Battery power dispatching optimization method for electrochemical energy storage power station

Abstract

The invention discloses a battery power dispatching optimization method for an electrochemical energy storage power station, and belongs to the technical field of electrochemical energy storage. Establishing a relationship between energy storage scheduling and battery life by constructing an energy storage scheduling model; and constructing an energy storage economy evaluation model, determining the economic life of the electrochemical energy storage power station, and optimizing an energy storage operation scheduling strategy, thereby prolonging the service life of the battery of the electrochemical energy storage power station, and maximizing the economic benefit of the energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of electrochemical energy storage technology, and relates to the optimization technology of electrochemical energy storage power stations. Specifically, it relates to a method for scheduling and optimizing the battery power of electrochemical energy storage power stations under the electricity spot market. Background Technology

[0002] Against the backdrop of global energy transition, electrochemical energy storage technology, due to its rapid response and flexible dispatch characteristics, has shown great potential in improving the absorption rate of new energy sources and ensuring grid stability, gradually becoming an indispensable key component of the electricity market. Energy storage power stations not only provide effective energy storage solutions for the power system but also offer new business opportunities and operational strategies for electricity market participants. Currently, the dispatch methods for electrochemical energy storage power stations mainly include peak-valley price arbitrage dispatch, demand-side response dispatch, power smoothing dispatch, ancillary service dispatch, and multi-objective optimization dispatch. However, existing battery power dispatch methods for electrochemical energy storage power stations have many shortcomings, making it difficult to fully realize the value of energy storage systems.

[0003] Existing energy storage economics assessment systems primarily focus on the direct market benefits of energy storage, such as revenue obtained through frequency regulation services and electricity price arbitrage. However, they lack systematic quantitative analysis of indirect benefits—such as the marginal aging cost of batteries and the dynamic characteristics of the electricity market. They neglect the volatility of electricity market prices and demand, as well as the impact of different charging and discharging strategies on battery life. As a result, the direct and indirect benefits of energy storage technology cannot be accurately assessed, which in turn affects the maximization of the economic benefits of energy storage power stations and the optimization of battery lifespan.

[0004] More importantly, most battery power dispatch methods for electrochemical energy storage power plants focus only on a single objective (such as maximizing economic benefits or minimizing power fluctuations), and have not yet established an evaluation framework that comprehensively considers the synergistic effects of energy storage technology on the power generation and grid sides. This deficiency not only limits the accuracy of economic and feasibility analyses of energy storage projects, but also hinders the realization of the potential value of energy storage technology in a wider range of power systems.

[0005] Therefore, there is an urgent need to develop a new economic evaluation method for energy storage power stations. Based on the economic evaluation results, and through reasonable scheduling and flexible operation, this method can provide a more reliable basis for investment decisions on energy storage projects, promote the active participation of energy storage power stations in electricity market transactions, maximize their advantages, and also promote the deep integration of energy storage technology into the power system, thereby accelerating the process of global energy transformation and contributing to the construction of a clean, efficient, and flexible modern power system. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a battery power dispatch optimization method for electrochemical energy storage power stations, including an economic evaluation method for electrochemical energy storage power stations under the electricity spot market and a power dispatch optimization management method, which can maximize the full life cycle revenue of energy storage power stations under the electricity spot market.

[0007] This invention comprehensively considers factors such as the marginal aging cost of batteries, energy arbitrage, and ancillary service revenue. By constructing an energy storage economic evaluation model, it conducts an economic benefit analysis of the entire life cycle of an energy storage power station, focusing on the impact of marginal aging costs on the physical lifespan of batteries. It also incorporates annual fixed operation and maintenance costs into the energy storage economic evaluation model to maximize the life-cycle revenue of energy storage power stations under the electricity spot market. At the same time, it defines the economic lifespan of electrochemical energy storage batteries for use in adjusting and optimizing operational strategies.

[0008] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0009] A battery power dispatch optimization method for an electrochemical energy storage power station includes the following steps:

[0010] 1) Data collection and analysis:

[0011] a. First, collect electricity demand data, electricity price data, and energy storage power station operation data, including technical parameters such as energy storage power station charge and discharge efficiency, battery cycle life, and capacity decay.

[0012] b. Secondly, collect research literature, market reports, etc. in relevant fields;

[0013] c. Clean, standardize, and extract features from the collected data.

