Virtual power plant operation scheduling optimization method and device, equipment and medium

Abstract

The invention provides a virtual power plant operation scheduling optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a dynamic adjustment carbon flow model, and dynamically adjusting the carbon emission of a virtual power plant through the dynamic adjustment carbon flow model; constructing a multi-scale low-carbon operation optimization model, and performing multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulating a demand side management strategy, and adjusting the power consumption condition in real time through the demand side management strategy; and making a low-carbon economic operation strategy, and carrying out operation scheduling optimization on the virtual power plant through the low-carbon economic operation strategy. According to the invention, a new carbon flow mode and a new low-carbon economic operation strategy are provided for low-carbon economic operation of the virtual power plant, so that the method can adapt to changes of an energy market, and the efficiency and sustainability of a power system are improved; and the potential of the virtual power plant in the aspects of low-carbon power system development promotion, digital and intelligent operation, power system operation efficiency and economy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to a method, device, electronic device, and storage medium for optimizing the operation and dispatch of a virtual power plant. Background Technique

[0002] With the transformation of the global energy structure and the development of a low-carbon economy, the virtual power plant (VPP), as a new power system operation mode, has received extensive attention. The virtual power plant realizes the optimal allocation and efficient management of power resources by integrating distributed energy resources such as wind power, solar energy, energy storage devices, and demand response.

[0003] Although the virtual power plant shows great potential in improving energy utilization efficiency and reducing operating costs, there are still some problems to be solved urgently in the carbon flow pattern and low-carbon economy operation strategy. First of all, the existing virtual power plant operation strategy fails to fully consider the dynamic adjustment of carbon flow. In a market environment with fluctuating carbon emission rights prices, an inflexible operation strategy is difficult to adapt to market changes, resulting in low efficiency in carbon emission management of the virtual power plant. In addition, most virtual power plant models only focus on scheduling at a single time scale, ignoring the importance of multi-scale scheduling for optimizing low-carbon operation. Secondly, the research on demand-side management strategies is insufficient. The existing technology is relatively backward in the real-time dynamic adjustment mechanism of demand response, making it difficult to balance power supply and demand and achieve the optimal operation of the power system. Finally, the research on low-carbon economy operation strategies corresponding to the carbon flow pattern of the virtual power plant is insufficient. The existing virtual power plant models and operation strategies lack targeted research and fail to form a set of feasible low-carbon operation strategies.

[0004] The foregoing description is for the purpose of providing general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] In view of the above technical problems, the present application provides a method, device, electronic device, and storage medium for optimizing the operation and dispatch of a virtual power plant, which solves the problems that the existing technology fails to fully consider the dynamic adjustment of carbon flow, multi-scale low-carbon operation optimization, insufficient research on demand-side management strategies, and insufficient evaluation and prediction of the impact of carbon emission trading policies on the operation of virtual power plants in the virtual power plant operation strategy.

[0006] To solve the above technical problems, the present application provides a method for optimizing the operation and dispatch of a virtual power plant, including the following steps:

[0007] Construct a dynamic adjustment carbon flow model, and dynamically adjust the carbon emissions of the virtual power plant through the dynamic adjustment carbon flow model;

[0008] Construct a multi-scale low-carbon operation optimization model, and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model;

[0009] Formulate a demand-side management strategy, and adjust the power consumption situation in real time through the demand-side management strategy;

[0010] Formulate a low-carbon economic operation strategy, and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy.

[0011] Furthermore, in some embodiments of the present application, the constructing a dynamic carbon flow adjustment model and dynamically adjusting the carbon emissions of the virtual power plant through the dynamic carbon flow adjustment model includes:

[0012] Collect the energy flow data and carbon emission data of each department inside the virtual power plant in real time;

[0013] Construct a corresponding dynamic carbon flow adjustment model based on the energy flow data and the carbon emission data;

[0014] Analyze the energy flow characteristics and carbon flow characteristics of the virtual power plant based on the dynamic carbon flow adjustment model, and dynamically adjust the carbon emissions of the virtual power plant according to the carbon emission rights market change information and production capacity information.

[0015] Furthermore, in some embodiments of the present application, the constructing a multi-scale low-carbon operation optimization model includes:

[0016] Collect the historical power generation data and real-time power generation data of the virtual power plant;

[0017] Define the objective function and constraint conditions of the multi-scale low-carbon operation optimization model;

[0018] Adjust the parameters of the multi-scale low-carbon operation optimization model based on the historical power generation data and the real-time power generation data according to a preset optimization algorithm to obtain an optimized multi-scale low-carbon operation optimization model.

[0019] Furthermore, in some embodiments of the present application, the multi-scale low-carbon operation optimization model includes a day-ahead robust optimization model, an intra-day rolling optimization model, and a real-time stage operation model. The performing multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model includes:

[0020] Perform day-ahead power generation and load scheduling optimization based on prediction data through the day-ahead robust optimization model;

[0021] Adjust the intra-day power generation plan according to the actual operation data and market information through the intra-day rolling optimization model;

[0022] Optimize the real-time scheduling of the virtual power plant in the real-time stage through the real-time stage operation model.

[0023] Further, in some embodiments of the present application, formulating a demand-side management strategy and adjusting the power consumption situation in real time through the demand-side management strategy includes:

[0024] Collect power consumption data on the demand side and analyze the corresponding power consumption patterns on the demand side based on the power consumption data;

[0025] Formulate a demand response plan and an incentive mechanism based on the power consumption pattern;

[0026] Monitor the grid load, generation data, and demand-side power consumption data in real time;

[0027] Adjust the power consumption situation on the demand side in real time through the demand-side management strategy based on the grid load, the generation data, and the demand-side power consumption data.

[0028] Further, in some embodiments of the present application, formulating a low-carbon economy operation strategy and optimizing the operation scheduling of the virtual power plant through the low-carbon operation strategy includes:

[0029] Analyze the power market demand, price trends, and changes in the carbon emission rights market to obtain a market analysis result;

[0030] Set a carbon emission target, evaluate the low-carbon technologies adopted, and determine the corresponding target low-carbon technologies;

[0031] Formulate a low-carbon economy operation strategy based on the market analysis result, the carbon emission target, and the target low-carbon technology. The low-carbon economy operation strategy includes a generation strategy and a scheduling strategy;

[0032] Optimize the power generation and scheduling of the virtual power plant based on the low-carbon economy operation strategy.

[0033] Further, in some embodiments of the present application, the method further includes:

[0034] Obtain the operation scheduling optimization result of the virtual power plant based on the dynamic carbon flow adjustment model, the multi-scale low-carbon operation optimization model, the demand-side management strategy, and the low-carbon economy operation strategy;

[0035] Evaluate the operation scheduling optimization result to obtain a corresponding evaluation result;

[0036] Adjust the dynamic carbon flow adjustment model, the multi-scale low-carbon operation optimization model, the demand-side management strategy, and the low-carbon economy operation strategy based on the evaluation result.

[0037] Accordingly, the present application provides a virtual power plant operation scheduling optimization device, including:

[0038] A dynamic carbon flow model adjustment module, configured to construct a dynamic carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamic carbon flow model;

[0039] A multi-scale low-carbon operation optimization model module, configured to construct a multi-scale low-carbon operation optimization model and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model;

[0040] A demand-side management strategy module, configured to formulate a demand-side management strategy and adjust the power consumption situation in real time through the demand-side management strategy;

[0041] A low-carbon economy operation strategy module, configured to formulate a low-carbon economy operation strategy and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy.

[0042] The present application also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the virtual power plant operation scheduling optimization method described above are implemented.

[0043] The present application also provides a storage medium storing a computer program that can be loaded and executed by a processor to implement the virtual power plant operation scheduling optimization method described above.

[0044] Implementing the embodiments of the present application has the following beneficial effects:

[0045] As described above, a virtual power plant operation scheduling optimization method, device, electronic device, and storage medium provided by the present application. The virtual power plant operation scheduling optimization method includes: constructing a dynamic carbon flow adjustment model and dynamically adjusting the carbon emissions of the virtual power plant through the dynamic carbon flow adjustment model; constructing a multi-scale low-carbon operation optimization model and performing multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulating a demand-side management strategy and adjusting the power consumption situation in real time through the demand-side management strategy; formulating a low-carbon economy operation strategy and performing operation scheduling optimization on the virtual power plant through the low-carbon operation strategy. The virtual power plant operation scheduling optimization solution provided by the present application can adapt to the changes in the energy market, improve the efficiency and sustainability of the power system, and enhance the potential of the virtual power plant in promoting the development of the low-carbon power system, digital and intelligent operation, power system operation efficiency, and economy by providing a new carbon flow pattern and low-carbon economy operation strategy for the low-carbon economy operation of the virtual power plant. It solves the problems in the existing technology that the dynamic adjustment of carbon flow, multi-scale low-carbon operation optimization, insufficient research on demand-side management strategies, and insufficient evaluation and prediction of the impact of carbon emission trading policies on the operation of virtual power plants are not fully considered in the virtual power plant operation strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 FIG. is a schematic diagram of the application scenario of the virtual power plant operation scheduling optimization method provided by the embodiment of the present application;

[0048] Figure 2 FIG. is a schematic flow chart of the virtual power plant operation scheduling optimization method provided by the embodiment of the present application;

[0049] Figure 3 FIG. is a schematic structural diagram of the virtual power plant operation scheduling optimization device provided by the embodiment of the present application;

[0050] Figure 4 FIG. is a schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0051] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0052] 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.

