Virtual power plant carbon footprint tracking and optimizing method in carbon market

Abstract

The invention discloses a virtual power plant carbon footprint tracking and optimization method in a carbon market, and the method comprises the steps: S1, proposing to establish a virtual power plant full life cycle carbon emission tracking accounting system, and carrying out the carbon emission tracking of typical resources of a virtual power plant; s2, establishing a low-carbon operation cost and income model of the virtual power plant in the carbon market, and obtaining the cost and income condition of the virtual power plant during typical resource scheduling; and S3, in the carbon market, considering the cost and income conditions of the virtual power plant during typical resource scheduling in the S2, establishing a virtual power plant carbon footprint optimization model, and inputting the typical resource carbon emission data in the S1 to obtain a virtual power plant carbon footprint optimization result. According to the method, the actual carbon emission of the emission main body can be accurately calculated, so that the typical resource carbon emission of the virtual power plant can be accurately tracked, the total emission of the hybrid energy power chain and the hybrid energy carbon footprint can be obtained, and the energy utilization efficiency and the low-carbon level of the virtual power plant can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of virtual power plant optimization operation, and in particular relates to a carbon footprint tracking and optimization method for a virtual power plant in a carbon market. Technical Background

[0002] Accelerating the transformation of energy structure towards low-carbonization, cleanliness, high efficiency and intelligence is the only way to achieve energy conservation and emission reduction goals. Carbon footprint tracking is an important tool for quantifying carbon emissions of virtual power plants. It can provide data support for optimizing the operation of virtual power plants and promote energy conservation and emission reduction in the energy industry. Building a carbon emission accounting system for the entire life cycle of virtual power plants is a prerequisite for achieving reasonable allocation of carbon assets and reliable transactions of electricity-carbon collaboration, and is the basis for promoting structural transformation and emission reduction and decarbonization in the energy and power industry.

[0003] Virtual power plants can efficiently aggregate and control various new loads and distributed power sources, have huge regulation potential, and provide solutions for efficient consumption of clean energy and energy structure transformation. However, carbon footprint tracking in multiple virtual power plants has considerable limitations. Energy trading and scheduling in virtual power plants also increase the difficulty of carbon footprint tracking. To improve the input-output accounting of carbon footprint, we should strengthen the carbon footprint analysis of key carbon emissions on energy generation and other industries.

[0004] Therefore, the present invention proposes carbon footprint tracking of virtual power plants under the carbon market, which can effectively solve the carbon footprint tracking of a large number of related power equipment, and further realize the carbon emission optimization of virtual power plants on this basis. It aims to give full play to the value of virtual power plants in load regulation and system control, establish a low-carbon operation cost and benefit model for virtual power plants, participate in green transactions in the electricity market, continuously improve market transaction rules, and better serve the market, which has important guiding significance for promoting the realization of energy conservation and emission reduction goals.

[0005] With the continuous development of new energy technologies, virtual power plant technology can improve the clean energy consumption capacity and support green and low-carbon energy transformation. There are many limitations and shortcomings in the traditional way of tracking the carbon footprint of virtual power plants. Therefore, the carbon footprint is optimized in the process of tracking the carbon footprint of virtual power plants. Summary of the invention

[0006] The present invention aims to overcome the defects of the prior art, proposes a method for tracking and calculating carbon emissions of virtual power plants throughout their life cycle taking into account carbon footprint, optimizes the carbon emissions of virtual power plants under the carbon market, and tracks the carbon footprint of typical resources of virtual power plants, thereby constructing a unified and standardized carbon footprint accounting system for virtual power plants; constructs a low-carbon operation cost and benefit model for virtual power plants under the carbon market, obtains the cost and benefit of virtual power plants when typical resources are scheduled, and effectively controls the carbon emissions of the VPP system while ensuring the economic benefits of the VPP; constructs a multi-time scale optimization scheduling model for virtual power plants within the day before under the carbon market, establishes a multi-time scale optimization scheduling of VPPs, considers multi-objective regulation within the virtual power plant to achieve carbon optimization, constructs a multi-scenario virtual power plant optimization scheduling model with uncertain factors, and improves the energy utilization efficiency and low-carbonization degree of virtual power plants.

[0007] The present invention provides a method for tracking and optimizing the carbon footprint of a virtual power plant in a carbon market, comprising the following steps:

[0008] S1. Propose to establish a carbon emission tracking and accounting system for the entire life cycle of virtual power plants, and track carbon emissions from typical resources of virtual power plants;

[0009] S2. Establish a low-carbon operation cost and revenue model for virtual power plants under the carbon market, and obtain the cost and revenue of virtual power plants under typical resource scheduling;

[0010] S3. In the carbon market, considering the cost and benefit of the virtual power plant during the typical resource scheduling described in S2, a virtual power plant carbon footprint optimization model is established, and the typical resource carbon emission data described in S1 is input to obtain the virtual power plant carbon footprint optimization result.

[0011] In the above-mentioned method for tracking and optimizing carbon footprint of a virtual power plant in a carbon market, S1 comprises the following steps:

[0012] S101: The carbon footprint tracking method of a virtual power plant includes analyzing resources based on new energy units, electric vehicle V2G technology, gas turbines or energy storage types, establishing a carbon emission tracking and accounting system for the entire life cycle of a virtual power plant, and obtaining typical resource carbon emission data.

[0013] In the above-mentioned carbon footprint tracking and optimization method for a virtual power plant in a carbon market, S1 further includes the following steps:

[0014] S102: Analyze the carbon footprint calculation of power generation using gas turbines. The life cycle carbon footprint of the power generation side includes the life cycle carbon emissions of the virtual power plant and the life cycle carbon emissions of the fuel non-use phase. Based on the selected functional unit and time range, the base value factor of the life cycle carbon emissions of the virtual power plant is calculated. and the base value factor of carbon emission in the non-use phase of fuel life cycle The unit is gCO2eq / kWh, the base value factor of carbon emission in the life cycle of power generation It can be expressed as:

[0015]

[0016] The carbon footprint of a power generation unit within a certain accounting period is calculated as follows:

[0017]

[0018] Among them, E PG,i is the power generation kWh of the i-th power generation unit in a certain accounting period, P P,i,t The power consumption kWh of the i-th power generation unit in a certain accounting period,

[0019] Taking the charging status of electric vehicles as an example to analyze the grid side, and the discharging status of electric vehicles as an example to analyze the user side, the life cycle carbon footprint of the grid side includes the life cycle carbon emissions of the grid during the non-operation phase and the carbon emissions caused by power transmission losses during the operation phase. Based on the selected functional units, system life and system rated power, the base value factors of the life cycle carbon emissions of the transmission, substation and distribution systems during the non-operation phase are calculated respectively. and The unit is gCO2eq / kWh.

