Energy storage capacity optimal configuration method and system for multi-dimensional constraint modeling

Abstract

The invention discloses an energy storage capacity optimal configuration method for multi-dimensional constraint modeling, particularly relates to the technical field of energy storage capacity planning, and is used for solving the problems of feasible solution space fragmentation and low global optimization efficiency caused by heterogeneous constraint conflicts in the prior art. The method comprises the following steps: acquiring multi-dimensional constraint data such as continuous technical parameters, operating conditions and discrete policies and rules of the energy storage system; quantizing conflict strength between constraints and distributing dynamic weights; converting the discrete rule into a continuous penalty function to construct a fusion constraint model; analyzing the solution space topological structure and monitoring the dynamic change of the solution space topological structure to generate a connectivity graph and an evolution trend graph; generating a self-adaptive search strategy based on the atlas; outputting a global optimal solution set and generating a verifiable energy storage capacity configuration scheme; and through dynamic weight distribution and solution space connectivity guidance, the multi-constraint collaborative optimization capability is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity planning. More specifically, the present invention relates to a method and system for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling. Background Art

[0002] In the field of optimizing the configuration of energy storage capacity, existing technologies usually transform the technical parameters, operating conditions, and external policy requirements of energy storage systems into constraint conditions through mathematical modeling, and use optimization algorithms to solve the optimal solution set of the objective function, which are widely applied in scenarios such as power system planning, renewable energy consumption, and improvement of power grid stability. However, with the complication of energy storage application scenarios, multi-dimensional constraint conditions (such as equipment physical characteristics, economic indicators, and environmental policy restrictions) have heterogeneity in mathematical form, logical relationship, and time scale, resulting in the need to coordinate conflicts and compatibility between different dimensions in model construction.

[0003] When existing technologies deal with the problem of optimizing energy storage capacity under heterogeneous constraint conditions, due to the lack of full coordination of implicit conflicts between different constraints, the feasible solution space is divided into multiple isolated regions, making it difficult for traditional optimization algorithms to effectively cross the fragmented regions of the solution space when searching for the global optimal solution, and it is easy to fall into local optimality or fail to meet the collaborative requirements of multi-dimensional constraints, ultimately resulting in a significant reduction in the economy, reliability, and compliance of the energy storage capacity configuration plan. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the existing technology, embodiments of the present invention provide a method and system for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling, comprising the following steps:

[0007] S1. Obtain multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints;

[0008] S2. Evaluate the degree of missing intersection of the feasible domain by the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints to allocate dynamic weights to the discrete policy rule constraints;

[0009] S3. Convert the boundary threshold of the discrete policy rule constraints into a continuous penalty function, and construct a fusion constraint model that integrates multi-dimensional constraints in combination with the dynamic weights;

[0010] S4. Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a map of the evolution trend of the solution space;

[0011] S5. Generate a search strategy based on the connectivity map and the map of the evolution trend of the solution space;

[0012] S6. Generate an energy storage capacity configuration plan according to the globally optimal solution set output by the search strategy.

[0013] In a preferred embodiment, obtain multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints. Specifically:

[0014] Divide the continuous technical parameter constraints into power limit type parameters and life decay model type parameters;

[0015] Convert the text rules of the discrete policy rule constraints into discrete mathematical expressions including boundary thresholds and trigger conditions;

[0016] Perform time series alignment processing on the grid load demand and the fluctuation range of renewable energy output in the operating condition constraints to generate a constraint data set with a unified time stamp.

[0017] In a preferred embodiment, evaluate the degree of lack of intersection in the feasible domain by the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints to assign dynamic weights to the discrete policy rule constraints, including:

[0018] Project the boundary thresholds of the discrete policy rule constraints into the feasible domain space of the continuous technical parameter constraints, and calculate the geometric intersection missing area between the boundary thresholds and the feasible domain of the continuous technical parameter constraints;

[0019] Based on the ratio of the geometric intersection missing area to the total area of the feasible domain of the continuous technical parameter constraints, determine the conflict intensity coefficient;

[0020] According to the conflict intensity coefficient and the preset weight mapping relationship, assign dynamic weights to the discrete policy rule constraints. The preset weight mapping relationship is an inverse proportional function relationship between the conflict intensity coefficient and the dynamic weight;

[0021] Embed the dynamic weight as the priority parameter of the discrete policy rule constraint into the objective function of the multi-objective optimization model.

[0022] In a preferred embodiment, convert the boundary thresholds of the discrete policy rule constraints into continuous penalty functions, and construct a fusion constraint model that combines multi-dimensional constraints, including:

[0023] Convert the boundary threshold of the discrete policy rule constraint into a piecewise exponential continuous function, and set a smooth transition interval at the boundary threshold to avoid function mutation;

[0024] Embed the dynamic weight as a scaling factor of the exponential term into the piecewise exponential continuous function to generate a weighted continuous penalty function term;

[0025] Based on the mathematical expressions of the weighted continuous penalty function term and the continuous technical parameter constraint, construct a fusion constraint model with the multi-objective weighted sum as the comprehensive optimization objective;

[0026] Perform dimension alignment processing on the input parameters of the fusion constraint model.

