Energy storage virtual power plant optimal dispatching method and system

Abstract

Provided are an energy storage virtual power plant optimal dispatching method and system. The method comprises: establishing a high-temperature flue gas absorption-electrolysis composite treatment equation of carbon emission of a gas turbine; designing a carbon emission pricing framework on the basis of the calculated carbon emission generated by the gas turbine; constructing a virtual power plant optimal economic dispatching model with an objective function of minimizing the total costs of a virtual power plant; establishing a stepped carbon emission trading model, and obtaining carbon trading costs on the basis of the stepped carbon emission trading model; and inputting molten salt energy storage operation and maintenance costs, the carbon trading costs, operational costs of the gas turbine, and demand response costs into the virtual power plant optimal economic dispatching model to obtain the most cost-effective virtual power plant dispatching optimization solution. The system comprises a composite treatment equation establishment module, a carbon emission pricing framework design module, a dispatching model construction module, a carbon trading cost calculation module, and an optimal dispatching calculation module which are connected in sequence.

Description

A method and system for optimizing the scheduling of energy storage virtual power plants

[0001] This application claims priority to Chinese Patent Application No. 202410691587.9, filed on May 30, 2024, entitled "A Method and System for Optimized Scheduling of an Energy Storage Virtual Power Plant", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application belongs to the field of power systems and energy management, and specifically relates to a method and system for optimizing the scheduling of energy storage virtual power plants. Background Technology

[0003] Virtual Power Plants (VPPs), as a highly intelligent control technology, integrate renewable energy, flexible power generation resources, controllable loads, and energy storage facilities, making them a core platform for coordinating regional energy supply and demand balance. To ensure the power industry achieves its energy conservation and emission reduction goals, the formulation of optimized dispatch strategies is particularly crucial. The scientific validity and rationality of these strategies are essential for ensuring the efficient operation of VPPs and maximizing their effectiveness. As a vital component of the power industry, the formulation of optimized dispatch strategies for VPPs is directly related to the achievement of the power industry's own energy conservation and emission reduction goals. Therefore, it is imperative to attach great importance to the research and application of optimized VPP dispatch strategies to ensure that they can make a greater contribution to environmental protection and sustainable development while guaranteeing energy security.

[0004] Gas turbines (GTs) possess excellent frequency regulation and peak shaving capabilities and are widely used in industry. P2G-GT coupling structures are employed to achieve electrical interconnection, thereby improving system flexibility and optimizing resource allocation. However, as GT installed capacity increases, its carbon emissions become significant. Carbon capture technology (CCS) is a key technology for reducing greenhouse gas emissions. In actual operation, the carbon dioxide concentration in the flue gas produced by GTs is relatively low. Coupled with a carbon capture system, this would lead to high operating costs, and the geological conditions required for carbon dioxide sequestration are demanding, with the risk of leakage and potential biosafety hazards. Therefore, there is an urgent need to research a carbon capture technology suitable for coupling with gas turbines.

[0005] Summary of the Invention

[0006] The purpose of this application is to provide a method and system for optimizing the scheduling of an energy storage virtual power plant, which utilizes the carbon dioxide generated by energy storage stationary gas turbine power generation and achieves carbon capture and utilization, with the goal of minimizing the total operating cost of the virtual power plant and achieving optimized scheduling.

[0007] This application adopts the following technical solution:

[0008] A method for optimizing the scheduling of energy storage virtual power plants includes:

[0009] Establish a composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines;

[0010] Based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, the carbon emissions generated by the gas turbine are calculated, and a carbon emission pricing framework is designed based on the calculated carbon emissions generated by the gas turbine.

[0011] Based on the designed carbon emission pricing framework, an optimal economic dispatch model for virtual power plants is constructed with the objective function of minimizing the total cost of virtual power plants.

[0012] By linking the carbon emissions generated by gas turbines to trading prices, a tiered carbon emissions trading model is established, and the carbon trading cost is obtained based on the tiered carbon emissions trading model.

[0013] By inputting the molten salt energy storage operation and maintenance costs, carbon trading costs, gas turbine operating costs, and demand response costs into the virtual power plant optimization economic dispatch model, the most economical virtual power plant dispatch optimization scheme is obtained.

