Community-type virtual power plant low-carbon optimization method and system considering carbon emission reduction benefits

Abstract

The invention discloses a community-type virtual power plant low-carbon optimization method considering carbon emission reduction benefits, and the method comprises the following steps: firstly, inputting community-type virtual power plant element related parameters, and setting a carbon emission reduction benefit accounting method covering electric vehicle charging, photovoltaic operation and load interaction behaviors; secondly, taking a carbon emission reduction benefit accounting result as an economic item, and combining an electric vehicle charging and discharging model, a photovoltaic output model and a load interaction model to construct a community-type virtual power plant low-carbon optimization model taking economic cost minimization as a target; and finally, solving the low-carbon optimization model, and outputting an electric vehicle charging and discharging strategy, a photovoltaic scheduling strategy and a load interaction regulation and control strategy of the community-type virtual power plant. According to the method, the carbon emission reduction benefits of electric vehicle charging, photovoltaic operation and load interaction are quantified, the carbon emission benefits are fully considered in the process of solving the community-type virtual power plant multi-resource cooperation strategy, and the dual goals of cost reduction and carbon reduction can be achieved.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant optimization technology, specifically relating to a low-carbon optimization method and system for community-based virtual power plants that considers carbon emission reduction benefits. Background Technology

[0002] Community-based virtual power plants, as key carriers for integrating distributed energy resources and promoting efficient energy use, play a crucial role in achieving a green energy transition through low-carbon operation. The electric vehicles, photovoltaics, and load resources encompassed by community-based virtual power plants possess flexibility and portability, making them core elements for flexible energy regulation and carbon emission reduction.

[0003] Current virtual power plant optimization focuses primarily on carbon trading costs arising from carbon emissions, failing to provide a detailed description of the carbon reduction benefits of electric vehicle charging / discharging, photovoltaic output, and load interaction. Furthermore, electric vehicle charging / discharging models do not adequately consider battery degradation and aging characteristics. As the carbon inclusive market continues to improve, the low-carbon benefits of clean energy and demand response should be fully considered. Therefore, in the optimization of community-based virtual power plants, the carbon reduction benefits of electric vehicle charging / discharging, photovoltaic output, and load interaction should be fully considered and integrated into the objective function.

[0004] Therefore, this application focuses on the carbon benefits brought about by the interaction of photovoltaic, load and electric vehicle in virtual power plants, and constructs a community-based low-carbon operation model for virtual power plants to fully explore the synergistic low-carbon potential of photovoltaic, load and electric vehicle resources and meet the needs of low-carbon and economical virtual power plant operation. Summary of the Invention

[0005] The purpose of this invention is to propose a low-carbon optimization method and system for community-based virtual power plants that considers carbon emission reduction benefits. This method introduces three carbon emission reduction benefits—electric vehicle charging and discharging, photovoltaic output, and load interaction—to calculate the benefits. It constructs a low-carbon optimization model for community-based virtual power plants with optimal economic cost, and coordinates optimization strategies for electric vehicles, photovoltaics, and load resources to reduce the overall energy cost of community virtual power plants. This achieves synergistic progress between economic operation and low-carbon development, and promotes the green and efficient transformation of community energy systems.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: a low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits, comprising the following steps:

[0007] S1. Input photovoltaic-related parameters and establish a photovoltaic power output model based on illumination-temperature correction.

[0008] S2. Input the electric vehicle parameters and construct an electric vehicle charge / discharge model that considers battery aging constraints, focusing on charge / discharge power and state of charge limitations to ensure that the regulation is within a reasonable range of battery life and user needs.

[0009] S3. Input load-related parameters and establish a load interaction behavior model.

[0010] S4. Construct a carbon emission reduction benefit accounting method that covers electric vehicle charging, photovoltaic operation, and load interaction behavior.

[0011] S5. Introducing the S1 solar power output model with illumination-temperature correction, the S2 electric vehicle charging and discharging model, and the S3 load interaction behavior model, a low-carbon optimization model for an economically optimal community-based virtual power plant is constructed based on S4.

[0012] S6. Solve the low-carbon optimization model of the community-based virtual power plant in S5 to obtain the low-carbon operation strategy of the community-based virtual power plant. As an improvement of this invention, the photovoltaic output model based on illumination-temperature correction in S1 is as follows:

[0013] The output P of the photovoltaic power station during time period t pv,t Calculation as follows

[0014] P pv,t =A pv ×G t ×η pv,t

[0015] In the formula: A pv The total area of ​​photovoltaic panels (m²) 2 ), G t The actual light intensity (W / m²) during time period t 2 ), η pv,t The photovoltaic conversion efficiency is corrected for time period t.