[0027] In a preferred embodiment, perform topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a solution space evolution trend map, including:

[0028] Randomly sample from the feasible solution space of the fusion constraint model to generate a candidate solution set, calculate the Euclidean distance between candidate solutions to construct an adjacency matrix, and the edge weight of the adjacency matrix is the reciprocal of the distance between candidate solutions;

[0029] Identify the isolated regions in the feasible solution space based on the adjacency matrix, and the isolated regions are defined as subsets of candidate solutions with edge weights lower than the preset connectivity threshold in the adjacency matrix;

[0030] Record the boundary solution coordinates and connectivity status changes of the isolated regions in real time to generate a historical record of the spatio-temporal distribution of the isolated regions;

[0031] Perform clustering analysis on the historical record of the spatio-temporal distribution, extract the migration paths and convergence characteristics of the isolated regions, and generate a solution space evolution trend map.

[0032] In a preferred embodiment, generate a search strategy based on the connectivity map and the solution space evolution trend map, including:

[0033] Set virtual transition nodes at the boundaries of the isolated regions in the feasible solution space;

[0034] Based on the migration paths and convergence characteristics in the solution space evolution trend map, predict the connectability of the isolated regions;

[0035] Calculate the crossing path from the current solution to the target solution through a path planning algorithm;

[0036] Dynamically adjust the search direction according to the convergence speed of the crossing path.

[0037] In a preferred embodiment, the virtual transition node is the intermediate jump point of the crossing path from the current solution to the target solution;

[0038] The prediction includes calculating the success rate of the historical crossing path and the iteration number threshold;

[0039] The path planning algorithm preferentially selects the direction with a high density of virtual transition nodes to expand the search branch;

[0040] The dynamic adjustment includes reducing the search weight of the path with a low convergence speed and increasing the exploration probability of the path with a high convergence speed.

[0041] In a preferred embodiment, an energy storage capacity configuration scheme is generated according to the globally optimal solution set output by the search strategy, including:

[0042] Screening out the optimal solution subset that meets the preset economic, reliability, and compliance indicators from the globally optimal solution set;

[0043] Mapping the solution vectors in the optimal solution subset to the energy storage capacity configuration parameters;

[0044] Based on the continuous technical parameter constraints and discrete policy rule constraints, performing feasibility verification on the mapped configuration parameters;

[0045] Generating an energy storage capacity configuration scheme including the configuration parameters, verification results, and optimization target weights, and the scheme output is an executable engineering deployment instruction.

[0046] In a preferred embodiment, the energy storage capacity configuration parameters include energy storage capacity, power limit, life attenuation coefficient, and charge-discharge strategy.

[0047] On the other hand, the present invention provides an energy storage capacity optimization configuration system for multi-dimensional constraint modeling, including the following modules:

[0048] Multi-dimensional data acquisition module: acquiring the multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints;

[0049] Conflict weight allocation module: evaluating the degree of lack of intersection of the feasible regions by the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints to allocate dynamic weights to the discrete policy rule constraints;

[0050] Constraint fusion modeling module: converting the boundary threshold of the discrete policy rule constraints into a continuous penalty function, and constructing a fusion constraint model that fuses multi-dimensional constraints in combination with the dynamic weights;

[0051] Topological analysis and monitoring module: performing topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity graph, and real-time monitoring of the dynamic changes of the feasible solution space to generate a solution space evolution trend graph;

[0052] Search strategy generation module: Generate a search strategy based on the connectivity map and the solution space evolution trend map;

[0053] Configuration plan generation module: Generate an energy storage capacity configuration plan according to the globally optimal solution set output by the search strategy.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. By coordinating the conflict of multi-dimensional constraints and the dynamic evolution characteristics of the solution space, the global optimization ability and engineering feasibility of energy storage capacity configuration are significantly improved. Aiming at the heterogeneity of multi-dimensional constraints, through the dynamic weight allocation mechanism and the conversion of continuous penalty functions, the implicit conflict between discrete policy rules and continuous technical parameters is effectively bridged; the dynamic weight quantifies the conflict intensity based on the lack of intersection of the feasible regions, adaptively reducing the priority of highly conflicting constraints and avoiding excessive fragmentation of the feasible solution space; at the same time, the continuous penalty function converts the discrete threshold into a smoothly transitioning mathematical expression, reducing the fragmentation of the solution space while maintaining the effectiveness of policy constraints; combined with the connectivity map and the evolution trend map generated by topological structure analysis and real-time monitoring, it can accurately locate the boundary of the isolated region and predict the migration path of the solution space, providing dynamic guidance for the optimization algorithm and significantly improving the quality and search efficiency of the global solution.

[0056] 2. A full-process closed-loop logic from multi-dimensional constraint modeling to configuration plan generation is constructed to ensure that the optimization results have both technical compliance and engineering operability; by integrating multi-dimensional parameters through the fusion constraint model, the technical characteristics, operation requirements and policy rules work together in a unified mathematical framework; the search strategy driven by the solution space evolution trend map, combined with the historical path convergence characteristics and virtual transition node guidance, realizes the intelligent crossing of the isolated region of the solution space and the dynamic optimization of the search direction; the finally generated configuration plan is output as an engineering instruction that can be directly deployed after multi-level feasibility verification, and through the dynamic weight and real-time feedback mechanism, the system maintains high adaptability and robustness in a complex and changeable operating environment. Description of the Drawings

[0057] Figure 1 It is a flowchart of a method for optimizing the energy storage capacity with multi-dimensional constraint modeling according to the present invention;

[0058] Figure 2 It is a structural schematic diagram of a system for optimizing the energy storage capacity with multi-dimensional constraint modeling according to the present invention. Detailed Embodiments

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] Embodiment 1: Figure 1 A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling of the present invention is provided, which includes the following steps:

[0061] S1. Obtain multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints;

[0062] S2. Evaluate the degree of lack of the intersection of the feasible regions by the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints to allocate dynamic weights to the discrete policy rule constraints;

[0063] S3. Convert the boundary thresholds of the discrete policy rule constraints into continuous penalty functions, and construct a fusion constraint model that combines multi-dimensional constraints with dynamic weights;

[0064] S4. Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a map of the evolution trend of the solution space;

[0065] S5. Generate a search strategy based on the connectivity map and the map of the evolution trend of the solution space;

[0066] S6. Generate an energy storage capacity configuration plan according to the global optimal solution set output by the search strategy.

