Comprehensive energy community optimization scheduling method and device for multi-vector electrified consumers

Abstract

The invention relates to the technical field of electric power systems, in particular to a comprehensive energy community optimization scheduling method and device for multi-vector electrified consumers, and the method comprises the steps: integrating energy producers, producers, the multi-vector electrified consumers, terminal users and energy storage resources to form a comprehensive energy community, on the basis of the flexible dispatching power demand of multi-vector electrified consumers and the characteristics of a plurality of subsystems corresponding to the multi-vector electrified consumers, constraint and objective functions of the integrated energy community are constructed, a Nash bargaining game model is adopted for optimization, benefits of all participants are balanced, overall optimal dispatching of the integrated energy community is achieved, the energy utilization efficiency is improved, and the energy utilization rate is increased. And the economic and environmental benefits of the whole community system are improved. Therefore, the problem of low resource allocation efficiency caused by multi-emphasis on economic optimization, ineffective combination of carbon emission constraint and a multi-energy market pricing mechanism and lack of cooperation among subsystems in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and particularly to an integrated energy community optimal scheduling method and device for multi-vector electrified consumers. Background Art

[0002] Driven by the global carbon neutrality goal, the power system urgently needs to improve the renewable energy consumption capacity through the clean reform of energy production and consumption. In related technologies, as an important carrier of the new power system, the integrated energy community integrates multiple energy forms such as electricity, gas, and heat, incorporates electrified consumers such as electric vehicles, power-to-chemicals, and power-to-heat into the demand-side response, and realizes the flexible scheduling of the energy system.

[0003] However, in related technologies, although the power substitution technology has improved flexibility, there are still the following problems: traditional centralized scheduling cannot coordinate the individual rational goals of operators in different energy sectors; existing methods mostly focus on economic optimization and do not effectively combine carbon emission constraints with multi-energy market pricing mechanisms; there is a lack of cooperation among the power, gas, and heat subsystems, resulting in low resource allocation efficiency and urgent need for improvement. Summary of the Invention

[0004] The present application provides an integrated energy community optimal scheduling method and device for multi-vector electrified consumers to solve the problems in related technologies, such as mostly focusing on economic optimization, not effectively combining carbon emission constraints with multi-energy market pricing mechanisms, and lacking cooperation among subsystems, resulting in low resource allocation efficiency.

[0005] The first aspect of the present application provides an integrated energy community optimal scheduling method for multi-vector electrified consumers, including the following steps: forming an integrated energy community composed of energy producers, prosumers, multi-vector electrified consumers, end users, and energy storage resources; constructing a first constraint of the integrated energy community based on the flexibly schedulable power demand of the multi-vector electrified consumers; constructing a collaborative optimization model and a second constraint of the integrated energy community based on the characteristics of multiple subsystems corresponding to the multi-vector electrified consumers; constructing an objective function of the integrated energy community by combining the first constraint, the collaborative optimization model, and the second constraint, and optimizing the objective function by using a preset Nash bargaining game model to achieve the optimal scheduling of the integrated energy community.

[0006] Through the above technical solution, the embodiments of the present application can create conditions for the unified management and optimal scheduling of energy by integrating to form an integrated energy community. A collaborative optimization model and constraints are constructed based on the characteristics of multiple subsystems, fully considering the characteristics and mutual relationships of different energy subsystems. An objective function is constructed in combination with various constraints, and the Nash bargaining game model is used for optimization to balance the interests of all participants, achieve the overall optimal scheduling of the integrated energy community, improve energy utilization efficiency, and enhance the economic and environmental benefits of the entire community system.

[0007] Optionally, in an embodiment of the present application, constructing the collaborative optimization model of the integrated energy community based on the characteristics of multiple subsystems corresponding to the multi-vector electrified consumers includes: generating an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmitting it between the multiple subsystems, so as to calculate the social welfare of the multiple subsystems based on the incentive signal; constructing the third constraint and the fourth constraint of the integrated energy community on the basis of the optimal network loss and maintaining energy balance of the integrated energy community; generating the collaborative optimization model in combination with the social welfare, the third constraint and the fourth constraint.

[0008] Through the above technical solution, the embodiments of the present application can stimulate subsystem operators to actively participate in energy optimization scheduling by generating incentive signals, so as to achieve the economic and environmental benefit goals of the whole community and promote the integrated energy community to develop in the direction of low carbon and high efficiency. Constructing the second constraint and the third constraint with the goal of minimizing network loss and maintaining energy balance helps to ensure the stable operation of the energy system, reduce the loss of energy during transmission and conversion, improve energy utilization efficiency, and ensure the real-time matching of energy supply and demand. Generating a collaborative optimization model in combination with social welfare, the second constraint and the third constraint can comprehensively consider the interests of each subsystem, the operation limitations of the energy system and the overall goals of the community, make the optimization model more in line with actual needs, realize the collaborative operation between subsystems, and then improve the overall performance and sustainable development ability of the integrated energy community.

[0009] Optionally, in an embodiment of the present application, the objective function is:

[0010]

[0011] where x PS Set of decision variables related to power system operation; x NGS Set of decision variables related to natural gas system operation; x DHS Set of decision variables related to heating system operation; sw PS Social welfare of the power system; sw NGS Social welfare of the natural gas system; sw DHSFor the social welfare of the heating system; For the real-time electricity price; For the real-time natural gas price.

[0012] Through the above technical solutions, the embodiments of the present application can take the maximization of the social welfare of each subsystem as the core, fully consider the characteristics and interest demands of different energy systems in the integrated energy community, and accurately optimize and adjust the operation strategies of each subsystem by setting a decision variable set related to the operation for each subsystem respectively. At the same time, taking the real-time electricity price and the real-time natural gas price into consideration enables the objective function to dynamically adapt to the fluctuations of the energy market price, guiding the operators of each subsystem to make decisions more in line with the actual market situation.

[0013] Optionally, in an embodiment of the present application, the formula of the Nash bargaining game model is:

[0014]

[0015] where N P is the participant; PS is the power system; NGS is the natural gas system; DHS is the heating system; S i is the strategy set of the participant; x PS is the decision variable set related to the operation of the power system; x NGS is the decision variable set related to the operation of the natural gas system; x DHS is the decision variable set related to the operation of the heating system; is the real-time electricity price; is the real-time natural gas price; B i is the corresponding benefit of the participant; sw PS is the social welfare of the power system; sw NGS is the social welfare of the natural gas system; sw DHS is the social welfare of the heating system.

