Capacity optimization configuration and dispatch method of hybrid energy storage system of optical storage micro-grid

Abstract

This invention relates to a capacity optimization configuration and scheduling method for a hybrid energy storage system in a photovoltaic-storage microgrid, belonging to the field of microgrid energy storage. It includes constructing an energy storage system within a microgrid composed of both new and retired batteries; building a two-layer optimization model with the lowest operating cost for the microgrid, including an energy storage capacity configuration model and a microgrid optimization scheduling model; predicting the capacity decay rate of the two types of batteries and the expected number of replacements throughout their lifespan under different operating strategies using a refined battery life loss model based on depth of discharge and equivalent cycle theory in the energy storage capacity configuration model; and outputting the daily power output of each unit in the microgrid system and the capacity planning results for new and old batteries after the algorithm is solved. This solves the problems of excessively high initial investment costs due to the sole use of new batteries in existing photovoltaic-storage microgrid energy storage configurations, and the failure to fully utilize low-cost retired battery resources.

Description

Technical Field

[0001] This invention relates to a capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system, belonging to the field of microgrid energy storage. Background Technology

[0002] In photovoltaic-storage microgrid systems, energy storage systems are core components for mitigating power fluctuations from renewable energy sources, achieving peak shaving and valley filling, and improving power supply reliability. To meet the power and energy balance requirements of the microgrid, it is essential to rationally configure the capacity of the energy storage system and formulate optimized operation and scheduling strategies. Currently, research on the capacity configuration and optimization of microgrid energy storage systems generally adopts economic objectives (such as minimizing the total lifecycle cost) to establish mathematical models, and uses intelligent algorithms such as particle swarm optimization and genetic algorithms for solving these models. In these studies, energy storage units are typically simplified to a single type of electrochemical battery (mainly novel lithium-ion batteries), and their power and capacity parameters are optimized based on this.

[0003] Defects and shortcomings of existing technology: Energy storage configuration options are limited and costly: existing technologies almost entirely utilize brand-new batteries as energy storage units. While brand-new batteries offer advantages such as high initial performance (e.g., high charge / discharge efficiency and long cycle life), their unit capacity purchase cost is extremely high. This high initial investment cost has become a key bottleneck restricting the large-scale promotion and application of energy storage systems and impacting the economic viability of microgrid projects.

[0004] Failure to effectively utilize low-cost retired battery resources: With the rapid development of the electric vehicle industry, a large number of power batteries are being retired after reaching their on-board service life. These retired batteries typically retain 70%-80% of their initial capacity, and their performance is sufficient to meet the needs of microgrids and other scenarios with relatively relaxed requirements for energy density and rate performance. The procurement cost of retired batteries is far lower than that of new batteries, offering significant economic advantages. However, existing research and technical solutions rarely incorporate retired batteries as a viable energy storage option into system-level capacity configuration and coordinated scheduling optimization, resulting in resource waste and missed opportunities to reduce overall system costs.

[0005] The synergistic advantages of different battery types are not fully utilized: New and retired batteries have natural complementarity in terms of technical characteristics and economics. New batteries offer superior performance but are expensive, making them suitable for high-power, fast-response tasks; retired batteries, while experiencing performance degradation, are inexpensive, making them suitable for large-capacity, energy storage. Existing technologies use only a single type of battery, failing to achieve the synergistic effect of "high-performance units handling peak demand, and low-cost units meeting basic energy needs" through optimized configuration. Therefore, there is room for improvement in the overall technical and economic efficiency of the system.

[0006] The system optimization perspective is limited to single battery characteristics: because it only considers brand-new batteries, existing optimization models are based on the same set of (high-performance) parameters when setting constraints (such as charge and discharge power limits, SOC operating window) and calculating costs. This leads to optimized operating strategies (such as depth of charge and discharge, power change rate) that may not be applicable to retired batteries, or when considering retired batteries, the model is not adjusted according to their actual characteristics (such as higher self-discharge rate, narrower SOC safe range, stricter power limits), resulting in inaccurate or unsafe optimization results.