[0053] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0056] Current virtual power plants have shown great potential in improving energy efficiency and reducing operating costs, but existing technologies still have some problems that need to be solved in terms of carbon flow patterns and low-carbon economic operation strategies.

[0057] First, the current virtual power plant operation strategy does not make dynamic adjustments to the carbon flow, making it difficult to adapt to the market environment of carbon emission rights price changes, and the operation strategy lacks flexibility. At the same time, most of the existing virtual power plant models only focus on a single time scale, such as day-ahead or intraday scheduling, and rarely consider multi-scale low-carbon operation optimization. In addition, most of the existing virtual power plant operation models only focus on the supply side, and there is little research on the management strategy on the demand side. However, the electricity consumption pattern and its management strategy on the demand side directly affect the operating efficiency, economy and environmental protection of the power system. The demand for flexibility has not been effectively met. The virtual power plant strategy in the current technology has not yet been able to effectively manage and optimize carbon emissions in its energy portfolio. Especially in the case of drastic fluctuations in carbon emission rights prices, there is a lack of corresponding strategies and plans to adjust output to adapt to market changes.

[0058] Second, the need for multi-scale scheduling has not been fully considered. The operation of a virtual power plant not only involves energy allocation and price setting in various time periods, but is also closely related to the long-term decisions of the power plant, such as equipment upgrades and new energy access. However, most of the current virtual power plant models focus on a single time scale, cannot fully utilize information related to multiple time scales, and cannot provide accurate references for long-term decisions.

[0059] Third, there is insufficient research on demand-side management strategies. For virtual power plants, in addition to optimizing supply strategies, more important is demand-side management. However, research on the demand side is relatively backward. For example, there is a lack of real-time dynamic adjustment mechanism for demand response, which makes it impossible to effectively balance demand and supply, and it is also difficult to achieve the optimal operation of the power system.

[0060] Finally, there is insufficient research on the low-carbon economic operation strategy corresponding to the carbon flow model of virtual power plants. In the context of energy transformation and low-carbon economy, virtual power plants should optimize carbon flow patterns, achieve low-carbon operation based on renewable energy, and ensure the stability and reliability of the power system. However, the current virtual power plant models and operation strategies lack targeted research and have failed to form a feasible low-carbon operation strategy.

[0061] In order to solve the above technical problems, the present application provides a virtual power plant operation scheduling optimization method, device, electronic device and storage medium.

[0062] Among them, the virtual power plant operation scheduling optimization device can be specifically integrated in an electronic device, which can be a smart phone, a tablet computer, a notebook computer or a desktop computer, but is not limited thereto. The electronic device can be directly or indirectly connected to the server through wired or wireless communication means. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. This application does not make any restrictions here.

[0063] Please refer to Figure 1 , Figure 1 which is an application environment diagram of the virtual power plant operation scheduling optimization method in an embodiment. Referring to Figure 1 , the virtual power plant operation scheduling optimization method can be applied to a virtual power plant operation scheduling optimization system. Among them, the virtual power plant operation scheduling optimization system can include a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 can specifically be a desktop terminal or a mobile terminal, and the mobile terminal can specifically be at least one of a mobile phone, a tablet computer, a notebook computer, etc. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers. The terminal 110 is used to construct a dynamically adjustable carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamically adjustable carbon flow model; construct a multi-scale low-carbon operation optimization model and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulate a demand-side management strategy and adjust the power consumption situation in real time through the demand-side management strategy; formulate a low-carbon economy operation strategy and perform operation scheduling optimization on the virtual power plant through the low-carbon operation strategy.

[0064] The following will be described in detail respectively. It should be noted that the description order of the following embodiments does not limit the priority order of the embodiments.

[0065] This application provides a virtual power plant operation scheduling optimization method, including: constructing a dynamically adjustable carbon flow model and dynamically adjusting the carbon emissions of the virtual power plant through the dynamically adjustable carbon flow model; constructing a multi-scale low-carbon operation optimization model and performing multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulating a demand-side management strategy and adjusting the power consumption situation in real time through the demand-side management strategy; formulating a low-carbon economy operation strategy and performing operation scheduling optimization on the virtual power plant through the low-carbon operation strategy.

[0066] Please refer to Figure 2 , Figure 2It is a schematic flow chart of the virtual power plant operation scheduling optimization method provided by the embodiments of the present application. The virtual power plant operation scheduling optimization method provided by this embodiment may specifically include the following steps:

[0067] S1. Construct a dynamically adjustable carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamically adjustable carbon flow model;

[0068] Specifically, for step S1, it includes collecting the energy flow data and carbon emission data of each department inside the virtual power plant in real time. Based on the energy flow data and carbon emission data, construct a dynamically adjustable carbon flow model that can reflect the energy flow characteristics and carbon flow characteristics of the virtual power plant. This model can dynamically adjust the carbon emissions of the virtual power plant according to the change information and production capacity information of the carbon emission rights market to adapt to the market environment and achieve low-carbon operation.

[0069] S2. Construct a multi-scale low-carbon operation optimization model and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model;

[0070] Specifically, for step S2, this step includes collecting the historical power generation data and real-time power generation data of the virtual power plant. Define the objective function and constraint conditions of the multi-scale low-carbon operation optimization model, and these objective functions and constraint conditions are aimed at optimizing the economy, stability and environmental protection of the power system. Based on the preset optimization algorithm, adjust the model parameters according to the historical and real-time data to obtain the optimized multi-scale low-carbon operation optimization model.

[0071] S3. Formulate a demand-side management strategy and adjust the power consumption situation in real time through the demand-side management strategy;

[0072] Specifically, for step S3, this step includes collecting the power consumption data on the demand side and analyzing the power consumption pattern on the demand side based on the power consumption data. Based on the power consumption pattern, formulate a demand response plan and an incentive mechanism, monitor the grid load, power generation data and demand-side power consumption data in real time, and adjust the power consumption situation in real time through the demand-side management strategy to balance power supply and demand.

[0073] S4. Formulate a low-carbon economic operation strategy and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy;

[0074] Specifically, for step S4, this step includes analyzing the power market demand, price trend and the change situation of the carbon emission rights market, setting a carbon emission target, and evaluating the low-carbon technologies adopted. Based on the market analysis results, carbon emission target and target low-carbon technologies, formulate a low-carbon economic operation strategy, including a power generation strategy and a scheduling strategy, and optimize the power generation and scheduling of the virtual power plant.

[0075] It can be seen that in this embodiment, by constructing a model capable of dynamically adjusting the carbon flow, the virtual power plant can dynamically adjust its carbon emissions according to the changes in the carbon emission rights market and its own production capacity, which helps to reduce carbon emissions and at the same time reduce the economic risks brought about by the changes in the carbon emission rights price; by constructing a multi-scale low-carbon operation model, optimal scheduling can be carried out in the day-ahead, intraday and real-time stages to achieve the balance of the economy, stability and environmental protection of the power system; by adjusting the power consumption in real time, the power supply and demand can be better matched, and the operation cost of the power system can be reduced, with remarkable effects; the constructed low-carbon economic operation strategy will promote the sustainable development of the virtual power plant, and at the same time achieve a substantial reduction in carbon emissions, providing a new solution for promoting the construction of a power system with more renewable energy access; by evaluating and predicting the impact of the carbon emission rights trading policy on the operation of the virtual power plant, it can help the virtual power plant to make strategic plans in advance and respond flexibly, and at the same time provide a reference for the government department to implement low-carbon policies; it is applicable to various types of virtual power plants and has strong versatility and popularization.

[0076] Further, in some embodiments, step S1, "construct a dynamically adjustable carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamically adjustable carbon flow model", may specifically include:

[0077] Collect the energy flow data and carbon emission data of each department inside the virtual power plant in real time;

[0078] Construct a corresponding dynamically adjustable carbon flow model based on the energy flow data and carbon emission data;

[0079] Analyze the energy flow characteristics and carbon flow characteristics of the virtual power plant based on the dynamically adjustable carbon flow model, and dynamically adjust the carbon emissions of the virtual power plant according to the changes in the carbon emission rights market information and production capacity information.

[0080] Specifically, for step S1, collect the energy flow data and carbon emission data of each department inside the virtual power plant in real time. These data are the basis for constructing a dynamically adjustable carbon flow model, involving the energy flow characteristics and carbon emission situations of different departments. Based on the collected energy flow data and carbon emission data, construct a model that can dynamically reflect the carbon flow situation inside the virtual power plant. This model can capture and analyze the energy and carbon emission characteristics inside the virtual power plant. Use the constructed model to analyze the energy flow characteristics and carbon flow characteristics of the virtual power plant. This includes identifying the energy usage patterns and carbon emission patterns of different departments, and how they respond to market changes and production capacity changes. According to the changes in the carbon emission rights market information and production capacity information, dynamically adjust the carbon emissions of the virtual power plant. This includes measures such as adjusting the energy mix, optimizing energy distribution, and improving energy efficiency to adapt to market changes and achieve low-carbon operation.

