Multi-dimensional collaborative optimization regional power dispatching method, device, equipment and medium

Abstract

The invention discloses a multi-dimensional collaborative optimization regional power dispatching method and device, equipment and a medium, and relates to the technical field of power system dispatching. Demand-type provinces and cities and supply-type provinces and cities are enabled to mutually dispatch surplus and deficiency of power through an inter-province mutual aid mode; meanwhile, optimization scheduling is carried out by considering the inter-provincial power scheduling cost, the power waste cost and the scheduling risk cost, the comprehensive cost of the power system under different conditions can be effectively reduced, and the operation economy of the power system is improved. Environmental benefit evaluation is further introduced, the overall renewable energy consumption proportion of the region is improved, the electricity abandoning phenomenon of the renewable energy is reduced, and then carbon emission in the power production process is reduced. And finally, the monthly power dispatching condition and the monthly power abandoning condition of each province and city in the region can be obtained by solving the comprehensive cost efficiency optimization model, so that the optimal configuration of the overall power resources of the region is realized, the adaptive capacity of a power system in the unbalanced state is enhanced, and the utilization efficiency of the power resources is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a method, apparatus, equipment and medium for regional power dispatching with multi-dimensional collaborative optimization. Background Art

[0002] As renewable energy continues to grow in the global energy mix, power system scheduling faces increasingly complex challenges. Renewable energy sources such as solar and wind, driven by their clean and sustainable nature, have become a key development direction for the power industry. However, renewable energy generation exhibits significant intermittent and fluctuating characteristics. For example, wind power generation depends on wind speed, while solar power generation is limited by sunlight intensity and duration. This poses significant challenges to the stability of the power system's generation side.

[0003] In the related art, when an imbalance in electricity supply and demand occurs in a certain region, two conventional methods are mainly used to maintain system stability. One is to start up backup units to increase power supply; the other is to reduce load, that is, to limit the electricity demand of some users and give priority to ensuring the power supply of key areas and important users. However, the applicant recognizes that neither of these two methods fully considers the characteristics of renewable energy power generation. Renewable energy power generation is unstable. In the case of a sudden increase in electricity demand, even if backup units are started and load is reduced, it is impossible to effectively absorb a large amount of excess renewable energy power, resulting in the power being abandoned. Not only is the resource utilization rate low, but it also causes resource waste, thus affecting the stability of the power system. Summary of the Invention

[0004] In view of this, the present application provides a regional power dispatching method, device, equipment and medium with multi-dimensional collaborative optimization. The main purpose is to solve the problem that existing methods have not fully taken into account the characteristics of renewable energy power generation. In the case of sudden increase in electricity demand, even if the backup units are started and the load is reduced, a large amount of excess renewable energy power cannot be effectively absorbed. Not only is the resource utilization rate low, but it will also cause resource waste, thereby affecting the stability of the power system.

[0005] According to a first aspect of the present application, a regional power dispatching method for multi-dimensional collaborative optimization is provided, the method comprising:

[0006] Acquire historical power data of a target area, and determine, in the target area, a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month based on the historical power data;

[0007] According to the plurality of demand-type provinces and cities and the plurality of supply-type provinces and cities corresponding to each month, extracting the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month from the regional power data of the target area;

[0008] Calculating the annual power dispatch cost using the monthly demand-based provincial and municipal power dispatch data, calculating the annual power waste cost and environmental benefits using the monthly supply-based provincial and municipal power waste data, and calculating the power dispatch risk of the target area based on the historical power data;

[0009] Constructing a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit;

[0010] Obtain the demand-based provincial and municipal power dispatching strategies and supply-based provincial and municipal power abandonment strategies corresponding to each month based on the minimization solution of the comprehensive cost-effectiveness optimization model by the solver, and use the demand-based provincial and municipal power dispatching strategies and supply-based provincial and municipal power abandonment strategies corresponding to each month as the dispatching optimization results of the target area.

[0011] According to a second aspect of the present application, a regional power dispatching device for multi-dimensional collaborative optimization is provided, the device comprising:

[0012] A determination module, configured to obtain historical power data of a target area, and determine a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month in the target area according to the historical power data;

[0013] An extraction module is configured to extract the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month from the regional power data of the target area according to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month;

[0014] a calculation module for calculating the annual power dispatch cost using the monthly demand-based provincial and municipal power dispatch data, calculating the annual power waste cost and environmental benefits using the monthly supply-based provincial and municipal power waste data, and calculating the power dispatch risk of the target area based on the historical power data;

[0015] A construction module, configured to construct a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit;

[0016] A solution module is used to obtain the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month, which are obtained by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and use the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month as the dispatching optimization result of the target area.

[0017] According to a third aspect of the present application, a device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0018] According to a fourth aspect of the present application, a medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0019] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0020] The present application provides a regional power dispatching method, device, equipment and medium for multi-dimensional collaborative optimization. The present application obtains historical power data of a target area, determines multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month in the target area based on the historical power data, extracts the demand-type province and city power dispatching data and supply-type province and city power waste data for each month from the regional power data of the target area based on the multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month, calculates the annual power dispatching cost using the demand-type province and city power dispatching data for each month, calculates the annual power waste cost and environmental benefits using the supply-type province and city power waste data for each month, and calculates the power dispatching risk of the target area based on the historical power data. Based on the annual power dispatching cost, power dispatching risk, annual power waste cost and environmental benefits, a comprehensive cost-effectiveness optimization model is constructed, obtains the demand-type province and city power dispatching strategy and supply-type province and city power waste strategy corresponding to each month obtained by minimizing the comprehensive cost-effectiveness optimization model based on a solver, and uses the demand-type province and city power dispatching strategy and supply-type province and city power waste strategy corresponding to each month as the dispatching optimization result of the target area. Through interprovincial mutual assistance, demand- and supply-oriented provinces and cities can adjust their power surpluses and shortages. This, combined with optimized dispatching that considers interprovincial power dispatching costs, power curtailment costs, and dispatching risk costs, can effectively reduce the overall cost of the power system under different circumstances and improve the economic efficiency of power system operation. Furthermore, by introducing environmental benefit assessments, the overall renewable energy consumption ratio in the region can be increased, renewable energy curtailment can be reduced, and carbon emissions from power generation can be reduced. Finally, by solving a comprehensive cost-effectiveness optimization model, monthly power dispatch and curtailment data for each province and city in the region can be obtained. This allows for optimal allocation of regional power resources, enhances the power system's adaptability to imbalances, and improves the efficiency of power resource utilization.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0023] Figure 1 A schematic flow chart of a method for multi-dimensional collaborative optimization of regional power dispatching provided by an embodiment of the present application is shown;

[0024] Figure 2 A schematic flow chart of another method for multi-dimensional collaborative optimization of regional power dispatching provided by an embodiment of the present application is shown;

[0025] Figure 3 A schematic diagram of the structure of a multi-dimensional collaborative optimization regional power dispatching method provided by an embodiment of the present application is shown;

[0026] Figure 4 A schematic diagram of the device structure of a device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0029] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0030] As an effective means of power dispatch, interprovincial mutual assistance not only improves the stability and economic efficiency of the power system but also provides strong support for addressing the instability of renewable energy. However, determining the cost-effectiveness of interprovincial mutual assistance and conducting in-depth analysis of its economic, risk, and environmental benefits are becoming increasingly important.

