Electrochemical energy storage system large-scale configuration optimization method and system based on life-economy combined constraint

Abstract

The invention provides a life-economy combined constraint-based large-scale configuration optimization method and system for an electrochemical energy storage system, and relates to the technical field of energy storage planning of an electric power system. According to the method, a life loss grading library and life budget constraints are constructed by obtaining load requirements, electricity price rules, operation constraints and energy storage module parameters; calculating the accumulated life consumption of the candidate configuration scale based on the operation instruction and a preset charging and discharging strategy, cutting the configuration which does not meet the life and operation constraint, and carrying out economical efficiency sorting in combination with the investment cost, the operation maintenance cost, the life loss conversion cost and the residual value income; and an optimal configuration result and an operation strategy are output through a combined checking and backspacing mechanism, so that collaborative optimization of the service life bearability and the economical efficiency is realized, and the long-term stability and the implementation of the configuration result are improved.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage planning technology, and in particular to a method and system for large-scale configuration optimization of electrochemical energy storage systems based on lifespan-economic constraints. Background Technology

[0002] In the engineering planning and scheme design of electrochemical energy storage systems, it is usually necessary to determine the capacity and power scale of the energy storage system, and to conduct economic calculations and comparisons of the schemes in combination with load demand data, electricity prices and settlement rules, while meeting grid connection, safety and operation constraints. Because electrochemical energy storage is subject to capacity decay and lifespan consumption as operating conditions change, existing configuration schemes often need to balance "meeting the benefit / cost target" with "acceptable lifespan".

[0003] As energy storage systems shift from one-time construction to a greater emphasis on life-cycle operational performance, configuration decisions no longer focus solely on initial investment or single revenue indicators. Instead, they must incorporate the impact of life-cycle degradation on available capacity, available power, and long-term economic viability into the constraints and evaluations of the configuration phase. Simultaneously, configuration methods need to be feasible on the engineering side: able to reproduce the calculation process given input data and operating rules, facilitating rapid scheme selection and outputting executable operational strategy parameters.

[0004] The shortcomings of existing technologies are mainly reflected in the following aspects: First, lifetime factors are often reflected in the configuration stage through experience reduction, equivalent iterative estimation, or ex-post verification, making it difficult to form a lifetime budget constraint that allows for direct tailoring of the feasible region for capacity and power scales. This results in low efficiency in candidate scale selection and the stability of the results depends on repeated manual adjustments. Second, when lifetime and economic efficiency are taken into account simultaneously, optimization modeling and numerical solution processes involving a large number of state variables and constraints are often adopted. This results in long computational chains, high implementation costs, and the risk of difficulty in reproducibility if the parameter definitions and mapping relationships are not clearly defined in the patent disclosure. Third, there is a lack of an engineering mechanism that establishes a clear correspondence between operating instructions and lifetime consumption. This leads to inconsistent definitions of lifetime loss conversion costs in economic evaluation and a lack of joint verification and backoff rules for the optimal candidate scale, resulting in insufficient reliability of the output results at the boundaries of lifetime or operating constraints. Based on the above problems, a large-scale configuration optimization method is needed that can tailor candidate scales with lifetime budget constraints during the configuration stage and complete economic ranking and verification backoff with a unified definition. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for optimizing the large-scale configuration of electrochemical energy storage systems based on a combined lifetime and economic constraints. By introducing a combined constraint mechanism of lifetime budget and economic evaluation during the large-scale configuration of electrochemical energy storage systems, the optimal selection of configuration scale is achieved under the premise of meeting lifetime affordability, thereby improving the long-term stability and engineering feasibility of the configuration results.

[0006] To achieve the above objectives, the present invention provides the following solution: A method for large-scale configuration optimization of electrochemical energy storage systems based on combined lifetime-economic constraints includes: The system acquires load demand data, electricity price and settlement rules, grid connection and safe operation constraints for the target scenario, and acquires the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset. Based on the candidate energy storage modules, a candidate configuration scale set is generated according to a preset combination rule. Based on the input dataset, extract the lifespan impact operation feature set, divide the lifespan impact operation feature set into intervals and establish the correspondence between the intervals and the lifespan loss coefficient to obtain the lifespan loss interval library, and determine the lifespan budget upper limit according to the target lifespan requirement to generate lifespan budget constraints. An operating instruction set is generated based on the electricity price and settlement rules. A preset charging and discharging strategy is used to map the operating instruction set into an equivalent utilization sequence. For each candidate configuration size in the candidate configuration size set, the cumulative lifetime consumption corresponding to the candidate configuration size is obtained by looking up the table from the lifetime loss classification library according to the equivalent utilization sequence. The candidate configuration size set is pruned according to the lifetime budget constraint and the operational constraint to obtain the candidate size set; The economic evaluation value is calculated and sorted for the candidate size set, and the one with the best ranking is selected as the optimal candidate size; wherein, the economic evaluation value includes investment cost, operation and maintenance cost, life loss depreciation cost determined by the cumulative life consumption, and residual value income; The optimal candidate size is jointly verified. If the verification fails, the second-best candidate size is selected in order of sorting and the joint verification is repeated until it passes. Then, the scaled configuration result and the corresponding operation strategy parameters are output. The joint verification includes a review of the satisfaction of the lifetime budget constraint and the operation constraint, and a review of the consistency of the calculation method of the economic evaluation value.

[0007] Preferably, generating a candidate configuration size set based on the candidate energy storage modules according to a preset combination rule includes: Set the capacity step size, power step size, and maximum number of modules; Under the constraints of the capacity step size and the power step size, the candidate energy storage modules are combined in number to form a triplet representation of the capacity scale, power scale and number of modules corresponding to each candidate configuration scale, and the candidate configuration scale set is constituted by the triplet representation.

[0008] Preferably, a lifespan impact operation feature set is extracted based on the input dataset, the lifespan impact operation feature set is divided into intervals and a correspondence between the intervals and lifespan loss coefficients is established to obtain a lifespan loss interval library, including: Determine lifetime-affected operational characteristics from the input dataset; these characteristics include energy throughput characteristics, charge / discharge depth characteristics, power rate characteristics, and charge / discharge switching frequency characteristics. Based on the lifetime degradation-related parameters of the candidate energy storage modules, grading boundaries are set for each lifetime-affecting operational feature, and interval grading is completed to obtain the grading results of each lifetime-affecting operational feature. Each of the grading results is used to generate a grading identifier using a preset combination key, and the grading identifier is then associated with a life loss coefficient to construct the life loss grading library.

[0009] Preferably, the method for determining the grading boundaries includes: A threshold table is established based on the lifetime degradation-related parameters of the candidate energy storage modules and corresponding to the lifetime-affected operational characteristics; the threshold table provides multiple lifetime-sensitive threshold intervals for each of the lifetime-affected operational characteristics. Extract the feature value sequence of the lifespan impact operation feature within the planning period from the input dataset to form a lifespan impact operation feature sample set; Candidate grading boundaries are determined based on the feature value sequence of the lifespan-affecting operational feature sample set according to a preset quantile rule; The candidate grading boundaries are aligned with the threshold table to retain grading boundaries that meet the coverage requirements of the lifetime sensitive threshold range.

