Method and system for constructing virtual power plant cost distribution model

Abstract

The invention discloses a virtual power plant cost distribution model construction method and system, and relates to the technical field of model construction, and the method comprises the following steps: S1, making a distribution plan; s2, optimizing the virtual power plant to participate in power market operation; s3, optimization based on the lowest day-ahead market transaction cost; s4, optimization based on the lowest carbon emission is carried out; and S5, optimization based on the lowest market transaction cost in the day. By constructing a day-ahead market and intra-day market two-stage optimization model and through output constraint, operation constraint and energy storage constraint on equipment, an optimization model for the virtual power plant to participate in the spot market is constructed, so that power generation manufacturers on the market can adjust own power generation plans in real time, the operation income of the virtual power plant is improved, and the power generation efficiency is improved. The method participates in the intraday electric quantity transaction according to the intraday transaction electricity price information in the intraday market, reduces the punishment cost of the deviation electric quantity, and guarantees the balance and stability of the active power of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of model construction, and in particular to a method and system for constructing a virtual power plant cost allocation model. Background Art

[0002] As the energy crisis becomes increasingly serious, the construction cost of distributed energy is gradually decreasing, and the energy utilization efficiency is gradually improving. Therefore, the development of distributed power sources has gradually received attention and has been widely promoted. The electricity spot market includes the day-ahead market and the intraday market. The day-ahead market refers to the formation of the next day's trading plan at a certain moment of the previous day. The dispatching agency of the trading center determines the next day's transaction volume and electricity price through the bid electricity and bid electricity price of each subject. For power generation companies, participating in the day-ahead market can obtain more transaction volume by adjusting their own power generation plans and reducing the unit operation costs. For users who purchase electricity, they can adjust their electricity consumption behavior through electricity price information. When the user's load demand is too high, they can meet their own load demand through the form of purchasing electricity from distribution companies and retailers. The day-ahead market transaction frequency is once a day, reflecting the relationship between the supply and demand balance and cost of short-term power load, making up for the shortcomings of the medium and long-term market, and meeting the short-term load imbalance. The intraday market is a supplement to the day-ahead market and is a load regulation for the imbalance in the operation of the power system.

[0003] Through energy Internet technology, virtual power plants can effectively integrate various distributed power generation units and controllable loads in a certain area and participate in the electricity market through coordination and optimization. However, distributed power sources are unevenly distributed and have high output uncertainty. Since virtual power plants usually include distributed photovoltaic and distributed wind turbine equipment, which have certain output volatility, it is impossible to accurately predict the output of the equipment. In addition, the power generation plan of power generation companies is related to the operating cost allocation plan of virtual power plants. The electricity volume reported by the market recently often deviates during intraday operation, resulting in unbalanced electricity volume, making it difficult to ensure the active power balance and stability of the power grid, and the operating benefits of virtual power plants cannot be maximized. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for constructing a virtual power plant cost allocation model to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for constructing a virtual power plant cost allocation model, comprising the following steps:

[0006] S1. Formulate allocation plan: Virtual power plants predict the output of renewable resources such as wind turbines and photovoltaics for the next day, and report the next day's trading power to the dispatching agency of the trading center in the day-ahead market to meet the user load, thereby maximizing the guarantee of transaction power. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration. It also formulates an allocation plan based on the characteristics of randomness of wind turbine and photovoltaic output that will produce power generation deviation;

[0007] S2. Optimizing the operation of virtual power plants in the power market: The operation of virtual power plants in the power market is optimized in two stages. The first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the lowest cost and the lowest carbon emissions are the optimization goals. The constraints include the operating characteristics of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant in the intraday market, cost reduction is the core.

[0008] S3. Optimization based on the lowest transaction cost in the day-ahead market: When a virtual power plant participates in the day-ahead market, the main electricity reported includes wind turbine output, photovoltaic output, and the output of some adjustable equipment. In the day-ahead market, the revenue sources include the revenue from reported electricity and the revenue from electricity sold to users. Fuel costs and energy storage operating costs are allocated based on the revenue sources.