[0031] In traditional power systems, when a regional imbalance in electricity supply and demand occurs, system stability is typically maintained by activating backup units or reducing load. While this traditional dispatching method can address supply and demand imbalances in the short term, it is often accompanied by economic losses, renewable energy curtailment, and the added environmental pressures of backup units. This traditional dispatching model faces significant pressure, especially as the proportion of renewable energy generation continues to increase.

[0032] To solve this problem, this application proposes a regional power dispatching method with multi-dimensional collaborative optimization, which obtains the historical power data of the target area, determines the demand-type provinces and cities and the supply-type provinces and cities corresponding to each month, and then extracts the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month. These data are used to calculate the annual power dispatching cost, power waste cost, environmental benefits and power dispatching risk respectively, and construct a comprehensive cost-effectiveness optimization model. Finally, the model is minimized by the solver to obtain the power dispatching strategy of the demand-type provinces and cities and the power waste strategy of the supply-type provinces and cities for each month as the dispatching optimization result. It can accurately identify the supply and demand roles of provinces and cities, improve the accuracy of power dispatching, reasonably allocate power resources and optimize power generation plans, and can also effectively reduce power costs, reduce transmission losses and spare capacity costs, promote energy structure optimization, thereby enhancing the stability and reliability of the power system, responding to emergencies and improving the system's risk resistance. The executor of this application may be an electric power dispatching system, which relies on the computing power of the server to provide services to users. The server may be an independent server, or it may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms and other basic cloud computing servers, so as to provide users with more scientific, more accurate and more timely electric power dispatching optimization strategies, and improve the operating efficiency and stability of the power system.

[0033] The embodiment of the present application provides a regional power dispatching method with multi-dimensional collaborative optimization, such as Figure 1 As shown, the method includes:

[0034] 101. Obtain historical electricity data for a target area, and determine, in the target area, a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month based on the historical electricity data.

[0035] In an embodiment of the present application, the power dispatching system determines multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month in the target area based on historical power data. After clearly distinguishing between demand-type and supply-type provinces and cities, the power dispatching department can accurately allocate power in different months based on the actual demand and supply capacity of each province and city, so that power generation resources can be used more efficiently.

[0036] 102. According to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month, extract the demand-type provinces and cities power dispatch data and the supply-type provinces and cities power waste data for each month from the regional power data of the target area.

[0037] In an embodiment of the present application, the power dispatching system extracts the power dispatching data for demand-based provinces and cities and the power waste data for supply-based provinces and cities for each month from the regional power data of the target area based on the multiple demand-based provinces and cities and the multiple supply-based provinces and cities corresponding to each month. The power dispatching data for demand-based provinces and cities can clearly reflect their power demand characteristics in different months, ensuring that while meeting the power needs of demand-based provinces and cities, the waste of resources caused by excessive dispatching is avoided. By analyzing the power waste data for supply-based provinces and cities, the proportion of renewable energy consumption can be increased, and the stability and reliability of power supply can be improved.

[0038] 103. Use the monthly demand-based provincial and municipal power dispatch data to calculate the annual power dispatch cost, use the monthly supply-based provincial and municipal power waste data to calculate the annual power waste cost and environmental benefits, and calculate the power dispatch risk of the target area based on historical power data.

[0039] In an embodiment of the present application, the power dispatching system calculates the annual power dispatching cost through the power dispatching data of the demand-type provinces and cities each month, which can comprehensively and meticulously reflect the costs incurred by resource allocation and other links in the entire dispatching process. By calculating the annual power waste cost based on the power waste data of the supply-type provinces and cities, the losses caused by power abandonment can be intuitively seen, thereby reducing the power abandonment rate and improving resource utilization efficiency. Moreover, by calculating the environmental benefits related to power abandonment, the environmental protection contribution in the power production process can be quantified, and the power dispatching risk of the target area based on historical power data can be calculated, so that factors that may affect the stability and reliability of power supply can be discovered in advance.

[0040] 104. Construct a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, power dispatching risk, annual power waste cost, and environmental benefits.

[0041] In the embodiments of the present application, traditional power dispatching decisions often only focus on a single or a few factors, such as power generation costs. The power dispatching system takes into account multiple key factors such as annual power dispatching costs, power dispatching risks, annual power waste costs, and environmental benefits, which can effectively reduce the comprehensive cost of the power system under different circumstances and improve the economy of power system operation. Through power dispatching risks and environmental benefits, the overall utilization efficiency of power resources is improved. Moreover, the inclusion of power dispatching risk factors in the model can help the power system identify and assess possible risks in advance, thereby enhancing the stability and reliability of the power system in the face of various risks.

[0042] 105. Obtain the demand-based provincial and municipal power dispatching strategy and supply-based provincial and municipal power abandonment strategy corresponding to each month by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and use the demand-based provincial and municipal power dispatching strategy and supply-based provincial and municipal power abandonment strategy corresponding to each month as the dispatching optimization result of the target area.

[0043] In the embodiments of this application, the power dispatch system formulates strategies based on the model solution results, which can promote resource complementarity between demand-oriented provinces and cities and supply-oriented provinces and cities, improve resource utilization efficiency, and enhance inter-regional power coordination. Furthermore, the solution process fully considers power dispatch risks. The optimized strategy can effectively reduce the dispatch risks caused by various uncertainties, enhance the power system's anti-interference ability, and ensure the stability and reliability of power supply.