[0016] The constraints include:

[0017] (1) Power limit constraint, that is, the actual output of a photovoltaic power station shall not exceed its maximum power generation capacity.

[0018] 0≤P pv,t ≤P pv,max

[0019] In the formula: P pv,max It is the maximum power output of a photovoltaic power station under ideal conditions.

[0020] (2) Light intensity constraints,

[0021] G min ≤G t ≤G max

[0022] In the formula: G min and G max These represent the lowest and highest possible light intensities.

[0023] As an improvement to the present invention, in S2, an electric vehicle charging and discharging model considering battery aging constraints is constructed, as follows:

[0024] The electric vehicle charging and discharging model considering battery aging constraints is as follows:

[0025]

[0026] Where: SOC n,t Let P be the battery load state of the nth electric vehicle during time period t, where n∈N. cha,n,t Let P be the charging power of the nth electric vehicle. dis,n,t Let η be the discharge power of the nth electric vehicle. cha η is the charging efficiency coefficient. dis γ is the discharge efficiency coefficient, Δt is the time interval, and γ is the discharge efficiency coefficient. aging,n Let E be the aging factor of the nth electric vehicle, considering the impact of battery aging on power. bat,max This is the maximum battery capacity.

[0027] The constraints include:

[0028] (1) Constraints on the degree of aging,

[0029] Quantifying charge and discharge aging losses and limiting the amount of aging within a reasonable range extends battery life and prevents overcharging and over-discharging from causing a decrease in battery life.

[0030] A n,t =A n,t-1 +γ aging,n ·|P cha,n,t -P dis,n,t |·ξ n,t ·Δt,t=1,2,...,T

[0031] ΔA=A n,T -A n,1 ≤A ev,max

[0032] In the formula: A n,t γ represents the cumulative aging amount of the nth electric vehicle during time period t. aging,n Let be the aging coefficient of the nth electric vehicle.

[0033] ξ n,t Let A be the correction factor for the nth electric vehicle in time period t, and ΔA be the change in battery aging within one period T. ev,max This represents the maximum permissible aging amount of the battery.

[0034] (2) Battery capacity constraints

[0035] SOC min ≤SOCn,t ≤SOC max

[0036] Where: SOC min For minimum battery capacity, SOC max For the maximum battery capacity,

[0037] (3) Charge and discharge power constraints

[0038] P cha,min ≤P cha,n,t ≤P cha,max

[0039] P dis,min ≤P dis,n,t ≤P dis,max

[0040] In the formula: P cha,min Let P be the minimum charging power for the nth electric vehicle. cha,max P is the maximum charging power of the nth electric vehicle. dis,min Let P be the minimum discharge power of the nth electric vehicle. dis,max Let be the maximum discharge power of the nth electric vehicle.

[0041] As an improvement to the present invention, the load interaction behavior model in S3 is as follows:

[0042] The total electricity consumption of transferable load is fixed throughout the entire dispatch cycle, expressed as follows:

[0043]

[0044] In the formula: j is the transferable load number, used to identify different individual transferable loads; J is the total number of transferable loads; P load,trans,j,t P represents the power after the transfer of the j-th transferable load in time period t. load,trans,0,j,t The power of the j-th transferable load before transfer in time period t.

[0045] The amount of load reduction that can be reduced in time period t is expressed as:

[0046]

[0047] In the formula: m is the reducible load number, used to identify different reducible load individuals; M is the total number of reducible loads; ΔP load,cut,m,t P represents the power reduction difference during the m-th load reduction period t. load,cut,0,m,t Let P be the power before the load reduction in the m-th load-reducible period t. load,cut,m,t Let t be the power after the load is reduced during the m-th load reduction period.

[0048] The constraints include:

[0049] (1) Power upper and lower limit constraints,

[0050] P load,trans,min,j ≤P load,trans,j,t ≤P load,trans,max,j

[0051] In the formula: P load,trans,min,j Let P be the minimum power of the j-th transferable load in time period t. load,trans,max,j Let be the maximum power of the j-th transferable load in time period t.

[0052] (2) Reduce the upper limit constraint.

[0053] 0≤P load,cut,m,t ≤P load,cut,max,m

[0054] In the formula: P load,cut,max Let be the maximum power that can be reduced during the m-th load period in time t.

