A site selection and capacity planning method and system for a grid-side energy storage device and an electronic device

Abstract

The application provides a site selection and capacity planning method and system for a power grid side energy storage device and an electronic device, relates to the technical field of power grid side energy storage planning, and comprises the following steps: after obtaining power grid operation data and external environment data of a target area, processing the data by using a power grid-energy storage digital twin model, obtaining site-capacity characteristic data, and generating a candidate site set in the target area according to the site-capacity characteristic data, and according to the space and boundary constraints of each candidate site, performing synchronous partition mapping on site selection variables and capacity determination variables to form a plurality of candidate subsets; generating an initial site selection and capacity determination scheme for the target area based on the sites in each candidate subset; performing simulation evaluation on the scheme by using annual time sequence operation data to obtain a performance index data set; and judging whether the performance index converges or not by combining a preset convergence rule, and if all the performance indexes converge, determining the initial site selection and capacity determination scheme at this time as a final scheme. The application realizes synchronous consideration of site selection and capacity determination.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grid-side energy storage planning, in particular to a grid-side energy storage device site selection and capacity planning method and system and electronic equipment. BACKGROUND

[0002] With high proportion of new energy grid-connected, grid-side energy storage devices have become the core equipment to improve system flexibility, alleviate curtailment of wind and light, and support spot market operation. Therefore, it is necessary to select the site and determine the capacity of the grid-side energy storage device. The traditional step-by-step decision mode is usually used, that is, first determine the capacity and then select the site, or first select the site and then determine the capacity, and finally form a planning scheme by simple splicing.

[0003] In related technologies, the capacity value of the energy storage device is highly dependent on its access location, and the station site value changes nonlinearly with the capacity scale, so there is a strong coupling relationship between them. Because the traditional step-by-step mode optimizes the capacity and station site as two independent stages in actual application, if the capacity is determined first, the real consumption capacity of the station site cannot be predicted, and if the station site is selected first, the reverse shaping of the capacity on the power flow will be ignored, resulting in distorted information transmission between stages and thus reducing the planning effect of the grid-side energy storage device. SUMMARY

[0004] The problem solved by the present application is how to improve the coupling relationship between site selection and capacity determination of the grid-side energy storage device.

[0005] To solve the above problems, the present application provides a grid-side energy storage device site selection and capacity planning method, system and electronic equipment.

[0006] In a first aspect, the present application provides a grid-side energy storage device site selection and capacity planning method, comprising:

[0007] obtaining grid operation data and external environment data of a target area;

[0008] obtaining station site-capacity characteristic data of the target area through a grid-energy storage digital twin model according to the grid operation data and the external environment data;

[0009] generating a candidate station site set in the target area according to the station site-capacity characteristic data;

[0010] synchronously partitioning and mapping site selection variables and capacity determination variables according to the spatial constraints and boundary constraints corresponding to each candidate station site in the candidate station site set, to obtain a plurality of candidate subsets of the candidate station site set;

[0011] generating an initial site selection and capacity determination scheme of the target area according to the candidate station sites in each candidate subset;

[0012] simulate and evaluate the annual time sequence operation data of the target region according to the initial site selection and capacity determination scheme, to obtain a performance index data set of the initial site selection and capacity determination scheme;

[0013] According to the performance index data set, combined with a preset convergence rule, it is judged whether each performance index in the performance index data set converges;

[0014] When all the performance indexes converge, the initial site selection and capacity determination scheme after the performance index converges is taken as the final site selection and capacity determination scheme.

[0015] Optionally, the site-capacity characteristic data of the target region is obtained by the grid-energy storage digital twin model according to the grid operation data and the external environment data, comprising:

[0016] The grid operation data and the external environment data are input into the grid-energy storage digital twin model synchronously;

[0017] The grid-energy storage digital twin model is used to perform power flow calculation and operation simulation on the grid operation data to obtain capacity characteristic data;

[0018] The grid-energy storage digital twin model is used to perform spatial accessibility and geological red line checking on the external environment data to obtain address characteristic data;

[0019] The capacity characteristic data and the address characteristic data are matched and fused in the node-site dimension to generate the site-capacity characteristic data.

[0020] Optionally, the candidate site set in the target region is generated according to the site-capacity characteristic data, comprising:

[0021] Multi-dimensional clustering and accessibility screening are performed on the site-capacity characteristic data to obtain a preliminary screening site list;

[0022] According to the geographical red line constraint corresponding to the address characteristic data of each candidate site in the preliminary screening site list and the grid access margin constraint corresponding to the capacity characteristic data, secondary filtering is performed to obtain a compliance site list;

[0023] The candidate sites in the compliance site list are identified according to node coordinates to generate the candidate site set.

[0024] Optionally, the site selection variables and the capacity determination variables are synchronously partitioned and mapped according to the spatial constraint and the boundary constraint corresponding to each candidate site in the candidate site set, to obtain a plurality of candidate subsets of the candidate site set, comprising:

[0025] extracting geographical position data of each candidate site in the candidate site set, and dividing the candidate site set into multiple geographical partitions according to the geographical position data;

[0026] taking the remaining available capacity data of the power transmission corresponding to the geographical partitions as boundary constraints, and taking the geographical position data as spatial constraints, and establishing a joint constraint domain of site selection variables and capacity determination variables according to the boundary constraints and the spatial constraints;

[0027] taking the joint constraint domain as a mapping rule, performing synchronous partition mapping on the site selection variables and the capacity determination variables to generate candidate subsets containing the same spatial constraints and boundary constraints;

[0028] summarizing each candidate subset according to the partition identifier to obtain multiple candidate subsets of the candidate site set.

[0029] Optionally, the generating of the initial site selection and capacity determination scheme of the target region according to the candidate sites in each candidate subset comprises:

[0030] obtaining the remaining available capacity data and the geographical position data corresponding to each candidate subset;

[0031] setting the remaining available capacity data as a capacity upper limit constraint of the subset, and converting the geographical position data into a spatial coordinate matrix;

[0032] constructing a site selection-capacity determination optimization model according to the capacity upper limit constraint and the spatial coordinate matrix;

[0033] obtaining the initial site selection and capacity determination scheme by taking values of site selection variables of each candidate site and synchronously determining energy storage rated power and rated capacity of the candidate site through the site selection-capacity determination optimization model.

[0034] Optionally, the simulation evaluation of the annual time sequence operation data of the target region according to the initial site selection and capacity determination scheme to obtain a performance index data set of the initial site selection and capacity determination scheme comprises:

[0035] inputting the initial site selection and capacity determination scheme into the grid-energy storage digital twin model;

[0036] performing hourly power flow simulation and operation simulation on the initial site selection and capacity determination scheme through the grid-energy storage digital twin model by calling the annual time sequence operation data of the target region to obtain capacity characteristic data and site characteristic data corresponding to the initial site selection and capacity determination scheme;

[0037] perform feature extraction on the capacity feature data and the address feature data to obtain operation characteristics of the initial site selection and capacity determination scheme in the target area, the operation characteristics including a node voltage curve, a line power flow curve, a storage energy state of charge curve, and a new energy power curtailment curve;

[0038] perform weighted processing on the node voltage curve, the line power flow curve, the storage energy state of charge curve, and the new energy power curtailment curve in an input performance index calculation module to obtain a performance index data set of the initial site selection and capacity determination scheme;

[0039] The performance index data set includes a new energy consumption rate, a peak-valley difference reduction rate, a node voltage qualified rate, a storage energy annualized yield, and a system carbon emission reduction amount.

[0040] Optionally, the determining, according to the performance index data set and in combination with a preset convergence rule, whether each performance index in the performance index data set converges includes:

[0041] comparing all performance indexes in the performance index data set with preset convergence rules corresponding to the performance indexes to obtain a deviation value of each performance index;

[0042] If the deviation value of the performance index is less than or equal to a preset deviation threshold, it is determined that the performance index converges.

[0043] If the deviation value of the performance index is greater than the preset deviation threshold, it is determined that the performance index does not converge.

[0044] Optionally, the method further includes:

[0045] If the deviation value of any of the performance indexes is greater than the preset deviation threshold, it is determined that the performance index data set does not converge.

[0046] generating a correction instruction according to the deviation value corresponding to the performance index that does not converge;

[0047] performing adaptive updating on model parameters of the power grid-storage digital twin model according to the correction instruction to obtain an updated power grid-storage digital twin model;

[0048] re-performing synchronous decision on the site selection variable and the capacity determination variable through the updated power grid-storage digital twin model to obtain an updated initial site selection and capacity determination scheme, until the deviation value of all the performance indexes is less than or equal to the preset deviation threshold.

