Virtual power plant low-carbon optimization method, system and equipment based on two-stage carbon transaction

Abstract

The invention relates to the technical field of energy power, and discloses a virtual power plant low-carbon optimization method, system and equipment based on two-stage carbon transaction, and the method comprises the steps: comprehensively considering the difference between equipment types and loads in each functional region and an electric vehicle aggregator based on a framework model divided according to functions; a two-stage carbon transaction model is established, in the first stage, optimal distribution of carbon quotas is achieved through carbon quota transactions among the subjects, and in the second stage, the subjects participate in stepped carbon transactions in the carbon market; in order to improve the enthusiasm of the electric vehicle to participate in carbon emission reduction, the carbon emission reduction amount is used as the obtained carbon quota, the electric vehicle fully participates in the carbon transaction, and the carbon quota is sold to obtain benefits; in view of different contribution degrees of each VPP system to the whole, the VPP system with a large contribution degree should obtain higher benefits, so that benefit distribution is performed by using the comprehensive contribution rate, and the comprehensive contribution rate is applied to the payment bargaining sub-problem, thereby improving fairness and rationality of benefit distribution.

Description

Technical Field

[0001] The present invention relates to the field of energy and power technology, and in particular to a low-carbon optimization method, system and equipment for a virtual power plant based on two-stage carbon trading. Background Art

[0002] As a clean and efficient energy management system that can achieve multi-energy coupling, virtual power plant (VPP) can couple various forms of energy such as electricity and heat. It can effectively improve energy utilization efficiency, reduce carbon emissions, and reduce energy waste and environmental pollution caused by the single energy supply of traditional energy supply systems. It is an important means for my country to achieve its low-carbon commitments and high-quality energy utilization.

[0003] At the market mechanism level, the carbon trading mechanism is considered to be an effective means to achieve low-carbon emission reduction goals. Under the carbon trading mechanism, there have been many literature studies on the low-carbon operation of virtual power plants with only a single interest subject, but few literatures analyze the interest interaction of multiple interest subjects under the carbon trading mechanism and the positive role of multi-subject cooperative operation in reducing carbon emissions. At the same time, the point-to-point transaction between virtual power plants is a decentralized transaction model. The transaction can be completed without the intervention of the distribution network operator, so that each virtual power plant member can not only participate in the upper carbon market, but also trade carbon quotas with other VPPs. In addition, the current carbon trading model cannot effectively guide electric vehicles to participate in carbon trading and give full play to their low-carbon benefits.

[0004] In the optimization of multi-VPP system cooperation, in addition to energy transfer, we also need to pay attention to the distribution of benefits between VPPs. Shapley value distribution will encounter the problem of "combinatorial explosion" when facing a large-scale microgrid, resulting in low computational efficiency. The standard Nash negotiation method only maximizes the benefits of all VPPs and distributes the profits equally, without taking into account the various differences in the contribution of each subject in the cooperation.

[0005] Therefore, based on the above problems, the present application provides a low-carbon optimization method, system and equipment for a virtual power plant based on two-stage carbon trading. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a low-carbon optimization method, system and equipment for a virtual power plant based on two-stage carbon trading, which comprehensively considers the differences in equipment types and loads in various functional areas and electric vehicle aggregators, and allocates carbon quotas according to the different contributions of each VPP system to the whole, so as to achieve fair and reasonable profit distribution for each VPP system.

[0007] In a first aspect, the present invention provides a low-carbon optimization method for a virtual power plant based on two-stage carbon trading, comprising the following steps:

[0008] S1. Constructing a cooperative operation framework model of a multi-VPP system; the multi-VPP system includes an electric vehicle aggregator and multiple functional areas;

[0009] S2. Calculate the carbon quota of electric vehicle aggregators and the carbon quota of each functional area based on the framework model; subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; calculate the carbon trading cost of each functional area based on the carbon emissions after the carbon quota is shared;

[0010] S3. Construct a first total cost minimization model for each functional area based on the framework model and the carbon trading cost, which is used to calculate the minimum total operating cost of each functional area; construct a second total cost minimization model for electric vehicle aggregators based on the framework model and the carbon quota of electric vehicle aggregators, which is used to calculate the minimum total operating cost of electric vehicle aggregators; the first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between each VPP system;

[0011] S4. Perform the first stage calculation based on the first sub-problem of Nash negotiation: construct a multi-VPP system cost minimization problem model based on the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP systems is minimized; calculate the trading volume of electricity, heat, and carbon quotas between each VPP system based on the multi-VPP system cost minimization problem model;

[0012] S5. Perform the second-stage calculation based on the second sub-problem of Nash negotiation: obtain multiple contribution factors corresponding to each VPP system, and obtain the comprehensive contribution rate of each VPP system according to the contribution factors and the weights corresponding to each contribution factor; construct a payment bargaining sub-problem model based on the comprehensive contribution rate and the multi-VPP system cost minimization problem model; and calculate the transaction prices of electricity, heat, and carbon quotas between each VPP system based on the payment bargaining sub-problem model.

[0013] In a second aspect, the present invention provides a virtual power plant low-carbon optimization system based on two-stage carbon trading, comprising:

[0014] Preprocessing module: construct a cooperative operation framework model of a multi-VPP system; the multi-VPP system includes an electric vehicle aggregator and multiple functional areas; calculate the carbon quota of the electric vehicle aggregator and the carbon quota of each functional area based on the framework model; subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; calculate the carbon trading cost of each functional area based on the carbon emissions after the carbon quota is shared;

[0015] Cost model construction module: construct the first total cost minimization model for each functional area according to the framework model and the carbon trading cost, which is used to calculate the minimum total operating cost of each functional area; construct the second total cost minimization model for electric vehicle aggregators according to the framework model and the carbon quota of electric vehicle aggregators, which is used to calculate the minimum total operating cost of electric vehicle aggregators; the first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between each VPP system;

[0016] The first phase calculation module: The first phase calculation is performed based on the first sub-problem of Nash negotiation: a multi-VPP system cost minimization problem model is constructed according to the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP system is minimized; the trading volume of electricity, heat and carbon quotas between each VPP system is calculated according to the multi-VPP system cost minimization problem model;

[0017] The second-stage calculation module: The second-stage calculation is performed based on the second sub-problem of Nash negotiation: multiple contribution factors corresponding to each VPP system are obtained, and the comprehensive contribution rate of each VPP system is obtained according to the contribution factors and the weights corresponding to each contribution factor; the payment bargaining sub-problem model is constructed according to the comprehensive contribution rate and the multi-VPP system cost minimization problem model; the transaction prices of electricity, heat and carbon quotas between each VPP system are calculated according to the payment bargaining sub-problem model.

