Regional electric power peak regulation resource cooperation system and method based on cloud computing

Abstract

The invention relates to the technical field of power dispatching, in particular to a regional power peak regulation resource cooperation system and method based on cloud computing. The method comprises the following steps: constructing a support vector machine regression model to predict regional electric power; on the basis of the prediction result, the generated power and the electricity storage quantity, calculating regional electric power peak regulation demands including residual electric power peak regulation time and power demands, and dividing an electric power peak regulation demand region, a stable electricity utilization region and an electric power surplus region; constructing a regional power peak regulation resource pool; considering a power transmission path, resource reserves and transmission loss, taking peak regulation time and power demand as constraints, constructing a cooperative scheduling model, and calculating response power of the power surplus area to the power peak regulation demand area; the electric power surplus area carries out electric power dispatching on the electric power peak regulation demand area according to the distributed response power; according to the method provided by the invention, the flexibility, the working efficiency and the stability of regional power peak regulation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a regional power peak-shaving resource coordination system and method based on cloud computing. Background Art

[0002] With the rapid development of the economy and society and the continuous growth of power load, the power system is facing increasingly severe challenges in balancing supply and demand. The regional power peak load problem has become a key issue in the operation of the power system. Due to the uneven distribution of power resources among regions, some regions are facing power supply shortages.

[0003] In order to meet the challenge of regional power peak load regulation, extensive research and practice have been conducted on the problem of power peak load regulation. Among them, the development of smart grid technology has provided new possibilities for solving the problem of regional power peak load regulation. Under the framework of smart grid, the problem of regional power peak load regulation has been solved in a more systematic and comprehensive way. By predicting and analyzing the regional power load, potential power supply and demand imbalance problems can be identified in advance, providing a basis for formulating power peak load regulation strategies. At the same time, by using advanced optimization algorithms and control strategies, the coordinated optimization and dispatching of power resources inside and outside the region can be realized, the regulation capacity of different types of power resources can be maximized, and the overall peak load regulation efficiency of the power system can be improved.

[0004] In addition, big data analysis technology has also been widely used in the field of power peak regulation. By mining and analyzing the massive operating data of the power system, we can find the changing patterns and influencing factors of power load, and provide data support for power peak regulation decisions. The introduction of machine learning and artificial intelligence technology has also provided new ideas for solving the problem of power peak regulation. By training intelligent algorithm models, we can achieve accurate prediction of power load and optimal allocation of power resources, and improve the automation and intelligence level of power peak regulation.

[0005] In view of this, the present invention proposes a regional power peak-shaving resource coordination system and method based on cloud computing. Summary of the invention

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a regional power peak load resource coordination method based on cloud computing, comprising: Construct a regional power consumption prediction model using support vector machine (SVR) to predict regional power consumption in the future based on the historical power consumption data of the region; Based on the regional power consumption prediction results and the regional power generation power and storage power, the regional power peak-shaving demand is calculated, and the power peak-shaving demand includes the remaining power peak-shaving time and the power peak-shaving power demand. Based on the regional power peak-shaving demand calculation results, the power consumption area in the future time period is divided into a power peak-shaving demand area, a stable power consumption area, and a power surplus area; A regional power peak-shaving resource pool is constructed based on regional power peak-shaving resources, wherein the regional power peak-shaving resources include power generating units and power storage equipment within the region; Based on the inter-regional power transmission paths and power peak-shaving resource reserves, and taking into account the inter-regional power transmission losses, a regional power peak-shaving resource coordinated scheduling model is constructed with the remaining power peak-shaving time and power peak-shaving power demand as constraints, and the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is calculated; The power surplus areas will dispatch power to the areas with power peak shaving demand according to the allocated power peak shaving response power.

[0007] Preferably, the construction of a regional power consumption prediction model of a support vector machine SVR includes: using a mathematical model of a support vector machine SVR to predict regional power consumption, assuming that the regional In time The power consumption is , the historical electricity consumption data is ,in is the input feature, n is the number of historical electricity consumption data, and the mathematical model of the support vector machine SVR is as follows: ; ; in, is the weight vector, For paranoid items, and Used to define the regression plane, and is the slack variable, is the maximum regression deviation, It is used to control the smoothness of the regression plane; is the input mapping function; is the transpose of the weight vector; It is the regularization term in the support vector machine SVR objective function; is the time of the nth historical electricity consumption data; Regional power consumption prediction model based on support vector machine SVR: ; in, and is the Lagrange multiplier, is the kernel function, It is a region The predicted power consumption at time t, where N is the total number of regions.

