A virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making

Abstract

The present invention discloses a virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making. The method includes: establishing a carbon-electricity multi-time joint decision-making system; inputting power trading data and virtual power plant operation data into a power trading pre-clearing model, and outputting pre-clearing power supply price parameters; inputting carbon trading data and virtual power plant operation data into a carbon trading pre-clearing model, and outputting carbon quota price parameters for the virtual power plant to participate in carbon trading; inputting pre-clearing power supply price parameters, carbon quota price parameters and virtual power plant operation data into a multi-time joint decision-making model, and outputting the virtual power plant's clearing amount and carbon quota quantity; arranging the output of the virtual power plant to achieve optimal scheduling of the virtual power plant. The present invention comprehensively considers power generation and carbon quota to achieve optimal scheduling of the virtual power plant, which can ensure the smooth completion of the virtual power plant's participation in carbon trading and reduce social carbon emissions.

Description

Technical Field

[0001] The present invention relates to a virtual power plant optimization scheduling method, and in particular to a virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making. Background Art

[0002] In recent years, electricity and carbon trading have accelerated. Given that electricity production often involves carbon emissions, virtual power plants face carbon emissions assessments. Therefore, virtual power plants tend to consider both power generation and carbon emissions in their scheduling.

[0003] Existing research often assumes a fixed carbon quota cost parameter, quantitatively assesses the carbon emission cost parameter based on unit emission characteristics, and incorporates the carbon emission cost into the virtual power plant's target model to guide the optimal scheduling of the virtual power plant. This approach assumes a fixed carbon quota cost parameter, but in reality, carbon quota cost parameters fluctuate in carbon trading. Existing research also fails to consider the existence of the virtual power plant's initial carbon quota in its decision-making, often assuming that the carbon quota is input based on the amount of electricity generated. However, in reality, virtual power plants are allocated an initial carbon quota at the beginning of the year. Ignoring this initial carbon quota often makes the scheduling plan less optimal. Summary of the Invention

[0004] In order to solve the problems existing in the background technology, the present invention provides a virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making. The method of the present invention is interwoven with carbon trading and electricity trading.

[0005] By establishing a carbon-electricity multi-time joint decision-making model for virtual power plants, power generation behavior and carbon emission behavior are taken into consideration in a coordinated manner, that is, the clearance volume and carbon quota are taken into consideration in a coordinated manner, thereby achieving optimal scheduling of virtual power plants, ensuring the completion rate of carbon trading, and reducing social carbon emissions.

[0006] The technical solution adopted in the present invention is:

[0007] The virtual power plant optimization scheduling method of the present invention includes the following steps:

[0008] Step 1: Establish a carbon-electricity multi-time joint decision-making system for virtual power plants that considers carbon-electricity joint constraints. The carbon-electricity multi-time joint decision-making system includes an electricity trading pre-clearing model that considers electricity trading constraints, a carbon trading pre-clearing model that considers carbon trading constraints, and a multi-time joint decision-making model that considers multi-time joint decision-making constraints for virtual power plants to participate in electricity trading and carbon trading.

[0009] Step 2: Obtain the power trading data and operating data of the virtual power plant participating in power trading, input the power trading data and operating data of the virtual power plant into the power trading pre-clearing model considering power trading constraints, and the power trading pre-clearing model outputs the pre-clearing power supply cost parameters of the virtual power plant participating in power trading.

[0010] Step 3: Obtain the carbon trading data of the virtual power plant participating in carbon trading, input the carbon trading data and the operating data of the virtual power plant into the carbon trading pre-clearing model that considers the carbon trading constraints, and the carbon trading pre-clearing model outputs the carbon quota cost parameters of the virtual power plant participating in carbon trading.

[0011] Step 4: Input the pre-clearing power supply cost parameters obtained in step 2, the carbon quota cost parameters obtained in step 3, and the operating data of the virtual power plant into the multi-time joint decision-making model considering multi-time joint decision-making constraints. The multi-time joint decision-making model outputs the clearing amount and carbon quota quantity of the virtual power plant.

[0012] Step 5: Finally, the carbon-electricity multi-time joint decision-making system that considers the carbon-electricity joint constraints outputs the pre-clearing power supply cost parameters, carbon quota cost parameters, the virtual power plant's clearing volume and the number of carbon quotas to arrange the virtual power plant's output and achieve optimal scheduling of the virtual power plant.

[0013] The specific cost is related to the amount of electricity pre-cleared by the virtual power plant and the amount of carbon quota.

[0014] In step 1, the virtual power plant is located in a power system, which includes several nodes, transmission lines, non-distributed generators, and virtual power plants. Each node is connected by various transmission lines. Each virtual power plant includes several non-distributed generators, energy storage devices, adjustable loads, and distributed generators within a preset area. Each generator, energy storage device, adjustable load, and distributed generator is located at a respective node in the power system. Distributed generators are specifically wind turbines or photovoltaic generators. The virtual power plant is an organization that integrates various devices into a whole through a control center to participate in power system operation, power trading, and carbon trading. The non-distributed generators of the power system located outside the virtual power plant are specifically coal-fired generators, while the non-distributed generators in the virtual power plant are specifically gas-fired generators.