[0014] 2) Construct an energy storage scheduling model:

[0015] a. Define the operating constraints of the energy storage system, such as charging and discharging power limits, state of charge range, etc.;

[0016] b. Considering factors such as charging and discharging efficiency and capacity decay, design an energy storage scheduling strategy, including determining the charging timing, discharging timing, and charging and discharging power;

[0017] c. Define the physical lifespan of the battery and establish a model relating energy storage scheduling to the physical lifespan of the battery;

[0018] d. Verify and optimize the accuracy of the energy storage scheduling model.

[0019] 3) Construct an energy storage economics assessment model:

[0020] a. Define evaluation indicators, including investment cost, operation and maintenance cost, battery swapping cost, aging cost, power station residual value, battery economic life and physical life, etc.

[0021] b. Construct a direct / indirect revenue model for the energy storage system; consider multiple revenue sources such as electricity price arbitrage and ancillary services, and evaluate the revenue-generating effect of energy storage on new energy power plants; calculate the net revenue of the energy storage power plant using the direct / indirect revenue model of the energy storage system and the fixed operation and maintenance costs of the energy storage power plant;

[0022] c. Establish and optimize an energy storage economics assessment model: Calculate the total revenue of the energy storage power station and the fixed operation and maintenance costs of the energy storage power station, and evaluate the net operating revenue of the energy storage power station throughout its entire life cycle, taking into account the short-term and long-term operation decisions of the energy storage power station.

[0023] 4) Scheduling optimization and result analysis

[0024] a. Determine the main optimization objectives, such as maximizing revenue, maximizing battery life, and system reliability. Analyze the impact of energy storage scheduling strategies on battery life and their economic efficiency. Use an energy storage economic evaluation model to determine the economic life of batteries in electrochemical energy storage power stations.

[0025] b. Establish a multi-objective optimization function considering factors such as economy and technology, and set weight coefficients for the objectives according to their importance;

[0026] c. Based on the predicted economic lifespan of power station batteries and historical data from the energy storage economic assessment model, generate multiple dispatch strategies. Optimize the operation decisions of energy storage power stations according to battery degradation characteristics, market conditions, and operating methods, including charging and discharging strategies and strategies for participating in electricity market transactions, in order to balance economic lifespan and battery lifespan.

[0027] d. Generate an optimal scheduling strategy report to provide operational decision-making recommendations for the entire lifecycle of energy storage power stations.

[0028] Compared with the prior art, the beneficial effects of the present invention include:

[0029] This invention provides a comprehensive framework for evaluating the profitability of electrochemical energy storage power stations and optimizes battery power dispatch. By constructing a profitability evaluation model for energy storage power stations, it can more accurately assess the economic benefits of energy storage power stations under the electricity spot market, thereby enabling optimized dispatch of electrochemical energy storage power stations. By defining and calculating the economic lifespan of batteries in electrochemical energy storage power stations, it can be used to more rationally plan the operation and maintenance of energy storage power stations. By optimizing energy storage operation and dispatch strategies, the lifespan of batteries in electrochemical energy storage power stations can be extended, maximizing the economic benefits of the energy storage system. This invention considers the relationship between energy storage dispatch and battery lifespan, improving the accuracy of long-term operating cost assessment. Attached Figure Description

[0030] Figure 1 This is a structural block diagram of the energy storage battery power revenue assessment model constructed in this invention.

[0031] Figure 2 This is a schematic diagram of the energy storage scheduling model constructed in this invention.

[0032] Figure 3 This is a schematic diagram illustrating the relationship between the economic life and physical life of an energy storage power station in a specific embodiment of the present invention. Detailed Implementation

[0033] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. The specific implementation steps are as follows:

[0034] This invention provides a battery power dispatch optimization method for electrochemical energy storage power stations. It establishes the relationship between energy storage dispatch and battery life by constructing an energy storage dispatch model; then it constructs an energy storage economic evaluation model; and by optimizing the energy storage operation and dispatch strategy, it extends the battery life of the electrochemical energy storage power station and maximizes the economic benefits of the energy storage system.