[0008] Preferably, the calculation of the regional power peak load demand based on the regional power consumption prediction result and the regional power generation power and storage capacity includes: calculating the regional The peak load demand of electricity is At current time To the future time Predicted peak power consumption Larger than area Power generation in When , the calculation area At current time Predicted electricity consumption at future time point t : ; If the area At current time Predicted electricity consumption at future time point t Larger than area The storage capacity When the sum of the power generation is: , then the region There is a demand for power peak load regulation. Mark as power peak demand area and calculate the area Remaining power peak-shaving time ; ; in, is the storage capacity of region i, It is a region At current time The power consumption at each moment; Get Region The peak power demand of : .

[0009] Preferably, when the area At current time To the future time Predicted peak power consumption Larger than area Power generation in When and area At current time Predicted electricity consumption at future time point t Less than or equal to area The storage capacity When the sum of the power generation is: ; area The power generation and storage of the region can meet its own electricity demand. Divided into stable electricity consumption areas; When the area At current time To the future time Predicted peak power consumption within Always smaller than area Power generation in Time, area At current time To the future time The power generated within the area is greater than the power consumed. There is no peak load demand and the area Divided into power surplus areas.

[0010] Preferably, the regional power peak-shaving resource pool is constructed based on regional power peak-shaving resources, including: assuming that the i-th region has power peak-shaving demand, the j-th region has power surplus, and the current time To the future time The power available for peak load regulation in the power surplus area j is Surplus power of the generator set And the output power of the storage device sum: ; ; Calculate the power available for peak load regulation in all power surplus areas : ; in, Indicates the number of regions with power surplus; the power available for power peak regulation in all power surplus regions Form a regional power peak-shaving resource pool.

[0011] Preferably, the method is based on the inter-regional power transmission path and the power peak-shaving resource reserves, while taking into account the inter-regional power transmission loss, taking the remaining power peak-shaving time and the power peak-shaving power demand as constraints, constructing a regional power peak-shaving resource collaborative scheduling model, and calculating the power peak-shaving response power of the power surplus area to the power peak-shaving demand area, including: assuming that the set of power surplus areas is A, and the set of power peak-shaving demand areas is B, then the power peak-shaving demand area , power surplus area , define the power variable The power surplus area j is the power peak demand area The power peak load response power; The objective function of minimizing the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is constructed as a regional power peak-shaving resource coordinated scheduling model to calculate the power peak-shaving response power of a single power surplus area to a single power peak-shaving demand area. : ; Calculation of power peak load response power in regional power peak load resource coordinated dispatch model based on constraint conditions ; The constraints include: The peak load response power of the power supply in each area shall not exceed The available peak power in each region ,Right now ; The constraints also include the power peak demand area The sum of the power peak load response power obtained from the regional power peak load resource pool is , considering the power transmission efficiency , ;and ; The constraints also include considering the transmission time from the power surplus area to the power peak demand area. ,but: ; According to the regional power peak load resource coordinated dispatch model, the power peak load response power of a single power surplus area to a single power peak load demand area is calculated. .

[0012] Preferably, the power surplus area dispatches power to the area with power peak load demand according to the allocated power peak load response power, including: dispatching the power peak load response power of the power surplus area to the area with power peak load demand according to the calculated power peak load response power of the power surplus area , power surplus areas respond to power peak load Respond to the peak demand area of ​​power load regulation and After a certain period of time, the regional power consumption is predicted again and the dispatch response power is calculated to ensure stable power consumption in the region.