[0020] The carbon emissions caused by losses during the operation of the power grid are closely related to the real-time operation status of the power system. Based on the power flow calculation, the power grid transmission power and power grid losses are obtained. The carbon footprint of the power grid side within a certain accounting period is calculated as follows:

[0021]

[0022] Where P TM,t , P TF,t and P D,t are the power passing through the transmission, transformation and distribution systems during the accounting period; ΔP L,t To calculate the power grid loss during the cycle,

[0023] The carbon footprint on the user side is the carbon emissions from power generation caused by user load demand, which is calculated as follows:

[0024]

[0025] Where P Load,t is the load during the calculation period.

[0026] In the above-mentioned carbon footprint tracking and optimization method for a virtual power plant in a carbon market, S1 further includes the following steps:

[0027] S103: Virtual power plants integrate resources through a full life cycle approach, analyzing the total emissions C of the hybrid energy power chain and the hybrid energy carbon footprint η,

[0028] The calculation formula for the total emissions C of the hybrid energy power chain is:

[0029] C=C tp +C hp +C wp +C k +C ng =CEC·Σ(E tp +E hp +E wp +E k +E ng ) (31)

[0030] Where CEC is the average carbon emission coefficient in the virtual power plant; C tp , C hp , C wp , C k , C ng are the carbon emissions of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources; E tp , E hp , E wp , E k , E ng They are the consumption of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources.

[0031] The carbon footprint of mixed energy is calculated based on the total emissions of the mixed energy power chain and the carbon sink capacity. The calculation formula is:

[0032]

[0033] The calculation formula of the carbon footprint diversity coefficient of mixed energy is:

[0034]

[0035] η ep =Σ(η tp / η tp ,η hp / η tp ,η np / η tp ,η wp / η tp ,η k / η tp ,η ng / η tp ) (34)

[0036] Where ηtp , η hp , η wp , η k , η ng They are the carbon footprint coefficients of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources respectively.

[0037] In the above-mentioned carbon footprint tracking and optimization method of a virtual power plant in a carbon market, the new energy unit adopts the life cycle energy chain analysis. Wind power and photovoltaic units are renewable new energy power generation methods, and their carbon emissions are generated in the production, construction and transportation processes. The measurement models of the remaining links are as follows:

[0038]

[0039] In the formula, E pne is the carbon emission coefficient of the production process of renewable energy unit; c jg

[0040] is the conversion coefficient between unit standard electric energy and energy consumption in the production and construction process; n is the number of categories of raw materials used in the production and construction process; u i 、m ci 、e ci is the loss rate, carbon emission coefficient and self-energy coefficient of the i-th raw material in the production and construction process; m cti 、e cti is the carbon emission coefficient and energy intensity value of transporting the i-th raw material during the production and construction process; m pj,t 、e pj,t is the carbon emission coefficient and self-energy coefficient of the i-th raw material used in the j-th production and construction process;

[0041]

[0042] In the formula, E tne is the carbon emission coefficient of the transportation process of renewable energy unit components; m ti

[0043] is the carbon emission coefficient caused by the transportation process of the i-th component material; e ti is the energy intensity coefficient of the material transportation process of the i-th component;

[0044] C ne =E pne P pne +E tne P tne (37)

[0045] In the formula, C ne is the total amount of carbon released by new energy; P pne is the energy consumed in the production process; P tenIt is the energy consumed during the transportation process.

[0046] In the above-mentioned carbon footprint tracking and optimization method of virtual power plants under a carbon market, the electric vehicle V2G technology, the electric vehicle is connected to the grid through the V2G technology, that is, as a load, it needs to be charged by the grid, and uses battery energy storage to discharge to the grid, that is, it has the dual attributes of consumption and generation. The carbon emissions in the production process of electric vehicles account for more than 45% of the carbon emissions in the entire life cycle of electric vehicles. Considering the carbon emissions in the production process C ev Assigned to each optimization schedule in the VPP model, the carbon emission amount C that the k-th electric vehicle needs to be allocated during the VPP scheduling period T k Calculated by the formula:

[0047]

[0048] In the formula, Δt c and η c are the charging power, charging time and charging loss coefficient of the electric vehicle battery in period t, Δt l and η l are the discharge power, discharge duration and discharge loss coefficient of the electric vehicle battery in period t, LC is the maximum equivalent full charge and discharge cycles, E r is the rated power of the battery.

[0049] In the above-mentioned carbon footprint tracking and optimization method for a virtual power plant under a carbon market, the gas turbine, assuming that the power generation efficiency of the distributed gas turbine unit is The power output of the gas turbine unit is P ng (t) can be expressed by the formula:

[0050]

[0051] Among them, LHV is the thermal value of natural gas, which is generally 3.34MJ / m 3 ,V ng (t) represents the volume of natural gas consumed by the gas turbine during period t,

[0052] The carbon emission intensity of a gas turbine is τ ng / kWh, the carbon emissions are:

[0053] C ng (t) = τ ng ×P ng (t) (40)

[0054] In the above-mentioned carbon footprint tracking and optimization method for virtual power plants in a carbon market, the life cycle carbon footprint of the energy storage system in the charging state includes the life cycle carbon emissions of the energy storage system and the power generation carbon emissions caused by the charging demand; the life cycle carbon footprint in the discharging state is the life cycle carbon emissions of the energy storage system. Based on the selected functional unit, system life and system rated power, the life cycle carbon emission base value factor of the energy storage system is calculated. The carbon footprint of the energy storage system within a certain accounting period is as follows:

[0055]

[0056] Where P ST_c,t and P ST_Dc,t They are respectively the charging and discharging amount of the energy storage system during the calculation period.