[0009] S4. Optimization based on the lowest carbon emissions: using the tiered environmental trading mechanism to reduce carbon dioxide emissions, the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission ranges;

[0010] S5. Optimization based on the lowest transaction cost in the intraday market: The virtual power plant adjusts the output strategy of each device within the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and is able to participate in the intraday market. In the intraday market, it participates in intraday electricity transactions based on the intraday transaction electricity price information, thereby reducing the penalty cost of deviation electricity.

[0011] Preferably, in step S1, the virtual power plant only reports the transaction electricity volume to the dispatching organization of the trading center and declares the price as zero. At this time, the virtual power plant, as the recipient of the electricity price, can maximize the guarantee of the transaction electricity volume and determine whether there is a deviation between the declared electricity volume and the actual power generation. When there is no deviation between the declared electricity volume and the actual power generation, it operates according to the power generation plan for the day. When there is a deviation between the declared electricity volume and the actual power generation, it is adjusted by the flexible units within the virtual power plant. If there is still a deviation in electricity volume, it is sold and purchased at the spot electricity price and then transferred to the virtual power plant for flexible unit adjustment. Otherwise, the power generation volume for the remaining period is formulated.

[0012] Preferably, in step S3, when the virtual power plant participates in the day-ahead market, the gas turbine and energy storage do not fully output, so the gas turbine and energy storage generate reserved capacity, which is used to participate in the intraday market to compensate for the deviation of the power reported by the virtual power plant. The operating cost of the gas turbine includes the start-stop cost and the natural gas cost, and the cost of the energy storage in the operation stage is mainly the cost generated during the charging and discharging process;

[0013] The formula for gas turbine operating cost is:

[0014]

[0015] C ss (t) = c start μ start (t)+c stop μ stop (t)

[0016] μ stop (t) = μ c (t-1)-μ c (t)+μ start (t)

[0017] In the formula, C op (t) represents the natural gas consumption cost, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, COP rj (t) represents the operating efficiency of the engine at time t, H r Indicates the calorific value of natural gas, C gas (t) represents the natural gas price at time t, C ss (t) represents the start-up and shutdown cost of the gas turbine, c start and c stop They represent the cost of a single start and shutdown of the gas turbine, μ c Indicates the start and stop status of the engine. c is 1, shutdown is 0, μ start (t) and μ stop (t) represents the start and stop signals of the engine, which are 0-1 variables. start When (t) is 0, it means that the unit is not started at time t. start When (t) is 1, it indicates that the unit enters the startup state. stop When (t) is 0, it means the unit is shut down at time t. stop When (t) is 1, it indicates that the unit enters the shutdown process;

[0018] The energy storage operating cost formula is:

[0019] C rqc (t) = P rqbf (t)Cf +P rqbc (t)C c

[0020] In the formula, C rqc (t) represents the energy storage operation cost, P rqbf (t) represents the discharge efficiency of energy storage in the day-ahead market, C f Represents the discharge cost coefficient of energy storage, P rqbc (t) represents the charging power of energy storage in the day-ahead market, C c Represents the charging cost coefficient.

[0021] Preferably, in step S4, when the carbon dioxide emissions do not exceed the benchmark amount, only the emission costs need to be borne without bearing the transaction costs. When the carbon dioxide emissions exceed the benchmark amount, the transaction costs need to be borne. In addition, the constraints for the virtual power plant to participate in the day-ahead market include distributed power output constraints, gas turbine equipment operation constraints and energy storage equipment operation constraints.

[0022] Preferably, in step S4, the operation of the gas engine needs to meet the start-stop constraint, power constraint and climbing constraint of the equipment, and the start-stop constraint condition of the gas engine equipment is: μ stop (t)+μ start (t)≤1, where μ start (t) and μ stop (t) respectively represent the start and stop signals of the engine at time t;

[0023] In the power constraint conditions, the gas turbine does not fully output when participating in the day-ahead market declaration. The power constraint conditions of the gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, η rj It indicates the proportion of gas turbines participating in the day-ahead market regulation. Indicates the maximum output of the gas turbine;

[0024] The climbing constraint conditions of the gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, P rqr (t-1) represents the declared output of energy storage at time t-1 in the day-ahead market.