[0055] As an improvement to this invention, in S4, a carbon emission reduction benefit accounting method covering electric vehicle charging, photovoltaic operation, and load interaction behavior is constructed, as detailed below.

[0056] (1) Calculation method for carbon emission reduction benefits of photovoltaic power generation

[0057] C pv_benefit =E pv ×λ c,pv ×γ pv

[0058]

[0059] In the formula: E pv For photovoltaic power generation, λ c,pv As the benchmark factor for photovoltaic carbon emission reduction, γ pv Additional adjustment coefficient for the carbon reduction benefits of photovoltaic power generation.

[0060] (2) Calculation method for carbon emission reduction benefits of electric vehicles

[0061] C ev_benefit =(E ev,cha ×λ c,ev,cha +E ev,dis ×λ c,ev,dis )×γ ev

[0062]

[0063] In the formula: E ev,cha Electric vehicle charging capacity, E ev,dis λ represents the discharge capacity of an electric vehicle. c,ev,cha The carbon emission reduction benchmark factor for electric vehicles participating in charging, λc,ev,dis γ is the baseline factor for carbon emission reduction when electric vehicles participate in discharge. ev Additional adjustment coefficient for the carbon emission reduction benefits of electric vehicles.

[0064] (4) Carbon emission reduction accounting method based on load interaction behavior

[0065] Carbon emission reduction benefits from load shifting are not considered at this time.

[0066] C load_benefit =E cut_trans ×λ c,load ×γ load

[0067]

[0068] In the formula: E cut_trans λ is the amount of load reduction that can be achieved. c,load As a baseline factor for load-interaction carbon emissions, γ load An additional adjustment factor for the carbon emission reduction benefits of load interaction.

[0069] The total economic benefits of the community-based virtual power plant are as follows:

[0070] F = C buy_e +C ope +C carbon_benefit

[0071] Economic Item 1: The electricity purchase cost function is as follows:

[0072] Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased.

[0073]

[0074] In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, P grid,t ρ is the power purchased by the virtual power plant from the grid during time period t. buy_e,t The price at which the virtual power plant purchases electricity from the grid during time period t.

[0075] Economic Item 2: The regulation cost function is as follows:

[0076]

[0077] Among them, C ope λ represents the operation and maintenance cost of charging and discharging electric vehicles. EV This is the operation and maintenance cost coefficient for electric vehicle charging and discharging. Economic item 3: The calculation function for the economic benefits of carbon emission reduction is as follows:

[0078] C carbon_benefit =C pv_benefit +Cev_benefit +C load_benefit .

[0079] As an improvement to this invention, the constraints of the economically optimal photovoltaic power generation, load, and electric vehicle collaborative optimization model include: power balance constraints and grid power purchase constraints.

[0080] (1) Power balance constraint,

[0081] P grid,t +P pv,t +P dis,n,t =P base,t +P load,trans,j,t +P load,cut,m,t +P cha,n,t

[0082] In the formula: P base,t The base load power for time period t is a known value.

[0083] (2) Power grid purchase constraints,

[0084] To ensure the safe and stable operation of the power grid, constraints are imposed on the maximum power purchase capacity of the distribution area and the power grid.

[0085] 0≤P grid,t ≤P grid_max

[0086] In the formula: P grid_max It is the maximum operating power allowed by the power grid.

[0087] A low-carbon optimization model for a community-based virtual power plant considering carbon emission reduction benefits is constructed. The collaborative optimization model is solved to obtain a low-carbon optimization method for a community-based virtual power plant that considers carbon emission reduction benefits, including:

[0088] By using YALMIP as an optimization modeling framework, the decision variables, objective function and constraints in the mixed integer programming problem are transformed into a standard mathematical model. Then, the CPLEX solver is used to optimize the model, and finally the optimized results of the users participating in load management are output.

[0089] A low-carbon optimization model for a community-based virtual power plant considering carbon emission reduction benefits is constructed to implement a low-carbon optimization method for a community-based virtual power plant considering carbon emission reduction benefits. The model includes:

[0090] A solar power output model with illumination-temperature correction.

[0091] Electric vehicle charge / discharge model and load interaction behavior model considering battery aging constraints;

[0092] A carbon emission reduction benefit accounting model covering electric vehicle charging and discharging, photovoltaic operation, and load interaction behavior;

[0093] The virtual power plant low-carbon optimization model achieves coordinated optimization of photovoltaic power generation, load and electric vehicles to reach the optimal economic state.