[0057] In the above-mentioned method for tracking and optimizing the carbon footprint of a virtual power plant in a carbon market, S2 comprises the following steps:

[0058] S201: In the carbon rights trading market, set up the carbon emission rights trading model and carbon emission rights

[0059] Use it or throw it away:

[0060] (1) Carbon emission rights allow trading mode. Under this mode, VPP can trade carbon emission rights in the carbon trading market. The carbon trading cost R Ca，t It is expressed as:

[0061] R Ca ,t =k ca (E ac ,t -E p,t ) (43)

[0062] (2) The use-or-discard model of carbon emission rights. Under this model, if the carbon emissions generated by the VPP are lower than the free quota, the quota can only be discarded; if it is higher than the free quota, it will be subject to excess penalties. Therefore, the carbon emission cost of the VPP is as follows:

[0063] R Ca,t =k ca2 max(E ac,t -E p,t ,0) (44)

[0064] Based on the two models of carbon emission rights trading model and carbon emission rights non-use-or-discard model, as well as the carbon emission measurement model based on the life cycle method, the VPP model carbon rights trading mechanism cost model taking into account the initial carbon quota can be expressed by the formula:

[0065]

[0066] In the formula, E all is the total amount of carbon emissions actually generated, E r is the initial carbon emission quota of the VPP model, C CO2 is the total cost of carbon trading in the VPP model; c is the carbon trading price in the carbon market; d is the length of the carbon emission interval; is α c is the carbon trading price growth coefficient;

[0067] S202: The total operating cost of the VPP model includes the cost of purchasing energy from the upper energy supply grid, the equipment operation and maintenance cost during the operation of the VPP model, and the cost of user-side participation in incentive-based demand response. N is the service provider's operating cost; C CO2 is the carbon trading cost of the service provider,

[0068] C N =C BUY +C OP +C IDR (46)

[0069] C BUY is the cost of energy purchased by the service provider from the upper energy network; C OP is the operation and maintenance cost of the service provider; C IDR It is the user-side incentive demand management fee;

[0070] S203: The optimization goal of VPP operators is comprehensive income F vpp Maximum, expressed as:

[0071]

[0072] Where, T is the scheduling period, which is 24 hours; R tsell R is the energy supply revenue of the VPP operator to the user at time t; tbuy is the energy purchase cost of the VPP operator from the power generation side at time t; R tgrid t

[0073] The interaction cost between VPP operators and the spot market at that moment, R tgrid <0 means selling electricity to the spot market,

[0074] R tgrid >0 means purchasing electricity from the spot market; c es,t 、c hs,t are the electricity and heat selling prices of the VPP operator to the user at time t; c eb,t 、c hb,t are the electricity and heat purchase prices of the VPP operator from the power generation side at time t; c mk,tis the electricity price in the spot market at time t; P es,t , H hs,t are the electrical and thermal output power of the power generation side at time t; P TL，t is the power passing through the tie line at time t, P TL，t <0 means selling electricity to the grid, P TL，t >0 means purchasing electricity from the grid.

[0075] In the above-mentioned method for tracking and optimizing carbon footprint of a virtual power plant in a carbon market, S3 includes the following steps:

[0076] S301: The virtual power plant proposes a multi-time scale optimization scheduling model. The established VPP multi-time scale optimization is divided into the following two stages:

[0077] (1) In the day-ahead long-time scheduling stage, scheduling is carried out 24 hours in advance, with a scheduling time scale of 1 hour. The minimum total cost of VPP is taken as the objective function. Each unit in the VPP will convert the wind and solar power output,

[0078] The load forecast and other system technical parameters are reported to the VPP coordination and control center. The control center formulates a 24-hour dispatch plan for the next day based on the collected parameters, combined with the purchase and sale electricity price and market carbon price.

[0079] minC vpp =min(C op +C th +C bs +C ce +C dr ]) (51)

[0080] In the formula, C VPP is the total operating cost of the virtual power plant; C op is the operation and maintenance cost of the virtual power plant equipment; C th is the operating cost of the gas turbine; C bs The cost of purchasing and selling electricity; C ce for the cost of carbon emissions;

[0081] C dr for demand response costs;

[0082] (2) In the intraday rolling optimization scheduling stage, scheduling is carried out 2 hours in advance, with a rolling optimization step of 30 minutes, and the scheduling time window is set to 3 hours, with a total of 8 periods, for optimal scheduling, and each time a scheduling plan for the next 4 hours is formed; the initial data of wind and solar power output and user load is expanded from 24 to 96 for scenario generation and reduction, and the VPP coordination control center adopts price-based demand response to reduce user load.

[0083]

[0084] In the formula, Total cost of VPP within the day; It is divided into wind, solar, storage and operation and maintenance costs, gas turbine operation costs and carbon emission costs during the T period of the day; It is the penalty fee for output deviation during the T period of the day; It is the intraday price-based demand response cost;

[0085] S302: Consider multi-objective regulation within the virtual power plant to achieve carbon optimization. Under the carbon emission constraints, the virtual power plant reduces the corresponding carbon emissions by adjusting the output changes of each energy unit, so that the virtual power plant has a certain degree of free carbon emission constraint space.

[0086] The beneficial effects of the present invention are as follows: based on the original carbon footprint virtual power plant tracking method, a tracking method for the carbon footprint of the virtual power plant throughout its life cycle is proposed, so as to accurately track the carbon emissions of typical resources of the virtual power plant, obtain carbon emission data of typical resources, and construct a multi-time scale and multi-objective optimization scheduling model for the virtual power plant within the day before under the carbon market, so as to improve the energy utilization efficiency and low carbonization degree of the virtual power plant; due to the pain points such as carbon emission data quality problems, industry technical standards and measurement methods not being unified in the carbon market, the present invention helps to build a transparent, effective and comprehensive carbon footprint full life cycle accounting quantification method; carbon footprint life cycle accounting helps to accurately calculate the actual carbon emissions of the emission subject, supports the open, transparent and highly reliable disclosure of carbon emission data by government agencies and the market, helps to allocate quotas efficiently and reasonably, helps to realize the openness and transparency of the carbon trading market, and ensures healthy market competition. It drives the coordinated development of related industries and businesses upstream and downstream of virtual power plants, promotes the participation of various types of power market entities in construction, promotes resource utilization and resource interaction, and strengthens market operation models and mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings required for describing the embodiments or technical solutions are briefly introduced below.