[0013] A regional power peak-shaving resource coordination system based on cloud computing, which is used to implement the regional power peak-shaving resource coordination method based on cloud computing, including a power consumption prediction module, a peak-shaving demand calculation module, a peak-shaving resource assembly module, a power peak-shaving response power calculation module and a power peak-shaving execution module; The power consumption prediction module adopts a regional power consumption prediction model constructed by a support vector machine (SVR) to predict regional power consumption in future time periods based on historical power consumption data of the region; The peak-shaving demand calculation module calculates the regional power peak-shaving demand based on the regional power consumption prediction result and the regional power generation power and power storage capacity. The power peak-shaving demand includes the remaining power peak-shaving time and the power peak-shaving power demand. Based on the regional power peak-shaving demand calculation result, the power consumption area in the future time period is divided into a power peak-shaving demand area, a stable power consumption area and a power surplus area; The peak-shaving resource building module builds a regional power peak-shaving resource pool based on regional power peak-shaving resources, wherein the regional power peak-shaving resources include power generating units and power storage equipment in the region; The power peak-shaving response power calculation module is based on the inter-regional power transmission path and power peak-shaving resource reserves, while taking into account the inter-regional power transmission loss, and taking the remaining power peak-shaving time and power peak-shaving power demand as constraints, to build a regional power peak-shaving resource collaborative scheduling model, and calculate the power peak-shaving response power of the power surplus area to the power peak-shaving demand area; The power peak-shaving execution module performs power peak-shaving scheduling for the power surplus area according to the allocated power peak-shaving response power and for the area with power peak-shaving demand.

[0014] Beneficial effect: The present invention constructs a regional power consumption prediction model based on a support vector machine (SVR) and uses the historical power consumption data of the region to predict the power consumption in future time periods, thereby improving the accuracy of power load prediction. Accurate power load prediction is an important prerequisite for achieving effective power peak regulation, which helps to discover potential power supply and demand imbalance problems early and provide a basis for formulating reasonable power peak regulation strategies.

[0015] Based on the regional power consumption prediction results and combined with the regional power generation power and power storage information, the present invention can accurately calculate the regional power peak-shaving demand, including the remaining power peak-shaving time and the power peak-shaving power demand; through the accurate identification of the power peak-shaving demand, the power peak-shaving demand area, the stable power consumption area and the power surplus area can be divided in a targeted manner, laying the foundation for the subsequent optimal allocation of power peak-shaving resources.

[0016] The present invention constructs a regional power peak-shaving resource pool, incorporates the generator sets and power storage equipment in the region into unified management and scheduling, and can achieve optimal configuration of power peak-shaving resources.

[0017] The present invention uses the regional power peak-shaving resource collaborative dispatching model, comprehensively considers factors such as the power transmission path between regions, the power peak-shaving resource reserves and transmission losses, and can calculate the optimal power peak-shaving response strategy for the power surplus region to the power peak-shaving demand region. This optimization configuration method can maximize the regulation capacity of power resources in different regions and improve the overall peak-shaving efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the flow of the regional power peak load resource coordination method based on cloud computing of the present invention; Figure 2 This is a schematic diagram of the structure of the regional power peak-shaving resource coordination system based on cloud computing of the present invention. DETAILED DESCRIPTION

[0019] In order to better understand the present application, a more detailed description will be made of various aspects of the present application with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application, and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0020] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in this application, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0021] It should also be understood that expressions such as "include", "including", "have", "contain" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0022] Unless otherwise defined, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that unless there is a clear explanation in this application, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0023] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] Example 1 Reference Figure 1 The first embodiment of the present invention provides a regional power peak-shaving resource coordination method based on cloud computing.

[0025] Step 1: A regional power consumption prediction model based on support vector machine (SVR) is constructed to predict regional power consumption in the future time period based on the historical power consumption data of the region.

[0026] The construction of the regional power consumption prediction model of the support vector machine SVR includes: using the mathematical model of the support vector machine SVR to predict the regional power consumption, setting the regional In time The power consumption is , the historical electricity consumption data is ,in is the input feature, n is the number of historical electricity consumption data, and the mathematical model of the support vector machine SVR is as follows: ; ; in, is the weight vector, For paranoid items, and Used to define the regression plane, and is the slack variable, is the maximum regression deviation, It is used to control the smoothness of the regression plane; is the input mapping function; is the transpose of the weight vector; is the regularization term in the support vector machine SVR objective function, is the time of the nth historical electricity consumption data.

[0027] Regional power consumption prediction model based on support vector machine SVR: ; Where N is the total number of regions, and is the Lagrange multiplier, is the kernel function, It is a region The power consumption predicted at time t. The regional power consumption prediction model based on support vector machine (SVR) can be used to predict the power consumption of the region by combining the input features of the future time period of the region.