[0081] In this embodiment, by collecting data in real time and dynamically adjusting carbon emissions, the virtual power plant can better adapt to the changes in the carbon emissions rights price and the market environment, improving the flexibility of its operation strategy; dynamically adjusting carbon emissions helps reduce the economic risks brought about by the fluctuations in the carbon emissions rights price and optimize the economic performance of the virtual power plant; by optimizing the energy mix and energy distribution, the virtual power plant can reduce carbon emissions, conforming to the development trend of the low-carbon economy; by dynamically adjusting carbon emissions, the virtual power plant can enhance its competitiveness in the energy market, especially in the carbon emissions rights trading market; by optimizing the carbon flow pattern, the virtual power plant can achieve low-carbon operation relying on renewable energy while ensuring the stability and reliability of the power system and promoting the sustainable development of the energy structure.

[0082] Further, in some embodiments, the "constructing a multi-scale low-carbon operation optimization model" in step S2 may specifically include:

[0083] Collect the historical power generation data and real-time power generation data of the virtual power plant;

[0084] Define the objective function and constraint conditions of the multi-scale low-carbon operation optimization model;

[0085] Based on a preset optimization algorithm, adjust the parameters of the multi-scale low-carbon operation optimization model according to the historical power generation data and real-time power generation data to obtain an optimized multi-scale low-carbon operation optimization model.

[0086] Specifically, for the construction of the multi-scale low-carbon operation optimization model in step S2, first, collect the historical power generation data and real-time power generation data of the virtual power plant, which are crucial for understanding the power generation behavior and performance of the virtual power plant. Then, define the objective function and constraint conditions of the multi-scale low-carbon operation optimization model. The objective function may include cost minimization, carbon emission minimization, etc., while the constraint conditions include power generation capacity, grid stability, environmental standards, etc. Next, select or develop a suitable optimization algorithm for adjusting the parameters of the multi-scale low-carbon operation optimization model, including linear programming, non-linear programming, mixed integer programming, etc. Finally, based on the preset optimization algorithm and the collected data, adjust the parameters of the multi-scale low-carbon operation optimization model to obtain an optimized model. This process involves multiple iterations to ensure that the model can accurately reflect the operation status of the virtual power plant and achieve the predetermined optimization goals. Use the optimized multi-scale low-carbon operation optimization model to perform multi-scale optimization on the virtual power plant. This includes optimization in the day-ahead, intra-day, and real-time stages to achieve a balance among the economy, stability, and environmental friendliness of the power system.

[0087] Through multi-scale optimization, the virtual power plant can manage power generation and load scheduling at different time scales more effectively, improving the overall operating efficiency; the optimization model helps to reduce the operating costs of the virtual power plant, including fuel costs, maintenance costs, and market transaction costs; the multi-scale low-carbon operation optimization model pays particular attention to reducing carbon emissions, helping the virtual power plant to achieve low-carbon operation, which is in line with the global trend of carbon emissions reduction; by optimizing power generation and load scheduling, the virtual power plant can better support grid stability and reduce grid fluctuations caused by supply-demand mismatches; the multi-scale optimization model can adapt to different market and environmental conditions, improving the response speed and adaptability of the virtual power plant to changes; by considering optimization at different time scales, the virtual power plant can carry out long-term planning, including equipment upgrades, new energy access, etc., laying a foundation for future sustainable development.

[0088] Furthermore, in some embodiments, the multi-scale low-carbon operation optimization model in this embodiment includes a day-ahead robust optimization model, an intraday rolling optimization model, and a real-time stage operation model. The "multi-scale optimization of the virtual power plant through the multi-scale low-carbon operation optimization model" in step S2 can specifically include:

[0089] Optimizing power generation and load scheduling in the day-ahead stage through the day-ahead robust optimization model based on forecast data;

[0090] Adjusting the power generation plan in the intraday stage according to the actual operation data and market information through the intraday rolling optimization model;

[0091] Performing real-time scheduling optimization on the virtual power plant in the real-time stage through the real-time stage operation model.

[0092] Specifically, for the multi-scale optimization of the virtual power plant through the multi-scale low-carbon operation optimization model in step S2, it includes: optimizing power generation and load scheduling in the day-ahead stage through the day-ahead robust optimization model using forecast data. This step involves using data such as weather forecasts and power market demand forecasts to plan the power generation and load distribution of the virtual power plant, with the aim of minimizing costs and carbon emissions while meeting demand. Adjusting the power generation plan in the intraday stage according to the actual operation data and market information through the intraday rolling optimization model. This step allows the virtual power plant to adjust its operation strategy based on real-time data to cope with forecast errors and market changes, ensuring the flexibility and adaptability of the power generation plan. Performing real-time scheduling optimization on the virtual power plant in the real-time stage through the real-time stage operation model. This step involves monitoring the grid conditions and power market dynamics and adjusting the power generation and load distribution in real time to respond to emergencies and market demand changes.

[0093] In specific embodiments, the multi-scale low-carbon operation model of the demand-side virtual power plant provided in this embodiment includes a day-ahead robust optimization model, an intraday rolling optimization model, and a real-time stage operation model.

[0094] 1. Day-ahead robust optimization model:

[0095] In the day-ahead stage, the objective function of the virtual power plant scheduling optimization model is as shown in the formula

[0096]

[0097] In the formula, and respectively represent the natural gas purchase cost, electric vehicle compensation cost, unit maintenance cost, electrical energy storage and thermal energy storage maintenance cost of the virtual power plant in period t. and respectively represent the electricity sales revenue and carbon emission right revenue of the virtual power plant in period t.

[0098] (1) Natural gas purchase cost

[0099]

[0100] In the formula, pr t gas is the unit price of natural gas, in yuan / MWh; P t gt is the electric power output of the gas turbine at time t; η gt is the power generation efficiency of the gas turbine; is the electric power output of the i-th gas turbine at time t.

[0101] (2) Electric vehicle compensation cost

[0102] The Monte Carlo method is used to predict the total charging load P of electric vehicles t ev , and the vehicle owners are compensated according to the virtual power plant regulation plan, as shown in the following formula:

[0103]

[0104] In the formula, is the battery purchase cost of the i-th EV; P i eν is the number of charge and discharge cycles of the i-th EV battery during its life cycle; is the battery capacity of the i-th EV; is the depth of discharge of the i-th EV battery.

[0105] (3) Unit maintenance cost

[0106]

[0107] In the formula, n whr and n ebThey are the total number of waste heat recovery units (WHR) and electric boilers respectively. and They are the maintenance cost coefficients of the i-th gas turbine, waste heat recovery, electric boiler, photovoltaic, and wind turbine respectively; and They are the thermal power outputs of the i-th waste heat recovery and electric boiler at time t; and They are the electric power outputs of the i-th photovoltaic and wind turbine at time t respectively.

[0108] (4) Maintenance costs of electric energy storage and heat storage

[0109]

[0110] In the formula, pr ees is the unit price of the maintenance cost of electric energy storage; They are the charging and discharging powers of the i-th electric energy storage in the t period respectively; pr hes is the unit price of the maintenance cost of heat storage; They are the heat storage and heat release powers of the i-th heat storage in the t period respectively; nees 、 nhes They are the total numbers of electric energy storage and heat storage respectively.

[0111] (5) Revenue from selling electricity of the virtual power plant

[0112]

[0113] In the formula, is the revenue from selling electricity of the virtual power plant in the t period; P t el,s 、pr t el,s are the electricity selling and purchasing powers of the virtual power plant to the power grid at time t; P t el,b 、pr t el,b They are the electricity selling and purchasing prices of the virtual power plant to the power grid respectively.

[0114] (6) Revenue from carbon emission rights of the virtual power plant

[0115]

[0116] In the formula, is the revenue obtained by the virtual power plant from selling carbon emission rights in the t period; pr c is the trading price of the unit carbon quota.

[0117] (7) Robust optimization model

[0118] The two-stage robust optimization method based on the column sum constraint generation algorithm is used to solve the day-ahead stage model, as shown in the formula.

[0119]

[0120] In the formula, Ψ1 is the output decision variable of each device of the virtual power plant at time t; is the actual output of the photovoltaic and wind turbine units; is the penalty for the actual output of renewable energy being lower than the predicted power, is the penalty coefficient, is the difference between the predicted power and the actual output of renewable energy; is the penalty for the actual output of renewable energy being higher than the predicted power, is the penalty coefficient, is the difference between the actual output and the predicted power of renewable energy; are the predicted values of the output of the photovoltaic and wind turbine units in the day-ahead stage, respectively; is the photovoltaic prediction deviation, generally taking ±10%; is the wind power prediction deviation, generally taking ±15%; To avoid overly conservative decisions, the robust coefficients Γ pν and Γ wt

[0121] 2. Intra-day rolling optimization model:

[0122] The intra-day stage is rolled and optimized hourly. The objective function of the intra-day stage with the minimum deviation between the external sales power of the virtual power plant and the day-ahead declared volume is shown in the following formula:

[0123]

[0124] In the formula, is the incremental gas purchase cost; is the deviation penalty in the intra-day stage; is the reduction in carbon market trading revenue; P t gt is the power increment of the gas turbine at time t; is the electric power increment of the i-th gas turbine at time t; is the penalty coefficient, ΔP t i+ is the difference between the output of the virtual power plant in the intra-day stage and that in the day-ahead stage; is the penalty coefficient, ΔP t i- is the difference between the output of the virtual power plant in the intra-day stage and that in the day-ahead stage; P t ′ el,b ,P t ′el,s are the electricity purchase and sale powers between the virtual power plant and the power grid during the intraday stage; Δ′Car t is the difference between the carbon quota and the total carbon emissions at time t during the intraday stage.