[0016] Through the above technical solutions, the embodiments of the present application can take the participants into account, comprehensively cover the main subsystems of the integrated energy community, and ensure that each energy system can participate in the decision-making process during the optimal dispatching. The setting of the strategy set comprehensively considers the operation decision variables of each subsystem as well as the real-time electricity price and natural gas price, enabling the participants to formulate strategies according to the market price dynamics and the characteristics of their own systems, enhancing the flexibility and adaptability of the model. Taking the social welfare of each subsystem as the benefit of the participant fully reflects the interest demands of each subsystem, motivating each subsystem to improve the overall efficiency of the integrated energy community while pursuing the maximization of its own interests. Through this game mechanism, each subsystem can reach an equilibrium state in mutual competition and cooperation, effectively coordinating the operation of different energy systems, improving the energy utilization efficiency, reducing the operation cost, and thus realizing the optimal dispatching and sustainable development of the integrated energy community.

[0017] The second aspect embodiment of the present application provides an integrated energy community optimal scheduling device for multi-vector electrified consumers, including: a combination module for forming an integrated energy community by energy producers, prosumers, multi-vector electrified consumers, end-users, and energy storage resources; a first construction module for constructing a first constraint of the integrated energy community based on the flexibly schedulable power demand of the multi-vector electrified consumers; a second construction module for constructing a collaborative optimization model and a second constraint of the integrated energy community based on the characteristics of multiple subsystems corresponding to the multi-vector electrified consumers; an optimal scheduling module for constructing an objective function of the integrated energy community by combining the first constraint, the collaborative optimization model, and the second constraint, and optimizing the objective function using a preset Nash bargaining game model to achieve the optimal scheduling of the integrated energy community.

[0018] Through the above technical solutions, the embodiments of the present application can create conditions for the unified management and optimal scheduling of energy by integrating to form an integrated energy community, construct a collaborative optimization model and constraints based on the characteristics of multiple subsystems, fully consider the characteristics and mutual relationships of different energy subsystems, construct an objective function by combining various constraints, and use the Nash bargaining game model for optimization to balance the interests of all parties involved, achieve the overall optimal scheduling of the integrated energy community, improve energy utilization efficiency, and enhance the economic and environmental benefits of the entire community system.

[0019] Optionally, in an embodiment of the present application, the second construction module includes: a calculation unit for generating an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmitting it between the multiple subsystems to calculate the social welfare of the multiple subsystems based on the incentive signal; a construction unit for constructing a third constraint and a fourth constraint of the integrated energy community on the basis of the optimal network loss of the integrated energy community and maintaining energy balance; a generation unit for generating the collaborative optimization model by combining the social welfare, the third constraint, and the fourth constraint.

[0020] Through the above technical solutions, the embodiments of the present application can generate incentive signals to stimulate subsystem operators to actively participate in energy optimization scheduling, so as to achieve the economic and environmental benefit goals of the community as a whole, and promote the development of the integrated energy community towards the direction of low carbon and high efficiency. Constructing the second constraint and the third constraint with the goal of minimizing network losses and maintaining energy balance helps to ensure the stable operation of the energy system, reduce the losses of energy during transmission and conversion, improve energy utilization efficiency, and ensure the real-time matching of energy supply and demand. Generating a collaborative optimization model by combining social welfare, the second constraint, and the third constraint can comprehensively consider the interests of each subsystem, the operation limitations of the energy system, and the overall goals of the community, making the optimization model more in line with actual needs, realizing the collaborative operation between subsystems, and further enhancing the overall performance and sustainable development ability of the integrated energy community.

[0021] Optionally, in an embodiment of the present application, the optimization scheduling module includes: The objective function is:

[0022]

[0023] where x PS Set of decision variables related to power system operations; x NGS Set of decision variables related to natural gas system operations; x DHS Set of decision variables related to heating system operations; sw PS Social welfare of the power system; sw NGS Social welfare of the natural gas system; sw DHS Social welfare of the heating system; Real-time electricity price; Real-time natural gas price.

[0024] Through the above technical solutions, the embodiments of the present application can take the maximization of the social welfare of each subsystem as the core, fully consider the characteristics and interest demands of different energy systems in the integrated energy community, accurately optimize and adjust the operation strategies of each subsystem by setting sets of decision variables related to operations for each subsystem respectively. At the same time, taking into account the real-time electricity price and the real-time natural gas price, the objective function can dynamically adapt to the fluctuations of the energy market price, guiding the operators of each subsystem to make decisions more in line with the actual situation of the market.

[0025] Optionally, in an embodiment of the present application, the optimization scheduling module includes: The formula of the Nash bargaining game model is:

[0026]

[0027] where N PLet \(P\) be the participants; \(PS\) be the power system; \(NGS\) be the natural gas system; \(DHS\) be the heating system; \(S\) i be the strategy set of the participants; \(x\) PS be the set of decision variables related to the operation of the power system; \(x\) NGS be the set of decision variables related to the operation of the natural gas system; \(x\) DHS be the set of decision variables related to the operation of the heating system; be the real-time electricity price; be the real-time natural gas price; \(B\) i be the corresponding profit of the participants; \(sw\) PS be the social welfare of the power system; \(sw\) NGS be the social welfare of the natural gas system; \(sw\) DHS be the social welfare of the heating system.

[0028] Through the above technical solutions, the embodiments of the present application can take the participants into account, comprehensively cover the main subsystems of the integrated energy community, and ensure that each energy system can participate in the decision-making process during the optimal dispatch. The setting of the strategy set comprehensively considers the operation decision variables of each subsystem as well as the real-time electricity price and natural gas price, enabling the participants to formulate strategies according to the market price dynamics and their own system characteristics, enhancing the flexibility and adaptability of the model. Taking the social welfare of each subsystem as the profit of the participants fully reflects the interest demands of each subsystem, and encourages each subsystem to improve the overall efficiency of the integrated energy community while pursuing its own maximum interests. Through this game mechanism, each subsystem can reach an equilibrium state in mutual competition and cooperation, effectively coordinate the operation of different energy systems, improve energy utilization efficiency, reduce operating costs, and thus achieve the optimal dispatch and sustainable development of the integrated energy community.

[0029] The third aspect of the embodiments of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the integrated energy community optimal dispatch method for multi-vector electrified consumers as described in the above embodiments.

[0030] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, it implements the integrated energy community optimal dispatch method as above.

[0031] The fifth aspect of the embodiments of the present application provides a computer program product, and the computer program is executed to be used to implement the integrated energy community optimal dispatch method as above.

[0032] In the embodiments of the present application, a comprehensive energy community is formed through integration, laying a foundation for unified energy management and optimal scheduling. A collaborative optimization model and constraints are constructed based on the characteristics of subsystems, fully considering the characteristics and mutual relationships of different energy subsystems. Combining various constraints, an objective function with the maximization of subsystem social welfare as the core is constructed, and the Nash bargaining game model is used for optimization. Incentive signals are generated to stimulate the participation of subsystem operators, and constraints are constructed to reduce network losses and maintain energy balance, ensuring the stability of the energy system, improving efficiency, and matching supply and demand. The objective function sets a decision variable set to precisely adjust the operation strategy of the subsystem and incorporates real-time prices to adapt to market fluctuations. The Nash bargaining game model covers the participation decisions of major subsystems, and the strategy set comprehensively considers factors to enhance flexibility. Using social welfare as the benefit to incentivize subsystems, promoting competition and cooperation among subsystems to reach equilibrium, improving energy utilization efficiency, reducing operating costs, and realizing the optimal scheduling and sustainable development of the comprehensive energy community.

