Virtual power plant optimal scheduling method and device, electronic equipment and storage medium

Abstract

The invention provides a virtual power plant optimal scheduling method and device, electronic equipment and a storage medium, and can be applied to the technical field of virtual power plants. The method comprises the following steps: acquiring resource data of distributed energy coordinated and managed by a virtual power plant and transaction data of various markets participated by the virtual power plant; constructing a power plant transaction model of the virtual power plant based on the resource data and the transaction data; based on the zero-sum game model and adjustable robust optimization, constructing a scheduling model with output maximization of the power plant transaction model as a target and constraint conditions of the scheduling model; and solving the scheduling model based on a genetic algorithm to obtain a scheduling strategy of the virtual power plant. According to the method, the influence of the carbon trading market on the virtual power plant is fully considered, the resource configuration of the virtual power plant is optimized, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plants, and in particular to a virtual power plant optimization scheduling method, device, electronic device and storage medium. Background Art

[0002] Virtual Power Plants (VPP) is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy sources such as distributed power sources, energy storage systems, and controllable loads, so as to participate in the power market and power grid operation as a special power plant. Virtual power plants can participate in power market transactions as a special power plant and optimize resource allocation based on market price data and supply and demand conditions.

[0003] Under the new round of power system reform, in the future power market, virtual power plants will inevitably consider multiple types of trading markets to enhance their competitive advantages and flexibility. Virtual power plants participating in the carbon trading market can effectively promote energy consumption, optimize resource allocation, and promote the development of a low-carbon economy.

[0004] However, existing virtual power plants have not given sufficient consideration to the carbon trading market, and the scheduling strategies of virtual power plants participating in the market have not fully considered the impact of carbon-electricity coupling on their resource scheduling, resulting in unreasonable resource allocation in virtual power plants. Summary of the invention

[0005] The present application provides a virtual power plant optimization scheduling method, device, electronic device and storage medium to solve the technical problem that existing virtual power plants do not give enough consideration to the carbon trading market, resulting in unreasonable resource allocation in virtual power plants.

[0006] According to the first aspect disclosed in the present application, the present application provides a virtual power plant optimization scheduling method, comprising:

[0007] Obtain resource data of distributed energy coordinated and managed by a virtual power plant, as well as transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak-shaving market, and output data of the carbon trading market;

[0008] Based on the resource data and the transaction data, construct a power plant transaction model of the virtual power plant;

[0009] Based on the zero-sum game model and adjustable robust optimization, a dispatch model with the goal of maximizing the output of the power plant trading model and constraints of the dispatch model are constructed;

[0010] The scheduling model is solved based on a genetic algorithm to obtain a scheduling strategy for the virtual power plant.

[0011] In a feasible implementation manner, the method further includes:

[0012] Based on the carbon emission quota allocation mechanism of the baseline method, a carbon emission quota model of the virtual power plant is constructed;

[0013] Based on the proportional relationship between carbon emissions and the unit output of the gas turbine, an actual carbon emission model of the virtual power plant is constructed;

[0014] Based on the carbon emission quota model and the actual carbon emission model, a ladder carbon trading model of the virtual power plant is constructed; wherein the ladder carbon trading model is used to represent the output data of the virtual power plant in the carbon trading market when the difference between the carbon emission quota and the actual carbon emissions of the virtual power plant is at different carbon emission interval lengths.

[0015] In a feasible implementation, the ladder carbon trading model satisfies the following formula:

[0016]

[0017] in, represents the output data of the carbon trading market, represents the actual carbon emission model, represents the carbon emission quota of the virtual power plant, represents the carbon emission length interval, Indicates the basic purchase price data of carbon emission quotas, Indicates the basic selling price data of carbon emission quotas, and Indicates the tiered purchase price data of carbon emission quotas, and It represents the tiered selling price data of carbon emission quotas, and α and δ represent floating coefficients.

[0018] In a feasible implementation, the carbon emission quota model satisfies the following formula:

[0019]

[0020] in, represents the power generation of distributed energy m in the virtual power plant at time t, v represents the carbon emission coefficient per unit of electricity, T represents the total number of scheduling periods, and M represents the total number of distributed energy resources;

[0021] The actual carbon emission model satisfies the following formula:

[0022]

[0023] in, represents the carbon emission factor, represents the output of the gas turbine in the electricity market at time t, It represents the output of the gas turbine in the peak-shaving market at time t.

[0024] In a feasible implementation manner, the scheduling model satisfies the following formula:

[0025]

[0026]

[0027] in, represents the maximum output data of the virtual power plant, represents the output data of the virtual power plant, represents the output data of the electric energy market, represents the output data of the peak-shaving market, represents the output data of the carbon trading market, represents the power generation cost data of gas turbines, represents the dispatch cost data of flexible load, represents the energy storage cost data of the energy storage system, x, u, y represent the pre-decision variable, uncertainty variable and re-decision variable respectively, and X, U, Y represent the decision space of pre-decision, output uncertainty data and re-decision respectively.

[0028] In a feasible implementation manner, the output uncertainty data satisfies the following formula:

[0029]

[0030] Among them, U represents the box uncertainty set of uncertainty variables, u represents the uncertainty variable, represents the actual output of the wind turbine at time t, represents the predicted output of the wind turbine at time t, represents the fluctuation range of wind turbine output at time t, , Indicates the fluctuation of wind turbines in a single time period. Represents an adjustable coefficient. By controlling the size of the adjustable coefficient, the robustness and conservatism can be changed.

[0031] In a feasible implementation manner, solving the scheduling model based on a genetic algorithm to obtain a scheduling strategy for the virtual power plant includes:

[0032] Based on the characteristic parameters of the scheduling model, a chromosome population is randomly generated; wherein the chromosome population includes a plurality of chromosomes;

[0033] According to a preset fitness function, the fitness value of each chromosome in the chromosome population is calculated; wherein the fitness function is constructed based on the power plant trading model and the penalty factor of the output uncertainty data;

[0034] Performing selection operation, crossover operation and mutation operation on the chromosome population to update the chromosome population;

[0035] The updated chromosome population is returned to the step of calculating the fitness value of each chromosome in the chromosome population, so as to repeat the updating iteration of the chromosome population until a preset maximum number of iterations is reached, and the chromosome with the largest fitness in the chromosome population is output as the scheduling strategy.

[0036] According to the second aspect disclosed in the present application, the present application provides a virtual power plant optimization scheduling device, comprising:

[0037] A power plant data acquisition module, used to acquire resource data of distributed energy coordinated and managed by a virtual power plant, and transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak load regulation market, and output data of the carbon trading market;

[0038] A transaction model building module, used to build a power plant transaction model of the virtual power plant based on the resource data and the transaction data;

[0039] A dispatch model building module, used to build a dispatch model with the output maximization of the power plant transaction model as the goal, and constraints of the dispatch model based on a zero-sum game model and adjustable robust optimization;

[0040] The scheduling strategy solving module is used to solve the scheduling model based on a genetic algorithm to obtain the scheduling strategy of the virtual power plant.

[0041] According to a third aspect disclosed in the present application, the present application provides an electronic device, including a processor, and a memory communicatively connected to the processor;

[0042] The memory stores computer-executable instructions;

[0043] The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of the first aspects.

[0044] According to the fourth aspect disclosed in the present application, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed, they are used to implement any method in the first aspect.

[0045] According to a fifth aspect disclosed in the present application, the present application provides a computer program product, including a computer program, and when the computer program is executed, it is used to implement any one of the methods in the first aspect.