[0007] For example, patent CN202211118369.3, "A Hybrid Energy Storage System Reuse and Control Method Based on Intelligent Algorithms," discloses a hybrid energy storage system reuse and control method based on intelligent algorithms, primarily using spent batteries. Based on a multi-parameter battery comprehensive model, it proposes a dual-strategy method combining short-term and long-term strategies. The short-term strategy uses the flamingo algorithm to optimize system capacity configuration, while the long-term strategy uses the sparrow search algorithm. The short-term strategy allows for continuous and uninterrupted energy supply to the system, while the short-term strategy, combined with the multi-parameter battery comprehensive model and control module, enables flexible control and configuration of the hybrid battery pack. The long-term strategy allows for intermittent adjustments to the system's operating strategy, optimizing the overall economic cost of operation. However, this method has the following drawbacks: capacity planning and operation scheduling are not coordinated and optimized; battery life loss is not incorporated into dynamic economic decision-making; and the new / old battery coordination mechanism is overly simplified, failing to achieve complementary advantages. Summary of the Invention

[0008] This invention proposes a capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system to solve the problems of excessively high initial investment costs due to the use of only brand-new batteries in existing photovoltaic-storage microgrid energy storage configurations, and the failure to fully utilize low-cost retired battery resources.

[0009] The technical solution adopted by the present invention to solve the above problems is as follows: The present invention includes the following steps: S1. Construct an energy storage system in a microgrid composed of both new and retired batteries, with cost characteristics based on differentiated cost parameters including purchase cost, cycle life, and charge / discharge efficiency. S2. Construct a two-layer optimization model with the lowest microgrid operating cost, specifically including an energy storage capacity configuration model, which is solved using a particle swarm optimization algorithm, with the differentiated cost parameters as input and the optimized energy storage capacity result as output; and a microgrid optimal scheduling model, which is solved using a genetic algorithm, with the optimized energy storage capacity result as input and the expected daily operating cost of the system as output. S3. By using the refined battery life loss model based on the depth of discharge and equivalent cycle theory in the energy storage capacity configuration model, different life decay parameters are set for new batteries and retired batteries respectively, so as to accurately predict the capacity decay rate of the two types of batteries under different operating strategies and the expected number of replacements in the whole life cycle, and the resulting replacement cost is included in the total cost calculation. S4. The optimization scheduling results of the microgrid optimization scheduling model are then transmitted in real time to the energy storage capacity configuration model for lifetime loss cost calculation. After the algorithm is solved, the daily output results of each unit of the microgrid system and the capacity planning results of new and old batteries in the energy storage system are output.

[0010] Furthermore, the energy storage capacity configuration model described in S2 includes constructing an objective function with the goal of minimizing the cost within the operating life of the microgrid system. Specifically, the initial investment cost of the battery, the energy storage replacement cost, and the residual value of the battery recycling are converted through a capital recovery coefficient, and then the annual maintenance cost of each device in the microgrid, the annual fuel consumption cost of the micro gas turbine, and the annual power exchange cost between the microgrid and the main grid are added to minimize the annual cost within the operating life of the microgrid system.

[0011] Furthermore, the energy storage capacity configuration model also includes an upper limit constraint on energy storage construction capacity, including: S21. The energy storage system contains only new batteries, and the capacity of the new batteries is less than or equal to the upper limit of the installed battery capacity in the microgrid energy storage system. S22. The capacity of the energy storage system when all batteries are old is constrained by the ratio of the capacity of all new batteries to the capacity of single retired batteries. S23, constrain the ratio of new batteries to retired batteries used in energy storage systems.

[0012] Furthermore, the microgrid optimization scheduling model described in S2 includes an objective function to minimize the overall cost of the microgrid in a typical day: the daily fuel cost of the microgrid system, the sum of the daily maintenance costs of each device, the daily power exchange cost with the main grid, and the daily battery discharge loss cost are included in the total daily operating cost of the microgrid system to minimize it.

[0013] Furthermore, the constraints of the microgrid optimization scheduling model include power balance constraints, micro gas turbine output constraints, power exchange constraints between the microgrid and the main grid, and energy storage constraints.

[0014] Furthermore, the refined battery life loss model based on the depth of discharge and equivalent cycle theory transforms the battery life loss of the i-th irregular discharge process into cost loss by using the proportion of the loss of the i-th discharge to the effective discharge ampere-hours of the battery's entire life cycle and the initial investment cost of the battery.

[0015] Furthermore, the loss of the battery during the i-th discharge is composed of the total amount of electricity actually released during the i-th discharge, the current ratio coefficient of the i-th irregular discharge process, and the cycle life ratio coefficient of the i-th irregular discharge process.

[0016] The beneficial effects of this invention are: 1. By constructing an energy storage system in a microgrid composed of both new and retired batteries, these two media naturally complement each other in key characteristics, creating space for in-depth optimization. First, in terms of cost characteristics, although new batteries have high initial purchase costs, they have the advantages of long cycle life and high charge-discharge efficiency; while retired batteries, although their performance parameters have degraded, have significantly lower purchase costs, giving them a prominent economic advantage.