[0049] In a second aspect, the present application provides a site selection and capacity determination planning system for a power grid side storage device, including:

[0050] A data collection unit is configured to collect power grid operation data and external environment data of a target region;

[0051] A twin modeling unit is configured to obtain site-capacity characteristic data of the target region by a power grid-ES digital twin model according to the power grid operation data and the external environment data;

[0052] A candidate site generation unit is configured to generate a candidate site set in the target region according to the site-capacity characteristic data;

[0053] A partition mapping unit is configured to perform synchronous partition mapping of site selection variables and capacity determination variables according to spatial constraints and boundary constraints corresponding to each candidate site in the candidate site set, to obtain a plurality of candidate subsets of the candidate site set;

[0054] An optimization decision unit is configured to generate an initial site selection and capacity determination scheme of the target region according to the candidate sites in each candidate subset;

[0055] A simulation evaluation unit is configured to perform simulation evaluation on annual time sequence operation data of the target region according to the initial site selection and capacity determination scheme, to obtain a performance index data set of the initial site selection and capacity determination scheme;

[0056] A convergence judgment unit is configured to judge whether each performance index in the performance index data set converges according to the performance index data set and in combination with a preset convergence rule; when all the performance indexes converge, the initial site selection and capacity determination scheme after the performance indexes converge is taken as a final site selection and capacity determination scheme.

[0057] In a third aspect, the present application provides an electronic device comprising a memory and a processor;

[0058] The memory is configured to store a computer program;

[0059] The processor is configured to implement the site selection and capacity determination planning method of the power grid side ES device when the computer program is executed.

[0060] The site selection and capacity planning method, system and electronic equipment of the grid-side energy storage device of the application obtain the grid operation data (such as power flow direction, node voltage, etc.) and external environment data (such as temperature, humidity, etc. factors affecting energy storage operation) of the target area, providing a comprehensive data basis for subsequent analysis. The grid-energy storage digital twin model is used to fuse the above data to obtain site-capacity characteristic data, which fully reflects the internal relationship between the energy storage access location and the capacity scale, providing key technical support for solving the coupling problem. Based on the site-capacity characteristic data, a candidate site set is generated, and the possible energy storage site location is preliminarily determined to provide an alternative range for subsequent site selection and capacity planning. Then, according to the spatial constraints (such as land area, topography, etc.) and boundary constraints (such as the distribution of surrounding power facilities, safety distance, etc.) of each candidate site in the candidate site set, the site selection variables and capacity variables are simultaneously mapped to obtain multiple candidate subsets. This process breaks the traditional step-by-step decision-making mode, realizes the simultaneous consideration of site selection and capacity, effectively avoids the information transmission distortion problem caused by step-by-step optimization, directly integrates the capacity factor into the site selection process to inversely shape the power flow, and fully considers the real accommodation capacity of the site during capacity planning, thereby solving the optimization problem under the strong coupling relationship between site selection and capacity. Further, an initial site selection and capacity planning scheme is generated according to the candidate sites in the candidate subset, and the performance index data set is obtained by simulating and evaluating the annual time series operation data of the target area. Through the simulation of actual operation data, the effectiveness and feasibility of the scheme are verified, and the mutual influence between site selection and capacity is further explored. According to the preset convergence rule, the performance index data set is judged, and when all the performance indexes converge, the initial scheme at this time is taken as the final site selection and capacity planning scheme, ensuring the stability and reliability of the scheme.

[0061] The application synchronously optimizes site selection and capacity, fully considers the strong coupling relationship between them, avoids the poor planning effect caused by information distortion in the traditional step-by-step decision-making mode, makes the final energy storage planning scheme more suitable for actual grid operation, and better improves the system flexibility, alleviates curtailment of wind and solar power, and supports the spot market operation. At the same time, the simulation evaluation of the annual time series operation data is introduced in the planning process, and the demand and characteristics of the grid under different operating conditions are fully considered, so that the planning scheme can adapt to the dynamic changes of the grid and improve the effectiveness and practicality of the energy storage system in actual operation. The introduction of synchronous partition mapping makes the decision of site selection and capacity more refined, can reasonably allocate energy storage resources according to the actual situation of different regions, avoids waste and over-construction of resources, and improves the investment benefit of the energy storage system. Through digital twin model and partition mapping technology, a better candidate scheme can be quickly generated and screened, unnecessary repeated optimization process is reduced, and the efficiency of planning work is improved. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flow chart of a method for site selection and capacity planning of a grid-side energy storage device according to an embodiment of the present application;

[0063] Figure 2 A structure block diagram of a system for site selection and capacity planning of a grid-side energy storage device according to an embodiment of the present application;

[0064] Figure 3 A structure diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the above objectives, features and advantages of the present application more clear and comprehensible, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, rather, these embodiments are provided so as to make the present application more thorough and complete. It should be understood that the drawings and embodiments of the present application are merely for exemplary purposes, and are not intended to limit the scope of protection of the present application.

[0066] It should be understood that each of the steps recited in the method embodiments of the present application can be executed in different orders, and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0067] The term “comprising” and variations thereof as used herein are open-ended, that is, “comprising but not limited to”; the term “based on” is “based at least in part on”; the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one additional embodiment”; the term “some embodiments” means “at least some embodiments”; the term “optional” means “optional in at least some embodiments.” Related definitions are given throughout the description. It should be noted that the concepts mentioned “first”, “second” and the like in the present application are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0068] It should be noted that the modification of “one” “multiple” mentioned in the present application is illustrative rather than limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as “one or more”.

[0069] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present application are authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant national and regional laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0070] In combination Figure 1 As shown in the embodiment of the present application, a site and capacity planning method of a grid-side energy storage device is provided, which comprises:

[0071] Obtain grid operation data and external environment data of a target area.

[0072] Specifically, the grid operation data and external environment data of the target area are obtained. The grid operation data includes but is not limited to grid topology, voltage and current parameters of each node, power flow direction and power size, power output data of power generation equipment, power consumption data of load, etc., which can reflect the electrical characteristics and operating characteristics of the grid under different operating conditions. The external environment data includes meteorological data (such as temperature, humidity, wind speed, etc.), geographic information (such as topography, altitude, land use type, etc.), policy and regulation (such as renewable energy quota system, energy storage subsidy policy, etc.), and social and economic data (such as economic development level, population density, industrial development trend, etc.) of the target area. These data can be obtained through various channels, for example, obtaining historical records and real-time monitoring data of grid operation from grid dispatching center, collecting meteorological and geographic information by using meteorological station, satellite remote sensing, etc., obtaining policy and regulation data by referring to government published policy documents, and collecting social and economic data by referring to statistical yearbook, economic census report, etc.

[0073] Through the grid-energy storage digital twin model, the site-capacity characteristic data of the target area is obtained according to the grid operation data and the external environment data.

[0074] Specifically, the grid- energy storage digital twin model is a virtual mapping and simulation model based on physical laws, mathematical equations, and data-driven algorithms, which can accurately simulate the interaction mechanism between the grid and the energy storage system. In this model, the grid operation data is used as the basic input to build a digital model of the target regional grid, including node parameter setting, line impedance configuration, and access location of power generation and load, etc. At the same time, external environmental data is integrated into the model to correct the operating characteristics of grid elements (such as the influence of temperature on line resistance) and evaluate the operating environment adaptability of the energy storage system (such as the potential impact of humidity on the service life of energy storage batteries). By running the digital twin model, the operating state of the grid under different energy storage site locations and different capacity scales is simulated, and then the site- capacity characteristic data reflecting the mutual relationship between the energy storage site location and the capacity scale are extracted, such as the support effect of the energy storage system on the surrounding node voltage when it is connected to the grid at different locations, the regulation ability of power flow, and the charging and discharging efficiency and cycle life attenuation of the energy storage system under different capacities. These data provide key basis for subsequent site selection and capacity planning.

[0075] In an embodiment of the present application, the grid- energy storage digital twin model is a grid- energy storage joint simulation model (based on multi-physical field coupling). The grid- energy storage joint simulation model is a multi-physical field coupling model that integrates the electrical characteristics of the grid, the dynamic characteristics of the energy storage system, and external environmental factors. This model realizes real-time mapping and simulation of the grid and the energy storage system through digital twin technology, and can accurately simulate the interaction mechanism between the grid and the energy storage system.

[0076] The model composition includes: a grid electrical sub-model based on mathematical models such as power flow calculation, short circuit calculation, and stability analysis in power system analysis, used to simulate the operating state of the grid. The energy storage system sub-model includes an electrochemical sub-model of the energy storage battery, a charging and discharging characteristic sub-model, and a control strategy sub-model of the energy storage system. The external environment model is used to consider the influence of environmental factors such as temperature and humidity on the grid elements and the energy storage system, and to reflect the actual operating environment by correcting the model parameters. The data-driven model is used to analyze the historical operation data using machine learning algorithms, optimize the model parameters, and improve the simulation accuracy.

[0077] For example, assume that the target area is a distribution network containing multiple substations and distributed energy access, and the site and capacity of the energy storage system need to be planned.

[0078] Data collection and input includes: grid operation data, including grid topology, node voltage, line impedance, generator and load power, etc. External environmental data, including temperature, humidity, solar intensity, etc. in the target area. Energy storage system data, including capacity, charging and discharging power, cycle life, etc. of energy storage batteries.

[0079] The power grid electrical model is built according to power grid operation data, a digital model of the power grid is constructed, node parameters and line impedances are set. The energy storage system model integration integrates the electrochemical model and charge-discharge characteristic model of the energy storage system into the power grid model, considering different access modes of different site locations. The external environmental factor correction corrects the operation characteristics of the power grid elements and the energy storage system according to external environmental data. For example, when the temperature rises, the line resistance increases, and the charge-discharge efficiency of the energy storage battery decreases.