[0033] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0035] Figure 1 is a flowchart of an optimal scheduling method for a comprehensive energy community of multi-vector electrified consumers according to an embodiment of the present application;

[0036] Figure 2 is a schematic diagram of the framework of a comprehensive energy community of multi-vector electrified consumers according to a specific embodiment of the present application;

[0037] Figure 3 is a flowchart of the optimal solution for a comprehensive energy community considering multi-vector electrified consumers according to a specific embodiment of the present application;

[0038] Figure 4 is a schematic diagram of power load, gas load, and heat demand within a scheduling period according to a specific embodiment of the present application;

[0039] Figure 5 is a schematic diagram of the output curves of wind power and photovoltaic units according to a specific embodiment of the present application;

[0040] Figure 6 is a schematic diagram of reshaping the power demand for Scenario 1 according to a specific embodiment of the present application;

[0041] Figure 7 is a schematic diagram of reshaping the power demand for Scenario 2 according to a specific embodiment of the present application;

[0042] Figure 8 Schematic diagram of reshaping the power demand for Scenario 3 according to a specific embodiment of the present application;

[0043] Figure 9 Schematic diagram of reshaping the power demand for Scenario 4 according to a specific embodiment of the present application;

[0044] Figure 10 Schematic diagram of reshaping the power demand for Scenario 5 according to a specific embodiment of the present application;

[0045] Figure 11 Schematic diagram of reshaping the power demand for Scenario 6 according to a specific embodiment of the present application;

[0046] Figure 12 Schematic diagram of real-time electricity price and gas price for Scenario 1 according to a specific embodiment of the present application;

[0047] Figure 13 Schematic diagram of real-time electricity price and gas price for Scenario 2 according to a specific embodiment of the present application;

[0048] Figure 14 Schematic diagram of real-time electricity price and gas price for Scenario 3 according to a specific embodiment of the present application;

[0049] Figure 15 Schematic diagram of real-time electricity price and gas price for Scenario 4 according to a specific embodiment of the present application;

[0050] Figure 16 Schematic diagram of real-time electricity price and gas price for Scenario 5 according to a specific embodiment of the present application;

[0051] Figure 17 Schematic diagram of real-time electricity price and gas price for Scenario 6 according to a specific embodiment of the present application;

[0052] Figure 18 Schematic diagram of the structure of an integrated energy community optimal scheduling device for a multi-vector electrified consumer provided according to an embodiment of the present application;

[0053] Figure 19 Schematic diagram of the structure example of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0054] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0055] The following describes the integrated energy community optimal scheduling method and device for multi-vector electrified consumers in the embodiments of the present application with reference to the accompanying drawings. In view of the problems in the related technologies mentioned in the above background technology, where there is more emphasis on economic optimization, the carbon emission constraint and the multi-energy market pricing mechanism are not effectively combined, and there is a lack of cooperation between subsystems, resulting in low resource allocation efficiency, the present application provides an integrated energy community optimal scheduling method for multi-vector electrified consumers. In this method, an integrated energy community can be formed by integration, creating conditions for the unified management and optimal scheduling of energy. A collaborative optimization model and constraints are constructed based on the characteristics of multiple subsystems, fully considering the characteristics and mutual relationships of different energy subsystems. A target function is constructed in combination with various constraints, and the Nash bargaining game model is used for optimization to balance the interests of all participants, achieve the overall optimal scheduling of the integrated energy community, improve energy utilization efficiency, and enhance the economic and environmental benefits of the entire community system. Thus, the problems in the related technologies, where there is more emphasis on economic optimization, the carbon emission constraint and the multi-energy market pricing mechanism are not effectively combined, and there is a lack of cooperation between subsystems, resulting in low resource allocation efficiency, are solved.

[0056] Specifically, Figure 1 FIG. is a schematic flowchart of an integrated energy community optimal scheduling method for multi-vector electrified consumers provided by an embodiment of the present application.

[0057] As Figure 1 shown, the integrated energy community optimal scheduling method for multi-vector electrified consumers includes the following steps:

[0058] In step S101, an integrated energy community is composed of an energy producer, a prosumer, a multi-vector electrified consumer, an end user, and a energy storage resource.

[0059] It should be noted that the integrated energy community includes energy producers, prosumers, electrified consumers, end-users, and energy storage resources. Among them, energy producers include, but are not limited to, UPG (Upper Power Grid), UGG (Upper Gas Grid), PV (Photovoltaic), and WT (Wind Turbine); prosumers include, but are not limited to, GB (Gas Boiler) and GFCHP (Gas-Fired Combined Heat and Power); energy storage resources include, but are not limited to, TES (Thermal Energy Storage) and GES (Gas Energy Storage); end-users refer to the users who finally consume energy, which can be household, commercial, or industrial users, directly consuming electricity, heat, or gas.

[0060] Specifically, multi-vector electrified consumers include, but are not limited to, one or more of V2G (Vehicle to Grid), P2C (Power to Chemical), P2H (Power to Heat), P2I (Power to Industry, such as industrial substitution in electric arc furnace steelmaking and electric kiln furnaces), and P2A (Power to Agriculture, such as agricultural production substitution in electric irrigation systems and electrification of agricultural machinery).

[0061] Preferably, they are V2G, P2C, and P2H.

[0062] Among them, V2G includes one or more of electric vehicles, electric railways, urban rail transit, and electric ships, preferably electric vehicles; P2C includes one or more of hydrogen production by electrolysis of water, CO2 electroreduction, chlorine production by electrolysis, and ammonia production by electroreduction, preferably CO2 electroreduction; P2H includes one or more of electric boilers, heat pumps, heat storage electric boilers, and decentralized electric heaters, preferably heat pumps.

[0063] The embodiments of the present application can construct an integrated energy community including multiple entities to achieve diversified supply, conversion, and storage of energy.

[0064] In step S102, based on the flexibly schedulable power demand of multi-vector electrified consumers, the first constraint of the integrated energy community is constructed.

[0065] Taking the optimized electric vehicle, electroreduction of CO2, and heat pump in step S101 as multi-vector electrified consumers as an example, based on their flexibly schedulable power demands, the first constraint of the integrated energy community is constructed, and this first constraint is the operation constraint of each energy facility.