[0125] 3. Real-time stage operation model:

[0126] At present, as a typical representative of controllable electric / thermal loads, the air-conditioning cluster load accounts for a large proportion in controllable electric / thermal loads. Therefore, the air-conditioning cluster is used as a time-decoupled load to make up for real-time deviations, and the objective function of the real-time scheduling stage is shown as follows:

[0127]

[0128] In the formula, is the deviation penalty during the real-time stage; is the adjustment cost of the air-conditioning cluster; is the penalty coefficient, ΔP t r+ is the difference between the output of the virtual power plant during the real-time stage and that during the intraday stage; is the penalty coefficient, ΔP t r- is the difference between the output of the virtual power plant during the real-time stage and that during the intraday stage; P t ′ el,s , P t ′ пеl,s are the electricity purchase and sale powers between the virtual power plant and the power grid during the real-time stage; pr t ac is the unit power adjustment cost of the air-conditioning cluster; P t ac is the virtual energy storage power output corresponding to the air-conditioning load at time t.

[0129] In a short period of time, it can be considered that T out (t) the outdoor temperature remains unchanged. Assume that when the room temperature is the most comfortable temperature T comf (t), the air conditioner is in a steady state, as shown in the formula:

[0130] dT room (t) / dt = 0

[0131]

[0132] In the above formula, T room (t) is the room temperature; is the initial power of the air-conditioning cluster; η ac is the air-conditioning thermoelectric conversion coefficient; R is the equivalent thermal resistance of the air-conditioning load.

[0133] Assume that a scheduling period is Δt, and the air conditioner runs at for a time of Δt1 and at for a time of Δt2. Each time period of the virtual energy storage discharge cycle of the air-conditioning load is as shown; the maximum discharge power of the virtual energy storage of the air-conditioning load is as shown in the formula; each time period of the virtual energy storage charging cycle of the air-conditioning load is as shown in the formula; the maximum charging power of the virtual energy storage of the air-conditioning load is as shown in the formula.

[0134]

[0135] In the formula, referring to the most comfortable temperature of the human body, T comf is taken as 26°C; let the lower limit of the air-conditioning temperature setting value be taken as 24.5°C, and the upper limit be taken as 24.5°C.

[0136] In this embodiment, through the day-ahead robust optimization model, the virtual power plant can make more accurate power generation and load scheduling plans in the day-ahead stage, reducing the scheduling risks caused by prediction errors; the intra-day rolling optimization model and the real-time stage operation model enable the virtual power plant to quickly respond to market changes and emergencies, improving the adaptability and flexibility to grid demands; the multi-scale optimization model helps the virtual power plant to minimize costs on different time scales and reduce operating costs by optimizing resource allocation; by optimizing power generation and load distribution, it helps to reduce unnecessary carbon emissions and promote the operation of a low-carbon economy; through the optimization model, the virtual power plant can improve its economic benefits, such as increasing revenue by participating more effectively in power market transactions; real-time scheduling optimization helps to maintain the stability and reliability of the power grid, especially when the power supply and demand fluctuate greatly.

[0137] Furthermore, in some embodiments, step S3, "formulating a demand-side management strategy and adjusting the power consumption situation in real time through the demand-side management strategy", may specifically include:

[0138] Collecting the power consumption data on the demand side and analyzing the corresponding power consumption patterns on the demand side based on the power consumption data;

[0139] Formulating a demand response plan and an incentive mechanism based on the power consumption patterns;

[0140] Real-time monitoring of the grid load, power generation data, and demand-side power consumption data;

[0141] Adjusting the power consumption situation on the demand side in real time through the demand-side management strategy based on the grid load, power generation data, and demand-side power consumption data.

[0142] Specifically, for step S3, the power consumption data on the demand side is collected, which includes but is not limited to the power load, power characteristics and power demand of users in each time period. Based on the collected power consumption data, the power consumption pattern corresponding to the demand side is analyzed to identify the power consumption behavior and demand changes of different users. According to the analysis results of the power consumption pattern, a demand response plan and incentive mechanism are formulated to encourage users to increase power consumption when the power supply is sufficient or the demand is low, and to reduce power consumption when the power supply is tight or the demand is peak. Real-time monitoring of grid load, power generation data and demand-side power consumption data is carried out to respond to changes in the power grid status in a timely manner. Through the demand-side management strategy, the power consumption of the demand side is adjusted in real time based on the grid load, power generation data and demand-side power consumption data to balance the supply and demand of electricity and optimize the allocation of power resources.

[0143] Through demand-side management, this embodiment can more effectively match electricity supply and demand, reduce the operating pressure of the power grid, and improve overall operating efficiency; demand-side management helps to reduce the operating cost of the power system because it can reduce the cost of purchasing electricity by reducing electricity demand during peak hours; real-time adjustment of electricity consumption can reduce fluctuations in the load of the power grid and enhance the stability and reliability of the power grid; by optimizing the power consumption pattern and reducing electricity consumption during peak hours, it helps to reduce carbon emissions and environmental pollution; demand response plans and incentive mechanisms can increase user participation and satisfaction, and users can reduce electricity bills or obtain incentives by adjusting their electricity usage behavior; demand-side management can better integrate renewable energy sources such as wind power and solar energy, and adapt to the intermittent and uncertain nature of these energy sources by adjusting electricity demand.

[0144] Further, in some embodiments, step S4 "formulates a low-carbon economic operation strategy, and optimizes the operation and scheduling of the virtual power plant through the low-carbon operation strategy" may specifically include:

[0145] Analyze the electricity market demand, price trends and changes in the carbon emission rights market to obtain market analysis results;

[0146] Set carbon emission targets, evaluate the low-carbon technologies used, and determine the corresponding target low-carbon technologies;

[0147] Formulate a low-carbon economic operation strategy based on market analysis results, carbon emission targets and target low-carbon technologies. The low-carbon economic operation strategy includes power generation strategy and dispatch strategy;

[0148] The power generation and scheduling of virtual power plants are optimized based on low-carbon economic operation strategies.

[0149] Specifically, for step S4, analyze the electricity market demand, price trends, and changes in the carbon emission rights market. This includes analyzing factors such as electricity spot prices, carbon emission rights prices, and the availability of renewable energy to predict market trends. Based on the market analysis results and policy requirements, set the carbon emission targets for the virtual power plant, including reducing a certain percentage of carbon emissions or meeting specific environmental protection standards. Evaluate available low-carbon technologies to determine which technologies are most suitable for the operation requirements and carbon emission targets of the virtual power plant, including energy efficiency improvement technologies, renewable energy integration technologies, carbon capture and storage technologies, etc. Based on the market analysis results, carbon emission targets, and target low-carbon technologies, formulate low-carbon economic operation strategies including power generation strategies and dispatching strategies. These strategies aim to optimize the operation of the virtual power plant to achieve the dual goals of economic and environmental benefits. According to the low-carbon economic operation strategies, optimize the power generation and dispatching of the virtual power plant, such as adjusting the allocation of power generation resources, optimizing the power generation schedule, and dispatching demand response, etc.

[0150] By implementing the low-carbon economic operation strategies in this embodiment, the virtual power plant can reduce carbon emissions, promote low-carbon operation, and meet the global emission reduction trend and environmental protection requirements; the optimization strategies help the virtual power plant reduce operating costs while meeting market demands and improve economic benefits; the low-carbon economic operation strategies make the virtual power plant more competitive in the electricity market, especially in the carbon emission rights trading and green energy markets; by optimizing power generation and dispatching, the virtual power plant can utilize energy more efficiently and reduce waste; this strategy helps government departments implement low-carbon policies and encourage the application of low-carbon technologies and the reduction of carbon emissions through market mechanisms; the low-carbon economic operation strategies help the power system maintain stability and reliability while meeting low-carbon requirements; by optimizing the carbon flow pattern and low-carbon economic operation strategies, the virtual power plant can achieve low-carbon operation relying on renewable energy while ensuring the stability and reliability of the power system and promoting the sustainable development of the energy structure.

[0151] In a specific embodiment, the strategies in this embodiment include:

[0152] 1. Energy flow characteristics of each department of the virtual power plant

[0153] (1) Department energy flow characteristics

[0154] The VPP groups multiple distributed energy resources and manages this set as a single virtual element associated with the system. The following will introduce the technologies used by our company to manage and operate each distributed energy resource (distributed energy).

