Extreme weather-oriented virtual power plant collaborative control method and device, and electronic equipment

Abstract

This invention relates to the field of power system dispatching technology, and discloses a method, device, and electronic equipment for collaborative control of virtual power plants in response to extreme weather. The method takes the revenue from carbon-green certificate joint trading and the revenue from energy storage operation as dual optimization objectives, and uses actual operational constraints such as power balance as the solution basis. It deeply couples and correlates the output of gas turbine units, carbon quota trading, green certificate trading, and energy storage charging and discharging behavior. On the one hand, it breaks through the limitations of a single trading mechanism, maximizing market trading revenue while achieving emission reduction targets, so that the low-carbon benefits and economic benefits of virtual power plants are achieved simultaneously. On the other hand, it uses the flexible dispatching of energy storage systems to smooth out fluctuations in wind and solar output under extreme weather, balance grid load, and support grid voltage stability, which greatly improves the emergency response capability and operational stability of virtual power plants in response to extreme weather, and fundamentally realizes the synergistic unity of trading strategies and physical dispatch.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, specifically to a method, apparatus, and electronic equipment for virtual power plant collaborative control in response to extreme weather conditions. Background Technology

[0002] With the rapid development of renewable energy, virtual power plants (VPPs), as coordination platforms for distributed energy, are playing an increasingly prominent role in the electricity market. However, extreme weather events (such as heavy rainfall, blizzards, and solar eclipses) often lead to fluctuations in photovoltaic output, surges in load demand, and unstable grid voltage, posing significant challenges to the normal operation of VPPs. Most existing VPP systems rely on single carbon trading or green certificate trading mechanisms, failing to adequately consider the risks and emergency responses brought about by extreme weather.

[0003] In addition, distributed energy storage systems, as part of virtual power plants, can provide power regulation and stability support under extreme weather conditions, but existing scheduling models have not yet explored in depth the synergistic optimization of energy storage systems and carbon-green certificate trading mechanisms. Summary of the Invention

[0004] This invention provides a method, device, and electronic equipment for the collaborative control of virtual power plants in response to extreme weather conditions. This addresses the problem that most existing virtual power plant systems rely on a single carbon trading or green certificate trading mechanism, fail to fully consider the risks and emergency responses brought about by extreme weather, and are unable to collaboratively optimize the energy storage system with the carbon-green certificate trading mechanism.

[0005] In a first aspect, the present invention provides a virtual power plant collaborative control method for extreme weather conditions. The method includes: when extreme weather is predicted for a target time period, acquiring grid load information, photovoltaic output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating costs, and preset constraints for the target time period. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic output, wind power output, energy storage charging and discharging power, and gas turbine output. Based on the preset constraints, a pre-constructed first objective function and a second objective function are solved to obtain a target scheduling strategy. The target scheduling strategy includes the target energy storage charging power for the target time period, the target... The system targets energy storage discharge power, carbon quota trading volume, and green certificate trading volume. The first objective function aims to maximize the combined trading revenue of carbon and green certificates. This revenue is calculated based on carbon trading revenue and green certificate trading revenue. Carbon trading revenue is determined based on gas turbine output information and carbon trading price information, while gas turbine output information is determined based on carbon quota trading volume. The second objective function aims to maximize energy storage revenue, calculated using electricity purchase price information, energy storage discharge power, electricity sales price information, energy storage charging power, and energy storage system operating costs. The system then uses these targets—target energy storage charging power, target energy storage discharge power, target carbon quota trading volume, and target green certificate trading volume—to implement coordinated control of the virtual power plant.

[0006] The virtual power plant collaborative control method for extreme weather provided by this invention uses carbon-green certificate joint trading revenue and energy storage operation revenue as dual optimization objectives, and takes actual operational constraints such as power balance as the solution basis. It deeply couples and correlates gas turbine output, carbon quota trading, green certificate trading and energy storage charging and discharging behavior. On the one hand, it breaks through the limitations of a single trading mechanism, maximizing market trading revenue while achieving emission reduction targets, so that the low-carbon benefits and economic benefits of the virtual power plant are achieved simultaneously. On the other hand, it uses the flexible scheduling of the energy storage system to smooth the fluctuations of wind and solar output under extreme weather, balance grid load, and support grid voltage stability, which greatly improves the emergency response capability and operational stability of the virtual power plant in the face of extreme weather. It fundamentally realizes the synergistic unity of trading strategy and physical scheduling, which not only improves the level of renewable energy consumption, but also makes the operation of the virtual power plant safer, more economical and efficient in complex meteorological and electricity market environments, significantly enhancing the core competitiveness and environmental adaptability of the virtual power plant in the electricity market.