[0067] S1. Obtain multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints. The specific implementation is as follows:

[0068] Continuous technical parameter constraints are divided into power limit parameters and life decay model parameters. Power limit parameters include the rated power of the energy storage device, the maximum charge-discharge rate, and the tolerance range of instantaneous power fluctuations, which are obtained by reading the technical specification documents of the energy storage device or the statistical results based on measured data; life decay model parameters include the mapping relationship between the number of cycles of the energy storage device and the capacity decay coefficient, and the influence coefficient of temperature on life, which are obtained by the fitting curve of laboratory accelerated aging tests or the regression analysis of historical operation data. The divided power limit parameters and life decay model parameters are stored as independent data tables respectively, and the numerical values in the data tables are normalized. The normalization process uses the min-max method to linearly transform each parameter value to the interval [0,1] to ensure the comparability of parameters with different dimensions in subsequent modeling. For example, the original data range of the rated power is 0 to 10 megawatts, and the corresponding per-unit value after normalization is 0 to 1; after the mapping relationship between the number of cycles and the capacity decay coefficient is fitted by a quadratic function, the number of cycles is normalized to the interval [0,1], and the capacity decay coefficient is scaled synchronously.

[0069] The text rules of discrete policy rule constraints are converted into discrete mathematical expressions containing boundary thresholds and trigger conditions. Specifically, the constraint clauses related to the operation of the energy storage are extracted from the policy documents, such as "charging operations are prohibited when the carbon emissions exceed 50 kilograms per megawatt-hour", and are converted into logical judgment expressions in the form of "if condition A is satisfied, then operation B is executed", where condition A is the numerical comparison of the boundary threshold and operation B is the behavior limit triggered by the constraint. For policy rules containing multiple conditions, such as "energy storage discharge power is not less than 20% of the total load and the SOC is higher than 40% during peak hours", it is converted into a compound logical expression in the form of "IF (time ∈ peak hours) AND (discharge power ≥ total load × 20%) AND (SOC > 40%) THEN allow discharge". The peak hours are defined as 8:00 to 12:00 and 18:00 to 22:00 every day, which is achieved by matching the timestamp interval. The converted discrete mathematical expressions are stored in the database and are associated with the continuous technical parameter constraints through the parameter name to establish an index.

[0070] Perform a temporal alignment process on the grid load demand and the fluctuation range of renewable energy output in the operating condition constraints to generate a constraint data set with a unified timestamp. The grid load demand data is provided in the form of historical records or predicted values at hourly intervals, and the fluctuation range of renewable energy output is provided in the form of power upper and lower limit curves at 15-minute intervals. Align data with different time resolutions to a unified time base through an interpolation algorithm. The interpolation algorithm selects linear interpolation or cubic spline interpolation according to the data type. For example, linear interpolation is used when the change in load data is gentle, and cubic spline interpolation is used for the photovoltaic output curve due to its large fluctuations. The aligned data set contains the load demand value, the upper and lower limit values of renewable energy output at each time point, and the timestamp format is "year-month-day hour:minute:second". For example, "2024-10-05 08:15:00" corresponds to a load demand of 100 MW, a photovoltaic output upper limit of 30 MW, and a wind power output lower limit of 10 MW. After the temporal alignment process, verify the integrity of the data set. The data filling rule for missing time points is as follows: if the number of consecutive missing time points does not exceed 3, fill with the average value of adjacent time points; if it exceeds 3, fill with the average value of the same time period on adjacent dates. The outlier rejection rule is as follows: when the load data exceeds 120% of the historical maximum value or is lower than 80% of the historical minimum value, it is considered an outlier; when the photovoltaic output exceeds 100% of the installed capacity, it is considered an outlier. Replace the outlier with a linear interpolation of the two adjacent valid values. The finally generated constraint data set is associated with the discrete mathematical expressions of power limit type parameters, life decay model type parameters, boundary thresholds, and trigger conditions through timestamps to form a unified input interface for multi-dimensional constraint data.

[0071] It should be noted that in step S1 of the operating condition constraints, the operating condition constraints (such as grid load demand, fluctuation range of renewable energy output) are the core components of multi-dimensional constraint data, providing dynamic input for subsequent conflict coordination and model construction; in step S3, the operating condition constraints generate a constraint data set with a unified timestamp through temporal alignment processing to ensure the consistency of the variables in the fusion constraint model in the time dimension; in step S4, the dynamic changes of the operating condition constraints directly affect the topological structure of the feasible solution space and the generation of the evolution trend map. For example, load fluctuations cause real-time adjustment of the solution space form; in step S6, the operating condition constraints are the key basis for verifying the feasibility of the configuration scheme to ensure that the charge and discharge strategy matches the actual operation requirements of the grid.