[0018] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method of the first aspect.

[0019] The embodiments of the present invention have the following technical effects:

[0020] The present invention is based on a framework model divided by function, which comprehensively considers the differences in equipment types and loads in various functional areas and electric vehicle aggregators; establishes a two-stage carbon trading model, in which the optimal allocation of carbon quotas is achieved through carbon quota transactions between various VPP system entities in the first stage, and in the second stage, each VPP system entity participates in a stepped carbon trading in the carbon market;

[0021] Among them, in order to further increase the enthusiasm of electric vehicles to participate in carbon emission reduction, their carbon emission reduction is used as the carbon quota they obtain, so that they can fully participate in carbon trading and sell their carbon quotas to obtain benefits; a tiered carbon trading mechanism is adopted to punish excess carbon quotas, which can promote carbon quota trading among various entities;

[0022] In view of the different contribution of each VPP system to the whole, the VPP system with a greater contribution should obtain higher benefits. Therefore, the comprehensive contribution rate is used to distribute benefits. The comprehensive contribution rate is applied to the payment bargaining sub-problem to improve the fairness and rationality of benefit distribution. The calculation of the comprehensive contribution rate takes into account four aspects, comprehensively examines the contribution of VPP under each dimension, and further improves the accuracy of benefit distribution.

[0023] This application further increases the enthusiasm of electric vehicle aggregators to participate in carbon emission reduction, enabling them to fully participate in carbon trading and sell their carbon emission quotas to gain benefits;

[0024] In summary, this application can take into account the economy and low carbon nature of the system and achieve fair and reasonable profit distribution for each VPP system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art are briefly introduced below.

[0026] Figure 1 It is a flow chart of a low-carbon optimization method for a virtual power plant based on two-stage carbon trading provided by an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a framework model provided by Embodiment 1 of the present invention;

[0028] Figure 3 is a schematic diagram of electric power transaction volume between various VPP systems provided in Embodiment 4 of the present invention;

[0029] Figure 4 is a schematic diagram of thermal power trading volume between various VPP systems provided in Example 4 of the present invention;

[0030] Figure 5 This is a schematic diagram of the electric energy transaction price between various VPP systems provided in Example 4 of the present invention;

[0031] Figure 6 is a schematic diagram of heat energy trading prices between various VPP systems provided in Example 4 of the present invention;

[0032] Figure 7 This is a schematic diagram of carbon quota trading volume between various VPP systems provided in Example 4 of the present invention;

[0033] Figure 8 This is a schematic diagram of carbon quota trading prices between various VPP systems provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. First, the following contents are explained:

[0035] (1) VPP (Virtual Power Plant) is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles, so that it can participate in the power market and power grid operation as a special power plant.

[0036] (2) Nash negotiation theory is an important concept in economics and game theory, especially when dealing with cooperation and allocation problems. Under the framework of Nash negotiation, it can usually be broken down into two main sub-problems:

[0037] 1. Alliance benefit maximization sub-problem: Among all possible cooperation plans, find a plan that can maximize the overall benefits of all participants. During the negotiation process, all parties will strive to reach an agreement so that the total benefits after cooperation exceed the sum of the benefits of each party acting alone. This sub-problem corresponds to the problem of minimizing the cost of all VPPs in this application.

[0038] 2. Cooperation benefit distribution sub-problem: On the basis of maximizing the benefits of the alliance, it is necessary to determine how to fairly distribute the additional benefits generated by the cooperation to each participant. This is a key negotiation link, because the rationality of the distribution plan directly affects the success of the negotiation and the willingness of all parties to cooperate. This sub-problem corresponds to the payment bargaining sub-problem expressed in this application.

[0039] By solving these two sub-problems, fair distribution among all parties can be achieved based on maximizing overall benefits, thereby promoting the smooth progress and sustainable development of cooperation.

[0040] (3) Carbon trading: This is a market artificially created by the government by controlling the total amount of emissions from energy-consuming enterprises. The government will determine a total amount of carbon emissions and allocate carbon emission quotas to enterprises according to certain rules. These quotas represent the amount of carbon dioxide that enterprises can legally emit.

[0041] If an enterprise adopts energy-saving and emission-reduction technologies or optimizes production processes, and its actual carbon emissions are lower than the quotas it has obtained, then this excess quota can be sold through the carbon trading market, thereby obtaining economic benefits. This mechanism encourages enterprises to actively take emission reduction measures.

[0042] If a company's actual carbon emissions exceed the quota it has been granted, it will need to purchase additional quotas on the market to complete the clearance. This punitive economic measure encourages companies to work hard to reduce carbon emissions in order to avoid high purchase costs.

[0043] (4) EV cluster refers to a large number of electric vehicles (EVs) organized in some way to form a collaborative working whole.

[0044] (5) P2P: A decentralized model in which energy is exchanged and utilized directly between nodes.

[0045] Example 1

[0046] Figure 1 Flow chart of a low-carbon optimization method for a virtual power plant based on two-stage carbon trading provided by an embodiment of the present invention. Figure 1 , specifically including:

[0047] S1. Build a cooperative operation framework model for multiple VPP systems; the VPP system includes electric vehicle aggregators and multiple functional areas.

[0048] In this application, for example, the multi-VPP system cooperative operation framework is as follows: Figure 1 As shown in the figure, it is composed of three typical urban areas: electric vehicle aggregators and industrial areas (VPP1), residential areas (VPP2) and commercial areas (VPP3). Each functional area can effectively integrate various types of distributed energy, loads, energy storage and other equipment. Here, VPP1, VPP2, and VPP3 can be repeated in multiples, or only one or two of the three can be included. The following content is based on Figure 2 In addition, electric vehicle aggregators can use VPP EV express.