[0080] By running the digital twin model, the operation state of the power grid under different energy storage site locations and different capacity scales is simulated. After the energy storage system is accessed at different energy storage site locations, the voltage changes of the surrounding nodes are observed. For example, after a 10MW energy storage system is accessed at node A, the voltage of node A is raised from 1.02p.u. to 1.05p.u., and the voltage of the surrounding nodes is also improved. The influence of the energy storage system on the power flow of the power grid after being accessed is analyzed. For example, the energy storage system discharges during the peak load period, reducing the power input from the upper-level power grid and optimizing the power flow distribution. Combined with external environmental data (such as humidity) and charge-discharge data of the energy storage system, the service life attenuation of the energy storage system is evaluated. For example, in an environment with a humidity of 70%, the cycle life of the energy storage system is reduced by 20% compared to an environment with a humidity of 50%.

[0081] The support effect of the energy storage system on the voltage of the surrounding nodes when accessed at different locations in the power grid, the regulation ability of the power flow, and the charge-discharge efficiency and cycle life attenuation of the energy storage system under different capacities are extracted. According to the simulation results, the optimal energy storage site location and capacity scale are determined. For example, it is recommended to access 10MW and 15MW energy storage systems at nodes A and B respectively to achieve the best voltage support and power flow regulation effect, while ensuring the service life of the energy storage system within an acceptable range. Through this power grid-energy storage joint simulation model, the performance of the energy storage system under different site and capacity configurations can be comprehensively evaluated, providing a scientific basis for the site selection and capacity planning of the energy storage system.

[0082] According to the site-capacity characteristic data, a candidate site set in the target area is generated.

[0083] Specifically, first, the station-site capacity feature data is analyzed in depth to identify potential station sites that have a significant positive impact on power grid operation under different capacity conditions. For example, those station sites that can effectively alleviate power grid congestion, improve voltage qualification rate, and reduce curtailed wind and solar power after the energy storage system is connected are screened out. Then, in combination with the geographic information and power grid layout of the target region, the rationality of the spatial distribution of these potential station sites is verified, and those unsuitable locations within the ecological protection red line, in geological disaster-prone areas, or occupied by other major projects in the power grid construction plan are excluded. Finally, by comprehensively considering the construction cost (such as land acquisition cost, construction difficulty and cost of connecting to the power grid, etc.) and operation and maintenance convenience (such as traffic accessibility and richness of surrounding operation and maintenance resources, etc.) of the energy storage system, a set of relatively optimal candidate station sites is determined to form a candidate station site set, laying a foundation for further optimization and screening.

[0084] According to the spatial constraints and boundary constraints corresponding to each candidate station site in the candidate station site set, the site selection variables and capacity determination variables are synchronously partitioned and mapped to obtain a plurality of candidate subsets of the candidate station site set.

[0085] Specifically, the spatial constraints of each candidate station site mainly include the land area, terrain flatness, and spacing requirements of surrounding buildings and facilities of its geographic location, which directly limit the size of the energy storage system that can be built; the boundary constraints involve the upper limit of the access capacity of the power grid around the station site, the voltage level range, the short-circuit current level, and other power system boundary conditions, as well as local environmental protection requirements, noise restrictions, and other environmental boundary conditions. When synchronously partitioning and mapping, the site selection variables (i.e., whether to build an energy storage system at the station site) and the capacity determination variables (i.e., the capacity of the energy storage system to be built) are considered jointly, and different feasible solution regions (i.e., candidate subsets) are divided according to the spatial and boundary constraint conditions of each candidate station site. For example, for a station site with ample land area and high access capacity of the power grid, a larger capacity range of feasible solutions can be mapped; while for a station site with limited land and strict access conditions of the power grid, only a smaller capacity or even no solution candidate subset can be mapped. In this way, the synchronization optimization of site selection and capacity determination is realized, ensuring that the selected station site and the determined capacity are matched with each other.

[0086] According to the candidate station sites in each of the candidate subsets, an initial site selection and capacity determination scheme for the target region is generated.

[0087] Specifically, for each candidate subset, the station-site-capacity characteristic data of the candidate sites therein, the spatial constraints and the boundary constraints are comprehensively considered to select appropriate station sites and determine the corresponding energy storage system capacity configurations. For example, in a certain candidate subset, the station-site-capacity characteristic data indicates that a station site at a certain capacity can significantly improve the operation efficiency of the power grid, while ensuring that the selected capacity meets the spatial and boundary constraints of the station site. Combining the selected station sites and their corresponding capacities in all candidate subsets, an initial site selection and capacity configuration scheme for the target region is formed. The scheme preliminarily determines the number, location and capacity size of the energy storage systems to be built in the target region, providing a benchmark scheme to be verified for subsequent simulation evaluation and optimization adjustment.

[0088] According to the initial site selection and capacity configuration scheme, the annual time series operation data of the target region is simulated and evaluated to obtain a performance index data set of the initial site selection and capacity configuration scheme.

[0089] Specifically, the annual time series operation data covers the operation conditions of the target region power grid in different seasons and time periods within a year, including load variation curves, power generation equipment output fluctuations (especially the intermittent and random characteristics of new energy generation), adjustment of power grid topology structure (such as line maintenance, open-loop and closed-loop operation, etc.), and external environmental factors (such as the influence of temperature changes on electricity load), etc. In the simulation and evaluation process, the energy storage systems in the initial site selection and capacity configuration scheme are connected to the power grid digital model, and the operation state of the power grid at each time is simulated according to the annual time series operation data, recording the charging and discharging behavior of the energy storage systems, the voltage deviation of each node of the power grid, the line flow distribution, the new energy consumption, the power grid operation cost and other key performance index data. Through the annual time series simulation, the influence of the initial site selection and capacity configuration scheme on the operation performance of the power grid under different operation conditions is comprehensively evaluated, providing detailed data support for subsequent optimization adjustment.

[0090] According to the performance index data set, combined with a preset convergence rule, it is judged whether each performance index in the performance index data set converges.

[0091] Specifically, the preset convergence rule is a set of criteria set according to the stability of power grid operation and planning objectives. For example, for the performance indicator of power grid voltage qualification rate, the convergence rule can be that after continuous multiple iterations of optimization, the change amplitude of the voltage qualification rate is less than a set threshold (such as 0.1%), and the final voltage qualification rate reaches or exceeds the power grid operation standard (such as 99.5%). For the utilization rate indicator of the energy storage system, the convergence rule can be that the fluctuation range of the utilization rate in the iteration process is controlled within a certain percentage (such as ±2%), and the utilization level reaches the expected level (such as not less than 70%). By comparing each performance indicator value in the performance indicator data set with the preset convergence rule, it is judged whether it meets the convergence condition. If it does not meet, the initial site selection and capacity determination scheme needs to be further optimized and adjusted, and the simulation evaluation and convergence judgment are performed again until all performance indicators converge.

[0092] When all the performance indicators converge, the initial site selection and capacity determination scheme after the performance indicators converge is taken as the final site selection and capacity determination scheme.

[0093] Specifically, when all the performance indicators converge, the initial site selection and capacity determination scheme after the performance indicators converge is taken as the final site selection and capacity determination scheme. This means that after multiple rounds of iteration optimization and simulation evaluation, the scheme has reached the best balance state in site selection and capacity determination, and can meet the requirements of stability, reliability and economy of power grid operation, while fully tapping the role of energy storage system in improving power grid flexibility and promoting new energy consumption. Determining this scheme as the final power grid side energy storage site selection and capacity determination planning scheme can directly guide the construction and layout of energy storage system, and provide strong support for the safe and stable operation of power grid and the large-scale access of new energy.

[0094] The site selection and capacity planning method of the grid-side energy storage device of the application obtains the grid operation data (such as power flow direction, node voltage, etc.) and external environmental data (such as temperature, humidity, etc. factors affecting energy storage operation) of the target area, providing a comprehensive data basis for subsequent analysis. The grid-energy storage digital twin model is used to fuse the above data to obtain site-capacity characteristic data, which fully reflects the internal relationship between the energy storage access location and the capacity scale, providing key technical support for solving the coupling problem. Based on the site-capacity characteristic data, a candidate site set is generated, and the possible energy storage site location is preliminarily determined to provide an alternative range for subsequent site selection and capacity planning. Then, according to the spatial constraints (such as land area, topography, etc.) and boundary constraints (such as the distribution of surrounding power facilities, safety distance, etc.) of each candidate site in the candidate site set, the site selection variables and capacity variables are simultaneously mapped to obtain multiple candidate subsets. This process breaks the traditional step-by-step decision-making mode, realizes the simultaneous consideration of site selection and capacity, effectively avoids the information transmission distortion problem caused by step-by-step optimization, directly integrates the capacity factor into the site selection process to reverse the shaping effect of the power flow, and fully considers the real accommodation capacity of the site during capacity planning, thereby solving the optimization problem under the strong coupling relationship between site selection and capacity. Further, an initial site selection and capacity planning scheme is generated according to the candidate sites in the candidate subset, and the performance index data set is obtained by simulating and evaluating the annual time series operation data of the target area. Through the simulation of actual operation data, the effectiveness and feasibility of the scheme are verified, and the mutual influence between site selection and capacity is further explored. According to the preset convergence rule, the performance index data set is judged, and when all the performance indexes converge, the initial scheme at this time is taken as the final site selection and capacity planning scheme, ensuring the stability and reliability of the scheme.