[0025] Preferably, in step S5, the deviation power includes the deviation power bid by the virtual power plant, expressed as ΔP pc (t) represents the deviation power of the virtual power plant bid at time t. When ΔP pcWhen (t)≥0, deviation electricity is generated at this time, and the flexible units are used to compensate for the deviation electricity and the demand response resources are used to reduce the user load of the virtual power plant. The demand resources include incentive demand response. When ΔP pc When (t) < 0, energy storage is used to absorb excess electricity and sell it to the grid at a lower price.

[0026] Preferably, in step S5, in the intraday market stage, the output of the flexibility resource is optimized according to the deviation compensation cost, and the expression is: cz (t) = ΔP pc (t)-P flex (t), where P cz (t) represents the deviation vector after the flexibility resource is adjusted at time t, P flex (t) represents the compensation deviation quantity. When P cz When (t)>0, the virtual power plant cannot balance the deviation power by itself. cz When (t)≤0, the virtual power plant can balance the deviation electricity by itself, and the virtual power plant can sell excess electricity at a lower price.

[0027] A system for constructing a virtual power plant cost allocation model, comprising:

[0028] A plan allocation unit, which predicts the output of renewable resources such as wind turbines and photovoltaics for the next day through a virtual power plant, reports the next day's trading power to the dispatching agency of the trading center in the day-ahead market to meet the user load, thereby maximizing the guarantee of transaction power. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration, and formulates an allocation plan based on the characteristics that the randomness of wind turbine and photovoltaic output will produce power generation deviations;

[0029] An operation optimization unit, wherein the operation optimization unit optimizes the operation of the virtual power plant participating in the power market in two stages, the first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the optimization objectives are the lowest cost and the lowest carbon emission. The constraints include the operation characteristic constraints of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant participating in the intraday market, the core is to reduce the cost;

[0030] The day-ahead market cost optimization unit, when the virtual power plant participates in the day-ahead market, mainly declares the power output including wind turbine output, photovoltaic output and the output of some adjustable equipment. In the day-ahead market, the revenue sources include the declared power revenue and the power revenue sold to users. The fuel cost and energy storage operation cost are allocated according to the revenue sources;

[0031] A carbon emission optimization unit, wherein the carbon emission optimization unit reduces carbon dioxide emissions by using a tiered environmental trading mechanism, wherein the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission intervals;

[0032] The intraday market cost optimization unit adjusts the output strategy of each device within the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and can participate in the intraday market. In the intraday market, it participates in intraday electricity transactions according to the intraday transaction electricity price information, thereby reducing the penalty cost of deviation electricity.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention constructs a two-stage optimization model of the day-ahead market and the intraday market. In the day-ahead market, the lowest cost and the lowest carbon emission are the optimization goals, and in the intraday market, the lowest transaction cost is the optimization goal. Through the output constraints, operation constraints and energy storage constraints of the equipment, an optimization model for the virtual power plant to participate in the spot market is constructed. The model of virtual power plants participating in the spot market enables power generation manufacturers in the market to adjust their own power generation plans in real time, thereby maximizing the operating income of the virtual power plants. In the intraday market, the virtual power plants participate in intraday electricity transactions according to the intraday transaction electricity price information, reduce the penalty cost of deviation electricity, optimize the output of flexible resources according to the deviation compensation cost and take the maximization of intraday transaction income as the goal, and ensure the active power balance and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the overall method flow provided by an embodiment of the present invention;

[0036] Figure 2 A block diagram of a system unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1-2 The present invention provides a technical solution: a method for constructing a virtual power plant cost allocation model, comprising the following steps:

[0039] S1. Formulate allocation plan: Virtual power plants predict the output of renewable resources such as wind turbines and photovoltaics for the next day, and report the next day's trading power to the dispatching agency of the trading center in the day-ahead market to meet the user load, thereby maximizing the guarantee of transaction power. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration. It also formulates an allocation plan based on the characteristics of randomness of wind turbine and photovoltaic output that will produce power generation deviation;

[0040] S2. Optimizing the operation of virtual power plants in the power market: The operation of virtual power plants in the power market is optimized in two stages. The first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the lowest cost and the lowest carbon emissions are the optimization goals. The constraints include the operating characteristics of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant in the intraday market, cost reduction is the core.

[0041] S3. Optimization based on the lowest transaction cost in the day-ahead market: When a virtual power plant participates in the day-ahead market, the main electricity reported includes wind turbine output, photovoltaic output, and the output of some adjustable equipment. In the day-ahead market, the revenue sources include the revenue from reported electricity and the revenue from electricity sold to users. Fuel costs and energy storage operating costs are allocated based on the revenue sources.

[0042] S4. Optimization based on the lowest carbon emissions: using the tiered environmental trading mechanism to reduce carbon dioxide emissions, the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission ranges;

[0043] S5. Optimization based on the lowest transaction cost in the intraday market: The virtual power plant adjusts the output strategy of each device within the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and is able to participate in the intraday market. In the intraday market, it participates in intraday electricity transactions based on the intraday transaction electricity price information, thereby reducing the penalty cost of deviation electricity.

[0044] In step S1, the virtual power plant only reports the transaction power to the dispatching agency of the trading center and declares the price as zero. At this time, as the recipient of the electricity price, the virtual power plant can maximize the guarantee of the transaction power and determine whether there is a deviation between the declared power and the actual power generation. When there is no deviation between the declared power and the actual power generation, it will operate according to the power generation plan of the day. When there is a deviation between the declared power and the actual power generation, the flexibility units within the virtual power plant will be adjusted. If there is still a deviation in power, it will be sold and purchased at the spot electricity price, and then transferred to the virtual power plant for flexibility unit adjustment. Otherwise, the power generation for the remaining period will be formulated;

[0045] In step S3, when the virtual power plant participates in the day-ahead market, the gas turbine and energy storage do not fully output, so the gas turbine and energy storage generate reserved capacity, which is used to participate in the intraday market to compensate for the deviation of the power reported by the virtual power plant. The operating cost of the gas turbine includes the start-stop cost and the natural gas cost. The cost of energy storage in the operation stage is mainly the cost generated during the charging and discharging process. The formula for the gas turbine operation cost is:

[0046]

[0047] C ss (t) = c start μ start (t)+c stop μ stop (t)

[0048] μ stop (t) = μ c (t-1)-μ c (t)+μ start (t)

[0049] In the formula, C op (t) represents the natural gas consumption cost, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, COP rj (t) represents the operating efficiency of the engine at time t, H r Indicates the calorific value of natural gas, C gas (t) represents the natural gas price at time t, C ss (t) represents the start-up and shutdown cost of the gas turbine, c start and c stop They represent the cost of a single start and shutdown of the gas turbine, μ c Indicates the start and stop status of the engine. c is 1, shutdown is 0, μ start (t) and μ stop (t) represents the start and stop signals of the engine, which are 0-1 variables. start When (t) is 0, it means that the unit is not started at time t. start When (t) is 1, it indicates that the unit enters the startup state. stop When (t) is 0, it means the unit is shut down at time t. stop When (t) is 1, it indicates that the unit enters the shutdown process;

[0050] The formula for energy storage operating cost is:

[0051] C rqc (t) = P rqbf (t)C f +P rqbc (t)Cc

[0052] In the formula, C rqc (t) represents the energy storage operation cost, P rqbf (t) represents the discharge efficiency of energy storage in the day-ahead market, C f represents the discharge cost coefficient of energy storage, P rqbc (t) represents the charging power of energy storage in the day-ahead market, C c represents the charging cost coefficient;

[0053] In step S4, when the carbon dioxide emissions do not exceed the benchmark amount, only the emission costs need to be borne, and no transaction costs need to be borne. When the carbon dioxide emissions exceed the benchmark amount, the transaction costs need to be borne. In addition, the constraints for the virtual power plant to participate in the day-ahead market include the output constraints of distributed power generation, the operation constraints of gas turbine equipment, and the operation constraints of energy storage equipment.