[0155] The efficient operation of many distributed energy sources requires intelligent and secure systems. VPP is part of a modern electrical system called the Smart Grid (SG). The SG works through advanced communication and control systems to ensure the efficient distribution of electricity, reduce grid losses, and maintain a high level of power supply quality and security. Currently, with the development of technology, there is an increasing interest in smart grids, which has promoted the application of strategies for integrating distributed energy sources into traditional power systems. Coordinating distributed energy sources with traditional power system participants requires powerful and innovative control systems and new operating models to facilitate the implementation of the next-generation electrical systems. This new concept of intelligent management operates through two-way power and communication flows and leverages the development of Internet of Things (IoT) devices, cybersecurity, big data, and artificial intelligence, etc. This new system is based on the full integration of different power generation sources, advanced communication devices, modern technologies, and complex control systems. Smart grids can automatically manage the two-way flow of electricity and communication. At the same time, they also drive the development of new market models, operating scenarios, and organizational structures. The SG is the ultimate evolution of traditional power systems. Therefore, VPP plays an important role in this concept and the modern power sector. The organization, management, and operation of distributed energy sources are carried out through the application of intelligent energy management systems. Energy Management System (EMS) technology was initially developed for managing the power generation - transmission system, but currently, digitization allows the development of these systems for distribution network power flow management systems, called Distribution Management Systems (DMS). Finally, these systems have evolved in smart grids for the intelligent management of distributed energy sources and are called Dynamic Energy Management Systems (DEMS). DEMS can be defined as intelligent modules capable of monitoring multiple supply and demand signals of the system in real time. The system can effectively control the power flow between different power plants, energy storage systems, and the grid to seek economic benefits and optimize available resources. These systems are the "brains" of the managed electrical system, which ensures its optimal function without endangering stability. The operation algorithms are based on multiple variables, such as the supply and demand in the power market, weather forecasts, energy spot prices, and the availability of distributed energy sources. Others have also previously discussed various optimization techniques and algorithms, their objective functions, and the types of mathematical formulas used for managing distributed energy sources and VPP and have systematically classified them. According to their architecture, these systems can operate centrally or distributively. The large-scale incorporation of distributed energy sources into traditional power systems and the development of DEMS technology have made energy consumers active participants in the power market. In this new model, consumers have the ability to commercialize energy, reserve electricity, and the ancillary services of the system, motivated by economic and energy efficiency benefits. The literature has identified this concept as "prosumers" and has described it as an essential element for future smart grids to conduct economic transactions through demand and small-scale power generation management. Therefore, to meet the technical and economic goals of smart grids, sufficient DEMS systems are needed.The system should be able to effectively manage the available resources and turn energy management into a profitable business by monitoring, controlling, and optimizing system performance. The algorithms implemented in each DEMS use a similar sequence. However, each DEMS is unique according to the predefined goals and the relevant distributed energy management system, and its capabilities to provide energy, power reserves, and ancillary services also vary. Distributed energy is an important energy source interconnected with the power grid. However, in order to provide flexibility to the power grid, they must be aggregated and controlled according to the needs of the system operator. The literature classifies smart energy management system technologies according to their applications as follows:.

[0156] (1) Home Energy Management System (HEMS):

[0157] They are intelligent systems composed of specific software and hardware that control storage systems, electric vehicles, controllable loads, and small power plants. HEMS monitors the demands and production of the house in real time and allows manual or automatic control of its elements. It is a system that optimizes energy use according to the economic parameters of the electricity market.

[0158] (2) Building Energy Management System (BEMS):

[0159] They are intelligent technologies aimed at optimizing energy resources in commercial and residential buildings. Their operating algorithms can be built under the concept of prosumers for energy, power reserve, and ancillary service supply. BEMS aims to reduce the energy consumption of buildings and improve efficiency without compromising the comfort of the occupants.

[0160] (3) Microgrid Energy Management System (MGMS):

[0161] They are management units integrated by hardware and complex software algorithms. MGMS is an intelligent technology that allows the integration of MG into the smart grid through a more advanced and efficient operating system. Its functions are based on processing information from a group of distributed energy sources and controllable loads to make decisions. These intelligent technologies strive to bring economic benefits to the system without compromising the stability of MG. It uses consumption and weather forecasting models to evaluate the production and demand of the system. MGMS can be integrated into the VPP using artificial intelligence systems and participate in the electricity market through services such as energy supply, power reserve, ancillary services, and demand management.

[0162] (4) Energy Management System Aggregator (EMSA):

[0163] EMSA is a complex control system for the VPP. The main purpose of this intelligent unit is to pool and manage the potential of each distributed energy source to form a virtual element that provides flexibility services to the system.

[0164] EMSA can optimize the resources provided by HEMS, BEMS, and MGMS. Its function is to manage the supply and demand of the group through a collaborative approach among relevant elements. Finally, it provides flexibility services through demand management and service in the electricity market.

[0165] The more distributed energy and prosumers need to be managed in the system, the more complex the network becomes. Aggregators must handle historical information on demand and weather to ensure the security and certainty of the offers submitted to the system operator. It is also important to understand the limitations of the distribution network to provide technically feasible offers. To better control distributed energy management, a bottom-up operation scheme is proposed. This distributed energy management concept changes the top-down operation mode of the traditional power system and promotes the technical and commercial integration of distributed energy.

[0166] The bottom-up operation scheme aims to cooperate with other relevant distributed energy to provide energy demand, starting from its own local network, then the progressive supply of the distribution network, and finally the rest of the system.

[0167] The management of distributed energy can be carried out in each subsystem of the distribution network. In these cases, two or more distributed VPPs can participate and provide services to the centralized VPP. The VPP organizes each element, verifies the technical availability of each distributed energy, meets local requirements, and establishes the best economic offer to participate in the wholesale market. In the bottom-up scheme, the distributed aggregator is responsible for ensuring the security and robustness of its controlled subsystems and the centralized aggregator, acting as an integration agent for the system operator. These elements are managed by multiple intelligent energy management system relationship diagrams and integrated into the traditional power system by distributed VPPs and central VPPs (integration agents).

[0168] (2) Carbon flow online regulation ability impact analysis model

[0169] Virtual power plants can become the most important part of the energy and reserve markets. The planning and bidding of VPPs in the electricity market are very different from those of traditional power plants. These differences can be summarized as follows: First, VPPs can play a dual role as producers or consumers (in the electricity market), while traditional power plants only play the role of producers in the electricity market; Second, traditional power plants do not have to supply their own workload, while VPPs are responsible for meeting their own loads; Third, VPPs can access the power grid from different points. Therefore, the bids of VPPs will be affected by the internal network structure and network limitations of VPPs. Based on the above differences, when participating in the electricity market and bidding, VPPs should consider the network security limitations within the VPP area, as well as the dispersion of distributed energy units and the balance of their production and consumption of electricity. In addition, VPPs should also accurately grasp the status of their distributed energy resources and the production capacity of each unit. Then, by estimating the consumption load of their users and the energy price and reservation price in the electricity market, they start to bid in the electricity market. To provide price and profitability in the electricity market, VPPs can determine the generation cost through implicit energy production sources. The cost function of each distributed energy is shown in the following formula. In addition, the generation cost of DGs (PDG) can be defined by the formula, and the generation cost of energy storage (P energy storage) is given below.

[0170]

[0171] C es (p es )=α es |P es |+P es

[0172] Among them, C col,k,t (P col,k,t ) represents the cost of cutting load k within the range of t; C DG,i,t (P DG,i,t ) represents the generation cost of DGs within the range of t; C es,j,t (P es,j,t ) represents the cost of energy storage j within the range of t; α and β are cost function coefficients.

[0173] Generally speaking, the optimization problem is defined as a non - linear mixed - integer program with hard temporal constraints. Mathematical methods are usually not suitable for solving this problem because they are generally model - or type - based and require an accurate model of the system to derive. In addition, starting from a single point, such algorithms are more likely to fall into local optimal states. These methods are difficult to handle constraints such as non - linear minimum up - and - down time limits. Various population - based algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), or grey wolf algorithm (GW), have been used in the optimization models of power systems. However, in this study, our company used the DE algorithm because the probability of this algorithm getting involved in local optima is relatively low, and it has an appropriate optimization speed when obtaining the answer.

[0174] The goal of the VPP is to obtain the maximum profit in participating in the electricity and trading markets by providing the electricity load of users and exchanging electricity with the electricity market. To this end, our company should consider the income from exchanging energy and reserves with the electricity market and providing electricity to VPP users, the cost of DG units, the cost of energy storage units, and the cost of load shedding. Therefore, the objective function (OF) can be divided into the following four different parts:

[0175]

[0176] Among them, λ E,t is the energy price of the electricity market; P t represents the proposed capacity for the VPP to buy or sell in the electricity market; λ R,t is the reservation price of the electricity market; R t represents the proposed reserve capacity of the VPP in the electricity market; λ L,t represents the energy rate of VPP consumers; L t is the consumer load of the VPP. The following formula shows the income from exchanging energy and reserves with the electricity market and providing electricity to VPP users. The following formula shows the cost of DG units.

[0177]

[0178] The following formula shows the cost of load shedding.