[0095] The application optimizes site selection and capacity simultaneously, fully considers the strong coupling relationship between the two, avoids the poor planning effect caused by information distortion in the traditional step-by-step decision-making mode, makes the final energy storage planning scheme more suitable for actual grid operation, and better improves the system flexibility, alleviates curtailment of wind and solar power, and supports spot market operation. At the same time, the simulation evaluation of annual time series operation data is introduced in the planning process, which fully considers the demand and characteristics of the grid under different operating conditions, so that the planning scheme can adapt to the dynamic changes of the grid, improving the effectiveness and practicality of the energy storage system in actual operation. The introduction of synchronous partition mapping makes the decision of site selection and capacity more refined, which can reasonably allocate energy storage resources according to the actual situation of different regions, avoid waste and over-construction of resources, and improve the investment benefit of the energy storage system. Through digital twin model and partition mapping technology, the optimal candidate scheme can be quickly generated and screened, reducing unnecessary repeated optimization process, and improving the efficiency of planning work.

[0096] Optionally, the station-capacity characteristic data of the target area is obtained from the grid- energy storage digital twin model based on the grid operation data and the external environment data, comprising:

[0097] The grid operation data and the external environment data are synchronously input into the grid- energy storage digital twin model;

[0098] The capacity characteristic data is obtained by performing power flow calculation and operation simulation on the grid operation data through the grid- energy storage digital twin model;

[0099] The address characteristic data is obtained by performing spatial accessibility and geological red line checking on the external environment data through the grid- energy storage digital twin model;

[0100] The capacity characteristic data and the address characteristic data are matched and fused in the node-station dimension to generate the station-capacity characteristic data.

[0101] Specifically, the grid operation data covers the grid topology, voltage and current parameters of each node, power flow direction and size, power output data of power generation equipment, and power consumption data of load, etc.; the external environment data covers meteorological data (such as temperature, humidity, wind speed, etc.), geographic information (such as topography, altitude, land use type, etc.), and social and economic data (such as economic development level, population density, industrial development trend, etc.) of the target area. These data can be obtained through data collection systems, weather stations, satellite remote sensing, etc., and after data cleaning, checking and standardization, they are synchronously transmitted to the grid- energy storage digital twin model to provide comprehensive and accurate input information for the operation of the model. For example, the grid operation data can be extracted from the real-time monitoring system and historical database of the grid dispatching center, the meteorological data can be obtained from the observation records of the local weather station and the weather forecast model, and the geographic information can be collected through satellite remote sensing images and geographic information system (GIS) data.

[0102] The digital twin model is based on the basic principles of power systems, such as Kirchhoff's law, Newton-Raphson method, etc., to establish an accurate mathematical model of the grid. In the model, the grid operation data is taken as input to perform power flow calculation and obtain the voltage, power distribution, etc. of each node, and then simulate the dynamic behavior of the grid under different operating conditions. By changing the capacity of the energy storage system, the influence of the energy storage system on the power flow distribution, voltage stability and power balance of the grid is analyzed, and the capacity characteristic data is extracted. For example, when the energy storage system is connected to the grid with different capacities, the voltage deviation of the key nodes of the grid, the reduction degree of the line flow, and the improvement of the new energy consumption capacity, etc. are observed, and according to the variation law of these indicators, the optimal capacity configuration range of the energy storage system is determined, forming the capacity characteristic data, which provides a basis for subsequent energy storage capacity determination.

[0103] The address feature data is obtained by checking the spatial accessibility and geological red line of the external environment data through the digital twin model. The geographic information of the target area is analyzed using geographic information system (GIS) technology to evaluate the spatial accessibility factors such as traffic convenience and the perfection of surrounding supporting facilities of each potential site. Meanwhile, combined with geological survey data, it checks whether the site is located in the prohibited construction area such as geological disaster hidden danger area and ecological protection red line area. For example, by analyzing the distance between the site and the main traffic trunk line, and the connection with water and power supply facilities, the spatial accessibility level of the site is determined. The geological structure information of the site is obtained by using geological radar, drilling and other means, and compared with the geological disaster database to judge whether the site has risks such as earthquake, landslide and debris flow, and whether it violates the geological red line regulations. These spatial accessibility and geological red line checking results are quantified as address feature data, which is used to screen out the site location that meets the construction conditions, and provides key reference for the site selection of energy storage system.

[0104] The capacity feature data (such as the improvement effect of energy storage capacity on power grid performance) and the address feature data (such as the construction feasibility and risk level of the site) are associated and integrated with the common dimension of power grid node and energy storage site location. For example, for each potential site, according to its node location in the power grid, the capacity feature data (such as reducing the node voltage deviation by X% at a certain capacity, increasing the new energy consumption power by Y kilowatt-hour, etc.) corresponding to the site is bound with the address feature data (such as the spatial accessibility score is M points, the geological safety score is N points, etc.) to form a comprehensive site-capacity feature data set. Through this matching and fusion method, the comprehensive performance and construction feasibility of energy storage system under different site and capacity conditions are comprehensively and systematically reflected, which provides accurate data support for the subsequent site selection and capacity planning.

[0105] In the optional embodiment, through the synchronous processing and comprehensive analysis of the grid operation data and external environment data, the generated site-capacity characteristic data can comprehensively and accurately reflect the suitable construction location and optimal capacity configuration of the energy storage system in the target region, providing a scientific and reliable basis for the site selection and capacity planning of the grid-side energy storage equipment, and avoiding the planning errors caused by scattered and one-sided data in the traditional method. The energy storage capacity configuration determined based on the capacity characteristic data can effectively improve the voltage stability, power balance capability and new energy consumption level of the power grid. For example, reasonable energy storage capacity can quickly respond to power fluctuations in the power grid, suppress voltage deviation, improve power quality, reduce wind and light curtailment caused by intermittent power generation of new energy, and enhance the reliability and flexibility of power grid operation. The spatial accessibility and geological red line checking function of the address characteristic data help to select high-quality sites with convenient transportation, perfect supporting facilities, no geological disaster hidden dangers and no policy violation problems, reduce the engineering risks in the construction process of the energy storage system and the safety hazards in the later operation, ensure the smooth implementation and long-term stable operation of the energy storage project, and improve the investment benefit and social benefit of the project.

[0106] Meanwhile, the embodiment combines the capacity optimization and site optimization of the energy storage system by matching and fusing in the node-site dimension, breaks the limitation of the traditional step-by-step decision mode, and realizes multi-dimensional and integrated planning optimization. The method of the embodiment which comprehensively considers the mutual relationship between capacity and site can fully tap the potential value of the energy storage system in the power grid, improve the overall performance and adaptability of the planning scheme, and make the energy storage system play a greater role in the operation of the power grid.

[0107] Optionally, the generating the candidate site set in the target region according to the site-capacity characteristic data comprises:

[0108] performing multi-dimensional clustering and accessibility screening on the site-capacity characteristic data to obtain a preliminary screening site list;

[0109] performing secondary filtering according to the geographical red line constraint corresponding to the address characteristic data of each candidate site in the preliminary screening site list and the grid access margin constraint corresponding to the capacity characteristic data to obtain a compliance site list;

[0110] identifying the candidate sites in the compliance site list according to node coordinates to generate the candidate site set.

[0111] Specifically, first, the dimensions of multi-dimensional clustering are determined, including the geographic coordinates of the site, the surrounding traffic convenience, the land use type, the expected capacity of the energy storage system, the voltage level and power margin of the grid access node, etc. The site-capacity feature data is grouped using clustering algorithms such as K-means clustering, and sites with similar features are classified into a class. For example, sites located in the suburbs of a city, close to major traffic arteries, with industrial or unused land, large expected capacity, and good grid access conditions are classified into a group. Based on clustering, combined with accessibility screening, factors such as the density of the surrounding traffic network, the distance from the main grid line, proximity to a substation or switch station, etc. are considered to screen sites that are convenient for transportation and easy to access the grid, forming a preliminary screening site list, and excluding sites that are remote and difficult to access.

[0112] According to the geographical red line constraints corresponding to the address feature data of each candidate site in the preliminary screening site list and the grid access margin constraints corresponding to the capacity feature data, secondary filtering is performed to obtain a compliant site list. The geographical red line constraints refer to the areas prohibited or restricted for construction such as ecological protection red line, basic farmland protection zone, geological disaster prone area, etc. The position of each candidate site is spatially overlaid and analyzed with these red line areas through a geographic information system (GIS), and sites located within the red line range are excluded. The grid access margin constraint refers to the remaining capacity of the grid at the access point of the candidate site, including the remaining transmission capacity of the line and the remaining capacity of the transformer, etc. Through grid flow calculation and capacity evaluation, the carrying capacity of the grid after the access of the energy storage system at each candidate site is calculated, and sites that meet the requirements of safe operation of the grid and have sufficient access margin are selected, thereby obtaining the compliant site list.