[0015] In step 2, the power transaction pre-clearing model considering power transaction constraints is as follows:

[0016]

[0017] Among them, θ t represents the operating time of the lth virtual power plant in the power system during period t; The bid price of the kth segment of the power of the dth load in the power system in the power transaction; represents the reported amount of the kth segment of the power of the dth load in the power system in the power transaction during the t period; α lib represents the bid of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t; represents the clearing amount of the bth segment of the power of the i-th non-distributed generator in the l-th virtual power plant in the power system during period t; A positive value indicates that the virtual power plant transmits electricity to the grid, and a negative value indicates that the virtual power plant transmits electricity from the grid; segmentation refers to the piecewise linear method commonly used in power trading to simplify the calculation complexity.

[0018] The power trading constraints are as follows:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Among them, B nm represents the susceptance of the transmission line between the nth and mth nodes in the power system; δ nt represents the phase angle of the nth node in the power system in period t, δ mt represents the phase angle of the mth node in the power system during period t; It represents the maximum power value of the line flow from the nth node to the mth node on the transmission line between the nth node and the mth node in the power system; represents the maximum clearing amount of the bth segment of the power of the i-th non-distributed generator unit in the l-th virtual power plant in the power system during period t; represents the maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction during period t; nt represents the pre-clearing power supply cost parameter of the nth node in the power system in period t; π represents the pi ratio; d∈Ψ n Indicates that when the dth load in the power system is located at the nth node, i∈Ψ n It means that when the i-th non-distributed generator of the l-th virtual power plant in the power system is located at the n-th node, w∈Ψn It means that when the wth non-distributed generator of the lth virtual power plant in the power system is located at the nth node, m∈Θ n Indicates that the mth node in the power system is the downstream node of the nth node.

[0026] The dual variable of .

[0027] The operation data of the virtual power plant input into the power transaction pre-clearing model is the operation time θ of the lth virtual power plant in the power system during period t. t The power transaction data of the virtual power plant participating in the power transaction input by the power transaction pre-clearing model includes the quotation of the kth segment of the power of the dth load in the power system in the power transaction And the k-th segment of the power reported by the d-th load in the power system during the t period The pre-clearing power supply cost parameter of the virtual power plant participating in the power transaction output by the power transaction pre-clearing model is the pre-clearing power supply cost parameter λ of the nth node in the power system in period t nt .

[0028] In step 3, the carbon trading pre-clearing model considering carbon trading constraints is as follows:

[0029]

[0030] in, represents the input bidding price of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, represents the input competition of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, Γ c Indicates the input party of carbon quota in carbon trading; represents the output bidding price of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, represents the output competition of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, Γ s Indicates the output party of carbon quota in carbon trading; represents the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading during period t. Other industry entities o specifically include high-carbon emission industries such as steel, petrochemicals, chemicals, building materials and nonferrous metals; It represents the output competition of carbon quotas of other industry entities o in carbon trading during period t.

[0031] The carbon trading constraints are as follows:

[0032]

[0033]

[0034]

[0035]

[0036] Among them, μ t C represents the carbon quota cost parameter for pre-clearing of carbon trading in period t; lt represents the carbon quota balance of the lth virtual power plant in the power system at the initial stage of carbon trading in period t; It represents the maximum output bidding of carbon quota of other industry entities in carbon trading.

[0037] μ t For the formula The dual variable of .

[0038] The operation data of the virtual power plant input into the carbon trading pre-clearing model includes the operation time θ of the lth virtual power plant in the power system during period t t The carbon trading data of the virtual power plant participating in the carbon trading input into the carbon trading pre-clearing model includes the input bidding price of the carbon quota of the lth virtual power plant in the power system in the carbon trading during period t. Output bidding of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading And the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading period t The carbon quota price parameter of the virtual power plant participating in carbon trading output by the carbon trading pre-clearing model is the carbon quota price parameter μ of the carbon trading pre-clearing in period t t .

[0039] The carbon quota balance C of the lth power plant in the power system during the period t at the beginning of the carbon trading lt , as follows:

[0040]

[0041] in, represents the total carbon quota initially allocated to the lth virtual power plant in the power system; represents the carbon emission factor of the i-th non-distributed generator in the l-th virtual power plant in the power system; represents the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system at time τ; i∈Ω l It indicates that the i-th non-distributed generator belongs to the l-th virtual power plant in the power system.

[0042] The operation data of the virtual power plant input into the carbon trading pre-clearing model also includes the total carbon quota initially allocated to the lth virtual power plant in the power system.

[0043] A virtual power plant receives its initial carbon quota at the beginning of the year. It allocates its total carbon quota across different short-term timescales and makes targeted trading decisions at different points in the carbon trading process based on its own power generation and remaining balance. By comprehensively considering the relationship between carbon quota competition and power generation in the current period, as well as the initial carbon quota decomposition, the virtual power plant, knowing its power generation in the t-1 period before the year (i.e., its cleared amount), can determine its own carbon quota usage in period t, i.e., its remaining carbon quota at the beginning of the carbon trading period.