[0094] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to achieve the aforementioned carbon reduction benefits: a community-based virtual power plant low-carbon optimization method.

[0095] A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned carbon reduction benefits in a community-based virtual power plant low-carbon optimization method.

[0096] Compared to existing technologies, this invention has the following significant advantages: The community-based virtual power plant low-carbon optimization method and system proposed in this invention, which considers carbon emission reduction benefits, addresses the problems of one-sided carbon emission reduction accounting and the disconnect between optimization models and low-carbon goals in existing technologies. By systematically integrating electric vehicles, photovoltaics, and load resources, it constructs an optimization system that integrates carbon emission reduction benefits, achieving synergistic optimization of low-carbon benefits and economic costs. Specific advantages are as follows:

[0097] First, accurately quantify carbon emission reduction benefits. Innovatively construct a carbon emission reduction benefit accounting method covering electric vehicle charging, photovoltaic power generation, and load interaction, changing the limitations of existing single-factor accounting, comprehensively and accurately quantifying the overall carbon emission reduction contribution of community-based virtual power plants, and providing reliable data support for low-carbon optimization.

[0098] Secondly, the optimized model takes multiple objectives into account. Carbon emission reduction benefits are incorporated as an economic term into the objective function to construct a low-carbon optimization model with the lowest economic cost. This differs from traditional models that only focus on economic or a single low-carbon objective, achieving dual optimization of reducing economic costs and improving carbon emission reduction benefits, thus meeting the needs of low-carbon and economical operation of virtual power plants.

[0099] Third, it enables efficient operation of multiple resources in a coordinated manner. It deeply integrates the basic models and constraints of electric vehicles, photovoltaics, and load resources, ensuring that each resource meets its own operating characteristics while improving the overall operating efficiency and stability of the virtual power plant during the optimization process.

[0100] Fourth, it enhances the comprehensive application value. Through this method and system, community-based virtual power plants can not only reduce operating costs but also maximize carbon emission reduction benefits, promote the green transformation of community energy systems, lay the foundation for participating in the carbon trading market and obtaining additional economic benefits, and achieve a win-win situation for both environmental and economic benefits. It has significant practical application value and promotion significance. Attached Figure Description

[0101] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0102] To enhance understanding of the present invention, the following detailed description of the solution is provided in conjunction with the accompanying drawings.

[0103] Example: See Figure 1 A low-carbon optimization method for community-based virtual power plants that considers carbon emission reduction benefits includes the following steps:

[0104] S1. Input photovoltaic-related parameters and establish a photovoltaic power output model based on illumination-temperature correction.

[0105] S2. Input the electric vehicle parameters and construct an electric vehicle charge / discharge model that considers battery aging constraints, focusing on charge / discharge power and state of charge limitations to ensure that the regulation is within a reasonable range of battery life and user needs.

[0106] S3. Input load-related parameters and establish a load interaction behavior model.

[0107] S4. Construct a carbon emission reduction benefit accounting method that covers electric vehicle charging, photovoltaic operation, and load interaction behavior.

[0108] S5. Introducing the S1 solar power output model with illumination-temperature correction, the S2 electric vehicle charging and discharging model, and the S3 load interaction behavior model, a low-carbon optimization model for an economically optimal community-based virtual power plant is constructed based on S4.

[0109] S6. Solve the low-carbon optimization model of the community-based virtual power plant in S5 to obtain the low-carbon operation strategy of the community-based virtual power plant. Specifically, the photovoltaic output model based on illumination-temperature correction in S1 is as follows:

[0110] The output P of the photovoltaic power station during time period t pv,t Calculation as follows

[0111] P pv,t =A pv ×G t ×η pv,t

[0112] In the formula: A pv The total area of ​​photovoltaic panels (m²) 2 ), G t The actual light intensity (W / m²) during time period t 2 ), η pv,t The photovoltaic conversion efficiency is corrected for time period t.

[0113] The constraints include:

[0114] (1) Power limit constraint, that is, the actual output of a photovoltaic power station shall not exceed its maximum power generation capacity.

[0115] 0≤Ppv,t ≤P pv,max

[0116] In the formula: P pv,max It is the maximum power output of a photovoltaic power station under ideal conditions.

[0117] (2) Light intensity constraints,

[0118] G min ≤G t ≤G max

[0119] In the formula: G min and G max These represent the lowest and highest possible light intensities.

