A scheduling system, comprising a processor, a memory, and a program stored in the memory and executable on the processor, characterized in that: When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.

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