[0049] Each functional area has different resource endowments and load characteristics, which are explained as follows:

[0050] VPP1 is generally located in the suburbs of the city. The open geographical location makes wind power more efficient. VPP1 includes wind turbines (WT), gas turbines (GT), gas boilers (GB), waste heat boilers (WHB), waste heat power generation (ORC), power-to-hydrogen (P2H), electrolysis of molten carbonate (EMC), hydrogen fuel cells (HFC), electric energy storage (ES), heat storage tanks (TS), hydrogen energy storage (HS) and user electricity, heat and cooling loads.

[0051] VPP2 includes photovoltaic (PV), gas turbine (GT), gas boiler (GB), waste heat boiler (WHB), electric refrigeration (EC), absorption refrigeration (AC), electric energy storage (ES), thermal storage tank (TS), hydrogen energy storage (HS) and user electricity, heating and cooling loads.

[0052] VPP3 includes photovoltaic (PV), gas turbine (GT), gas boiler (GB), waste heat boiler (WHB), electric energy storage (ES), heat storage tank (TS), hydrogen energy storage (HS) and user electricity, heat and cooling loads.

[0053] This application introduces electric vehicle carbon quotas to encourage electric vehicles to use clean energy to charge as much as possible. The difference between electric vehicle aggregators and functional areas is that electric vehicles do not involve resources such as gas and heat energy, and obviously cannot be equated with functional areas.

[0054] It should be noted that the following uses VPP i , i=1,2,...,EV represents each VPP system, unless otherwise specified as a functional area, it includes EV, and EV refers to electric vehicle.

[0055] S2. Calculate the carbon quota of electric vehicle aggregators and each functional zone based on the framework model; subtract the carbon quota of the corresponding functional zone from the actual carbon emissions of each functional zone to obtain the carbon emissions of each functional zone after the carbon quota is shared; calculate the carbon trading cost of each functional zone based on the carbon emissions after the carbon quota is shared.

[0056] Taking into account the differences in equipment types and loads in various VPP systems, each VPP system sells or purchases quotas in the carbon market, transfers excess quotas to those with insufficient carbon quotas, and achieves the optimal allocation of quotas among entities. However, the purchase expenditure of the same amount of quotas is much higher than the sales income, which will seriously reduce the economic efficiency of the system.

[0057] It should be noted that the following parameters are all set based on a period of one day, that is, based on changes in 24 hours.

[0058] (1) The carbon emissions of each functional area mainly come from energy purchases from the superior energy network, gas turbines and gas boilers, which are existing technologies. The actual carbon emissions of each functional area can be expressed as:

[0059]

[0060] Where: and are the electric power and thermal power purchased from the upstream network at time t, respectively; They are VPP at time t i Energy purchase from the superior energy network, energy supply from gas turbines, energy supply from gas boilers and actual carbon emissions of the system; is the amount of CO2 electrolyzed by the EMC device at time t; and are the electrical output and thermal output of the gas turbine at time t, is the thermal output of the gas boiler at time t; 11 , ε 12 , ε 21 , ε 22 , σ3 are the carbon emission coefficients of the upper power grid, upper gas grid, gas turbine and gas boiler respectively. It should be noted that the above energy purchase refers to the carbon emissions of external energy when purchasing energy from outside. Energy supply refers to the carbon emissions generated by internal equipment; that is, energy purchase refers to the purchase of energy, and energy supply refers to the supply of energy by internal equipment.

[0061] (2) The carbon quota of electric vehicles is the carbon emissions reduced relative to fuel vehicles. That is, the carbon quota of electric vehicle aggregators is the carbon dioxide generated by fuel vehicles traveling the same mileage minus the carbon emissions generated by the charging power of electric vehicles. The calculation formula is as follows:

[0062]

[0063] Where: is the carbon quota of the electric vehicle aggregator at time t; is the charging load of the vth electric vehicle at time t; L ev E is the mileage that an electric vehicle can travel per unit of electricity; gas The carbon emissions of a fuel car driving 1km; The renewable energy output in the electric vehicle charging power at time t; is the fossil energy output of the electric vehicle charging power at time t, Ex h is the carbon emission factor per unit of electricity from fossil energy; V refers to the number of electric vehicles.

[0064] (3) The carbon quota of each functional area can be expressed as:

[0065]

[0066] Where: are the carbon quotas for purchasing energy from the upper energy grid, gas turbines and gas boilers respectively; τ e , τ h The carbon quotas for electricity and heat supply per unit respectively; For VPP i of carbon quotas.

[0067] (4) The actual carbon emissions of each functional area minus the carbon quota of the corresponding functional area is used to obtain the carbon emissions after the carbon quota of each functional area is shared, which can be expressed as:

[0068] in, VPP at time t i Supply to VPP j Carbon quota, D = {VPP i , i=1,2,3,....}.

[0069] It should be noted that the calculated carbon emissions after carbon quota sharing can be positive or negative.

[0070] Here, we construct a tiered carbon trading rule. Each functional area will participate in the external tiered carbon trading for the part of its actual carbon emissions that exceeds its carbon quota, and obtain the carbon trading cost of each functional area. The above process ensures that the carbon emissions of multiple VPP systems participating in market transactions are minimal by imposing trading penalties on high carbon emissions, and the carbon trading cost of each functional area is It can be expressed as:

[0071]

[0072] in, is the carbon trading base price; δ is the price growth rate; L is the length of the carbon emission interval.

[0073] The tiered carbon trading rules here can be set according to actual conditions, and this application does not make specific restrictions on this, so as to achieve the purpose of incentivizing each VPP system to take emission reduction measures.

[0074] S3. Constructing the first total cost minimization model of each functional area based on the framework model and the carbon trading cost, so as to calculate the minimum value of the total operating cost of each functional area;

[0075] A second total cost minimization model for electric vehicle aggregators is constructed based on the framework model and the carbon quota of electric vehicle aggregators to calculate the minimum total cost of operation of electric vehicle aggregators;

[0076] The first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between various VPP systems.