[0120] In S2, an electric vehicle charging and discharging model considering battery aging constraints is constructed, as follows:

[0121] The electric vehicle charging and discharging model considering battery aging constraints is as follows:

[0122]

[0123] Where: SOC n,t Let P be the battery load state of the nth electric vehicle during time period t, where n∈N. cha,n,t Let P be the charging power of the nth electric vehicle. dis,n,t Let η be the discharge power of the nth electric vehicle. cha η is the charging efficiency coefficient. dis γ is the discharge efficiency coefficient, Δt is the time interval, and γ is the discharge efficiency coefficient. aging,n Let E be the aging factor of the nth electric vehicle, considering the impact of battery aging on power. bat,max This is the maximum battery capacity.

[0124] The constraints include:

[0125] (1) Constraints on the degree of aging,

[0126] Quantifying charge and discharge aging losses and limiting the amount of aging within a reasonable range extends battery life and prevents overcharging and over-discharging from causing a decrease in battery life.

[0127] A n,t =A n,t-1 +γ aging,n ·|P cha,n,t -P dis,n,t |·ξ n,t ·Δt,t=1,2,...,T

[0128] ΔA=A n,T -A n,1 ≤A ev,max

[0129] In the formula: A n,t γ represents the cumulative aging amount of the nth electric vehicle during time period t. aging,n Let ξ be the aging coefficient of the nth electric vehicle. n,t Let A be the correction factor for the nth electric vehicle in time period t, and ΔA be the change in battery aging within one period T. ev,max This represents the maximum permissible aging amount of the battery.

[0130] (2) Battery capacity constraints

[0131] SOC min ≤SOC n,t ≤SOC max

[0132] Where: SOC min For minimum battery capacity, SOC max For the maximum battery capacity,

[0133] (3) Charge and discharge power constraints

[0134] P cha,min ≤P cha,n,t ≤P cha,max

[0135] P dis,min ≤P dis,n,t ≤P dis,max

[0136] In the formula: P cha,min Let P be the minimum charging power for the nth electric vehicle. cha,max P is the maximum charging power of the nth electric vehicle. dis,min Let P be the minimum discharge power of the nth electric vehicle. dis,max Let n be the maximum discharge power of the nth electric vehicle.

[0137] The load interaction behavior model in S3 is as follows:

[0138] The total electricity consumption of transferable load is fixed throughout the entire dispatch cycle, expressed as follows:

[0139]

[0140] In the formula: j is the transferable load number, used to identify different individual transferable loads; J is the total number of transferable loads; P load,trans,j,t P represents the power after the transfer of the j-th transferable load in time period t. load,trans,0,j,t Let be the power of the j-th transferable load before transfer in time period t, and let be the reduction amount of the load that can be reduced in time period t.

[0141]

[0142] In the formula: m is the reducible load number, used to identify different reducible load individuals; M is the total number of reducible loads; ΔP load,cut,m,t P represents the power reduction difference during the m-th load reduction period t. load,cut,0,m,t Let P be the power before the load reduction in the m-th load-reducible period t. load,cut,m,t Let t be the power after the load is reduced during the m-th load reduction period.

[0143] The constraints include:

[0144] (1) Power upper and lower limit constraints,

[0145] P load,trans,min,j ≤P load,trans,j,t ≤P load,trans,max,j

[0146] In the formula: P load,trans,min,j Let P be the minimum power of the j-th transferable load in time period t. load,trans,max,j Let be the maximum power of the j-th transferable load in time period t.

[0147] (2) Reduce the upper limit constraint.

[0148] 0≤P load,cut,m,t ≤P load,cut,max,m

[0149] In the formula: P load,cut,max Let be the maximum power that can be reduced during the m-th load period in time t.

[0150] In S4, a carbon emission reduction benefit accounting method covering electric vehicle charging, photovoltaic operation, and load interaction behavior is constructed, as detailed below.

[0151] (1) Calculation method for carbon emission reduction benefits of photovoltaic power generation

[0152] C pv_benefit =E pv ×λ c,pv ×γ pv

[0153]

[0154] In the formula: E pv For photovoltaic power generation, λ c,pv As the benchmark factor for photovoltaic carbon emission reduction, γ pv Additional adjustment coefficient for the carbon reduction benefits of photovoltaic power generation.