[0046] Compared with the prior art, this application has the following beneficial effects:

[0047] The present application provides a virtual power plant optimization scheduling method, device, electronic device and storage medium. By fully considering the impact of the carbon trading market on the virtual power plant, a power plant trading model considering the carbon trading market is constructed, and the scheduling model is further constructed with the goal of maximizing the output of the power plant trading model. Finally, the scheduling model is solved based on a genetic algorithm to obtain the optimal scheduling strategy for the virtual power plant. Comprehensively considering the transactions of virtual power plants in the electricity-carbon-peaking market, a low-carbon and economic scheduling strategy for virtual power plants is created, which enhances its scheduling flexibility in multiple markets, improves the benefits of virtual power plants themselves, optimizes the resource allocation of virtual power plants, and improves resource utilization efficiency.

[0048] Among them, on the basis of analyzing the market structure, the ladder carbon trading model is introduced, and the ladder is simulated by the time-of-use electricity price. Compared with the carbon trading mechanism with fixed carbon price, the ladder carbon trading model can give a more detailed carbon trading model to take into account the carbon emission reduction and the benefits of virtual power plants, and achieve a win-win situation for virtual power plants in carbon emission reduction and electricity market. And considering the participation of virtual power plants in multi-class market dispatch, further considering the uncertainty of new energy power generation, the output uncertainty of wind power is modeled by adjustable robustness. Adjustable robustness can not only consider the negative impact caused by the uncertainty of new energy processing, but also change the conservatism of the original robust optimization. Then, through the idea of ​​engineering game, the zero-sum game idea of ​​engineering game theory is introduced, the uncertainty data is rationalized as the game subject, and the zero-sum game model between uncertainty data and virtual power plants participating in multi-class markets is constructed. Combined with adjustable robustness, a two-stage min max min dispatch model is constructed. For the non-convex problem of the dispatch model, a genetic algorithm is introduced to solve it, and a dispatch strategy that can effectively optimize the resource allocation of virtual power plants is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] Figure 1A schematic diagram of a system architecture of a virtual power plant provided in an embodiment of the present application;

[0051] Figure 2 A flowchart of a virtual power plant optimization scheduling method provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of a step-by-step carbon price data for a carbon emission quota provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of forecast data of wind power and flexible load at different time periods provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of electricity price data of a distribution network at different time periods provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of the dispatch results of a virtual power plant in various markets in a different scenario provided in an embodiment of the present application;

[0056] Figure 7 A schematic diagram of the dispatch results of a virtual power plant in various markets under scenarios 3 and 4 provided in an embodiment of the present application;

[0057] Figure 8 A schematic diagram of internal output distribution of a virtual power plant in scenario 4 provided in an embodiment of the present application;

[0058] Fig. 9 A schematic diagram of peak load distribution of a virtual power plant in scenario 4 provided in an embodiment of the present application;

[0059] Fig.10 A schematic diagram of the structure of a virtual power plant optimization scheduling device provided in an embodiment of the present application;

[0060] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0061] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] Virtual Power Plants (VPP) is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy sources such as distributed power sources, energy storage systems, and controllable loads, so that it can participate in the power market and power grid operation as a special power plant.

[0064] As a system that integrates distributed energy through software and intelligent technology, virtual power plants do not rely on traditional centralized power generation facilities. Instead, they aggregate, coordinate and optimize distributed power sources, energy storage, adjustable loads and other resources through advanced information and communication technology and automation technology. Virtual power plants can participate in power market transactions as a special power plant and optimize resource allocation according to market price data grids and supply and demand conditions. Therefore, when participating in market transactions, virtual power plants need to formulate effective scheduling strategies to cope with fluctuations in market price data grids and changes in supply and demand.

[0065] Due to the new round of power system reform, virtual power plants in the future power market will inevitably consider multiple types of trading markets to enhance their competitive advantages and flexibility. Among them, the carbon trading market is a market formed by artificial regulations. Its core is to buy and sell the emission rights of greenhouse gases such as carbon dioxide as commodities to achieve the goal of global greenhouse gas emission reduction. If the virtual power plant participates in the carbon trading market and the virtual power plant can receive the market information released by the power trading center in a timely and effective manner, then the market trading behavior of the virtual power plant needs to consider the coupling of the carbon trading market with the electric energy market and the peak-shaving market, and formulate a reasonable scheduling plan. If the operator of the virtual power plant believes that it can make more profits in the electric energy market, it will allocate more regulation capacity to provide conventional power sales services; if it believes that it can make more profits in the carbon trading market, it will reduce the output of fossil energy units in the electric energy market; if it believes that the peak-shaving market makes more profits, it will reduce the capacity of the electric energy market.

[0066] However, existing virtual power plants have not given enough consideration to the carbon trading market. The scheduling strategies of virtual power plants participating in the market have not fully considered the impact of carbon-electricity coupling on the allocation of their output data resources, and have not considered the linkage between carbon price data and electricity price data. It is impossible to achieve the coordinated transactions of virtual power plants in the electricity and carbon markets, and it is difficult to effectively guarantee the output income and resource utilization efficiency of virtual power plants when scheduling resources in virtual power plants, resulting in unreasonable resource allocation of virtual power plants.

[0067] In response to the above technical problems, this application proposes a virtual power plant optimization scheduling method. By fully considering the impact of the carbon trading market on the virtual power plant, the optimal resource scheduling strategy of the virtual power plant is determined, thereby optimizing the resource allocation of the virtual power plant and improving resource utilization efficiency.

[0068] Next, combine Figure 1 , the system architecture of the virtual power plant involved in the virtual power plant optimization scheduling method of this application is explained.

[0069] Figure 1 A schematic diagram of the system architecture of a virtual power plant provided in an embodiment of the present application. Figure 1 The system architecture includes virtual power plants, distributed energy resources coordinated and managed by virtual power plants, and multiple markets in which virtual power plants participate. Distributed energy resources and multiple markets can interact with virtual power plants. Distributed energy resources include gas turbines, wind turbines, energy storage systems, and flexible loads. Multiple markets include electric energy markets, peak load regulation markets, and carbon trading markets.

[0070] Specifically, as a controllable distributed power source in a virtual power plant, a gas turbine mainly uses natural gas as fuel and converts the chemical energy in other combustible gases into mechanical energy, which in turn drives the generator to generate electricity. It has the characteristics of stable and controllable output, fast start and stop and ramp speed, which can smooth load fluctuations and improve the benefits of virtual power plants.

[0071] Specifically, wind turbines are an important representative of clean energy in virtual power plants. They use wind energy to generate electricity, which has the advantages of being renewable and pollution-free. Through coordinated optimization with other distributed energy sources, the uncertainty and volatility of wind turbine output can be reduced, and the stability of the power system can be improved.

[0072] Specifically, energy storage systems play a vital role in virtual power plants, smoothing the intermittent output of renewable energy and ensuring the stable operation of the power system. At the same time, energy storage systems can also serve as backup power sources to provide emergency power supply in the event of grid failure or power shortage.

[0073] Specifically, flexible loads in virtual power plants mainly realize flexible scheduling and management of loads. Through advanced control, metering and communication technologies, virtual power plants can aggregate flexible loads with different characteristics, such as controllable loads, charging pile networks, microgrids, etc., to achieve large-scale coordinated and optimized operation.