[0044] In step 4, the multi-time joint decision model for power plants to participate in electricity trading and carbon trading, taking into account multi-time joint decision constraints, is as follows:

[0045]

[0046] Among them, φ l represents the objective function of the multi-time joint decision model; n:i∈Ψ n Indicates that when n is the node where the i-th generator set of the l-th power plant in the power system is located; a lib represents the cost coefficient of the bth segment of the power of the i-th generator unit of the l-th power plant in the power system during period t; curt,t P represents the compensation cost parameter for load reduction in the power system during period t; curt,t represents the load interruption of the power system during period t; α i,t represents the participation factor of the i-th non-distributed generator in the l-th virtual power plant in the power system during period t, α i,t Indicates the proportion of the i-th non-distributed generator participating in the uncertainty balance; d i represents the regulation cost parameter of the i-th non-distributed generator in the l-th virtual power plant in the power system; ξ t Represents the total uncertainty of wind and solar power in the power system during period t.

[0047] The multi-time joint decision constraints are as follows:

[0048]

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[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

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[0057]

[0058]

[0059] Among them, α li(b-1) ETq represents the price quoted by the i-th non-distributed generator of the l-th virtual power plant in the power system in the b-1-th segment of power in the power transaction; lt represents the number of carbon quotas of the lth virtual power plant in the power system in time period t in carbon trading; σ lt represents the carbon quota decomposition factor of the lth virtual power plant in the power system during period t in carbon trading; P WT,t (v) represents the output power of each wind turbine in the power system at the actual wind speed v during time period t; v ci 、v r and v co Respectively represent the rated wind speed, cut-in wind speed and cut-out wind speed of each wind turbine in the power system; Represents the rated power of each wind turbine in the power system; P PV,t represents the output power of each photovoltaic generator in the power system during period t; the photovoltaic generators in the power system constitute a photovoltaic system, f PV Indicates the power loss ratio of the photovoltaic system, which is the ratio of the output power of the photovoltaic system to the rated output power; I is the photovoltaic power generation capacity of each photovoltaic generator in the power system during period t; T is the actual illumination of the power system; I s is the preset illuminance, that is, the illuminance under standard test conditions; α P Represents the temperature coefficient; T ce Represents the surface temperature of the solar panels of each photovoltaic generator in the power system; T ce,ST represents the preset surface temperature of the solar panel of each photovoltaic generator in the power system, that is, the surface temperature under standard test conditions; SOC(t) and SOC(t-1) represent the state of charge of each energy storage device in the power system at time period t and time t-1, respectively; represents the charging power of each energy storage device in the power system during time period t; η represents the charging and discharging efficiency of each energy storage device in the power system; Δt is the charging and discharging time of each energy storage device in the power system; represents the discharge power of each energy storage device in the power system during time period t; and denote the minimum and maximum clearing amounts of the ith non-distributed generator in the lth virtual power plant in the power system, and denote the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system in period t and period t-1, respectively; and are the ramp-down rate and ramp-up rate of the ith non-distributed generator in the ith virtual power plant in the power system; h max It represents the maximum call rate of controllable load in a single period in the power system; P load,t and P load,t+1 represents the electric load level of each virtual power plant in the power system at time t and time t+1; P lcurt,t and P lcurt,t+1 denote the load interruption of the lth virtual power plant in the power system at time period t and time t+1 respectively; It represents the maximum value of the sum of the continuous call rates in two adjacent time periods; P WT,i,t represents the output power of each wind turbine connected to the ith non-distributed generator of the lth virtual power plant in the power system during period t; P PV,i,t represents the output power of each PV generator of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during period t; and They represent the discharge power and charging power of each energy storage device of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during time period t.

[0060] The operation data of the virtual power plant input into the multi-time joint decision-making model includes the operation time θ of the lth virtual power plant in the power system during period t t and the total carbon quota initially allocated to the lth virtual power plant in the power system The output of the virtual power plant output by the multi-time joint decision model is specifically the output of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t. The carbon quota quantity of the virtual power plant output by the multi-time joint decision-making model is specifically the carbon quota quantity ETq of the lth virtual power plant in the power system in time period t in carbon trading lt .

[0061] In step 5, the carbon-electricity combined constraints are as follows:

[0062]

[0063]

[0064]

[0065]

[0066]

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[0069]

[0070]

[0071]

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[0088] in, The maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction; and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of It represents the maximum power value of the line flow from the mth node to the nth node on the transmission line between the mth node and the nth node in the power system; and Representation formula The dual variable of Representation formula The dual variable of represents the maximum input bidding amount of carbon quota of the lth virtual power plant in the power system in the carbon trading during period t; The maximum output bidding amount of carbon quota in carbon trading by the lth virtual power plant in the power system during period t; Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of .

[0089] The carbon-electricity joint constraint is the KKT condition for the pre-clearing problem of electricity trading and carbon trading, that is, the problem to be solved is converted into a single-layer optimization problem. The solver can be used to directly solve the carbon-electricity joint constraint to obtain the final output result.