[0072] S2. Resolve the implicit conflict between discrete policy rule constraints and continuous technical parameter constraints, and evaluate the degree of missing intersection of the feasible regions to allocate dynamic weights to the discrete policy rule constraints. The specific implementation is as follows:

[0073] Project the boundary threshold of the discrete policy rule constraint onto the feasible region space of the continuous technical parameter constraint, and calculate the missing area of the geometric intersection between the boundary threshold and the feasible region of the continuous technical parameter constraint. Specifically, the projection operation maps the boundary threshold of the discrete policy rule constraint into the coordinate system of the feasible region formed by the value range of the continuous technical parameter constraint. For example, if the feasible region of the power limit of the continuous technical parameter constraint is from 0 to 10 MW, and the discrete policy rule constraint requires that the power shall not exceed 8 MW, then the covered area of the projected feasible region is from 0 to 8 MW, the non-overlapping part is from 8 to 10 MW, and the missing area of the geometric intersection is the area corresponding to 2 MW. The calculation method of the missing area of the geometric intersection is the polygon area superposition method or the numerical integration method, which is specifically selected according to the shape of the feasible region: for a rectangular feasible region, it is calculated by multiplying the length and width; for a feasible region with a curved boundary, it is calculated by integrating after piecewise linear approximation.

[0074] Based on the ratio of the missing area of the geometric intersection to the total area of the feasible region of the continuous technical parameter constraint, determine the conflict intensity coefficient. The total area of the feasible region is the area corresponding to the value range of the continuous technical parameter constraint. For example, the total area of the power limit feasible region from 0 to 10 MW is the area corresponding to 10 MW. The calculation formula of the conflict intensity coefficient is the missing area of the geometric intersection divided by the total area of the feasible region, and the result is a value between 0 and 1. For example, when the missing area of the geometric intersection is the area corresponding to 2 MW and the total area of the feasible region is the area corresponding to 10 MW, the conflict intensity coefficient is 0.2; when the missing area is the area corresponding to 8 MW, the conflict intensity coefficient is 0.8. The conflict intensity coefficient is used to quantify the conflict degree between the discrete policy rule constraint and the continuous technical parameter constraint. The larger the value, the stronger the conflict.

[0075] According to the mapping relationship between the conflict intensity coefficient and the preset weight, assign a dynamic weight to the discrete policy rule constraint. The preset weight mapping relationship is an inverse proportional function relationship between the conflict intensity coefficient and the dynamic weight, which is specifically defined as the dynamic weight is equal to the benchmark weight divided by (the conflict intensity coefficient multiplied by the adjustment factor), where the benchmark weight is a preset fixed value (for example, the benchmark weight is 1.0), and the adjustment factor is used to control the sensitivity of the weight change with the conflict intensity (for example, the adjustment factor is 2.0). For example, when the conflict intensity coefficient is 0.2, the dynamic weight is 1.0 / (0.2×2.0) = 2.5; when the conflict intensity coefficient is 0.8, the dynamic weight is 1.0 / (0.8×2.0) = 0.625. Through the inverse proportional function relationship, the discrete policy rule constraint with a lower conflict intensity is assigned a higher weight to strengthen the binding force, and the constraint with a higher conflict intensity is assigned a lower weight to weaken its restriction on the feasible region.

[0076] Embed the dynamic weight as the priority parameter of the discrete policy rule constraint into the objective function of the multi-objective optimization model. The objective function consists of multi-dimensional sub-objectives such as economy, reliability, and compliance. The original weight of each sub-objective is preset according to actual needs (for example, the economy weight is 0.6, the reliability weight is 0.3, and the compliance weight is 0.1). After the dynamic weight is adjusted, the weight of the compliance sub-objective is the original compliance weight multiplied by the dynamic weight. For example, when the dynamic weight is 2.5, the compliance weight is adjusted to 0.1×2.5 = 0.25; when the dynamic weight is 0.625, the compliance weight is adjusted to 0.1×0.625 = 0.0625. The adjusted objective function combines each sub-objective in the form of weighted summation and inputs it into the optimization algorithm for solution. For example, the expression of the total objective function is: economic index×0.6 + reliability index×0.3 + compliance index×adjusted compliance weight.

[0077] S3. Convert the boundary threshold of the discrete policy rule constraint into a continuous penalty function, and construct a fusion constraint model that combines multi-dimensional constraints. The specific implementation is as follows:

[0078] Convert the boundary threshold of the discrete policy rule constraint into a piecewise exponential continuous function. The piecewise exponential continuous function sets a smooth transition interval at the boundary threshold to avoid function mutation. Specifically, for the boundary threshold of the discrete policy rule constraint, such as "the discharge power of the energy storage system during the peak period shall not be lower than 20 megawatts", first determine its threshold range (such as discharge power ≥ 20 megawatts), and define a smooth transition interval near the threshold (such as 18 megawatts to 22 megawatts). Within the smooth transition interval, the penalty function value increases exponentially from zero: when the discharge power is lower than 18 megawatts, the penalty value is zero; within the interval of 18 megawatts to 22 megawatts, the penalty value gradually increases according to the exponential function as the discharge power decreases, and the growth rate of the exponential function is determined by the width of the smooth transition interval (for example, when the interval width is 4 megawatts, the growth rate is set to double the penalty value per megawatt); when the discharge power exceeds 22 megawatts, the penalty value remains at a constant maximum. The setting of the smooth transition interval avoids the mutation of the traditional step function at the threshold, ensures the differentiability of the objective function, and facilitates the convergence of the optimization algorithm.