[0179]

[0180] In the formula, P col,k,t is the cut - off load k within the range of t in the electricity market, and R col,k,t gives the cut - off load k within the range of t in the reserve market. Therefore, the OF of the VPP in the electricity market proposed by our company can be defined as the equation:

[0181] OF = A1 - A2 - A3 - A4

[0182] Here, the electricity market can be regarded as pay - as - you - go or uniform offer. The forward market is closed by capacity offer. According to the objective function of electricity market trading proposed in the formula, it should be solved by considering a series of constraints. Some of these constraints are related to the energy balance and production and consumption reserves, which are given in the following formulas respectively. In these formulas, the power losses in the virtual power grid are considered.

[0183]

[0184] Among them, Load t is the load provided by the VPP only in the energy market; represents the power loss of the VPP only in the energy market; η es,j represents the energy storage efficiency of j; R es,j,t is the load provided by the VPP only in the reserve market; R Loss,t represents the power loss of the VPP only in the reserve market; R t is the recommended reserve capacity of the VPP in the electricity market; R DG,i,t is DG i the generated electricity within the range of t in the reserve market; R es,j,t represents the reserve capacity of the energy storage device j within the range of t; R col,k,t is the curtailed load k within the range of t.

[0185] Other categories that need to be considered are the restrictions related to distributed energy in the VPP, as shown in the following formula. The formula shows the restrictions of distributed generation devices.

[0186]

[0187] Among them, are the minimum and maximum values of DG i respectively; R DG,i,tt is the generated electricity of DG i in the reserve market; MSR t, is the increase rate of distributed generation i in the reserve market; are the start - up or shut - down durations of unit i within the time t respectively; are the minimum start - up or shut - down times of unit i respectively; z (i,t) is the graphical state of unit i.

[0188] The inequality stipulates the restrictions of the energy storage unit.

[0189]

[0190] |P es,t,j |≤R CH,j

[0191] Among them, RCH,j The maximum charge-discharge capacity inequality of energy storage device j stipulates the limits of intermittent loads.

[0192]

[0193] Among them, is the upper limit of the load that can be shed. The last type of equations are the grid constraints provided by the formula. These equations show the passage of electricity in the radial distribution network.

[0194]

[0195] In addition, the inequality expresses the constraint security of the virtual power plant.

[0196]

[0197] where V max and V min are the upper and lower voltage limits. P and Q are the active power and reactive power respectively; R and X also represent the resistance and reactance of I and J; δ i and δ j are the phase angles of i and j respectively. Like other evolutionary methods, a set of input parameters are required to initialize the optimization process. These parameters include the population size, the maximum number of iterations, and other parameters required by DE.

[0198] Power System Operation Model Considering Demand Response and Integrated Flexible Carbon Capture Power Plant

[0199] To address global warming and reduce CO2 emissions, vigorously developing wind power is one of the important measures. However, affected by the natural wind energy resource endowment, wind power generation has strong volatility and randomness, and its large-scale grid connection brings many pressures to the peak shaving of the power system. Carbon capture power plants are ideal cooperative power sources for wind power. By reasonably dispatching carbon capture power plants, the adverse effects of wind power on the power system can be alleviated. Considering the coordinated cooperation between integrated flexible carbon capture power plants and wind power, supplemented by load-side demand response to smooth the load curve, low-carbon economic dispatching is achieved.

[0200] (1) Price-based Demand Response Model

[0201] (1) Objective function:

[0202] Considering the peak shaving and valley filling characteristics of price-based demand response, the objective function adopts the minimum sum of the squares of load fluctuations that meet the peak shaving and valley filling requirements. The objective function is as follows:

[0203]

[0204] In the formula, J is the sum of the squares of the daily load fluctuations, and P t optOptimized load power during period t.

[0205] (2) Constraints

[0206] Regarding the constraints, electricity price constraints, power transfer constraints, user satisfaction constraints, and total demand allocation constraints are considered as follows:

[0207]

[0208] (2) Low-carbon economic dispatch model of a power system with wind power considering an integrated flexible carbon capture power plant

[0209] In the system of wind power-carbon capture low-carbon economic dispatch, the wind farm does not produce CO2 during power generation, but its large-scale grid connection will exacerbate the peak shaving pressure of the power system. Through the reasonable regulation of the integrated flexible carbon capture power plant, it can ensure the low-carbon operation of thermal power plants while making up for the deficiency of strong volatility of wind power and ensure the safe and stable operation of the power system.

[0210] 1) Objective function

[0211] The dispatch strategy is low-carbon economic dispatch. Therefore, the objective function is a cost function, including thermal power fuel cost, thermal power start-up and shutdown cost, carbon trading cost, and carbon capture equipment depreciation cost. At the same time, considering the problem of wind power curtailment, the wind power curtailment penalty cost is included. The specific situation is as follows:

[0212] minC = min(C K +C T +C H +C Q +C z )

[0213] Where C is the total operating cost of the power system dispatch, C K is the total start-up and shutdown cost of thermal power units, C T is the carbon trading cost, C H is the thermal power fuel cost, C Q is the wind power curtailment penalty cost, C Z is the carbon capture equipment depreciation cost. The total start-up and shutdown cost of thermal power units, CK, and the fuel cost, CH, can ensure the safe and stable operation of the power system through reasonable regulation of thermal power output. Sometimes, to ensure economy, it is necessary to control the start-up and shutdown of thermal power units. Therefore, the thermal power units mainly include start-up and shutdown costs and fuel costs during the dispatch process, as follows:

[0214]

[0215] Where, S i is the unit start-up and shutdown cost of thermal power unit i, n is the total number of thermal power units, u i,t is the start-up and shutdown state of thermal power unit, 1 is on, 0 is off, PGi,t is the total output of thermal power unit i in period t, a i , b i , c i are the fuel cost parameters of thermal power unit i;

[0216] 2) Carbon trading cost

[0217] During the operation of the CT power system, carbon dioxide will be generated. To restrict the operation of high-carbon power plants and achieve low-carbon dispatching of the power system, China has established a carbon trading market. The carbon trading cost is calculated based on the carbon emission and free quota, and its calculation formula is as follows:

[0218]

[0219] In the formula, σ T is the carbon trading price, E c is the net carbon emission of the power system in a dispatching cycle, t, Δt is the period length, and h is the carbon quota coefficient of the thermal power unit;

[0220] 3) Penalty cost for wind curtailment C Q

[0221] To improve the accommodation of wind power, the penalty cost for wind curtailment is added to the model, and the calculation formula is:

[0222]

[0223] In the formula, σ Q is the penalty cost coefficient for wind curtailment, P W,t is the predicted output of wind power in period t, P WS,t is the grid-connected power of wind power in period t

[0224] 4) Depreciation cost of carbon capture equipment C Z

[0225] Considering the high cost of carbon capture equipment, the depreciation cost of carbon capture equipment is introduced into the model, as follows:

[0226]

[0227] In the formula, C ZJ is the total price of carbon capture equipment except the liquid storage tank under the benchmark conditions, C GJ is the cost required for the expansion and transformation of the regenerator compressor, ω is the net salvage rate, N T is the depreciation life of carbon capture equipment except the liquid storage tank, P CY is the price per unit volume of the liquid storage tank, V CY is the volume of the liquid storage tank, N C is the depreciation life of the liquid storage tank.

[0228] (2) Constraint conditions

[0229] In the low-carbon economic dispatch model of a power system considering the integrated flexible operation mode of a carbon capture power plant, the internal operation constraints of each power plant and the safe operation constraints of the power grid should be fully considered. The main constraints are as follows:

[0230] Power balance constraint: When network losses are not considered, the sum of the net output power of thermal power plants and the output power of wind farms is equal to the load, that is:

[0231]

[0232] Where \(P\) Ji,t is the net output power of the \(i\)-th thermal power unit at time \(t\), and \(P\) el,t is the electrical load at time \(t\).

[0233] Integrated flexible carbon capture power plant constraint

[0234] The integrated flexible carbon capture power plant adds a liquid storage tank on the basis of the split-flow carbon capture power plant. The specific constraints are as follows:

[0235]

[0236] Where \(P\) Gi,t is the carbon capture equivalent power of the \(i\)-th thermal power unit at time \(t\), \(P\) Di is the carbon capture maintenance energy consumption of the \(i\)-th thermal power unit, is the maximum total output power of the \(i\)-th thermal power unit. \(E\) ingCO2i,t is the CO₂ treatment rate of the \(i\)-th thermal power unit at time \(t\), \(E\) Pi,t is the total CO₂ emission rate of the \(i\)-th thermal power unit at time \(t\), \(\delta\) Bi,t is the flue gas split ratio of the \(i\)-th thermal power unit at time \(t\), \(E\) CGi,t is the CO₂ supply rate of the liquid storage tank equipped with the \(i\)-th thermal power unit at time \(t\), \(E\) Pi,t is the net CO₂ emission rate of the \(i\)-th thermal power unit at time \(t\), \(V\) CAi,t is the solution flow rate of the liquid storage tank supplying CO₂ for the \(i\)-th thermal power unit at time \(t\), \(V\) CFLi,t is the liquid storage volume of the rich liquid tank of the \(i\)-th thermal power unit at time \(t\), \(V\) CPLi,t is the liquid storage volume of the lean liquid tank of the \(i\)-th thermal power unit at time \(t\), \(V\) CRi is the capacity of the liquid storage tank configured for the \(i\)-th thermal power unit, \(V\) CFLi,0 is the initial liquid storage volume of the rich liquid tank of the \(i\)-th thermal power unit, \(V\) CPLi,0 is the initial liquid storage volume of the lean liquid tank of the \(i\)-th thermal power unit, \(V\) CFLi,24 is the liquid storage volume of the rich liquid tank at the end of the scheduling period of the \(i\)-th thermal power unit, \(V\) CPLi,24 is the liquid storage volume of the lean liquid tank at the end of the scheduling period of the \(i\)-th thermal power unit.