[0007] In one optional implementation, the step of solving a pre-constructed first objective function and a second objective function based on preset constraints to obtain a target scheduling strategy includes: obtaining a risk assessment model, which is used to characterize the relationship between the scheduling strategy and the conditional value of risk; determining a global overall objective function based on the risk assessment model, the first objective function, and the second objective function; and solving the global overall objective function based on a preset optimization algorithm to obtain the target scheduling strategy.

[0008] The method provided by this optional implementation integrates conditional value at risk into the optimization framework, combining the dual objectives of carbon-green certificate joint trading and energy storage revenue into a global overall objective. Through a unified optimization algorithm, it achieves synergistic optimization of trading strategies and energy storage scheduling, and effectively avoids operational risks caused by extreme weather and market fluctuations. The resulting scheduling strategy is both economically efficient and risk-resistant, and is more adaptable to complex and ever-changing extreme weather operation scenarios.

[0009] In one optional implementation, obtaining the risk assessment model includes: obtaining interval information of input parameters, including wind and solar power output parameters, grid load parameters, price parameters, and extreme weather parameters; determining parameter information for multiple scenarios based on the interval information of the input parameters, with different parameter information for different scenarios; determining the total revenue calculation model for each scenario based on the parameter information of each scenario, the total revenue calculation model being used to characterize the correlation between total revenue and scheduling strategy; and determining the risk assessment model based on the total revenue calculation models for multiple scenarios and a pre-set confidence level.

[0010] The method provided by this optional implementation method constructs a risk assessment model through multi-scenario parameters, which can comprehensively cover various operating conditions such as extreme weather, wind and solar power output, grid load and market price fluctuations. It can accurately quantify the potential risks of dispatching strategies, making risk assessment more realistic and authentic, providing a reliable risk basis for the collaborative optimization of virtual power plants, and enabling the final dispatching strategy to better adapt to the complex and ever-changing scenarios under extreme weather, thereby improving the robustness and practicality of the strategy.

[0011] In one optional implementation, the step of coordinating the control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume includes: scheduling the energy storage system based on the energy storage charging power and energy storage discharging power of the target time period; scheduling the carbon quota purchase volume of the target time period based on the carbon quota trading volume; and scheduling the green certificate trading volume of the target time period based on the green certificate trading volume.

[0012] The method provided by this optional implementation method synchronizes and coordinates the physical scheduling of the energy storage system with the carbon quota and green certificate trading scheduling, realizing integrated and precise control of the physical operation and market trading of the virtual power plant. It relies on the charging and discharging of energy storage to ensure the grid load balance and system stability under extreme weather conditions, and matches low-carbon compliance requirements and economic benefit targets through carbon and green certificate trading, making the overall control more coordinated and the execution more efficient, taking into account the operational safety, low-carbon benefits and economic benefits of the virtual power plant.

[0013] In an optional implementation, the method further includes switching the photovoltaic converter from constant power factor mode to grid voltage support mode when grid voltage fluctuations are detected.

[0014] The method provided by this optional implementation can quickly switch the operation mode of the photovoltaic converter when the grid voltage fluctuates, actively provide voltage support to the grid, effectively suppress voltage anomalies caused by extreme weather, ensure the stable operation of the grid and the reliable operation of the virtual power plant, and greatly improve the system's emergency response capability to extreme weather.

[0015] In an optional implementation, the method further includes: adjusting the flexible load when the power grid load change is detected to meet preset conditions.

[0016] The method provided by this optional implementation can quickly smooth out grid load fluctuations by flexibly adjusting flexible loads, further improving the virtual power plant's ability to adapt to and adjust load changes, and enhancing the system's operational stability and emergency response capabilities under extreme weather conditions.

[0017] Secondly, the present invention provides a virtual power plant collaborative control device for extreme weather. The device includes: when extreme weather is predicted for a target time period, acquiring grid load information, photovoltaic output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating cost, and preset constraints for that target time period. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic output, wind power output, energy storage charging and discharging power, and gas turbine output. Based on the preset constraints, a pre-constructed first objective function and a second objective function are solved to obtain a target scheduling strategy. The target scheduling strategy includes the target energy storage charging power for the target time period, the target... The system targets energy storage discharge power, carbon quota trading volume, and green certificate trading volume. The first objective function aims to maximize the combined trading revenue of carbon and green certificates. This revenue is calculated based on carbon trading revenue and green certificate trading revenue. Carbon trading revenue is determined based on gas turbine output information and carbon trading price information, while gas turbine output information is determined based on carbon quota trading volume. The second objective function aims to maximize energy storage revenue, calculated using electricity purchase price information, energy storage discharge power, electricity sales price information, energy storage charging power, and energy storage system operating costs. The system then uses these targets—target energy storage charging power, target energy storage discharge power, target carbon quota trading volume, and target green certificate trading volume—to implement coordinated control of the virtual power plant.

[0018] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the virtual power plant collaborative control method for extreme weather described in the first aspect or any corresponding embodiment.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the virtual power plant collaborative control method for extreme weather described in the first aspect or any corresponding embodiment thereof.