[0079] Embed the dynamic weight as a scaling factor of the exponential term into a piecewise exponential continuous function to generate a weighted continuous penalty function term. The dynamic weight is obtained by calculating based on the conflict intensity coefficient in step S2, and its embedding method is: multiply the dynamic weight by the power parameter of the exponential function to adjust the growth rate of the penalty function. For example, if the growth rate of the original exponential function is doubling the penalty value per megawatt (i.e., the exponential term coefficient is 0.6931), and the dynamic weight is 2.5, the adjusted growth rate becomes doubling the penalty value 2.5 times per megawatt (i.e., the exponential term coefficient is 0.6931×2.5≈1.7328). Through the scaling of the dynamic weight, when the conflict intensity is low (the dynamic weight is high), the penalty function grows steeper in the transition interval, strengthening the constraint on behaviors close to the threshold; when the conflict intensity is high (the dynamic weight is low), the function grows more gently, weakening the constraint effect to alleviate the fragmentation of the solution space.

[0080] Based on the mathematical expressions of the weighted continuous penalty function term and the continuous technical parameter constraints, construct a fusion constraint model with the multi-objective weighted sum as the comprehensive optimization objective. The multi-objective weighted sum model linearly superimposes sub-objectives such as economy, reliability, and compliance according to preset weights. For example, the economic sub-objective is the minimization of the investment cost of the energy storage system, expressed as the total cost; the reliability objective is the minimization of the sum of squares of the power supply gap, expressed as the cumulative sum of the squares of the difference between the actual load and the power supply capacity; the compliance objective is the minimization of the sum of all weighted penalty function terms. The objective function expression of the fusion constraint model is the weighted sum of the above sub-objectives, and the weights of each sub-objective are preset according to the actual scenario requirements. For example, the economic weight is set to 0.6, the reliability weight is set to 0.3, and the compliance weight is set to 0.1. The constraint conditions of the fusion constraint model include the continuous technical parameter constraints obtained in step S1 (such as power limit ≤ 10 megawatts) and the continuous penalty function terms converted from the discrete policy rule constraints (such as the penalty term corresponding to the discharge power ≥ 20 megawatts).

[0081] Align the dimensions of the input parameters of the fusion constraint model to ensure the consistency of the variables of the discrete policy rules and the continuous technical parameters in terms of time and space scales. The time scale alignment is achieved by unifying the timestamps. For example, align the peak period definition in the discrete policy rules (such as 8:00 to 12:00 every day) with the hourly or minute-level time series data of the continuous technical parameters to the same time benchmark (such as 15-minute interval timestamps). The space scale alignment is achieved by unifying the units. For example, unify the power unit to megawatts and the carbon emission unit to kilograms per megawatt-hour. The aligned input parameters are integrated through database association indexing to ensure that all constraint parameters at the same time point can be synchronously called. For example, if the load demand corresponding to the timestamp "2024-10-05 08:15:00" is 100 megawatts, the upper limit of photovoltaic output is 30 megawatts, and the carbon emission is 40 kilograms, and this time point is in the peak period, then call the discrete policy rule constraint corresponding to the peak period (such as discharge power ≥ 20 megawatts), convert it into a continuous penalty function term, and input it into the fusion constraint model together with other parameters.

[0082] S4. Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate an evolution trend map of the solution space. The specific implementation is as follows:

[0083] Randomly sample from the feasible solution space of the fusion constraint model to generate a candidate solution set, calculate the Euclidean distance between candidate solutions to construct an adjacency matrix, and the edge weight of the adjacency matrix is the reciprocal of the distance between candidate solutions. The feasible solution space of the fusion constraint model is jointly defined by the weighted continuous penalty function terms generated in step S3 and the continuous technical parameter constraints. The candidate solution set is obtained by random sampling using the Monte Carlo method. Each candidate solution contains multi-dimensional parameters such as energy storage capacity, power limit, and life attenuation coefficient. The calculation of the Euclidean distance is based on the coordinates of the candidate solutions in the feasible solution space. The edge weight of the adjacency matrix is set to the reciprocal of the Euclidean distance. For example, when the distance is 2.0049, the edge weight is 1 divided by 2.0049. The row and column indices of the adjacency matrix correspond to the candidate solution numbers, and the matrix element values represent the connectivity strength between the corresponding candidate solutions.

[0084] Identify isolated regions in the feasible solution space based on the adjacency matrix. An isolated region is defined as a subset of candidate solutions with edge weights lower than a preset connectivity threshold in the adjacency matrix. The preset connectivity threshold is set according to historical data or empirical values. For example, if the threshold is set to 0.1, it means that there is no connectivity relationship between candidate solutions with edge weights lower than 0.1. The method for identifying isolated regions is as follows: Traverse each row of the adjacency matrix. If all the edge weights in a row are lower than the threshold, mark the candidate solution corresponding to that row as a member of the isolated region. For example, if all the edge weights in the adjacent rows of candidate solution A are less than 0.1, then candidate solution A belongs to the isolated region. The boundary solution coordinates of the isolated region are determined through geometric space analysis. For example, in a two-dimensional feasible solution space, the boundary solutions of the isolated region are the set of its outer contour points.