[0066] Specifically, on the first hand, for electric vehicles, the power system operator needs the responsive electric vehicle cluster to adjust its charging time according to the dispatching order. Assuming there are dispatchable electric vehicles in the integrated energy community, and the charging power of each vehicle at time t is p EV , then the flexible power demand of the electric vehicle charging piles in the system is:

[0067]

[0068] Among them, is because electric vehicles are required to advance their originally scheduled charging plans to time t; is because vehicles are required to postpone their originally scheduled charging plans from time t to other time periods. Considering the daily travel needs, the charging demand of each electric vehicle must be met within 24 hours, and it can be obtained that:

[0069]

[0070] Among them, T = 24 hours defines the dispatching period set; I CS is the number of dispatchable electric vehicles in the system. In addition, as part of the total power load, each electric vehicle must adjust its demand according to the dispatching order while maintaining the safety and stability of the load-side network. Introducing the acceptable response load ratio γ of the power system, the hourly dispatchable power demand of the charging pile is restricted by the following factors:

[0071]

[0072] Among them, is the hourly power load (kW) of the integrated energy community. In summary, formulas (1) to (3) are the operation constraints of electric vehicles.

[0073] On the second hand, for the electroreduction of CO2, the power system operator realizes "power to chemicals" through the electrochemical park to efficiently absorb the remaining renewable energy power in the system. The production chain of the electrochemical park includes two main steps: carbon capture and electrochemical reduction of CO2. In the carbon capture process, alkaline amine absorbents are used to capture CO2 from the flue gas discharged from gas boilers and gas cogeneration plants. The captured CO2 is concentrated and introduced into the electrolyzer, where it is activated by the catalyst and converted into high-value chemicals. The CO2 conversion process in the electrolyzer is as follows:

[0074]

[0075] Among them, E is the bias voltage applied by the electrolyzer (V vs. RHE). Consider the hydrogen evolution side reaction in the aqueous electrolyte:

[0076] 2H2O + 2e - → H2 + 2OH - , E = 0 V (5)

[0077] In CO2 conversion, the selectivity of the product is affected by the applied potential E, the catalyst, and the electrolyte composition. The Faraday efficiency quantifies the ratio between the chemical product and the required charge quantity, reflecting the probability of generating a specific product:

[0078]

[0079] Among them, n is the number of electrons transferred by different products; N is the number of moles of the product (mol); F is the Faraday constant (96485 C / mol); I is the total current in the electrolyzer (A); Δτ is the electrolysis time (s). According to the law of conservation of charge, the sum of the Faraday efficiencies of all products must be equal to 1, that is:

[0080]

[0081] Among them, J = {CO, CH2O2, CH4O, C2H6O, C2H4, H2}. The electricity production driven by the surplus renewable energy power brings economic and environmental benefits to the integrated energy community: the chemical products produced by the electricity production sold to downstream industries can generate additional economic benefits; the carbon emissions reduced by the electricity production traded in the local carbon market can bring environmental benefits. Therefore, the chemical production and CO2 emission reduction during the electricity production in period t are expressed as:

[0082]

[0083]

[0084] Among them, is the total amount of chemicals produced by the electrochemical park (kg); is the CO2 emission consumed by the electrochemical park (kg); α EP is the conversion coefficient of the park investment cost; is the maximum conversion efficiency of product j (kg / kWh); and Mr j are the molar masses of CO2 and product j (kg / mol) respectively; I WT and I PV represent the numbers of wind turbines and photovoltaic panels operating in the integrated energy community; and is the surplus renewable energy power generated by the fan and the photovoltaic panel i during the time period t. In summary, formulas (4) to (9) are the operation constraints of the electroreduction CO2 device.

[0085] Thirdly, for heat pumps, the power system operator realizes "power-to-heat" through heat pumps, converts the heat energy demand into a dispatchable power demand, and helps the power system alleviate the problem of supply-demand mismatch. Generally, the relationship between the energy input and output of a heat pump is determined by the coefficient of performance:

[0086]

[0087] where and are the heat output and power consumption (kW) of heat pump i respectively; and define the minimum and maximum heat powers (kW) output by heat pump unit i respectively. Formulas (10) and (11) are the operation constraints of the heat pump.

[0088] In summary, the operation constraints of each energy device constitute the first constraint of the integrated energy community.

[0089] The embodiments of the present application can formulate operation constraints for different multi-vector electrification consumers respectively. For electric vehicles, by adjusting the charging time through responsive dispatching, it can not only meet the daily travel charging needs of users, but also maintain the safety and stability of the load-side network. By using the acceptable response load ratio of the power system to limit the dispatchable power demand, the grid load can be effectively balanced. For the electroreduction CO2 part, according to the law of conservation of charge, the energy conversion is ensured to be reasonable, which can not only efficiently absorb the surplus renewable energy power in the system, but also bring economic benefits and environmental benefits. The heat pump part uses the coefficient of performance to establish the energy input-output relationship and sets the heat power range constraint, which can convert the heat energy demand into a dispatchable power demand and effectively alleviate the problem of supply-demand mismatch in the power system.

[0090] In step S103, based on the characteristics of multiple subsystems corresponding to the multi-vector electrification consumers, a collaborative optimization model and a second constraint of the integrated energy community are constructed.

[0091] Taking the preferably selected electric vehicle, electroreduction CO2, and heat pump as multi-vector electrification consumers in step S101 as an example, the corresponding subsystems are the power system, the natural gas system, and the heating system respectively.

[0092] Specifically, based on the characteristics of multiple subsystems corresponding to multi-vector electrified consumers, a collaborative optimization model for an integrated energy community is constructed, including: generating an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmitting it among multiple subsystems, so as to calculate the social welfare of multiple subsystems based on the incentive signal; constructing the third constraint and the fourth constraint of the integrated energy community on the basis of the optimal network loss and energy balance of the integrated energy community; generating a collaborative optimization model by combining social welfare, the third constraint and the fourth constraint.

[0093] In the actual implementation process, the collaborative optimization model proposed in the embodiment of the present application is guided by the integrated energy-carbon pricing in the local energy market to coordinate multi-vector electrified consumers. The optimization process is divided into two stages: the first stage, the local market pricing stage incorporates the additional fuel cost related to the marginal carbon tax on the source side and considers the income from carbon emission reduction on the load side; the second stage, the energy management stage focuses on optimizing the real-time operation of each subsystem in the integrated energy community, including energy supply planning, trading volume and storage scheduling.

[0094] On the one hand, the first stage focuses on transmitting effective incentive signals among interconnected energy subsystems to ensure that the integrated energy community achieves economic benefits and scientific carbon emission reduction. Assuming that the local energy market operates as a perfectly competitive retail market, each subsystem operator submits bid information to the balancing responsible party, including the energy purchase and sales volume, and negotiates with external operators about the required energy transactions at the same time. The social welfare of each subsystem is defined as the net difference between the end-user income and the operating cost.