[0028] The present invention adopts the support vector machine regression model SVR to predict regional electric power consumption. The SVR model has good generalization ability and nonlinear fitting ability, can effectively capture the changing trend of electric power consumption, and provide an important basis for subsequent power peak demand analysis.

[0029] Step 2: Based on the regional power consumption forecast results and the regional power generation power and power storage capacity, calculate the regional power peak demand, the power peak demand includes the remaining power peak time and the power peak power demand, and based on the regional power peak demand calculation results, divide the power consumption area in the future time period into power peak demand area, stable power consumption area and power surplus area.

[0030] Calculation area The peak load demand of electricity is At current time To the future time Predicted peak power consumption Larger than area Power generation in When , the calculation area At current time Predicted electricity consumption at future time point t : ; If the area At current time Predicted electricity consumption at future time point t Larger than area The storage capacity When the sum of the power generation is: , then the region There is a demand for power peak load regulation. Mark as power peak demand area and calculate the area Remaining power peak-shaving time : ; in, is the storage capacity of region i, It is a region At current time The power consumption at each moment.

[0031] The remaining power peak load time It is a region The remaining time during which the power generation and storage can guarantee stable power consumption in the region is required to be during the remaining power peak load time. Internal area Transmit power at a rate greater than the peak power demand, otherwise the region During the remaining power peak load period It will not be possible to ensure stable electricity supply in the area.

[0032] Then the area The peak power demand of : .

[0033] When the area At current time To the future time Predicted peak power consumption Larger than area Power generation in When and area At current time Predicted electricity consumption at future time point t Less than or equal to area The storage capacity When the sum of the power generation is: ; The power generation and storage within region i can meet its own electricity demand, and region i is divided into a stable power consumption area.

[0034] When the area At current time To the future time Predicted peak power consumption within Always smaller than area Power generation in Time, area At current time To the future time The power generated within the area is greater than the power consumed. There is no peak load demand and the area Divided into power surplus areas.

[0035] Based on the prediction results of regional power consumption, combined with the regional power generation and storage information, the regional power peak-shaving demand can be analyzed and calculated. Specifically, when the predicted regional power consumption peak exceeds the regional power generation, it means that the region may face the problem of insufficient power supply; at this time, it is necessary to calculate the predicted power consumption of the region in the future time period and compare it with the regional power storage and power generation; if the predicted power consumption is greater than the sum of the power storage and power generation, it indicates that there is a power peak-shaving demand in the region.

[0036] For areas with power peak-shaving needs, it is necessary to further calculate the remaining power peak-shaving time, that is, the remaining time that the region's own power generation and storage can maintain stable power consumption. The calculation of this time takes into account the region's storage capacity, predicted power consumption and real-time power consumption. During the remaining power peak-shaving time, sufficient electricity needs to be transferred in from other regions to meet the region's power demand.

[0037] In addition to the peak demand areas, it is also necessary to identify stable power consumption areas and power redundancy areas; the characteristic of stable power consumption areas is that the predicted power consumption is less than or equal to the sum of power storage and power generation, indicating that the power supply in the area can meet the power demand. In the power redundancy area, the predicted power consumption is always less than the power generation, indicating that there is a power surplus in the area, which can be used as a supplier of peak power.

[0038] Step 3: construct a regional power peak-shaving resource pool based on regional power peak-shaving resources, wherein the regional power peak-shaving resources include power generating sets and power storage equipment in the region.

[0039] There is There is a demand for peak power regulation in the first region. There is a power surplus in the regions, and the current time To the future time In the segment, the power surplus area The power available for peak load regulation Surplus power of the generator set And the output power of the storage device sum: ; ; Calculate the power available for peak load regulation in all power surplus areas : ; in, Indicates the number of regions with power surplus; the power available for power peak regulation in all power surplus regions Form a regional power peak-shaving resource pool.

[0040] In order to achieve inter-regional power peak regulation, it is necessary to build a regional power peak regulation resource pool; the resource pool includes the generators and power storage equipment in each region. For regions with power redundancy, their surplus power generation and output power of power storage equipment can be included in the resource pool as power resources available for peak regulation; through the management and scheduling of the resource pool, the optimal allocation of power resources between regions can be achieved, and the overall peak regulation capacity of the power system can be improved.