[0085] Record the boundary solution coordinates and the changes in connectivity status of the isolated region in real time to generate a historical record of the spatio-temporal distribution of the isolated region. The historical record of the spatio-temporal distribution includes timestamps, boundary solution coordinates, and connectivity status. For example, at the timestamp "2024-10-05 08:15:00", record that the power limit range of the boundary solutions of the isolated region is 8 to 10 megawatts, the life decay coefficient range is 0.2 to 0.3, and mark the closest distance between this isolated region and adjacent regions as 1.5 megawatts. The real-time record is stored in a time series database, and the status of the isolated region at each time point is aligned with the unified timestamp in step S1.

[0086] Perform clustering analysis on the historical record of the spatio-temporal distribution to extract the migration paths and convergence characteristics of the isolated regions, and generate a map of the evolution trend of the solution space. The clustering analysis uses the K-means algorithm to divide the historical record of the spatio-temporal distribution into several categories according to the location, area, and connectivity status of the isolated regions. For example, the clustering results show that the isolated regions exhibit periodic migration in the power limit dimension and a trend of converging to the low decay area in the life decay dimension. The migration paths are generated by connecting the central points of the same clusters, and the convergence characteristics are quantified by the change in the within-cluster variance. The map of the evolution trend of the solution space is presented in the form of a spatio-temporal heat map, with the horizontal axis being the timestamp and the vertical axis being the power limit or life decay coefficient, and the depth of the color indicating the frequency of the occurrence of the isolated region.

[0087] S5. Generate a search strategy based on the connectivity map and the map of the evolution trend of the solution space. The specific implementation is as follows:

[0088] Set virtual transition nodes at the boundaries of isolated regions in the feasible solution space. The virtual transition nodes are intermediate jump points for the path from the current solution to the target solution. Specifically, the positions of the virtual transition nodes are set based on the boundary solution coordinates identified in step S4. For example, when the boundary solution of the isolated region is in the power limit dimension from 8 to 10 MW and the lifetime decay coefficient dimension from 0.2 to 0.3, insert a virtual transition node near the coordinate point where power = 8 MW and lifetime decay coefficient = 0.3. The insertion rule for the virtual transition node is: generate several candidate points around the boundary solution, and select the candidate point with the minimum sum of the spatial distances to the current solution and the target solution space as the virtual transition node. The virtual transition node does not participate in the calculation of the actual solution and is only used to guide the path search direction.

[0089] Based on the migration paths and convergence characteristics in the solution space evolution trend map, predict the connectivity of the isolated regions. The prediction process includes calculating the success rate and iteration number threshold of the historical crossing paths. The success rate of the historical crossing paths is determined by statistically calculating the ratio of the number of path connections to the number of attempts at the same isolated region at past time points in the spatio-temporal distribution historical record in step S4. For example, if a certain isolated region has a certain proportion of paths successfully crossed in historical searches, it is considered to have a higher connectivity. The iteration number threshold is set based on the average number of iterations of the historical successful crossing paths. For example, set the threshold as the average number of iterations of historical successful cases plus twice the standard deviation. If the number of iterations in the current search exceeds the threshold and the success rate is lower than the preset lower limit, then determine that the isolated region is not connected and terminate the search for it.

[0090] Calculate the crossing path from the current solution to the target solution through the path planning algorithm. The path planning algorithm preferentially expands the search branches in the direction with a high density of virtual transition nodes. The path planning algorithm uses an improved A* algorithm, and the virtual transition node density is introduced as a weight factor in its heuristic function. The virtual transition node density is calculated by counting the number of virtual transition nodes in the candidate search directions. For example, when the number of virtual transition nodes in candidate direction A is significantly more than that in direction B, preferentially expand the search branch in direction A. The generation logic of the crossing path is: starting from the current solution, generate child nodes in the direction with a high density of virtual transition nodes and gradually approach the target solution.

[0091] Dynamically adjust the search direction according to the convergence speed of the crossing path. The convergence speed is measured by the decrease amplitude of the objective function value within the unit number of iterations. For example, a path with a rapid decrease is identified as a path with a high convergence speed. The dynamic adjustment rule is: reduce the search weight of the path with a low convergence speed and increase the exploration probability of the path with a high convergence speed. The adjustment method of the search weight is: if the path convergence speed is lower than the preset threshold, decay its weight proportionally; if it is higher than the threshold, increase it proportionally. The exploration probability is distributed by the weights after normalization. For example, a path with a high weight obtains a higher exploration priority.

[0092] S6. Generate an energy storage capacity configuration plan based on the globally optimal solution set output by the search strategy. The specific implementation is as follows:

[0093] Screen out the optimal solution subset that meets the preset economic, reliability, and compliance indicators from the globally optimal solution set. The globally optimal solution set is output by the search strategy in step S5, including the optimized objective function values of multiple candidate solutions and the corresponding solution vectors. The screening process is based on the preset thresholds of economic, reliability, and compliance indicators. For example, the economic indicator requires that the energy storage investment cost is less than 10 million yuan, the reliability indicator requires that the power supply gap is less than 5 megawatt-hours, and the compliance indicator requires that the carbon emission does not exceed 50 kilograms per megawatt-hour. During screening, traverse each candidate solution in the globally optimal solution set. If its economic, reliability, and compliance indicators all meet the preset thresholds, add it to the optimal solution subset. If there are multiple candidate solutions that meet the conditions, sort them in ascending order of the optimized objective function value and select the top 10% of the solutions as the optimal solution subset.