[0095] For the power system, its social welfare sw PS (yuan) consists of five parts: the income from selling products of the electrochemistry park and trading carbon emission reduction volume by the power system, the income from selling electricity to operate heat pumps by the heating system, the income obtained from power end-users, the cost of purchasing carbon electricity from the superior power grid, and the cost of purchasing electricity generated by gas-fired cogeneration units from the heating system. The remaining renewable energy power in the integrated energy community is completely consumed by the electrochemistry park and heat pumps and does not need to be returned to the superior power grid:

[0096]

[0097] Among them, P j is the market price (yuan / kg) of product j of the electrochemistry park; P CET is the carbon emission trading price in the local retail carbon market (yuan / kg); is the real-time electricity price (kWh); b EU is the electricity price of power users (yuan / kWh); P UPG and c EECT are the thermal power price of the superior power grid and the equivalent carbon tax of electricity (yuan / kWh) respectively; and are the hourly power outputs (kW) of the superior power grid and the gas-fired combined heat and power unit i, respectively; I HP 、I UPG and I GFCHP represent the numbers of heat pumps, nodes connected to the superior power grid, and gas-fired combined heat and power units, respectively.

[0098] For the natural gas system, the social welfare sw NGS (yuan) consists of four parts: the revenue from selling natural gas to the heating system for operating gas-fired combined heat and power units and heat pumps, the revenue obtained from natural gas end-users, and the cost of purchasing carbon gas from the superior natural gas pipeline network:

[0099]

[0100] Among them, is the real-time natural gas price (kWh); and are the hourly gas consumption (kW) of the gas-fired combined heat and power unit and the gas boiler i, respectively; is the hourly gas consumption of the superior gas network (kW); b GU is the natural gas end-user utility (yuan / kWh); is the natural gas load of the natural gas transmission system (kW); P UGG and c GECT are the natural gas electricity price and the natural gas equivalent carbon tax (yuan / kWh) transmitted by the superior gas network, respectively; I GB and I UGG represent the numbers of gas boilers and nodes connected to the superior gas network, respectively.

[0101] For the heating system, its social welfare sw DHS (yuan) consists of five parts: the revenue from selling the electricity generated by the gas-fired combined heat and power unit to the power system, the revenue obtained from heat end-users, the cost of purchasing natural gas from the natural gas system to operate the gas-fired combined heat and power unit and the gas boiler, and the cost of purchasing electricity from the power system to operate the heat pump:

[0102]

[0103] Among them, b HU is the utility of the heat end-user (yuan / kWh); is the actual heat demand of the heating system (kW).

[0104] On the other hand, the second stage focuses on formulating energy scheduling within each subsystem to maintain real-time energy balance, minimize network losses, and effectively regulate flexible resources. It is assumed that each subsystem operator independently manages its energy system and supervises the entire process of energy production, distribution, conversion, and consumption. The real-time energy balance equations for the power system, natural gas system, and heating system, which are the third constraints, are expressed as follows:

[0105]

[0106] Among them, and are the actual power generation (kW) of the wind turbine and photovoltaic power station respectively; and are the charging and discharging gas flow rates (kW) of the gas storage device respectively; and η TES,c are the charging and discharging heat flow rates (kW) of the heat storage device and its charging efficiency respectively; Θ n defines the set of buses / nodes directly connected to bus / node n; p mn,t 、g mn,t and q mn,t are the active power flowing through line m-n, the gas flow rate flowing through pipeline m-n, and the heat flow rate flowing through pipeline m-n (kW) respectively; I GES and I TES represent the numbers of the gas storage device and the heat storage device respectively.

[0107] Furthermore, for the power system, the mathematical model expression of the power energy flow p mn,t in the power grid is as follows:

[0108]

[0109] Among them, is the reactive power flowing through line m-n; V m / n,t 、θ m / n,t 、G mn and B mn are the bus voltage amplitude, the phase angle difference between buses m and n, the conductance and susceptance values in the nodal admittance matrix respectively.

[0110] For the natural gas system, the mathematical model expression of the gas flow g mn,t in the natural gas network is as follows:

[0111]

[0112] Among them, pr m / n is the nodal pressure of the pipeline; y mnis the total transmission coefficient ((m3 / h)·Pa) of pipeline m-n, which depends on the physical properties of pipeline m-n with a fixed gas composition, including specific constant y, reference gas pressure pr b (Pa), reference gas temperature T b (K), pipeline diameter d mn (m), pipeline length l mn (m), specific gravity γ of natural gas G , average pipeline temperature compression coefficient λ a and friction coefficient f mn .

[0113] For a heating system, different from the instantaneity of current transmission, the heat flow in the heat network will experience a significant time delay. When adopting a constant current variable temperature control strategy, a functional method can be used to model the dynamic temperature distribution in the heating pipeline. The mathematical model among the nodal heat flow, water mass flow rate, and the inlet and outlet temperatures of each heating pipeline is expressed as follows:

[0114]

[0115] where c W is the specific heat capacity of water (J / kg·K), m mn is the water mass flow rate (kg / s), T m,t , T n,t and are the inlet, outlet, and ambient temperatures (K) of pipeline m-n, respectively. τ n , represent the delay time (h) and relative attenuation degree, respectively, characterizing the delayed heat transfer and heat loss:

[0116]

[0117] where l n is the pipeline length (m) from the heat source to node n; is the water flow velocity (m / s) at node n; ω is the angular frequency (rad / s). h1 and h2 are determined by the following equations:

[0118]

[0119] where k and k WP are the convective heat transfer coefficients (W / m 2 ·K) between the environment and the outer wall of the pipeline, and between the water flow and the inner wall of the pipeline, respectively; and are the outer and inner circumferences (m) of the pipe wall, respectively; A W and A P are the cross-sectional areas (m 2 ) of the water flow and the pipe wall, respectively; cP is the specific heat capacity of the pipe wall (J / kg·K); ρ W and ρ P are the densities of water and the pipe wall (kg / m 3 ). In summary, formulas (18) to (22) are the fourth constraint of the integrated energy community, that is, the energy flow constraint of each subsystem.

[0120] It should be noted that in addition to the operation constraints, energy balance constraints, and network energy flow constraints of the above energy equipment, there are also constraints on the respective network transmission capacities of each subsystem, that is, the second constraint of the integrated energy community:

[0121] |p mn,t | ≤ p mn,max (23)

[0122] |g mn,t | ≤ g mn,max (24)

[0123] |q mn,t | ≤ q mn,max (25)

[0124] where p mn,max , g mn,max , and q mn,max are the transmission limits (kW) of the power line m-n, gas pipeline m-n, and heating pipeline m-n, respectively.