[0074] Specifically, the peak-shaving market refers to a market mechanism that balances power supply and demand by adjusting the output of generators according to the peak-valley changes in the power system load, thereby maintaining the safe and stable operation of the power system. Peak-shaving is an important task in the operation of the power system, which aims to balance the supply and demand of power and ensure the safe and stable operation of the power grid. Since renewable energy power generation (such as wind power and photovoltaic power generation) is random, intermittent and volatile, its power generation is difficult to accurately predict and regulate, so it is necessary to rely on peak-shaving resources to balance the supply and demand of the power grid. Virtual power plants aggregate distributed power sources, energy storage systems, controllable loads and other resources to form a flexibly dispatchable power resource pool to provide peak-shaving services for the power grid. Among them, the peak-shaving market is divided into the valley-filling market and the peak-shaving market.

[0075] Specifically, the electric energy market is one of the core markets in the power market, mainly involving the buying and selling of electric energy. As a special power trading entity, virtual power plants can participate in the electric energy market to achieve optimal allocation and efficient use of electric energy. Virtual power plants can form tradable power products by predicting and aggregating the power generation of distributed resources, and conduct buying and selling transactions in the electric energy market. Among them, the electric energy market is divided into the day-ahead electric energy market and the real-time electric energy market.

[0076] Specifically, the carbon trading market is one of the effective means to control greenhouse gas emissions, and it promotes emission reduction and low-carbon development through market mechanisms. As a new type of market entity, virtual power plants can participate in the carbon trading market, obtain carbon emission reductions through energy conservation, emission reduction and environmental protection measures, and trade them in the market. Virtual power plants can obtain carbon emission reductions by optimizing resource allocation and operation scheduling, reducing fossil energy consumption and pollutant emissions. These carbon emission reductions can be traded in the carbon trading market, bringing additional economic output data to virtual power plants and motivating them to continue to promote green and low-carbon development.

[0077] It should be noted that Figure 2 This is only a schematic diagram of a system architecture provided in the embodiment of the present application. Figure 2 The actual form of the various devices included in the Figure 2 The interaction mode or connection mode between various devices is limited, and in the specific application of the technical solution, it can be set according to actual needs.

[0078] In combination with the system architecture of the virtual power plant described above, the technical solution of a virtual power plant optimization scheduling method provided by this application is described in detail below through a specific embodiment. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar content may not be repeated in different embodiments.

[0079] Figure 2 A flowchart of a virtual power plant optimization scheduling method provided in an embodiment of the present application is shown in FIG. Figure 2 In some embodiments, the process of the virtual power plant optimization scheduling method includes the following steps:

[0080] S201, obtaining resource data of distributed energy coordinated and managed by the virtual power plant, as well as transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak-shaving market, and output data of the carbon trading market.

[0081] Among them, the virtual power plant can collect the resource data of distributed energy and the transaction data of the trading market in real time to prepare data for subsequent scheduling. Specifically, the resource data includes the relevant data of the distributed resources within the virtual power plant, and the transaction data includes the relevant data of the trading market in which the virtual power plant participates.

[0082] S202: construct a power plant transaction model for the virtual power plant based on resource data and transaction data.

[0083] Among them, the output data model of the virtual power plant needs to consider the output data in each trading market and the cost data of each distributed energy. Specifically, the power plant trading model satisfies the following formula:

[0084]

[0085] in, represents the output data of the virtual power plant, represents the output data of the electric energy market, represents the output data of the peak-shaving market, represents the output data of the carbon trading market, represents the power generation cost data of gas turbines, represents the dispatch cost data of flexible load, Represents the energy storage cost data of the energy storage system.

[0086] Specifically, when the output data of the energy market is positive, it indicates that the cost of electricity purchased by the virtual power plant from the electric energy market exceeds the output data of electricity sold, and the virtual power plant needs to purchase electricity from the electric energy market to maintain normal operation. When it is negative, it indicates that the output data of electricity sold by the virtual power plant to the electric energy market exceeds the cost of electricity purchased. At this time, the electric energy market is an output data market for the virtual power plant. Similarly, when the output data of the carbon trading market is positive, it means that the carbon emissions of the virtual power plant exceed the prescribed carbon emission quota, and it is necessary to purchase carbon emission quotas from the carbon trading market. At this time, the output data of the carbon trading market becomes the cost data that the virtual power plant needs to pay for operation. When the output data of the carbon trading market is negative, it indicates that the carbon emission quota of the virtual power plant exceeds the actual carbon emissions. The virtual power plant can sell excess carbon quotas to the carbon trading market to obtain income. At this time, the carbon trading market is an income market for the virtual power plant.

[0087] S203, based on the zero-sum game model and adjustable robust optimization, construct a dispatch model with the goal of maximizing the output of the power plant trading model and the constraints of the dispatch model.

[0088] Among them, zero-sum game is also called zero-sum game. In contrast to non-zero-sum game, it is a concept in game theory and belongs to non-cooperative game. It refers to the parties involved in the game. Under strict competition, the output data of one party must mean the loss of the other party. The sum of the output data and losses of the parties in the game is always "zero", so there is no possibility of cooperation between the two parties. From the perspective of game theory, the system decision maker is most concerned about the impact of uncertainty on its output data, that is, whether the scheduling plan can still obtain higher output data under uncertainty factors, and the uncertainty of nature tries to degrade its output data. From this point of view, the uncertainty of nature is rationalized as a virtual player, and the uncertainty of the system decision maker and nature can be modeled as a two-person zero-sum game model.

[0089] Adjustable robust optimization (ARO) is an optimization method for dealing with uncertainty, which allows the subsequent decisions to be adjusted according to the partial disclosure of uncertainty (i.e., new information obtained after the first stage of decision making) during the decision-making process. Adjustable robust optimization is usually divided into two stages: the first stage is to make a preliminary decision before the uncertainty is fully revealed, and the second stage is to adjust the preliminary decision according to the new information. Therefore, combined with the two-stage decision modeling process of adjustable robustness, the general form of the zero-sum game model satisfies the following formula:

[0090]

[0091] Among them, x, u, y represent the pre-decision variable, uncertainty variable and re-decision variable respectively, and X, U, Y represent the decision space of pre-decision, uncertainty data and re-decision respectively. represents the output data function, represents an inequality constraint, Represents an equality constraint.

[0092] Specifically, the decision order of min-max-min means that x first gives a strategy, then the uncertainty u gives the worst scenario, and finally y makes a decision.

[0093] Preferably, based on the zero-sum game model and adjustable robust optimization, the scheduling model satisfies the following formula:

[0094]

[0095]

[0096] in, represents the maximum output data of the virtual power plant, represents the output data of the virtual power plant, represents the output data of the electric energy market, represents the output data of the peak-shaving market, represents the output data of the carbon trading market, represents the power generation cost data of gas turbines, represents the dispatch cost data of flexible load, represents the energy storage cost data of the energy storage system, x, u, y represent the pre-decision variable, uncertainty variable and re-decision variable respectively, and X, U, Y represent the decision space of pre-decision, output uncertainty data and re-decision respectively.

[0097] Specifically, the pre-decision variables of the virtual power plant are composed of the start and stop variables of the controllable units, the charging and discharging status of the energy storage, and the power purchase and sales status. The re-decision variables are composed of the output of each unit, the power market purchase and sales amount, the peak-shaving dispatching amount and the carbon emissions.

[0098] Preferably, the output data of the carbon trading market is obtained through a ladder carbon trading model. The construction process of the ladder carbon trading model includes:

[0099] Step 1: Construct a carbon emission quota model for virtual power plants based on the carbon emission quota allocation mechanism of the baseline method.