[0044] An embodiment of the present application provides a regional power dispatching method for multi-dimensional collaborative optimization. Compared with the prior art, the embodiment of the present invention obtains historical power data of the target area, determines multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month in the target area based on the historical power data, extracts the demand-type provincial and municipal power dispatching data and supply-type provincial and municipal power waste data of each month from the regional power data of the target area based on the multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month, uses the demand-type provincial and municipal power dispatching data of each month to calculate the annual power dispatching cost, uses the supply-type provincial and municipal power waste data of each month to calculate the annual power waste cost and environmental benefits, and calculates the power dispatching risk of the target area based on the historical power data. Based on the annual power dispatching cost, power dispatching risk, annual power waste cost, and environmental benefits, a comprehensive cost-effectiveness optimization model is constructed, obtains the demand-type provincial and municipal power dispatching strategy and supply-type provincial and municipal power waste strategy corresponding to each month obtained by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and uses the demand-type provincial and municipal power dispatching strategy and supply-type provincial and municipal power waste strategy corresponding to each month as the dispatching optimization result of the target area. Through interprovincial mutual assistance, demand- and supply-oriented provinces and cities can adjust their power surpluses and shortages. This, combined with optimized dispatching that considers interprovincial power dispatching costs, power curtailment costs, and dispatching risk costs, can effectively reduce the overall cost of the power system under different circumstances and improve the economic efficiency of power system operation. Furthermore, by introducing environmental benefit assessments, the overall renewable energy consumption ratio in the region can be increased, renewable energy curtailment can be reduced, and carbon emissions from power generation can be reduced. Finally, by solving a comprehensive cost-effectiveness optimization model, monthly power dispatch and curtailment data for each province and city in the region can be obtained. This allows for optimal allocation of regional power resources, enhances the power system's adaptability to imbalances, and improves the efficiency of power resource utilization.

[0045] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, the embodiment of the present application provides another regional power dispatching method with multi-dimensional collaborative optimization, such as Figure 2 As shown, the method includes:

[0046] 201. Obtain historical power data of a target area, perform random sampling on the historical power data, and obtain a random power data matrix.

[0047] In an embodiment of the present application, the power dispatching system determines multiple provinces and cities in the target area, and extracts historical data of each province and city from the historical power data, wherein the historical data includes load power data, wind power generation data, hydropower generation data, photovoltaic power generation data, thermal power generation data, and power value data corresponding to each month of the province and city. Then, the historical data of each province and city are used to construct a target matrix, and the covariance calculation is performed on the target matrix to obtain a covariance matrix. The target matrix and the covariance matrix are randomly sampled to obtain a random power data matrix. Optionally, based on the monthly historical data of different types of power generation related data and random electricity prices in each province and city, and combined with regional power market policies, the historical data are randomly sampled through Monte Carlo simulation to obtain a random power data matrix, thereby accurately predicting the values of random variables such as future power generation and electricity prices, which can reflect possible market fluctuations and uncertainties and provide rich scenario data for subsequent analysis.

[0048] 202. Determine, in the target area, a plurality of demand-type provinces and cities and a plurality of supply-type provinces and cities corresponding to each month based on the random electricity data matrix.

[0049] In an embodiment of the present application, the power dispatch system calculates the monthly power supply and demand gap for each province and city based on the obtained load data and power generation data, and then categorizes different provinces and cities into two types: demand-type and supply-type, based on whether there is excess load or surplus power generation. Demand-type provinces and cities are defined as those with a higher load than power generation, indicating that the province or city faces a power shortage; supply-type provinces and cities are defined as those with a higher power generation than load, indicating that the province or city has excess power resources and needs to reduce power or dispatch power to other demand-type provinces and cities to help alleviate power shortages and maintain power system stability.

[0050] Specifically, for each month, the user load and total power generation of each province and city are extracted from the random power data matrix, and the power supply and demand gap of each province and city is calculated using the user load and total power generation of each province and city, as shown in the following formula 1:

[0051] Formula 1: ΔQ i,j =Q l,i,j -Q g,i,j ,

[0052] Where ΔQ i,j is the electricity supply and demand difference of province / city j in month i, Q l,i,j is the user load of province j in month i, Q g,i,j is the total power generation of province or city j in month i.

[0053] Next, for each province and city, the power supply and demand gap in the province and city is tested;

[0054] If the power supply and demand difference of a province or city is greater than 0, the province or city will be regarded as the demand-type province or city corresponding to the month;

[0055] If the electricity supply and demand difference of a province or city is less than 0, the province or city will be regarded as the supply-type province or city corresponding to the month.

[0056] 203. According to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month, extract the demand-type provinces and cities power dispatching data and the supply-type provinces and cities power waste data for each month from the regional power data of the target area.

[0057] In an embodiment of the present application, for each month, the power dispatching system extracts the target power data corresponding to the month from the regional power data. Then, the inter-provincial power dispatching data corresponding to each demand-type province and city is extracted from the target power data to obtain a plurality of inter-provincial power dispatching data, and the plurality of inter-provincial power dispatching data is used as the demand-type province and city power dispatching data for the month. The demand-type province and city power dispatching data can clearly reflect its power demand characteristics in different months, ensuring that while meeting the power demand of the demand-type provinces and cities, it avoids the waste of resources caused by excessive dispatching. Then, the abandoned power data corresponding to each supply-type province and city is extracted from the target power data to obtain a plurality of abandoned power data, and the plurality of abandoned power data is used as the supply-type province and city power abandonment data for the month. By analyzing the supply-type province and city power abandonment data, the absorption ratio of renewable energy is increased, and the stability and reliability of power supply are improved.

[0058] 204. Extract the annual medium- and long-term market dispatch data and inter-provincial spot market dispatch data for each month from the demand-based provincial and municipal power dispatch data for each month.

[0059] In the embodiment of the present application, in the inter-provincial mutual assistance scenario, demand-oriented provinces and cities can make up for power supply gaps by purchasing external power, and supply-oriented provinces and cities can consume surplus power by selling power resources. Therefore, the inter-provincial power dispatching costs and the power dispatching risk costs involved in the dispatching process are taken into consideration. Therefore, the power dispatching system extracts the annual medium- and long-term market dispatching data and inter-provincial spot market dispatching data for each month from the power dispatching data of demand-oriented provinces and cities in each month, as shown in the following formula 2:

[0060] Formula 2:

[0061] in, is the annual medium- and long-term market dispatch data of the demand-oriented province or city j0 in month i, is the planned dispatching amount of thermal power in the demand-type province or city j0 in month i, is the planned dispatching amount of wind power in the demand-type province or city j0 in month i, is the planned PV dispatching quantity of demand-type province or city j0 in month i, is the planned dispatching amount of hydropower in the demand-type province or city j0 in month i, is the planned dispatch volume of nuclear power in the demand-type province or city j0 in month i.