[0237] Thermal power unit related constraints

[0238] As a flexible regulating power source in the power system, thermal power units can achieve low-carbon economic dispatching of the power system through reasonable dispatching. It mainly needs to meet the constraints of output limit, ramping rate, and start-up and shutdown time, as follows:

[0239]

[0240] -R dn,i Δt ≤ P Gi,t -P Gi,t-1 ≤ R up,i Δt

[0241]

[0242] In the formula is the minimum output power of thermal power unit i, is the maximum output power of thermal power unit i, R dn,i is the down-ramping rate of thermal power unit i, R up,i is the up-ramping rate of thermal power unit i, is the time that the i-th thermal power unit has been continuously in operation at time t-1, is the time that the i-th thermal power unit has been continuously shut down at time t-1, is the minimum continuous operation time of the i-th thermal power unit, is the minimum continuous shutdown time of the i-th thermal power unit.

[0243] Wind power output constraint

[0244] Considering that the wind power dispatching output needs to be less than its own predicted value, the constraint conditions are as follows:

[0245] 0 ≤ P WS,t ≤ P W,t

[0246] Spinning reserve constraint:

[0247]

[0248] In the formula is the maximum net output power of the i-th thermal power unit, is the minimum net output power of the i-th thermal power unit, R Jup,i,t is the up-ramping rate of the net output of the i-th thermal power unit at time t, R Jdn,i,t is the down-ramping rate of the net output of the i-th thermal power unit at time t, μ1 is the reserve capacity coefficient considering load uncertainty, μ2 is the reserve capacity coefficient considering load uncertainty, P wcp is the installed capacity of the wind farm.

[0249] Further, in some embodiments, the method provided in this embodiment further includes:

[0250] S51. Obtain the optimization results of the operation scheduling of the virtual power plant based on the dynamically adjusted carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economy operation strategy;

[0251] S52. Evaluate the optimization results of the operation scheduling to obtain the corresponding evaluation results;

[0252] S53. Adjust the dynamically adjusted carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economy operation strategy based on the evaluation results.

[0253] Specifically, the virtual power plant operation scheduling optimization method provided in this embodiment further includes the following steps: Based on the dynamically adjusted carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economy operation strategy, obtain the optimization results of the virtual power plant operation scheduling. Evaluate the obtained optimization results of the operation scheduling, including cost-benefit analysis, environmental impact assessment, system stability testing, etc., to determine the effectiveness and efficiency of the optimization measures. According to the evaluation results, adjust the dynamically adjusted carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economy operation strategy. This step ensures that the models and strategies can continuously adapt to market changes and technological progress.

[0254] Through evaluation and adjustment in this embodiment, the operation scheduling strategy of the virtual power plant can be continuously improved to adapt to new market conditions and technological developments; the virtual power plant can respond more quickly to market changes and policy adjustments, improving its adaptability and flexibility; continuous evaluation and adjustment help to allocate resources more effectively, improve energy utilization efficiency, and reduce costs; through continuous optimization, the virtual power plant can achieve higher economic benefits, such as by reducing unnecessary energy waste and lowering operating costs; continuous environmental impact assessment helps to reduce the negative environmental impact of the virtual power plant operation and promote sustainable development; through real-time adjustment and optimization, the virtual power plant can better maintain the stability of the power system, especially in the face of supply and demand fluctuations and external disturbances; continuous optimization of the demand-side management strategy can improve user satisfaction through more reasonable electricity prices and more reliable power supply.

[0255] It can be seen that the virtual power plant operation scheduling optimization method provided in this embodiment enables the virtual power plant to dynamically adjust carbon emissions according to changes in the carbon emission rights market and its own production capacity by constructing a model capable of dynamically adjusting the carbon flow. This not only helps reduce carbon emissions but also effectively reduces the economic risks brought about by changes in carbon emission rights prices. A multi-scale low-carbon operation model is constructed, which can perform optimal scheduling in the day-ahead, intra-day, and real-time stages to achieve a balance among the economy, stability, and environmental protection of the power system. Through the demand-side management strategy of this embodiment, the power consumption can be adjusted in real time to better match the power supply and demand, reduce the operation cost of the power system, and the effect is remarkable. The low-carbon economic operation strategy constructed in this embodiment will greatly promote the sustainable development of the virtual power plant, and at the same time achieve a significant reduction in carbon emissions, undoubtedly providing a new solution for promoting the construction of a power system with more renewable energy access. By evaluating and predicting the impact of carbon emission rights trading policies on the operation of virtual power plants, it can help virtual power plants make strategic plans in advance and respond flexibly. At the same time, it can also provide a reference for government departments to implement low-carbon policies. This embodiment is applicable to various types of virtual power plants and has strong versatility and scalability. It can be seen that this embodiment has significant advantages and positive impacts in promoting the development of low-carbon power systems, strengthening the digital and intelligent operation of power plants, and improving the operation efficiency and economy of power systems.

[0256] To facilitate the better implementation of a virtual power plant operation scheduling optimization method according to an embodiment of the present application, an embodiment of the present application also provides a virtual power plant operation scheduling optimization device. The meanings of the terms are the same as those in the above virtual power plant operation scheduling optimization method, and the specific implementation details can refer to the descriptions in the method embodiments.

[0257] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the virtual power plant operation scheduling optimization device provided in an embodiment of the present application. The virtual power plant operation scheduling optimization device may specifically include a dynamic carbon flow adjustment model module 201, a multi-scale low-carbon operation optimization model module 202, a demand-side management strategy module 203, and a low-carbon economic operation strategy module 204, which are specifically as follows:

[0258] The dynamic carbon flow adjustment model module 201 is configured to construct a dynamic carbon flow adjustment model and dynamically adjust the carbon emissions of the virtual power plant through the dynamic carbon flow adjustment model;

[0259] The multi-scale low-carbon operation optimization model module 202 is configured to construct a multi-scale low-carbon operation optimization model and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model;

[0260] The demand-side management strategy module 203 is configured to formulate a demand-side management strategy and adjust the power consumption situation in real time through the demand-side management strategy;

[0261] The low-carbon economy operation strategy module 204 is used to formulate low-carbon economy operation strategies and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategies.

[0262] Further, in some embodiments, the dynamic carbon flow model module 201 is specifically configured to:

[0263] Collect the energy flow data and carbon emission data of each department inside the virtual power plant in real time;

[0264] Construct a corresponding dynamic carbon flow model based on the energy flow data and carbon emission data;

[0265] Analyze the energy flow characteristics and carbon flow characteristics of the virtual power plant based on the dynamic carbon flow model, and dynamically adjust the carbon emissions of the virtual power plant according to the carbon emission right market change information and production capacity information.

[0266] Further, in some embodiments, the multi-scale low-carbon operation optimization model module 202 is specifically configured to:

[0267] Collect the historical power generation data and real-time power generation data of the virtual power plant;

[0268] Define the objective function and constraint conditions of the multi-scale low-carbon operation optimization model;

[0269] Adjust the parameters of the multi-scale low-carbon operation optimization model based on the historical power generation data and real-time power generation data according to the preset optimization algorithm to obtain the optimized multi-scale low-carbon operation optimization model.

[0270] Optimize the power generation and load scheduling in the day-ahead stage based on the prediction data through the day-ahead robust optimization model;

[0271] Adjust the power generation plan in the intra-day stage according to the actual operation data and market information through the intra-day rolling optimization model;

[0272] Optimize the real-time scheduling of the virtual power plant in the real-time stage through the real-time stage operation model.

[0273] Further, in some embodiments, the demand-side management strategy module 203 is specifically configured to:

[0274] Collect the power consumption data on the demand side and analyze the corresponding power consumption patterns on the demand side based on the power consumption data;

[0275] Formulate a demand response plan and an incentive mechanism based on the power consumption patterns;

[0276] Monitor the grid load, power generation data, and demand-side power consumption data in real time;

[0277] Based on the grid load, generation data, and demand-side power consumption data, the power consumption of the demand side is adjusted in real time through demand-side management strategies.

[0278] Furthermore, in some embodiments, the low-carbon economic operation strategy module 204 is specifically configured to:

[0279] Analyze the power market demand, price trends, and changes in the carbon emission rights market to obtain market analysis results;

[0280] Set carbon emission targets, evaluate the low-carbon technologies adopted, and determine the corresponding target low-carbon technologies;

[0281] Formulate low-carbon economic operation strategies based on the market analysis results, carbon emission targets, and target low-carbon technologies. The low-carbon economic operation strategies include generation strategies and dispatching strategies;

[0282] Optimize the generation and dispatching of the virtual power plant based on the low-carbon economic operation strategies.