[0014] An optional improvement in this application is the establishment of a composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines, including:

[0015] Where: P GT,t For the input power of gas turbine cogeneration, L CH4 Indicates the low calorific value of natural gas. This indicates the power output of the gas turbine combined heat and power (CHP) system. This indicates the amount of natural gas it consumes. and This indicates the power output of the gas turbine combined heat and power (CHP) system. Indicating the comparison of gas turbine / heat production efficiency, η p η q This indicates the power generation and heat production efficiency of gas turbine combined heat and power (CHP). e represents the carbon emissions from gas turbine combined heat and power (CHP) generation. gt Its carbon emission coefficient, Carbon dioxide fixed for molten salt energy storage, α EMC For its carbon capture efficiency, ψ EMC,t The flue gas split ratio, For solid carbon generated by electrolyzing CO2 in molten salt energy storage, α CO2-C For fixed coefficients, Let γ be the energy consumption of molten salt energy storage at time t. EMCThe electrical power consumed to electrolyze one unit of carbon dioxide. The waste heat coefficient, and These represent the external electrical and thermal outputs of the gas turbine, η. WHB For the efficiency of waste heat boilers, P represents the upper and lower limits of the thermoelectric ratio. GT,max P GT,min This indicates the upper and lower limits of the gas turbine's output.

[0016] An optional improvement to this application is that the expression for the tiered carbon emissions trading model is:

[0017] Where: B represents the carbon trading base price; L represents the length of the carbon emission range; μ represents the compensation coefficient of the carbon trading price; and κ is the growth coefficient. Represents actual carbon emissions; at time t, the corresponding carbon trading cost of the system. A positive value indicates that the system is purchasing carbon emission rights at that moment, while a negative value indicates that the system is selling carbon emission rights during that period.

[0018] The optional improvement of this application is that the tiered carbon emission trading model includes: a carbon emission allowance model and an actual carbon emission model;

[0019] The carbon emission allowance model is as follows: in For carbon quotas, λ GT This refers to the carbon quota factor for gas turbines;

[0020] The actual carbon emission model is as follows: in Indicates actual carbon emissions, This indicates the CO2 emissions from the gas turbine. Surface molten salt energy storage can capture and utilize CO2.

[0021] An optional improvement in this application is that the virtual power plant optimized economic dispatch model... The expression is:

[0022] An optional improvement in this application is the reduction of molten salt energy storage operation and maintenance costs. Gas turbine operating costs and demand response costs They are represented as follows:

[0023] in, Tables show the operation and maintenance cost coefficients for molten salt energy storage. It is the price per unit volume of natural gas purchased by the gas-fired power plant at time t; The volume of natural gas purchased; These are the compensation unit prices for reducing electrical load and transferring electrical load, respectively. These are the reduced electrical power and the transferred electrical power, respectively.

[0024] An energy storage virtual power plant optimized scheduling system includes:

[0025] The composite treatment equation establishment module is used to establish composite treatment equations for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines.

[0026] The carbon emission pricing framework design module is used to calculate the carbon emissions generated by the gas turbine based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, and to design the carbon emission pricing framework based on the calculated carbon emissions generated by the gas turbine.

[0027] The scheduling model construction module is used to construct an optimal economic scheduling model for virtual power plants with the objective function of minimizing the total cost of virtual power plants, based on the designed carbon emission pricing framework.

[0028] The carbon trading cost calculation module is used to link the carbon emissions generated by gas turbines with the trading price, establish a tiered carbon emission trading model, and obtain the carbon trading cost based on the tiered carbon emission trading model.

[0029] The optimized scheduling calculation module is used to input the molten salt energy storage operation and maintenance costs, carbon trading costs, gas turbine operating costs, and demand response costs into the virtual power plant optimized economic scheduling model to obtain the most economical virtual power plant scheduling optimization scheme.