[0041] Step 4: Based on the inter-regional power transmission paths and power peak-shaving resource reserves, and taking into account the inter-regional power transmission losses, a regional power peak-shaving resource coordinated scheduling model is constructed with the remaining power peak-shaving time and power peak-shaving power demand as constraints, and the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is calculated.

[0042] Assume that the set of power surplus areas is A, and the set of power peak demand areas is B, then the power peak demand areas , power surplus area , define the power variable It is the power peak-shaving response power of power surplus area j to power peak-shaving demand area i.

[0043] The objective function of minimizing the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is constructed as a regional power peak-shaving resource coordinated scheduling model to calculate the power peak-shaving response power of a single power surplus area to a single power peak-shaving demand area. : ; Calculation of power peak load response power in regional power peak load resource coordinated dispatch model based on constraint conditions .

[0044] The constraint condition includes that the peak-shaving power response power of the jth region in the regional peak-shaving resource pool does not exceed the peak-shaving power available for the jth region. ,Right now .

[0045] The constraint condition also includes that the sum of the power peak load response power obtained by the power peak load demand area i from the regional power peak load resource pool is , considering the power transmission efficiency , ;and .

[0046] The constraints also include considering the transmission time from the power surplus area to the power peak demand area. ,but: ; According to the regional power peak load resource coordinated dispatch model, the power peak load response power of a single power surplus area to a single power peak load demand area is calculated. .

[0047] On the basis of constructing a regional power peak-shaving resource pool, it is necessary to further establish a coordinated dispatch model for regional power peak-shaving resources; this model comprehensively considers factors such as inter-regional power transmission paths, power peak-shaving resource reserves, and power transmission losses. Taking the remaining power peak-shaving time and power peak-shaving power demand of the area with power peak-shaving demand as constraints, through mathematical modeling and optimization solution, the optimal response strategy for the power redundant area to provide power support to the power peak-shaving demand area can be calculated, that is, the power peak-shaving response power of each power redundant area to each power peak-shaving demand area. The goal of the coordinated dispatch model is to minimize the total amount of power peak-shaving response in the power redundant area as much as possible while ensuring the stability of power consumption in the power peak-shaving demand area, so as to achieve the optimal allocation of power resources between regions.

[0048] Step 5: The power surplus area dispatches power to the area with power peak load demand according to the allocated power peak load response power.

[0049] The power peak-shaving response power of the power surplus area to the power peak-shaving demand area is calculated , power surplus areas respond to power peak load The peak-shaving response is carried out for the area with peak power demand. After time t is reached, the regional power consumption is predicted again and the dispatch response power is calculated to ensure stable power consumption in the region.

[0050] According to the calculation results of the regional power peak-shaving resource coordinated dispatching model, the power redundant area needs to dispatch power to the power peak-shaving demand area according to the allocated power peak-shaving response power; comprehensively consider factors such as the real-time operating status of the power grid and the working status of power equipment to ensure the reliability and safety of power dispatching.

[0051] Through reasonable power dispatching, the power supply pressure in the power peak demand area can be effectively alleviated to ensure the normal power demand of users in the region; at the same time, the power peak response in the power redundant area can also help improve the overall operating efficiency of the power system and promote the optimal allocation of power resources among regions.

[0052] Example 2 Reference Figure 2 The second embodiment of the present invention provides a regional power peak-shaving resource coordination system based on cloud computing.

[0053] The system includes an electric power prediction module, a peak-shaving demand calculation module, a peak-shaving resource assembly module, an electric power peak-shaving response power calculation module and an electric power peak-shaving execution module.

[0054] The power consumption prediction module adopts a regional power consumption prediction model constructed by a support vector machine (SVR) to predict regional power consumption in future time periods based on historical power consumption data of the region.

[0055] The peak-shaving demand calculation module calculates the regional power peak-shaving demand based on the regional power consumption prediction results and the regional power generation power and power storage capacity. The power peak-shaving demand includes the remaining power peak-shaving time and the power peak-shaving power demand. Based on the regional power peak-shaving demand calculation results, the power consumption area in the future time period is divided into power peak-shaving demand area, stable power consumption area and power surplus area.