[0054] The output constraints of distributed generation are:

[0055]

[0056] Among them, P w (t) represents the declared output of the wind turbine at time t, P v (t) represents the declared output of photovoltaic power at time t, is the maximum declared output of the fan, The maximum declared contribution for photovoltaics;

[0057] The operating constraints of energy storage equipment are:

[0058] 0≤Prqbf(t)≤Pbf_max,0≤Prqbc(t)≤Pbc_max

[0059] Among them, P bf_max With P bc_max are the maximum charging power and maximum discharging power of the energy storage battery, P rqbf (t) represents the discharge efficiency of energy storage in the day-ahead market, P rqbc (t) represents the charging power of energy storage in the day-ahead market;

[0060] In step S4, the operation of the gas turbine needs to meet the start-stop constraint, power constraint and climbing constraint of the equipment. The start-stop constraint condition of the gas turbine equipment is: μ stop (t)+μ star ( t t)≤1, where μ star ( t t) and μ stop (t) respectively represent the start and stop signals of the engine at time t;

[0061] In the power constraint conditions, the gas turbine does not fully output when participating in the day-ahead market declaration. The power constraint conditions of the gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, η rj It indicates the proportion of gas turbines participating in the day-ahead market regulation. Indicates the maximum output of the gas turbine;

[0062] The climbing constraints of gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, P rqr (t-1) represents the declared output of energy storage at time t-1 in the day-ahead market;

[0063] In step S5, the deviation power includes the deviation power bid by the virtual power plant, expressed as ΔP pc (t) represents the deviation power of the virtual power plant bid at time t. When ΔP pc When (t)≥0, deviation electricity is generated, and the flexible units are used to compensate for the deviation electricity and the demand response resources are used to reduce the load of virtual power plant users. When ΔP pc When (t) < 0, energy storage is used to absorb excess electricity and sell it to the grid at a lower price;

[0064] In step S5, in the intraday market stage, the output of flexibility resources is optimized according to the deviation compensation cost and the goal of maximizing the intraday trading profit. The expression is: P cz (t) = ΔP pc (t)-P flex (t), where P cz (t) represents the deviation vector after the flexibility resource is adjusted at time t, P flex (t) represents the compensation deviation quantity. When P cz When (t)>0, the virtual power plant cannot balance the deviation power by itself. cz When (t)≤0, the virtual power plant can balance the deviation electricity by itself, and the virtual power plant can sell excess electricity at a lower price;

[0065] A system for constructing a virtual power plant cost allocation model, comprising:

[0066] Plan allocation unit 1, plan allocation unit 1 predicts the output of wind turbines, photovoltaics and other renewable resources for the next day through the virtual power plant, reports the next day's trading power to the dispatching agency of the trading center in the day-ahead market to meet the user load, thereby maximizing the guarantee of transaction power. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration, and formulates an allocation plan based on the characteristics of power generation deviation caused by the randomness of wind turbine and photovoltaic output;

[0067] Operation optimization unit 2: Operation optimization unit 2 optimizes the operation of the virtual power plant participating in the power market in two stages. The first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the lowest cost and the lowest carbon emission are the optimization goals. The constraints include the operating characteristics of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant participating in the intraday market, cost reduction is the core.

[0068] Day-ahead market cost optimization unit 3: When a virtual power plant participates in the day-ahead market, the main declared electricity includes wind turbine output, photovoltaic output and the output of some adjustable equipment. In the day-ahead market, the revenue sources include the declared electricity revenue and the electricity sold to users. The fuel cost and energy storage operation cost are allocated according to the revenue sources.