[0155] (2) Calculation method for carbon emission reduction benefits of electric vehicles

[0156] C ev_benefit =(E ev,cha×λ c,ev,cha +E ev,dis ×λ c,ev,dis )×γ ev

[0157]

[0158] In the formula: E ev,cha Electric vehicle charging capacity, E ev,dis λ represents the discharge capacity of an electric vehicle. c,ev,cha The carbon emission reduction benchmark factor for electric vehicles participating in charging, λ c,ev,dis γ is the baseline factor for carbon emission reduction when electric vehicles participate in discharge. ev Additional adjustment coefficient for the carbon emission reduction benefits of electric vehicles.

[0159] (5) Carbon emission reduction accounting method based on load interaction behavior

[0160] Carbon emission reduction benefits from load shifting are not considered at this time.

[0161] C load_benefit =E cut_trans ×λ c,load ×γ load

[0162]

[0163] In the formula: E cut_trans λ is the amount of load reduction that can be achieved. c,load As a baseline factor for load-interaction carbon emissions, γ load An additional adjustment factor for the carbon emission reduction benefits of load interaction.

[0164] The total economic benefits of the community-based virtual power plant are as follows:

[0165] F = C buy_e +C ope +C carbon_benefit

[0166] Economic Item 1: The electricity purchase cost function is as follows:

[0167] Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased.

[0168]

[0169] In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, P grid,t ρ is the power purchased by the virtual power plant from the grid during time period t. buy_e,t The price at which the virtual power plant purchases electricity from the grid during time period t.

[0170] Economic Item 2: The regulation cost function is as follows:

[0171]

[0172] Among them, C ope λ represents the operation and maintenance cost of charging and discharging electric vehicles. EV This is the operation and maintenance cost coefficient for electric vehicle charging and discharging. Economic item 3: The calculation function for the economic benefits of carbon emission reduction is as follows:

[0173] C carbon_benefit =C pv_benefit +C ev_benefit +C load_benefit .

[0174] The constraints for constructing the economically optimal photovoltaic power generation, load, and electric vehicle collaborative optimization model include: power balance constraints and grid power purchase constraints.

[0175] (1) Power balance constraint,

[0176] P grid,t +P pv,t +P dis,n,t =P base,t +P load,trans,j,t +P load,cut,m,t +P cha,n,t

[0177] In the formula: P base,t The base load power for time period t is a known value.

[0178] (2) Power grid purchase constraints,

[0179] To ensure the safe and stable operation of the power grid, constraints are imposed on the maximum power purchase capacity of the distribution area and the power grid.

[0180] 0≤P grid,t ≤P grid_max

[0181] In the formula: P grid_max It is the maximum operating power allowed by the power grid.

[0182] Example 2: Constructing a low-carbon optimization model for a community-based virtual power plant considering carbon emission reduction benefits, solving the collaborative optimization model, and obtaining a low-carbon optimization method for a community-based virtual power plant considering carbon emission reduction benefits, including:

[0183] By using YALMIP as an optimization modeling framework, the decision variables, objective function and constraints in the mixed integer programming problem are transformed into a standard mathematical model. Then, the CPLEX solver is used to optimize the model, and finally the optimized results of the users participating in load management are output.

[0184] Example 3: Constructing a low-carbon optimization model for a community-based virtual power plant that considers carbon emission reduction benefits, to implement a low-carbon optimization method for a community-based virtual power plant that considers carbon emission reduction benefits. The model includes:

[0185] A solar power output model with illumination-temperature correction.

[0186] Electric vehicle charge / discharge model and load interaction behavior model considering battery aging constraints;

[0187] A carbon emission reduction benefit accounting model covering electric vehicle charging and discharging, photovoltaic operation, and load interaction behavior;

[0188] The virtual power plant low-carbon optimization model achieves coordinated optimization of photovoltaic power generation, load and electric vehicles to reach the optimal economic state.

[0189] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits, characterized in that, Includes the following steps: S1. Input photovoltaic-related parameters and establish a photovoltaic power output model based on illumination-temperature correction. S2. Input the electric vehicle parameters and construct an electric vehicle charge / discharge model that considers battery aging constraints, focusing on charge / discharge power and state of charge limitations to ensure that the regulation is within a reasonable range of battery life and user needs. S3. Input load-related parameters and establish a load interaction behavior model. S4. Construct a carbon emission reduction benefit accounting method that covers electric vehicle charging, photovoltaic operation, and load interaction behavior. S5. Introducing the S1 solar power output model with illumination-temperature correction, the S2 electric vehicle charging and discharging model, and the S3 load interaction behavior model, a low-carbon optimization model for an economically optimal community-based virtual power plant is constructed based on S4. S6. Solve the low-carbon optimization model of the community-type virtual power plant in S5 to obtain the low-carbon operation strategy of the community-type virtual power plant.