[0030] An optional improvement to this application is that the composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines, established in the composite treatment equation establishment module, includes:

[0031] Where: P GT,t For the input power of gas turbine cogeneration, L CH4 Indicates the low calorific value of natural gas. This indicates the power output of the gas turbine combined heat and power (CHP) system. This indicates the amount of natural gas it consumes. and This indicates the power output of the gas turbine combined heat and power (CHP) system. Indicating the comparison of gas turbine / heat production efficiency, η p η q This indicates the power generation and heat production efficiency of gas turbine combined heat and power (CHP). e represents the carbon emissions from gas turbine combined heat and power (CHP) generation. gt Its carbon emission coefficient, Carbon dioxide fixed for molten salt energy storage, α EMC For its carbon capture efficiency, ψ EMC,t The flue gas split ratio, For solid carbon generated by electrolyzing CO2 in molten salt energy storage, α CO2-C For fixed coefficients, Let γ be the energy consumption of molten salt energy storage at time t. EMC The electrical power consumed to electrolyze one unit of carbon dioxide. The waste heat coefficient, and These represent the external electrical and thermal outputs of the gas turbine, η. WHB For the efficiency of waste heat boilers, P represents the upper and lower limits of the thermoelectric ratio. GT,max P GT,min This indicates the upper and lower limits of the gas turbine's output.

[0032] An optional improvement to this application is that the expression for the tiered carbon emission trading model established in the carbon trading cost calculation module is as follows:

[0033] Where: B represents the carbon trading base price; L represents the length of the carbon emission range; μ represents the compensation coefficient of the carbon trading price; and κ is the growth coefficient. Represents actual carbon emissions; at time t, the corresponding carbon trading cost of the system. A positive value indicates that the system is purchasing carbon emission rights at that moment, while a negative value indicates that the system is selling carbon emission rights during that period.

[0034] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the energy storage virtual power plant optimization scheduling method.

[0035] Compared with the prior art, this application has at least the following beneficial technical effects:

[0036] The energy storage virtual power plant optimization scheduling method and system provided in this application, based on the electrochemical principle of molten salt, designs an innovative coupled model of energy storage and gas turbine virtual power plants. In this model, the energy storage device not only stores energy, but more importantly, it can capture carbon dioxide generated during gas turbine power generation and convert it into reusable materials through an electrochemical process. This effectively reduces the carbon emissions of the virtual power plant while achieving energy storage, thereby lowering overall operating costs. This coupled model not only improves energy utilization efficiency but also significantly promotes the green and low-carbon development of the power industry.

[0037] The energy storage virtual power plant optimization scheduling method and system provided in this application innovatively introduces a tiered carbon trading mechanism for virtual power plants participating in the carbon trading market. By finely adjusting the carbon base price and price growth rate, this mechanism effectively guides virtual power plants to proactively control their carbon emissions while pursuing the lowest possible operating costs. Specifically, when a virtual power plant's carbon emissions are below its carbon quota, the regulatory effect of the price growth rate becomes insignificant, and the carbon base price becomes the primary control mechanism. This design provides virtual power plant operators with an important reference for formulating internal unit output plans, further promoting the green, low-carbon, and efficient development of the power industry.

[0038] In summary, this application constructs an innovative coupled model of an energy storage and gas turbine virtual power plant based on the electrochemical principles of molten salt. To guide the virtual power plant in effectively controlling carbon emissions during operation, this application also introduces a tiered carbon trading mechanism. Furthermore, aiming to minimize the operating costs of the virtual power plant system, this application establishes a low-carbon economic optimization scheduling model, which can effectively optimize the scheduling of the energy storage virtual power plant. The virtual power plant system established in this application not only integrates advanced energy storage and gas turbine technologies but also achieves efficient energy conversion and storage through electrochemical principles. Simultaneously, by introducing a tiered carbon trading mechanism, the system can effectively reduce carbon emissions while ensuring energy supply, meeting the requirements of a low-carbon economy. Moreover, the established low-carbon economic optimization scheduling model can scientifically and rationally guide the operation and scheduling of the virtual power plant, achieving efficient energy utilization and cost minimization, providing strong support for the sustainable development of the power industry. Attached Figure Description

[0039] Figure 1 is a block diagram of a virtual power plant for energy storage.

[0040] Figure 2 shows the analysis of the base price for carbon trading.

[0041] Figure 3 is a chart analyzing the price growth rate.