[0094] Map the solution vectors in the optimal solution subset to the energy storage capacity configuration parameters. The energy storage capacity configuration parameters include energy storage capacity, power limit, life attenuation coefficient, and charge-discharge strategy. The solution vector is a multi-dimensional variable combination. For example, the first dimension of the solution vector corresponds to the energy storage capacity (unit: megawatt-hour), the second dimension corresponds to the power limit (unit: megawatt), the third dimension corresponds to the life attenuation coefficient, and the fourth dimension corresponds to the trigger threshold of the charge-discharge strategy. During the mapping process, each dimension of the solution vector is converted into actual configuration parameters according to the preset rules. For example, the energy storage capacity dimension is multiplied by a reference coefficient (such as 10) to obtain the actual capacity value, the power limit dimension is directly used as the maximum charge-discharge rate, the life attenuation coefficient is used as the input parameter of the capacity attenuation model, and the charge-discharge strategy trigger threshold is converted into time or power conditions.

[0095] Based on the continuous technical parameter constraints and discrete policy rule constraints, verify the feasibility of the mapped configuration parameters. The verification process includes two levels: The first level checks whether the configuration parameters meet the continuous technical parameter constraints in step S1. For example, verify whether the power limit is within the rated power range of the device (such as 0 to 10 megawatts) and whether the life attenuation coefficient is within the allowable range of laboratory tests (such as 0 to 0.5); The second level checks whether the configuration parameters meet the discrete policy rule constraints converted in step S1. For example, verify whether the charge-discharge strategy meets the peak period discharge power requirement (such as not less than 20 megawatts) and whether the carbon emission is lower than the policy threshold (such as 50 kilograms per megawatt-hour). If any level of verification fails, eliminate the configuration parameter and go back to step S5 to regenerate candidate solutions.

[0096] Generate an energy storage capacity configuration plan that includes configuration parameters, verification results, and optimization target weights. The output of the plan is an executable engineering deployment instruction. In the configuration plan, parameters such as energy storage capacity and power limit are expressed in standard engineering units (such as megawatt-hours, megawatts). The verification result is marked as "passed" or "failed" and the specific reasons for failure. The optimization target weights inherit the dynamic weight allocation results in step S3 (such as economic weight 0.6, compliance weight 0.25). The format of the engineering deployment instruction is set according to the requirements of the target system interface. For example, it is converted into a configuration file in JSON format, which includes an energy storage device parameter table, a charge and discharge strategy schedule, and a constraint compliance statement. After the deployment instruction is transmitted to the energy storage control system through an industrial communication protocol, the configuration parameters are automatically loaded and executed.

[0097] Embodiment 2: Figure 2 The structural schematic diagram of an energy storage capacity optimization configuration system with multi-dimensional constraint modeling according to the present invention is given. An energy storage capacity optimization configuration system with multi-dimensional constraint modeling includes the following modules:

[0098] Multi-dimensional data acquisition module: Acquire multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints;

[0099] Conflict weight allocation module: Evaluate the degree of lack of intersection of the feasible region by the implicit conflict between discrete policy rule constraints and continuous technical parameter constraints to allocate dynamic weights to discrete policy rule constraints;

[0100] Constraint fusion modeling module: Convert the boundary threshold of discrete policy rule constraints into a continuous penalty function, and construct a fusion constraint model that combines multi-dimensional constraints with dynamic weights;

[0101] Topological analysis and monitoring module: Conduct topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and real-time monitor the dynamic changes of the feasible solution space to generate a solution space evolution trend map;

[0102] Search strategy generation module: Generate a search strategy based on the connectivity map and the solution space evolution trend map;

[0103] Configuration plan generation module: Generate an energy storage capacity configuration plan according to the global optimal solution set output by the search strategy.

[0104] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0105] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application of the technical solution and the invention constraints. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0106] In addition, in each embodiment of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0107] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0108] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling, characterized in that It includes the following steps: S1. Obtain the multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints; S2. Evaluate the degree of lack of the intersection of the feasible regions through the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints, so as to allocate dynamic weights to the discrete policy rule constraints; S3. Convert the boundary thresholds of the discrete policy rule constraints into continuous penalty functions, and construct a fusion constraint model that integrates multi-dimensional constraints in combination with the dynamic weights; S4. Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a map of the evolution trend of the solution space; S5. Generate a search strategy based on the connectivity map and the map of the evolution trend of the solution space; S6. Generate an energy storage capacity configuration plan according to the globally optimal solution set output by the search strategy.

2. The energy storage capacity optimization configuration method for multi-dimensional constraint modeling according to claim 1, characterized in that Obtain the multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints. Specifically: Divide the continuous technical parameter constraints into power limit type parameters and life attenuation model type parameters; Convert the text rules of the discrete policy rule constraints into discrete mathematical expressions including boundary thresholds and trigger conditions; Perform time series alignment processing on the grid load demand and the fluctuation range of renewable energy output in the operating condition constraints to generate a constraint data set with a unified timestamp.