2. The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits according to claim 1, characterized in that, The photovoltaic output model in S1 based on illumination-temperature correction is as follows: The output P of the photovoltaic power station during time period t pv,t Calculation as follows P pv,t =A pv ×G t ×η pv,t In the formula: A pv The total area of ​​photovoltaic panels (m²) 2 ), G t The actual light intensity (W / m²) during time period t 2 ), η pv,t The photovoltaic conversion efficiency is corrected for time period t. The constraints include: (1) Power limit constraint, that is, the actual output of a photovoltaic power station shall not exceed its maximum power generation capacity. 0≤P pv,t ≤P pv,max In the formula: P pv,max It is the maximum power output of a photovoltaic power station under ideal conditions. (2) Light intensity constraints, G min ≤G t ≤G max In the formula: G min and G max These represent the lowest and highest light intensities.

3. The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits according to claim 1, characterized in that, In S2, an electric vehicle charging and discharging model considering battery aging constraints is constructed, as follows: The electric vehicle charging and discharging model considering battery aging constraints is as follows: Where: SOC n,t Let P be the battery load state of the nth electric vehicle during time period t, where n∈N. cha,n,t Let P be the charging power of the nth electric vehicle. dis,n,t Let η be the discharge power of the nth electric vehicle. cha η is the charging efficiency coefficient. dis γ is the discharge efficiency coefficient, Δt is the time interval, and γ is the discharge efficiency coefficient. aging,n Let E be the aging factor of the nth electric vehicle, considering the impact of battery aging on power. bat,max This is the maximum battery capacity. The constraints include: (1) Constraints on the degree of aging, Quantifying charge and discharge aging losses and limiting the amount of aging within a reasonable range extends battery life and prevents overcharging and over-discharging from causing a decrease in battery life. From n,t =A n,t-1 +γ aging,n ·|P cha,n,t -P dis,n,t |·ξ n,t ·Δt,t=1,2,...,T ΔA=A n,T -IN n,1 ≤A ev,max In the formula: A n,t γ represents the cumulative aging amount of the nth electric vehicle during time period t. aging,n Let ξ be the aging coefficient of the nth electric vehicle. n,t Let A be the correction factor for the nth electric vehicle in time period t, and ΔA be the change in battery aging within one period T. ev,max This represents the maximum permissible aging amount of the battery. (2) Battery capacity constraints SOC min ≤SOC n,t ≤SOC max Where: SOC min For minimum battery capacity, SOC max For the maximum battery capacity, (3) Charge and discharge power constraints P cha,min ≤P cha,n,t ≤P cha,max P dis,min ≤P dis,n,t ≤P dis,max In the formula: P cha,min Let P be the minimum charging power for the nth electric vehicle. cha,max P is the maximum charging power of the nth electric vehicle. dis,min Let P be the minimum discharge power of the nth electric vehicle. dis,max Let be the maximum discharge power of the nth electric vehicle.

4. The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits according to claim 1, characterized in that, The load interaction behavior model in S3 is as follows: The total electricity consumption of transferable load is fixed throughout the entire dispatch cycle, expressed as follows: In the formula: j is the transferable load number, used to identify different individual transferable loads, and J is the total number of transferable loads. Quantity, P load,trans,j,t P represents the power after the transfer of the j-th transferable load in time period t. load,trans,0,j,t The power of the j-th transferable load before transfer in time period t. The amount of load reduction that can be reduced in time period t is expressed as: In the formula: m is the reducible load number, used to identify different reducible load individuals, and M is the total reducible load. Number, ΔP load,cut,m,t P represents the power reduction difference during the m-th load reduction period t. load,cut,0,m,t Let P be the power before the load reduction in the m-th load-reducible period t. load,cut,m,t Let t be the power after the load is reduced during the m-th load reduction period. The constraints include: (1) Power upper and lower limit constraints, P load,trans,min,j ≤P load,trans,j,t ≤P load,trans,max,j In the formula: P load,trans,min,j Let P be the minimum power of the j-th transferable load in time period t. load,trans,max,j Let be the maximum power of the j-th transferable load in time period t. (2) Reduce the upper limit constraint. 0≤P load,cut,m,t ≤P load,cut,max,m In the formula: P load,cut,max Let be the maximum power that can be reduced during the m-th time period t.