[0017] 2. A two-layer optimization model for minimizing microgrid operating costs was constructed, coupling an outer-layer capacity allocation model with an inner-layer optimization scheduling model. The outer-layer model optimizes the capacity ratio of new / old batteries with the goal of minimizing the total lifecycle cost, while the inner-layer model optimizes the real-time scheduling strategy given the capacity, and simultaneously feeds back the battery lifespan loss cost incurred during the scheduling process to the outer layer. This closed-loop feedback mechanism achieves simultaneous solution and collaborative optimization of the two core decision-making problems of "how much to allocate" and "how to use," ensuring that the optimization result reaches global optimum from the perspective of the entire system lifecycle, and avoiding resource allocation misallocation caused by fragmented decision-making.

[0018] 3. A refined battery life degradation model based on depth of discharge and equivalent cycle theory is introduced, which can set different life degradation parameters for new batteries and retired batteries respectively, thereby accurately predicting the capacity degradation rate of the two types of batteries under different operating strategies and the expected number of replacements throughout the entire life cycle, and incorporating the resulting replacement cost into the total cost calculation, avoiding long-term economic misjudgment caused by ignoring life degradation.

[0019] 4. Through a unified and rigorous two-layer optimization framework, the two core decision-making problems of "how much to allocate" and "how to use" are solved simultaneously. The final output is not a fixed formula, but rather an optimal hybrid energy storage capacity configuration scheme and a precisely matched collaborative operation and scheduling strategy, generated for specific scenario requirements while pursuing the goal of minimizing total lifecycle cost. This gives the technical solution strong universality, excellent economic efficiency, and direct engineering guidance value. Attached Figure Description

[0020] Figure 1 This is a flowchart of the present invention; Figure 2 This is the information transmission diagram of the inner and outer layers of the present invention. Detailed Implementation

[0021] Specific implementation method one: Combining Figure 1 and Figure 2 This embodiment is described below: S1. Construct an energy storage system in a microgrid composed of both new and retired batteries, based on differentiated cost parameters including purchase cost, cycle life, and charge / discharge efficiency in terms of cost characteristics. Models of various components in a microgrid system Photovoltaic and gas turbine models The focus is on addressing the capacity allocation and scheduling issues of new and old batteries in energy storage systems. Conventional models are used for photovoltaic and gas turbine systems without specific modifications, as detailed below: In actual operation, photovoltaic systems typically employ Maximum Power Point Tracking (MPPT) technology to ensure efficient system operation. Under this operating mode, the power absorbed by the system during time period t must adhere to physical constraints, meaning it cannot exceed the maximum output power for that period. This constraint can be expressed mathematically as:

[0022] In the formula: Photovoltaic power absorption capacity during time period t ( ); Maximum output power of photovoltaic (PV) ).

[0023] The cost of generating electricity using a micro gas turbine is as follows:

[0024] In the formula Fuel cost (RMB); Annual fuel consumption cost of micro gas turbine (RMB); Gas turbine output power ( ); Low calorific value of fuel ( ); : Gas turbine power generation efficiency.

[0025] The relationship between the efficiency and output power of a gas turbine is shown in the following formula:

[0026] In the formula Gas turbine power generation efficiency; Gas turbine output power ( ).

[0027] (2) Energy storage system model The charging and discharging model of the energy storage battery is as follows:

[0028] In the formula Battery SOC at time t; Battery self-discharge rate; Battery SOC during time period t-1; Battery charging power at time t; Battery charging efficiency; Battery construction capacity ( ); Discharge duration.

[0029]

[0030] In the formula Battery discharge efficiency.

[0031] S2. Construct a two-layer optimization model with the lowest microgrid operating cost, specifically including an energy storage capacity configuration model, which is solved using a particle swarm optimization algorithm, with the differentiated cost parameters as input and the optimized energy storage capacity result as output; and a microgrid optimal scheduling model, which is solved using a genetic algorithm, with the optimized energy storage capacity result as input and the expected daily operating cost of the system as output. Energy storage capacity configuration model The energy storage capacity configuration model includes an objective function designed to minimize the annual cost of the microgrid system over its operational lifespan. Specifically, it involves converting the initial battery investment cost, energy storage replacement cost, and battery recycling residual value using a capital recovery coefficient, then adding the annual maintenance costs of each device within the microgrid, the annual fuel consumption cost of the micro gas turbine, and the annual power exchange cost between the microgrid and the mains grid, to achieve the lowest possible annual cost over the microgrid system's operational lifespan. The specific objective function is as follows:

[0032] In the formula Initial investment cost of battery (RMB); Energy storage replacement cost (RMB); Battery recycling residual value (yuan); Capital recovery coefficient; Annual maintenance cost (RMB) for each device in the microgrid; Annual fuel consumption cost of micro gas turbine (RMB); Annual power exchange cost between microgrids and the main grid.