[0283] Furthermore, in some embodiments, the device further includes an adjustment module, which is specifically configured to:

[0284] Obtain the operation and dispatching optimization results of the virtual power plant based on the dynamic adjustment carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economic operation strategy;

[0285] Evaluate the operation and dispatching optimization results to obtain the corresponding evaluation results;

[0286] Adjust the dynamic adjustment carbon flow model, multi-scale low-carbon operation optimization model, demand-side management strategy, and low-carbon economic operation strategy based on the evaluation results.

[0287] In summary, the virtual power plant operation scheduling optimization device provided in this embodiment constructs a dynamically adjusted carbon flow model by dynamically adjusting the carbon flow model module 201, and dynamically adjusts the carbon emissions of the virtual power plant through the dynamically adjusted carbon flow model; constructs a multi-scale low-carbon operation optimization model through the multi-scale low-carbon operation optimization model module 202, and performs multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulates a demand-side management strategy through the demand-side management strategy module 203, and adjusts the power consumption situation in real time through the demand-side management strategy; formulates a low-carbon economic operation strategy through the low-carbon economic operation strategy module 204, and optimizes the operation scheduling of the virtual power plant through the low-carbon operation strategy. The virtual power plant operation scheduling optimization device provided in this embodiment can adapt to the changes in the energy market, improve the efficiency and sustainability of the power system, and enhance the potential of the virtual power plant in promoting the development of low-carbon power systems, digital and intelligent operations, power system operation efficiency and economy by providing a new carbon flow pattern and low-carbon economic operation strategy for the low-carbon economic operation of the virtual power plant, and solve the problems that the existing technology fails to fully consider the dynamic adjustment of carbon flow, multi-scale low-carbon operation optimization, insufficient research on demand-side management strategies, and insufficient evaluation and prediction of the impact of carbon emission trading policies on the operation of virtual power plants in the virtual power plant operation strategy.

[0288] In addition, an embodiment of the present application also provides an electronic device, as Figure 4 shown, which shows a schematic structural diagram of the electronic device involved in the embodiment of the present application. Specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art can understand that Figure 4 the structure of the electronic device shown in

[0289] does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Among them:

[0290] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and the virtual power plant operation scheduling optimization method by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device. In addition, the memory 302 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 302 can also include a memory controller to provide the processor 301 with access to the memory 302.

[0291] The electronic device further includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 303 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0292] The electronic device may further include an input unit 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0293] Although not shown, the electronic device may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to achieve various functions as follows:

[0294] Build a dynamic carbon flow adjustment model, and dynamically adjust the carbon emissions of the virtual power plant through the dynamic carbon flow adjustment model; build a multi-scale low-carbon operation optimization model, and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulate a demand-side management strategy, and adjust the power consumption situation in real time through the demand-side management strategy; formulate a low-carbon economy operation strategy, and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy.

[0295] For the specific implementation of each of the above operations, reference can be made to the previous embodiments, which will not be elaborated here.

[0296] The embodiments of the present application provide a new carbon flow pattern and a low-carbon economic operation strategy for the low-carbon economic operation of a virtual power plant, which can adapt to the changes in the energy market, improve the efficiency and sustainability of the power system, enhance the potential of the virtual power plant in promoting the development of a low-carbon power system, digital and intelligent operation, and the operation efficiency and economy of the power system, and solve the problems that the existing technology fails to fully consider the dynamic adjustment of carbon flow, multi-scale low-carbon operation optimization, insufficient research on demand-side management strategies, and insufficient evaluation and prediction of the impact of carbon emission trading policies on the operation of the virtual power plant in the operation strategy of the virtual power plant.

[0297] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0298] Therefore, the embodiments of the present application provide a storage medium that stores multiple instructions that can be loaded by a processor to execute the steps in any of the virtual power plant operation scheduling optimization methods provided by the embodiments of the present application. For example, the instructions can perform the following steps:

[0299] Construct a dynamic adjustment carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamic adjustment carbon flow model; construct a multi-scale low-carbon operation optimization model and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; formulate a demand-side management strategy and adjust the power consumption situation in real time through the demand-side management strategy; formulate a low-carbon economic operation strategy and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy.

[0300] For the specific implementation of each of the above operations, reference can be made to the previous embodiments and will not be elaborated here.

[0301] Among them, the storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), a magnetic disk or an optical disc, etc. Since the instructions stored in the storage medium can execute the steps in any of the virtual power plant operation scheduling optimization methods provided by the embodiments of the present application, the beneficial effects that can be achieved by any of the virtual power plant operation scheduling optimization methods provided by the embodiments of the present application can be realized. For details, refer to the previous embodiments and will not be elaborated here.

[0302] The above has introduced in detail a virtual power plant operation scheduling optimization method, device, electronic device, and storage medium provided by the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A virtual power plant operation scheduling optimization method, characterized in that: The steps include: Constructing a dynamic adjustment carbon flow model, and dynamically adjusting the carbon emissions of the virtual power plant through the dynamic adjustment carbon flow model; Constructing a multi-scale low-carbon operation optimization model, and performing multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; Formulate demand side management strategies and adjust power consumption in real time through the demand side management strategies; A low-carbon economic operation strategy is formulated, and the operation scheduling of the virtual power plant is optimized through the low-carbon operation strategy.

2. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The dynamically adjusting carbon flow model is constructed, and the carbon emissions of the virtual power plant are dynamically adjusted by the dynamically adjusting carbon flow model, including: Collecting energy flow data and carbon emission data of various departments within the virtual power plant in real time; Constructing a corresponding dynamically adjusted carbon flow model based on the energy flow data and the carbon emission data; The energy flow characteristics and carbon flow characteristics of the virtual power plant are analyzed based on the dynamically adjusted carbon flow model, and the carbon emissions of the virtual power plant are dynamically adjusted according to the carbon emission rights market change information and production capacity information.

3. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The construction of a multi-scale low-carbon operation optimization model includes: Collecting historical power generation data and real-time power generation data of the virtual power plant; Defining the objective function and constraints of the multi-scale low-carbon operation optimization model; Based on a preset optimization algorithm, the parameters of the multi-scale low-carbon operation optimization model are adjusted according to the historical power generation data and the real-time power generation data to obtain an optimized multi-scale low-carbon operation optimization model.

4. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The multi-scale low-carbon operation optimization model includes a day-ahead robust optimization model, an intra-day rolling optimization model, and a real-time stage operation model. The multi-scale optimization of the virtual power plant by using the multi-scale low-carbon operation optimization model includes: Optimizing power generation and load scheduling in the day-ahead phase based on forecast data using the day-ahead robust optimization model; Adjusting the power generation plan during the intraday period according to actual operation data and market information through the intraday rolling optimization model; The real-time scheduling optimization of the virtual power plant is performed in the real-time stage through the real-time stage operation model.

5. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The formulating of a demand side management strategy and adjusting the power consumption in real time by using the demand side management strategy include: Collect power consumption data on the demand side, and analyze the corresponding power consumption pattern on the demand side based on the power consumption data; Developing demand response plans and incentive mechanisms based on the electricity usage patterns; Real-time monitoring of grid load, power generation data and demand-side power consumption data; The demand-side power consumption is adjusted in real time based on the grid load, the power generation data and the demand-side power consumption data through the demand-side management strategy.

6. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The formulating of a low-carbon economic operation strategy and optimizing the operation and scheduling of the virtual power plant through the low-carbon operation strategy includes: Analyze the electricity market demand, price trends and changes in the carbon emission rights market to obtain market analysis results; Set carbon emission targets, evaluate the low-carbon technologies used, and determine the corresponding target low-carbon technologies; Formulate a low-carbon economic operation strategy based on the market analysis results, the carbon emission target and the target low-carbon technology, wherein the low-carbon economic operation strategy includes a power generation strategy and a scheduling strategy; The power generation and scheduling of the virtual power plant are optimized based on the low-carbon economic operation strategy.

7. The virtual power plant operation scheduling optimization method according to claim 1 is characterized in that: The method further comprises: Obtaining an operation scheduling optimization result of the virtual power plant based on the dynamically adjusted carbon flow model, the multi-scale low-carbon operation optimization model, the demand-side management strategy and the low-carbon economic operation strategy; Evaluate the operation scheduling optimization result to obtain a corresponding evaluation result; The dynamically adjusted carbon flow model, the multi-scale low-carbon operation optimization model, the demand-side management strategy and the low-carbon economic operation strategy are adjusted based on the evaluation results.

8. A virtual power plant operation scheduling optimization device, characterized in that: include: A dynamically adjusted carbon flow model module is used to construct a dynamically adjusted carbon flow model and dynamically adjust the carbon emissions of the virtual power plant through the dynamically adjusted carbon flow model; A multi-scale low-carbon operation optimization model module, used to construct a multi-scale low-carbon operation optimization model, and perform multi-scale optimization on the virtual power plant through the multi-scale low-carbon operation optimization model; A demand side management strategy module, used to formulate a demand side management strategy and adjust power consumption in real time through the demand side management strategy; The low-carbon economic operation strategy module is used to formulate a low-carbon economic operation strategy and optimize the operation scheduling of the virtual power plant through the low-carbon operation strategy.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the virtual power plant operation scheduling optimization method as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the virtual power plant operation scheduling optimization method as described in any one of claims 1 to 7.

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