5. The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits according to claim 1, characterized in that, In S4, a carbon emission reduction benefit accounting method covering electric vehicle charging, photovoltaic operation, and load interaction behavior is constructed, as detailed below. (1) Calculation method for carbon emission reduction benefits of photovoltaic power generation C pv_benefit =And pv ×λ c,pv ×γ pv In the formula: E pv For photovoltaic power generation, λ c,pv As the benchmark factor for photovoltaic carbon emission reduction, γ pv Additional adjustment coefficient for the carbon reduction benefits of photovoltaic power generation. (2) Calculation method for carbon emission reduction benefits of electric vehicles C ev_benefit =(And ev,cha ×λ c,ev,cha +E ev,dis ×λ c,ev,dis )×γ ev In the formula: E ev,cha E-charge capacity for electric vehicles ev,dis λ represents the discharge capacity of an electric vehicle. c,ev,cha The carbon emission reduction benchmark factor for electric vehicles participating in charging, λ c,ev,dis γ is the baseline factor for carbon emission reduction when electric vehicles participate in discharge. ev Additional adjustment coefficient for the carbon emission reduction benefits of electric vehicles. (3) Carbon emission reduction accounting method based on load interaction behavior Carbon emission reduction benefits from load shifting are not considered at this time. C load_benefit =And cut_trans ×λ c,load ×γ load In the formula: E cut_trans λ is the amount of load reduction that can be achieved. c,load As a baseline factor for load-interaction carbon emissions, γ load For load mutual Additional adjustment coefficient for carbon emission reduction benefits. The total economic benefits of the community-based virtual power plant are as follows: F=C buy_e +C ope +C carbon_benefit Economic Item 1: The electricity purchase cost function is as follows: Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased. In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, P grid,t The virtual power plant's power purchase from the grid during time period t rate, ρ buy_e,t The price at which the virtual power plant purchases electricity from the grid during time period t. Economic Item 2: The regulation cost function is as follows: Among them, C ope λ represents the operation and maintenance cost of charging and discharging electric vehicles. EV This is the operation and maintenance cost coefficient for electric vehicle charging and discharging. Economic item 3: The calculation function for the economic benefits of carbon emission reduction is as follows: C carbon_benefit =C pv_benefit +C ev_benefit +C load_benefit 。 6. The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits according to claim 1, characterized in that, The constraints for constructing the economically optimal photovoltaic power generation, load, and electric vehicle collaborative optimization model include: power balance constraints and grid power purchase constraints. (1) Power balance constraint, P grid,t +P pv,t +P dis,n,t =P base,t +P load,trans,j,t +P load,cut,m,t +P cha,n,t In the formula: P base,t The base load power for time period t is a known value. (2) Power grid purchase constraints, To ensure the safe and stable operation of the power grid, constraints are imposed on the maximum power purchase capacity of the distribution area and the power grid. 0≤P grid,t ≤P grid_max In the formula: P grid_max It is the maximum operating power allowed by the power grid.

7. Constructing a low-carbon optimization model for a community-based virtual power plant that considers carbon emission reduction benefits, characterized in that... The low-carbon optimization method for community-based virtual power plants considering carbon emission reduction benefits, as described in any one of claims 1-6, is characterized by solving a collaborative optimization model to obtain the low-carbon optimization method for community-based virtual power plants with carbon emission reduction benefits, comprising: By using YALMIP as an optimization modeling framework, the decision variables, objective function and constraints in the mixed integer programming problem are transformed into a standard mathematical model. Then, the CPLEX solver is used to optimize the model, and finally the optimized results of the users participating in load management are output.

8. A low-carbon optimization model for a community-based virtual power plant considering carbon emission reduction benefits is constructed to implement the low-carbon optimization method for a community-based virtual power plant considering carbon emission reduction benefits as described in any one of claims 1-6, characterized in that... The model includes: A solar power output model with illumination-temperature correction. Electric vehicle charge / discharge model and load interaction behavior model considering battery aging constraints; A carbon emission reduction benefit accounting model covering electric vehicle charging and discharging, photovoltaic operation, and load interaction behavior; The virtual power plant low-carbon optimization model achieves coordinated optimization of photovoltaic power generation, load and electric vehicles to reach the optimal economic state.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements a community-based virtual power plant low-carbon optimization method for carbon reduction benefits as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that: When the computer instructions are executed by the processor, they implement the low-carbon optimization method for community-based virtual power plants that achieves carbon reduction benefits as described in any one of claims 1-6.

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