[0088] Figure 1 It is a technical flow chart of the present invention;

[0089] Figure 2 It is the carbon footprint tracking framework in the present invention;

[0090] Figure 3 This is a schematic diagram of the multi-time scale rolling optimization principle of the present invention within the previous day;

[0091] Figure 4 It is the game diagram of the virtual power plant in the present invention. DETAILED DESCRIPTION

[0092] The present invention will be further described below in conjunction with the accompanying drawings.

[0093] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, Figure 1 The technical flow chart shown clearly and completely describes the technical solution in the embodiment of the present invention, namely: analyzing multiple types of resources through the carbon footprint tracking method, obtaining the carbon emission data of mixed resources of the virtual power plant, constructing a multi-time scale optimization scheduling model according to the operating costs and benefits of the virtual power plant, and using the differential evolution algorithm to solve the multi-scenario optimization scheduling model.

[0094] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] The present invention proposes a carbon footprint tracking and optimization method for a virtual power plant in a carbon market, which includes the following contents:

[0096] S1. Propose to establish a carbon emission tracking and accounting system for the entire life cycle of virtual power plants, and track carbon emissions from typical resources of virtual power plants;

[0097] S2. Establish a low-carbon operation cost and revenue model for virtual power plants under the carbon market, and obtain the cost and revenue of virtual power plants under typical resource scheduling;

[0098] S3. In the carbon market, considering the cost and benefit of the virtual power plant during the typical resource scheduling described in S2, a virtual power plant carbon footprint optimization model is established, and the typical resource carbon emission data described in S1 is input to obtain the virtual power plant carbon footprint optimization result.

[0099] The S1 comprises the following steps:

[0100] S101: The carbon footprint tracking method of virtual power plants can be analyzed from various types of resources such as new energy units, electric vehicle V2G technology, gas turbines, energy storage, etc., to establish a carbon emission tracking and accounting system for the entire life cycle of virtual power plants and obtain typical resource carbon emission data. Figure 2 The figure shows a carbon footprint tracking framework diagram. The tracking order is various resources on the power generation side, power transmission side and power consumption side, and the carbon footprint is tracked through the direction of power transmission.

[0101] 1) New energy units

[0102] The life cycle energy chain is used to analyze renewable energy power generation methods such as wind power and photovoltaic units. Their carbon emissions are mainly generated in the production, construction, and transportation processes. Carbon emissions in the energy supply process are mostly generated in the production, transportation, and unit maintenance of replacement equipment. A large number of literature analyses point out that the greenhouse gas emissions of renewable energy units in this process are negligible.

[0103] The quantitative model is as follows

[0104]

[0105] In the formula, E pne is the carbon emission coefficient of the production process of renewable energy unit; c jg is the conversion coefficient between unit standard electric energy and energy consumption in the production and construction process; n is the number of categories of raw materials used in the production and construction process; u i 、m ci 、e ci is the loss rate, carbon emission coefficient and self-energy coefficient of the i-th raw material in the production and construction process; m cti 、e cti is the carbon emission coefficient and energy intensity value of transporting the i-th raw material during the production and construction process; m pj,t 、e pj,t is the carbon emission coefficient and self-energy coefficient of the i-th raw material used in the j-th production and construction process;

[0106]

[0107] In the formula, E tne is the carbon emission coefficient of the transportation process of renewable energy unit components; m ti is the carbon emission coefficient caused by the transportation process of the i-th component material; e ti is the energy intensity coefficient of the material transportation process of the i-th component;

[0108] C ne =E pne P pne +E tne P tne (55)

[0109] In the formula, C ne is the total amount of carbon released by new energy; P pne It is the energy consumed in the production process;

[0110] P ten It is the energy consumed during the transportation process.

[0111] 2) V2G technology

[0112] Electric vehicles are connected to the grid through V2G technology, which means they can be used as loads and need to be charged by the grid, and can also use battery storage to discharge energy to the grid, that is, they have the dual attributes of consumption and generation. The carbon emissions in the production process of electric vehicles account for more than 45% of the carbon emissions in the entire life cycle of electric vehicles. Considering the carbon emissions in the production process C evAssigned to each optimization schedule in the VPP model, the carbon emission amount C that the k-th electric vehicle needs to be allocated during the VPP scheduling period T k

[0113] It can be calculated by the formula:

[0114]

[0115] In the formula, Δt c and η c are the charging power, charging time and charging loss coefficient of the electric vehicle battery in period t, Δt l and η l are the discharge power, discharge duration and discharge loss coefficient of the electric vehicle battery in period t, LC is the maximum equivalent full charge and discharge cycles, E r is the rated power of the battery.

[0116] 3) Gas turbine

[0117] The distributed power source in the VPP model of the present invention uses a micro gas turbine to generate electricity. Assume that the power generation efficiency of the distributed gas turbine unit is The power output of the gas turbine unit is P ng (t) can be expressed by the formula:

[0118]

[0119] Among them, LHV is the thermal value of natural gas, which is generally 3.34MJ / m 3 ,V ng (t) represents the volume of natural gas consumed by the gas turbine during period t.

[0120] The carbon emission intensity of a gas turbine is τ ng / kWh, the carbon emissions are:

[0121]

[0122] 4) The life cycle carbon footprint of the energy storage system in the charging state includes the life cycle carbon emissions of the energy storage system and the carbon emissions of power generation caused by charging demand; the life cycle carbon footprint in the discharging state is the life cycle carbon emissions of the energy storage system. Based on the selected functional unit, system life and system rated power, the life cycle carbon emission base value factor of the energy storage system is calculated. The carbon footprint of the energy storage system within a certain accounting period is as follows:

[0123]

[0124] Where P ST_c,t and PST_Dc,t They are respectively the charging and discharging amount of the energy storage system during the calculation period.

[0125] S102: Take gas turbines as an example to analyze the calculation of carbon footprint of power generation. The life cycle carbon footprint of the power generation side includes the life cycle carbon emissions of the virtual power plant and the life cycle carbon emissions of the fuel non-use phase. Based on the selected functional unit and time range, the base value factor of the life cycle carbon emissions of the virtual power plant is calculated.