[0113] Using a geographic information system (GIS) platform, the geographic coordinates of each compliant candidate site are converted into node coordinates in the grid topology structure, which are displayed on an electronic map in an intuitive manner, and a correspondence between the sites and the grid nodes is established. At the same time, the detailed information of each candidate site, such as the site name, geographic coordinates, land use type, expected capacity range, grid access margin, etc. is integrated into a data structured candidate site set, providing a clear and complete candidate site information library for subsequent site selection and capacity planning.

[0114] In this optional embodiment, by multi-dimensional clustering and reachability screening, the geographical, transportation, power grid access and other factors of the station site are comprehensively considered to accurately identify the station site with potential construction value, avoid subsequent problems caused by blind selection of station site, and improve the scientificity and rationality of planning. The secondary filtering process strictly follows the geographical red line constraint and power grid access margin constraint to ensure that the selected station site meets the requirements of national policies and regulations and safe operation of power grid, effectively avoids the risks of ecological environment destruction, land resource waste and power grid safety accidents caused by improper selection of station site, and guarantees the legality and safety of the energy storage project. Moreover, the compliant station sites are identified according to node coordinates to form a set, which provides intuitive and clear information of the selected station sites for subsequent planning work, facilitating further analysis and optimization. This systematic station site screening and sorting method improves the efficiency of planning work while ensuring the quality of candidate station sites, which helps to develop a more optimal power grid-side energy storage site selection and capacity planning scheme. The generated candidate station site set fully considers the adaptability of the station site to the power grid, can flexibly adjust and optimize the station site selection and capacity configuration according to the development needs of the power grid and the progress of energy storage technology, so that the planning scheme has better adaptability and sustainability, and provides strong support for the long-term stable development of the power grid.

[0115] Optionally, the site selection variable and the capacity determination variable are synchronously partitioned and mapped according to the spatial constraint and the boundary constraint corresponding to each candidate station site in the candidate station site set, to obtain a plurality of candidate subsets of the candidate station site set, including:

[0116] Geographical position data of each candidate station site in the candidate station site set is extracted, and the candidate station site set is divided into a plurality of geographical partitions according to the geographical position data;

[0117] The remaining available capacity data of the power transmission corresponding to the geographical partition is taken as the boundary constraint, and the geographical position data is taken as the spatial constraint, and a joint constraint domain of the site selection variable and the capacity determination variable is established according to the boundary constraint and the spatial constraint;

[0118] The joint constraint domain is taken as a mapping rule, and the site selection variable and the capacity determination variable are synchronously partitioned and mapped to generate a candidate subset containing the same spatial constraint and boundary constraint;

[0119] Each candidate subset is summarized according to the partition identifier to obtain a plurality of candidate subsets of the candidate station site set.

[0120] Specifically, the explicit geographic location data includes the longitude and latitude coordinates of the station site, the administrative division to which it belongs, the type of topography, and the like. Using geographic information system (GIS) software, these geographic location data are visualized and displayed in combination with natural geographic features (such as mountains, rivers, and the like) and the administrative boundaries of the power grid, and a spatial clustering algorithm (such as DBSCAN clustering) or a manually set geographic zoning rule (such as division according to the county, township administrative division, or division according to the power supply area of the power grid) is used to divide the set of candidate station sites into a plurality of geographic zones. For example, candidate station sites located in the same county-level administrative division and having a geographic coordinate distance within a certain threshold (such as 10 kilometers) are divided into the same geographic zone, ensuring that the station sites in each geographic zone have relative concentration and similarity in geographic location. For each geographic zone, the remaining available capacity of the power transmission network in the geographic zone under the current operating state is calculated as a boundary constraint for the access of the energy storage system, through the power grid operating data and the capacity of the power transmission line. At the same time, based on the geographic location data, the available land area and the degree of flatness of the terrain of the candidate station sites in each geographic zone are determined as spatial constraint parameters. In combination with these boundary constraints and spatial constraints, a joint constraint domain of the site selection variable (indicating whether to select a certain candidate station site for construction) and the capacity determination variable (indicating the capacity of the energy storage system constructed at the station site) is established. For example, for each candidate station site, the capacity of the energy storage system cannot exceed the remaining available capacity of the power transmission in the geographic zone, and cannot exceed the maximum size of the energy storage system that can be carried by the land area at the station site location, thereby forming a joint constraint domain with boundary constraints and spatial constraints as boundary conditions.

[0121] In the joint constraint domain of each geographic zone, the site selection variable and the capacity determination variable are simultaneously optimized and solved according to the planning objectives of the energy storage system (such as minimizing construction cost, maximizing new energy consumption, maximizing power grid operating benefit, and the like) using a multi-objective optimization algorithm (such as genetic algorithm, particle swarm algorithm, and the like). In this process, according to the limiting conditions of the joint constraint domain, the site selection and capacity determination combinations that satisfy the spatial constraints and boundary constraints are selected to form a plurality of candidate subsets. The schemes in each candidate subset have the same spatial constraints and boundary constraints, which makes the construction and operating conditions of these schemes in the geographic zone similar, facilitating subsequent comparison and evaluation. The generated candidate subsets are classified and summarized according to the identification of the geographic zones, ensuring that the candidate subsets corresponding to each geographic zone can be clearly identified and managed. For example, a unique zone identifier is set for each geographic zone, all candidate subsets belonging to the same geographic zone are classified together, and the detailed information of each candidate subset is recorded, such as the candidate station sites included, the corresponding energy storage capacity configuration, the construction cost estimate, the expected power grid operating benefit, and the like. In this way, the plurality of candidate subsets obtained by summarization provide a comprehensive and systematic candidate scheme library for subsequent planning decisions, facilitating the selection of the most suitable energy storage site and capacity configuration by the planning personnel according to the characteristics and needs of different geographic zones.

[0122] In this optional embodiment, the candidate site set is refined into multiple candidate subsets with the same spatial constraints and boundary constraints through geographical zoning and synchronous zoning mapping, so that the energy storage planning can be carried out at a more detailed geographical level, fully considering the differences and particularities of different regions, avoiding the "one-size-fits-all" planning mode, and improving the adaptability of the planning scheme to the actual geographical environment and power grid conditions. The establishment of the joint constraint domain and the synchronous zoning mapping process strictly follow the spatial constraints and boundary constraints, ensuring that the site selection and capacity determination scheme in the generated candidate subset is feasible in terms of geographical space and power grid access capacity, avoiding the problem that the planning scheme cannot be implemented due to the neglect of physical limitation conditions, and improving the reliability and practicality of the planning.

[0123] In addition, within the same geographical zoning, through the synchronous optimization of site selection and capacity determination, the limited land resources and power grid access capacity can be reasonably allocated, realizing the optimal combination of the spatial layout and capacity configuration of the energy storage system, improving resource utilization efficiency, reducing construction cost and operation risk, and at the same time improving the support benefit of the energy storage system to the power grid operation. The generated multiple candidate subsets provide a rich selection of schemes for planners, and according to different planning objectives and priorities, such as focusing on new energy consumption, power grid peak shaving capacity improvement or economic cost control, the best scheme can be quickly selected from the candidate subsets, enhancing the flexibility and adaptability of the planning to different objectives, and helping to cope with the uncertainty of power grid development.

[0124] Optionally, the generating of the initial site selection and capacity determination scheme of the target region according to the candidate sites in each of the candidate subsets comprises:

[0125] obtaining the power transmission remaining available capacity data and the geographical position data corresponding to each of the candidate subsets;

[0126] setting the power transmission remaining available capacity data as the capacity upper limit constraint of the subset, and converting the geographical position data into a spatial coordinate matrix;

[0127] constructing a site selection-capacity determination optimization model according to the capacity upper limit constraint and the spatial coordinate matrix;

[0128] synchronously deciding the site selection variable value of each of the candidate sites and the energy storage rated power and rated capacity of the candidate sites through the site selection-capacity determination optimization model, to obtain the initial site selection and capacity determination scheme.

[0129] Specifically, the transmission residual available capacity data refers to the available capacity of the transmission line in the geographical area covered by each candidate subset after meeting the current power grid operation demand, which can be obtained from the real-time monitoring data of the power grid dispatching system and the capacity evaluation report of the power grid planning department. The geographical position data includes the latitude and longitude coordinates, topographic features, surrounding traffic network distribution and other information of each candidate site, which are usually extracted from the geographic information system (GIS) database or field survey records. In the planning model, the transmission residual available capacity of each candidate subset is taken as the maximum capacity limit of the energy storage system that can be configured in the subset, ensuring that the access of the energy storage system will not exceed the carrying capacity of the power grid. At the same time, the latitude and longitude coordinates in the geographical position data are converted into a unified spatial coordinate matrix using a coordinate conversion algorithm, with each candidate site corresponding to a coordinate point in the matrix. Other data in the matrix can include topographic slope, distance to the nearest traffic trunk, and other geographical feature parameters, so as to quantitatively analyze the influence of geographical factors on the construction of energy storage stations in the model.