[0069] Carbon emission optimization unit 4, carbon emission optimization unit 4 uses a tiered environmental trading mechanism to reduce carbon dioxide emissions, and the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission ranges;

[0070] The intraday market cost optimization unit 5 adjusts the output strategy of each device in the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and can participate in the intraday market. In the intraday market, it participates in intraday electricity transactions according to the intraday transaction electricity price information, thereby reducing the penalty cost of deviation electricity.

[0071] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0072] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a virtual power plant cost allocation model, characterized in that The following steps are involved: S1. Formulate allocation plan: The virtual power plant predicts the output of renewable resources such as wind turbines and photovoltaics for the next day, and reports the next day's trading power to the dispatching agency of the trading center in the day-ahead market to meet the user load. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration. It also formulates an allocation plan based on the characteristics of randomness of wind turbine and photovoltaic output, which will cause power generation deviation; S2. Optimizing the operation of virtual power plants in the power market: The operation of virtual power plants in the power market is optimized in two stages. The first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the lowest cost and the lowest carbon emissions are the optimization goals. The constraints include the operating characteristics of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant in the intraday market, cost reduction is the core. S3. Optimization based on the lowest transaction cost in the day-ahead market: When a virtual power plant participates in the day-ahead market, the main electricity reported includes wind turbine output, photovoltaic output, and the output of some adjustable equipment. In the day-ahead market, the revenue sources include the revenue from reported electricity and the revenue from electricity sold to users. Fuel costs and energy storage operating costs are allocated based on the revenue sources. S4. Optimization based on the lowest carbon emissions: using the tiered environmental trading mechanism to reduce carbon dioxide emissions, the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission ranges; S5. Optimization based on the lowest transaction cost in the intraday market: The virtual power plant adjusts the output strategy of each device within the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and is able to participate in the intraday market. In the intraday market, it participates in intraday electricity transactions based on the intraday transaction electricity price information, thereby reducing the penalty cost of deviation electricity.

2. The method for constructing a virtual power plant cost allocation model according to claim 1, characterized in that: In step S1, the virtual power plant only reports the trading electricity volume to the dispatching agency of the trading center and declares the price as zero. At this time, the virtual power plant, as the recipient of the electricity price, determines whether there is a deviation between the reported electricity volume and the actual power generation. When there is no deviation between the reported electricity volume and the actual power generation, it operates according to the power generation plan for the day. When there is a deviation between the reported electricity volume and the actual power generation, the flexibility units within the virtual power plant will adjust. If there is still a deviation in electricity volume, it will be sold and purchased at the spot electricity price, and then transferred to the virtual power plant for flexibility unit adjustment. Otherwise, the power generation volume for the remaining period will be formulated.

3. The method for constructing a virtual power plant cost allocation model according to claim 1, characterized in that: In step S3, when the virtual power plant participates in the day-ahead market, the gas turbine and energy storage do not fully output, so the gas turbine and energy storage generate retained spare capacity, which is used to participate in the intraday market to compensate for the deviation in the electricity reported by the virtual power plant. The operating cost of the gas turbine includes the start-stop cost and the natural gas cost, and the cost of the energy storage in the operation stage is mainly the cost generated during the charging and discharging process.

4. The method for constructing a virtual power plant cost allocation model according to claim 1, characterized in that: In step S4, when the carbon dioxide emissions do not exceed the benchmark amount, only the emission costs need to be borne, and no transaction costs need to be borne. When the carbon dioxide emissions exceed the benchmark amount, the transaction costs need to be borne. In addition, the constraints for the virtual power plant to participate in the day-ahead market include distributed power output constraints, gas turbine equipment operation constraints, and energy storage equipment operation constraints.