[0100] Among them, due to the actual situation in my country, free allocation based on the baseline method is widely used to provide producers with carbon emission quotas. This article adopts free allocation, that is, free carbon emission quotas are pre-allocated to producers. And from the distributed energy aggregated by the virtual power plant, it can be seen that the main source of carbon emissions is gas turbines. Therefore, specifically, the carbon emission quota model satisfies the following formula:

[0101]

[0102] in, It represents the power generation of distributed energy m in the virtual power plant at time t, v represents the carbon emission coefficient per unit of electricity, T represents the total number of scheduling periods, and M represents the total number of distributed energy.

[0103] Specifically, the baseline method refers to sorting the carbon emissions per unit product of enterprises from small to large, and selecting a certain proportion of emissions (such as the top 10% of enterprises) as the baseline. This baseline represents the best practice level of carbon emissions per unit product in the industry. The company's output is multiplied by the baseline to determine the company's free quota, which means that the company's carbon emissions quota will be related to its production scale and the best practice level in the industry.

[0104] Step 2: Based on the proportional relationship between carbon emissions and the unit output of the gas turbine, an actual carbon emission model of the virtual power plant is constructed.

[0105] Among them, the actual carbon emissions of the virtual power plant are proportional to the output of the gas turbine unit. The greater the output of the gas turbine unit, the greater the actual carbon emissions.

[0106] Specifically, the actual carbon emission model satisfies the following formula:

[0107]

[0108] in, represents the carbon emission factor, represents the output of the gas turbine in the electricity market at time t, It represents the output of the gas turbine in the peak-shaving market at time t.

[0109] Step 3: Construct a ladder carbon trading model for the virtual power plant based on the carbon emission quota model and the actual carbon emission model; wherein the ladder carbon trading model is used to represent the output data of the virtual power plant in the carbon trading market when the difference between the carbon emission quota and the actual carbon emissions of the virtual power plant is at different carbon emission interval lengths.

[0110] In the carbon trading market, there are two trading situations for operators of virtual power plants. One is that the actual carbon emissions of virtual power plants during actual operation are higher than the carbon emission quota, and they need to purchase excess carbon emissions from the carbon trading market; the other is that the actual carbon emissions are lower than the carbon emission quota, and they can sell the excess quota to the carbon trading market. Therefore, in order to more accurately promote the effect of carbon emission reduction, the output data of carbon trading is stepped.

[0111] Specifically, the ladder carbon trading model satisfies the following formula:

[0112]

[0113] in, represents the output data of the carbon trading market, represents the actual carbon emission model, represents the carbon emission quota of the virtual power plant, represents the carbon emission length interval, Indicates the basic purchase price data of carbon emission quotas, Indicates the basic selling price data of carbon emission quotas, and Indicates the tiered purchase price data of carbon emission quotas, and It represents the tiered selling price data of carbon emission quotas, and α and δ represent floating coefficients.

[0114] Among them, see Figure 3 From the diagram of the step-by-step carbon price data of carbon emission quotas, we can see that the first quadrant represents that the actual carbon emissions of the virtual power plant are less than the carbon emission quota. At this time, the virtual power plant can sell the carbon emission quota to the carbon trading market to profit; the third quadrant represents that the actual carbon emissions of the virtual power plant are greater than the carbon emission quota, and it is necessary to purchase carbon emission quotas from the carbon trading market.

[0115] Preferably, the output uncertainty data satisfies the following formula:

[0116]

[0117] Among them, U represents the box uncertainty set of uncertainty variables, u represents the uncertainty variable, represents the actual output of the wind turbine at time t, represents the predicted output of the wind turbine at time t, represents the fluctuation range of wind turbine output at time t, , Indicates the fluctuation of wind turbines in a single time period. Represents an adjustable coefficient. By controlling the size of the adjustable coefficient, the robustness and conservatism can be changed.

[0118] Among them, the output of wind turbines is affected by the weather, the prediction accuracy is low, and it has strong volatility. Random programming cannot guarantee the safety of extreme scenarios, so adjustable robustness is used to characterize the uncertainty of wind turbine output. Specifically, a box-type uncertainty set is used.

[0119] Specifically, the output data of the peak load market satisfies the following formula:

[0120]

[0121]

[0122]

[0123] Where T represents the total number of scheduling periods, It indicates the dispatching power of the virtual power plant participating in peak load regulation. It indicates the dispatching power of the virtual power plant to fill the valley. and They represent the peak shaving price data and valley filling price data of the peak shaving market respectively. , , and They represent the peak shaving power of wind power, the peak shaving power of gas turbines, the peak shaving power of flexible loads and the peak shaving power of energy storage. and They represent the valley filling power of flexible load and the valley filling power of energy storage respectively.

[0124] Specifically, the output data of the electric energy market satisfies the following formula:

[0125]

[0126] in, and They represent the purchase price data and sales price data of the distribution network respectively. and They represent the purchased electricity and sold electricity of the virtual power plant respectively. It indicates the dispatching power of virtual power plant participating in valley filling.

[0127] Specifically, the power generation cost data of the gas turbine satisfies the following formula:

[0128]

[0129] in, and represents the cost factor of the gas turbine, represents the output of gas turbines in the electric energy market, Represents the output of gas turbines in the peak-shaving market.

[0130] Specifically, the load response cost data can be translated to meet the following formula:

[0131]

[0132] in, represents the demand response compensation price data grid, represents the power of flexible load reduction of the virtual power plant at time t, Indicates the load increase, and They represent the peak shaving power and valley filling power of flexible load respectively.

[0133] Specifically, the energy storage cost data of the energy storage system satisfies the following formula:

[0134]

[0135] in, Represents the unit energy storage charging and discharging cost data, and They represent the energy storage discharge and energy storage charge respectively. and They represent the peak shaving power and valley filling power of energy storage respectively.

[0136] Preferably, the constraints include gas turbine constraints, wind power constraints, energy storage constraints, flexible load constraints, power purchase and sales constraints, and power balance constraints.

[0137] Specifically, the wind power constraint satisfies the following formula:

[0138]

[0139]

[0140]

[0141] in, It represents the electricity market output of the wind turbine at time t, and SP is the opening period of the peak load market.

[0142] Among them, wind power constraints construct the feasible domain of wind power output in the peak-shaving market and the electric energy market, that is, the output of wind power in each market cannot exceed the forecast value of the corresponding period, and the sum of the output of the two periods cannot exceed the output forecast value of the corresponding period.

[0143] Specifically, the gas turbine constraints are as follows:

[0144]

[0145]

[0146]

[0147] in, and They represent the minimum output limit and maximum output limit of the gas turbine respectively. Represents the output of gas turbines in the electrical energy market.

[0148] Among them, the gas turbine constraints construct the feasible domain of gas turbine output in the peak-shaving market and the electric energy market, that is, the minimum output of each market must exceed the minimum technical output and be less than the maximum technical output. At the same time, the sum of the outputs of the two markets must be less than the maximum technical output and greater than the minimum technical output.

[0149] Specifically, the flexible load constraint is as follows:

[0150]

[0151]

[0152] Among them, SV represents the opening period of the valley-filling market. represents the power increase of the flexible load during period t, represents the total load before dispatch, represents the total load after flexible load scheduling, Indicates the upper limit to which the flexible load can be increased.