[0077] Based on the resource and load differences between functional areas and electric vehicle aggregators, the two are discussed separately here:

[0078] (1) Functional area

[0079] There are interactions between functional zones and between functional zones and electric vehicle aggregators during operation, and each functional zone will have operation and maintenance costs, network access costs, etc. Therefore, based on the above settings, the first total cost minimization model of the functional zone is:

[0080]

[0081] Among them, minU i is the minimum total cost of electric vehicle aggregator operation; C i，grid For VPP i The cost of interacting with the upper network; C i，op For VPP i Operation and maintenance costs; C i，trade For each functional area including VPP EV The network access fee generated when the energy of other VPP systems including For VPP i Carbon trading costs; i，trade For VPP i With VPP EV Energy interaction benefits of other VPPs including. The specific calculation formula is as follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Where: and They are VPP at time t i Electric and thermal power sold to upstream networks; and They represent the electricity price and heat price of the upper network at time t respectively; and They represent the electricity price and heat price bought back by the superior network at time t respectively; p g，s is the natural gas price, is the amount of natural gas purchased, and They are VPP at time t i Electric power and thermal power purchased from the upper network; D = {VPP i , i=1,2,3,....}; c WT 、c PV 、c GT 、c GB 、c WHB 、c ORC 、c P2H 、c EMC 、c HFC 、c EC 、c AC 、c ES 、cTS 、c HS are the operating cost coefficients of each equipment respectively;

[0088] are the power of each device respectively; k is the conversion coefficient of network fee; c is the benefit per unit of solid carbon; is the carbon trading cost of each VPP system, is the solid carbon produced by electrolysis at time t; and VPP at time t i Supply to VPP j The electric power, thermal power and carbon quota of the company, with positive values ​​indicating sales and negative values ​​indicating purchases; and VPP at time t i With VPP j The electricity price, thermal energy price and carbon quota price traded between them, where p is a lowercase letter.

[0089] Among them, the unknown parameters include the transaction volume and transaction price of electricity, heat and carbon quota between each VPP system, which are the parameters to be solved, specifically:

[0090] The first cost minimization model is based on the first constraint condition, which is used to constrain the output, energy conversion and energy balance of equipment in each functional area and is a part of the model.

[0091] The first constraint condition mainly restricts power balance, gas turbines, gas boilers, EMC, waste heat boilers and waste heat power generation, electric hydrogen production, hydrogen fuel cells, electric refrigeration, absorption refrigeration, energy storage and flexible loads. The specific contents are as follows:

[0092] ①Power balance

[0093]

[0094] in, Power forecasting for wind power; Predict power for photovoltaics; is the heat load after demand response; is the gas turbine power consumption at time t; is the gas power consumption of GB at time t; for the amount of natural gas purchased; is the hydrogen production power of P2H; is the hydrogen consumption power of HFC at time t; is the electric load after demand response at time t.

[0095] ② Gas turbine

[0096]

[0097] Where: η p，t and η q，t are the electrical efficiency and thermal efficiency of the gas turbine at time t; H gas It is the low calorific value of natural gas; The upper and lower limits of GT output; is the spare capacity of GT; They are the upper and lower limits of the gas turbine climbing power respectively.

[0098] ③Gas boiler

[0099]

[0100] Where: η GB is the heating efficiency of GB; The upper and lower limits of GB output; They are the upper and lower limits of GB climbing power respectively.

[0101] ④EMC

[0102]

[0103] Where: is the amount of CO2 electrolyzed by the EMC device at time t; β EMC is the electrolysis efficiency of the EMC device; α is the flue gas split ratio; is the solid carbon content produced by electrolysis at time t; are the energy consumption and waste heat generated by EMC at time t respectively; C , PEMC, HEMC They are respectively the solid carbon produced by electrolysis unit CO2, the energy consumption required, and the waste heat power generated.

[0104] The EMC here produces solid carbon and uses the remaining heat to supply the heat load while helping the system reduce carbon emissions.

[0105] ⑤ Waste heat boiler and waste heat power generation

[0106]

[0107] Where: is the thermal power collected by the system waste heat bus at time t; Enter the heat power of ORC and WHB at time t; are the proportions of waste heat input to ORC and WHB at time t to the total waste heat; is the ORC power output at time t; η ORC is the power generation efficiency of ORC; η WHB The heating efficiency of WHB.

[0108] ⑥ Hydrogen production by electricity

[0109]

[0110] Where: are the P2H power consumption and hydrogen production power at time t respectively; η P2H is the conversion efficiency of P2H; They are the upper and lower limits of P2H power consumption respectively; They are the upper and lower limits of P2H power consumption climbing respectively; It is the spare capacity of P2H.

[0111] ⑦ Hydrogen fuel cell

[0112]

[0113] Where: is the hydrogen consumption power of HFC at time t; are the electrical power and thermal power of HFC at time t respectively; η HFC，p，t , η HFC，q，t are the electrical efficiency and thermal efficiency of HFC at time t respectively; They are the upper and lower limits of HFC electric power respectively; They are the upper and lower limits of HFC power ramping, respectively; It is the spare capacity of HFC.

[0114] ⑧Electric refrigeration

[0115]

[0116] Where: is the EC cooling power at time t; η EC The efficiency of electric refrigeration; is the power consumption of EC at time t; They are the upper and lower limits of EC cooling power respectively.

[0117] ⑨Absorption Refrigeration

[0118]

[0119] Where: is the AC cooling power at time t; η AC is the efficiency of absorption refrigeration; is the AC heat consumption power at time t; They are the upper and lower limits of AC cooling power respectively.

[0120] ⑩Energy storage

[0121]

[0122] The superscript m indicates the energy type, ES indicates electric energy storage, TS indicates thermal energy storage tank, and HS indicates hydrogen energy storage; is the capacity of the energy storage device at time t; φ m is the self-loss coefficient of the energy storage device; are the charging and discharging efficiencies of the energy storage device, respectively; They are the charging and discharging power of the energy storage device respectively.