[0042] Figure 4 is a structural block diagram of an energy storage virtual power plant optimization scheduling system according to this application. Detailed Implementation

[0043] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] This application provides a method for optimizing the scheduling of energy storage virtual power plants, including:

[0045] Establish a composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines;

[0046] Based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, the carbon emissions generated by the gas turbine are calculated, and a carbon emission pricing framework is designed based on the calculated carbon emissions generated by the gas turbine.

[0047] Based on the designed carbon emission pricing framework, an optimal economic dispatch model for virtual power plants is constructed with the objective function of minimizing the total cost of virtual power plants.

[0048] By linking the carbon emissions generated by gas turbines to trading prices, a tiered carbon emissions trading model is established, and the carbon trading cost is obtained based on the tiered carbon emissions trading model.

[0049] By inputting the molten salt energy storage operation and maintenance costs, carbon trading costs, gas turbine operating costs, and demand response costs into the virtual power plant optimization economic dispatch model, the most economical virtual power plant dispatch optimization scheme is obtained.

[0050] Example 1

[0051] As shown in Figure 1, in terms of system architecture and operational logic, the core of the entire system consists of a gas turbine cogeneration system equipped with molten salt energy storage and an electricity-to-gas (EPC) unit. During the daily operation of the virtual power plant, this gas turbine cogeneration system integrating molten salt energy storage technology plays a crucial role, working collaboratively to meet the power load demands of the grid. Simultaneously, the virtual power plant is also responsible for coordinating load regulation between the molten salt energy storage gas turbine cogeneration system and the EPC unit to ensure the balance and stability of the system's thermal power. The introduction of molten salt energy storage technology not only improves the system's energy efficiency but also effectively immobilizes the carbon dioxide generated during the gas turbine cogeneration process, achieving carbon capture and efficient utilization. The flue gas diversion device in the system optimizes the management of the power and heat output capacity of the gas turbine cogeneration system by finely adjusting the flue gas diversion ratio, further promoting the decoupling of heat and electricity and enabling the system to respond more flexibly to external load changes. Furthermore, the demand response mechanism also plays an important role in the system. By scientifically guiding and incentivizing users to develop more reasonable electricity consumption plans, demand response helps optimize the allocation of power resources, reduce peak grid pressure, and promote energy conservation, emission reduction, and sustainable social development.

[0052] The high-temperature flue gas emitted from the gas turbine can be guided to molten salt energy storage via a flue gas channel. Inside this system, the exhaust gas undergoes a combined absorption-electrolysis treatment process, as detailed in the following model:

[0053] In the formula: P GT,t For the input power of gas turbine cogeneration, L CH4 Indicates the low calorific value of natural gas. This indicates the power output of the gas turbine combined heat and power (CHP) system. This indicates the amount of natural gas it consumes. and η represents the power output of the combined heat and power (CHP) generation from the gas turbine. p η q This indicates the power generation and heat production efficiency of gas turbine cogeneration. e represents the carbon emissions from gas turbine combined heat and power (CHP) generation. gt Its carbon emission coefficient. Carbon dioxide fixed for molten salt energy storage, α EMC For its carbon capture efficiency, ψ EMC,t This refers to the flue gas split ratio; For solid carbon generated by electrolyzing CO2 in molten salt energy storage, α CO2-C It is a fixed coefficient. Let γ be the energy consumption of molten salt energy storage at time t. EMC The electrical power consumed to electrolyze a unit of carbon dioxide; The waste heat coefficient, and These represent the external electrical and thermal outputs of the gas turbine, η. WHB For waste heat boiler efficiency; Indicates the upper and lower limits of the thermoelectric ratio; P GT,max P GT,min This indicates the upper and lower limits of the gas turbine's output.

[0054] The core of the carbon trading mechanism is the establishment of a market trading system based on carbon emission permits to achieve precise control over total carbon emissions. In this system, regulatory agencies first set a carbon emission cap for each emitting entity (such as gas turbines, factories, etc.) according to certain standards or principles. Then, each emitting entity arranges its production and emission activities according to this cap. If an entity's actual carbon emissions are lower than its allocated allowances, it can sell its unused allowances on the carbon trading market to gain economic benefits. Conversely, if an entity's carbon emissions exceed its allocated allowances, it must purchase additional carbon emission rights on the carbon trading market to ensure its emission activities comply with the prescribed standards. This trading mechanism effectively encourages emitting entities to reduce their carbon emissions through technological innovation, energy conservation, and emission reduction, thereby meeting their production needs while minimizing their environmental impact.

