A method for optimizing configuration of mobile energy storage capacity and quantity in rural station area

Abstract

The present application relates to a kind of rural station area mobile energy storage capacity and quantity optimization configuration method, first, the historical load data, real-time operation data and photovoltaic output data of agricultural area, residential area, photovoltaic area are collected and analyzed, to establish the correlation between photovoltaic penetration and station area power quality problem, then, based on station area type, load fluctuation amplitude, photovoltaic output time series data, construct station area type-load fluctuation-photovoltaic output three-dimensional analysis model, to form demand quantification framework, then, establish the total capacity and quantity calculation model of mobile energy storage considering economy and security constraints, and the model is solved, the optimal total capacity and quantity of mobile energy storage are output, finally, based on the demand-supply dynamic matching mechanism of multiple station area established by multi-agent cooperation theory, mobile energy storage across station area dynamic allocation and flexible flow are realized;Overall, the present application has the advantages of economic efficiency, safe and reasonable, cross-regional flexible scheduling convenient.

Description

Technical Field

[0001] This invention belongs to the field of mobile energy storage technology for power grids, and specifically relates to a method for optimizing the configuration of mobile energy storage capacity and quantity in rural transformer substations. Background Technology

[0002] With the accelerated construction of new power systems, rural power grids, as a crucial link at the end of the power system, are undergoing profound changes in the energy forms and load characteristics they carry. The high proportion of distributed photovoltaic and small-scale wind power in rural transformer substations, along with the rapid growth of diversified loads such as electric vehicle charging stations and smart agricultural equipment, have resulted in rural power grids exhibiting a "dual-high" characteristic (high proportion of renewable energy and high proportion of power electronic equipment), posing a severe challenge to traditional operation and management models. Currently, rural power grids have significant technical shortcomings in flexible construction and adaptive control, particularly in the overall rigidity of energy storage resource allocation. Specifically, existing fixed energy storage devices are costly to deploy, lack flexibility, and cannot be dynamically allocated according to the differentiated needs of different transformer substations (such as planting areas, breeding areas, and densely populated residential areas), resulting in "support gaps" during peak load periods and "resource redundancy" during off-peak periods. Therefore, to solve these problems, it is essential to develop an economical, efficient, safe, and reasonable method for optimizing the allocation of mobile energy storage capacity and quantity in rural transformer substations with cross-regional flexible dispatch. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide an economical, efficient, safe, reasonable, and flexible cross-regional method for optimizing the configuration of mobile energy storage capacity and quantity in rural transformer substations.

[0004] The objective of this invention is achieved as follows: a method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas, comprising the following steps: Step 1: Collect historical load data, real-time operation data, and distributed photovoltaic output data from agricultural areas, densely populated residential areas, and photovoltaic areas. Then, use time series analysis to extract the periodic patterns of seasonal overloads, thereby identifying the start and end times and duration characteristics of overloads in different transformer areas, and analyzing the spatial and temporal distribution characteristics of reverse overloads and overvoltage faults caused by distributed photovoltaic access, and establishing the correlation between photovoltaic penetration rate and power quality problems in transformer areas. Step 2: Based on transformer substation type, load fluctuation amplitude, and photovoltaic output time series data, a three-dimensional analysis model of "transformer substation type-load fluctuation-photovoltaic output" is constructed, with transformer substation type as the first dimension, load fluctuation amplitude as the second dimension, and photovoltaic output characteristics as the third dimension. Subsequently, clustering algorithm and multiple regression analysis method are used to quantify the dynamic energy storage demand of different transformer substations from the two dimensions of power demand and capacity demand, thereby forming a standardized demand quantification framework. Step 3: With the economic efficiency of the entire life cycle cost of mobile energy storage equipment as a constraint and the safe and stable operation of the distribution network as a boundary constraint, establish a calculation model for the total capacity and number of mobile energy storage vehicles that considers economic and safety constraints. Then, with energy storage capacity, rated power and access location as decision variables, and with the optimization objectives of minimizing comprehensive cost and voltage deviation, combined with the boundary conditions of distribution network safety constraints, solve the above-established model to output the optimal total capacity and optimal number of mobile energy storage vehicles suitable for rural multi-transformer area scenarios. Step 4: Establish a real-time load monitoring network for multiple transformer substations. Use wireless communication technology to collect and interact with status information such as load power, photovoltaic output, and node voltage in each substation in real time. Then, based on the multi-agent collaborative theory, treat each substation as an independent intelligent agent and the mobile energy storage vehicle as a mobile shared resource to build a dynamic "demand-supply" matching mechanism. Next, combine the real-time load gap of the substations, the remaining energy storage capacity, and the geographical distribution information of the substations to design a dynamic scheduling path optimization algorithm and build theoretical models for two typical scenarios: peak load and photovoltaic fluctuation. This allows for adaptive adjustment of the mobile energy storage charging and discharging strategy and cross-substation scheduling path to achieve flexible allocation of energy storage resources.