[0125] The embodiments of the present application can be guided by energy-carbon integrated pricing and optimized in two stages. In the local market pricing stage, the carbon tax cost and emission reduction income are considered to make the energy pricing more reasonable and promote the energy trading planning of each subsystem from the dual perspectives of economic and environmental benefits; in the energy management stage, the real-time operation of the subsystem is optimized to ensure the efficient use of energy. By defining the social welfare of each subsystem and clarifying the composition of economic benefits, the optimization motivation of the operator is stimulated. In terms of constraint construction, the real-time energy balance equation (the third constraint) maintains the real-time balance of energy supply and demand of each subsystem, reducing energy waste and shortage; the detailed energy flow constraint (the fourth constraint) accurately describes the energy flow characteristics of the power, natural gas, and heating systems, providing a scientific basis for energy scheduling; the network transmission capacity constraint (the second constraint) fully considers the actual transmission limitations to ensure the safe operation of the energy system.

[0126] In step S104, the objective function of the integrated energy community is constructed by combining the first constraint, the collaborative optimization model, and the second constraint, and the objective function is optimized using a preset Nash bargaining game model to achieve the optimal scheduling of the integrated energy community.

[0127] Specifically, taking the power system, natural gas system, and heating system preferably selected in step S103 as examples, its objective function is:

[0128]

[0129] where x PS is the set of decision variables related to the operation of the power system; x NGS is the set of decision variables related to the operation of the natural gas system; x DHS is the set of decision variables related to the operation of the heating system; sw PS is the social welfare of the power system; sw NGS is the social welfare of the natural gas system; sw DHS is the social welfare of the heating system; is the real-time electricity price; is the real-time natural gas price.

[0130] Specifically, considering that the collaboration and communication among energy sectors are restricted by information asymmetry, communication costs, and trade secrets, etc., subsystem operators may conceal all or part of the information in the decision-making process to safeguard their interests. Therefore, the Nash bargaining game framework is adopted to optimize the decision-making process. Each subsystem operator integrates the bidding information from the producers, prosumers, consumers, and storage resources it manages to conduct energy transactions in the local energy market, aiming to maximize its social welfare. The calculation formula of the Nash bargaining game model is:

[0131]

[0132] where N P is the set of participants; PS is the power system; NGS is the natural gas system; DHS is the heating system; S i is the set of strategies of the participants; X PS is the set of decision variables related to the operation of the power system; x NGS is the set of decision variables related to the operation of the natural gas system; x DHS is the set of decision variables related to the operation of the heating system; is the real-time electricity price; is the real-time natural gas price; B i is the corresponding payoff of the participants; sw PS is the social welfare of the power system; sw NGS is the social welfare of the natural gas system; sw DHS is the social welfare of the heating system.

[0133] Specifically, the game model consists of three elements: the participants N P , their set of strategies S i and the corresponding payoff B i . These elements are described as follows:

[0134] 1) Participants: Electricity, gas and heating system operators participate in the local retail market as unified representatives of their respective market roles;

[0135] 2) Strategy set: Energy carbon pricing strategy for external market game to adjust real-time gas prices and electricity prices Energy management strategies for internal operations Optimize energy dispatch x PS 、x NGS and x DHS ;

[0136] 3) Benefit: The benefit of each participant corresponds to the social welfare sw defined in the objective function PS , sw NGS and sw DHS .

[0137] In actual implementation, the game process between stakeholders is iterated until equilibrium is reached. In this process, all operators have equal market positions while making independent and self-interested decisions. When other subsystems have determined their strategies, a given subsystem adjusts its strategy to maximize its benefits among the available options, which is called the optimal response. A stable equilibrium is achieved when the strategy solution satisfies the optimal response of all subsystems while ensuring that no operator can unilaterally adjust its strategy to obtain additional benefits. This equilibrium condition is expressed as follows:

[0138]

[0139] The embodiment of the present application can construct an objective function by combining various constraints, and optimize it using the Nash bargaining game model, which comprehensively integrates multiple factors such as energy equipment operation, system characteristics and transmission restrictions, so that the objective function is practical and scientific, and can accurately guide the optimization direction of the integrated energy community. The Nash bargaining game framework is adopted, and the information asymmetry, communication costs and commercial secrets between energy departments are fully considered. The operators of each subsystem make decisions based on their own interests on an equal footing. The participants in the game model include operators of electricity, natural gas and heating systems. Their strategy set takes into account external energy carbon pricing and internal energy management to achieve the coordinated optimization of energy prices and scheduling; taking the social welfare of each subsystem as the benefit, it encourages operators to pursue their own interests while promoting the overall benefit improvement of the integrated energy community. The game iteration process of stakeholders can achieve a stable equilibrium, ensure that the strategies of each subsystem achieve the best response, avoid unilateral changes in strategies by operators to destroy the overall balance, effectively realize the optimized scheduling of the integrated energy community, improve energy utilization efficiency, reduce operating costs, and ensure the stable operation of the energy system.

[0140] by Figure 2Taking the integrated energy community of the multi-vector electrified consumers shown as an example, the solution process of its collaborative optimization scheduling method is as Figure 3 shown. Six simulation scenarios are set for comparative simulation analysis to verify the feasibility and advantages of the proposed method. Scenario 1: An integrated energy community integrating three electrified consumers, namely electric vehicles, electrochemical parks, and heat pumps; Scenario 2: An integrated energy community considering only electrochemical parks and heat pumps; Scenario 3: An integrated energy community considering only electric vehicles and heat pumps; Scenario 4: An integrated energy community considering only heat pumps; Scenario 5: An integrated energy community considering only electric vehicles and electrochemical parks; Scenario 6: A traditional combined heat and power energy community without considering electric vehicles, electrochemical parks, and heat pumps.

[0141] Set the parameters of the system as follows: The power load, gas load, and heat demand during the scheduling period are as Figure 4 shown, and the output curves of wind power and photovoltaic units are as Figure 5 shown. The economic indicators of the system are shown in Table 1, and Table 1 is as follows:

[0142]

[0143] Specifically, this application conducts simulation solutions for the optimal scheduling of six scenarios in a 24-hour day through GAMS software, and obtains the real-time electricity and gas prices, system emission reduction amounts, and target optimization results under the six scenarios. The results show that:

[0144] Figures 6 to 11 Summarizes the reshaping of electricity demand in the six scenarios. Initially, the power load curve does not match the local renewable energy generation curve. For example, during the period from 16:00 to 19:00, the renewable energy generation is at a low valley while the power demand reaches a peak. As Figure 6 shown, the integrated energy community considering electric vehicles, electrochemical parks, and heat pumps effectively reshapes the electricity demand during the scheduling period. Comparing Scenario 2 ( Figure 7 ), Scenario 3 ( Figure 8 ), and Scenario 5 ( Figure 10 ), in which electric vehicles, chemical industrial parks, and heat pump electrified consumers are excluded respectively, the results show that electric vehicles have the most significant impact on improving electricity demand. In Scenario 4 ( Figure 9 ), when there are multiple heating options and sufficient heat sources, the role of heat pumps in demand reshaping is minimal. Although electrochemical parks are not as effective as electric vehicles in reshaping electricity demand, Scenario 1 ( Figure 6 ) and Scenario 3 ( Figure 8) The comparison between them shows that consumers in the electrochemical park contribute to achieving a smoother electricity demand curve. These simulation results indicate that the proposed integrated energy community effectively reshapes electricity demand by coordinating the dispatch of multi-vector electrified consumers, ensuring better tracking of local renewable energy generation. Electric vehicles exhibit excellent load shaping capabilities, while consumers in the electrochemical park help reduce load fluctuations.