[0010] Preferably, the lifetime budget upper limit is determined based on the target lifetime requirement, and lifetime budget constraints are generated, including: The target lifetime requirement is quantified as at least one of a capacity retention rate threshold and a target lifetime in years. Based on the lifetime decay-related parameters, the upper limit of the allowable cumulative lifetime consumption corresponding to the capacity retention rate threshold or the target lifetime is determined as the lifetime budget upper limit; The lifetime budget upper limit is used to limit the cumulative lifetime consumption corresponding to any candidate configuration size to not exceed the lifetime budget upper limit, thus forming the lifetime budget constraint.

[0011] Preferably, the operation instruction set is generated based on the electricity price and settlement rules, including: The time range corresponding to the target scenario is divided into multiple runtime segments; For each of the aforementioned operating periods, charging instructions and discharging instructions are determined based on the load demand data and the electricity price and settlement rules corresponding to the operating period; operating periods that are not determined to be charging instructions or discharging instructions are considered to be in standby mode. And set corresponding power upper limits or energy targets for the charging command and the discharging command; The set of execution instructions is composed of the instructions of each of the aforementioned runtime segments.

[0012] Preferably, a preset charging and discharging strategy is used to map the operating instruction set into an equivalent utilization sequence, and the cumulative lifetime consumption is obtained by looking up the equivalent utilization sequence from the lifetime consumption classification database, including: For each candidate configuration size in the candidate configuration size set, the operating instruction set is limited and efficiency corrected according to the rated capacity, rated power and efficiency parameters corresponding to the candidate configuration size to obtain the equivalent utilization sequence corresponding to the candidate configuration size; A tiered identifier is generated based on the equivalent utilization sequence corresponding to the candidate configuration size, and the lifetime loss coefficient corresponding to the tiered identifier is retrieved from the lifetime loss tiered database. The lifetime loss coefficient is accumulated over the operating period to obtain the cumulative lifetime consumption corresponding to the candidate configuration scale.

[0013] Preferably, calculating and ranking the economic evaluation values ​​for the candidate size set includes: For each candidate configuration scale in the candidate scale set, the investment cost is determined based on the number of modules corresponding to the candidate configuration scale and the rated capacity and rated power of the candidate energy storage modules; The operation and maintenance costs are determined according to the pre-designed rules; the life loss conversion cost is determined according to the cumulative life consumption corresponding to the candidate configuration scale and according to the preset conversion rules; and the residual value income is determined according to the difference between the life budget upper limit and the cumulative life consumption corresponding to the candidate configuration scale and according to the pre-designed rules. The investment cost, the operation and maintenance cost, and the depreciation cost are combined into a cost item, and the residual value is deducted to obtain the economic evaluation value corresponding to the candidate configuration scale. The candidate size set is sorted according to the economic evaluation value.

[0014] Preferably, the optimal candidate size is jointly verified. If the verification fails, the next best candidate size is selected in order of ranking and the joint verification is repeated, including: The optimal candidate size is used as the current verification object, and the current verification object is verified to satisfy the lifetime budget constraint and the operational constraint; wherein, the verification of the lifetime budget constraint includes verifying that the cumulative lifetime consumption corresponding to the current verification object does not exceed the lifetime budget upper limit; The review of the operational constraints includes reviewing the power and energy boundary satisfaction of the current verification object under the grid connection and safe operation constraints; Verify that the economic evaluation value of the current verification object is consistent with the calculation method of the economic evaluation value used in the sorting stage; If any review fails, the next best candidate size is selected in order to replace the current review object and the joint review is repeated until it passes. Then the scaled configuration result and the corresponding running strategy parameters are output.

[0015] A large-scale configuration optimization system for electrochemical energy storage systems based on combined lifetime-economic constraints, comprising: The input data construction and candidate scale generation unit is used to obtain load demand data, electricity price and settlement rules, grid connection and safe operation constraints of the target scenario, and obtain the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset, and generate a candidate configuration scale set based on the candidate energy storage modules according to a preset combination rule; The life loss classification library construction and life budget constraint generation unit is used to extract the life impact operation feature set based on the input dataset, classify the life impact operation feature set into intervals and establish the correspondence between the classification and the life loss coefficient to obtain the life loss classification library, and determine the life budget upper limit according to the target life requirement to generate life budget constraints. The operation instruction mapping and cumulative lifetime consumption calculation unit is used to generate an operation instruction set according to the electricity price and settlement rules, map the operation instruction set into an equivalent utilization sequence using a preset charging and discharging strategy, and for each candidate configuration scale in the candidate configuration scale set, look up the cumulative lifetime consumption corresponding to the candidate configuration scale from the lifetime loss classification library according to the equivalent utilization sequence. The candidate size pruning unit based on lifetime budget is used to prune the candidate configuration size set according to the lifetime budget constraint and the operation constraint to obtain a candidate size set. An economic evaluation and optimal size ranking unit is used to calculate and rank the economic evaluation values ​​of the candidate size set, and select the best ranked one as the optimal candidate size; wherein, the economic evaluation values ​​include investment costs, operation and maintenance costs, life loss depreciation costs determined by the cumulative life consumption, and residual value income; The joint verification and large-scale configuration result output unit is used to jointly verify the optimal candidate size. When the verification fails, the second-best candidate size is selected in order of sorting and the joint verification is repeated until it passes. Then, the large-scale configuration result and the corresponding operation strategy parameters are output. The joint verification includes the verification of the satisfaction of the lifetime budget constraint and the operation constraint, and the verification of the consistency of the calculation method of the economic evaluation value.

[0016] The present invention discloses the following technical effects: This invention introduces a lifespan budget constraint and economic evaluation mechanism simultaneously during the configuration phase, so that the large-scale configuration of electrochemical energy storage systems no longer depends solely on a single cost or benefit indicator, but uses lifespan affordability as a prerequisite constraint to screen and optimize the scale of candidate configurations. This avoids the problems of lifespan overrun and rapid decay of usable capacity that occur in energy storage systems after they are put into operation in the prior art, and improves the feasibility and stability of the configuration results throughout the entire life cycle.

[0017] This invention constructs a lifetime attenuation classification library and maps the running instructions into an equivalent utilization sequence through a preset charging and discharging strategy. Then, it obtains the cumulative lifetime consumption through a lookup table. This transforms the complex lifetime attenuation calculation into an engineering-feasible classification mapping process, reducing the dependence of configuration optimization on complex modeling and high computational load. It also makes the lifetime assessment process have a clear data source and processing path, which is easy to reproduce and implement.

[0018] This invention introduces a feasible domain pruning mechanism based on lifetime budget constraints after the candidate configuration scale is generated, so that configuration schemes that do not meet lifetime requirements are eliminated in the early stage. This avoids repeated economic calculations on a large number of infeasible schemes, improves the efficiency of the configuration optimization process, and ensures that all configuration schemes that enter the subsequent economic ranking have lifetime feasibility.

[0019] This invention directly incorporates the cumulative lifespan consumption into the composition of the economic evaluation value. By using a unified approach to calculate the lifespan loss conversion cost and residual value income, the impact of lifespan can be quantified and reflected in the economic ranking results. This overcomes the problem of the separation between lifespan factors and economic evaluation in the prior art, thereby achieving a more reasonable balance between economic benefits and lifespan consumption in the configuration results.