[0123] Energy storage equipment capacity constraints:

[0124] Where: and are the maximum and minimum storage capacities of the energy storage device m, respectively.

[0125] Energy storage equipment charging and discharging power constraints:

[0126]

[0127] Where: is a variable from 0 to 1, used to indicate whether the energy storage device m can be charged and discharged at the same time. Indicates that the energy storage device is charged. Indicates that the energy storage device releases energy; They respectively represent the upper limits of the charging and discharging power of the energy storage device m.

[0128] The energy storage equipment should have the same starting conditions in each scheduling cycle, expressed as:

[0129] In the formula, is the initial capacity of the energy storage device m; It is the remaining capacity of energy storage device m after running one cycle.

[0130] Flexible load

[0131] The electric and thermal loads participating in VPP dispatch can be expressed as:

[0132] In the formula, are the electric and thermal loads before demand response at time t, respectively; and They are the electric and thermal loads after demand response at time t.

[0133]

[0134]

[0135] Where: are the power of electrical and thermal loads that can be moved at time t; α t，TSE , α i，TSH They are the ratio of time-shiftable electricity to total electricity load and the ratio of heat load to total heat load respectively.

[0136]

[0137] Where: They are respectively the power that can be interrupted at time t and the thermal load power; α i，IE , α i，IH They are the ratio of interruptible electrical load to total electrical load and the ratio of interruptible thermal load to total thermal load respectively.

[0138] (2) Electric vehicle aggregators

[0139] Electric vehicle aggregators interact with various functional areas. Electric vehicles usually choose to charge their batteries when electricity prices are low and discharge their batteries when electricity prices are high to minimize costs. The operating cost minimization objective function of electric vehicle aggregators, that is, the second total cost minimization model, is expressed as:

[0140] Among them, the unknown parameters are also included, namely, the amount of electricity trading between electric vehicle aggregators and functional areas, the amount of carbon quota trading, the electricity trading price, and the carbon quota trading price, which are as follows: It only includes the carbon quota trading volume interacting between electric vehicle aggregators and various functional areas.

[0141] Likewise, the second cost minimization model is based on the second constraint condition, which is used to constrain the output, energy conversion and energy balance of each device and is a part of the model.

[0142] The second constraint condition includes: electric vehicle charging and discharging power constraint, EV charging and discharging state constraint and EV capacity constraint. The specific contents are as follows:

[0143] ① Electric vehicle charging and discharging power constraints:

[0144]

[0145] Where: is the driving distance of the vth electric vehicle in period t; is the net charging demand of the vth electric vehicle, is the charging power of the vth electric vehicle in period t, is the discharge power of the vth electric vehicle in period t, For electric vehicle aggregators to provide VPP i Power purchase, For VPP i Purchase electric power from EV aggregators.

[0146] ②EV charging and discharging state constraints:

[0147]

[0148] Where: Boolean variable μ v，ch，t , μ v，dc，t They are whether the vth electric vehicle is in the charging or discharging state at time t, if yes, set to 1, otherwise set to 0; is the maximum charging power of the vth electric vehicle, is the maximum discharge power of the vth electric vehicle.

[0149] ③EV capacity constraints:

[0150]

[0151] Where: is the available capacity of electric vehicles at time t; are the charging and discharging efficiency of electric vehicles respectively; are the maximum and minimum capacities of the electric vehicle respectively, and Δt is the time interval.

[0152] S4. Perform the first-stage calculation based on the first sub-problem of Nash negotiation: construct a multi-VPP system cost minimization problem model based on the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP systems is minimized; calculate the trading volume of electricity, heat, and carbon quotas between each VPP system based on the multi-VPP system cost minimization problem model.

[0153] Phase 1: The problem of maximizing alliance benefits can be equivalent to the problem of minimizing costs. The cost saved is the benefit achieved by cooperation, and the total cost is the sum of the operating costs of each VPP system. Therefore, it can be expressed as:

[0154] The transaction volume can be calculated, that is This allows the operating status of the equipment in each VPP system to be determined.

[0155] S5. Perform the second-stage calculation based on the second sub-problem of Nash negotiation: obtain multiple contribution factors of each VPP system, and obtain the comprehensive contribution rate of each VPP system according to the contribution factors and the weights corresponding to each contribution factor; construct the payment bargaining sub-problem model based on the comprehensive contribution rate and the multi-VPP system cost minimization problem model;

[0156] The transaction prices of electricity, heat and carbon quotas between various VPP systems are calculated based on the payment bargaining sub-problem model.

[0157] Phase 2: After achieving the lowest total cost among multiple VPP systems, a reasonable method is needed to distribute the benefits to each VPP system. Given that each VPP system contributes differently to the whole, the VPP system with a greater contribution should obtain higher benefits. Therefore, this application uses the multi-factor comprehensive contribution rate to distribute benefits, that is, bargaining to maximize benefits.

[0158] ①Energy sharing contribution factor

[0159] A nonlinear function based on natural logarithm is used to quantify the contribution of multiple VPP systems in energy sharing. Each VPP system negotiates with each other based on its own contribution as bargaining power to determine the energy transaction price between them and fairly distribute the benefits of energy sharing. The details are as follows:

[0160] First, calculate the total energy provided by each VPP system during the scheduling period and the total energy obtained

[0161]

[0162]

[0163]

[0164] An exponential function based on the natural constant e is used to form a nonlinear energy mapping function to quantify the bargaining power of each VPP system according to its contribution to energy sharing. i，1 :

[0165]

[0166] Where: For VPP i The amount of electricity supplied, For VPP i The heat supply, For VPP i The carbon quota supply; and are the maximum values ​​of the electricity, heat and carbon quota supply energy in each VPP system; For VPP i The amount of power received, For VPP i The amount of heat energy received, For VPP i The amount of carbon quota received; and They are the maximum values ​​of the electricity, heat and carbon quota received by each VPP system.

[0167] In addition, this application also takes into account the following factors to consider the contribution of each VPP system to the entire system in multiple dimensions when allocating benefits, including economic factors, environmental protection and low-carbon factors, user satisfaction, and safety and reliability factors.