[0126] and the base value factor of carbon emission in the non-use phase of fuel life cycle The units are

[0127] gCO2eq / kWh. Therefore, the base value factor of carbon emission in the life cycle of power generation is It can be expressed as:

[0128]

[0129] The carbon footprint of a power generation unit within a certain accounting period is calculated as follows:

[0130]

[0131] Among them, E PG,i is the power generation kWh of the i-th power generation unit in a certain accounting period, P P,i,t The power consumption kWh of the i-th power generation unit in a certain accounting period.

[0132] Taking the charging state of electric vehicles as an example to analyze the grid side, and the discharging state of electric vehicles as an example to analyze the user side, the life cycle carbon footprint of the grid side includes the life cycle carbon emissions of the grid during the non-operation phase and the carbon emissions generated by the power transmission loss during the operation phase. Based on the selected functional units, system life and system rated power, the base value factors of the life cycle carbon emissions of the transmission, substation and distribution systems during the non-operation phase are calculated respectively. and The unit is gCO2eq / kWh.

[0133] The carbon emissions caused by losses during the operation of the power grid are closely related to the real-time operation status of the power system. Based on the power flow calculation, the power grid transmission power and power grid losses are obtained. The carbon footprint of the power grid side within a certain accounting period is calculated as follows:

[0134]

[0135] Where P TM,t , P TF,t and P D,t are the power passing through the transmission, transformation and distribution systems during the accounting period; ΔP L,t It is the grid loss within the calculation period.

[0136] The carbon footprint on the user side is the carbon emissions from power generation caused by user load demand, which is calculated as follows:

[0137]

[0138] Where P Load,t is the load during the calculation period.

[0139] S103: The virtual power plant integrates resources through a full life cycle approach to analyze the total emissions C of the hybrid energy power chain and the hybrid energy carbon footprint η.

[0140] The calculation formula for the total emissions C of the hybrid energy power chain is:

[0141] C=C tp +C hp +C wp +C k +C ng =CEC·Σ(E tp +E hp +E wp +E k +E ng ) (65)

[0142] Where CEC is the average carbon emission coefficient in the virtual power plant; C tp , C hp , C wp , C k , C ng are the carbon emissions of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources; E tp , E hp , E wp , E k , E ng They are the consumption of thermal power, hydropower, wind power, electric vehicles and controllable distributed power sources respectively.

[0143] Mixed energy carbon footprint. Based on the total emissions of the mixed energy power chain and the carbon sink capacity, the carbon footprint η of the mixed energy can be calculated. The calculation formula is:

[0144]

[0145] The calculation formula of the carbon footprint diversity coefficient of mixed energy is:

[0146]

[0147] η ep =∑(η tp / η tp ,η hp / η tp,η np / η tp ,η wp / η tp ,η k / η tp ,η ng / η tp ) (68)

[0148] Where η tp , η hp , η wp , η k , η ng They are the carbon footprint coefficients of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources respectively.

[0149] The S2 comprises the following steps:

[0150] S201: In the carbon trading market, in order to maximize their benefits in carbon trading, all participating carbon emission resources will actively transform to a low-carbon production and operation mode, effectively promoting energy conservation and emission reduction in all walks of life, and making carbon emission rights have economic performance. In order to explore the superiority of the carbon trading mechanism, a carbon emission rights trading mode and a carbon emission rights use-or-discard mode are set up.

[0151] 1) Carbon emission rights allow trading mode. In this mode, VPP can trade carbon emission rights in the carbon trading market, and its carbon trading cost R Ca，t It can be expressed as:

[0152] R Ca,t =k ca (E ac,t -E p,t ) (69)

[0153] 2) Use-or-discard carbon emission rights model. In this model, if the carbon emissions generated by the VPP are lower than the free quota, the quota can only be discarded; if it is higher than the free quota, it will be subject to excess penalties. Therefore, the carbon emission costs of the VPP are as follows:

[0154] R Ca,t =k ca2 max(E ac,t -E p,t ,0) (70)

[0155] Therefore, based on the two models of carbon emission rights trading mode and carbon emission rights non-use and discard mode, as well as the carbon emission measurement model based on the life cycle method, the VPP model carbon rights trading mechanism cost model taking into account the initial carbon quota can be expressed by the formula:

[0156]

[0157] In the formula, Eall is the total amount of carbon emissions actually generated, E r is the initial carbon emission quota of the VPP model, C CO2 is the total cost of carbon trading in the VPP model; c is the carbon trading price in the carbon market; d is the length of the carbon emission interval; is α c is the carbon trading price growth coefficient.

[0158] S202: The total operating cost of the VPP model mainly includes the cost of purchasing energy from the upper energy supply grid, the equipment operation and maintenance cost during the operation of the VPP model, and the cost of user-side participation in incentive-based demand response. In addition, in order to achieve the low-carbon optimization operation of the VPP model, the carbon rights trading fee under the established life cycle measurement model is included in the service provider's operating cost, and a low-carbon optimization model of the comprehensive energy service provider based on the life cycle is obtained. N is the service provider's operating cost; C CO2 Is a service provider carbon trading

[0159] cost

[0160] C N =C BUY +C OP +C IDR (72)

[0161] C BUY is the cost of energy purchase by the service provider from the upper energy network; C OP is the operation and maintenance cost of the service provider; C IDR It is the user-side incentive demand management fee;

[0162] S203: The optimization goal of VPP operators is comprehensive income F vpp The maximum can be expressed as:

[0163]

[0164] Where, T is the scheduling period, which can be 24 hours; R tsell R is the energy supply revenue of the VPP operator to the user at time t; tbuy R is the energy purchase cost of the VPP operator from the power generation side at time t; tgrid is the interaction cost between the VPP operator and the spot market at time t, R tgrid <0 means selling electricity to the spot market, R tgrid >0 means purchasing electricity from the spot market; c es,t 、c hs,t are the electricity and heat prices sold by the VPP operator to the user at time t; c eb,t 、c hb,t are the electricity and heat purchase prices of the VPP operator from the power generation side at time t; cmk,t is the electricity price in the spot market at time t; P es,t , H hs,t are the electrical and thermal output power of the power generation side at time t; P TL，t is the power passing through the tie line at time t, P TL，t <0 means selling electricity to the grid, P TL，t >0 means purchasing electricity from the grid.