[0020] This invention verifies the optimal candidate size at three levels—lifetime constraints, operational constraints, and consistency of economic criteria—by setting up a joint verification and sorting rollback mechanism. When the verification fails, it automatically switches to the second-best candidate size, thereby ensuring that the final output of the scalable configuration result is not only theoretically optimal but also stable and reliable under constrained boundary conditions, thus enhancing the applicability of the method in practical engineering applications. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of the method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The purpose of this invention is to provide a method and system for large-scale configuration optimization of electrochemical energy storage systems based on the combined constraints of lifetime and economic efficiency. By incorporating lifetime loss into the economic evaluation and configuration screening process in a tiered mapping manner, the large-scale configuration of energy storage systems achieves coordination and unity between lifetime constraints and economic benefits, thereby enhancing the rationality and reliability of configuration decisions.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a method for large-scale configuration optimization of electrochemical energy storage systems based on combined lifetime-economic constraints, including: Step 100: Obtain load demand data, electricity price and settlement rules, grid connection and safe operation constraints for the target scenario, and obtain the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset, and generate a candidate configuration scale set based on the candidate energy storage modules according to the preset combination rules; Step 200: Extract the lifespan impact operation feature set based on the input dataset, divide the lifespan impact operation feature set into intervals and establish the correspondence between the intervals and the lifespan loss coefficient to obtain the lifespan loss interval library, and determine the lifespan budget upper limit according to the target lifespan requirement to generate lifespan budget constraints. Step 300: Generate an operation instruction set based on electricity price and settlement rules, map the operation instruction set to an equivalent utilization sequence using a preset charging and discharging strategy, and for each candidate configuration scale in the candidate configuration scale set, look up the cumulative lifetime consumption corresponding to the candidate configuration scale from the lifetime loss classification database according to the equivalent utilization sequence. Step 400: Prune the candidate configuration size set according to the lifetime budget constraint and operational constraint to obtain the candidate size set; Step 500: Calculate and sort the economic evaluation values ​​of the candidate size set, and select the one with the best ranking as the optimal candidate size; wherein, the economic evaluation value includes investment cost, operation and maintenance cost, life loss depreciation cost determined by cumulative life consumption, and residual value income; Step 600: Perform joint verification on the optimal candidate size. If the verification fails, select the second-best candidate size in order of sorting and repeat the joint verification until it passes. Then output the scaled configuration result and the corresponding operation strategy parameters. The joint verification includes verification of the satisfaction of life budget constraints and operation constraints, as well as verification of the consistency of the calculation method of economic evaluation value.

[0027] In this embodiment, step 100 is as follows: This embodiment first reads the rated capacity and rated power of candidate energy storage modules from the input dataset, and defines the "preset combination rule" as a set of combination constraints used to constrain the enumeration range of candidate configuration sizes. This set of combination constraints includes at least a capacity step size, a power step size, and an upper limit on the number of modules. The capacity step size is used to limit the capacity increment between two adjacent candidate configuration sizes. In this embodiment, the capacity step size can be 50. The power step size is used to limit the power increment between two adjacent candidate configuration sizes. In this embodiment, the power step size can be 25. The upper limit on the number of modules is used to limit the maximum number of combinations of candidate energy storage modules in the same candidate configuration size. In this embodiment, the upper limit on the number of modules can be 200. This gives the generation of the candidate configuration size set a defined boundary and granularity.

[0028] In this embodiment, the candidate configuration scale is further represented as a triple consisting of capacity scale, power scale, and number of modules. The capacity scale is the total capacity of all candidate energy storage modules in the candidate configuration scale, calculated by the number of modules. The power scale is the total power of all candidate energy storage modules in the candidate configuration scale, calculated by the number of modules. The number of modules is the number of candidate energy storage modules participating in the combination. For example, when the rated capacity of a candidate energy storage module is 100 and the rated power is 50, a module count of 50 corresponds to a capacity scale of 5000 and a power scale of 2500. Under the combined constraints of the capacity step size and the power step size, this embodiment increases the number of modules incrementally from the minimum feasible number without exceeding the upper limit of the module count, generating a corresponding triple representation for each module count.

[0029] In this embodiment, after generating each triplet, its validity is screened, retaining only triplets that simultaneously meet the power and energy boundary requirements of the grid connection and safe operation constraints. The screened triplets are then aggregated to form the candidate configuration size set. To facilitate establishing a consistent correspondence with the equivalent utilization sequence, cumulative lifetime consumption, and economic evaluation value in subsequent steps, this embodiment assigns a unique identifier to each candidate configuration size in the candidate configuration size set. Through this method, a consistent candidate configuration size set can be repeatedly obtained under the conditions of the same input dataset, the same capacity step size, the same power step size, and the same upper limit on the number of modules.

[0030] In this embodiment, step 200 first determines the lifetime-affecting operational characteristics based on the input dataset, forming a lifetime-affecting operational characteristic set. This lifetime-affecting operational characteristic set includes four categories: energy throughput characteristics, charge / discharge depth characteristics, power ratio characteristics, and charge / discharge switching frequency characteristics. Specifically, the energy throughput characteristic characterizes the cumulative charge / discharge energy scale within a statistical window, which in this embodiment can be 24; the charge / discharge depth characteristic characterizes the proportion of charge / discharge energy relative to the rated capacity within the statistical window; the power ratio characteristic characterizes the proportion of charge / discharge power relative to the rated power; and the charge / discharge switching frequency characteristic characterizes the frequency of switching between charging and discharging states within the statistical window. Through the above definitions, this embodiment clarifies the composition, value scope, and statistical boundaries of the lifetime-affecting operational characteristic set to support the subsequent generation of interval grading and lifetime budget constraints.

[0031] This embodiment further sets grading boundaries for each lifetime-affecting operational feature based on the lifetime degradation-related parameters of the candidate energy storage modules, and completes interval grading. In this embodiment, a "grading boundary" is defined as a set of thresholds that divides the continuous values ​​of the same lifetime-affecting operational feature into multiple discrete intervals, used to map the values ​​of the lifetime-affecting operational feature to indexable grading results. For example, the grading boundary for the charge / discharge depth feature can cover 10 to 90 in this embodiment, and the grading boundary for the power rate feature can cover 20 to 100, thereby dividing the operating states at different depths and power rates into discrete grading levels. Through the above interval grading, this embodiment obtains the grading results for each lifetime-affecting operational feature, and ensures that the subsequent configuration of the lifetime loss coefficient can stably correspond to the discrete grading levels.

[0032] In a preferred embodiment, this embodiment uses a combination of threshold tables and quantile rules to determine the grading boundaries. This embodiment establishes a threshold table corresponding to the lifespan-affecting operational characteristics based on the lifespan degradation-related parameters of the candidate energy storage modules. The threshold table provides multiple lifespan-sensitive threshold intervals for each lifespan-affecting operational characteristic. Simultaneously, this embodiment extracts the feature value sequences of the lifespan-affecting operational characteristics within the planning period from the input dataset, forming a lifespan-affecting operational characteristic sample set. Candidate grading boundaries are then determined according to preset quantile rules. In this embodiment, 10 quantiles can be selected to ensure that the candidate grading boundaries cover the main intervals of the sample distribution. Subsequently, this embodiment aligns the candidate grading boundaries with the threshold table, retaining grading boundaries that meet the lifespan-sensitive threshold interval coverage requirements. This ensures that the grading boundaries balance lifespan sensitivity and scenario representativeness, preventing the grading boundaries from deviating from the lifespan degradation-sensitive segment and reducing the effectiveness of lifespan constraints.