[0055] 1) The carbon emission quota model is used to establish the initial carbon emission rights allocation rules and total control to ensure that the overall emissions meet policy objectives.

[0056] In the formula: For carbon quotas, λ GT This is the carbon quota coefficient for gas turbines.

[0057] 2) Actual carbon emission models are used to simulate and track the actual carbon emission levels of each gas turbine, and to record and verify its emission data.

[0058] In the formula: Indicates actual carbon emissions, This indicates the CO2 emissions from the gas turbine. Surface molten salt energy storage can capture and utilize CO2.

[0059] 3) Tiered Carbon Emission Trading Model: This application designs a carbon emission pricing framework, the core of which is to closely link the carbon emissions of gas turbines with their trading prices. Once a gas turbine's carbon emissions exceed its allocated allowances, it will face a progressively increasing purchase cost. This escalating cost design aims to provide strong economic incentives for gas turbines, driving them to continuously seek technological and managerial innovations to reduce emissions. Simultaneously, for gas turbines that can effectively control emissions and even generate excess allowances, this model also provides a fair trading platform for carbon emissions. These gas turbines can obtain economic returns by selling excess allowances, further incentivizing them to continue optimizing their emission reduction strategies. The tiered carbon emission trading model can be expressed as:

[0060] In the formula: B represents the carbon trading base price; L represents the length of the carbon emission range; μ represents the compensation coefficient for the carbon trading price; κ is the growth coefficient; and at time t, the corresponding carbon trading cost of the system. A positive value indicates that the system is purchasing carbon emission rights at that moment, while a negative value indicates that the system is selling carbon emission rights during that period.

[0061] To achieve optimal scheduling of virtual power plants, an economic scheduling model needs to be constructed, which aims to minimize the total cost of the virtual power plants. This economic model includes the operation and maintenance costs of molten salt energy storage. Carbon trading costs Gas turbine operating costs Demand response cost The virtual power plant optimal economic dispatch model is as follows:

[0062] Molten salt energy storage operation and maintenance costs Gas turbine operating costs Demand response cost It can be represented as:

[0063] In the formula: Tables show the operation and maintenance cost coefficients for molten salt energy storage. It is the price per unit volume of natural gas purchased by the gas-fired power plant at time t; The volume of natural gas purchased; These are the compensation unit prices for reducing electrical load and transferring electrical load, respectively. These are the reduced electrical power and the transferred electrical power, respectively.

[0064] Example 2

[0065] Different carbon trading parameters directly affect the operation of VPPs. While analyses have been conducted on factors such as the base price, interval length, and price growth rate in tiered carbon trading mechanisms, they have not been integrated with EMC (Enhanced Monetary Control), and carbon emissions in each period have exceeded carbon trading allowances. This paper combines EMC with the tiered carbon trading mechanism to analyze the impact of different carbon base prices and price growth rates on system carbon emissions.

[0066] As shown in Figure 2, before the carbon base price is below 150 yuan, the carbon emissions decrease as the carbon base price increases. This is because the system reduces carbon emissions by adjusting the flue gas split ratio in order to sell more carbon emission credits. When the carbon base price reaches 150 yuan, due to the limitations of power balance and EMC efficiency, the system cannot reduce carbon emissions by adjusting the flue gas split ratio, so the carbon emissions tend to stabilize.

[0067] Figure 3 illustrates the impact of the price growth rate on total cost and carbon emissions when the carbon base price is 120 yuan. As the price growth rate increases, the cost of carbon emissions exceeding the carbon allowance rises. The system adjusts the output of each device to reduce carbon emissions. When the price growth rate reaches 0.5, the total cost and carbon emissions tend to stabilize because the carbon emissions are less than the carbon allowance at this point, rendering the price growth rate ineffective.

[0068] In summary, when the carbon base price is below 150 yuan and the price growth rate is below 0.5%, carbon emissions decrease as carbon trading parameters increase. However, when the carbon base price exceeds 150 yuan and the price growth rate reaches 0.5%, it becomes impossible to reduce carbon emissions by adjusting parameters. Therefore, VPP operators can flexibly adjust the output of their internal equipment based on the carbon base price and price growth rate to reduce operating costs.