[0020] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the virtual power plant collaborative control method for extreme weather as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the first type of virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a virtual power plant collaborative control device for extreme weather according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the execution of the virtual power plant collaborative control method for extreme weather depends is described herein. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0028] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0029] With the rapid development of renewable energy, virtual power plants (VPPs), as coordination platforms for distributed energy, are playing an increasingly prominent role in the electricity market. However, extreme weather events (such as heavy rainfall, blizzards, and solar eclipses) often lead to fluctuations in photovoltaic output, surges in load demand, and unstable grid voltage, posing significant challenges to the normal operation of VPPs. Most existing VPP systems rely on single carbon trading or green certificate trading mechanisms, failing to adequately consider the risks and emergency responses brought about by extreme weather.

[0030] In addition, distributed energy storage systems, as part of virtual power plants, can provide power regulation and stability support under extreme weather conditions, but existing scheduling models have not yet explored in depth the synergistic optimization of energy storage systems and carbon-green certificate trading mechanisms.

[0031] In view of this, this application provides a method for collaborative control of virtual power plants under extreme weather conditions, which can be applied to a single server to achieve collaborative control of virtual power plants under extreme weather conditions. The method provided in this application uses carbon-green certificate joint trading revenue and energy storage operation revenue as dual optimization objectives, and takes actual operational constraints such as power balance as the solution basis. It deeply couples and correlates gas turbine output, carbon quota trading, green certificate trading, and energy storage charging and discharging behavior. On the one hand, it breaks through the limitations of a single trading mechanism, maximizing market trading revenue while achieving emission reduction targets, allowing the low-carbon benefits and economic benefits of virtual power plants to be achieved simultaneously. On the other hand, it leverages the flexible scheduling of energy storage systems to smooth wind and solar power output fluctuations under extreme weather conditions, balance grid load, and support grid voltage stability, significantly improving the emergency response capability and operational stability of virtual power plants in the face of extreme weather. This fundamentally achieves the synergistic unity of trading strategies and physical scheduling, improving the level of renewable energy consumption and making the operation of virtual power plants safer, more economical, and more efficient in complex meteorological and electricity market environments, significantly enhancing the core competitiveness and environmental adaptability of virtual power plants in the electricity market.

[0032] According to an embodiment of the present invention, a method for collaborative control of virtual power plants in extreme weather is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] This embodiment provides a virtual power plant collaborative control method for extreme weather conditions, which can be used in the aforementioned server. Figure 2 This is a flowchart of a virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: When extreme weather is predicted for the target period, obtain the grid load information, photovoltaic power output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating cost, and preset constraints for the target period. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output.

[0034] For example, the target time period can be a future time period that requires scheduling and pre-optimization control of the virtual power plant, such as one hour or one scheduling cycle in the future; extreme weather refers to severe weather that will seriously affect the operation of the power grid and distributed energy, such as heavy rainfall, blizzards, solar eclipses, extreme high temperatures, and low temperatures, which can lead to sudden changes in wind and solar power output, load surges, and power grid fluctuations; power grid load information refers to the total power consumption and electricity consumption data of users and equipment on the power grid during the target time period, reflecting the overall electricity demand; photovoltaic power output information refers to the actual or predicted active power that the photovoltaic power station can generate during the target time period; photovoltaic power output information refers to the target time period The predicted active power output of the internal photovoltaic power station refers to the predicted active power output of the wind turbine units within the target period; the carbon trading price information refers to the unit price of carbon emission rights traded on the market, used to calculate the revenue or cost of carbon trading; the green certificate trading price information refers to the unit price of green electricity certificates traded on the market, with green certificates representing the environmental value of renewable energy generation; the electricity purchase price information refers to the unit price at which the virtual power plant purchases electricity from the grid; the electricity sales price information refers to the unit price at which the virtual power plant sells electricity to the grid; and the energy storage system operating cost refers to the related costs such as losses, maintenance, and depreciation incurred during the charging and discharging of energy storage batteries. In this embodiment, the power balance constraint can be expressed by the following formula:

[0035] in, Indicates the power grid load; This represents the active power output of the photovoltaic system at time t, i.e., photovoltaic output. This represents the active power output of the wind power system at time t, i.e., the wind power output. This indicates the discharge power of the energy storage system, ensuring additional power support is provided when load demand is high; Indicates the output of the gas turbine unit; This represents the charging power of the energy storage system at time t.

[0036] Furthermore, the preset constraints also include constraints on the change in the State of Charge (SOC) of the energy storage system. The management of energy storage systems is crucial, especially in response to extreme weather conditions. The performance of an energy storage system is primarily characterized by the battery's State of Charge (SOC). Changes in SOC reflect the battery's charging and discharging states. At each moment, the formula for updating the SOC is:

[0037] in, This indicates the battery charging state of the energy storage system at time t; This indicates the battery's SOC at the previous moment; This refers to the battery capacity of the energy storage system.