[0033] In this embodiment, after obtaining the grading results of various lifetime-affecting operational characteristics, each grading result is used to generate a grading identifier according to a preset combination key, and a correspondence between the grading identifier and the lifetime loss coefficient is established to construct a lifetime loss grading library. The preset combination key is used to specify the composition order and components of the grading identifier, ensuring that the same operational state has a unique grading identifier under the combination of grading results for energy throughput characteristics, charge / discharge depth characteristics, power rate characteristics, and charge / discharge switching frequency characteristics. This embodiment defines the lifetime loss coefficient as a coefficient characterizing the contribution of the operational state corresponding to the grading identifier to lifetime consumption, and configures a corresponding lifetime loss coefficient for each grading identifier based on the lifetime decay-related parameters of the candidate energy storage modules. This allows subsequent steps to retrieve the corresponding lifetime loss coefficient from the lifetime loss grading library using the grading identifier, achieving a lookup-based expression of lifetime consumption.

[0034] This embodiment also determines the lifetime budget upper limit and generates a lifetime budget constraint based on the target lifetime requirement. This embodiment quantifies the target lifetime requirement as at least one of a capacity retention rate threshold and a target lifetime duration, where the capacity retention rate threshold can be 80 and the target lifetime duration can be 10. This embodiment defines the lifetime budget upper limit as the upper limit of the allowable cumulative lifetime consumption within a planning period, where the planning period can be 1 year. Based on the lifetime degradation-related parameters of the candidate energy storage modules, this embodiment maps the capacity retention rate threshold or the target lifetime duration to the allowable cumulative lifetime consumption upper limit, and uses this allowable cumulative lifetime consumption upper limit as the lifetime budget upper limit. This limits the cumulative lifetime consumption corresponding to any candidate configuration scale from exceeding the lifetime budget upper limit, forming a lifetime budget constraint, thereby creating a lifetime feasibility criterion that can be directly used for screening during the configuration phase.

[0035] In step 200, regarding the sub-step of determining the set of lifespan-affecting operational features, this embodiment can define the values ​​of the lifespan-affecting operational features within the statistical window as follows: in, This is an energy throughput characteristic used to characterize the cumulative charge and discharge energy within a statistical window; The set of discrete time periods corresponding to the statistical window; For the first The charging and discharging power values ​​for each time period are taken with opposite signs for charging and discharging; The duration of adjacent discrete time periods; This refers to the depth of charge / discharge characteristics; For the first The cumulative energy change over a period of time; The rated capacity of the candidate energy storage module; It is a power ratio characteristic; The rated power of the candidate energy storage module; Characteristic of charge / discharge switching frequency; This is an indicator function; it takes the value 1 if the condition inside the parentheses is true, and 0 otherwise. It is a symbolic function.

[0036] In step 200, regarding the sub-step of "classifying intervals and establishing the correspondence between intervals and lifetime loss coefficients to construct a lifetime loss interval classification library," this embodiment can formalize the lookup relationship between interval identifiers and lifetime loss coefficients as follows: in, The tiered identifier is composed of the tiered results of energy throughput characteristics, charge / discharge depth characteristics, power rate characteristics, and charge / discharge switching number characteristics. These are the grading mapping functions for the corresponding features, and their outputs are the interval numbers into which the feature falls, where the interval numbers are determined by the grading boundaries; This is the lifespan loss coefficient, used to characterize the contribution of the corresponding operating state to lifespan consumption. This is a lookup mapping for the life loss classification library, used to output the corresponding life loss coefficient based on the classification identifier.

[0037] In step 200, regarding the sub-step of determining the upper limit of the lifetime budget based on the target lifetime requirement and generating the lifetime budget constraint, this embodiment can define the correspondence between the upper limit of the lifetime budget and the target lifetime requirement as follows: in, The lifespan budget ceiling represents the maximum allowable cumulative lifespan consumption within the planning period; This is the capacity retention threshold; The allowable capacity loss ratio; Target lifespan; This represents the reference capacity loss ratio corresponding to the unit lifetime consumption, which is derived from the calibration results of lifetime degradation-related parameters of the candidate energy storage modules. The reference lifespan corresponding to the unit lifespan consumption is derived from the calibration results of lifespan degradation-related parameters of the candidate energy storage modules; This is used to determine the upper limit of the lifetime budget when both capacity retention constraints and lifetime constraints are given.

[0038] Further, in this embodiment, step 300 is first used to generate an executable set of operating instructions, which serves as a unified input for subsequent equivalent utilization sequence mapping and lifetime consumption calculation. This embodiment divides the time range corresponding to the target scenario into multiple operating segments, and defines a "operating segment" as the smallest decision-making time unit that remains unchanged under the same electricity price and settlement rules. In this embodiment, the duration of the operating segment can be 1, and the number of operating segments within the planning period is determined to be a discrete number consistent with the length of the planning period. Subsequently, within each operating segment, based on the load demand data and the corresponding electricity price and settlement rules, this embodiment determines whether the operating segment needs to perform charging or discharging, and generates charging or discharging instructions accordingly. Operating segments not determined to be charging or discharging are considered as standby states in this embodiment, indicating that no charging or discharging behavior occurs in the operating segment, thereby ensuring that the operating instruction set has complete coverage of all operating segments within the planning period.

[0039] This embodiment further sets corresponding power upper limits or energy targets for charging and discharging commands, so that the running command set not only indicates the action type but also has executable boundary conditions. In this embodiment, the "power upper limit" is defined as the maximum allowed charging or discharging power within the corresponding operating segment, and the "energy target" is defined as the expected charging or discharging energy within the corresponding operating segment. Values ​​are assigned to the power upper limit or the energy target based on the power and energy boundary restrictions in the grid connection and safe operation constraints; for example, in this embodiment, the power upper limit can be 5000, and the energy target can be 3000. This embodiment combines the action type of each operating segment with its power upper limit or energy target in chronological order to form a running command set, allowing the running command set to directly serve as constraint input for subsequent mapping processes, avoiding the unreproducible nature caused by only having "charging / discharging" without boundaries.

[0040] This embodiment then employs a preset charge / discharge strategy to map the operating instruction set into an equivalent utilization sequence, and performs the mapping separately for each candidate configuration scale in the candidate configuration scale set to ensure a one-to-one correspondence between lifetime consumption and candidate configuration scale. This embodiment defines the "preset charge / discharge strategy" as a set of mapping rules that convert the charging / discharging actions of each operating segment into equivalent energy throughput and equivalent charge / discharge depth under the constraints of the operating instruction set; wherein, "limiting" is used to ensure that the actual charging / discharging power of the candidate configuration scale in each operating segment does not exceed the rated power corresponding to that candidate configuration scale, and "efficiency correction" is used to ensure that the equivalent energy throughput of the candidate configuration scale in each operating segment reflects the energy difference caused by the efficiency parameter. This embodiment, for each candidate configuration scale, performs limiting and efficiency correction on the operating instruction set based on the rated capacity, rated power, and efficiency parameters corresponding to that candidate configuration scale to obtain an equivalent utilization sequence corresponding to that candidate configuration scale; wherein, for the operating segment in standby state, this embodiment sets both the equivalent energy throughput and the equivalent charge / discharge depth to 0 to ensure that the standby state participates in sequence coverage but does not introduce a non-real contribution to lifetime consumption.