[0062] Among them, annual medium- and long-term market dispatch represents the monthly power generation of various power sources contracted in advance. The spot market conducts differential trading as a means to address power dispatch risks and improve economic efficiency. To address imbalances and improve the efficiency of power resource utilization, this application conducts cost calculations from the perspective of demand-driven provinces and cities in interprovincial power dispatch.

[0063] 205. The total cost of annual medium- and long-term market dispatch between provinces is calculated using the annual medium- and long-term market dispatch data for each month.

[0064] In the embodiment of the present application, the power dispatching system uses the annual medium- and long-term market dispatch data of each month to calculate and obtain the total annual medium- and long-term market dispatch cost between provinces, as shown in the following formula 3:

[0065] Formula 3:

[0066] in, is the total annual medium- and long-term market dispatch cost among provinces, is the planned dispatching amount of thermal power in the demand-type province or city j0 in month i, is the planned dispatching amount of wind power in the demand-type province or city j0 in month i, is the planned PV dispatching quantity of demand-type province or city j0 in month i, is the planned dispatching amount of hydropower in the demand-type province or city j0 in month i, is the planned dispatch volume of nuclear power in the demand-type province or city j0 in month i, is the thermal power value of demand-type province or city j0 in month i, is the wind power value of demand-type province or city j0 in month i, is the photovoltaic value of demand-type province or city j0 in month i, is the hydropower value of demand-type province j0 in month i, is the nuclear power value of demand-type province / city j0 in month i, and a is the number of demand-type provinces / city j0 in month i. If there are multiple demand-type provinces / city, they will jointly consume the power resources of the remaining supply-type provinces / city.

[0067] 206. Using the inter-provincial spot market dispatch data of each month, we can calculate the total annual dispatch cost of the inter-provincial spot market.

[0068] In the embodiment of the present application, the power dispatching system uses the inter-provincial spot market dispatching data of each month to calculate and obtain the total annual dispatching cost of the inter-provincial spot market, as shown in the following formula 4:

[0069] Formula 4:

[0070] in, is the total annual dispatch cost of the inter-provincial spot market, is the inter-provincial spot market dispatch volume in month i, is the inter-provincial spot market dispatch value in month i. In the spot market, multiple demand-oriented provinces and cities are merged into one entity to calculate the inter-provincial spot market dispatch volume.

[0071] 207. The sum of the total annual dispatch costs of the inter-provincial medium- and long-term markets and the total annual dispatch costs of the inter-provincial spot markets shall be taken as the annual electricity dispatch costs.

[0072] In the embodiment of the present application, the power dispatching system uses the sum of the total annual inter-provincial medium- and long-term market dispatching cost and the total annual inter-provincial spot market dispatching cost as the annual power dispatching cost, as shown in the following formula 5:

[0073] Formula 5:

[0074] in, is the annual power dispatch cost, is the total annual medium- and long-term market dispatch cost among provinces, is the total annual dispatch cost of the inter-provincial spot market.

[0075] By comprehensively considering the cost differences and benefits of different markets, using the spot market to flexibly adjust electricity volume to cope with price fluctuations, improving the power system's ability to respond to market changes and risks, and achieving efficient flow and reasonable allocation of power resources among provinces, resource utilization efficiency can be improved.

[0076] 208. Use the monthly electricity waste data of supply-oriented provinces and cities to calculate the annual electricity waste costs and environmental benefits.

[0077] In the embodiment of the present application, the power dispatching system uses the monthly supply-oriented provincial and municipal power waste data to calculate the annual power waste cost, as shown in the following formula 6:

[0078] Formula 6:

[0079] Among them, C1 is the annual electricity waste cost, Q thp_ab,(i,j1) is the thermal power curtailment of supply-type province or city j1 in month i, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in the supply-type province or city j1 in month i, P thp_ab,(i,j1) is the marginal cost of thermal power in the supply-oriented province or city j1 in month i, P wp_ab,(i,j1) is the marginal cost of wind power in the supply-oriented province or city j1 in month i, P pv_ab,(i,j1)is the marginal cost of photovoltaic power generation in the supply-oriented province or city j1 in month i, P hp_ab,(i,j1) is the marginal cost of hydropower in the supply-type province or city j1 in month i, and b is the quantity of the supply-type province or city j1 in month i.

[0080] Next, this application considers environmental benefit assessment from the perspective of power abandonment type. The higher the proportion of renewable energy generation consumed, the better the environmental benefit. The power dispatching system obtains the environmental benefit coefficient and calculates the environmental benefit using the environmental benefit coefficient and the monthly power abandonment data for supply-oriented provinces and cities, as shown in the following formula 7:

[0081] Formula 7:

[0082] Among them, γ is the environmental benefit, λ is the environmental benefit coefficient, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in supply-type province / city j1 in month i, and b is the number of supply-type provinces / city j1 in month i. By clarifying the positive relationship between the proportion of renewable energy consumption and environmental benefits, it is possible to incentivize renewable energy consumption and thus enhance renewable energy utilization.

[0083] 209. Perform Monte Carlo sampling on historical power data to obtain sampling data, and calculate the power dispatch risk of the target area based on the sampling data.

[0084] In the embodiment of the present application, the power dispatching system performs Monte Carlo sampling on historical power data to obtain sampled data. Then, the confidence level and risk value are obtained, and the confidence level, risk value, and sampled data are used to calculate the power dispatching risk of the target area, as shown in the following formula 8:

[0085] Formula 8:

[0086] in, is the power dispatch risk, which is used to characterize the average excess cost of the power system, VaR is the value at risk, β is the confidence level, β = 0.95, N is the number of samples included in the sampling data, ω n is the nth sample of Monte Carlo sampling, is the annual power dispatch cost of the nth sample in the sampling data, Indicates taking CVaR measures the average excess cost, taking into account not only the probability of risk occurrence but also the mean of losses exceeding the value at risk (VaR), providing strong support for coping with uncertainty.