[0130] According to the capacity upper limit constraint and the spatial coordinate matrix, a site selection-capacity determination optimization model is constructed. The optimization model takes the minimization of the construction and operation cost of the energy storage system and the maximization of the operation benefit of the power grid as the dual objective functions, wherein the construction and operation cost includes the purchase cost, installation cost, maintenance cost, land leasing cost and the like of the energy storage equipment, and the operation benefit involves the improvement of new energy consumption, the reduction of power grid loss, the improvement of voltage stability and the like. The constraint conditions of the model include: the rated power and rated capacity of the energy storage system cannot exceed the capacity upper limit constraint set by the transmission residual available capacity data; the selection of the energy storage site needs to meet the geographical condition limit reflected by the spatial coordinate matrix, for example, the site cannot be located in an area with too steep topographic slope, and the distance to the traffic trunk cannot exceed a certain distance to facilitate equipment transportation and operation and maintenance; the technical parameters of the energy storage system such as charge and discharge efficiency and cycle life need to meet the actual equipment performance; and the power balance and voltage deviation of the power grid need to meet the standards for safe and stable operation of the power system.

[0131] In this embodiment, the site selection variable is a binary variable, taking a value of 1 to indicate the construction of the energy storage system at the candidate site, and taking a value of 0 to indicate no construction. The optimization model is solved by using an intelligent optimization algorithm (such as genetic algorithm, simulated annealing algorithm, etc.). The algorithm adjusts the value of the site selection variable of each candidate site and the size of the rated power and rated capacity of the energy storage through continuous iterative search, so that the dual objective functions reach an optimal balance state under the condition of meeting all the constraint conditions. Finally, according to the optimal solution obtained by the optimization algorithm, it is determined which candidate sites to construct the energy storage system and the rated power and rated capacity of the energy storage at each site, thereby forming an initial site selection-capacity determination scheme for the target area.

[0132] In the optional embodiment, by constructing a site-sizing optimization model and making a synchronous decision, various factors such as cost, benefit and power grid operation are comprehensively considered, and an initial site-sizing scheme that is optimal in both technology and economy can be generated, thereby effectively improving the scientificity and rationality of energy storage planning. The transmission residual available capacity is taken as an upper limit constraint of capacity, so that the configuration of the energy storage system is matched with the actual carrying capacity of the power grid, and over-provisioning or insufficient resources are avoided, thereby improving the utilization efficiency of power grid resources. Meanwhile, the geographical factors are considered in combination with the spatial coordinate matrix, so that the layout of the energy storage station is more reasonable, and the construction and operation cost is reduced.

[0133] In addition, the power balance and voltage deviation of the power grid are considered in the model, so that the access of the energy storage system will not have a negative impact on the safe and stable operation of the power grid, but can improve the operation performance of the power grid through the optimized energy storage configuration, such as enhancing the new energy consumption capacity, reducing the power grid loss, and improving the voltage stability. The embodiment can flexibly generate the corresponding initial site-sizing scheme according to the actual situation such as the transmission residual available capacity and geographical characteristics of different target regions, and has strong adaptability and flexibility.

[0134] Optionally, the performance index data set of the initial site-sizing scheme is obtained by simulating and evaluating the annual time-series operation data of the target region according to the initial site-sizing scheme, and the performance index data set of the initial site-sizing scheme includes:

[0135] The initial site-sizing scheme is input into the power grid-energy storage digital twin model;

[0136] The annual time-series operation data of the target region is called by the power grid-energy storage digital twin model, and the initial site-sizing scheme is subjected to hourly power flow simulation and operation simulation, so as to obtain the capacity characteristic data and the address characteristic data corresponding to the initial site-sizing scheme;

[0137] The capacity characteristic data and the address characteristic data are subjected to feature extraction, so as to obtain the operation characteristics of the initial site-sizing scheme in the target region, and the operation characteristics include the node voltage curve, the line power flow curve, the energy storage state of charge curve and the new energy curtailment power curve;

[0138] The node voltage curve, the line power flow curve, the energy storage state of charge curve and the new energy curtailment power curve are input into the performance index calculation module for weighted processing, so as to obtain the performance index data set of the initial site-sizing scheme;

[0139] The performance index data set includes the new energy consumption rate, the peak-valley difference reduction rate, the node voltage qualified rate, the energy storage annualized yield and the system carbon emission reduction amount.

[0140] Specifically, first, the location information (such as longitude and latitude coordinates, access grid node number, etc.), rated power and rated capacity parameters of the energy storage system, and the charge and discharge characteristic curve (including charge and discharge efficiency, self-discharge rate, maximum charge and discharge power limit, etc.) of the energy storage system in the initial site selection and capacity scheme are integrated into a structured data file format (such as XML or JSON format). Then, through the data interface of the digital twin model, the data file is imported into the model, so that the model can identify and configure the specific location and parameter settings of the energy storage system in the virtual power grid.

[0141] The annual time sequence operation data includes the hourly change curve of power grid load, the hourly output prediction data of new energy power generation (wind power, photovoltaic, etc.), the operation plan of conventional generating units, the maintenance plan of power grid equipment, and the change of power grid topology structure, etc. These data are stored in the database of the model and are called by time period in time sequence. In each time period, the digital twin model performs power flow calculation and operation simulation according to the current power grid operation state and the configuration of the energy storage system, and obtains the results data of the voltage of each node of the power grid, the distribution of line power flow, and the state of charge (SOC) of the energy storage system in the time period. These result data are recorded in real time during the simulation process to form capacity characteristic data (such as the charge and discharge capacity and power output of the energy storage system) and address characteristic data (such as the voltage fluctuation amplitude of each node and the line power flow overrun situation).

[0142] The operation characteristics include node voltage curve, line power flow curve, energy storage state of charge curve, and new energy curtailment power curve. The characteristic extraction process mainly uses data processing algorithms and curve fitting techniques. For example, for the node voltage curve, the node voltage data of each time period in a year is collected, arranged in time sequence and fitted into a continuous curve to reflect the trend of the node voltage change over time; for the line power flow curve, the change of the line power flow in a day or a year is obtained in the same way. The energy storage state of charge curve is drawn according to the charge and discharge record of the energy storage system, showing the SOC change process of the energy storage system in different time periods. The new energy curtailment power curve is obtained by comparing the difference between the new energy generation prediction value and the actual power grid acceptance value, and the wind and light curtailment power in each time period is calculated to form the curve of the curtailment power change over time.

[0143] The performance index calculation module preconfigures a series of performance index calculation formulas and weight coefficients. For example, the calculation formula of the new energy consumption rate is (total new energy generation - total abandoned electricity) / total new energy generation x 100%, and the weight coefficient can be set to a higher value according to the importance of the planning target; the peak-valley difference reduction rate is calculated by comparing the peak-valley difference changes of the grid load curve before and after the energy storage system is connected, and the weight coefficient is also set according to the grid peak shaving demand. Similarly, the node voltage qualification rate, the energy storage annualized yield, and the system carbon emission reduction amount, etc. have corresponding calculation methods and weights. In the calculation process, the data of each operation characteristic curve is substituted into the corresponding formula for calculation, and multiplied by the weight coefficient, and finally the performance index data set is formed by summarizing, which comprehensively quantitatively evaluates the comprehensive performance of the initial site selection and capacity determination scheme.

[0144] In this optional embodiment, through the digital twin model, the annual time series operation data is simulated in each time period, combined with feature extraction and weighting processing, which can comprehensively and carefully evaluate the performance of the initial site selection and capacity determination scheme in new energy consumption, grid peak shaving, voltage support, energy storage yield and carbon emission reduction, etc. to provide a comprehensive quantitative basis for the optimization and adjustment of the scheme, avoiding the planning defects caused by one-sided evaluation. The performance index data set presents the pros and cons of the scheme with intuitive quantitative data, helping decision makers clearly understand the impact and benefits of different planning schemes, so as to make more accurate and scientific decisions, improve the rationality and effectiveness of the grid side energy storage planning, and maximize the investment benefit and grid operation benefit of the energy storage project. The problems and performance short boards found in the simulation and evaluation process can be targeted to optimize and adjust the initial site selection and capacity determination scheme. For example, if the node voltage qualification rate of a certain area is low, the energy storage configuration of the area can be appropriately increased or the control strategy of the energy storage can be adjusted; if the new energy consumption rate is not ideal, the energy storage layout can be optimized or the energy storage capacity of the key station can be increased. Through repeated iteration and optimization, the quality of the planning scheme is continuously improved. This embodiment is based on the simulation and evaluation of the annual time series operation data, fully considers the seasonal changes of grid operation, load fluctuations and the uncertainty of new energy generation, etc. so that the performance index data set can truly reflect the performance of the energy storage system in actual operation, providing reliable prediction and protection for the stable operation of the grid and the reliable implementation of the energy storage project.

[0145] Optionally, the performance index data set is obtained by the following steps:

[0146] The performance index data set is obtained by the following steps:

[0147] If the deviation value of the performance index is less than or equal to a preset deviation threshold, it is determined that the performance index converges.

[0148] If the deviation value of the performance indicator is greater than the preset deviation threshold, it is determined that the performance indicator does not converge.