[0041] After obtaining the equivalent utilization sequence corresponding to the candidate configuration scale, this embodiment generates a tier identifier based on the equivalent utilization sequence and retrieves the lifetime loss coefficient through the lifetime loss tier database. In this embodiment, the "tier identifier" is defined as a discrete index that uniquely represents the equivalent utilization state of a certain operating segment. Its generation method is as follows: the energy throughput characteristics, charge / discharge depth characteristics, power ratio characteristics, and charge / discharge switching frequency characteristics corresponding to the equivalent utilization sequence in that operating segment are mapped to their respective interval tier results, and a tier identifier is formed by combining them with a preset key. This embodiment then retrieves the lifetime loss coefficient corresponding to the tier identifier from the lifetime loss tier database, ensuring that the equivalent utilization state of each operating segment can obtain its corresponding lifetime loss coefficient, thereby transforming the continuously changing operating process into a lookupable and reproducible lifetime evaluation process.

[0042] Finally, this embodiment accumulates the lifetime loss coefficients by runtime segment to obtain the cumulative lifetime consumption corresponding to the candidate configuration size. In this embodiment, "cumulative lifetime consumption" is defined as the cumulative result of the lifetime loss coefficients for all runtime segments within the planning period, reflecting the total lifetime consumption level of the candidate configuration size under the constraints of the running instruction set and the preset charge / discharge strategy. In this embodiment, the planning period can be 1, and the corresponding number of runtime segments can be 24, thus ensuring that the cumulative range of lifetime consumption is consistent with the statistical window, facilitating direct comparison with the lifetime budget upper limit. Through the above process, this embodiment achieves a complete closed loop of running instruction set generation, equivalent utilization sequence mapping, lifetime loss coefficient lookup, and cumulative lifetime consumption calculation, ensuring that each candidate configuration size has a corresponding and reproducible cumulative lifetime consumption output.

[0043] In step 300, regarding the sub-step of generating an operating instruction set based on electricity prices and settlement rules, this embodiment can represent the operating instructions as a discrete action sequence as follows: in, For the first The runtime instruction action has a value of 1 indicating a charging instruction, a value of -1 indicating a discharging instruction, and a value of 0 indicating a standby state; For the first The electricity price or settlement price corresponding to the operating period; For low price reference threshold, The high-price reference threshold is determined by the electricity price and settlement rules within the planning period for the electricity price sequence, for example, by the low and high quantiles of the electricity price sequence; For indicator functions; For the first Instruction power target for the runtime segment; The upper limit of allowable power is given to constrain grid connection and safe operation; For the first The load demand data during the runtime period is used to limit the discharge from exceeding the load demand and the charging from exceeding the allowable boundary.

[0044] In step 300, regarding the sub-step of mapping the running instruction set to an equivalent utilization sequence using a preset charging and discharging strategy, and performing limiting and efficiency correction for each candidate configuration size in the candidate configuration size set, this embodiment can express the limiting and efficiency correction rules corresponding to the candidate configuration size as follows: in, An index for the size of the candidate configuration; The number of candidate energy storage modules corresponding to the candidate configuration scale; and These are the rated capacity and rated power of the candidate energy storage modules, respectively. and The first The capacity and power scales corresponding to the candidate configuration sizes; For the first Candidate configuration size in the Actual charging and discharging power during operation; This is a limiting function used to limit the commanded power target to a certain level. Within the range; For the first Candidate configuration size in the The cumulative energy change at the end of the runtime period. This represents the cumulative energy change at the end of the previous operating period. For charging efficiency, The discharge efficiency is derived from the efficiency parameter. To take the positive part of the function; This refers to the runtime of the session; when From time to time This corresponds to the equivalent utilization of the standby state being zero.

[0045] In step 300, regarding the sub-step of obtaining the cumulative lifetime consumption corresponding to the candidate configuration size by looking up a table from the lifetime consumption tier library based on the equivalent utilization sequence, this embodiment can represent the tier identifier generation, lifetime consumption coefficient retrieval, and accumulation as follows: in, The set of runtime segments within the planning period; The first Candidate configuration size in the The energy throughput characteristics, charge / discharge depth characteristics, power rate characteristics, and charge / discharge switching frequency characteristics corresponding to the runtime segment are determined using the same criteria as those in step 200. The definitions remain consistent, except that the power and energy variables are replaced with the first... Candidate configuration size corresponding and ; These are the classification mapping functions for the corresponding features; For the first Runtime segment segment identifier; For the lookup mapping of the lifespan loss classification database; The life loss coefficient corresponding to the grade label; In order to be with the first The cumulative lifetime consumption corresponding to the candidate configuration size is used to compare with the lifetime budget upper limit generated in step 200.

[0046] Optionally, step 400 in this embodiment is used to prune the candidate configuration size set based on lifetime budget constraints and operational constraints to form a candidate size set that is feasible in both lifetime and operation. This embodiment uses the candidate configuration size set generated in step 100 as the pruning object, and defines "pruning" as the process of performing compliance judgments on each candidate configuration size and eliminating configuration sizes that do not meet the constraints; wherein, the lifetime budget constraint uses the lifetime budget upper limit determined in step 200 as the judgment benchmark, and in this embodiment, the lifetime budget upper limit can be 1. For each candidate configuration size in the candidate configuration size set, this embodiment retrieves the cumulative lifetime consumption obtained in step 300 for that candidate configuration size and establishes a corresponding lifetime feasibility judgment record to ensure that the pruning process can be repeatedly executed under a unified lifetime evaluation caliber.

[0047] This embodiment first performs a lifetime budget constraint determination: the cumulative lifetime consumption corresponding to each candidate configuration scale is compared with the lifetime budget upper limit. When the cumulative lifetime consumption is greater than the lifetime budget upper limit, this embodiment determines the candidate configuration scale as lifetime infeasible and eliminates it; when the cumulative lifetime consumption is not greater than the lifetime budget upper limit, this embodiment retains the candidate configuration scale as a lifetime feasible candidate scale. Subsequently, this embodiment performs an operational constraint determination on the lifetime feasible candidate scale, wherein the operational constraints include at least the grid-connected allowable power range and energy boundary requirements; in this embodiment, the upper limit of the grid-connected allowable power range can be 5000. When the power scale corresponding to the candidate configuration scale exceeds the grid-connected allowable power range, this embodiment determines the candidate configuration scale as operationally infeasible and eliminates it, thereby preventing ungridable configuration scales from entering the subsequent economic evaluation stage.

[0048] This embodiment further performs an energy boundary check on the candidate configuration sizes that have passed the power boundary check, that is, it determines whether the capacity size corresponding to the candidate configuration size meets the energy requirements of the operating instruction set within the planning period. In this embodiment, the minimum capacity size corresponding to the operating constraint can be 5000. When the capacity size of the candidate configuration size is less than the minimum capacity size, this embodiment determines that the candidate configuration size is infeasible and eliminates it. Finally, this embodiment gathers the candidate configuration sizes that simultaneously meet the lifetime budget constraint and the operating constraint to form a candidate size set, and maintains a consistent correspondence between the candidate size set and the equivalent utilization sequence and cumulative lifetime consumption in step 300, so as to be used for the calculation and sorting of the economic evaluation value in step 500, thereby outputting a candidate size set with clear boundaries, feasibility, and reproducibility in the configuration stage.