[0090] The beneficial effects of the present invention are:

[0091] This method is a novel approach for optimizing the scheduling of virtual power plants. It achieves the coordinated optimization of carbon trading and electricity trading strategy decisions within virtual power plants, effectively guiding their optimal scheduling. Furthermore, it can enhance carbon trading activity, ensure smooth completion, and reduce carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a logic block diagram of the method of the present invention;

[0093] Figure 2 This is a schematic diagram of the output of each unit of the virtual power plant of the present invention. DETAILED DESCRIPTION

[0094] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0095] like Figure 1 As shown, the virtual power plant optimization scheduling method of the present invention includes the following steps:

[0096] Step 1: Establish a carbon-electricity multi-time joint decision-making system for virtual power plants that considers carbon-electricity joint constraints. The carbon-electricity multi-time joint decision-making system includes an electricity trading pre-clearing model that considers electricity trading constraints, a carbon trading pre-clearing model that considers carbon trading constraints, and a multi-time joint decision-making model that considers multi-time joint decision-making constraints for virtual power plants to participate in electricity trading and carbon trading.

[0097] In step 1, the virtual power plant is located in the power system, which consists of several nodes, transmission lines, non-distributed generators, and virtual power plants. Each node is connected by transmission lines. Each virtual power plant includes several non-distributed generators, energy storage devices, adjustable loads, and distributed generators within a predefined area. Each generator, energy storage device, adjustable load, and distributed generator are located at their respective nodes in the power system. Distributed generators can be wind turbines or photovoltaic generators. The virtual power plant is an organization that integrates these devices into a single entity through a control center to participate in power system operation, power trading, and carbon trading. Non-distributed generators in the power system located outside the virtual power plant can be coal-fired generators, while non-distributed generators within the virtual power plant can be gas-fired generators.

[0098] Step 2: Obtain the power trading data and operating data of the virtual power plant participating in power trading, input the power trading data and operating data of the virtual power plant into the power trading pre-clearing model considering power trading constraints, and the power trading pre-clearing model outputs the pre-clearing power supply cost parameters of the virtual power plant participating in power trading.

[0099] In step 2, the power trading pre-clearing model considering power trading constraints is as follows:

[0100]

[0101] Among them, θ t represents the operating time of the lth virtual power plant in the power system during period t; The bid price of the kth segment of the power of the dth load in the power system in the power transaction; represents the reported amount of the kth segment of the power of the dth load in the power system in the power transaction during the t period; α librepresents the bid of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t; represents the clearing amount of the bth segment of the power of the i-th non-distributed generator in the l-th virtual power plant in the power system during period t; A positive value indicates that the virtual power plant transmits electricity to the grid, while a negative value indicates that the virtual power plant transmits electricity from the grid; segmentation refers to the piecewise linear method commonly used in power trading to simplify the calculation complexity;

[0102] The power trading constraints are as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Among them, B nm represents the susceptance of the transmission line between the nth and mth nodes in the power system; δ nt represents the phase angle of the nth node in the power system in period t, δ mt represents the phase angle of the mth node in the power system during period t; It represents the maximum power value of the line flow from the nth node to the mth node on the transmission line between the nth node and the mth node in the power system; represents the maximum clearing amount of the bth segment of the power of the i-th non-distributed generator unit in the l-th virtual power plant in the power system during period t; represents the maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction during period t; nt represents the pre-clearing power supply cost parameter of the nth node in the power system in period t; π represents the pi ratio; d∈Ψ n Indicates that when the dth load in the power system is located at the nth node, i∈Ψ n It means that when the i-th non-distributed generator of the l-th virtual power plant in the power system is located at the n-th node, w∈Ψ n It means that when the wth non-distributed generator of the lth virtual power plant in the power system is located at the nth node, m∈Θ n Indicates that the mth node in the power system is the downstream node of the nth node.

[0110] λ nt For the formula The dual variable of .

[0111] The operation data of the virtual power plant input into the power transaction pre-clearing model is the operation time θ of the lth virtual power plant in the power system during period t. t The power transaction data of the virtual power plant participating in the power transaction input by the power transaction pre-clearing model includes the quotation of the kth segment of the power of the dth load in the power system in the power transaction And the k-th segment of the power reported by the d-th load in the power system during the t period The pre-clearing power supply cost parameter of the virtual power plant participating in the power transaction output by the power transaction pre-clearing model is the pre-clearing power supply cost parameter λ of the nth node in the power system in period t nt .

[0112] Step 3: Obtain the carbon trading data of the virtual power plant participating in carbon trading, input the carbon trading data and the operating data of the virtual power plant into the carbon trading pre-clearing model that considers the carbon trading constraints, and the carbon trading pre-clearing model outputs the carbon quota cost parameters of the virtual power plant participating in carbon trading.

[0113] In step 3, the carbon trading pre-clearing model considering carbon trading constraints is as follows:

[0114]

[0115] in, represents the input bidding price of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, represents the input competition of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, Γ c Indicates the input party of carbon quota in carbon trading; represents the output bidding price of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, represents the output competition of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, Γ s Indicates the output party of carbon quota in carbon trading; represents the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading during period t. Other industry entities o specifically include high-carbon emission industries such as steel, petrochemicals, chemicals, building materials and nonferrous metals; It represents the output competition of carbon quotas of other industry entities o in carbon trading during period t.

[0116] The carbon trading constraints are as follows:

[0117]

[0118]

[0119]

[0120]

[0121] Among them, μ t C represents the carbon quota cost parameter for pre-clearing of carbon trading in period t; lt represents the carbon quota balance of the lth virtual power plant in the power system at the initial stage of carbon trading in period t; It represents the maximum output bidding of carbon quota of other industry entities in carbon trading.