[0153] Among them, the flexible load constraint builds the dispatchable range of the flexible load, which means that the total electricity consumption of the flexible load cannot be changed, only the electricity consumption period can be changed. At the same time, it means that the peak shaving, valley filling electricity and the increase and decrease of the electricity market of the flexible load cannot exceed the original planned electricity consumption of the flexible load at the corresponding moment. At the same time, the reduction of the flexible load at a certain moment and the sum of the electricity participating in the peak regulation shall not be less than zero and greater than the original planned maximum electricity load. The sum of the increase and valley filling electricity of the flexible load in a certain period shall not be greater than the maximum increase of the load in this period.

[0154] Specifically, the power purchase and sales constraints are as follows:

[0155]

[0156] in, Indicates the maximum amount of electricity purchased. Indicates the maximum amount of electricity sold. Represents a Boolean variable, which represents the current power purchasing status of the virtual power plant. It is 1 when purchasing electricity from the electric energy market, otherwise it is 0.

[0157] Specifically, the energy storage constraint is as follows:

[0158]

[0159] in, Indicates the amount of energy storage discharge, represents the energy storage charge at time t, Indicates the energy storage discharge sign. Indicates the maximum discharge capacity of energy storage, Indicates the maximum charge capacity of energy storage. represents the energy storage discharge efficiency, represents the energy storage charging efficiency, S(t) represents the capacity of the energy storage at time t, and They represent the maximum and minimum capacity of energy storage respectively.

[0160] Among them, the energy storage constraint constructs the feasible domain of the charging and discharging capacity of energy storage in the electricity market and the peak-shaving market, indicating that the maximum charging and discharging capacity of energy storage in each market must not exceed the rated charging and discharging value of the energy storage itself. At the same time, due to the capacity limitations of the energy storage itself, the capacity of the energy storage is constrained to prevent charging and discharging when the energy storage capacity does not meet the charging and discharging requirements.

[0161] Specifically, the power balance constraint is as follows:

[0162]

[0163] Among them, the power balance constraint constructs the balance between power generation and power consumption in the power system.

[0164] S204, solving the scheduling model based on the genetic algorithm to obtain the scheduling strategy of the virtual power plant.

[0165] Among them, by solving the scheduling model, the optimal scheduling result of the virtual power plant under the current conditions can be obtained, and the optimal scheduling strategy can be obtained for the virtual power plant operator. The scheduling strategy includes the output results of the virtual power plant's gas turbines, wind turbines, energy storage systems, and flexible loads in each time period. The virtual power plant can determine the output of each unit of the gas turbine, wind turbine, energy storage system, and flexible load based on the output results, and guide the virtual power plant to perform internal resource allocation under the corresponding scheduling strategy.

[0166] Among them, since the above scheduling model contains nonlinear terms, a genetic algorithm is used to solve it. Each chromosome of the genetic algorithm represents the distribution of uncertainty data to uncertainty at each moment. The virtual power plant determines its own scheduling plan based on the uncertainty situation, and then feeds its own output data back to the genetic algorithm. Genetic Algorithm (GA) is a search heuristic algorithm that simulates the natural evolution process. It belongs to the broader category of evolutionary algorithms (EA). Genetic algorithms solve optimization problems by simulating natural selection and genetic mechanisms. This algorithm usually starts with a randomly generated initial population. Each individual in the population represents a potential solution. The population is iteratively updated through genetic operations (such as selection, crossover (hybridization), and mutation), thereby gradually approaching the optimal solution.

[0167] Preferably, the genetic algorithm solution process includes:

[0168] Step 1: Based on the characteristic parameters of the scheduling model, a chromosome population is randomly generated; wherein the chromosome population includes multiple chromosomes.

[0169] Therein, a random initial population is generated, and each individual in the population is represented by an encoded string (such as a binary string), which corresponds to a potential solution of the scheduling model.

[0170] Step 2: Calculate the fitness value of each chromosome in the chromosome population according to a preset fitness function; wherein the fitness function is constructed based on the power plant trading model and the penalty factor of the output uncertainty data.

[0171] Among them, the fitness of each individual in the population is calculated, that is, the quality or performance of the solution corresponding to the individual. This is usually done by defining a fitness function, which maps the solution to the problem to a scalar value used to evaluate the quality of the solution.

[0172] Specifically, the fitness function satisfies the following formula:

[0173]

[0174] in, represents the distribution value of the uncertainty of wind turbine output at time t, represents the penalty factor, Indicates the adjustable coefficient.

[0175] Step 3: Perform selection, crossover and mutation operations on the chromosome population to update the chromosome population.

[0176] Among them, according to the fitness value, individuals are selected from the current population to produce the next generation population. Individuals with high fitness are more likely to be selected, and this process simulates the principle of "survival of the fittest" in natural selection. Specifically, the selection operation can be based on roulette selection, tournament selection, random selection, etc.

[0177] Among them, the selected individuals are crossovered in pairs, and new individuals are generated by exchanging some parts of their encoding strings. This step simulates the gene recombination in biological evolution. Specifically, the crossover operation can be based on one-point crossover, multi-point crossover, uniform crossover, etc.

[0178] Among them, certain bits in the individual's encoding string are flipped (for binary encoding) or replaced (for other encoding methods) with a certain probability to introduce new genetic information. Mutation is an important means of introducing randomness in genetic algorithms, which helps to avoid premature convergence and explore new areas of the solution space. Specifically, the mutation operation can be based on substring mutation, insertion mutation, and replacement mutation.

[0179] Step 4, returning the updated chromosome population to the step of calculating the fitness value of each chromosome in the chromosome population, to repeatedly update the chromosome population until the preset maximum number of iterations is reached, and outputting the chromosome with the largest fitness in the chromosome population as the scheduling strategy.

[0180] The above steps (fitness evaluation, selection, crossover, mutation) are repeated until a certain stopping criterion is met (such as reaching the maximum number of iterations, the fitness reaching a threshold, the quality of the solution no longer significantly improving, etc.), and the chromosome with the largest fitness in the chromosome population is output as the optimal scheduling strategy for the virtual power plant.

[0181] In this embodiment, by fully considering the impact of the carbon trading market on the virtual power plant, a power plant trading model considering the carbon trading market is constructed, and a scheduling model is further constructed with the goal of maximizing the output of the power plant trading model. Finally, the scheduling model is solved based on a genetic algorithm to obtain the optimal scheduling strategy for the virtual power plant. Comprehensively considering the transactions of virtual power plants in the electricity-carbon-peaking market, a low-carbon and economic scheduling strategy for virtual power plants is created, which enhances its scheduling flexibility in multiple markets, improves the benefits of virtual power plants themselves, optimizes the resource allocation of virtual power plants, and improves resource utilization efficiency.

[0182] Among them, on the basis of analyzing the market structure, the ladder carbon trading model is introduced, and the ladder is simulated by the time-of-use electricity price. Compared with the carbon trading mechanism with fixed carbon price, the ladder carbon trading model can give a more detailed carbon trading model to take into account the carbon emission reduction and the benefits of virtual power plants, and achieve a win-win situation for virtual power plants in carbon emission reduction and electricity market. And considering the participation of virtual power plants in multi-class market dispatch, further considering the uncertainty of new energy power generation, the output uncertainty of wind power is modeled by adjustable robustness. Adjustable robustness can not only consider the negative impact caused by the uncertainty of new energy processing, but also change the conservatism of the original robust optimization. Then, through the idea of ​​engineering game, the zero-sum game idea of ​​engineering game theory is introduced, and the uncertainty data is rationalized as the game subject. A zero-sum game model between uncertainty data and virtual power plants participating in multi-class markets is constructed. Combined with adjustable robustness, a two-stage min max min dispatch model is constructed. For the non-convex problem of the dispatch model, a genetic algorithm is introduced to solve it, and a dispatch strategy that can effectively optimize the output data resource allocation of virtual power plants is obtained.