[0033] The cost calculation methods for each part are as follows:

[0034] In the formula Initial investment cost of battery (RMB); The battery capacity installed in the microgrid energy storage system ( ); Construction cost per unit capacity of batteries (RMB / ) ).

[0035]

[0036] In the formula Battery recycling residual value (yuan); The number of times the battery needs to be replaced within the planning period. Battery residual value rate; :discount rate; Microgrid planning and operation cycle (years); : Indicates the number of times the equipment has been replaced (the xth replacement).

[0037]

[0038] In the formula : Capital recovery coefficient, used to... , , Apportioned to equipment lifespan In each year of the year.

[0039]

[0040] In the formula The sum of annual maintenance costs for photovoltaic and gas turbine (RMB); Daily equipment operation and maintenance cost (RMB) for a typical day.

[0041]

[0042] In the formula Annual fuel consumption cost of micro gas turbine (RMB); Daily fuel cost (RMB) of a typical gas turbine.

[0043]

[0044] In the formula Annual power exchange cost between microgrid and main grid (RMB); Daily power exchange cost (RMB) between a typical microgrid and a large power grid.

[0045]

[0046] In the formula Energy storage replacement cost (RMB); : Planned operating life of the microgrid (years); : The cumulative lifespan loss cost (in yuan) of batteries in an energy storage system within one year; Initial investment cost of battery (RMB).

[0047] The constraints are based on the actual situation that the land area for microgrid energy storage construction remains unchanged (no expansion). First, the energy storage configuration is optimized by considering only new batteries in the energy storage system. Then, the capacity limit is determined based on the optimized new battery capacity value and the capacity ratio of the same type of retired battery cells when the energy storage configuration combination is used for two cases: using all retired batteries and new batteries, and using a mixture of retired batteries.

[0048]

[0049] Energy storage construction capacity upper limit constraints:

[0050] In the formula New battery capacity installed in microgrid energy storage systems ( ); The upper limit of battery capacity installed in a microgrid energy storage system ( ).

[0051] When the energy storage system uses only old batteries:

[0052] In the formula The installed capacity of old batteries in a microgrid energy storage system ( ); : The optimized new battery construction capacity result when using only new batteries ( ); : Percentage of retired individual battery cells.

[0053] When new and retired batteries are used together in an energy storage system, the following requirements must be met:

[0054] Microgrid Optimal Scheduling Model The microgrid optimal scheduling model includes an objective function to minimize the overall cost of the microgrid during a typical day: It incorporates the microgrid's daily fuel cost, the sum of daily equipment maintenance costs, the daily power exchange cost with the main grid, and the cost of losses from cumulative battery discharge into the minimum total daily operating cost of the microgrid system. The objective function is as follows:

[0055] In the formula : Total daily operating cost of the microgrid system (RMB); Daily fuel cost (RMB) for a microgrid system; : Daily maintenance cost of all equipment (RMB); Daily power exchange cost with the main power grid (RMB); Cost of loss (in yuan) resulting from the cumulative discharge of the battery in a single day.

[0056]

[0057] In the formula Daily cumulative discharge loss (yuan); Discharge loss (in yuan) during the i-th discharge process.

[0058] Constraints: The constraint system of the inner-layer optimization model focuses on the daily operation phase of the microgrid, ensuring that the system maintains safe and stable operation of equipment while satisfying energy balance. This constraint framework ensures that all components of the system operate normally within the design critical range by strictly controlling the operating parameters of each device.

[0059] (1) Power balance constraint Power balance constraints establish a real-time supply and demand matching mechanism for microgrid systems, ensuring a dynamic balance between system power supply capacity and load demand at any given time. The constraint formula is as follows:

[0060] In the formula Transmission power between microgrids and the main grid ( ); Output power of micro gas turbine ( ); Photovoltaic power absorption capacity ( ); : Battery discharge power in energy storage system ( ); User-side load demand ( ); : Battery charging power in energy storage system ( ).