[0122] μ t For the formula The dual variable of .

[0123] The operation data of the virtual power plant input into the carbon trading pre-clearing model includes the operation time θ of the lth virtual power plant in the power system during period t t The carbon trading data of the virtual power plant participating in the carbon trading input into the carbon trading pre-clearing model includes the input bidding price of the carbon quota of the lth virtual power plant in the power system in the carbon trading during period t. Output bidding of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading And the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading period t The carbon quota price parameter of the virtual power plant participating in carbon trading output by the carbon trading pre-clearing model is the carbon quota price parameter μ of the carbon trading pre-clearing in period t t .

[0124] The carbon quota balance C of the lth power plant in the power system at the beginning of carbon trading in period t lt , as follows:

[0125]

[0126] in, represents the total carbon quota initially allocated to the lth virtual power plant in the power system; represents the carbon emission factor of the i-th non-distributed generator in the l-th virtual power plant in the power system; represents the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system at time τ; i∈Ω l It indicates that the i-th non-distributed generator belongs to the l-th virtual power plant in the power system.

[0127] The operation data of the virtual power plant input into the carbon trading pre-clearing model also includes the total carbon quota initially allocated to the lth virtual power plant in the power system.

[0128] A virtual power plant receives its initial carbon quota at the beginning of the year. It allocates its total carbon quota across different short-term timescales and makes targeted trading decisions at different points in the carbon trading process based on its own power generation and remaining balance. By comprehensively considering the relationship between carbon quota competition and power generation in the current period, as well as the initial carbon quota decomposition, the virtual power plant, knowing its power generation in the t-1 period before the year (i.e., its cleared amount), can determine its own carbon quota usage in period t, i.e., its remaining carbon quota at the beginning of the carbon trading period.

[0129] Step 4: Input the pre-clearing power supply cost parameters obtained in step 2, the carbon quota cost parameters obtained in step 3, and the operating data of the virtual power plant into the multi-time joint decision-making model considering multi-time joint decision-making constraints. The multi-time joint decision-making model outputs the clearing amount and carbon quota quantity of the virtual power plant.

[0130] In step 4, the multi-time joint decision model for power plants to participate in electricity trading and carbon trading, considering multi-time joint decision constraints, is as follows:

[0131]

[0132] Among them, φ l represents the objective function of the multi-time joint decision model; n:i∈Ψ n Indicates that when n is the node where the i-th generator set of the l-th power plant in the power system is located; a lib represents the cost coefficient of the bth segment of the power of the i-th generator unit of the l-th power plant in the power system during period t; curt,t P represents the compensation cost parameter for load reduction in the power system during period t; curt,t represents the load interruption of the power system during period t; α i,t represents the participation factor of the i-th non-distributed generator in the l-th virtual power plant in the power system during period t, α i,t Indicates the proportion of the i-th non-distributed generator participating in the uncertainty balance; d i represents the regulation cost parameter of the i-th non-distributed generator in the l-th virtual power plant in the power system; ξ t Represents the total uncertainty of wind and solar power in the power system during period t.

[0133] The multi-time joint decision constraints are as follows:

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[0145] Among them, α li(b-1) ETq represents the price quoted by the i-th non-distributed generator of the l-th virtual power plant in the power system in the b-1-th segment of power in the power transaction; lt represents the number of carbon quotas of the lth virtual power plant in the power system in time period t in carbon trading; σ lt represents the carbon quota decomposition factor of the lth virtual power plant in the power system during period t in carbon trading; P WT,t (v) represents the output power of each wind turbine in the power system at the actual wind speed v during time period t; v ci 、v r and v co Respectively represent the rated wind speed, cut-in wind speed and cut-out wind speed of each wind turbine in the power system; Represents the rated power of each wind turbine in the power system; P PV,t represents the output power of each photovoltaic generator in the power system during period t; the photovoltaic generators in the power system constitute a photovoltaic system, f PV Indicates the power loss ratio of the photovoltaic system, which is the ratio of the output power of the photovoltaic system to the rated output power; I is the photovoltaic power generation capacity of each photovoltaic generator in the power system during period t; T is the actual illumination of the power system; I s is the preset illuminance, that is, the illuminance under standard test conditions; α P Represents the temperature coefficient; T ce Represents the surface temperature of the solar panels of each photovoltaic generator in the power system; T ce,STrepresents the preset surface temperature of the solar panel of each photovoltaic generator in the power system, that is, the surface temperature under standard test conditions; SOC(t) and SOC(t-1) represent the state of charge of each energy storage device in the power system at time period t and time t-1, respectively; represents the charging power of each energy storage device in the power system during time period t; η represents the charging and discharging efficiency of each energy storage device in the power system; Δt is the charging and discharging time of each energy storage device in the power system; represents the discharge power of each energy storage device in the power system during time period t; and denote the minimum and maximum clearing amounts of the ith non-distributed generator in the lth virtual power plant in the power system, and denote the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system in period t and period t-1, respectively; and are the ramp-down rate and ramp-up rate of the ith non-distributed generator in the ith virtual power plant in the power system; h max It represents the maximum call rate of controllable load in a single period in the power system; P load,t and P load,t+1 represents the electric load level of each virtual power plant in the power system at time t and time t+1; P lcurt,t and P lcurt,t+1 denote the load interruption of the lth virtual power plant in the power system at time period t and time t+1 respectively; It represents the maximum value of the sum of the continuous call rates in two adjacent time periods; P WT,i,t represents the output power of each wind turbine connected to the ith non-distributed generator of the lth virtual power plant in the power system during period t; P PV,i,t represents the output power of each PV generator of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during period t; and They represent the discharge power and charging power of each energy storage device of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during time period t.