[0183] Below, a specific embodiment will be used to illustrate the exemplary application of the virtual power plant scheduling method of the present application in an actual application scenario.

[0184] 1. Parameter setting.

[0185] To verify the effectiveness of the above scheduling model, a 24-hour day is set as a scheduling cycle and 1 hour is set as a time period. Figure 4 , preset gas turbine parameters, load call cost is 100 yuan / MW, energy storage use cost is 120 yuan / MW, distribution network electricity price data is shown in Figure 5 The electricity price data of the virtual power plant is 0.9 times the electricity purchase price data. The peak-shaving electricity price data and time period are shown in Table 1. The uncertain parameters Take 6, the basic carbon price data is 50 yuan per ton, and use 4 different trading scenarios to simulate and compare the output benefits of the virtual power plant.

[0186] Scenario 1: The virtual power plant only participates in electricity market transactions.

[0187] Scenario 2: Virtual power plants participate in electricity energy market and peak load market transactions.

[0188] Scenario 3: The virtual power plant participates in carbon-electricity-peaking market transactions at the same time, and the carbon trading market does not use tiered carbon price data.

[0189] Scenario 4: The virtual power plant participates in carbon-electricity-peaking market transactions at the same time, and the carbon trading market adopts tiered carbon price data.

[0190] 2. Analysis of simulation results.

[0191] The output benefits of each scenario are shown in Table 1.

[0192] Table 1 Output benefits of virtual power plants in various scenarios

[0193]

[0194] Refer to Table 1. By comparing scenarios 1 and 2, it can be seen that the output income of scenario 2 is 26,388 yuan higher than that of scenario 1. Compared with a single market, participating in multiple markets at the same time can increase income. Since both scenarios 1 and 2 introduce carbon trading, their carbon emissions are similar.

[0195] Refer to Table 1. By comparing scenarios 2 and 3, it can be seen that the revenue of scenario 3 is 6,367 yuan higher than that of scenario 2, of which the revenue of scenario 3 in the electric energy market is 182 yuan less than that of scenario 2, the revenue of the peak-shaving market remains unchanged, and the carbon emissions are reduced by 9.94 tons. This is mainly because scenario 3 takes carbon trading into account, so the carbon emissions are reduced. The reduction in carbon emissions is mainly due to the reduction in the output of fossil fuel units (gas turbines) in the electric energy market. Since the electricity price data of the electric energy market is relatively low, it is significantly affected by carbon trading, while the peak-shaving market is less affected by carbon trading due to the high electricity price data, which also leads to the unchanged revenue of the peak-shaving market.

[0196] This shows that with the addition of the carbon trading market, the output of fossil units will be more sensitive to electricity price data. For the market, the electricity price data can be adjusted to match the carbon price data to achieve new energy consumption or flexible adjustment. When new energy is in large demand, the electricity price data is lowered to match the carbon price data. Fossil units are subject to the dual influence of electricity price data and carbon price data and will consciously reduce their output. However, when the power grid urgently needs peak-shaving resources, higher electricity price data is needed to eliminate the impact of carbon trading on the output of fossil units, which increases the cost of electricity market operators.

[0197] Refer to Table 1. By comparing scenarios 3 and 4, it can be seen that the income of scenario 4 is 1,652 yuan higher than that of scenario 3, and the carbon emissions are reduced by 4.97 tons. This is mainly because the use of ladder carbon trading has led to more severe restrictions on the output range of fossil fuel units. In scenarios 3 and 4, the income of the peak-shaving market remains unchanged. The reason is that the electricity price data of the peak-shaving market is relatively high. The power market operator considers the restrictions of the carbon trading market on fossil fuel units. In order to achieve power balance, the power market operator raises the electricity price data of the peak-shaving market, eliminating part of the impact of the carbon trading market on the output of fossil fuel units. The difference between ladder carbon trading and traditional carbon trading will be described in detail in the impact of ladder carbon trading on virtual power plant scheduling.

[0198] 3. The impact of different markets on virtual power plant dispatch.

[0199] See also Figure 6, taking scenarios 1, 2, and 3 as examples, this paper explains the impact of various trading markets on virtual power plant scheduling. Figure 6 In the data, the dispatch amount of scenario 1 in the electric energy market is less than that of scenarios 2 and 3 from time 1 to 8. This is mainly because scenarios 2 and 3 participated in the valley filling and peak load regulation market from time 1 to 8, and purchased more electricity from the electric energy market to meet the internal load and energy storage charging. Therefore, under the same power generation, scenarios 2 and 3 have more surplus electricity to sell to the market, while scenario 1 does not participate in the peak load regulation market, so it will not purchase electricity from the market. The internal power generation will give priority to satisfying the internal power balance, so the surplus power generation is less than that of scenarios 2 and 3, resulting in a decrease in the power sales of scenario 1 during this period.

[0200] At 9:00 to 12:00 and 18:00 to 21:00, since Scenarios 2 and 3 both participated in the peak-shaving market with higher electricity price data, the dispatch volume of Scenarios 1 in the electricity energy market was higher than that of Scenarios 2 and 3. In addition, since the reduction of flexible loads in the peak-shaving market is also the peak-shaving output, the dispatch volume of Scenarios 1 in the electricity energy market is lower than that of Scenarios 2 and 3 in the peak-shaving market during this period.

[0201] At 17:00, the dispatch volume of scenario 1 in the electric energy market is also lower than that of scenarios 2 and 3. This is mainly because at this moment, scenario 1 is the worst scenario, that is, the wind power output is the lower limit, while it is not the worst scenario for scenarios 2 and 3, that is, the wind power output is the predicted value, so the dispatch value of scenario 1 in the electric energy market at 17:00 is lower than that of scenarios 2 and 3. Compared with scenarios 2 and 3, due to the higher price of the peak-shaving market, the negative impact of carbon prices on fossil fuel units is eliminated, so the dispatch volume of scenarios 2 and 3 in the peak-shaving market is the same. The negative impact of the carbon trading market on fossil fuel units is mainly reflected in the electric energy market with lower electricity price data. At 15 and 16, due to the impact of carbon trading, scenario 3 reduced the output of fossil fuel units in the electric energy market.

[0202] From the above analysis, we can see that, firstly, for virtual power plants with smaller capacity, the benefits obtained through peak load ancillary services are better than those in the electricity market. This is mainly because the capacity of virtual power plants is small and it is difficult to form a stable scale effect. In addition, the various types of flexible resources in virtual power plants are more suitable for playing a role in the ancillary service market. Secondly, with the continuous improvement of the carbon trading market, the higher the carbon price, the greater the negative effect on fossil fuel units. If market operators want to maintain the balance of power generation and consumption of the power grid, they need higher electricity prices to eliminate the negative impact of the carbon trading market on fossil fuel units.

[0203] 4. The impact of tiered carbon trading on virtual power plant dispatch.

[0204] See also Figure 7From the dispatch results of scenarios 3 and 4 in the figure, it can be seen that on the basis of the basic carbon price, the step carbon prices are 50, 100 and 200 respectively. The overall carbon price level is still not high, so it still has no impact on the peak-shaving market with higher electricity prices. The peak-shaving market dispatch volumes of scenarios 3 and 4 are basically the same. Although there are slight deviations at 10 and 20 seconds, it is mainly due to the different energy storage discharge times. The overall peak-shaving market dispatch volumes of scenarios 3 and 4 are still the same.