[0087] It should be noted that when the total surplus of power resources in supply-oriented provinces and cities is greater than the total load demand in demand-oriented provinces and cities, power curtailment still exists. At this time, it is necessary to comprehensively consider the three requirements of economy, reliability, and environmental protection, and make optimization suggestions for power trading and power dispatch for demand-oriented provinces and cities in the inter-provincial mutual assistance scenario. Let j0 represent a demand-oriented province and city, and j1 represent a supply-oriented province and city. Assume that there are a demand-oriented province and city and b supply-oriented province and city among n provinces and cities. Then for each month, as shown in the following formula 9:

[0088] Formula 9:

[0089] Where ΔQ (i,j0) is the total electricity demand of all demand-type provinces and cities j0 in month i, ΔQ (i,j1) is the total power surplus of all supply-type provinces and cities j1 in month i, Q trade,(i,j0) is the electricity purchase amount of demand-type province or city j0 in month i, Q trade,(i,j1) is the electricity sales of supply-oriented province / city j1 in month i, Q ab,(i,j1) is the total amount of electricity wasted in the supply province or city j1 in month i, It means that power resources are only dispatched within the region, and power trading volume includes annual medium- and long-term market trading volume and spot market trading volume.

[0090] 210. Based on the annual power dispatching cost, power dispatching risk, annual power waste cost and environmental benefits, a comprehensive cost-effectiveness optimization model is constructed.

[0091] In the embodiment of the present application, the power dispatching system constructs a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, power dispatching risk, annual power waste cost, and environmental benefits, as shown in the following formula 10:

[0092] Formula 10:

[0093] Among them, C aid For the comprehensive cost-effectiveness optimization model, is the annual power dispatch cost, The comprehensive cost-effectiveness optimization model integrates multiple key factors, including annual power dispatch costs, power dispatch risks, annual power waste costs, and environmental benefits. This model avoids the limitations of focusing on a single factor and comprehensively assesses the operating status of the power system. Furthermore, through comprehensive analysis and optimization of these factors, it guides the rational allocation of power resources.

[0094] 211. Obtain the demand-based provincial and municipal power dispatching strategy and supply-based provincial and municipal power abandonment strategy corresponding to each month by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and use the demand-based provincial and municipal power dispatching strategy and supply-based provincial and municipal power abandonment strategy corresponding to each month as the dispatching optimization result of the target area.

[0095] In the embodiment of the present application, the strategy formulated by the power dispatching system based on the model solution results can promote resource complementarity between demand-oriented provinces and cities and supply-oriented provinces and cities, improve resource utilization efficiency, and enhance inter-regional power coordination, thereby achieving the optimal configuration of regional power resources as a whole. By reducing dispatching costs and waste costs, resources can be used more effectively; considering environmental benefits, it can promote the consumption of clean energy, make resource allocation more in line with sustainable development needs, and improve the overall operating efficiency of the power system. Moreover, the power dispatching risk is fully considered in the solution process, and the optimized strategy can effectively reduce the dispatching risk caused by various uncertain factors, enhance the anti-interference ability of the power system, and ensure the stability and reliability of power supply.

[0096] In existing dispatching methods, when power supply and demand are unbalanced, the annual individual startup costs, load reduction losses, and curtailment costs for each province and city are quantitatively analyzed to construct an economic cost model for a scenario without inter-provincial mutual assistance. Specifically, in a scenario without inter-provincial mutual assistance, demand-driven provinces and cities use standby units and load reduction to maintain a balance between power supply and demand. Therefore, based on the standby unit capacity and load reduction, an economic cost model is constructed for each demand-driven province and city, as shown in the following formula 11:

[0097] Formula 11:

[0098] Among them, C2 is the economic cost model, P s,j The output of the standby unit in month i for province or city j, C s,j is the comprehensive cost of standby units in month i in province / city j, P rl,j is the load reduction in province j in month i, C rl,j is the economic value of unit load in month i in province or city j.

[0099] For supply-oriented provinces and cities, the balance of electricity supply and demand is achieved by curtailing power generation, including the cost of abandoning renewable energy sources such as wind power and photovoltaic power, the cost of abandoning hydropower, and the cost of abandoning traditional thermal power, as shown in the following formula 12:

[0100] Formula 12:

[0101] Among them, Q wp_ab,(i,j) is the amount of wind power abandoned in province or city j in month i, P wp_ab,(i,j) is the marginal cost of wind power in province j in month i, Qpv_ab,(i,j) is the amount of photovoltaic waste in province or city j in month i, P pv_ab,(i,j) is the marginal cost of photovoltaic power generation in province j in month i, Q hp_ab,(i,j) is the amount of hydropower wasted in province or city j in month i, P hp_ab,(i,j) is the marginal cost of hydropower in province j in month i, Q thp_ab,(i,j) is the amount of thermal power wasted in province j in month i, P thp_ab,(i,j) is the marginal cost of thermal power in province or city j in month i.

[0102] Then, by adding the costs of demand-oriented provinces and cities and supply-oriented provinces and cities and introducing environmental benefit assessment, we can obtain the overall regional comprehensive cost-benefit model under the non-inter-provincial mutual assistance scenario, as shown in the following formula 13:

[0103] Formula 13: C non =C2+C3+γ,

[0104] Among them, C non is a comprehensive cost-benefit model, C2 is the economic cost model of demand-oriented provinces and cities, C3 is the cost of power abandonment in supply-oriented provinces and cities, and γ is the environmental benefit.

[0105] Finally, the comprehensive cost-effectiveness model is minimized to obtain the monthly standby unit output, load reduction and power abandonment of each province and city, and compared with the dispatch optimization results obtained in this application. The comprehensive cost-effectiveness optimization model of this application provides practical suggestions for the purchase and sale of electricity in the context of inter-provincial mutual assistance to achieve the optimal allocation and dispatch of power resources. While improving the economic benefits of inter-provincial power transactions, it enhances the adaptability of the power system in dealing with imbalanced conditions, thereby promoting the coordinated development and sustainable utilization of regional power markets. From the perspective of power purchasers, this application proposes optimization suggestions for power purchase strategies for demand-based provinces and cities in the region, and proposes power abandonment suggestions for supply-based provinces and cities. By deciding the purchase amount of each type of power source on a monthly basis, while ensuring the reliability of power supply, the overall interests of regional demand-based provinces and cities are maximized, regional power resource dispatch is optimized, and the overall renewable energy absorption ratio in the region is increased, thereby improving the utilization efficiency of power resources.