[0005] Furthermore, the distribution network safety constraints in step 3 include the overload protection threshold of the transformer area, the stable operating range of the grid voltage, and the tolerance limit of the mobile energy storage device, which are determined according to the rural distribution network operation specifications.

[0006] Furthermore, the total life cycle cost of the mobile energy storage device in step 3 includes equipment procurement cost, on-site operation and maintenance cost, equipment depreciation cost, and scrapping and replacement cost.

[0007] Furthermore, in step 4, when the theoretical model is adaptively adjusted, in the peak load scenario, the mobile energy storage vehicle is deployed in advance to the heavy-load area for discharge support; in the photovoltaic fluctuation scenario, the mobile energy storage vehicle charges and consumes energy during the peak photovoltaic power generation period, and discharges to make up for insufficient photovoltaic power output.

[0008] Furthermore, the algorithm for solving the calculation model of the total capacity and number of mobile energy storage vehicles in step 3 is a multi-objective particle swarm optimization algorithm.

[0009] The beneficial effects of this invention are as follows: This invention breaks through the traditional unified configuration mode by constructing a three-dimensional analysis model of "transformer type-load fluctuation-photovoltaic output" based on transformer area type, load fluctuation amplitude, and photovoltaic output time series data, with transformer area type as the first dimension, load fluctuation amplitude as the second dimension, and photovoltaic output characteristics as the third dimension. This enables precise quantification of the spatiotemporal differences in energy storage demand, thereby adapting to the complex source-load scenarios of rural power grids. This invention integrates economic constraints and grid operation safety constraints by using the economic efficiency of the entire life cycle of mobile energy storage devices as constraints and the safe and stable operation of the distribution network as boundary constraints, thus solving the problem of single-dimensional configuration defects. Furthermore, by solving the calculation model of the total capacity and number of mobile energy storage vehicles using the target particle swarm optimization algorithm, this invention can output the optimal energy storage capacity and the optimal number of vehicles, ensuring safe operation requirements such as no overload and stable voltage in the distribution area, while minimizing equipment investment and operation and maintenance costs. This invention establishes a dynamic matching mechanism for demand and supply across multiple energy storage areas based on the theory of multi-agent collaboration. This mechanism enables the dynamic allocation and flexible movement of mobile energy storage vehicles across energy storage areas, completely solving the problems of high idle rates and rigid scheduling in traditional fixed energy storage, and improving the overall utilization rate of energy storage equipment. In summary, this invention has the advantages of being economical and efficient, safe and reasonable, and convenient for flexible scheduling across regions. Detailed Implementation

[0010] The present invention will now be further described.