[0035] When constructing an economic evaluation model for energy storage, perform the following operations (such as...). Figure 1 As shown):

[0036] (1) Data collection and analysis:

[0037] a. Collect operational data, electricity price data, and electricity demand data for energy storage power stations, including charge / discharge efficiency, battery cycle life, capacity decay, regional marginal electricity price, and ancillary service market prices. Data sources can include historical operating records from power companies and market data from power trading centers. The data period should be no less than one year, with a sampling interval of 15 minutes or 1 hour. Collect operational data for energy storage power stations, including but not limited to the battery type used (LFP, NCM, etc.), battery charge / discharge efficiency (between 85% and 95%), battery self-discharge effect (0.1%-0.3% per day), battery cycle life at different depths of charge / discharge, capacity decay rate (typically 1%-3% per year), response time, rate density, and energy density.

[0038] b. Collect research literature and market reports in related fields such as energy storage market development trend reports, electricity market policy documents, energy storage project case studies, corporate website annual reports, and battery technology development;

[0039] c. Preprocess the collected data. Data cleaning: remove outliers and fill in missing values; standardization: unify data of different dimensions to the same scale; feature extraction: extract time features (such as hour, date, season), weather features (such as temperature, humidity, wind speed), etc.

[0040] (2) Construct an energy storage scheduling model (e.g.) Figure 2 As shown):

[0041] a. Define the operating constraints of the energy storage system, such as: charge / discharge power (P) limit: (P min ≤P(t)≤P max State of Charge (SOC) range: SOC min ≤SOC(t)≤SOC max Charge / discharge efficiency: η t (S t )wait.

[0042] b. Design energy storage dispatch strategy: Integrate renewable energy and energy storage into the economic dispatch of the power system, optimize the configuration, promote the grid connection and consumption of renewable energy and reduce the cost of power generation; Based on the forecast results and electricity price information, conduct economic analysis on different energy storage technologies, system configurations and uncertainties, optimize charging and discharging decisions, and determine the optimal configuration and dispatch strategy of the energy storage system; The objective function can be set as maximizing economic benefits or minimizing the amount of abandoned electricity.

[0043] c. Establish an energy storage scheduling model: Based on the processed data, identify key factors affecting battery life (deep cycling, high-rate charge / discharge, etc.), and utilize the relationship between energy storage scheduling and battery life to obtain the impact of cycle aging and calendar aging; select an electrochemical model and determine the model input parameters; then define the battery state of health (SOH) index, and establish a mapping relationship between scheduling strategies and input parameters such as charge / discharge depth, charge / discharge rate, and temperature to predict the physical life of batteries under different scheduling strategies. When the battery state of health (SOH) ≤ 80%, it is considered the end of the battery's physical life, and the number of years the battery has been used is the battery's physical life.

[0044] d. Verify and optimize model accuracy: Use historical operational data to test the model, compare the differences between the model's predictions and the actual operational data; divide the dataset into training and test sets for cross-validation, adjust the key parameters of the model, observe output changes, and optimize these key parameters to improve model accuracy.

[0045] (3) Construct an energy storage economic evaluation model to simulate the battery usage and revenue curves of energy storage power stations under different dispatch objectives:

[0046] a. Define evaluation metrics: This model includes, but is not limited to, the following parameters: battery marginal aging cost, energy arbitrage revenue, ancillary service revenue, investment cost, operation and maintenance cost, aging cost, battery charge and discharge efficiency, and battery self-efficacy.

[0047] Discharge rate, state of health (SOH) of the battery, economic life of the energy storage power station, etc.

[0048] b. Construct a direct / indirect revenue model for energy storage systems to calculate the revenue of energy storage power stations, including the optimal long-term revenue and net revenue of energy storage power stations;

[0049] The optimal long-term return of an energy storage power station is expressed as:

[0050]

[0051] δ t =(1+r) -α

[0052] Using the exponential discount factor (δ) t ) and short-term revenue of energy storage power stations (R t To achieve the optimal long-term return (R) of energy storage power stations under the electricity spot market. ESS (max), where r represents the discount rate, α is the battery's lifespan, x is the optimal marginal aging cost over the battery's lifespan, and R... ESS For optimal long-term returns of energy storage power stations;

[0053] The net revenue of an energy storage power station is expressed as follows:

[0054]

[0055] Among them, P t This refers to the charging and discharging volume of the energy storage power station during time period t. This refers to the net revenue of the energy storage power station, which includes revenue from battery charging and discharging (RM). t (Market revenue) and battery reserve revenue A s (i.e., revenue from ancillary services), the expense item includes total battery aging cost. Fixed operation and maintenance costs of energy storage power stations C fix F represents the feasible operating group for the energy storage power station; Π h It is the regional marginal electricity price. and These are the power generation and charging amount of the energy storage system during time period t, in μ. t It is the aging cost of the battery's life cycle, where Δt is the change in time t, and d t d represents the degree of battery aging. t It is battery cycle aging and calendar aging. The sum of .