[0106] An embodiment of the present application provides a regional power dispatching method for multi-dimensional collaborative optimization. Compared with the prior art, the embodiment of the present invention obtains historical power data of the target area, determines multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month in the target area based on the historical power data, extracts the demand-type provincial and municipal power dispatching data and supply-type provincial and municipal power waste data of each month from the regional power data of the target area based on the multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month, uses the demand-type provincial and municipal power dispatching data of each month to calculate the annual power dispatching cost, uses the supply-type provincial and municipal power waste data of each month to calculate the annual power waste cost and environmental benefits, and calculates the power dispatching risk of the target area based on the historical power data. Based on the annual power dispatching cost, power dispatching risk, annual power waste cost, and environmental benefits, a comprehensive cost-effectiveness optimization model is constructed, obtains the demand-type provincial and municipal power dispatching strategy and supply-type provincial and municipal power waste strategy corresponding to each month obtained by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and uses the demand-type provincial and municipal power dispatching strategy and supply-type provincial and municipal power waste strategy corresponding to each month as the dispatching optimization result of the target area. Through interprovincial mutual assistance, demand- and supply-oriented provinces and cities can adjust their power surpluses and shortages. This, combined with optimized dispatching that considers interprovincial power dispatching costs, power curtailment costs, and dispatching risk costs, can effectively reduce the overall cost of the power system under different circumstances and improve the economic efficiency of power system operation. Furthermore, by introducing environmental benefit assessments, the overall renewable energy consumption ratio in the region can be increased, renewable energy curtailment can be reduced, and carbon emissions from power generation can be reduced. Finally, by solving a comprehensive cost-effectiveness optimization model, monthly power dispatch and curtailment data for each province and city in the region can be obtained. This allows for optimal allocation of regional power resources, enhances the power system's adaptability to imbalances, and improves the efficiency of power resource utilization.

[0107] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a regional power dispatching device with multi-dimensional collaborative optimization, such as Figure 3 As shown, the device includes: a determination module 301, an extraction module 302, a calculation module 303, a construction module 304 and a solution module 305.

[0108] A determination module 301 is configured to obtain historical power data of a target area, and determine a plurality of demand-type provinces and cities and a plurality of supply-type provinces and cities corresponding to each month in the target area based on the historical power data;

[0109] An extraction module 302 is configured to extract, from the regional power data of the target area, the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month according to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month;

[0110] a calculation module 303 for calculating the annual power dispatch cost using the monthly demand-based provincial and municipal power dispatch data, calculating the annual power waste cost and environmental benefits using the monthly supply-based provincial and municipal power waste data, and calculating the power dispatch risk of the target area based on the historical power data;

[0111] A construction module 304 is configured to construct a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit;

[0112] The solution module 305 is used to obtain the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month based on the solver's minimization solution of the comprehensive cost-effectiveness optimization model, and use the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month as the dispatching optimization result of the target area.

[0113] In a specific application scenario, the determination module 301 is used to determine multiple provinces and cities in the target area, extract historical data of each province and city from the historical power data, and the historical data include load power data, wind power generation data, hydropower generation data, photovoltaic power generation data, thermal power generation data, and power value data corresponding to each month of the province and city; use the historical data of each province and city to construct a target matrix, perform covariance calculation on the target matrix to obtain a covariance matrix, and randomly sample the target matrix and the covariance matrix to obtain a random power data matrix; for each month, extract the user load and total power generation of each province and city from the random power data matrix, and use the user load and total power generation of each province and city to calculate the power supply and demand difference of each province and city.

[0114] ΔQ i,j =Q l,i,j -Q g,i,j ,

[0115] Where ΔQ i,j is the electricity supply and demand difference of province / city j in month i, Q l,i,j is the user load of province j in month i, Q g,i,j is the total power generation of province or city j in month i; for each province or city, the power supply and demand difference of the province or city is detected; if the power supply and demand difference of the province or city is greater than 0, the province or city is regarded as the demand-type province or city corresponding to the month; if the power supply and demand difference of the province or city is less than 0, the province or city is regarded as the supply-type province or city corresponding to the month.

[0116] In a specific application scenario, the extraction module 302 is used to extract the target power data corresponding to each month from the regional power data; extract the inter-provincial power dispatching data corresponding to each of the demand-type provinces and cities from the target power data to obtain multiple inter-provincial power dispatching data, and use the multiple inter-provincial power dispatching data as the demand-type provincial and municipal power dispatching data for the month; extract the abandoned power data corresponding to each of the supply-type provinces and cities from the target power data to obtain multiple abandoned power data, and use the multiple abandoned power data as the supply-type provincial and municipal power waste data for the month.

[0117] In a specific application scenario, the calculation module 303 is used to extract the annual medium- and long-term market dispatch data and inter-provincial spot market dispatch data of each month from the demand-based provincial and municipal power dispatch data of each month; and calculate using the annual medium- and long-term market dispatch data of each month to obtain the total cost of inter-provincial annual medium- and long-term market dispatch.

[0118]

[0119] in, is the total annual medium- and long-term market dispatch cost among provinces, is the planned dispatching amount of thermal power in the demand-type province or city j0 in month i, is the planned dispatching amount of wind power in the demand-type province or city j0 in month i, is the planned PV dispatching quantity of demand-type province or city j0 in month i, is the planned dispatching amount of hydropower in the demand-type province or city j0 in month i, is the planned dispatch volume of nuclear power in the demand-type province or city j0 in month i, is the thermal power value of demand-type province or city j0 in month i, is the wind power value of demand-type province or city j0 in month i, is the photovoltaic value of demand-type province or city j0 in month i, is the hydropower value of demand-type province j0 in month i, is the nuclear power value of demand-type province or city j0 in month i, a is the number of demand-type province or city j0 in month i; the inter-provincial spot market dispatch data of each month is used to calculate the total annual dispatch cost of the inter-provincial spot market,

[0120]

[0121] in, is the total annual dispatch cost of the inter-provincial spot market, is the inter-provincial spot market dispatch volume in month i, is the dispatch value of the inter-provincial spot market in month i; the sum of the total annual mid- and long-term inter-provincial market dispatch cost and the total annual dispatch cost of the inter-provincial spot market is taken as the annual power dispatch cost,

[0122]

[0123] in, is the annual power dispatch cost, is the total annual medium- and long-term market dispatch cost among provinces, is the total annual dispatch cost of the inter-provincial spot market.

[0124] In a specific application scenario, the calculation module 303 is used to calculate the annual power waste cost using the monthly supply-type provincial and municipal power waste data.