[0168] ② Carbon emission reduction contribution factor

[0169] Carbon emission reduction contribution factor of functional area d i，2 The calculation formula is as follows:

[0170]

[0171] Where: The amount of carbon emissions reduced after cooperation between various functional areas; is the carbon emissions of each functional area before cooperation, The carbon emissions after cooperation among various functional areas.

[0172] ③User satisfaction contribution factor

[0173] User satisfaction contribution factor d of functional area i，3 The calculation is as follows:

[0174]

[0175] in, Improve user satisfaction after cooperation among various VPP systems; They are the electricity and heat satisfaction of users in each functional area before cooperation; They are the users' electrical and thermal satisfaction after the cooperation in each functional area.

[0176] ④ Risk resistance contribution factor

[0177] Functional area's anti-risk contribution factor d i，4 The calculation is as follows:

[0178]

[0179] Among them, R i，max is the maximum dispatchable reserve capacity in each functional area, S i For spare capacity, is the predicted power of wind power or photovoltaic power, and the “ / ” in the formula means “or”; Forecasting power for loads; is the actual power of wind power or photovoltaic power, and the “ / ” in the formula means “or”; is the actual power of the load.

[0180] In the process of multi-VPP system cooperation, the comprehensive contribution rate is an important factor in determining the final profit distribution coefficient of each VPP system. This application quantifies this indicator from four perspectives: energy sharing, carbon emission reduction, user satisfaction, and risk resistance, so as to make the contribution statistics of each VPP system more comprehensive and accurate. i For: i =ω1d i，1 +ω2d i，2 +ω3d i，3 +ω4d i，4 , where ω1, ω2, ω3, and ω4 are the weights of each contribution factor.

[0181] It should be noted that there is no limitation on the setting of weight values ​​here, and they can be set according to actual needs.

[0182] After obtaining the comprehensive contribution rate, substitute it into the following payment bargaining sub-problem model:

[0183]

[0184] in, is the total system cost before multi-VPP cooperation, and The asterisk in the upper right corner indicates the value obtained based on the results of the first stage calculation. The above formula can be used to calculate I i，trade The specific calculation can be obtained value to obtain the transaction price with the best efficiency.

[0185] For example, the alternating direction multiplier method (ADMM) can be used for two-stage calculations. The alternating direction multiplier method (ADMM) has good convergence for large-scale variable optimization problems and can fully guarantee the privacy of information when each subject cooperates in operation. Based on the above-established model, the ADMM algorithm is used to solve the optimal solution for each parameter of the model.

[0186] The alternating direction multiplier method is a prior art and will not be described in detail in this application. Other methods may also be used to implement it according to actual conditions and are not limited here.

[0187] In summary, by setting up the above-mentioned multi-VPP system based on unknown parameters, comprehensively considering the differences in equipment types and loads in various functional areas and electric vehicle aggregators, and distributing benefits according to the degree of sharing, the purpose of low-carbon optimization of carbon trading virtual power plants can be achieved.

[0188] Example 2

[0189] Based on the content of Example 1, the present application provides a virtual power plant low-carbon optimization system based on two-stage carbon trading, including:

[0190] Preprocessing module: Construct a cooperative operation framework model of a multi-VPP system; the multi-VPP system includes electric vehicle aggregators and multiple functional areas; calculate the carbon quota of electric vehicle aggregators and the carbon quota of each functional area based on the framework model; subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; calculate the carbon trading cost of each functional area based on the carbon emissions after the carbon quota is shared;

[0191] Cost model construction module: construct the first total cost minimization model for each functional area based on the framework model and carbon trading costs, which is used to calculate the minimum total operating cost of each functional area; construct the second total cost minimization model for electric vehicle aggregators based on the framework model and the carbon quota of electric vehicle aggregators, which is used to calculate the minimum total operating cost of electric vehicle aggregators; the first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between each VPP system;

[0192] The first phase calculation module: The first phase calculation is performed based on the first sub-problem of Nash negotiation: a multi-VPP system cost minimization problem model is constructed based on the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP system is minimized; the trading volume of electricity, heat, and carbon quotas between each VPP system is calculated based on the multi-VPP system cost minimization problem model;

[0193] The second-stage calculation module: The second-stage calculation is performed based on the second sub-problem of Nash negotiation: multiple contribution factors corresponding to each VPP system are obtained, and the comprehensive contribution rate of each VPP system is obtained according to the contribution factors and the weights corresponding to each contribution factor; the payment bargaining sub-problem model is constructed according to the comprehensive contribution rate and the multi-VPP system cost minimization problem model; the transaction prices of electricity, heat and carbon quotas between each VPP system are calculated according to the payment bargaining sub-problem model.

[0194] For other contents, please refer to Example 1 and will not be described in detail here.

[0195] Example 3

[0196] The present application provides an electronic device, including: a memory, a processor, and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method described in Example 1. For other contents, please refer to Example 1 and will not be repeated here.

[0197] Example 4

[0198] Based on the content of Example 1, this embodiment provides a specific embodiment:

[0199] (1) Parameter setting

[0200] The multi-VPP system consisting of three different functional areas in a certain region is used as the research scenario. VPP1 is located in an area with low building density and mainly uses wind power; VPP2 and VPP3 are located in areas with high building density, which is not conducive to the installation of wind power, so the form of renewable energy is mainly photovoltaic. The energy price of the upper network is shown in Table 1; the basic parameters of the system equipment are shown in Table 2.

[0201] Table 1 Energy purchase and sale prices of power grid, heat grid and gas grid

[0202]

[0203] Table 2 System equipment parameters

[0204]

[0205] (2) Analysis of optimization results of electricity and heat energy trading in multi-VPP systems

[0206] Under the cooperative operation mode, the mutual assistance of electricity and heat power among VPP members has a positive effect on improving the comprehensive benefits of operation. Figure 3 and Figure 4 Optimization results of the interaction between electrical energy and thermal energy of each VPP system.