5. The method for constructing a virtual power plant cost allocation model according to claim 4, characterized in that: In step S4, the operation of the gas engine needs to meet the start-stop constraint, power constraint and climbing constraint of the equipment. The start-stop constraint condition of the gas engine equipment is: μ stop (t)+μ start (t)≤1, where μ start (t) and μ stop (t) respectively represent the start and stop signals of the engine at time t; In the power constraint conditions, the gas turbine does not fully output when participating in the day-ahead market declaration. The power constraint conditions of the gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, η rj It indicates the proportion of gas turbines participating in the day-ahead market regulation. Indicates the maximum output of the gas turbine; The climbing constraint conditions of the gas turbine equipment are: Among them, P rqr (t) represents the declared output of energy storage at time t in the day-ahead market, P rqr (t-1) represents the declared output of energy storage at time t-1 in the day-ahead market.

6. The method for constructing a virtual power plant cost allocation model according to claim 1, characterized in that: In step S5, the deviation power includes the deviation power bid by the virtual power plant, expressed as ΔP pc (t) represents the deviation power of the virtual power plant bid at time t. When ΔP pc When (t)≥0, deviation electricity is generated, and the flexible units are used to compensate for the deviation electricity and the demand response resources are used to reduce the load of virtual power plant users. When ΔP pc When (t) < 0, energy storage is used to absorb excess electricity and sell it to the grid at a lower price.

7. The method for constructing a virtual power plant cost allocation model according to claim 6, characterized in that: In step S5, during the intraday market stage, the cost is compensated according to the deviation and the goal is to maximize the intraday trading profit. The expression is: cz (t) = ΔP pc (t)-P flex (t), where P cz (t) represents the deviation vector after the flexibility resource is adjusted at time t, P flex (t) represents the compensation deviation quantity. When P cz When (t)>0, the virtual power plant cannot balance the deviation power by itself. cz When (t)≤0, the virtual power plant can balance the deviation electricity by itself, and the virtual power plant can sell excess electricity at a lower price.

8. A system for constructing a virtual power plant cost allocation model, characterized in that: The system for constructing a virtual power plant cost allocation model comprises a method for constructing a virtual power plant cost allocation model according to any one of items 1 to 7, including: A plan allocation unit (1), wherein the plan allocation unit (1) predicts the output of wind turbines, photovoltaic power generation and other renewable resources on the next day through a virtual power plant, reports the next day's transaction power to the dispatching agency of the trading center in the day-ahead market to meet the user load, thereby maximizing the guarantee of transaction power. The virtual power plant jointly reports the power to formulate a power generation plan and sends it to the intraday market for integration, and formulates an allocation plan based on the characteristics that the randomness of wind turbine and photovoltaic output will produce power generation deviation; An operation optimization unit (2), wherein the operation optimization unit (2) optimizes the operation of the virtual power plant participating in the power market in two stages, wherein the first stage is the day-ahead market optimization, and the second stage is the intraday market optimization. In the optimization model of the day-ahead market, the optimization objectives are the lowest cost and the lowest carbon emission. The constraints include the operation characteristic constraints of the equipment and the supply and demand balance constraints. In the optimization model of the virtual power plant participating in the intraday market, the core is to reduce the cost. A day-ahead market cost optimization unit (3), wherein when the virtual power plant participates in the day-ahead market, the main electricity reported by the day-ahead market cost optimization unit (3) includes wind turbine output, photovoltaic output and output of some adjustable equipment. In the day-ahead market, the revenue sources include revenue from reported electricity and revenue from electricity sold to users. Fuel costs and energy storage operating costs are allocated according to the revenue sources; A carbon emission optimization unit (4), wherein the carbon emission optimization unit (4) reduces carbon dioxide emissions by using a tiered environmental trading mechanism, wherein the tiered environmental trading mechanism calculates different environmental costs for different carbon dioxide emission intervals; An intraday market cost optimization unit (5), the day-ahead market cost optimization unit (5) adjusts the output strategy of each device within the virtual power plant according to the intraday transaction unit price information announced by the dispatching agency of the trading center, and can participate in the intraday market, participate in intraday electricity transactions in the intraday market according to the intraday transaction electricity price information, and reduce the penalty cost of deviation electricity.

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