[0069] Example 3

[0070] As shown in Figure 4, the energy storage virtual power plant optimized scheduling system provided in this application includes:

[0071] The composite treatment equation establishment module is used to establish composite treatment equations for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines.

[0072] The carbon emission pricing framework design module is used to calculate the carbon emissions generated by the gas turbine based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, and to design the carbon emission pricing framework based on the calculated carbon emissions generated by the gas turbine.

[0073] The scheduling model construction module is used to construct an optimal economic scheduling model for virtual power plants with the objective function of minimizing the total cost of virtual power plants, based on the designed carbon emission pricing framework.

[0074] The carbon trading cost calculation module links the carbon emissions generated by gas turbines to the trading price, establishes a tiered carbon emissions trading model, and calculates the carbon trading cost based on this model.

[0075] The optimized scheduling calculation module is used to reduce the operation and maintenance costs of molten salt energy storage. Carbon trading costs Gas turbine operating costs and demand response costs The data is input into the virtual power plant optimization economic dispatch model to obtain the most economical virtual power plant dispatch optimization scheme.

[0076] Example 4

[0077] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the energy storage virtual power plant optimization scheduling method.

[0078] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0082] Although this application has been described in detail above with general descriptions and specific embodiments, some modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, such modifications or improvements made without departing from the spirit of this application are all within the scope of protection claimed in this application.

Claims

1. A method for optimized scheduling of an energy storage virtual power plant, characterized in that, include: Establish a composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines; Based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, the carbon emissions generated by the gas turbine are calculated, and a carbon emission pricing framework is designed based on the calculated carbon emissions generated by the gas turbine. Based on the designed carbon emission pricing framework, an optimal economic dispatch model for virtual power plants is constructed with the objective function of minimizing the total cost of virtual power plants. By linking the carbon emissions generated by gas turbines to trading prices, a tiered carbon emissions trading model is established, and the carbon trading cost is obtained based on the tiered carbon emissions trading model. By inputting the molten salt energy storage operation and maintenance costs, carbon trading costs, gas turbine operating costs, and demand response costs into the virtual power plant optimization economic dispatch model, the most economical virtual power plant dispatch optimization scheme is obtained.

2. The energy storage virtual power plant optimized scheduling method according to claim 1, characterized in that, The established composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines includes: Where: P GT,t For the input power of gas turbine cogeneration, L CH4 Indicates the low calorific value of natural gas. This indicates the power output of the gas turbine combined heat and power (CHP) system. This indicates the amount of natural gas it consumes. and This indicates the power output of the gas turbine combined heat and power (CHP) system. Indicating the comparison of gas turbine / heat production efficiency, η p η q This indicates the power generation and heat production efficiency of gas turbine combined heat and power (CHP). e represents the carbon emissions from gas turbine combined heat and power (CHP) generation. gt Its carbon emission coefficient, Carbon dioxide fixed for molten salt energy storage, α EMC For its carbon capture efficiency, ψ EMC,t The flue gas split ratio, For solid carbon generated by electrolyzing CO2 in molten salt energy storage, α CO2-C To solidify Fixed coefficient, Let γ be the energy consumption of molten salt energy storage at time t. EMC The electrical power consumed to electrolyze one unit of carbon dioxide. The waste heat coefficient, and These represent the external electrical and thermal outputs of the gas turbine, η. WHB For the efficiency of waste heat boilers, P represents the upper and lower limits of the thermoelectric ratio. GT,max P GT,min This indicates the upper and lower limits of the gas turbine's output.

3. The energy storage virtual power plant optimized scheduling method according to claim 1, characterized in that, The expression for the tiered carbon emissions trading model is: Where: B represents the carbon trading base price; L represents the length of the carbon emission range; μ represents the compensation coefficient for the carbon trading price. κ The growth coefficient; Represents actual carbon emissions; at time t, the corresponding carbon trading cost of the system. A positive value indicates that the system is purchasing carbon emission rights at that moment, while a negative value indicates that the system is selling carbon emission rights during that period.