[0049] Further, in this embodiment, step 500 is used to form a unified economic evaluation within the candidate scale set and determine the optimal candidate scale accordingly. This embodiment uses the candidate scale set obtained in step 400 as the evaluation object, and defines the "economic evaluation value" as a quantitative representation of the comprehensive economic effect of the candidate configuration scale under the same planning period. The economic evaluation value includes at least four sub-items: investment cost, operation and maintenance cost, lifetime depreciation cost, and residual value benefit. This embodiment establishes an evaluation record for each candidate configuration scale in the candidate scale set, and retrieves the number of modules, rated capacity and rated power of the candidate energy storage modules, and the cumulative lifetime consumption and lifetime budget upper limit corresponding to the candidate configuration scale as input items for the evaluation record. In this embodiment, the planning period can be 1 to ensure that the economic evaluation and lifetime budget constraints have a consistent time caliber.

[0050] This embodiment first determines the investment cost. The "investment cost" is defined as the cost item incurred during the construction phase of the candidate configuration scale. The investment cost is determined based on the number of modules corresponding to the candidate configuration scale and the rated capacity and rated power of the candidate energy storage modules. In this embodiment, the unit capacity cost of the candidate energy storage module can be taken as 1200, and the unit power cost can be taken as 300. The investment cost of the candidate configuration scale is obtained by converting the number of modules corresponding to the candidate configuration scale with the rated capacity and rated power of the candidate energy storage modules using a unified pricing caliber. This ensures that the construction investment in capacity and power dimensions for different candidate configuration scales can be compared under the same cost caliber.

[0051] This embodiment further determines the operation and maintenance costs based on pre-designed allocation rules, and determines the lifespan depreciation cost and residual value. In this embodiment, "operation and maintenance cost" is defined as a component of the operation and maintenance costs within the planning period, and is calculated according to pre-designed allocation rules. In this embodiment, the allocation ratio for operation and maintenance costs can be 2, so that operation and maintenance costs vary with the scale of investment costs. In this embodiment, "lifespan depreciation cost" is defined as a cost component that converts lifespan depreciation into economic costs, and is determined based on the cumulative lifespan depreciation corresponding to the candidate configuration scale and according to preset allocation rules. In this embodiment, the cost per unit of lifespan depreciation can be 50,000, so that differences in cumulative lifespan depreciation can be transformed into comparable economic differences. In this embodiment, "residual value" is defined as the residual value component corresponding to the remaining lifespan budget, and is determined based on the difference between the upper limit of the lifespan budget and the cumulative lifespan depreciation corresponding to the candidate configuration scale, and according to pre-designed allocation rules. In this embodiment, the residual value per unit of remaining lifespan budget can be 30,000, so that the larger the remaining lifespan budget, the higher the residual value.

[0052] In this embodiment, after obtaining each item, the investment cost, operation and maintenance cost, and life loss depreciation cost are combined into a cost item, and residual value is deducted to obtain an economic evaluation value corresponding to the candidate configuration scale. In this embodiment, the economic evaluation value adopts the unified caliber of "cost item minus residual value" to ensure that different candidate configuration scales can be directly ranked and compared. This embodiment then sorts the candidate scale set according to the economic evaluation value and selects the candidate configuration scale with the best economic evaluation value as the optimal candidate scale. To ensure the determinism of the ranking result, in the case of the same economic evaluation value, this embodiment can give priority to selecting the candidate configuration scale with fewer modules or smaller cumulative life loss as the optimal candidate scale, so as to form a reproducible optimal candidate scale selection rule.

[0053] Furthermore, in this embodiment, step 600 is used to jointly verify the optimal candidate size before outputting the scalable configuration result, to ensure that the output result is consistent and feasible in three dimensions: lifetime budget constraint, operational constraint, and economic evaluation caliber. This embodiment defines "joint verification" as a process of consistent verification of the same candidate configuration size under multiple constraint calibers, and determines the candidate configuration size with the best ranking in step 500 as the optimal candidate size, which is then used as the current verification object. To ensure the determinism of the joint verification, this embodiment retrieves the cumulative lifetime consumption, lifetime budget upper limit, power scale, capacity scale, and economic evaluation value items consistent with the current verification object from the corresponding records in steps 300 and 500 during each verification, thereby avoiding unstable verification conclusions due to inconsistent data calibers.

[0054] This embodiment first verifies the lifetime budget constraint, specifically ensuring that the cumulative lifetime consumption corresponding to the current verification object does not exceed the lifetime budget upper limit. In this embodiment, the lifetime budget upper limit can be 1. When the cumulative lifetime consumption exceeds the lifetime budget upper limit, this embodiment determines that the lifetime budget constraint verification fails and triggers an alternative mechanism. Subsequently, this embodiment verifies the operational constraints, which at least include the grid-connected allowable power range and energy boundary satisfaction. In this embodiment, the upper limit of the grid-connected allowable power range can be 5000. When the power scale corresponding to the current verification object exceeds the upper limit of the grid-connected allowable power range, this embodiment determines that the operational constraint verification fails. Simultaneously, this embodiment verifies the energy boundary satisfaction. In this embodiment, the minimum capacity scale corresponding to the operational constraint can be 5000. When the capacity scale corresponding to the current verification object is less than the minimum capacity scale, this embodiment also determines that the operational constraint verification fails, thereby ensuring that the verified configuration scale is feasible under both grid-connected power and operational energy boundaries.

[0055] This embodiment also verifies the consistency of the calculation method for the economic evaluation value to prevent the lack of reproducibility of the output results due to different accrual or conversion rules used in the ranking and verification stages. This embodiment defines "calculation method consistency verification" as the process of comparing the consistency of the accrual rules and value methods for investment costs, operation and maintenance costs, life loss conversion costs, and residual value income. In this embodiment, the accrual ratio for operation and maintenance costs can be 2, the unit life loss conversion cost can be 50,000, and the unit life budget remaining conversion income can be 30,000. This embodiment verifies the consistency of the above rules with the ranking stage during the verification stage and verifies that the economic evaluation value is still formed according to the unified method of "cost items minus residual value income". If any item's accrual rule or value method is found to be inconsistent with the ranking stage, this embodiment determines that the consistency verification of the economic evaluation value fails and triggers an alternative mechanism to ensure that the economic evaluation value corresponding to the optimal candidate size in the final output has traceable and reproducible support.

[0056] When any review fails, this embodiment selects the next best candidate size according to the sorting result obtained in step 500 to replace the current verification object, and repeats the lifetime budget constraint review, operational constraint review, and economic evaluation value consistency review until the verification passes. To ensure the determinism of the replacement process, this embodiment moves only one sorting position backward each time and re-triggers a full review until the first candidate configuration size that passes the verification is obtained as the scaled configuration result. After the verification passes, this embodiment outputs the scaled configuration result and the corresponding operation strategy parameters. In this embodiment, the operation strategy parameters include at least the time sequence of charging instructions, discharging instructions, and standby states consistent with the operation instruction set, as well as the power upper limit or energy target corresponding to each charging instruction and discharging instruction, so that the output result can be reproduced under the same input dataset conditions and used for parameterized reference in subsequent operation stages.