[0038] Step S202: Solve the pre-constructed first objective function and second objective function based on preset constraints to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine output information and carbon trading price information. The gas turbine output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs.

[0039] For example, in this embodiment of the application, the first objective function is as follows:

[0040]

[0041]

[0042] in, This indicates that the maximum value of the following expression is being calculated. This represents the total revenue from the carbon-green certificate joint trading, which is the sum of carbon trading revenue and green certificate trading revenue. The carbon trading revenue is determined by factors such as carbon emissions from gas turbine units, carbon quota trading volume, and carbon trading price, reflecting the profit or cost of virtual power plants in the carbon market. The green certificate trading revenue is determined by factors such as renewable energy generation, green certificate trading volume, and green certificate trading price, reflecting the profit or cost of virtual power plants in the green certificate market; the adjustment factor is used to balance the contribution of carbon trading and green certificate trading to the total revenue.

[0043] Carbon trading revenue can be calculated using the following formula:

[0044] in, Indicates the carbon trading price, This represents the carbon emissions at time t.

[0045] Carbon emissions are calculated based on the output and carbon emission factor of the gas turbine unit. The carbon emissions can be calculated using the following formula:

[0046] in, This indicates the processing of gas turbine unit i at time t. and This represents the carbon emission coefficient of the gas turbine unit, which represents the carbon emissions per unit power.

[0047] Based on the carbon emission calculation formula, the system can calculate the carbon emissions of virtual power plants in the carbon emissions trading market in real time, thereby calculating the required carbon allowances. When carbon trading market prices are high, virtual power plants may need to purchase more allowances to ensure they meet emission requirements and avoid penalties for insufficient allowances.

[0048] The returns from green certificate trading can be calculated using the following formula:

[0049] in, Indicates the price of green certificates. This represents the number of green certificates allocated at time t.

[0050] The number of green certificates is calculated based on the renewable energy generation of the virtual power plant, which can be obtained using the following formula:

[0051] in, This represents the number of green certificates corresponding to each kilowatt-hour of electricity generated; the meanings of the other variables will not be elaborated further.

[0052] Trading in the green certificate market can help virtual power plants reduce their need to purchase carbon emission allowances and provide them with an additional source of revenue. During periods of high renewable energy output, virtual power plants can increase the economic benefits of their systems by increasing the trading volume of green certificates.

[0053] The second objective function can be expressed as follows:

[0054] in, Indicates energy storage revenue, This represents the electricity price at time t. This represents the electricity purchase price at time t. This represents the operating cost of the energy storage system; the meanings of the other variables will not be elaborated further.

[0055] By maximizing the discharge benefits of energy storage systems and minimizing charging costs, virtual power plants can improve their economic efficiency in the electricity market. Simultaneously, optimizing charging and discharging strategies can effectively enhance the absorption capacity of renewable energy and reduce grid load fluctuations.

[0056] Step S203: Coordinate the control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume.

[0057] For example, in this embodiment, instructions are sent to the energy storage converter based on the target energy storage charging and discharging power for the target time period. During periods of surplus wind and solar power or low electricity prices, the converter charges at the target power and discharges at the target power during periods of peak load or high electricity prices. This helps to smooth out fluctuations in wind and solar power output under extreme weather conditions, balance grid load, and obtain peak-valley price difference revenue. Simultaneously, corresponding buy and sell instructions are submitted to the carbon trading market based on the target carbon quota trading volume to make up for carbon emission gaps or realize surplus quota revenue. Instructions are also submitted to the green certificate trading market based on the target green certificate trading volume to sell green certificates corresponding to renewable energy to obtain environmental value or buy green certificates to offset carbon emissions and reduce compliance costs. Finally, the three types of scheduling results are synchronously fed back to the control platform to verify constraints such as power balance and carbon emission compliance. If there are deviations, a new round of optimization is triggered to ensure the safe operation of the virtual power plant under extreme weather conditions and the synergistic achievement of low-carbon benefits and economic gains.

[0058] The virtual power plant collaborative control method for extreme weather provided in this embodiment takes carbon-green certificate joint trading revenue and energy storage operation revenue as dual optimization objectives, and uses actual operational constraints such as power balance as the solution basis. It deeply couples and correlates gas turbine output, carbon quota trading, green certificate trading and energy storage charging and discharging behavior. On the one hand, it breaks through the limitations of a single trading mechanism, maximizing market trading revenue while achieving emission reduction targets, so that the low-carbon benefits and economic benefits of the virtual power plant are achieved simultaneously. On the other hand, it uses the flexible scheduling of the energy storage system to smooth the fluctuations of wind and solar output under extreme weather, balance grid load, and support grid voltage stability, which greatly improves the emergency response capability and operational stability of the virtual power plant in the face of extreme weather. It fundamentally realizes the synergistic unity of trading strategy and physical scheduling, which not only improves the level of renewable energy consumption, but also makes the operation of the virtual power plant safer, more economical and efficient in complex meteorological and electricity market environments, significantly enhancing the core competitiveness and environmental adaptability of the virtual power plant in the electricity market.