[0149] Specifically, first, the target value or expected range set for each performance indicator in the preset convergence rule is determined. For example, for the new energy consumption rate, the target value is set to 95%; for the peak-valley difference reduction rate, an expectation of 20% is reached; the target of the node voltage qualification rate is 99.5%; the target of the energy storage annualized yield is set to 1000 yuan per megawatt hour; and the target of the system carbon emission reduction is to reduce 1000 tons of carbon dioxide per year. Then, the current value of each performance indicator actually calculated from the performance indicator data set is extracted, and the deviation value between the current value and the target value is calculated. The deviation value can be expressed as an absolute deviation (the absolute value of the current value minus the target value) or a relative deviation (the absolute deviation divided by the target value). If the deviation value of the performance indicator is less than or equal to the preset deviation threshold, it is determined that the performance indicator converges. The preset deviation threshold is set according to the accuracy requirement of the planning and the acceptable error range of the actual engineering. For example, for the new energy consumption rate, the preset deviation threshold is set to ±2%; for the peak-valley difference reduction rate, it is set to ±1.5%; the deviation threshold of the node voltage qualification rate is ±0.5%; the deviation threshold of the energy storage annualized yield is ±5%; and the deviation threshold of the system carbon emission reduction is ±5%. When the deviation values of all performance indicators are less than or equal to the respective preset deviation thresholds, it is considered that the planning scheme has reached the expected performance target, and the performance indicator converges. At this time, the iteration optimization process can be stopped, and the current site sizing scheme is taken as the final scheme. If the deviation value of the performance indicator is greater than the preset deviation threshold, it is determined that the performance indicator does not converge. If it is found that the deviation values of some or all performance indicators exceed the preset deviation threshold, it indicates that the current site sizing scheme fails to meet the planning target in some key performance aspects. At this time, it is necessary to further analyze which performance indicators do not meet the requirements and the specific situation of the deviation, so as to adjust and optimize the scheme accordingly.

[0150] In this optional embodiment, by comparing the performance indicators with the preset convergence rules, it can be accurately evaluated whether the current planning scheme meets the predetermined performance target, ensuring the feasibility and effectiveness of the scheme. This quantitative evaluation method based on deviation value avoids the uncertainty of subjective judgment, improves the scientificity and accuracy of planning decision. When the performance indicators do not converge, the deviation value provides a clear optimization direction, enabling the planner to improve the performance indicators that do not meet the standard. For example, if the new energy consumption rate deviation is large, the energy storage layout can be adjusted to increase the energy storage configuration in the new energy-rich area; if the node voltage qualification rate is not up to standard, the reactive power regulation strategy of the energy storage system or the location of the energy storage site can be optimized. This clear feedback mechanism improves the efficiency and pertinence of the optimization process. By setting a preset deviation threshold, the convergence of the scheme can be judged in the iterative optimization process, avoiding unnecessary repeated calculation and resource waste. Once the scheme converges, the optimization process can be terminated, saving calculation time and cost; if it does not converge, optimization and adjustment continue to ensure that the final scheme reaches the optimal state, improving the efficiency and economy of the overall planning process. The converged performance indicators indicate that the planning scheme can reliably achieve the expected performance target, providing a strong guarantee for the implementation of the grid-side energy storage project. In actual operation, the energy storage system can effectively improve the grid operation performance according to the planning target, promote new energy consumption, and enhance the stability of the power grid.

[0151] Optionally, further comprising:

[0152] When the deviation value of any of the performance indicators is greater than the preset deviation threshold, it is determined that the performance indicator dataset does not converge;

[0153] According to the deviation value corresponding to the performance indicator that does not converge, a correction instruction is generated;

[0154] According to the correction instruction, the model parameters of the grid-energy storage digital twin model are adaptively updated to obtain an updated grid-energy storage digital twin model;

[0155] Through the updated grid-energy storage digital twin model, the selected site variables and the fixed capacity variables are re-decided synchronously to obtain an updated initial site and capacity selection scheme, until the deviation values of all performance indicators are less than or equal to the preset deviation threshold.

[0156] Specifically, the system automatically detects the deviation values of all performance indicators and compares them with the preset deviation thresholds one by one. For example, if the deviation value of new energy consumption rate exceeds the preset threshold of ±2%, or the deviation value of node voltage qualification rate exceeds the preset threshold of ±0.5%, it is determined that the entire performance indicator dataset does not reach the convergence state. The generation of correction instructions is based on the direction and size of the deviation value, as well as the importance and priority of the performance indicators. For example, if the deviation value of new energy consumption rate is -3% (i.e., the actual value is 3% lower than the target value), the correction instructions may include increasing the energy storage configuration capacity in the new energy-rich area, optimizing the charging and discharging strategy of the energy storage system to better track the new energy generation curve, etc.; if the deviation value of node voltage qualification rate is -0.8%, the correction instructions may point to adjusting the reactive power output capability of the energy storage system or re-siting to be close to the node with larger voltage fluctuations.

[0157] The updating of model parameters in this embodiment involves multiple aspects, such as the rated capacity, rated power, and charging and discharging efficiency of the energy storage system, the parameters of grid equipment (such as line impedance and transformer ratio), and control strategy parameters (such as the charging and discharging control logic of the energy storage system and the reactive power compensation strategy). According to the specific content of the correction instructions, the corresponding parameters are modified through the parameter adjustment interface of the model. For example, if the correction instruction requires increasing the energy storage configuration capacity in a certain area, the rated capacity parameter of the energy storage site in that area is correspondingly increased in the digital twin model; if the charging and discharging strategy needs to be optimized, the control logic algorithm of the energy storage system in the model is adjusted to better match the changes in new energy generation and grid load. Using the updated digital twin model, the siting and sizing optimization process is re-run, including re-performing multi-dimensional clustering and reachability screening, synchronous partition mapping, constructing and solving the siting and sizing optimization model, etc. Based on the updated model parameters, the optimization algorithm generates a new siting and sizing scheme and performs simulation evaluation again. This process is repeated, with the correction instructions being generated and the model being updated each time based on the deviation values of the non-converged performance indicators, until the deviation values of all performance indicators are controlled within the preset deviation threshold, thereby ensuring that the final siting and sizing scheme meets the expected performance targets.

[0158] In this optional embodiment, the automatic generation of correction instructions and the adaptive updating of the digital twin model enable the automatic iterative optimization of the energy storage planning scheme. Without the need for repeated manual intervention, the planning efficiency is greatly improved, especially when facing complex power grids and large-scale energy storage configurations, the optimal scheme can be quickly converged, saving a lot of time and labor costs. This process can dynamically adapt to changes in power grid operation characteristics and planning targets. When the power load characteristics, new energy generation, or policy targets change, the energy storage configuration scheme can be adjusted in time by updating the model parameters and re-optimizing the decision, ensuring that it always meets the latest operation requirements and planning targets, enhancing the timeliness and adaptability of the planning scheme.

[0159] Moreover, the correction mechanism based on the deviation value driving in the embodiment can accurately adjust the performance indicators that do not meet the standards, ensuring that all performance indicators ultimately meet the preset convergence rules. This provides reliable performance guarantees for the implementation of grid-side energy storage projects, ensuring that the energy storage system can effectively improve the performance of the power grid in actual operation, achieve the expected goals of new energy consumption, voltage support, peak shaving, and improve the investment return rate and success rate of the project. Through continuous adaptive updating, the grid-energy storage digital twin model can continuously correct its parameters and assumptions, making it more close to the actual power grid operation. After multiple iterations and optimizations, the accuracy and reliability of the model are significantly improved, providing stronger technical support for subsequent energy storage planning and other power grid-related decisions.

[0160] In combination with Figure 2 The application also provides a site selection and capacity planning system for a grid-side energy storage device, which comprises:

[0161] a data acquisition unit configured to acquire power grid operation data and external environment data of a target region;

[0162] a twin modeling unit configured to obtain site-capacity characteristic data of the target region by using a grid-energy storage digital twin model based on the power grid operation data and the external environment data;

[0163] a candidate site generation unit configured to generate a candidate site set in the target region based on the site-capacity characteristic data;

[0164] a partition mapping unit configured to perform synchronous partition mapping of site selection variables and capacity determination variables based on spatial constraints and boundary constraints corresponding to each candidate site in the candidate site set, to obtain a plurality of candidate subsets of the candidate site set;

[0165] an optimization decision unit configured to generate an initial site selection and capacity determination scheme for the target region based on the candidate sites in each candidate subset;

[0166] a simulation evaluation unit configured to perform simulation evaluation on annual time series operation data of the target region based on the initial site selection and capacity determination scheme, to obtain a performance indicator data set of the initial site selection and capacity determination scheme;

[0167] a convergence judgment unit configured to determine whether each performance indicator in the performance indicator data set converges based on the performance indicator data set and in combination with a preset convergence rule; and when all the performance indicators converge, the initial site selection and capacity determination scheme after the performance indicators converge is taken as a final site selection and capacity determination scheme.

[0168] The site selection and capacity planning system of the grid-side energy storage device has the same advantages as the site selection and capacity planning method of the grid-side energy storage device compared with the prior art, which will not be repeated here.

[0169] In combination Figure 3 The application further provides an electronic device, comprising a memory and a processor.

[0170] The memory is used for storing a computer program.

[0171] The processor is used for realizing the site selection and capacity planning method of the grid-side energy storage device when the computer program is executed.

[0172] The electronic device has the same advantages as the site selection and capacity planning method of the grid-side energy storage device compared with the prior art, which will not be repeated here.