4. The energy storage virtual power plant optimized scheduling method according to claim 1, characterized in that, The tiered carbon emissions trading model includes: the carbon emission allowance model and the actual carbon emissions model; The carbon emission allowance model is as follows: in For carbon quotas, λ GT This refers to the carbon quota factor for gas turbines; The actual carbon emission model is as follows: in Indicates actual carbon emissions, This indicates the CO2 emissions from the gas turbine. Surface molten salt energy storage can capture and utilize CO2.

5. The energy storage virtual power plant optimized scheduling method according to claim 1, characterized in that, Virtual power plant optimized economic dispatch model The expression is:

6. The energy storage virtual power plant optimized scheduling method according to claim 5, characterized in that, Molten salt energy storage operation and maintenance costs Gas turbine operating costs and demand response costs They are represented as follows: in, Tables show the operation and maintenance cost coefficients for molten salt energy storage. It is the price per unit volume of natural gas purchased by the gas-fired power plant at time t; The volume of natural gas purchased; These are the compensation unit prices for reducing electrical load and transferring electrical load, respectively. These are the reduced electrical power and the transferred electrical power, respectively.

7. An optimized scheduling system for energy storage virtual power plants, characterized in that, include: The composite treatment equation establishment module is used to establish composite treatment equations for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines. The carbon emission pricing framework design module is used to calculate the carbon emissions generated by the gas turbine based on the established composite treatment equation of high-temperature flue gas absorption-electrolysis, and to design the carbon emission pricing framework based on the calculated carbon emissions generated by the gas turbine. The scheduling model construction module is used to construct an optimal economic scheduling model for virtual power plants with the objective function of minimizing the total cost of virtual power plants, based on the designed carbon emission pricing framework. The carbon trading cost calculation module is used to link the carbon emissions generated by gas turbines with the trading price, establish a tiered carbon emission trading model, and obtain the carbon trading cost based on the tiered carbon emission trading model. The optimized scheduling calculation module is used to input the molten salt energy storage operation and maintenance costs, carbon trading costs, gas turbine operating costs, and demand response costs into the virtual power plant optimized economic scheduling model to obtain the most economical virtual power plant scheduling optimization scheme.

8. The energy storage virtual power plant optimized scheduling system according to claim 7, characterized in that, The composite treatment equation establishment module establishes a composite treatment equation for high-temperature flue gas absorption-electrolysis of carbon emissions from gas turbines, including: Where: P GT,t For the input power of gas turbine cogeneration, L CH4 Indicates the low calorific value of natural gas. This indicates the power output of the gas turbine combined heat and power (CHP) system. This indicates the amount of natural gas it consumes. and This indicates the power output of the gas turbine combined heat and power (CHP) system. Indicating the comparison of gas turbine / heat production efficiency, η p η q This indicates the power generation and heat production efficiency of gas turbine combined heat and power (CHP). e represents the carbon emissions from gas turbine combined heat and power (CHP) generation. gt Its carbon emission coefficient, Carbon dioxide fixed for molten salt energy storage, α EMC For its carbon capture efficiency, ψ EMC,t The flue gas split ratio, For solid carbon generated by electrolyzing CO2 in molten salt energy storage, α CO2-C For fixed coefficients, Let γ be the energy consumption of molten salt energy storage at time t. EMC The electrical power consumed to electrolyze one unit of carbon dioxide. The waste heat coefficient, and These represent the external electrical and thermal outputs of the gas turbine, η. WHB For the efficiency of waste heat boilers, P represents the upper and lower limits of the thermoelectric ratio. GT,max P GT,min This indicates the upper and lower limits of the gas turbine's output.

9. The energy storage virtual power plant optimized scheduling system according to claim 7, characterized in that, In the carbon trading cost calculation module, the expression for the tiered carbon emission trading model is as follows: Where: B represents the carbon trading base price; L represents the length of the carbon emission range; μ represents the compensation coefficient of the carbon trading price; and κ is the growth coefficient. Represents actual carbon emissions; at time t, the corresponding carbon trading cost of the system. A positive value indicates that the system is purchasing carbon emission rights at that moment, while a negative value indicates that the system is selling carbon emission rights during that period.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the energy storage virtual power plant optimization scheduling method according to any one of claims 1-6.

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