[0207] Regarding the sharing of electric energy, during the periods of 00:00-14:00 and 17:00-24:00, VPP1 has a relatively large surplus of power generation, so it transmits electric energy to VPP2 and VPP3, which are short of electric energy due to the inability of photovoltaic power generation at night. During the period of 15:00-16:00, VPP1 is short of power supply due to the surge in power load during the day, while the photovoltaic power generation of VPP2 and VPP3 is relatively abundant at this time, so they will transmit power to VPP1 respectively, and VPP2 will mainly supply power to the EV cluster, corresponding to the electric vehicle aggregator in Example 1. The charging of the EV cluster is concentrated in the periods of 00:00-14:00 and 18:00-22:00, and the remaining periods sell electricity to other VPPs to obtain revenue. Regarding the sharing of thermal energy, VPP1 delivers thermal energy to other members throughout the scheduling cycle; this is because the capacity of the heat-generating unit of VPP1 is large, and its own nighttime heat load is small, which can support the heat load requirements of other members in the multi-VPP system. During the entire dispatching cycle, VPP2 and VPP3 need to purchase a large amount of heat energy, except for VPP2 selling a small amount of heat energy during the period from 14:00 to 16:00. This is because the heat load of VPP2 and VPP3 is high and they cannot achieve self-sufficiency in heat energy.

[0208] The VPP systems negotiate and determine the transaction price based on the asymmetric bargaining method. The transaction prices of electricity and heat after asymmetric bargaining between VPPs are shown in Figure 5 and Figure 6 .

[0209] The transaction prices between VPPs in each period are between the prices for selling energy to the superior energy grid and the prices for purchasing energy. This means that VPP members are more willing to purchase energy at a price lower than the energy grid selling price and sell energy at a price higher than the energy grid purchasing price.

[0210] In terms of trading electricity prices, VPP3 supplements power supply for VPP2, and its trading electricity price with VPP2 rises suddenly during the peak of VPP2's electricity load (9:00-21:00). VPP3 realizes arbitrage by selling electricity at an increase in its trading electricity price with VPP2. On the contrary, VPP2 has a lower electricity load demand during the 12:00-17:00 period, and the trading electricity prices of VPP1 and VPP2 are lower. Similarly, EV has a large demand for charging during the 1:00-11:00 period, and mainly purchases electricity from VPP1 and VPP3. Therefore, the trading electricity price of VPP1 and EV clusters is low, while the trading electricity price of VPP3 and EV clusters is high. On the contrary, the EV cluster supplements VPP2 with electricity during its discharge period, which helps EV make a profit by increasing its trading electricity price with VPP2.

[0211] In terms of transaction heat price, VPP2 has a higher heat load during the period of 11:00-13:00, and the transaction heat prices of VPP1 and VPP2 are higher, which helps VPP1 to sell heat energy for profit. On the contrary, VPP2 has a lower heat load during the period of 7:00-10:00, and the transaction heat prices of VPP1 and VPP2 are lower. In addition, the load time distribution of VPP2 and VPP3 is relatively similar, and the transaction heat prices of VPP2 and VPP3 are lower and the trend is gentle.

[0212] (3) Analysis of Multi-VPP Carbon Quota Trading Optimization Results

[0213] See also Figure 7 It can be seen that in order to avoid high carbon trading penalties, VPP2 and VPP3 choose to purchase carbon quotas from VPP1 and electric vehicle aggregators with surplus carbon quotas. Specifically, VPP1 makes full use of EMC technology and HFC equipment to reduce carbon emissions, and there is a certain surplus of carbon quotas during the entire scheduling cycle. In particular, during the electric vehicle charging period, the VPP system has a large photovoltaic output from 7:00-11:00 and 15:00-17:00, and the proportion of renewable energy is large, and the quotas sold by electric vehicles are higher than VPP1. Therefore, the interaction of carbon quotas between VPP systems can improve the carbon quota utilization efficiency of multiple VPP systems, thereby reducing the carbon trading costs of VPPs based on tiered carbon trading.

[0214] The carbon quota trading price between VPPs can be found at Figure 8 . The carbon quota trading price in each period is lower than the VPP carbon trading cost based on the tiered carbon trading. Specifically, when the carbon quota of electric vehicles is sufficient, the trading prices of VPP2 and VPP3 are higher, which can stimulate the enthusiasm of electric vehicles to participate in quota trading. Therefore, the price difference between the carbon quota trading price and the benchmark price prompts the interaction of carbon quotas between VPP systems, thereby improving the economic benefits of each VPP system.

[0215] (4) Cost and benefit distribution analysis

[0216] The comprehensive contribution rate of each VPP is shown in Table 3.

[0217] Table 3 Comprehensive contribution rate of each VPP

[0218] VPP <![CDATA[d i,1 ]]> <![CDATA[d i,2 ]]> <![CDATA[d i,3 ]]> <![CDATA[d i,4 ]]> <![CDATA[d i ]]> 1 0.51 -0.16 0.24 0.47 0.41 2 0.24 0.70 0.39 0.31 0.31 3 0.22 0.46 0.37 0.22 0.26 EV 0.03 0.00 0.00 0.00 0.02

[0219] As shown in Table 3, from the perspective of energy sharing, VPP1 has a larger energy contribution value, that is, its energy mapping function has a larger exponent, and the multi-VPP system tends to allocate more cooperation benefits to VPP1. From the perspective of carbon emission reduction, VPP2 and VPP3 have a higher carbon emission reduction contribution rate. From the perspective of user satisfaction, VPP2 and VPP3 make up for the power shortage by receiving energy in most periods, so their user satisfaction has increased significantly. From the perspective of risk resistance, since there are more dispatchable resources in VPP1, it can better deal with the risks faced by VPP, so VPP1 has a higher risk resistance contribution rate. In summary, the distribution of benefits to each VPP according to the comprehensive contribution degree is very fair, which can strengthen the cooperation willingness of each VPP, consolidate the cooperative relationship, and maintain the stability of the cooperative operation of the multi-VPP system. The cost and benefit distribution of each VPP before and after participating in the transaction is shown in Table 4.