[0061] (2) Output constraints of micro gas turbines In microgrids, to ensure stable combustion and maintain basic efficiency of the micro gas turbines, a minimum operating power is set, while the maximum power is set to the equipment's rated capacity to prevent mechanical stress and thermal damage caused by overload operation. This constraint ensures equipment safety while providing the microgrid with flexible adjustment capabilities. The constraint formula is as follows:

[0062] In the formula Minimum output power of micro gas turbines ( ); Output power of micro gas turbine ( ); Rated output power of micro gas turbine ( ).

[0063] (3) Power exchange constraints between microgrids and the main grid The power exchange constraint between the microgrid and the main grid limits the power interaction strength between the microgrid system and the upstream power supply network. This constraint must be strictly limited within the rated transmission power limit of the lines, as shown in the following formula:

[0064] In the formula The upper limit of power transmission between microgrids and main grid lines ( ); The exchange power between the microgrid and the main power grid ( ).

[0065] (4) Energy storage constraints Energy storage in microgrids needs to meet safe operating conditions, including SOC constraints and power constraints. The constraint formulas are as follows: .

[0066] In the formula The state of charge of the battery at time t; The minimum state of charge allowed during battery operation; The maximum state of charge allowed during battery operation.

[0067] Energy storage in microgrids needs to meet safe operating conditions and also needs to satisfy the following:

[0068] In the formula Actual battery charging power ( ); Maximum charging power during battery operation ( ).

[0069] Energy storage in microgrids needs to meet safe operating conditions and also needs to satisfy the following:

[0070] In the formula Actual battery discharge power ( ); The maximum discharge power of the battery during operation ( ).

[0071] The following states must be satisfied at the beginning and end of the cycle:

[0072] In the formula The battery's SOC (State of Charge) value at the start of the operating cycle; : The SOC state value of the battery at the end of the operating cycle.

[0073] S3. By using the refined battery life loss model based on the depth of discharge and equivalent cycle theory in the energy storage capacity configuration model, different life decay parameters are set for new batteries and retired batteries respectively, so as to accurately predict the capacity decay rate of the two types of batteries under different operating strategies and the expected number of replacements in the whole life cycle, and the resulting replacement cost is included in the total cost calculation. As one of the devices with a relatively short service life in microgrid systems, the batteries in energy storage systems will have their original service life greatly reduced due to frequent charging and discharging and unreasonable charging and discharging depths. Therefore, studying and analyzing the impact of energy storage charging and discharging power and charging and discharging depth on the lifespan of energy storage in microgrids with economic efficiency as the research objective, and establishing a corresponding battery life loss model, is of practical significance for solving practical microgrid engineering construction problems.

[0074] Depth of discharge, number of charge-discharge cycles, and discharge rate are the main factors affecting the lifespan of battery energy storage in microgrids. A battery lifespan simulation and prediction framework quantifies the battery capacity decay process under given conditions, and its mathematical expression is:

[0075] In the formula The number of battery cycles (times) at rated depth of discharge and rated discharge current; Rated depth of discharge; Rated capacity under rated discharge current ( ).

[0076] Since the charging process has a relatively small impact on battery life loss at medium or low charge levels, only the discharge process is considered to affect battery life loss. To accurately describe this life loss mechanism, a data-driven battery life simulation scheme was developed. This scheme establishes an equivalent rectified charge-discharge condition model by simulating a series of different cycles under defined conditions, paying particular attention to key cycles significantly affected by depth of discharge and ambient temperature. The loss during the i-th discharge consists of the total amount of electricity actually released during the i-th discharge, the current ratio coefficient of the i-th irregular discharge process, and the cycle life ratio coefficient of the i-th irregular discharge process, from which the equivalent cycle calculation formula can be obtained:

[0077] In the formula The i-th irregular discharge process under non-rated conditions is equivalent to the effective discharge quantity under rated conditions. ); : Current ratio coefficient of the i-th irregular discharge process; : Cycle lifetime ratio coefficient of the i-th irregular discharge process; : The total amount of electricity actually released during the i-th discharge process ( ).

[0078] Considering the impact of energy transfer time on battery aging, the aging characteristics during charge and discharge processes can be assessed consistently over a wider range. When the ratio of battery power to nominal power is used in the calculation, it can be expressed as:

[0079] In the formula Rated discharge current ( ); The actual current intensity during the i-th discharge process ( ); Rated power ( ); : Actual discharge power during the i-th discharge process ( ).