[0059] This embodiment provides a virtual power plant collaborative control method for extreme weather conditions, which can be used in the aforementioned server. Figure 3 This is a flowchart of a virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: When extreme weather is predicted for the target period, obtain the following information for the target period: grid load, photovoltaic power output, wind power output, carbon trading price, green certificate trading price, electricity purchase price, electricity sales price, energy storage system operating cost, and preset constraints. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output. For details, please refer to [link to relevant documentation]. Figure 1 Step S201 of the illustrated embodiment will not be described again here.

[0060] Step S302: Solve the pre-constructed first objective function and second objective function based on preset constraints to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine output information and carbon trading price information. The gas turbine output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs.

[0061] Specifically, step S302 includes: Step S3021: Obtain the risk assessment model, which is used to characterize the relationship between scheduling strategies and conditional value of risk.

[0062] In some optional implementations, step S3021 above includes: Step a1: Obtain the range information of the input parameters, which include wind and solar power output parameters, grid load parameters, price parameters, and extreme weather parameters.

[0063] For example, in the embodiments of this application, the range information of the input parameters can be determined based on past experience, and the embodiments of this application do not impose specific limitations.

[0064] Step a2: Determine the parameter information for multiple scenarios based on the range information of the input parameters. The parameter information is different for different scenarios.

[0065] For example, in this embodiment of the application, based on uncertainties such as extreme weather warnings (blizzards, solar eclipses, etc.), market price fluctuations (carbon prices, green certificate prices, electricity prices), wind and solar power output fluctuations, and load fluctuations, a system is generated. N Each scenario represents a possible future operating state.

[0066] Step a3: Determine the total revenue calculation model for each scenario based on the parameter information of each scenario. The total revenue calculation model is used to characterize the relationship between total revenue and scheduling strategy.

[0067] For example, in the embodiments of this application, the total revenue of the i-th scenario This includes revenue from carbon-green certificate trading and revenue from energy storage charging and discharging; the specific calculation process will not be elaborated here.

[0068] Step a4: Calculate the total revenue based on multiple scenarios and determine the risk assessment model based on pre-set reliability.

[0069] For example, in this embodiment of the application, the risk assessment model can be as follows:

[0070] in, This represents the risk assessment value at a given confidence level. It calculates the average return for all scenarios where the return is lower than the Value at Risk (VaR), accurately depicting the average loss or gain in the worst-case scenario. Confidence level represents the tolerance for risk; The value at a confidence level, , It refers to the number of scenes.

[0071] The calculation process is as follows: Based on the fluctuations in wind power output, photovoltaic power output, load demand, carbon price, green certificate price, and electricity market price under extreme weather scenarios, multiple revenue scenarios are constructed, and the revenue value of the virtual power plant in each scenario is calculated. Then, sort the revenue values ​​corresponding to all scenarios in ascending order to obtain the sequence:

[0072] in, This represents the total number of scenes.

[0073] Finally, based on the confidence level Take the sorted number 1 Each revenue value is used as ,Right now , This indicates rounding up to the nearest integer.

[0074] Step S3022: Determine the global objective function based on the risk assessment model, the first objective function, and the second objective function.

[0075] For example, in this embodiment of the application, the global objective function can be expressed by the following formula:

[0076] in, Represents the overall benefit. This represents the risk assessment value. This indicates the returns from carbon-green certificate joint trading. Indicates the operating revenue of the energy storage system. This represents the risk preference coefficient.

[0077] Step S3023: Solve the global objective function based on the preset optimization algorithm to obtain the target scheduling strategy.

[0078] For example, in this embodiment, the preset optimization algorithm may include the Artificial Lemmings Algorithm (ALA). Under extreme weather conditions, market volatility can be very high, and virtual power plants must flexibly respond to market changes to maximize their economic benefits. To solve the scheduling problem of energy storage systems, this embodiment introduces the Artificial Lemmings Algorithm (ALA), which mathematically models four different behaviors of lemmings in nature, including long-distance migration, burrowing, foraging, and predator avoidance.