[0220] Table 4 Cost and benefit distribution

[0221]

[0222] As shown in Table 4, the overall benefit of the VPP alliance has increased by 17778.47 yuan. The asymmetric benefit distribution proposed in the present invention based on the amount of energy contributed by each VPP in the energy sharing process has increased the benefits by 7311.22 yuan, 5470.11 yuan, 4626.39 yuan and 370.75 yuan respectively; the benefit increase ratios are 14.55%, 6.15%, 9.04% and 28.96% respectively. This shows that through P2P technology energy sharing, the interests of the participating entities are effectively improved, and each entity can obtain a fair amount of benefit distribution according to its comprehensive contribution, which further verifies the rationality of the benefit distribution mechanism proposed in the present invention.

[0223] In summary, it is verified that the application method can take into account the economy and low carbon nature of the system and achieve fair and reasonable income distribution for each VPP system.

[0224] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A low-carbon optimization method for virtual power plants based on two-stage carbon trading, characterized in that: The steps include: S1. Constructing a cooperative operation framework model of a multi-VPP system; the multi-VPP system includes an electric vehicle aggregator and multiple functional areas; S2. Calculate the carbon quota of electric vehicle aggregators and the carbon quota of each functional area based on the framework model; subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; calculate the carbon trading cost of each functional area based on the carbon emissions after the carbon quota is shared; S3. Constructing the first total cost minimization model of each functional area based on the framework model and the carbon trading cost, so as to calculate the minimum value of the total operating cost of each functional area; A second total cost minimization model for electric vehicle aggregators is constructed based on the framework model and the carbon quota of electric vehicle aggregators to calculate the minimum total cost of operation of electric vehicle aggregators; The first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between each VPP system; S4. Perform the first stage calculation based on the first sub-problem of Nash negotiation: construct a multi-VPP system cost minimization problem model based on the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP systems is minimized; calculate the trading volume of electricity, heat, and carbon quotas between each VPP system based on the multi-VPP system cost minimization problem model; S5. Perform the second stage calculation based on the second sub-problem of Nash negotiation: obtain multiple contribution factors corresponding to each VPP system, and obtain the comprehensive contribution rate of each VPP system according to the contribution factor and the weight corresponding to each contribution factor; construct the payment bargaining sub-problem model according to the comprehensive contribution rate and the multi-VPP system cost minimization problem model; The transaction prices of electricity, heat and carbon quotas between various VPP systems are calculated based on the payment bargaining sub-problem model.

2. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The functional areas include one or more of industrial areas, residential areas and commercial areas divided according to functions.

3. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The carbon quota of the electric vehicle aggregator is the carbon dioxide generated by the same mileage of a fuel vehicle minus the carbon emissions generated by the charging power of the electric vehicle.

4. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The carbon trading costs of each functional area are calculated based on the carbon emissions after carbon quota sharing, including: Subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; construct a tiered carbon trading rule, and trade the carbon emissions after the carbon quota is shared according to the tiered carbon trading rule to obtain the carbon trading cost of each functional area; The tiered carbon trading rules include different carbon trading cost calculation formulas corresponding to multiple different carbon emission ranges.

5. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The first total cost minimization model takes the minimum value of the sum of the operating costs of each functional area; the first total cost minimization model includes a first constraint condition; the second total cost minimization model takes the minimum value of the operating cost of the electric vehicle aggregator; the second total cost minimization model includes a second constraint condition.

6. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The contribution factors include: energy sharing contribution factor, carbon emission reduction contribution factor, user satisfaction contribution factor, and risk resistance contribution factor; The sum of the contribution factors and the corresponding weight products is the comprehensive contribution rate of each VPP system.

7. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 6 is characterized in that: The carbon emission reduction contribution factor is the ratio of the carbon emission reduction of each functional area after carbon trading to the carbon emission reduction of the total functional area; The user satisfaction contribution factor is the ratio of customer satisfaction after carbon trading in each functional area to customer satisfaction in the total functional area; The risk resistance contribution factor is the ratio of the risk resistance of each functional area after carbon trading to the total risk resistance of the functional areas.

8. The low-carbon optimization method for virtual power plants based on two-stage carbon trading according to claim 1 is characterized in that: The multi-VPP system cost minimization problem model and the payment bargaining sub-problem model use the alternating direction multiplier method to calculate unknown parameters.

9. A virtual power plant low-carbon optimization system based on two-stage carbon trading, characterized in that: Includes the following: Preprocessing module: construct a cooperative operation framework model of a multi-VPP system; the multi-VPP system includes an electric vehicle aggregator and multiple functional areas; calculate the carbon quota of the electric vehicle aggregator and the carbon quota of each functional area based on the framework model; subtract the carbon quota of the corresponding functional area from the actual carbon emissions of each functional area to obtain the carbon emissions of each functional area after the carbon quota is shared; calculate the carbon trading cost of each functional area based on the carbon emissions after the carbon quota is shared; Cost model construction module: construct the first total cost minimization model of each functional area based on the framework model and carbon trading costs, which is used to calculate the minimum value of the total operating cost of each functional area; construct the second total cost minimization model of the electric vehicle aggregator based on the framework model and the carbon quota of the electric vehicle aggregator, which is used to calculate the minimum value of the total operating cost of the electric vehicle aggregator; The first total cost minimization model and the second total cost minimization model include unknown parameters; the unknown parameters include the transaction volume and transaction price of electricity, heat, and carbon quota between each VPP system; The first stage calculation module: The first stage calculation is performed based on the first sub-problem of Nash negotiation: a multi-VPP system cost minimization problem model is constructed according to the first total cost minimization model and the second total cost minimization model, so that the sum of the total operating costs of the multi-VPP system is minimized; The trading volumes of electricity, heat and carbon quotas between the VPP systems are calculated according to the multi-VPP system cost minimization problem model; The second-stage calculation module: The second-stage calculation is performed based on the second sub-problem of Nash negotiation: multiple contribution factors corresponding to each VPP system are obtained, and the comprehensive contribution rate of each VPP system is obtained according to the contribution factors and the weights corresponding to each contribution factor; the payment bargaining sub-problem model is constructed according to the comprehensive contribution rate and the multi-VPP system cost minimization problem model; the transaction prices of electricity, heat and carbon quotas between each VPP system are calculated according to the payment bargaining sub-problem model.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method according to any one of claims 1 to 8.

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