[0057] As an example, in the specific implementation of this invention, to facilitate a unified comparison and constraint of the lifespan consumption of different candidate configuration scales under different operating scenarios, this invention uses a normalized processing method to represent the planning period and lifespan-related indicators. The planning period is set to 1, which does not represent the actual time length, but rather a standardized planning period unit after unified mapping, used as a benchmark period for lifespan consumption accumulation and economic evaluation. The actual planning period can correspond to a settlement period, an operating year, or any time span set by the user, all of which are mapped to the standardized planning period unit through proportional conversion during calculation. Similarly, the lifespan budget upper limit is set to 1, representing the maximum equivalent lifespan proportion allowed to be consumed within the standard planning period. This value is a dimensionless representation after normalization, and its corresponding actual lifespan consumption amount is determined by the rated lifespan parameters and lifespan degradation characteristics of the energy storage module. During the cumulative lifetime consumption calculation, the lifetime loss corresponding to each operating condition is converted into an equivalent lifetime consumption ratio according to the same normalization rule, and then accumulated within the standard planning period. When the accumulated result does not exceed the lifetime budget upper limit, the corresponding candidate configuration scale is determined to meet the lifetime constraint condition. Through the above normalization process, energy storage configuration schemes with different capacity scales, different operating strategies, and different lifetime characteristics can be judged for lifetime constraints and compared economically under a unified dimension and a unified benchmark.

[0058] In other embodiments of the present invention, different standard planning cycle units or lifetime budget benchmark values ​​may be used for calculation while keeping the normalization process unchanged. This does not affect the overall technical concept and constraint logic of the present invention.

[0059] In the specific implementation of this invention, to ensure consistency in the calculation methods between lifespan constraints and economic evaluation, the lifespan loss conversion cost and residual value revenue are both calculated and accounted for based on a unified lifespan measurement benchmark. The inputs for calculating the lifespan loss conversion cost include: the rated lifespan parameters of the energy storage module, the initial investment cost, the cumulative equivalent lifespan consumption ratio within the planning period, and a preset lifespan conversion rule. The lifespan conversion rule characterizes the correspondence between the lifespan consumption ratio and the investment cost, and can be determined using a linear mapping method, a piecewise mapping method, or a lookup method based on a lifespan loss tier database.

[0060] In a preferred embodiment, the cumulative equivalent lifespan consumption ratio within the planning period is compared with the rated full lifespan of the energy storage module. The initial investment cost is then proportionally allocated over the entire lifespan to obtain the lifespan loss conversion cost corresponding to the lifespan consumption within the planning period. This lifespan loss conversion cost is included in the economic evaluation indicators. Simultaneously, the inputs for calculating the residual value revenue include: the initial lifespan state of the energy storage module, the cumulative equivalent lifespan consumption ratio within the planning period, and a preset residual value assessment rule. The residual value assessment rule characterizes the correspondence between the remaining lifespan ratio and the recoverable value, and can be determined based on a linear reduction model or a piecewise residual value model. During the calculation process, the remaining equivalent lifespan ratio of the energy storage module at the end of the planning period is obtained based on the cumulative equivalent lifespan consumption ratio, and the corresponding residual value revenue is determined according to the residual value assessment rule. This residual value revenue is included as a revenue item in the economic evaluation. By using the unified method of calculating the depreciation cost and residual value, the life constraints, operating strategies, and economic evaluations are all based on the same life measurement and depreciation standards. This avoids deviations in economic evaluation caused by inconsistent life consumption measurement methods and ensures the objectivity of economic comparison results between different candidate configuration scales.

[0061] In other embodiments of the present invention, while keeping the life measurement benchmark and conversion caliber unchanged, the life conversion rules and residual value assessment rules can also be adjusted according to different regional electricity pricing mechanisms, investment recovery strategies or asset management requirements, without affecting the overall technical concept of the present invention.

[0062] Corresponding to the above methods, such as Figure 2 As shown, this embodiment also provides a large-scale configuration optimization system for electrochemical energy storage systems based on lifetime-economic constraints, including: The input data construction and candidate scale generation unit is used to obtain load demand data, electricity price and settlement rules, grid connection and safe operation constraints of the target scenario, and obtain the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset, and generate a candidate configuration scale set based on the candidate energy storage modules according to a preset combination rule; The life loss classification library construction and life budget constraint generation unit is used to extract the life impact operation feature set based on the input dataset, classify the life impact operation feature set into intervals and establish the correspondence between the classification and the life loss coefficient to obtain the life loss classification library, and determine the life budget upper limit according to the target life requirement to generate life budget constraints. The operation instruction mapping and cumulative lifetime consumption calculation unit is used to generate an operation instruction set according to the electricity price and settlement rules, map the operation instruction set into an equivalent utilization sequence using a preset charging and discharging strategy, and for each candidate configuration scale in the candidate configuration scale set, look up the cumulative lifetime consumption corresponding to the candidate configuration scale from the lifetime loss classification library according to the equivalent utilization sequence. The candidate size pruning unit based on lifetime budget is used to prune the candidate configuration size set according to the lifetime budget constraint and the operation constraint to obtain a candidate size set. An economic evaluation and optimal size ranking unit is used to calculate and rank the economic evaluation values ​​of the candidate size set, and select the best ranked one as the optimal candidate size; wherein, the economic evaluation values ​​include investment costs, operation and maintenance costs, life loss depreciation costs determined by the cumulative life consumption, and residual value income; The joint verification and large-scale configuration result output unit is used to jointly verify the optimal candidate size. When the verification fails, the second-best candidate size is selected in order of sorting and the joint verification is repeated until it passes. Then, the large-scale configuration result and the corresponding operation strategy parameters are output. The joint verification includes the verification of the satisfaction of the lifetime budget constraint and the operation constraint, and the verification of the consistency of the calculation method of the economic evaluation value.

[0063] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0064] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for large-scale configuration optimization of electrochemical energy storage systems based on combined lifetime-economic constraints, characterized in that, include: The system acquires load demand data, electricity price and settlement rules, grid connection and safe operation constraints for the target scenario, and acquires the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset. Based on the candidate energy storage modules, a candidate configuration scale set is generated according to a preset combination rule. Based on the input dataset, extract the lifespan impact operation feature set, divide the lifespan impact operation feature set into intervals and establish the correspondence between the intervals and the lifespan loss coefficient to obtain the lifespan loss interval library, and determine the lifespan budget upper limit according to the target lifespan requirement to generate lifespan budget constraints. An operating instruction set is generated based on the electricity price and settlement rules. A preset charging and discharging strategy is used to map the operating instruction set into an equivalent utilization sequence. For each candidate configuration size in the candidate configuration size set, the cumulative lifetime consumption corresponding to the candidate configuration size is obtained by looking up the table from the lifetime loss classification library according to the equivalent utilization sequence. The candidate configuration size set is pruned according to the lifetime budget constraint and the operational constraint to obtain the candidate size set; The economic evaluation value is calculated and sorted for the candidate size set, and the one with the best ranking is selected as the optimal candidate size; wherein, the economic evaluation value includes investment cost, operation and maintenance cost, life loss depreciation cost determined by the cumulative life consumption, and residual value income; The optimal candidate size is jointly verified. If the verification fails, the second-best candidate size is selected in order of sorting and the joint verification is repeated until it passes. Then, the scaled configuration result and the corresponding operation strategy parameters are output. The joint verification includes a review of the satisfaction of the lifetime budget constraint and the operation constraint, and a review of the consistency of the calculation method of the economic evaluation value.

2. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, Based on the candidate energy storage modules, a candidate configuration size set is generated according to a preset combination rule, including: Set the capacity step size, power step size, and maximum number of modules; Under the constraints of the capacity step size and the power step size, the candidate energy storage modules are combined in number to form a triplet representation of the capacity scale, power scale and number of modules corresponding to each candidate configuration scale, and the candidate configuration scale set is constituted by the triplet representation.

3. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, Based on the input dataset, a lifespan impact feature set is extracted. This feature set is then divided into intervals, and a correspondence between these intervals and lifespan loss coefficients is established, resulting in a lifespan loss interval library, including: Determine lifetime-affected operational characteristics from the input dataset; these characteristics include energy throughput characteristics, charge / discharge depth characteristics, power rate characteristics, and charge / discharge switching frequency characteristics. Based on the lifetime degradation-related parameters of the candidate energy storage modules, grading boundaries are set for each lifetime-affecting operational feature, and interval grading is completed to obtain the grading results of each lifetime-affecting operational feature. Each of the grading results is used to generate a grading identifier using a preset combination key, and the grading identifier is then associated with a life loss coefficient to construct the life loss grading library.

4. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 3, characterized in that, The methods for determining the grading boundaries include: A threshold table is established based on the lifetime degradation-related parameters of the candidate energy storage modules and corresponding to the lifetime-affected operational characteristics; the threshold table provides multiple lifetime-sensitive threshold intervals for each of the lifetime-affected operational characteristics. Extract the feature value sequence of the lifespan impact operation feature within the planning period from the input dataset to form a lifespan impact operation feature sample set; Candidate grading boundaries are determined based on the feature value sequence of the lifespan-affecting operational feature sample set according to a preset quantile rule; The candidate grading boundaries are aligned with the threshold table to retain grading boundaries that meet the coverage requirements of the lifetime sensitive threshold range.

5. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, Based on the target lifespan requirement, determine the upper limit of the lifespan budget and generate lifespan budget constraints, including: The target lifetime requirement is quantified as at least one of a capacity retention rate threshold and a target lifetime in years. Based on the lifetime decay-related parameters, the upper limit of the allowable cumulative lifetime consumption corresponding to the capacity retention rate threshold or the target lifetime is determined as the lifetime budget upper limit; The lifetime budget upper limit is used to limit the cumulative lifetime consumption corresponding to any candidate configuration size to not exceed the lifetime budget upper limit, thus forming the lifetime budget constraint.

6. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, A set of operating instructions is generated based on the electricity price and settlement rules, including: The time range corresponding to the target scenario is divided into multiple runtime segments; For each of the aforementioned operating periods, charging instructions and discharging instructions are determined based on the load demand data and the electricity price and settlement rules corresponding to the operating period; operating periods that are not determined to be charging instructions or discharging instructions are considered to be in standby mode. And set corresponding power upper limits or energy targets for the charging command and the discharging command; The set of execution instructions is composed of the instructions of each of the aforementioned runtime segments.

7. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, A preset charging and discharging strategy is used to map the operating instruction set into an equivalent utilization sequence, and the cumulative lifetime consumption is obtained by looking up a table from the lifetime consumption classification library based on the equivalent utilization sequence, including: For each candidate configuration size in the candidate configuration size set, the operating instruction set is limited and efficiency corrected according to the rated capacity, rated power and efficiency parameters corresponding to the candidate configuration size to obtain the equivalent utilization sequence corresponding to the candidate configuration size; A tiered identifier is generated based on the equivalent utilization sequence corresponding to the candidate configuration size, and the lifetime loss coefficient corresponding to the tiered identifier is retrieved from the lifetime loss tiered database. The lifetime loss coefficient is accumulated over the operating period to obtain the cumulative lifetime consumption corresponding to the candidate configuration scale.

8. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, Calculate and rank the economic evaluation values ​​for the candidate size set, including: For each candidate configuration scale in the candidate scale set, the investment cost is determined based on the number of modules corresponding to the candidate configuration scale and the rated capacity and rated power of the candidate energy storage modules; The operation and maintenance costs are determined according to the pre-designed rules; the life loss conversion cost is determined according to the cumulative life consumption corresponding to the candidate configuration scale and according to the preset conversion rules; and the residual value income is determined according to the difference between the life budget upper limit and the cumulative life consumption corresponding to the candidate configuration scale and according to the pre-designed rules. The investment cost, the operation and maintenance cost, and the depreciation cost are combined into a cost item, and the residual value is deducted to obtain the economic evaluation value corresponding to the candidate configuration scale. The candidate size set is sorted according to the economic evaluation value.

9. The method for large-scale configuration optimization of electrochemical energy storage systems based on lifetime-economic constraints according to claim 1, characterized in that, The optimal candidate size is jointly verified. If the verification fails, the second-best candidate size is selected in order of ranking and the joint verification is repeated, including: The optimal candidate size is used as the current verification object, and the current verification object is verified to satisfy the lifetime budget constraint and the operational constraint; wherein, the verification of the lifetime budget constraint includes verifying that the cumulative lifetime consumption corresponding to the current verification object does not exceed the lifetime budget upper limit; The review of the operational constraints includes reviewing the power and energy boundary satisfaction of the current verification object under the grid connection and safe operation constraints; Verify that the economic evaluation value of the current verification object is consistent with the calculation method of the economic evaluation value used in the sorting stage; If any review fails, the next best candidate size is selected in order to replace the current review object and the joint review is repeated until it passes. Then the scaled configuration result and the corresponding running strategy parameters are output.

10. A large-scale configuration optimization system for electrochemical energy storage systems based on combined lifetime-economic constraints, characterized in that, include: The input data construction and candidate scale generation unit is used to obtain load demand data, electricity price and settlement rules, grid connection and safe operation constraints of the target scenario, and obtain the rated capacity, rated power, efficiency parameters and life decay related parameters of the candidate energy storage modules to form an input dataset, and generate a candidate configuration scale set based on the candidate energy storage modules according to a preset combination rule; The life loss classification library construction and life budget constraint generation unit is used to extract the life impact operation feature set based on the input dataset, classify the life impact operation feature set into intervals and establish the correspondence between the classification and the life loss coefficient to obtain the life loss classification library, and determine the life budget upper limit according to the target life requirement to generate life budget constraints. The operation instruction mapping and cumulative lifetime consumption calculation unit is used to generate an operation instruction set according to the electricity price and settlement rules, map the operation instruction set into an equivalent utilization sequence using a preset charging and discharging strategy, and for each candidate configuration scale in the candidate configuration scale set, look up the cumulative lifetime consumption corresponding to the candidate configuration scale from the lifetime loss classification library according to the equivalent utilization sequence. The candidate size pruning unit based on lifetime budget is used to prune the candidate configuration size set according to the lifetime budget constraint and the operation constraint to obtain a candidate size set. An economic evaluation and optimal size ranking unit is used to calculate and rank the economic evaluation values ​​of the candidate size set, and select the best ranked one as the optimal candidate size; wherein, the economic evaluation values ​​include investment costs, operation and maintenance costs, life loss depreciation costs determined by the cumulative life consumption, and residual value income; The joint verification and large-scale configuration result output unit is used to jointly verify the optimal candidate size. When the verification fails, the second-best candidate size is selected in order of sorting and the joint verification is repeated until it passes. Then, the large-scale configuration result and the corresponding operation strategy parameters are output. The joint verification includes the verification of the satisfaction of the lifetime budget constraint and the operation constraint, and the verification of the consistency of the calculation method of the economic evaluation value.

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