[0079] Two objective functions jointly characterize the comprehensive optimization objective of the virtual power plant under extreme weather conditions, encompassing both the trading and physical aspects. The carbon-green certificate joint trading objective function quantifies carbon trading revenue and green certificate revenue (and can incorporate CVaR risk measurement to reflect the uncertainties brought about by extreme weather and market fluctuations). The energy storage dispatch objective function quantifies the operating revenue and costs of energy storage in multiple markets such as energy / peak shaving / reactive power. ALA, as a unified solver, encodes the carbon-green certificate trading decision variables and the energy storage and multi-market participation decision variables into the same high-dimensional candidate solution. Under the constraints of power balance, SOC evolution, and output boundary, it calculates the combined revenue from joint trading and multi-market energy storage. The "risk term (CVaR)" constitutes the fitness function for iterative search. During the solution process, ALA achieves global exploration of the multi-market strongly coupled nonlinear solution space through long-distance migration and burrowing, achieves fine development of the neighborhood of the optimal solution through the foraging spiral, and achieves escape and re-search by using the Levy flight predator avoidance mechanism when extreme weather causes the objective function to change shape abruptly or fall into local optima. In this way, the optimal solution of carbon-green certificate trading strategy and energy storage multi-market scheduling strategy is obtained. Finally, the central coordination and control unit transforms the optimal solution into executable charging and discharging and market participation instructions and issues them in real time.

[0080] The initialization operation of an artificial lemming population can be represented as:

[0081] In the formula: The initial lemming population location matrix;z i,j Let be the position value of the i-th lemming individual in the j-th dimension; rand is a random value in the range [0,1]. LB j Let be the lower bound of the j-th dimension; UB j Let be the upper bound of the j-th dimension; N Population size; Dim Dimensions of the search space.

[0082] 1) Exploration Phase: When lemmings experience food shortages due to overpopulation during long-distance migration, they will randomly embark on long-distance migrations. At this time, lemmings will explore and search their space based on their current location and the locations of random individuals within the population, seeking food-rich habitats to obtain better survival conditions and resources. It is important to note that the direction and distance of lemming migration are not fixed and are influenced by various factors such as the ecological environment. The following equation is provided to simulate this behavior:

[0083] In the formula: This represents the position of the i-th search entity at iteration t+1; This represents the current optimal solution; F This is the direction adjustment factor; A vector of random numbers representing Brownian motion; a vector The vector is a random element of dimension 1×Dim. This vector controls the movement of the current best individual and random individuals in the population and is used to represent the interaction between individuals during the migration process. This represents the current position of the i-th searched individual; This refers to a search individual randomly selected from the population; a It is an integer index between 1 and n.

[0084] Another behavior of lemmings is digging complex tunnels in their habitat to provide themselves with safe hiding places and food storage spaces. They will randomly dig new burrows based on the existing burrow locations and the location of any individual in the population. This practice helps them quickly escape the threat of predators and find food more efficiently. The formula for updating burrow locations is as follows:

[0085] In the formula: L It is a random number related to the current iteration number; This indicates a search individual randomly selected from the population; b It is a random integer index value between 1 and n. L and Used to describe the interactions between individual lemmings when digging new burrows.

[0086] 2) Development Stage: Lemmings use their keen sense of smell and hearing to locate food sources by moving extensively and randomly within their burrows. Based on the abundance and availability of food, they generally establish a small foraging range within their habitat, and will randomly roam within this range to obtain more food. The model for this stage employs a spiral winding mechanism, as detailed below:

[0087]

[0088] In the formula: s A function to simulate a lemming spiraling around its optimal position; r The radius of the helix; Z best,j(t) Z represents the j-th dimension component of the optimal lemming position at time t; i,j (t) represents the j-th dimension component of the i-th lemming position at time t. rand It is a random number uniformly distributed within the interval [0, 1].

[0089] The core of the final stage of modeling is the lemming's avoidance and self-protection behavior when encountering danger. These burrows serve as hiding places for lemmings. If they detect a predator, they use their unique running ability to return to the burrow and will also perform deceptive maneuvers to escape the predator's pursuit. The corresponding mathematical expression is:

[0090] In the formula: W This is the dynamic escape gain, whose value decreases as the iteration progresses, used to quantify the decay of the probability of an individual lemming escaping predation; Levy (x) is the Lévy flight function, reflecting the escape trajectory; t is the current iteration number; T max This represents the maximum number of iterations.

[0091] This algorithm can search for the optimal solution globally and quickly adjust the scheduling strategy to ensure that the virtual power plant can respond quickly and optimize resource allocation under extreme weather conditions.

[0092] Step S303 involves coordinated control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume. For details, please refer to [link to relevant documentation]. Figure 2 Step S303 of the illustrated embodiment will not be described again here.

[0093] In some alternative implementations, the method further includes switching the photovoltaic converter from constant power factor mode to grid voltage support mode when grid voltage fluctuations are detected.

[0094] For example, in this embodiment of the application, under extreme weather conditions, when the grid voltage fluctuates, the photovoltaic converter will switch from constant power factor mode to grid voltage support mode.

[0095] In some alternative implementations, the method further includes adjusting the flexible load when the power grid load change is detected to meet preset conditions.

[0096] For example, in this application embodiment, the responsiveness of flexible loads is adjusted according to changes in grid load and market signals, and the system's revenue is maximized in multiple markets.