[0011] Example: A method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas, comprising the following steps: Step 1: Collect historical load data, real-time operation data, and distributed photovoltaic output data from agricultural areas, densely populated residential areas, and photovoltaic areas. Then, use time series analysis to extract the periodic patterns of seasonal overloads, thereby identifying the start and end times and duration characteristics of overloads in different transformer areas, and analyzing the spatial and temporal distribution characteristics of reverse overloads and overvoltage faults caused by distributed photovoltaic access, and establishing the correlation between photovoltaic penetration rate and power quality problems in transformer areas. Step 2: Based on transformer substation type, load fluctuation amplitude, and photovoltaic output time series data, a three-dimensional analysis model of "transformer substation type-load fluctuation-photovoltaic output" is constructed, with transformer substation type as the first dimension, load fluctuation amplitude as the second dimension, and photovoltaic output characteristics as the third dimension. Subsequently, clustering algorithm and multiple regression analysis method are used to quantify the dynamic energy storage demand of different transformer substations from the two dimensions of power demand and capacity demand, thereby forming a standardized demand quantification framework. Step 3: Using the economic efficiency of the mobile energy storage device's entire lifecycle cost as a constraint and the safe and stable operation of the distribution network as a boundary constraint, establish a calculation model for the total capacity and number of mobile energy storage vehicles, considering both economic and safety constraints. The distribution network safety constraints include the overload protection threshold for transformer substations, the stable operating range of the grid voltage, and the tolerance limit of the mobile energy storage device, all determined according to rural distribution network operation specifications. Then, using energy storage capacity, rated power, and access location as decision variables, and minimizing overall cost and voltage deviation as optimization objectives, combined with the distribution network safety constraint boundary conditions, solve the established model using a multi-objective particle swarm optimization algorithm. This outputs the optimal total capacity and optimal number of mobile energy storage vehicles suitable for rural multi-transformer substation scenarios. In this process, the entire lifecycle cost of the mobile energy storage device includes equipment procurement cost, on-site operation and maintenance cost, equipment depreciation cost, and scrapping and replacement cost. Step 4: Establish a real-time load monitoring network for multiple transformer substations. Utilize wireless communication technology to collect and interact with real-time status information such as load power, photovoltaic output, and node voltage for each substation. Then, based on multi-agent collaboration theory, treat each substation as an independent agent and mobile energy storage vehicles as mobile shared resources, constructing a dynamic "demand-supply" matching mechanism. Next, combining real-time load gaps, remaining energy storage capacity, and geographical distribution information of the substations, design a dynamic scheduling path optimization algorithm and construct theoretical models for two typical scenarios: peak load and photovoltaic fluctuations. This allows for adaptive adjustment of mobile energy storage charging and discharging strategies and cross-substation scheduling paths, achieving flexible allocation of energy storage resources. During this process, the theoretical model adaptively adjusts as follows: in peak load scenarios, mobile energy storage vehicles are pre-deployed to heavily loaded substations for discharge support; in photovoltaic fluctuation scenarios, mobile energy storage vehicles charge and consume energy during periods of high photovoltaic power generation and discharge to compensate for insufficient photovoltaic output.

[0012] In use, this invention first collects historical load data, real-time operation data, and distributed photovoltaic output data from agricultural areas, densely populated residential areas, and photovoltaic areas. Then, based on the collected data, time series analysis is used to extract the periodic patterns of seasonal overloads, thereby identifying the start and end times and duration characteristics of overloads in different distribution areas, and analyzing the spatial and temporal distribution characteristics of reverse overloads and overvoltage faults caused by distributed photovoltaic access. This establishes a correlation between photovoltaic penetration rate and power quality issues in distribution areas. Then, based on distribution area type, load fluctuation amplitude, and photovoltaic output time series data, ... The invention constructs a three-dimensional analysis model of "Distribution Area Type - Load Fluctuation - Photovoltaic Output" using three dimensions: distribution area type as the first dimension, load fluctuation amplitude as the second dimension, and photovoltaic output characteristics as the third dimension. Subsequently, clustering algorithms and multiple regression analysis are employed to quantify the dynamic energy storage demand of different distribution areas from two dimensions: power demand and capacity demand. This forms a standardized demand quantification framework. This approach breaks through the traditional unified configuration model, achieving precise quantification of the spatiotemporal differences in energy storage demand. Furthermore, the invention uses the economic efficiency of the entire life cycle cost of mobile energy storage devices as a constraint and the safe and stable operation of the distribution network as a boundary condition. This approach integrates economic constraints with grid operation safety constraints, establishing a calculation model for the total capacity and number of mobile energy storage vehicles that considers both economic and safety constraints. Finally, using energy storage capacity, rated power, and access location as decision variables, and minimizing overall cost and voltage deviation as optimization objectives, combined with distribution network safety constraint boundary conditions, a multi-objective particle swarm optimization algorithm is used to solve the established model. This outputs the optimal total capacity and optimal number of mobile energy storage vehicles suitable for rural multi-distribution area scenarios. After completing the above operations, a real-time load monitoring network for multiple distribution areas is established, and... This invention employs wireless communication technology to achieve real-time acquisition and interaction of status information such as load power, photovoltaic output, and node voltage in each distribution area. Then, based on multi-agent collaborative theory, a dynamic "demand-supply" matching mechanism is constructed. Combining real-time load gaps in distribution areas, remaining energy storage capacity, and geographical distribution information, a dynamic scheduling path optimization algorithm is designed. This enables the dynamic allocation and flexible movement of mobile energy storage vehicles across distribution areas, completely solving the problems of high idle rates and rigid scheduling in traditional fixed energy storage, and improving the overall utilization rate of energy storage equipment. In summary, this invention has the advantages of being economical and efficient, safe and reasonable, and convenient for flexible cross-regional scheduling.