[0173] Although the application discloses as above, the protection scope of the application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the application, and these changes and modifications shall fall within the protection scope of the application.

Claims

1. A method for site selection and capacity planning of grid-side energy storage devices, characterized in that, The method comprises the following steps: acquiring power grid operation data and external environment data of a target region; obtaining site-capacity characteristic data of the target region through a power grid- energy storage digital twin model according to the power grid operation data and the external environment data; generating a candidate site set in the target region according to the site-capacity characteristic data; synchronously partitioning and mapping site selection variables and capacity determination variables according to spatial constraints and boundary constraints corresponding to each candidate site in the candidate site set to obtain a plurality of candidate subsets of the candidate site set; specifically, extracting geographic position data of each candidate site in the candidate site set, and dividing the candidate site set into a plurality of geographic partitions according to the geographic position data; taking power transmission residual available capacity data corresponding to the geographic partitions as boundary constraints, and taking the geographic position data as spatial constraints, and establishing a joint constraint domain of the site selection variables and the capacity determination variables according to the boundary constraints and the spatial constraints; taking the joint constraint domain as a mapping rule to perform synchronous partitioning and mapping on the site selection variables and the capacity determination variables to generate candidate subsets containing the same spatial constraints and boundary constraints; wherein the synchronous partitioning and mapping comprises: synchronously optimizing and solving the site selection variables and the capacity determination variables in the joint constraint domain by using a multi-objective optimization algorithm to filter out combinations of site selection variable values and capacity determination variable values that meet the spatial constraints and the boundary constraints, and form a plurality of candidate subsets, wherein the site selection scheme and the capacity determination scheme in each candidate subset have the same spatial constraints and boundary constraints; summarizing each candidate subset according to partition identifiers to obtain a plurality of candidate subsets of the candidate site set; generating an initial site selection and capacity determination scheme of the target region according to the candidate sites in each candidate subset; performing simulation evaluation on annual time sequence operation data of the target region according to the initial site selection and capacity determination scheme to obtain a performance indicator data set of the initial site selection and capacity determination scheme; wherein the performance indicator data set includes new energy consumption rate, peak-valley difference reduction rate, node voltage qualification rate, energy storage annualized yield, and system carbon emission reduction amount; judging whether each performance indicator in the performance indicator data set converges according to the performance indicator data set and in combination with a preset convergence rule; when all the performance indicators converge, taking the initial site selection and capacity determination scheme after the performance indicators converge as a final site selection and capacity determination scheme.

2. The method for sizing and siting of grid-side energy storage device according to claim 1, characterized in that, The method of obtaining site-capacity characteristic data of the target region through the power grid- energy storage digital twin model according to the power grid operation data and the external environment data comprises the following steps: synchronously inputting the power grid operation data and the external environment data into the power grid- energy storage digital twin model; performing power flow calculation and operation simulation on the power grid operation data through the power grid- energy storage digital twin model to obtain capacity characteristic data; performing spatial accessibility and geological red line checking on the external environment data through the power grid- energy storage digital twin model to obtain address characteristic data; The capacity feature data and the address feature data are matched and fused in the node-site dimension to generate the site-capacity feature data.

3. The method for sizing and siting of grid-side energy storage device according to claim 2, characterized in that, The generating of the candidate site set in the target region according to the site-capacity feature data comprises: Performing multi-dimensional clustering and reachability screening on the site-capacity feature data to obtain a preliminary screening site list; According to the geographical red line constraint corresponding to the address feature data of each candidate site in the preliminary screening site list and the grid access margin constraint corresponding to the capacity feature data, secondary filtering is performed to obtain a compliance site list; The candidate sites in the compliance site list are identified according to node coordinates to generate the candidate site set.

4. The method for sizing and siting of grid-side energy storage device according to claim 1, characterized in that, The generating of the initial site selection and capacity determination scheme of the target region according to the candidate sites in each candidate subset comprises: Obtaining the remaining available capacity data and the geographical position data corresponding to each candidate subset; Setting the remaining available capacity data as the capacity upper limit constraint of the subset, and converting the geographical position data into a spatial coordinate matrix; According to the capacity upper limit constraint and the spatial coordinate matrix, a site selection-capacity determination optimization model is constructed; Through the site selection-capacity determination optimization model, the value of the site selection variable of each candidate site and the rated power and rated capacity of the candidate site are determined synchronously to obtain the initial site selection and capacity determination scheme.

5. The method for sizing and siting of grid-side energy storage device according to claim 1, characterized in that, The simulation evaluation of the annual time sequence operation data of the target region according to the initial site selection and capacity determination scheme comprises: Inputting the initial site selection and capacity determination scheme into the grid-energy storage digital twin model; Through the grid-energy storage digital twin model, the annual time sequence operation data of the target region is called to perform hourly power flow simulation and operation simulation on the initial site selection and capacity determination scheme, to obtain the capacity feature data and the address feature data corresponding to the initial site selection and capacity determination scheme; The capacity feature data and the address feature data are extracted to obtain the operation characteristics of the initial site selection and capacity determination scheme in the target region, and the operation characteristics comprise node voltage curve, line power flow curve, energy storage state of charge curve, and new energy curtailment power curve; The node voltage curve, the line power flow curve, the energy storage state of charge curve, and the new energy curtailment power curve are input into a performance index calculation module for weighted processing to obtain the performance index data set of the initial site selection and capacity determination scheme.

6. The method for sizing and siting of grid-side energy storage device according to claim 5, characterized in that, The judging of whether each performance index in the performance index data set converges according to the performance index data set and in combination with a preset convergence rule comprises: Comparing all performance indexes in the performance index data set with the preset convergence rule corresponding to the performance index to obtain the deviation value of each performance index; If the deviation value of the performance index is less than or equal to a preset deviation threshold, it is determined that the performance index converges; If the deviation value of the performance index is greater than the preset deviation threshold, it is determined that the performance index does not converge.

7. The method for sizing and siting of grid-side energy storage device according to claim 6, characterized in that, Further comprising: determining that the performance indicator dataset does not converge when the deviation value of any of the performance indicators is greater than the preset deviation threshold value; generating a correction instruction according to the deviation value corresponding to the performance indicator that does not converge; performing adaptive update on the model parameters of the grid- energy storage digital twin model according to the correction instruction, to obtain an updated grid- energy storage digital twin model; re-performing synchronous decision on the site selection variables and the capacity determination variables through the updated grid- energy storage digital twin model, to obtain an updated initial site selection and capacity determination scheme, until the deviation values of all the performance indicators are less than or equal to the preset deviation threshold value.

8. A system for planning site selection and capacity of a grid-side energy storage device, characterized in that, comprise: a data acquisition unit configured to acquire grid operation data and external environment data of a target region; a twin modeling unit configured to obtain site- capacity characteristic data of the target region by a grid- energy storage digital twin model according to the grid operation data and the external environment data; a candidate site generation unit configured to generate a candidate site set in the target region according to the site- capacity characteristic data; a partition mapping unit configured to perform synchronous partition mapping on site selection variables and capacity determination variables according to spatial constraints and boundary constraints corresponding to each candidate site in the candidate site set, to obtain a plurality of candidate subsets of the candidate site set; specifically comprising: extracting geographic location data of each candidate site in the candidate site set, and dividing the candidate site set into a plurality of geographic partitions according to the geographic location data; taking power transmission residual available capacity data corresponding to the geographic partitions as boundary constraints, and taking the geographic location data as spatial constraints, establishing a joint constraint domain of the site selection variables and the capacity determination variables according to the boundary constraints and the spatial constraints; taking the joint constraint domain as a mapping rule, performing synchronous partition mapping on the site selection variables and the capacity determination variables to generate candidate subsets containing the same spatial constraints and boundary constraints; wherein the synchronous partition mapping comprises: in the joint constraint domain, performing synchronous optimization and solving on the site selection variables and the capacity determination variables by using a multi-objective optimization algorithm, screening out combinations of site selection variable values and capacity determination variable values that satisfy the spatial constraints and the boundary constraints to form a plurality of candidate subsets, wherein the site selection scheme and the capacity determination scheme in each candidate subset have the same spatial constraints and boundary constraints; summarizing each candidate subset according to partition identifiers to obtain a plurality of candidate subsets of the candidate site set; an optimization decision unit configured to generate an initial site selection and capacity determination scheme of the target region according to the candidate sites in each candidate subset; a simulation evaluation unit configured to perform simulation evaluation on annual time series operation data of the target region according to the initial site selection and capacity determination scheme, to obtain a performance indicator dataset of the initial site selection and capacity determination scheme; wherein the performance indicator dataset comprises new energy consumption rate, peak valley difference reduction rate, node voltage qualified rate, energy storage annualized income, and system carbon emission reduction amount. A convergence judging unit is configured to judge whether each performance index in the performance index dataset converges according to the performance index dataset and in combination with a preset convergence rule; and when all the performance indexes converge, take the initial sizing scheme after the performance indexes converge as a final sizing scheme.

9. An electronic device, comprising: comprising a memory and a processor; the memory is configured to store a computer program; the processor is configured to implement the sizing planning method of the grid-side energy storage device according to any one of claims 1-7 when executing the computer program.

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