[0146] The operation data of the virtual power plant input into the multi-time joint decision-making model includes the operation time θ of the lth virtual power plant in the power system during period t t and the total carbon quota initially allocated to the lth virtual power plant in the power system The output of the virtual power plant output by the multi-time joint decision model is specifically the output of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t. The carbon quota quantity of the virtual power plant output by the multi-time joint decision-making model is specifically the carbon quota quantity ETq of the lth virtual power plant in the power system in time period t in carbon trading lt .

[0147] Step 5: Finally, the carbon-electricity multi-time joint decision-making system that considers the carbon-electricity joint constraints outputs the pre-clearing power supply cost parameters, carbon quota cost parameters, the virtual power plant's clearing volume and the number of carbon quotas to arrange the virtual power plant's output and achieve optimal scheduling of the virtual power plant.

[0148] The specific cost is related to the amount of electricity pre-cleared by the virtual power plant and the amount of carbon quota.

[0149] In step 5, the carbon-electricity combined constraints are as follows:

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[0175] in, The maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction; and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of It represents the maximum power value of the line flow from the mth node to the nth node on the transmission line between the mth node and the nth node in the power system; and Representation formula The dual variable of Representation formula The dual variable of represents the maximum input bidding amount of carbon quota of the lth virtual power plant in the power system in the carbon trading during period t; The maximum output bidding amount of carbon quota in carbon trading by the lth virtual power plant in the power system during period t; Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of .

[0176] The carbon-electricity joint constraint is the KKT condition for the pre-clearing problem of electricity trading and carbon trading, that is, the problem to be solved is converted into a single-layer optimization problem. The solver can be used to directly solve the carbon-electricity joint constraint to obtain the final output result.

[0177] The specific embodiments of the present invention are as follows:

[0178] Taking the IEEE 30-node power system as an example, the power system includes 30 power nodes and 41 transmission lines, two coal-fired generators (connected to nodes 1 and 2), and one virtual power plant. This virtual power plant includes two gas-fired generators (connected to nodes 5 and 8), one wind turbine (connected to node 11), and one photovoltaic generator (connected to node 13). In addition to the virtual power plant and two coal-fired generators, carbon trading also includes three other industry entities. The parameters of each generator are shown in Table 1, and the initial quotas of each carbon trading entity are shown in Table 2.

[0179] Table 1 Parameters of each generator

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[0181] Table 2 Initial carbon quota situation

[0182] main body Initial carbon quota (tons CO2) Coal-fired generator 1 143008 Coal-fired generator 2 188428.8 Virtual Power Plant 1 202994.8 Other industry entities1 200000 Other industry entities2 200000 Other industry entities3 162465.5

[0183] like Figure 2 The figure shows the annual power generation, or output, of each generator in the virtual power plant. Distributed generators are subject to fluctuations in output due to factors such as natural disasters. While gas-fired units are more flexible and stable than distributed generators, they have a higher carbon emission factor and tend to use up their initially allocated carbon allowances more quickly. At this point, any additional power generation incurs a carbon allowance price parameter, reducing their competitiveness under carbon trading.

[0184] Table 3 shows the carbon allowance decomposition factors for virtual power plants under 10 typical scenarios. The method proposed in this paper adjusts the carbon allowance decomposition factors based on the pre-clearance of carbon trading. Virtual power plants choose to input carbon allowances when the carbon allowance cost parameter is low, minimizing the use of their own initial allowances. When the carbon allowance cost parameter is high, virtual power plants prioritize the use of their allocated initial allowances, thereby reducing their declared carbon allowances in carbon trading.

[0185] Table 3 Carbon quota decomposition factors under 10 typical scenarios

[0186] Scenario T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 Carbon quota decomposition factor 0 0.056 0.132 0.134 0.26 0.164 0.11 0.012 0.043 0.089

[0187] Table 4 shows the social carbon emissions. When considering the multi-time joint decision-making of carbon and electricity, the virtual power plant can optimize its own decomposition factors, thus having greater optimization space and further reducing carbon emissions.