[0145] Table 2 and Figures 12 to 17 summarize the fluctuation ranges (peaks and valleys) of electricity and gas prices under six scenarios. Table 2 is shown as follows:

[0146]

[0147]

[0148]

[0149]

[0150] In Scenario 1, which includes all types of electrified consumers, the energy price curve is relatively stable. In Scenario 2, which excludes electric vehicles, the energy price fluctuates significantly. For example, the gas price reaches a peak of 1250.09 yuan / kWh and a valley of -837.17 yuan / kWh. In Scenario 3, which excludes the electrochemical park, due to the reduced competition between power producers and end-users for renewable energy power, the energy price drops to negative values during most periods. However, extreme cases of energy prices still exist, such as a peak electricity price of 1280.52 yuan / kWh at 14:00, a valley electricity price of -1033.21 yuan / kWh at 13:00, and a peak gas price of 107.43 yuan / kWh at 13:00. In Scenario 4, which excludes electric vehicles and the electrochemical park, the electricity price increases, with a valley electricity price of -57.36 yuan / kWh at 17:00; in Scenario 5, which excludes heat pumps, the energy price deteriorates extremely, with a peak-to-valley difference in natural gas of 7202.48 yuan / kWh and a peak-to-valley difference in electricity of 3200.63 yuan / kWh; in Scenario 6, which adopts a conventional combined heat and power operation mode, the energy price is mainly affected by the marginal costs of thermal power, renewable energy power, and natural gas from the upstream gas pipeline network. The valley electricity prices for electricity and natural gas occur at 5:00, reaching -6.86 yuan / kWh and -7.38 yuan / kWh respectively. The price changes observed in the six scenarios highlight the impact of different compositions of electrified consumers on the energy price curve within the integrated energy community. The results show that the cooperation among electric vehicles, the electrochemical park, and heat pump multi-vector electrified consumers effectively absorbs the surplus renewable energy, balances energy supply and demand, and stabilizes the fluctuations in natural gas and electricity prices.

[0151] Table 3 shows the optimization results of the integrated energy community under six scenarios. Table 3 is shown as follows:

[0152] Scenario Operating cost (yuan) <![CDATA[CO2 emission reduction (kg)]]> Remaining renewable energy electricity (kWh) 1 1955410 106762 396740 2 2016950 146248 543478 3 2103660 0 396287 4 2220110 0 543315 5 2811710 226825 842911 6 3202530 0 947637

[0153] As can be seen from Table 3, compared with the traditional system, the daily operating cost of the integrated energy community considering multi-vector electrified consumers is reduced by 1,247,120 yuan, and the CO2 emissions are reduced by 10,672 kg. In the scenarios covering the electrochemical park (Scenarios 1, 2, and 5), the electrochemical park makes full use of the surplus renewable energy power for chemical production. Therefore, compared with Scenario 6, Scenario 1 reduces the actual renewable energy curtailment by 947.637 kWh. Although Scenarios 2 and 5 increase the utilization rate of the surplus renewable energy power for chemical production, due to the flexible coordination of multi-vector electrified consumers, Scenario 1 shows the best economic benefits and achieves the lowest operating cost of 1,955,410 yuan.

[0154] According to the integrated energy community optimal scheduling method for multi-vector electrified consumers proposed in the embodiments of the present application, an integrated energy community can be formed by integration, creating conditions for the unified management and optimal scheduling of energy. A collaborative optimization model and constraints are constructed based on the characteristics of multiple subsystems, fully considering the characteristics and mutual relationships of different energy subsystems. An objective function is constructed in combination with various constraints, and the Nash bargaining game model is used for optimization to balance the interests of all parties and achieve the overall optimal scheduling of the integrated energy community, improving energy utilization efficiency and enhancing the economic and environmental benefits of the entire community system.

[0155] Next, an integrated energy community optimal scheduling device for multi-vector electrified consumers proposed in the embodiments of the present application is described with reference to the accompanying drawings.

[0156] Figure 18 It is a block diagram of an integrated energy community optimal scheduling device for multi-vector electrified consumers according to an embodiment of the present application.

[0157] As Figure 18 shown, the integrated energy community optimal scheduling device 10 for multi-vector electrified consumers includes: a combination module 100, a first construction module 200, a second construction module 300, and an optimal scheduling module 400.

[0158] Specifically, the combination module 100 is used to form an integrated energy community from energy producers, prosumers, multi-vector electrified consumers, end-users, and energy storage resources.

[0159] The first construction module 200 is used to construct the first constraint of the integrated energy community based on the flexibly schedulable power demand of multi-vector electrified consumers.

[0160] The second construction module 300 is used to construct a collaborative optimization model and a second constraint of the integrated energy community based on the characteristics of multiple subsystems corresponding to multi-vector electrified consumers.

[0161] The optimal scheduling module 400 is used to construct an objective function of the integrated energy community by combining the first constraint, the collaborative optimization model and the second constraint, and optimize the objective function by using a preset Nash bargaining game model to achieve the optimal scheduling of the integrated energy community.

[0162] Optionally, in an embodiment of the present application, the second construction module 300 includes: a calculation unit, a construction unit and a generation unit.

[0163] Among them, the calculation unit is used to generate an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmit it between multiple subsystems, and calculate the social welfare of multiple subsystems based on the incentive signal.

[0164] The construction unit constructs a third constraint and a fourth constraint of the integrated energy community on the basis of the optimal network loss and energy balance maintenance of the integrated energy community.

[0165] The generation unit is used to generate a collaborative optimization model by combining social welfare, the third constraint and the fourth constraint.

[0166] Optionally, in an embodiment of the present application, the optimal scheduling module 400 includes: the objective function is:

[0167]

[0168] Among them, x PS Set of decision variables related to power system operation; x NGS Set of decision variables related to natural gas system operation; x DHS Set of decision variables related to heating system operation; sw PS Social welfare of the power system; sw NGS Social welfare of the natural gas system; sw DHS Social welfare of the heating system; Real-time electricity price; Real-time natural gas price.

[0169] Optionally, in an embodiment of the present application, the optimal scheduling module 400 includes: the calculation formula of the Nash bargaining game model is:

[0170]

[0171] Among them, N P Is the participant; PS is the power system; NGS is the natural gas system; DHS is the heating system; Si is the strategy set of the participants; x PS is the set of decision variables related to the operation of the power system; x NGS is the set of decision variables related to the operation of the natural gas system; x DHS is the set of decision variables related to the operation of the heating system; is the real-time electricity price; is the real-time natural gas price; B i is the corresponding profit of the participants; sw PS is the social welfare of the power system; sw NGS is the social welfare of the natural gas system; sw DHS is the social welfare of the heating system.