[0125]

[0126] Among them, C1 is the annual electricity waste cost, Q thp_ab,(i,j1) is the thermal power curtailment of supply-type province or city j1 in month i, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in the supply-type province or city j1 in month i, P thp_ab,(i,j1) is the marginal cost of thermal power in the supply-oriented province or city j1 in month i, P wp_ab,(i,j1) is the marginal cost of wind power in the supply-oriented province or city j1 in month i, P pv_ab,(i,j1) is the marginal cost of photovoltaic power generation in the supply-oriented province or city j1 in month i, P hp_ab,(i,j1) is the marginal cost of hydropower in the supply-type province or city j1 in month i, b is the number of supply-type provinces or cities j1 in month i; obtain the environmental benefit coefficient, and calculate the environmental benefit using the environmental benefit coefficient and the power waste data of the supply-type provinces or cities in each month,

[0127]

[0128] Among them, γ is the environmental benefit, λ is the environmental benefit coefficient, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in the supply-type province or city j1 in month i, and b is the number of supply-type provinces or cities j1 in month i.

[0129] In a specific application scenario, the calculation module 303 is used to perform Monte Carlo sampling on the historical power data to obtain sampled data; obtain confidence and risk value, and calculate the power dispatch risk of the target area using the confidence, risk value and sampled data.

[0130]

[0131] in, is the power dispatch risk, VaR is the risk value, β is the confidence level, N is the number of samples included in the sampling data, ω n is the nth sample of Monte Carlo sampling, is the annual power dispatch cost of the nth sample in the sample data,

[0132] In a specific application scenario, the construction module 304 is used to construct a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit.

[0133]

[0134] Among them, C aid is the comprehensive cost-effectiveness optimization model, is the annual power dispatch cost, is the power dispatching risk, C1 is the annual power abandonment cost, and γ is the environmental benefit. The embodiment of the present application provides a device. Compared with the prior art, the embodiment of the present invention obtains historical power data of the target area, determines multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month in the target area based on the historical power data, extracts the demand-type province and city power dispatching data and supply-type province and city power abandonment data of each month from the regional power data of the target area based on the multiple demand-type provinces and cities and multiple supply-type provinces and cities corresponding to each month, calculates the annual power dispatching cost using the demand-type province and city power dispatching data of each month, calculates the annual power abandonment cost and environmental benefits using the supply-type province and city power abandonment data of each month, and calculates the power dispatching risk of the target area based on the historical power data. Based on the annual power dispatching cost, power dispatching risk, annual power abandonment cost, and environmental benefits, a comprehensive cost-effectiveness optimization model is constructed, obtains the demand-type province and city power dispatching strategy and supply-type province and city power abandonment strategy corresponding to each month obtained by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and uses the demand-type province and city power dispatching strategy and supply-type province and city power abandonment strategy corresponding to each month as the dispatching optimization result of the target area. Through interprovincial mutual assistance, demand- and supply-oriented provinces and cities can adjust their power surpluses and shortages. This, combined with optimized dispatching that considers interprovincial power dispatching costs, power curtailment costs, and dispatching risk costs, can effectively reduce the overall cost of the power system under different circumstances and improve the economic efficiency of power system operation. Furthermore, by introducing environmental benefit assessments, the overall renewable energy consumption ratio in the region can be increased, renewable energy curtailment can be reduced, and carbon emissions from power generation can be reduced. Finally, by solving a comprehensive cost-effectiveness optimization model, monthly power dispatch and curtailment data for each province and city in the region can be obtained. This allows for optimal allocation of regional power resources, enhances the power system's adaptability to imbalances, and improves the efficiency of power resource utilization.

[0135] It should be noted that for other corresponding descriptions of the functional units involved in the multi-dimensional collaborative optimization regional power dispatching device provided in the embodiment of the present application, please refer to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0137] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

[0139] In an exemplary embodiment, see Figure 4 A device is also provided, comprising a bus, a processor, a memory, and a communication interface. The device may also include an input / output interface and a display device, wherein the various functional units can communicate with each other via the bus. The memory stores a computer program, and the processor is configured to execute the program stored in the memory and implement the multi-dimensional collaborative optimization regional power dispatch method described in the above embodiment.

[0140] A medium stores a computer program, which, when executed by a processor, implements the steps of the multi-dimensional collaborative optimization regional power dispatching method.

[0141] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented through hardware or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0142] Those skilled in the art will understand that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present application.

[0143] Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the implementation scenario description, or can be modified accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module or further split into multiple submodules.

[0144] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0145] The above disclosure only describes several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A regional power dispatching method with multi-dimensional collaborative optimization, characterized in that: include: Acquire historical power data of a target area, and determine, in the target area, a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month based on the historical power data; According to the plurality of demand-type provinces and cities and the plurality of supply-type provinces and cities corresponding to each month, extracting the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month from the regional power data of the target area; Calculating the annual power dispatch cost using the monthly demand-based provincial and municipal power dispatch data, calculating the annual power waste cost and environmental benefits using the monthly supply-based provincial and municipal power waste data, and calculating the power dispatch risk of the target area based on the historical power data; Constructing a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit; Obtain the demand-based provincial and municipal power dispatching strategies and supply-based provincial and municipal power abandonment strategies corresponding to each month based on the minimization solution of the comprehensive cost-effectiveness optimization model by the solver, and use the demand-based provincial and municipal power dispatching strategies and supply-based provincial and municipal power abandonment strategies corresponding to each month as the dispatching optimization results of the target area.

2. The method according to claim 1, characterized in that The determining, in the target area according to the historical power data, a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month includes: Determine a plurality of provinces and cities in the target area, and extract historical data of each province and city from the historical power data, wherein the historical data includes load power data, wind power generation data, hydropower generation data, photovoltaic power generation data, thermal power generation data, and power value data corresponding to each month of the province and city; Using the historical data of each province and city to construct a target matrix, performing covariance calculation on the target matrix to obtain a covariance matrix, and randomly sampling the target matrix and the covariance matrix to obtain a random electricity data matrix; For each month, extract the user load and total power generation of each province and city from the random power data matrix, and calculate the power supply and demand difference of each province and city using the user load and total power generation of each province and city. ΔQ i,j =Q l,i,j -Q g,i,j , Where ΔQ i,j is the electricity supply and demand difference of province / city j in month i, Q l,i,j is the user load of province j in month i, Q g,i,j is the total power generation of province or city j in month i; For each of the provinces and cities, detecting the power supply and demand difference of the province and city; If the power supply and demand difference of the province or city is greater than 0, the province or city will be regarded as the demand-type province or city corresponding to the month; If the electricity supply and demand difference of the province or city is less than 0, the province or city will be regarded as the supply-type province or city corresponding to the month.