3. A method for optimizing the configuration of energy storage capacity based on multi-dimensional constraint modeling according to claim 1, characterized in that, Evaluate the degree of lack of the intersection of the feasible regions through the implicit conflict between the discrete policy rule constraints and the continuous technical parameter constraints, so as to allocate dynamic weights to the discrete policy rule constraints, including: Project the boundary thresholds of the discrete policy rule constraints into the feasible region space of the continuous technical parameter constraints, and calculate the geometric intersection missing area between the boundary thresholds and the feasible region of the continuous technical parameter constraints; Based on the ratio of the geometric intersection missing area to the total area of the feasible region of the continuous technical parameter constraints, determine the conflict intensity coefficient; According to the preset weight mapping relationship between the conflict intensity coefficient and the dynamic weight, allocate dynamic weights to the discrete policy rule constraints. The preset weight mapping relationship is an inverse proportional function relationship between the conflict intensity coefficient and the dynamic weight; Embed the dynamic weight as a priority parameter of the discrete policy rule constraint into the objective function of the multi-objective optimization model.

4. A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling according to claim 1, characterized in that, Convert the boundary thresholds of the discrete policy rule constraints into continuous penalty functions, and construct a fusion constraint model that integrates multi-dimensional constraints in combination with the dynamic weights, including: Convert the boundary thresholds of the discrete policy rule constraints into piecewise exponential continuous functions, and set a smooth transition interval at the boundary thresholds of the piecewise exponential continuous functions to avoid function mutation; Embed the dynamic weight as a scaling factor of the exponential term into the piecewise exponential continuous function to generate a weighted continuous penalty function term; Based on the weighted continuous penalty function term and the mathematical expression of the continuous technical parameter constraints, construct a fusion constraint model with the multi-objective weighted sum as the comprehensive optimization objective; Perform dimension alignment processing on the input parameters of the fusion constraint model.

5. A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling according to claim 1, characterized in that Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a solution space evolution trend map, including: Randomly sample from the feasible solution space of the fusion constraint model to generate a candidate solution set, calculate the Euclidean distance between candidate solutions to construct an adjacency matrix, and the edge weight of the adjacency matrix is the reciprocal of the distance between candidate solutions; Identify isolated regions in the feasible solution space based on the adjacency matrix. An isolated region is defined as a subset of candidate solutions with edge weights lower than a preset connectivity threshold in the adjacency matrix; Record the boundary solution coordinates and connectivity status changes of the isolated regions in real time to generate a historical record of the spatio-temporal distribution of the isolated regions; Perform clustering analysis on the historical record of spatio-temporal distribution, extract the migration paths and convergence characteristics of the isolated regions, and generate a solution space evolution trend map.

6. The method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling according to claim 1, wherein Generate a search strategy based on the connectivity map and the solution space evolution trend map, including: Set virtual transition nodes at the boundaries of the isolated regions in the feasible solution space; Predict the connectivability of the isolated regions based on the migration paths and convergence characteristics in the solution space evolution trend map; Calculate the crossing path from the current solution to the target solution through a path planning algorithm; Dynamically adjust the search direction according to the convergence speed of the crossing path.

7. A method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling according to claim 6, characterized in that, The virtual transition node is the intermediate jump point of the crossing path from the current solution to the target solution; The prediction includes calculating the success rate and iteration number threshold of the historical crossing path; The path planning algorithm preferentially expands the search branches in the direction with a high density of virtual transition nodes; The dynamic adjustment includes reducing the search weight of paths with a low convergence speed and increasing the exploration probability of paths with a high convergence speed.

8. A method for optimizing the configuration of energy storage capacity based on multi-dimensional constraint modeling according to claim 1, characterized in that Generate an energy storage capacity configuration plan according to the globally optimal solution set output by the search strategy, including: Screen out the optimal solution subset that meets the preset economic, reliability, and compliance indicators from the globally optimal solution set; Map the solution vectors in the optimal solution subset to the energy storage capacity configuration parameters; Based on the continuous technical parameter constraints and discrete policy rule constraints, verify the feasibility of the mapped configuration parameters; Generate an energy storage capacity configuration plan including configuration parameters, verification results, and optimization target weights, and the plan output is an executable engineering deployment instruction.

9. A method for optimizing the configuration of energy storage capacity based on multi-dimensional constraint modeling according to claim 8, characterized in that, The energy storage capacity configuration parameters include energy storage capacity, power limit, life attenuation coefficient, and charge-discharge strategy.

10. A system for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling, which is used to implement the method for optimizing the configuration of energy storage capacity with multi-dimensional constraint modeling according to any one of claims 1-9, characterized in that, Include the following modules: Multi-dimensional data acquisition module: Acquire multi-dimensional constraint data of the energy storage system, including continuous technical parameter constraints, operating condition constraints, and discrete policy rule constraints; Conflict weight allocation module: Evaluate the degree of lack of intersection in the feasible domain through the implicit conflict between discrete policy rule constraints and continuous technical parameter constraints, and allocate dynamic weights to discrete policy rule constraints; Constraint fusion modeling module: Convert the boundary threshold of discrete policy rule constraints into a continuous penalty function, and construct a fusion constraint model that fuses multi-dimensional constraints in combination with dynamic weights; Topological analysis and monitoring module: Conduct a topological structure analysis on the feasible solution space of the fusion constraint model to construct a connectivity map, and monitor the dynamic changes of the feasible solution space in real time to generate a solution space evolution trend map; Search strategy generation module: Generate a search strategy based on the connectivity map and the solution space evolution trend map; Configuration scheme generation module: Generate an energy storage capacity configuration scheme based on the globally optimal solution set output by the search strategy.

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