[0013] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas, characterized in that, Includes the following steps: Step 1: Collect historical load data, real-time operation data, and distributed photovoltaic output data from agricultural areas, densely populated residential areas, and photovoltaic areas. Then, use time series analysis to extract the periodic patterns of seasonal overloads, thereby identifying the start and end times and duration characteristics of overloads in different transformer areas, and analyzing the spatial and temporal distribution characteristics of reverse overloads and overvoltage faults caused by distributed photovoltaic access, and establishing the correlation between photovoltaic penetration rate and power quality problems in transformer areas. Step 2: Based on transformer substation type, load fluctuation amplitude, and photovoltaic output time series data, a three-dimensional analysis model of "transformer substation type-load fluctuation-photovoltaic output" is constructed, with transformer substation type as the first dimension, load fluctuation amplitude as the second dimension, and photovoltaic output characteristics as the third dimension. Subsequently, clustering algorithm and multiple regression analysis method are used to quantify the dynamic energy storage demand of different transformer substations from the two dimensions of power demand and capacity demand, thereby forming a standardized demand quantification framework. Step 3: With the economic efficiency of the entire life cycle cost of mobile energy storage equipment as a constraint and the safe and stable operation of the distribution network as a boundary constraint, establish a calculation model for the total capacity and number of mobile energy storage vehicles that considers economic and safety constraints. Then, with energy storage capacity, rated power and access location as decision variables, and with the optimization objectives of minimizing comprehensive cost and voltage deviation, combined with the boundary conditions of distribution network safety constraints, solve the above-established model to output the optimal total capacity and optimal number of mobile energy storage vehicles suitable for rural multi-transformer area scenarios. Step 4: Establish a real-time load monitoring network for multiple transformer substations. Use wireless communication technology to collect and interact with status information such as load power, photovoltaic output, and node voltage in each substation in real time. Then, based on the multi-agent collaborative theory, treat each substation as an independent intelligent agent and the mobile energy storage vehicle as a mobile shared resource to build a dynamic "demand-supply" matching mechanism. Next, combine the real-time load gap of the substations, the remaining energy storage capacity, and the geographical distribution information of the substations to design a dynamic scheduling path optimization algorithm and build theoretical models for two typical scenarios: peak load and photovoltaic fluctuation. This allows for adaptive adjustment of the mobile energy storage charging and discharging strategy and cross-substation scheduling path to achieve flexible allocation of energy storage resources.

2. The method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas as described in claim 1, characterized in that: The distribution network safety constraints in step 3 include the overload protection threshold of the transformer area, the stable operating range of the grid voltage, and the tolerance limit of mobile energy storage devices, which are determined according to the rural distribution network operation specifications.

3. The method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas as described in claim 1, characterized in that: The total life cycle cost of the mobile energy storage device in step 3 includes the equipment procurement cost, on-site operation and maintenance cost, equipment depreciation cost, and scrapping and replacement cost.

4. The method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas as described in claim 1, characterized in that: In step 4, when the theoretical model is adaptively adjusted, the mobile energy storage vehicle is deployed in advance to the heavy-load area to provide discharge support in the peak load scenario. In the photovoltaic fluctuation scenario, the mobile energy storage vehicle charges and consumes photovoltaic power during the peak photovoltaic power generation period and discharges to make up for insufficient photovoltaic power output.

5. The method for optimizing the configuration of mobile energy storage capacity and quantity in rural power distribution areas as described in claim 1, characterized in that: The algorithm for solving the model for calculating the total capacity and number of mobile energy storage vehicles in step 3 is a multi-objective particle swarm optimization algorithm.