[0097] This embodiment provides a virtual power plant collaborative control method for extreme weather conditions, which can be used in the aforementioned server. Figure 4 This is a flowchart of a virtual power plant collaborative control method for extreme weather according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: When extreme weather is predicted for the target period, acquire the following information for the target period: grid load, photovoltaic power output, wind power output, carbon trading price, green certificate trading price, electricity purchase price, electricity sales price, energy storage system operating cost, and preset constraints. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0098] Step S402: Solve the pre-constructed first and second objective functions based on preset constraints to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine unit output information and carbon trading price information. The gas turbine unit output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0099] Step S403: Coordinate the control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume.

[0100] Specifically, step S403 includes: Step S4031: Schedule the energy storage system based on the energy storage charging power and energy storage discharging power during the target time period.

[0101] For example, based on the energy storage charging and discharging power during the target period, and combined with the current grid operating conditions and market signals, control commands are issued to the energy storage converter. During periods of surplus wind and solar power output or low electricity market purchase prices, the energy storage system is charged at the target charging power to absorb clean energy or store low-priced electricity. During periods of insufficient wind and solar power output, peak grid load, or peak electricity prices, the energy is released at the target discharging power to supplement the power gap or obtain peak-valley price difference revenue. At the same time, the operating parameters such as the state of charge, voltage, and current of the energy storage system are monitored in real time to ensure that the charging and discharging process strictly follows safety constraints such as power limits and upper and lower limits of the state of charge, avoiding overcharging, over-discharging, or power exceeding limits. While ensuring the safe and stable operation of the energy storage system itself, the fluctuations in wind and solar power output under extreme weather conditions are smoothed out, and the power balance between the virtual power plant and the grid is maintained.

[0102] Step S4032: Schedule the carbon allowance purchase volume for the target period based on the carbon allowance trading volume.

[0103] For example, the trading logic is determined by combining the carbon emission forecast results of gas turbine units: if the target trading volume is positive, a corresponding number of carbon allowance purchase instructions are submitted to the carbon trading market to make up for the carbon emission gap generated by the gas turbine unit's power generation, thus meeting carbon emission compliance requirements and avoiding penalties for exceeding emission limits; if the target trading volume is negative, a corresponding number of carbon allowance sale instructions are submitted to realize the surplus allowances in the market to obtain economic benefits. During the transaction execution process, the constraint matching relationship between the carbon allowance holdings and the total emissions is simultaneously verified to ensure that the virtual power plant still meets the carbon market compliance requirements after the transaction. At the same time, the transaction results are coordinated with energy storage dispatch, wind and solar power output regulation, and other links to ensure the synergistic achievement of the virtual power plant's economic benefits and low-carbon compliance goals under extreme weather conditions.

[0104] Step S4033: Schedule the transaction volume of green certificates for the target period based on the transaction volume of green certificates.

[0105] For example, based on the target trading volume of green certificates, corresponding trading instructions are issued to the green certificate trading platform. The buy or sell operation of green certificates is determined according to the trading volume value and executed. At the same time, the matching and verification are carried out in combination with the wind and solar renewable energy power generation in the virtual power plant. Under the premise of meeting the green certificate trading rules, the precise scheduling of green certificates is achieved, taking into account both low-carbon compliance and trading revenue.

[0106] This embodiment also provides a virtual power plant collaborative control device for extreme weather conditions. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0107] This embodiment provides a virtual power plant collaborative control device for extreme weather conditions, such as... Figure 5 As shown, it includes: The acquisition module 501 is used to acquire grid load information, photovoltaic power output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating cost, and preset constraints when extreme weather is predicted to occur during the target period. The preset constraints include power balance constraints, which stipulate that the grid load is determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output. The solver module 502 is used to solve the pre-constructed first objective function and second objective function based on preset constraints to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine output information and carbon trading price information. The gas turbine output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs. The control module 503 is used to coordinate the control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume.

[0108] In some alternative implementations, the solver module 502 includes: The acquisition submodule is used to acquire the risk assessment model, which is used to characterize the relationship between scheduling strategies and conditional value of risk. The first determination submodule is used to determine the global overall objective function based on the risk assessment model, the first objective function, and the second objective function. The solution submodule is used to solve the global objective function based on a preset optimization algorithm to obtain the target scheduling strategy.

[0109] In some optional implementations, the acquisition submodule includes: The acquisition unit is used to acquire the range information of the input parameters, which include wind and solar power output parameters, grid load parameters, price parameters, and extreme weather parameters. The first determining unit is used to determine the parameter information of multiple scenarios based on the interval information of the input parameters, and the parameter information of different scenarios is different; The second determining unit is used to determine the total revenue calculation model for each scenario based on the parameter information of each scenario. The total revenue calculation model is used to characterize the relationship between the total revenue and the scheduling strategy. The third determining unit is used for the total revenue calculation model based on multiple scenarios and the risk assessment model with pre-set reliability.