[0056]

[0057] Z t Represents the battery capacity within time t, 2Z t η represents the total energy generated during charging and discharging in a single cycle. t,θ The number of cycles a power storage station performs at a given depth of charge, where θ is the depth of charge / discharge. It represents the calendar aging of the battery at time t, where T is the battery's entire lifespan, and D is the total usage of the energy storage station's batteries before the end of their lifespan.

[0058] c. Based on the aforementioned mathematical formulas, a full lifecycle economic evaluation model for energy storage is established to simulate the operation of energy storage power stations and maximize long-term (full lifecycle) benefits by summarizing short-term gains. Specifically, given the price forecasts (regional marginal electricity prices and ancillary service market prices, etc.) and the configuration of the energy storage power station (i.e., battery SOC and SOH), the values ​​of battery charging, discharging, and output are determined through an energy storage dispatch model, and the energy storage power station's revenue is calculated using a direct / indirect revenue model for the energy storage system. Next, the full lifecycle revenue of the energy storage power station is evaluated by summing and aggregating the short-term gains before the end of the battery's physical life. Then, the net revenue of the energy storage power station is calculated based on its long-term revenue and fixed operating and maintenance costs. Combining the short-term and long-term operating decisions of the energy storage power station, the economic viability of the energy storage power station throughout its entire lifecycle is assessed.

[0059] (4) Scheduling optimization and result analysis

[0060] a. Based on the operational needs of the energy storage power station, identify the main optimization objectives, such as maximizing revenue, extending battery life, and ensuring the highest system reliability. Then, using an energy storage economic evaluation model, simulate battery usage and net revenue curves under different scheduling objectives by obtaining different charging and discharging strategies (i.e., battery charging and discharging capacity, charging and discharging efficiency, etc.) and ancillary service methods. Calculate the economic life of the energy storage system batteries. If the energy storage project cannot achieve a positive net profit during operation, i.e., net profit... If the time is right, then this moment is considered to be the end of the economic life of the energy storage power station battery, and the battery's service life (α) is the economic life of the battery. The impact of energy storage dispatch strategy on battery life and its economic efficiency are then derived.

[0061] b. Determine the set of optimization objectives: f = {f1, f2, ..., f...} n}, where f1 can be economic benefit, f2 can be battery life, and so on; set target weight coefficients: w={w1,w2,...,w n}, satisfying ∑w i =1; Considering economic and technical factors, construct a weighted summation type multi-objective optimization function: F = w1f1 + w2f2 + ... + w n f n Determine the constraints: such as power and capacity limitations of the energy storage system, grid dispatch requirements, and SOC limits;

[0062] c. Based on the prediction and historical data of the energy storage scheduling model, generate multiple scheduling strategies and select the optimal scheduling strategy: use a mixed integer linear programming model to describe the problem and use a genetic algorithm to generate an initial population, each individual representing a feasible scheduling strategy. Then, encode each strategy, including the charging and discharging power of each time period and the decision to participate in market transactions. Use the multi-objective function in (4)b to score each strategy, use the tournament selection method to select the best strategy, analyze the impact of energy storage scheduling strategy on battery life and its economics, use the energy storage economics assessment model to calculate the net income and then determine the economic life of the battery of the electrochemical energy storage power station. That is, when the annual net income of the energy storage power station is 0, the service life of the battery is the economic life of the battery. Compare the economic life with the physical life (the year when SOH drops to 80%). Then apply crossover and mutation operations to generate new strategies, repeat the evaluation-selection-evolution process until the termination condition is met. If extreme situations are encountered (such as drastic fluctuations in electricity prices), sensitivity analysis can be performed.