[0188] Table 4: Social carbon emissions

[0189] Carbon emissions (10,000 tons CO2) 89.523

Claims

1. A virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making, characterized by: The steps include: Step 1: Establish a carbon-electricity multi-time joint decision-making system for the virtual power plant that considers carbon-electricity joint constraints. The carbon-electricity multi-time joint decision-making system includes an electricity trading pre-clearing model that considers electricity trading constraints, a carbon trading pre-clearing model that considers carbon trading constraints, and a multi-time joint decision-making model that considers multi-time joint decision-making constraints for the virtual power plant's participation in electricity and carbon trading. Step 2: Obtain the power transaction data and operation data of the virtual power plant participating in power trading, and input the power transaction data and operation data of the virtual power plant into the power trading pre-clearing model that considers power trading constraints. The power trading pre-clearing model outputs the pre-clearing power supply cost parameters of the virtual power plant participating in power trading; Step 3: Obtain carbon trading data of virtual power plants participating in carbon trading, input the carbon trading data and the operating data of the virtual power plants into a carbon trading pre-clearing model that considers carbon trading constraints. The carbon trading pre-clearing model outputs the carbon quota cost parameters for the virtual power plants participating in carbon trading. Step 4: Input the pre-clearing power supply price parameters obtained in step 2, the carbon quota price parameters obtained in step 3, and the operation data of the virtual power plant into the multi-time joint decision-making model considering multi-time joint decision-making constraints. The multi-time joint decision-making model outputs the clearing amount and carbon quota quantity of the virtual power plant; Step 5: Finally, the carbon-electricity multi-time joint decision-making system that considers the carbon-electricity joint constraints outputs the pre-clearing power supply cost parameters, carbon quota cost parameters, the virtual power plant's clearing volume and the number of carbon quotas to arrange the virtual power plant's output and achieve optimal scheduling of the virtual power plant.

2. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 1 is characterized by: In the above-mentioned step 1, the virtual power plant is located in the power system, which includes several nodes, transmission lines, non-distributed generators and virtual power plants. Each node is connected by various transmission lines. Each virtual power plant includes several non-distributed generators, energy storage equipment, loads and distributed generators in a preset area. Each generator, energy storage equipment, load and distributed generator is respectively located at its own node in the power system; the distributed generator is specifically a wind turbine or a photovoltaic generator.

3. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 2 is characterized by: In step 2, the power transaction pre-clearing model considering power transaction constraints is as follows: Among them, θ t represents the operating time of the lth virtual power plant in the power system during period t; The bid price of the kth segment of the power of the dth load in the power system in the power transaction; represents the reported amount of the kth segment of the power of the dth load in the power system in the power transaction during the t period; α lib represents the bid of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t; represents the clearing amount of the bth segment of the power of the i-th non-distributed generator in the l-th virtual power plant in the power system during period t; The power trading constraints are as follows: Among them, B nm represents the susceptance of the transmission line between the nth and mth nodes in the power system; δ nt represents the phase angle of the nth node in the power system in period t, δ mt represents the phase angle of the mth node in the power system during period t; It represents the maximum power value of the line flow from the nth node to the mth node on the transmission line between the nth node and the mth node in the power system; represents the maximum clearing amount of the bth segment of the power of the i-th non-distributed generator unit in the l-th virtual power plant in the power system during period t; represents the maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction during period t; nt represents the pre-clearing power supply cost parameter of the nth node in the power system in period t; π represents the pi ratio; d∈Ψ n Indicates that when the dth load in the power system is located at the nth node, i∈Ψ n It means that when the i-th non-distributed generator of the l-th virtual power plant in the power system is located at the n-th node, w∈Ψ n It means that when the wth non-distributed generator of the lth virtual power plant in the power system is located at the nth node, m∈Θ n Indicates that the mth node in the power system is the downstream node of the nth node; The operation data of the virtual power plant input into the power transaction pre-clearing model is the operation time θ of the lth virtual power plant in the power system during period t. t The power transaction data of the virtual power plant participating in the power transaction input by the power transaction pre-clearing model includes the quotation of the kth segment of the power of the dth load in the power system in the power transaction And the k-th segment of the power reported by the d-th load in the power system during the t period The pre-clearing power supply cost parameter of the virtual power plant participating in the power transaction output by the power transaction pre-clearing model is the pre-clearing power supply cost parameter λ of the nth node in the power system in period t nt .

4. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 2 is characterized by: In step 3, the carbon trading pre-clearing model considering carbon trading constraints is as follows: in, represents the input bidding price of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, represents the input competition of carbon quota of the lth virtual power plant in the power system in period t in carbon trading, Γ c Indicates the input party of carbon quota in carbon trading; represents the output bidding price of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, represents the output competition of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading, Γ s Indicates the output party of carbon quota in carbon trading; represents the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading period t, represents the output competition of carbon quotas of other industry entities o in carbon trading during period t; The carbon trading constraints are as follows: Among them, μ t C represents the carbon quota cost parameter for pre-clearing of carbon trading in period t; lt represents the carbon quota balance of the lth virtual power plant in the power system at the initial stage of carbon trading in period t; It represents the maximum output bidding amount of carbon quota of other industry entities in carbon trading; The operation data of the virtual power plant input into the carbon trading pre-clearing model includes the operation time θ of the lth virtual power plant in the power system during period t t The carbon trading data of the virtual power plant participating in the carbon trading input into the carbon trading pre-clearing model includes the input bidding price of the carbon quota of the lth virtual power plant in the power system in the carbon trading during period t. Output bidding of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading And the output bidding of carbon quotas of other industry entities o except the power industry in the carbon trading period t The carbon quota price parameter of the virtual power plant participating in carbon trading output by the carbon trading pre-clearing model is the carbon quota price parameter μ of the carbon trading pre-clearing in period t t .

5. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 4 is characterized by: The carbon quota balance C of the lth power plant in the power system during the period t at the beginning of the carbon trading lt , as follows: in, represents the total carbon quota initially allocated to the lth virtual power plant in the power system; represents the carbon emission factor of the i-th non-distributed generator in the l-th virtual power plant in the power system; represents the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system at time τ; i∈Ω l Indicates that the i-th non-distributed generator belongs to the l-th virtual power plant in the power system; The operation data of the virtual power plant input into the carbon trading pre-clearing model also includes the total carbon quota initially allocated to the lth virtual power plant in the power system.

6. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 2 is characterized by: In step 4, the multi-time joint decision model for power plants to participate in electricity trading and carbon trading, taking into account multi-time joint decision constraints, is as follows: Among them, φ l represents the objective function of the multi-time joint decision model; n:i∈Ψ n Indicates that when n is the node where the i-th generator set of the l-th power plant in the power system is located; a lib represents the cost coefficient of the bth segment of the power of the i-th generator unit of the l-th power plant in the power system during period t; curt,t P represents the compensation cost parameter for load reduction in the power system during period t; curt,t represents the load interruption of the power system during period t; α i,t represents the participation factor of the ith non-distributed generator in the lth virtual power plant in the power system during period t; d i represents the regulation cost parameter of the i-th non-distributed generator in the l-th virtual power plant in the power system; ξ t represents the total uncertainty of wind and solar power in the power system during period t; The multi-time joint decision constraints are as follows: Among them, α li(b-1) ETq represents the price quoted by the i-th non-distributed generator of the l-th virtual power plant in the power system in the b-1-th segment of power in the power transaction; lt represents the number of carbon quotas of the lth virtual power plant in the power system in time period t in carbon trading; σ lt represents the carbon quota decomposition factor of the lth virtual power plant in the power system during period t in carbon trading; P WT,t (v) represents the output power of each wind turbine in the power system at the actual wind speed v during time period t; v ci 、v r and v co Respectively represent the rated wind speed, cut-in wind speed and cut-out wind speed of each wind turbine in the power system; Represents the rated power of each wind turbine in the power system; P PV,t represents the output power of each photovoltaic generator in the power system during period t; the photovoltaic generators in the power system constitute a photovoltaic system, f PV Indicates the power loss ratio of the photovoltaic system, which is the ratio of the output power of the photovoltaic system to the rated output power; I is the photovoltaic power generation capacity of each photovoltaic generator in the power system during period t; T is the actual illumination of the power system; I s is the preset illumination; α P Represents the temperature coefficient; T ce Represents the surface temperature of the solar panels of each photovoltaic generator in the power system; T ce,ST represents the preset surface temperature of the solar panel of each photovoltaic generator in the power system; SOC(t) and SOC(t-1) represent the state of charge of each energy storage device in the power system at time period t and time t-1 respectively; represents the charging power of each energy storage device in the power system during time period t; η represents the charging and discharging efficiency of each energy storage device in the power system; Δt is the charging and discharging time of each energy storage device in the power system; represents the discharge power of each energy storage device in the power system during time period t; and denote the minimum and maximum clearing amounts of the ith non-distributed generator in the lth virtual power plant in the power system, and denote the clearing amount of the i-th non-distributed generator in the l-th virtual power plant in the power system in period t and period t-1, respectively; and They represent the down-ramp rate and up-ramp rate of the ith non-distributed generator in the ith virtual power plant in the power system respectively; η max Indicates the maximum load call rate of a single period in the power system; P load,t and P load,t+1 represents the electric load level of each virtual power plant in the power system at time t and time t+1; P lcurt,t and P lcurt,t+1 denote the load interruption of the lth virtual power plant in the power system at time period t and time t+1 respectively; It represents the maximum value of the sum of the continuous call rates in two adjacent time periods; P WT,i,t represents the output power of each wind turbine connected to the ith non-distributed generator of the lth virtual power plant in the power system during period t; P PV,i,t represents the output power of each PV generator of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during period t; and denote the discharge power and charging power of each energy storage device of the i-th non-distributed generator connected to the l-th virtual power plant in the power system during time period t; The operation data of the virtual power plant input into the multi-time joint decision-making model includes the operation time θ of the lth virtual power plant in the power system during period t t and the total carbon quota initially allocated to the lth virtual power plant in the power system The output of the virtual power plant output by the multi-time joint decision model is specifically the output of the bth segment of the power of the i-th non-distributed generator of the l-th virtual power plant in the power system during period t. The carbon quota quantity of the virtual power plant output by the multi-time joint decision-making model is specifically the carbon quota quantity ETq of the lth virtual power plant in the power system in time period t in carbon trading lt .

7. The virtual power plant optimization scheduling method based on carbon-electricity multi-time joint decision-making according to claim 2 is characterized by: In step 5, the carbon-electricity combined constraints are as follows: in, The maximum reported amount of the kth segment of the power of the dth load in the power system in the power transaction; and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of It represents the maximum power value of the line flow from the mth node to the nth node on the transmission line between the mth node and the nth node in the power system; and Representation formula The dual variable of Representation formula The dual variable of represents the maximum input bidding amount of carbon quota of the lth virtual power plant in the power system in time period t in carbon trading; The maximum output bidding amount of carbon quota in carbon trading by the lth virtual power plant in the power system during period t; Representation formula The dual variable of and Representation formula The dual variable of and Representation formula The dual variable of .

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