[0110] In some alternative implementations, the control module 503 includes: The first scheduling submodule is used to schedule the energy storage system based on the energy storage charging power and energy storage discharging power during the target time period; The second scheduling submodule is used to schedule the carbon quota purchase volume for the target period based on the carbon quota trading volume. The third scheduling submodule is used to schedule the transaction volume of green certificates for a target period based on the transaction volume of green certificates.

[0111] In some alternative embodiments, the above-described apparatus further includes: The switching module is used to switch the photovoltaic converter from constant power factor mode to grid voltage support mode when grid voltage fluctuations are detected.

[0112] In some alternative embodiments, the above-described apparatus further includes: The adjustment module is used to adjust the flexible load when the power grid load change is detected to meet preset conditions.

[0113] The virtual power plant collaborative control device for extreme weather provided in this embodiment of the invention can execute the virtual power plant collaborative control method for extreme weather provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0114] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0115] The following is a detailed reference. Figure 6This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0116] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0117] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the virtual power plant collaborative control method for extreme weather according to embodiments of the present invention.

[0118] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0119] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the virtual power plant collaborative control method for extreme weather shown in the above embodiments is implemented.

[0120] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0121] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A virtual power plant collaborative control method for extreme weather, characterized in that, The method includes: When extreme weather is predicted for a target period, the system acquires grid load information, photovoltaic power output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating cost, and preset constraints for that period. The preset constraints include power balance constraints, whereby the grid load is determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output. Based on the preset constraints, the pre-constructed first objective function and second objective function are solved to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine output information and carbon trading price information. The gas turbine output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs. The virtual power plant is controlled collaboratively based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume.

2. The method according to claim 1, characterized in that, The step of solving the pre-constructed first objective function and second objective function based on the preset constraints to obtain the target scheduling strategy includes: Obtain a risk assessment model, which is used to characterize the relationship between scheduling strategies and conditional value of risk; The global objective function is determined based on the aforementioned risk assessment model, the first objective function, and the second objective function. The global objective function is solved based on a preset optimization algorithm to obtain the target scheduling strategy.

3. The method according to claim 2, characterized in that, The risk assessment model includes: Obtain the range information of the input parameters, which include wind and solar power output parameters, grid load parameters, price parameters, and extreme weather parameters; Based on the range information of the input parameters, parameter information for multiple scenarios is determined, and the parameter information for different scenarios is different; Based on the parameter information of each scenario, a total revenue calculation model for the corresponding scenario is determined. The total revenue calculation model is used to characterize the relationship between total revenue and scheduling strategy. The total revenue calculation model based on multiple scenarios and the risk assessment model with pre-set reliability.

4. The method according to any one of claims 1 to 3, characterized in that, The steps for coordinated control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume include: The energy storage system is scheduled based on the energy storage charging power and energy storage discharging power during the target time period. The carbon allowance purchase volume for the target period is scheduled based on the carbon allowance trading volume. The trading volume of green certificates for a target period is scheduled based on the trading volume of green certificates.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When fluctuations in grid voltage are detected, the photovoltaic converter will switch from constant power factor mode to grid voltage support mode.

6. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When the power grid load change is detected to meet the preset conditions, the flexible load is adjusted.

7. A virtual power plant collaborative control device for extreme weather, characterized in that, The device includes: The acquisition module is used to acquire grid load information, photovoltaic power output information, wind power output information, carbon trading price information, green certificate trading price information, electricity purchase price information, electricity sales price information, energy storage system operating cost, and preset constraints when extreme weather is predicted to occur during the target period. The preset constraints include power balance constraints, which are determined based on photovoltaic power output, wind power output, energy storage charging and discharging power, and gas turbine output. The solution module is used to solve the pre-constructed first objective function and second objective function based on the preset constraints to obtain the target scheduling strategy. The target scheduling strategy includes the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume for the target time period. The first objective function is constructed with the goal of maximizing the joint trading revenue of carbon and green certificates. The joint trading revenue is calculated based on the carbon trading revenue and the green certificate trading revenue. The carbon trading revenue is determined based on the gas turbine output information and carbon trading price information. The gas turbine output information is determined based on the carbon quota trading volume. The second objective function is constructed with the goal of maximizing energy storage revenue. The energy storage revenue is calculated based on the electricity purchase price information, energy storage discharging power, electricity sales price information, energy storage charging power, and energy storage system operating costs. The control module is used to coordinate the control of the virtual power plant based on the target energy storage charging power, target energy storage discharging power, target carbon quota trading volume, and target green certificate trading volume.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the virtual power plant collaborative control method for extreme weather as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the virtual power plant collaborative control method for extreme weather as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the virtual power plant collaborative control method for extreme weather as described in any one of claims 1 to 6.

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