[0063] d. Organize and optimize results: Generate an optimal dispatch strategy report, including the optimal dispatch strategy's timeline, expected economic benefits, battery life assessment, and capacity degradation curve. This report provides recommendations for the energy storage power station's full lifecycle operation decisions and helps formulate strategies for participating in the electricity market (how to allocate capacity in different markets). For example: determine the optimal marginal aging cost of the energy storage power station in the spot electricity market to maximize its full lifecycle revenue; optimize the energy storage power station's operational decisions based on battery degradation characteristics, market conditions, and operational strategies. In the early stages of the project (e.g., the first 3 years): focus on energy arbitrage, fully utilizing battery performance to obtain high returns; in the mid-term (e.g., 4-6 years): gradually increase the proportion of ancillary services to balance revenue and battery aging; in the later stages (after 7 years): focus on ancillary services to maintain stable income.

[0064] The invention will be further illustrated by the following examples.

[0065] Example 1:

[0066] Assuming an independent energy storage power station, based on CAISO data for a certain region in 2018, the regional marginal electricity price and non-spinning standby service price are used to represent the annual price situation over the battery's life cycle, with an average peak-valley price difference of approximately $32 / MWh.

[0067] (1) Characteristics of energy storage systems:

[0068] Using lithium iron phosphate (LFP) batteries

[0069] Initial charge / discharge efficiency: 95%

[0070] Self-discharge rate is 0

[0071] (2) Battery life characteristics:

[0072] At 100% charge-discharge depth, the capacity retention rate is approximately 80% after 2000 cycles, at which point the battery's physical lifespan is considered to have ended. The point at which the annual net income of an energy storage power station drops to zero is considered the end of the battery's economic life. The average calendar aging rate is 1% capacity loss per year.

[0073] (3) Operation and maintenance costs:

[0074] It requires 24-hour staffing, and the costs of manual operation and maintenance, as well as equipment maintenance, are relatively high. Operation and maintenance cost of large-scale industrial-grade energy storage power stations: $16 / kW-year

[0075] Operation and maintenance cost of commercial and industrial energy storage power stations: $27 / kW-year

[0076] The physical and economic lifespans of the aforementioned energy storage power stations were evaluated and calculated using an energy storage economic assessment model. Figure 3 This study demonstrates the relationship between the net lifecycle revenue and state of health (SOH) of commercial and industrial energy storage power stations in the spot market. When the model calculates the optimal marginal aging cost ($45 / MWh-throughput), the economic lifespan of the station (8 years) is significantly shorter than its physical lifespan (9 years). Higher operation and maintenance costs accelerate the decline in net revenue, causing the batteries to lose their economic value before physical failure. In other words, even if the energy storage station's batteries are still operational in the 9th year, their revenue is insufficient to cover fixed operating and maintenance costs, failing to generate net profit for the station. At this point, the batteries can be recycled or used in other economically viable applications.

[0077] The above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A battery power dispatch optimization method for an electrochemical energy storage power station, characterized in that, The relationship between energy storage scheduling and battery life is established by constructing an energy storage scheduling model; then, an energy storage economic evaluation model is constructed to determine the economic life of batteries in electrochemical energy storage power stations, and energy storage operation and scheduling strategies are optimized to extend the lifespan of batteries in electrochemical energy storage power stations and maximize the economic benefits of the energy storage system; the steps include the following: 1) Data acquisition and processing, including electricity demand data, electricity price data, and energy storage power station operation data; 2) Construct an energy storage scheduling model, establish the relationship between energy storage scheduling and battery life, and verify the accuracy of the model; 3) Construct an energy storage economic evaluation model to simulate the battery usage and revenue of energy storage power stations under different dispatch objectives; Includes the following steps: 31) Determine the evaluation indicators, including: investment cost, operation and maintenance cost, battery swapping cost, aging cost, power station residual value, battery economic life and physical life; 32) Construct a revenue model for energy storage systems; The optimal long-term return of an energy storage power station is expressed as: Among them, R ESS max represents the optimal long-term return for the energy storage power station; R t For energy storage power stations, x represents the short-term benefits; x represents the optimal marginal aging cost of the battery over its lifecycle; δ represents the energy storage power station's battery. t δ is the exponential discount factor; t =(1+r) -α Where r represents the discount rate and α is the battery life; The net revenue of an energy storage power station is expressed as follows: Among them, P t This refers to the charging and discharging volume of the energy storage power station during time period t. It is the net revenue of the energy storage power station, Π h It is the regional marginal electricity price. and The energy storage system generates and charges electricity during time period t; F represents the feasible operating group of the energy storage power station; μ t It is the cost of battery lifecycle aging. It refers to the calendar aging of the battery during period t; A s For battery backup revenue, C fix Fixed operating and maintenance costs for energy storage power stations; d t This indicates the degree of battery aging. 33) Optimize the constructed energy storage system revenue model and evaluate the net operating revenue of the energy storage power station throughout its entire life cycle; 4) Perform scheduling optimization, including: 41) Determine the optimization objective and construct the multi-objective optimization function and constraints; 42) The battery usage and revenue of energy storage power stations under different scheduling objectives were simulated; 43) Calculate the economic life of the energy storage power station system; 44) Based on model predictions and historical data, multiple scheduling strategies are generated, and the optimal scheduling strategy is selected, thereby realizing the optimization of battery power scheduling in electrochemical energy storage power stations.