[0056] The peak-shaving resource building module builds a regional power peak-shaving resource pool based on regional power peak-shaving resources, and the regional power peak-shaving resources include power generating sets and power storage equipment in the region.

[0057] The power peak-shaving response power calculation module is based on the power transmission path between regions and the power peak-shaving resource reserves, while taking into account the loss of power transmission between regions, and takes the remaining power peak-shaving time and power peak-shaving power demand as constraints, to construct a regional power peak-shaving resource collaborative scheduling model, and calculates the power peak-shaving response power of the power surplus region to the power peak-shaving demand region.

[0058] The power peak-shaving execution module performs power peak-shaving scheduling for the power surplus area according to the allocated power peak-shaving response power and for the area with power peak-shaving demand.

[0059] In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0060] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A regional power peak load resource coordination method based on cloud computing, characterized in that: include: Construct a regional power consumption prediction model using support vector machine (SVR) to predict regional power consumption in the future based on the historical power consumption data of the region; Based on the regional power consumption prediction results and the regional power generation power and storage power, the regional power peak-shaving demand is calculated, and the power peak-shaving demand includes the remaining power peak-shaving time and the power peak-shaving power demand. Based on the regional power peak-shaving demand calculation results, the power consumption area in the future time period is divided into a power peak-shaving demand area, a stable power consumption area, and a power surplus area; A regional power peak-shaving resource pool is constructed based on regional power peak-shaving resources, wherein the regional power peak-shaving resources include power generating units and power storage equipment within the region; Based on the inter-regional power transmission paths and power peak-shaving resource reserves, and taking into account the inter-regional power transmission losses, a regional power peak-shaving resource coordinated scheduling model is constructed with the remaining power peak-shaving time and power peak-shaving power demand as constraints, and the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is calculated; The power surplus areas will dispatch power to the areas with power peak shaving demand according to the allocated power peak shaving response power.

2. The regional power peak load resource coordination method based on cloud computing according to claim 1 is characterized in that: The construction of the regional power consumption prediction model of the support vector machine SVR includes: using the mathematical model of the support vector machine SVR to predict the regional power consumption, setting the regional In time The power consumption is , the historical electricity consumption data is ,in is the input feature, n is the number of historical electricity consumption data, and the mathematical model of the support vector machine SVR is as follows: ； ； in, is the weight vector, For paranoid items, and Used to define the regression plane, and is the slack variable, is the maximum regression deviation, It is used to control the smoothness of the regression plane; is the input mapping function; is the transpose of the weight vector; It is the regularization term in the support vector machine SVR objective function; is the time of the nth historical electricity consumption data; The regional power consumption prediction model is constructed based on the mathematical model of support vector machine SVR: ； in, and is the Lagrange multiplier, is the kernel function, It is a region The predicted power consumption at time t, where N is the total number of regions.

3. The regional power peak load resource coordination method based on cloud computing according to claim 2 is characterized in that: The calculation of the regional power peak load demand based on the regional power consumption prediction result and the regional power generation power and storage capacity includes: Calculation area The peak load demand of electricity is At current time To the future time Predicted peak power consumption Larger than area Power generation in When , the calculation area At current time Predicted electricity consumption at future time point t : ； If the area At current time Predicted electricity consumption at future time point t Larger than area The amount of power stored in When the sum of the power generation is: , then the area There is a demand for power peak load regulation. Mark as power peak demand area and calculate the area The remaining power peak-shaving time ; ； in, is the storage capacity of region i, It is a region At current time The power consumption at each moment; Get Region The peak power demand of : .

4. The regional power peak load resource coordination method based on cloud computing according to claim 3 is characterized in that: The method further comprises: when the area At current time To the future time Predicted peak power consumption Larger than area Power generation in When and area At current time Predicted electricity consumption at future time point t Less than or equal to area The amount of power stored in When the sum of the power generation is: ； area The power generation and storage of the region can meet its own electricity demand. Divided into stable electricity consumption areas; When the area At current time To the future time Predicted peak power consumption within Always smaller than area Power generation in Time, area At current time To the future time The power generated within the area is greater than the power consumed. There is no peak load demand and the area Divided into power surplus areas.