3. The method according to claim 1, characterized in that The extracting, from the regional power data of the target area according to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month, the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month, comprises: For each month, extracting target power data corresponding to the month from the regional power data; Extracting inter-provincial power dispatching data corresponding to each of the demand-type provinces and cities from the target power data to obtain a plurality of inter-provincial power dispatching data, and using the plurality of inter-provincial power dispatching data as the demand-type provincial and municipal power dispatching data for the month; The power abandonment data corresponding to each of the supply-type provinces and cities is extracted from the target power data to obtain a plurality of power abandonment data, and the plurality of power abandonment data are used as the power abandonment data of the supply-type provinces and cities for the month.

4. The method according to claim 1, wherein The calculation of the annual power dispatching cost using the demand-based provincial and municipal power dispatching data for each month includes: Extracting the annual medium- and long-term market dispatch data and inter-provincial spot market dispatch data for each month from the demand-based provincial and municipal power dispatch data for each month; The total cost of annual medium- and long-term market dispatch between provinces is calculated using the annual medium- and long-term market dispatch data for each month. in, is the total annual medium- and long-term market dispatch cost among provinces, is the planned dispatching amount of thermal power in the demand-type province or city j0 in month i, is the planned dispatching amount of wind power in the demand-type province or city j0 in month i, is the planned PV dispatching quantity of demand-type province or city j0 in month i, is the planned dispatching amount of hydropower in the demand-type province or city j0 in month i, is the planned dispatch volume of nuclear power in the demand-type province or city j0 in month i, is the thermal power value of demand-type province or city j0 in month i, is the wind power value of demand-type province or city j0 in month i, is the photovoltaic value of demand-type province or city j0 in month i, is the hydropower value of demand-type province j0 in month i, is the nuclear power value of demand-type province or city j0 in month i, a is the number of demand-type province or city j0 in month i; The inter-provincial spot market dispatch data for each month is used to calculate the total annual dispatch cost of the inter-provincial spot market. in, is the total annual dispatch cost of the inter-provincial spot market, is the inter-provincial spot market dispatch volume in month i, is the inter-provincial spot market dispatch value in month i; The sum of the total annual inter-provincial medium- and long-term market dispatching cost and the total annual inter-provincial spot market dispatching cost is taken as the annual power dispatching cost. in, is the annual power dispatch cost, is the total annual medium- and long-term market dispatch cost among provinces, is the total annual dispatch cost of the inter-provincial spot market.

5. The method according to claim 1, wherein The calculation of the annual power waste costs and environmental benefits using the monthly power waste data of the supply-oriented provinces and cities includes: The annual electricity waste cost is calculated using the monthly supply-oriented provincial and municipal electricity waste data. Among them, C1 is the annual electricity waste cost, Q thp_ab,(i,j1) is the thermal power curtailment of supply-type province or city j1 in month i, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in the supply-type province or city j1 in month i, P thp_ab,(i,j1) is the marginal cost of thermal power in the supply-oriented province or city j1 in month i, P wp_ab,(i,j1) is the marginal cost of wind power in the supply-oriented province or city j1 in month i, P pv_ab,(i,j1) is the marginal cost of photovoltaic power generation in the supply-oriented province or city j1 in month i, P hp_ab,(i,j1) is the marginal cost of water and electricity in the supply-type province or city j1 in month i, and b is the quantity of the supply-type province or city j1 in month i; Obtaining an environmental benefit coefficient, and calculating the environmental benefit using the environmental benefit coefficient and the monthly power waste data for the supply-oriented provinces and cities, Among them, γ is the environmental benefit, λ is the environmental benefit coefficient, Q wp_ab,(i,j1) is the amount of wind power curtailment in the supply-type province or city j1 in month i, Q pv_ab,(i,j1) is the photovoltaic power curtailment in the supply-type province or city j1 in month i, Q hp_ab,(i,j1) is the amount of hydropower curtailment in the supply-type province or city j1 in month i, and b is the number of supply-type provinces or cities j1 in month i.

6. The method according to claim 1, characterized in that The calculating the power dispatch risk of the target area based on the historical power data includes: Performing Monte Carlo sampling on the historical power data to obtain sampled data; Obtaining confidence and risk value, and calculating the power dispatch risk of the target area using the confidence, the risk value and the sampled data, in, is the power dispatch risk, VaR is the risk value, β is the confidence level, N is the number of samples included in the sampling data, ω n is the nth sample of Monte Carlo sampling, is the annual power dispatch cost of the nth sample in the sample data, It means taking the non-negative part of CACyear(Q,ωn)-VaR.

7. The method according to claim 1, characterized in that The comprehensive cost-effectiveness optimization model is constructed based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit. Among them, C aid is the comprehensive cost-effectiveness optimization model, is the annual power dispatch cost, is the power dispatch risk, C1 is the annual power waste cost, and γ is the environmental benefit.

8. A regional power dispatching device with multi-dimensional collaborative optimization, characterized in that: include: A determination module, configured to obtain historical power data of a target area, and determine a plurality of demand-oriented provinces and cities and a plurality of supply-oriented provinces and cities corresponding to each month in the target area according to the historical power data; An extraction module is configured to extract the power dispatching data of the demand-type provinces and cities and the power waste data of the supply-type provinces and cities for each month from the regional power data of the target area according to the multiple demand-type provinces and cities and the multiple supply-type provinces and cities corresponding to each month; a calculation module for calculating the annual power dispatch cost using the monthly demand-based provincial and municipal power dispatch data, calculating the annual power waste cost and environmental benefits using the monthly supply-based provincial and municipal power waste data, and calculating the power dispatch risk of the target area based on the historical power data; A construction module, configured to construct a comprehensive cost-effectiveness optimization model based on the annual power dispatching cost, the power dispatching risk, the annual power waste cost, and the environmental benefit; A solution module is used to obtain the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month, which are obtained by minimizing the comprehensive cost-effectiveness optimization model based on the solver, and use the demand-based provincial and municipal power dispatching strategy and the supply-based provincial and municipal power abandonment strategy corresponding to each month as the dispatching optimization result of the target area.

9. A device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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