2. The battery power dispatch optimization method for an electrochemical energy storage power station as described in claim 1, characterized in that, Step 2) Construct an energy storage scheduling model. Specifically, based on the processed data, identify key factors affecting battery life, including deep cycling and high-rate charge / discharge. Utilize the relationship between energy storage scheduling and battery life to obtain the impact of cycle aging and calendar aging. Select an electrochemical model and determine the model input parameters. Then define the battery state of health (SOH) index and establish a mapping relationship between scheduling strategies and input parameters such as charge / discharge depth, charge / discharge rate, and temperature to predict the physical life of the battery under different scheduling strategies. When the battery state of health (SOH) ≤ 80%, it is considered the end point of the battery's physical life, and the number of years the battery has been used is defined as the battery's physical life.

3. The battery power dispatch optimization method for an electrochemical energy storage power station as described in claim 1, characterized in that, Battery aging degree d t For battery cycle aging and calendar aging The sum, expressed as: Among them, Z t Represents the battery capacity within time t, 2Z t η represents the total energy generated during charging and discharging in a single cycle. t,θ Let θ be the cycle number of the energy storage power station at a certain charging depth, and θ be the charging / discharging depth. It represents the calendar aging of the battery at time t, where T is the battery's entire lifespan, and D is the total usage of the energy storage station's batteries before the end of their lifespan.

4. The battery power dispatch optimization method for an electrochemical energy storage power station as described in claim 1, characterized in that, The energy storage economic evaluation model constructed in step 33) simulates the operation of the energy storage power station, and then maximizes the long-term, i.e., the whole life cycle benefit, by summarizing the short-term benefits; specifically including: Determine price forecasts and energy storage power station configurations, determine battery charging, discharging and output values ​​through an energy storage scheduling model, and calculate the revenue of energy storage power stations using an energy storage system revenue model. The total lifecycle benefits of an energy storage power station are assessed by summing up the short-term benefits before the end of the battery's physical life. The net income of an energy storage power station is calculated based on its long-term revenue and fixed operating and maintenance costs. The economic viability of the energy storage power station throughout its entire life cycle is also assessed based on its short-term and long-term operating decisions.

5. The battery power dispatch optimization method for an electrochemical energy storage power station as described in claim 1, characterized in that, In step 41), the optimization objectives include maximizing revenue, maximizing battery life, and maximizing system reliability. Further, based on the energy storage economic evaluation model, different charging and discharging strategies and ancillary service methods are obtained to simulate battery usage and net revenue curves under different scheduling objectives. The economic life of the energy storage system battery is specifically calculated when the energy storage project cannot achieve a positive net profit during operation, i.e., net profit... When the time is reached, it is the end of the economic life of the energy storage power station battery. The battery's service life α is the economic life of the battery. From this, we can derive the impact of energy storage dispatch strategy on battery life and its economic efficiency.

6. The battery power dispatch optimization method for an electrochemical energy storage power station as described in claim 5, characterized in that, In step 44), selecting the optimal scheduling strategy from multiple scheduling strategies includes: A mixed-integer linear programming model is used, and an initial population is generated using a genetic algorithm, where each individual represents a feasible scheduling strategy. Each scheduling strategy is coded, including the charging and discharging power and market trading decisions for each time period. Each scheduling strategy is scored using a multi-objective function, and the best strategy is selected using a tournament selection method. The impact of energy storage scheduling strategies on battery life and their economic efficiency are analyzed. The net revenue was calculated using an energy storage economics assessment model, and the economic life of the batteries in the electrochemical energy storage power station was further determined. Compare economic life with physical life; New scheduling strategies are generated by applying crossover and mutation operations; Repeat the above process until the termination condition is met to obtain the optimal scheduling strategy.

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