[0165] The S3 comprises the following steps:

[0166] S301: Virtual power plant proposes a multi-time scale optimization scheduling model, such as Figure 3 As shown in the diagram, the principle diagram of multi-time scale rolling optimization within the previous day, the established VPP multi-time scale optimization mode is divided into the following

[0167] Two phases:

[0168] 1) In the day-ahead long-time-scale scheduling stage, scheduling is carried out 24 hours in advance, and the scheduling time scale is 1 hour. Multiple uncertain scenarios such as thermal, hydropower, wind power, electric vehicles, real-time electricity prices and user loads are obtained through scenario generation, and the interval method is used to deal with the uncertainty of carbon prices and demand response. Taking the minimum total cost of VPP as the objective function, each unit within the VPP reports the system technical parameters such as wind and solar output and load forecast to the VPP coordination and control center. The control center formulates a scheduling plan for the next 24 hours based on these collected parameters, combined with the purchase and sale electricity price and market carbon price.

[0169] minC vpp =min(C op +C th +C bs +C ce +C dr ]) (77)

[0170] In the formula, C VPP is the total operating cost of the virtual power plant; C op The operation and maintenance costs of virtual power plant equipment;

[0171] C th is the operating cost of the gas turbine; C bs The cost of purchasing and selling electricity; C ce is the carbon emission cost; C dr is the demand response cost.

[0172] 2) In the intraday rolling optimization scheduling stage, scheduling is carried out 2 hours in advance, with a rolling optimization step of 30 minutes, and the scheduling time window is set to 3 hours, a total of 8 periods, and optimal scheduling is carried out, each time forming a scheduling plan for the next 4 hours; the initial data of wind and solar power output and user load are expanded from 24 to 96 for scenario generation and reduction, and the VPP coordination control center adopts price-based demand response to reduce user load. The operating status of the gas turbine is determined by the results of the day-ahead optimization, and the sum of the penalty cost of the intraday and day-ahead power deviation and the total cost of the VPP is minimized as the objective function, and the daily output, daily power purchase and sale, and daily energy storage charging and discharging plans of each unit are formulated.

[0173]

[0174] In the formula, Total cost of VPP within the day; Intraday T

[0175] Costs of wind, solar, storage, operation and maintenance, gas turbine operation costs and carbon emission costs during the period; It is the penalty fee for output deviation during the T period of the day; is the intraday price-based demand response cost.

[0176] S302: Consider multi-objective regulation within the virtual power plant to achieve carbon optimization. The virtual power plant control architecture that considers carbon emissions is mainly divided into four parts: new energy power generation sites, traditional generators, energy storage equipment, and power loads. Among them, new energy power generation sites use clean energy to replace traditional high-pollution and high-emission thermal power and gas generators, effectively reducing carbon emissions from power generation; energy storage devices store and consume excess energy in a time-sharing manner, meeting users' peak electricity demands while reducing power abandonment losses;

[0177] In addition to traditional loads, various types of clean energy loads including electric vehicles, charging piles, etc. are added to give full play to the regulatory role of virtual power plants in the allocation of electricity resources.

[0178] Under the carbon emission constraint, the virtual power plant reduces the corresponding carbon emissions by adjusting the output changes of each energy unit, so that the virtual power plant has a certain degree of free carbon emission constraint space. When carbon emissions reach a certain limit and cannot be internally controlled, the virtual power plant will accept the corresponding carbon emission penalties according to the maximum carbon emission limit. In addition, the key to significantly reducing overall carbon emissions is that the frequency of use of gas-fired equipment is higher than that of coal-fired equipment. Finally, the use of power interaction to meet the demand for remaining power shortages can achieve the purpose of low-carbon regulation.

[0179] like Figure 4As shown in the figure, it is a simulation diagram of a virtual power plant. The VPP energy trading mechanism is a two-layer game mode. The upper layer is the master-slave game of stage 1, and the lower layer is the cooperative game of stage 2. The upper layer of the master-slave game is that the VPP operator aims to maximize its own interests. Through the binary search method, it is cyclically iterated to obtain the purchase and sale price and the purchase and sale volume, such as the VPP operator, the internal output results of the VPP, and the power transaction volume between VPPs; if the upper layer game cannot converge, it will continue to iterate through the binary search constraint. If it can converge, the lower layer cooperative game will be carried out. The lower layer is the cooperative game between VPPs. By initializing the penalty factor dual variable and the energy transaction price, the power transaction price between VPPs is solved; the two-layer trading mechanism formulates a price strategy according to the VPP operator, and only exchanges power transaction and transaction price information.

[0180] By obtaining the purchase and sale electricity prices between VPP and the power grid and the interactive electricity prices between VPPs through the upper and lower double-layer game, the interactivity between VPPs can be improved, the total cost of VPP and the cost of each member can be reduced, and the reasonable distribution of VPP benefits can be achieved. The quadratic programming embedded differential evolution algorithm is adopted. In the initial stage, the grid operator and the operator of the integrated energy user system perform secondary optimization based on the price information provided by the virtual power plant (VPP) operator. They feed back the optimization results to the VPP operator. The VPP operator calculates its own benefits based on this feedback information, selects and adjusts the pricing strategy, and then passes these strategies to the grid operator. The above process is iterated until an equilibrium state is reached, that is, the optimal pricing strategy is found.

[0181]

[0182] In the formula, and are the strategies of the jth iteration of the VPP operator and the energy supply side and the user side respectively; They are the optimal strategies for VPP operators, energy supply side, and user side respectively.

[0183] In response to the operating cost and benefit issues of low-carbon virtual power plants, the present invention proposes a new method for tracking the carbon footprint of virtual power plants throughout their life cycle, which helps to accurately calculate the actual carbon emissions of the emission entities, so as to achieve accurate tracking of carbon emissions from typical resources of virtual power plants, and obtain the total emissions of the hybrid energy power chain and the hybrid energy carbon footprint.