[0205] The impact of the tiered carbon trading on scenario 4 is still reflected in the electricity market. At 13:00, scenario 4 lowered its output in the electricity market and reduced the output of fossil fuel units. If you want to further reduce carbon emissions, you need to increase the carbon price. Figure 5 It can be seen that from the perspective of power market operators, since the power grid must meet power balance, the peak-shaving electricity price data is relatively high, and the addition of carbon trading has further pushed up the peak-shaving electricity price data. For power market operators, the addition of carbon trading helps to absorb new energy, but when new energy power generation is less and fossil fuel units are needed to make up for the shortfall, carbon trading will push up electricity prices and increase the costs of power market operators. For virtual power plants with distributed new energy units, carbon trading helps to increase their profits.

[0206] 4. Internal output distribution of virtual power plants.

[0207] See also Figure 8 and Fig. 9 , Figure 8 This is a schematic diagram of the internal output distribution of the virtual power plant in scenario 4. Fig. 9 This is a schematic diagram of the peak load distribution of the virtual power plant in scenario 4. The peak load shaving period is from 9 to 12 o'clock and from 18 to 21 o'clock. Since the peak load shaving electricity price data is high, the output of the virtual power plant operator during the peak load shaving period is all during the peak load shaving period, and the output of the electricity energy market at this time is 0. Limited by the dual factors of tiered carbon trading and electricity prices, the output of gas turbines in the energy market is relatively small, but the output in the peak load shaving market is relatively large. When filling the valley to shave the peak, since the valley filling peak load benefits and the electricity purchase cost are calculated separately, the valley filling peak load is mainly carried out during the period with lower electricity price data. The overall output priority is wind power, flexible load, energy storage and gas turbines, which is in line with their output costs.

[0208] In order to explore the low-carbon and economic dispatch strategy of virtual power plants under the dual carbon goals, the robust dispatch method of virtual power plants considering ladder carbon trading is studied, and a zero-sum game model based on engineering game theory is established. The above examples verify that:

[0209] 1. Compared with the carbon trading mechanism with a fixed carbon price, the ladder carbon trading can take into account both carbon emission reduction and the benefits of virtual power plants. The scheduling model provided in this application achieves a win-win situation for virtual power plants in carbon emission reduction and the electricity market.

[0210] 2. Comprehensively considering the transactions of virtual power plants in the electricity-carbon-peaking market, it enhances their dispatching flexibility in multiple markets, improves their own benefits, optimizes the resource allocation of virtual power plants, and improves resource utilization efficiency.

[0211] Fig.10 is a structural diagram of a virtual power plant optimization scheduling device provided in an embodiment of the present application, see Fig.10 The virtual power plant scheduling device includes various functional modules for implementing the aforementioned virtual power plant scheduling method, and any functional module can be implemented by software and / or hardware.

[0212] In some embodiments, the virtual power plant dispatching device 1000 includes a power plant data acquisition module 1001, a transaction model construction module 1002, a dispatching model construction module 1003 and a dispatching strategy solving module 1004. Among them:

[0213] The power plant data acquisition module 1001 is used to acquire resource data of distributed energy coordinated and managed by the virtual power plant, as well as transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak-shaving market, and output data of the carbon trading market;

[0214] The transaction model building module 1002 is used to build a power plant transaction model of the virtual power plant based on resource data and transaction data;

[0215] The dispatch model construction module 1003 is used to construct a dispatch model with the output maximization of the power plant transaction model as the goal and the constraints of the dispatch model based on the zero-sum game model and adjustable robust optimization;

[0216] The scheduling strategy solving module 1004 is used to solve the scheduling model based on the genetic algorithm to obtain the scheduling strategy of the virtual power plant.

[0217] In some embodiments, the transaction model building module 1002 is further specifically used to:

[0218] Based on the carbon emission quota allocation mechanism of the baseline method, a carbon emission quota model for virtual power plants is constructed;

[0219] Based on the proportional relationship between carbon emissions and the unit output of gas turbines, an actual carbon emission model of the virtual power plant is constructed;

[0220] Based on the carbon emission quota model and the actual carbon emission model, a ladder carbon trading model of the virtual power plant is constructed; wherein, the ladder carbon trading model is used to represent the output data of the virtual power plant in the carbon trading market when the difference between the carbon emission quota and the actual carbon emissions of the virtual power plant is at different carbon emission interval lengths.

[0221] In some embodiments, in the trading model building module 1002, the ladder carbon trading model satisfies the following formula:

[0222]

[0223] in, represents the output data of the carbon trading market, represents the actual carbon emission model, represents the carbon emission quota of the virtual power plant, represents the carbon emission length interval, Indicates the basic purchase price data of carbon emission quotas, Indicates the basic selling price data of carbon emission quotas, and Indicates the tiered purchase price data of carbon emission quotas, and It represents the tiered selling price data of carbon emission quotas, and α and δ represent floating coefficients.

[0224] In some embodiments, in the transaction model building module 1002, the carbon emission quota model satisfies the following formula:

[0225]

[0226] in, represents the power generation of distributed energy m in the virtual power plant at time t, v represents the carbon emission coefficient per unit of electricity, T represents the total number of scheduling periods, and M represents the total number of distributed energy resources;

[0227] The actual carbon emission model satisfies the following formula:

[0228]

[0229] in, represents the carbon emission factor, represents the output of the gas turbine in the electricity market at time t, It represents the output of the gas turbine in the peak-shaving market at time t.

[0230] In some embodiments, in the scheduling model building module 1003, the scheduling model satisfies the following formula:

[0231]

[0232]

[0233] in, represents the maximum output data of the virtual power plant, represents the output data of the virtual power plant, represents the output data of the electric energy market, represents the output data of the peak-shaving market, represents the output data of the carbon trading market, represents the power generation cost data of gas turbines, represents the dispatch cost data of flexible load, represents the energy storage cost data of the energy storage system, x, u, y represent the pre-decision variable, uncertainty variable and re-decision variable respectively, and X, U, Y represent the decision space of pre-decision, output uncertainty data and re-decision respectively.

[0234] In some embodiments, in the transaction model building module 1002, the output uncertainty data satisfies the following formula:

[0235]

[0236] Among them, U represents the box uncertainty set of uncertainty variables, u represents the uncertainty variable, represents the actual output of the wind turbine at time t, represents the predicted output of the wind turbine at time t, represents the fluctuation range of wind turbine output at time t, , Indicates the fluctuation of wind turbines in a single time period. Represents an adjustable coefficient. By controlling the size of the adjustable coefficient, the robustness and conservatism can be changed.

[0237] In some embodiments, the scheduling model solving module is specifically used to:

[0238] Based on the characteristic parameters of the scheduling model, a chromosome population is randomly generated; wherein the chromosome population includes a plurality of chromosomes;

[0239] According to a preset fitness function, the fitness value of each chromosome in the chromosome population is calculated; wherein the fitness function is constructed based on the power plant trading model and the penalty factor of the output uncertainty data;

[0240] Perform selection, crossover and mutation operations on the chromosome population, update the chromosome population, return the updated chromosome population to the step of calculating the fitness value of each chromosome in the chromosome population, and repeat the updating iteration of the chromosome population until the preset maximum number of iterations is reached, and output the chromosome with the largest fitness in the chromosome population as the scheduling strategy.