[0172] It should be noted that the foregoing explanation of the embodiment of the integrated energy community optimal scheduling method for multi-vector electrified consumers is also applicable to the integrated energy community optimal scheduling device for multi-vector electrified consumers in this embodiment, and will not be repeated here.

[0173] According to the integrated energy community optimal scheduling device for multi-vector electrified consumers provided by the embodiments of the present application, an integrated energy community can be formed by integration, creating conditions for the unified management and optimal scheduling of energy. A collaborative optimization model and constraints are constructed based on the characteristics of multiple subsystems, fully considering the characteristics and mutual relationships of different energy subsystems. An objective function is constructed in combination with various constraints, and the Nash bargaining game model is used for optimization to balance the interests of all parties and achieve the overall optimal scheduling of the integrated energy community, improve energy utilization efficiency, reduce operating costs, and enhance the stability and environmental benefits of the entire scheduling system.

[0174] Figure 19 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

[0175] A memory 1901, a processor 1902, and a computer program stored on the memory 1901 and executable on the processor 1902.

[0176] When the processor 1902 executes the program, it implements the integrated energy community optimal scheduling method for multi-vector electrified consumers provided in the foregoing embodiment.

[0177] Furthermore, the electronic device further includes:

[0178] A communication interface 1903 for communication between the memory 1901 and the processor 1902.

[0179] The memory 1901 is used to store a computer program executable on the processor 1902.

[0180] Memory 1901 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0181] If memory 1901, processor 1902, and communication interface 1903 are implemented independently, communication interface 1903, memory 1901, and processor 1902 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 19 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0182] Optionally, in a specific implementation, if memory 1901, processor 1902, and communication interface 1903 are integrated on a single chip, memory 1901, processor 1902, and communication interface 1903 can communicate with each other through an internal interface.

[0183] Processor 1902 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0184] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned integrated energy community optimization scheduling method for multi-vector electrified consumers is implemented.

[0185] The embodiments of the present application also provide a computer program product, and the computer program is executed to implement the above-mentioned integrated energy community optimization scheduling method for multi-vector electrified consumers.

[0186] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0187] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0188] Any process or method description depicted in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a manner that is not in the order shown or discussed, including in a substantially simultaneous manner according to the involved functions or in the reverse order, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0189] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0190] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0191] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above-described embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0192] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0193] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A comprehensive energy community optimization dispatch method for multi-vector electrified consumers, characterized in that: Applied to the model building stage, wherein the method comprises the following steps: An integrated energy community consisting of energy producers, prosumers, multi-vector electrification consumers, end users and energy storage resources; constructing a first constraint of the integrated energy community based on the flexibly dispatchable power demand of the multi-vector electrification consumers; constructing a collaborative optimization model and a second constraint of the integrated energy community based on the characteristics of multiple subsystems corresponding to the multi-vector electrification consumers; The objective function of the integrated energy community is constructed by combining the first constraint, the collaborative optimization model and the second constraint, and the objective function is optimized using a preset Nash bargaining game model to achieve optimal scheduling of the integrated energy community.

2. The method according to claim 1, characterized in that The collaborative optimization model of the integrated energy community is constructed based on the characteristics of multiple subsystems corresponding to the multi-vector electrification consumers, including: Generate an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmit it to the multiple subsystems to calculate the social welfare of the multiple subsystems based on the incentive signal; On the basis of the optimal network loss of the integrated energy community and maintaining energy balance, constructing the third constraint and the fourth constraint of the integrated energy community; The collaborative optimization model is generated by combining the social welfare, the third constraint and the fourth constraint.

3. The method according to claim 1, characterized in that The objective function is: Among them, x PS The set of decision variables related to power system operation; x NGS The set of decision variables related to the operation of the natural gas system; x DHS The set of decision variables related to the operation of the heating system; sw PS For the social welfare of the power system; SW NGS For the social benefits of the natural gas system; sw DHS for the social benefits of the heating system; is the real-time electricity price; Real-time natural gas prices.

4. The method according to claim 1, characterized in that The Nash bargaining game model formula is: Among them, N P are participants; PS is the power system; NGS is the natural gas system; DHS is the heating system; S i is the strategy set of the participants; x PS The set of decision variables related to power system operation; x NGS The set of decision variables related to the operation of the natural gas system; x DHS the set of decision variables related to the operation of the heating system; is the real-time electricity price; is the real-time natural gas price; B i The corresponding benefits for the participants; sw PS For the social welfare of the power system; sw NGS For the social benefits of the natural gas system; sw DHS For the social benefits of heating systems.

5. An integrated energy community optimization dispatching device for multi-vector electrification consumers, characterized in that: Applied in the model building phase, including: Combination modules for integrated energy communities consisting of energy producers, prosumers, multi-vector electrification consumers, end users, and energy storage resources; A first building module, configured to build a first constraint of the integrated energy community based on the flexibly dispatchable power demand of the multi-vector electrification consumer; A second building module is used to build a collaborative optimization model and a second constraint of the integrated energy community based on a plurality of subsystem characteristics corresponding to the multi-vector electrification consumer; An optimization scheduling module is used to construct an objective function of the integrated energy community by combining the first constraint, the collaborative optimization model and the second constraint, and optimize the objective function using a preset Nash bargaining game model to achieve optimal scheduling of the integrated energy community.

6. The device according to claim 5, characterized in that The second building block comprises: A calculation unit, used to generate an incentive signal according to the economic benefits of the integrated energy community and the purpose of scientific carbon emission reduction, and transmit the incentive signal to the multiple subsystems to calculate the social welfare of the multiple subsystems based on the incentive signal; A construction unit, configured to construct a third constraint and a fourth constraint of the integrated energy community on the basis of optimal network loss and maintaining energy balance in the integrated energy community; A generating unit is used to generate the collaborative optimization model by combining the social welfare, the third constraint and the fourth constraint.

7. The device according to claim 5, characterized in that The optimization scheduling module includes: the objective function is: Among them, x PS The set of decision variables related to power system operation; x NGS The set of decision variables related to the operation of the natural gas system; x DHS The set of decision variables related to the operation of the heating system; sw PS For the social welfare of the power system; sw NGS For the social benefits of the natural gas system; sw DHS for the social benefits of the heating system; is the real-time electricity price; Real-time natural gas prices.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the integrated energy community optimization scheduling method for multi-vector electrified consumers as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the integrated energy community optimization scheduling method for multi-vector electrification consumers as described in any one of claims 1-4.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the integrated energy community optimization dispatch method for multi-vector electrified consumers as described in any one of claims 1-4.

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