5. The regional power peak load resource coordination method based on cloud computing according to claim 4 is characterized in that: The regional power peak-shaving resource pool is constructed based on regional power peak-shaving resources, including: assuming that the i-th region has power peak-shaving demand, the j-th region has power surplus, and the current time To the future time The power available for peak load regulation in the power surplus area j is Surplus power of the generator set And the output power of the storage device sum: ； ； Calculate the power available for peak load regulation in all power surplus areas : ； in, Indicates the number of regions with power surplus; the power available for power peak regulation in all power surplus regions Form a regional power peak-shaving resource pool.

6. The regional power peak load resource coordination method based on cloud computing according to claim 5 is characterized in that: Based on the inter-regional power transmission path and power peak-shaving resource reserves, while taking into account the inter-regional power transmission loss, taking the remaining power peak-shaving time and power peak-shaving power demand as constraints, a regional power peak-shaving resource collaborative scheduling model is constructed to calculate the power peak-shaving response power of the power surplus area to the power peak-shaving demand area, including: Assume that the set of power surplus areas is A, and the set of power peak demand areas is B, then the power peak demand areas , power surplus area , define the power variable The power surplus area j is the power peak demand area The power peak load response power; The objective function of minimizing the power peak-shaving response power of the power surplus area to the power peak-shaving demand area is constructed as a regional power peak-shaving resource coordinated scheduling model to calculate the power peak-shaving response power of a single power surplus area to a single power peak-shaving demand area. : ； Calculation of power peak load response power in regional power peak load resource coordinated dispatch model based on constraint conditions ; The constraints include: The peak load response power of the power supply in each area shall not exceed The available peak power in each region ,Right now ; The constraints also include the power peak demand area The sum of the power peak load response power obtained from the regional power peak load resource pool is , considering the power transmission efficiency , ;and ; The constraints also include considering the transmission time from the power surplus area to the power peak demand area. ,but: ； According to the regional power peak load resource coordinated dispatch model, the power peak load response power of a single power surplus area to a single power peak load demand area is calculated. .

7. The regional power peak load resource coordination method based on cloud computing according to claim 6 is characterized in that: The power surplus area performs power dispatching to the area with power peak-shaving demand according to the allocated power peak-shaving response power, including: dispatching the power peak-shaving response power of the power surplus area to the area with power peak-shaving demand according to the calculated power peak-shaving response power of the power surplus area , power surplus areas respond to power peak load Respond to the peak demand area of ​​power load regulation and After a certain period of time, the regional power consumption is predicted again and the dispatch response power is calculated to ensure stable power consumption in the region.

8. A regional power peak-shaving resource coordination system based on cloud computing, which is used to implement the regional power peak-shaving resource coordination method based on cloud computing as claimed in any one of claims 1 to 7, characterized in that: It includes an electric power prediction module, a peak-shaving demand calculation module, a peak-shaving resource assembly module, an electric power peak-shaving response power calculation module and an electric power peak-shaving execution module; The power consumption prediction module adopts a regional power consumption prediction model constructed by a support vector machine (SVR) to predict regional power consumption in future time periods based on historical power consumption data of the region; The peak-shaving demand calculation module calculates the regional power peak-shaving demand based on the regional power consumption prediction result and the regional power generation power and power storage capacity. The power peak-shaving demand includes the remaining power peak-shaving time and the power peak-shaving power demand. Based on the regional power peak-shaving demand calculation result, the power consumption area in the future time period is divided into a power peak-shaving demand area, a stable power consumption area and a power surplus area; The peak-shaving resource building module builds a regional power peak-shaving resource pool based on regional power peak-shaving resources, wherein the regional power peak-shaving resources include power generating units and power storage equipment in the region; The power peak-shaving response power calculation module is based on the inter-regional power transmission path and power peak-shaving resource reserves, while taking into account the inter-regional power transmission loss, and taking the remaining power peak-shaving time and power peak-shaving power demand as constraints, to build a regional power peak-shaving resource collaborative scheduling model, and calculate the power peak-shaving response power of the power surplus area to the power peak-shaving demand area; The power peak-shaving execution module performs power peak-shaving scheduling for the power surplus area according to the allocated power peak-shaving response power and for the area with power peak-shaving demand.

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