[0241] The virtual power plant optimization scheduling device 1000 provided in the embodiment of the present application is used to execute the technical solution provided in the aforementioned virtual power plant optimization scheduling method embodiment. Its implementation principle and technical effect are similar to those in the aforementioned method embodiment and will not be repeated here.

[0242] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, all or part of them can be integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements, or all in the form of hardware, or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the power plant data acquisition module 1001 can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above power plant data acquisition module 1001. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in software form.

[0243] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, see Fig.11 The electronic device 1100 includes a processor 1101 and a memory 1102 communicatively connected to the processor 1101;

[0244] Memory 1102 stores computer executable instructions;

[0245] The processor 1101 executes the computer execution instructions stored in the memory 1102 to implement the technical solution of the aforementioned virtual power plant optimization scheduling method.

[0246] In the above-mentioned electronic device 1100, the memory 1102 and the processor 1101 are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus. The memory 1102 stores computer execution instructions for implementing the aforementioned virtual power plant optimization scheduling method, including at least one software function module that can be stored in the memory 1102 in the form of software or firmware. The processor 1101 executes various functional applications and data processing by running the software programs and modules stored in the memory 1102.

[0247] The memory 1102 includes at least one type of readable storage medium, including but not limited to random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable read-only memory (EEPROM). The memory 1102 is used to store programs, and the processor 1101 executes the programs after receiving the execution instruction. Furthermore, the software programs and modules in the above-mentioned memory 1102 may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.

[0248] The processor 1101 may be an integrated circuit chip having signal processing capability. The above-mentioned processor 1101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), etc. The disclosed methods, steps and logic block diagrams in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor, or the processor 1101 may be any conventional processor, etc.

[0249] The electronic device 1100 is used to execute the technical solution provided by the aforementioned virtual power plant optimization scheduling method embodiment. Its implementation principle and technical effect are similar to those in the aforementioned method embodiment and will not be repeated here.

[0250] An embodiment of the present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed, they are used to implement the technical solution of the virtual power plant optimization scheduling method as described above.

[0251] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The computer-readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0252] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the control device of the virtual power plant optimization scheduling device.

[0253] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the technical solution of the aforementioned virtual power plant optimization scheduling method.

[0254] In the above embodiments, those skilled in the art will appreciate that the implementation of the above method embodiments can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless network, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0255] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0256] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the appended claims.

[0257] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A virtual power plant optimization scheduling method, characterized in that: include: Obtain resource data of distributed energy coordinated and managed by a virtual power plant, as well as transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak-shaving market, and output data of the carbon trading market; Based on the resource data and the transaction data, construct a power plant transaction model of the virtual power plant; Based on the zero-sum game model and adjustable robust optimization, a dispatch model with the goal of maximizing the output of the power plant trading model and constraints of the dispatch model are constructed; The scheduling model is solved based on a genetic algorithm to obtain a scheduling strategy for the virtual power plant.

2. The method according to claim 1, characterized in that: The method further comprises: Based on the carbon emission quota allocation mechanism of the baseline method, a carbon emission quota model of the virtual power plant is constructed; Based on the proportional relationship between carbon emissions and the unit output of the gas turbine, an actual carbon emission model of the virtual power plant is constructed; Based on the carbon emission quota model and the actual carbon emission model, a ladder carbon trading model of the virtual power plant is constructed; wherein the ladder carbon trading model is used to represent the output data of the virtual power plant in the carbon trading market when the difference between the carbon emission quota and the actual carbon emissions of the virtual power plant is at different carbon emission interval lengths.

3. The method according to claim 2, characterized in that The ladder carbon trading model satisfies the following formula: ， in, represents the output data of the carbon trading market, represents the actual carbon emission model, represents the carbon emission quota of the virtual power plant, represents the carbon emission length interval, Indicates the basic purchase price data of carbon emission quotas, Indicates the basic selling price data of carbon emission quotas, and Indicates the tiered purchase price data of carbon emission quotas, and It represents the tiered selling price data of carbon emission quotas, and α and δ represent floating coefficients.

4. The method according to claim 2, characterized in that: The carbon emission quota model satisfies the following formula: ， in, represents the power generation of distributed energy m in the virtual power plant at time t, v represents the carbon emission coefficient per unit of electricity, T represents the total number of scheduling periods, and M represents the total number of distributed energy resources; The actual carbon emission model satisfies the following formula: ， in, represents the carbon emission factor, represents the output of the gas turbine in the electricity market at time t, It represents the output of the gas turbine in the peak-shaving market at time t.

5. The method according to any one of claims 1 to 4, characterized in that: The scheduling model satisfies the following formula: ， ， in, represents the maximum output data of the virtual power plant, represents the output data of the virtual power plant, represents the output data of the electric energy market, represents the output data of the peak-shaving market, represents the output data of the carbon trading market, represents the power generation cost data of gas turbines, represents the dispatch cost data of flexible load, represents the energy storage cost data of the energy storage system, x, u, y represent the pre-decision variable, uncertainty variable and re-decision variable respectively, and X, U, Y represent the decision space of pre-decision, output uncertainty data and re-decision respectively.

6. The method according to claim 5, characterized in that The output uncertainty data satisfies the following formula: ， Among them, U represents the box uncertainty set of uncertainty variables, u represents the uncertainty variable, represents the actual output of the wind turbine at time t, represents the predicted output of the wind turbine at time t, represents the fluctuation range of wind turbine output at time t, , Indicates the fluctuation of wind turbines in a single time period. Represents an adjustable coefficient. By controlling the size of the adjustable coefficient, the robustness and conservatism can be changed.

7. The method according to any one of claims 1 to 4, characterized in that: Solving the scheduling model based on a genetic algorithm to obtain a scheduling strategy for the virtual power plant includes: Based on the characteristic parameters of the scheduling model, a chromosome population is randomly generated; wherein the chromosome population includes a plurality of chromosomes; According to a preset fitness function, the fitness value of each chromosome in the chromosome population is calculated; wherein the fitness function is constructed based on the power plant trading model and the penalty factor of the output uncertainty data; Performing selection operation, crossover operation and mutation operation on the chromosome population to update the chromosome population; The updated chromosome population is returned to the step of calculating the fitness value of each chromosome in the chromosome population, so as to repeat the updating iteration of the chromosome population until a preset maximum number of iterations is reached, and the chromosome with the largest fitness in the chromosome population is output as the scheduling strategy.

8. A virtual power plant optimization scheduling device, characterized in that: include: A power plant data acquisition module, used to acquire resource data of distributed energy coordinated and managed by a virtual power plant, and transaction data of multiple types of markets in which the virtual power plant participates; wherein the resource data includes power generation cost data of gas turbines, output uncertainty data of wind turbines, energy storage cost data of energy storage systems, and dispatch cost data of flexible loads; and the transaction data includes output data of the electric energy market, output data of the peak load regulation market, and output data of the carbon trading market; A transaction model building module, used to build a power plant transaction model of the virtual power plant based on the resource data and the transaction data; A dispatch model building module, used to build a dispatch model with the output maximization of the power plant transaction model as the goal, and constraints of the dispatch model based on a zero-sum game model and adjustable robust optimization; The scheduling strategy solving module is used to solve the scheduling model based on a genetic algorithm to obtain the scheduling strategy of the virtual power plant.

9. An electronic device, characterized in that: comprising a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed, they are used to implement the method according to any one of claims 1 to 7.

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