[0184] This paper constructs a multi-time scale and multi-objective optimization scheduling model for virtual power plants in the carbon market, aiming to improve the energy utilization efficiency and low carbonization level of virtual power plants. A VPP energy two-tier trading mechanism is proposed, and the quadratic programming embedded differential evolution algorithm is used to solve the model and obtain the optimal pricing strategy.

[0185] The present invention is described in detail above in conjunction with the embodiments of the accompanying drawings. A person skilled in the art can make various variations of the present invention according to the above description. Therefore, certain details in the embodiments should not constitute a limitation of the present invention, and the scope of protection of the present invention shall be defined by the scope of the attached claims.

Claims

1. A method for tracking and optimizing the carbon footprint of a virtual power plant in a carbon market, comprising the following steps: S1. Propose to establish a carbon emission tracking and accounting system for the entire life cycle of virtual power plants, and track carbon emissions from typical resources of virtual power plants; S2. Establish a low-carbon operation cost and revenue model for virtual power plants under the carbon market, and obtain the cost and revenue of virtual power plants under typical resource scheduling; S3. In the carbon market, considering the cost and benefit of the virtual power plant during the typical resource scheduling described in S2, a virtual power plant carbon footprint optimization model is established, and the typical resource carbon emission data described in S1 is input to obtain the virtual power plant carbon footprint optimization result.

2. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 1, characterized in that: The S1 comprises the following steps: S101: The carbon footprint tracking method of a virtual power plant includes analyzing resources based on new energy units, electric vehicle V2G technology, gas turbines or energy storage types, establishing a carbon emission tracking and accounting system for the entire life cycle of a virtual power plant, and obtaining typical resource carbon emission data.

3. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The S1 further comprises the following steps: S102: Analyze the carbon footprint calculation of power generation using gas turbines. The life cycle carbon footprint of the power generation side includes the life cycle carbon emissions of the virtual power plant and the life cycle carbon emissions of the fuel non-use phase. Based on the selected functional unit and time range, the base value factor of the life cycle carbon emissions of the virtual power plant is calculated. and the base value factor of carbon emission in the non-use phase of fuel life cycle The unit is gCO2eq / kWh, the base value factor of carbon emission in the life cycle of power generation It can be expressed as: The carbon footprint of a power generation unit within a certain accounting period is calculated as follows: Among them, E PG,i is the power generation kWh of the i-th power generation unit in a certain accounting period, P P,i,t The power consumption kWh of the i-th power generation unit in a certain accounting period, Taking the charging status of electric vehicles as an example to analyze the grid side, and the discharging status of electric vehicles as an example to analyze the user side, the life cycle carbon footprint of the grid side includes the life cycle carbon emissions of the grid during the non-operation phase and the carbon emissions caused by power transmission losses during the operation phase. Based on the selected functional units, system life and system rated power, the base value factors of the life cycle carbon emissions of the transmission, substation and distribution systems during the non-operation phase are calculated respectively. and The unit is gCO2eq / kWh. The carbon emissions caused by losses during the operation of the power grid are closely related to the real-time operation status of the power system. Based on the power flow calculation, the power grid transmission power and power grid losses are obtained. The carbon footprint of the power grid side within a certain accounting period is calculated as follows: Where P TM,t , P TF,t and P D,t are the power passing through the transmission, transformation and distribution systems during the accounting period; ΔP L,t To calculate the power grid loss during the cycle, The carbon footprint on the user side is the carbon emissions from power generation caused by user load demand, which is calculated as follows: Where P Load,t is the load during the calculation period.

4. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The S1 further comprises the following steps: S103: Virtual power plants integrate resources through a full life cycle approach, analyzing the total emissions C of the hybrid energy power chain and the hybrid energy carbon footprint η, The calculation formula for the total emissions C of the hybrid energy power chain is: C=C tp +C hp +C wp +C k +C ng =CEC·Σ(E tp +E hp +E wp +E k +E ng ) (5) Where CEC is the average carbon emission coefficient in the virtual power plant; C tp , C hp , C wp , C k , C ng are the carbon emissions of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources; E tp , E hp , E wp , E k , E ng They are the consumption of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources. The carbon footprint of mixed energy is calculated based on the total emissions of the mixed energy power chain and the carbon sink capacity. The calculation formula is: The calculation formula of the carbon footprint diversity coefficient of mixed energy is: or ep =Σ(η tp / or tp ,or hp / or tp ,or np / or tp ,or wp / or tp ,or k / or tp ,or ng / or tp ) (8) Where η tp , η hp , η wp , η k , η ng They are the carbon footprint coefficients of thermal power, hydropower, wind power, electric vehicles, and controllable distributed power sources respectively.

5. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The new energy units adopt the life cycle energy chain analysis. Wind power and photovoltaic units are renewable energy power generation methods. Their carbon emissions are generated in the production, construction and transportation processes. The measurement models of the remaining links are as follows: In the formula, E pne is the carbon emission coefficient of the production process of renewable energy unit; c jg is the conversion coefficient between unit standard electric energy and energy consumption in the production and construction process; n is the number of categories of raw materials used in the production and construction process; u i 、m ci 、e ci is the loss rate, carbon emission coefficient and self-energy coefficient of the i-th raw material in the production and construction process; m cti 、e cti is the carbon emission coefficient and energy intensity value of transporting the i-th raw material during the production and construction process; m pj,t 、e pj,t is the carbon emission coefficient and self-energy coefficient of the i-th raw material used in the j-th production and construction process; In the formula, E tne is the carbon emission coefficient of the transportation process of renewable energy unit components; m ti is the carbon emission coefficient caused by the transportation process of the i-th component material; e ti is the energy intensity coefficient of the material transportation process of the i-th component; C ne =And pne P pne +E tne P tne (11) In the formula, C ne is the total amount of carbon released by new energy; P pne is the energy consumed in the production process; P ten It is the energy consumed during the transportation process.

6. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The electric vehicle V2G technology, electric vehicles are connected to the grid through V2G technology, that is, as a load, they need to be charged by the grid, and use battery energy storage to discharge to the grid, that is, they have the dual attributes of consumption and generation. The carbon emissions in the production process of electric vehicles account for more than 45% of the carbon emissions in the entire life cycle of electric vehicles. Considering the carbon emissions in the production process C ev Assigned to each optimization schedule in the VPP model, the carbon emission amount C that the k-th electric vehicle needs to be allocated during the VPP scheduling period T k Calculated by the formula: In the formula, Δt c and η c are the charging power, charging time and charging loss coefficient of the electric vehicle battery in period t, Δt l and η l are the discharge power, discharge duration and discharge loss coefficient of the electric vehicle battery in period t, LC is the maximum equivalent full charge and discharge cycles, E r is the rated power of the battery.

7. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The gas turbine, assuming that the power generation efficiency of the distributed gas turbine unit is The power output of the gas turbine unit is P ng (t) can be expressed by the formula: Among them, LHV is the thermal value of natural gas, which is generally 3.34MJ / m 3 ,V ng (t) represents the volume of natural gas consumed by the gas turbine during period t, The carbon emission intensity of a gas turbine is τ ng / kWh, the carbon emissions are: C ng (t)=τ ng ×P ng (t) (14) 8. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 2, characterized in that: The life cycle carbon footprint of the energy storage system in the charging state includes the life cycle carbon emissions of the energy storage system and the carbon emissions of power generation caused by charging demand; the life cycle carbon footprint in the discharging state is the life cycle carbon emissions of the energy storage system. Based on the selected functional unit, system life and system rated power, the life cycle carbon emission base value factor of the energy storage system is calculated. The carbon footprint of the energy storage system within a certain accounting period is as follows: Where P ST_c,t and P ST_Dc,t They are respectively the charging and discharging amount of the energy storage system during the calculation period.

9. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 1, characterized in that: The S2 comprises the following steps: S201: In the carbon rights trading market, set up a carbon emission rights trading mode and a carbon emission rights use-or-discard mode: (1) Carbon emission rights allow trading mode. Under this mode, VPP can trade carbon emission rights in the carbon trading market. The carbon trading cost R Ca，t It is expressed as: R Ca,t =k ca (AND ac,t -AND p,t ) (17) (2) The use-or-discard model of carbon emission rights. Under this model, if the carbon emissions generated by the VPP are lower than the free quota, the quota can only be discarded; if it is higher than the free quota, it will be subject to excess penalties. Therefore, the carbon emission cost of the VPP is as follows: R Ca,t =k ca2 max(E ac,t -AND p,t ,0) (18) Based on the two models of carbon emission rights trading model and carbon emission rights non-use-or-discard model, as well as the carbon emission measurement model based on the life cycle method, the VPP model carbon rights trading mechanism cost model taking into account the initial carbon quota can be expressed by the formula: In the formula, E all is the total amount of carbon emissions actually generated, E r is the initial carbon emission quota of the VPP model, C CO2 is the total cost of carbon trading in the VPP model; c is the carbon trading price in the carbon market; d is the length of the carbon emission interval; is α c is the carbon trading price growth coefficient; S202: The total operating cost of the VPP model includes the cost of purchasing energy from the upper energy supply grid, the equipment operation and maintenance cost during the operation of the VPP model, and the cost of user-side participation in incentive-based demand response. N is the service provider's operating cost; C CO2 is the carbon trading cost of the service provider, C N =C BUY +C OP +C IDR (20) C BUY is the cost of energy purchase by the service provider from the upper energy network; C OP is the operation and maintenance cost of the service provider; C IDR It is the user-side incentive demand management fee; S203: The optimization goal of VPP operators is comprehensive income F vpp Maximum, expressed as: Where, T is the scheduling period, which is 24 hours; R tsell R is the energy supply revenue of the VPP operator to the user at time t; tbuy R is the energy purchase cost of the VPP operator from the power generation side at time t; tgrid is the interaction cost between the VPP operator and the spot market at time t, R tgrid <0 means selling electricity to the spot market, R tgrid >0 means purchasing electricity from the spot market; c es,t 、c hs,t are the electricity and heat prices sold by the VPP operator to the user at time t; c eb,t 、c hb,t are the electricity and heat purchase prices of the VPP operator from the power generation side at time t; c mk,t is the electricity price in the spot market at time t; P es,t , H hs,t are the electrical and thermal output power of the power generation side at time t; P TL，t is the power passing through the tie line at time t, P TL，t <0 means selling electricity to the grid, P TL，t >0 means purchasing electricity from the grid.

10. The carbon footprint tracking and optimization method of a virtual power plant in a carbon market according to claim 1, characterized in that: The S3 comprises the following steps: S301: The virtual power plant proposes a multi-time scale optimization scheduling model. The established VPP multi-time scale optimization is divided into the following two stages: (1) In the day-ahead long-time-scale scheduling phase, scheduling is carried out 24 hours in advance with a scheduling time scale of 1 hour. The objective function is to minimize the total cost of the VPP. Each unit within the VPP reports system technical parameters such as wind and solar output and load forecast to the VPP coordination and control center. The control center formulates a scheduling plan for the next 24 hours based on the collected parameters, combined with the purchase and sale electricity price and the market carbon price. minC vpp =min(C op +C th +C bs +C ce +C dr ]) (25) In the formula, C VPP is the total operating cost of the virtual power plant; C op is the operation and maintenance cost of the virtual power plant equipment; C th is the operating cost of the gas turbine; C bs The cost of purchasing and selling electricity; C ce is the carbon emission cost; C dr for demand response costs; (2) In the intraday rolling optimization scheduling stage, scheduling is carried out 2 hours in advance, with a rolling optimization step of 30 minutes, and the scheduling time window is set to 3 hours, with a total of 8 periods, for optimal scheduling, and each time a scheduling plan for the next 4 hours is formed; the initial data of wind and solar power output and user load is expanded from 24 to 96 for scenario generation and reduction, and the VPP coordination control center adopts price-based demand response to reduce user load. In the formula, Total cost of VPP within the day; It is divided into wind, solar, storage and operation and maintenance costs, gas turbine operation costs and carbon emission costs during the T period of the day; It is the penalty fee for output deviation during the T period of the day; It is the intraday price-based demand response cost; S302: Consider multi-objective regulation within the virtual power plant to achieve carbon optimization. Under the carbon emission constraints, the virtual power plant reduces the corresponding carbon emissions by adjusting the output changes of each energy unit, so that the virtual power plant has a certain degree of free carbon emission constraint space.

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