[0080]

[0081] In the formula : Standard discharge process corresponds to cycle life; : The actual cycle life corresponding to the i-th non-standard discharge process.

[0082] The depth of discharge is calculated from the actual discharge depth during the i-th discharge process, and its mathematical expression is:

[0083] In the formula : The actual discharge depth of the i-th discharge process.

[0084] The parameters a, b, and c in the formula are all positive fitting coefficients, and the fitting data are usually determined by the battery manufacturer through experiments.

[0085] The above analysis process allows for the accurate calculation of the effective discharge ampere-hours of the battery under different equivalent to rated conditions in each uncertain condition. As the battery accumulates through multiple cycles during use, it can be determined that the battery needs to be replaced when the following conditions are met.

[0086]

[0087] In the formula The loss of the battery during the i-th discharge ( ); Effective discharge ampere-hours over the entire battery lifecycle ( ) ).

[0088] A refined battery life loss model based on depth of discharge and equivalent cycle theory transforms the battery life loss during the i-th irregular discharge process into cost loss by using the proportion of the loss during the i-th discharge to the total effective discharge ampere-hours of the battery's entire lifespan and the initial investment cost of the battery. Specifically, the cost of battery life loss during the i-th irregular discharge process can be expressed as:

[0089] In the formula : Battery life loss cost (RMB) during the i-th irregular discharge process; Initial investment cost of the battery (RMB).

[0090] S4. The optimization scheduling results of the microgrid optimization scheduling model are then transmitted in real time to the energy storage capacity configuration model for lifetime loss cost calculation. After the algorithm is solved, the daily output results of each unit of the microgrid system and the capacity planning results of new and old batteries in the energy storage system are output.

[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for capacity optimization configuration and scheduling of a photovoltaic-storage microgrid hybrid energy storage system, characterized in that, Includes the following steps: S1. Construct an energy storage system in a microgrid composed of both new and retired batteries, with cost characteristics based on differentiated cost parameters including purchase cost, cycle life, and charge / discharge efficiency. S2. Construct a two-layer optimization model with the lowest microgrid operating cost, specifically including an energy storage capacity configuration model, which is solved using a particle swarm optimization algorithm, with the differentiated cost parameters as input and the optimized energy storage capacity result as output; and a microgrid optimal scheduling model, which is solved using a genetic algorithm, with the optimized energy storage capacity result as input and the expected daily operating cost of the system as output. S3. By using the refined battery life loss model based on the depth of discharge and equivalent cycle theory in the energy storage capacity configuration model, different life decay parameters are set for new batteries and retired batteries respectively, so as to accurately predict the capacity decay rate of the two types of batteries under different operating strategies and the expected number of replacements in the whole life cycle, and the replacement cost generated therefrom is included in the total cost calculation. S4. The optimization scheduling results of the microgrid optimization scheduling model are then transmitted in real time to the energy storage capacity configuration model for lifetime loss cost calculation. After the algorithm is solved, the daily output results of each unit of the microgrid system and the capacity planning results of new and old batteries in the energy storage system are output.

2. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 1, characterized in that, The energy storage capacity configuration model described in S2 includes constructing an objective function with the goal of minimizing the cost within the operating life of the microgrid system. Specifically, the initial investment cost of the battery, the energy storage replacement cost, and the residual value of the battery recycling are converted through a capital recovery coefficient. Then, the annual maintenance cost of each device in the microgrid, the annual fuel consumption cost of the micro gas turbine, and the annual power exchange cost between the microgrid and the main grid are added to minimize the annual cost within the operating life of the microgrid system.

3. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 2, characterized in that, The energy storage capacity configuration model also includes an upper limit constraint on energy storage construction capacity, including: S21. The energy storage system contains only new batteries, and the capacity of the new batteries is less than or equal to the upper limit of the installed battery capacity in the microgrid energy storage system. S22. The capacity of the energy storage system when all batteries are old is constrained by the ratio of the capacity of all new batteries to the capacity of single retired batteries. S23, constrain the ratio of new batteries to retired batteries used in energy storage systems.

4. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 3, characterized in that, The microgrid optimization scheduling model described in S2 includes an objective function to minimize the overall cost of the microgrid in a typical day: the daily fuel cost of the microgrid system, the sum of the daily maintenance costs of each device, the daily power exchange cost with the main grid, and the daily loss cost caused by the cumulative discharge of the battery are included in the total daily operating cost of the microgrid system.

5. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 4, characterized in that, The constraints of the microgrid optimization scheduling model include power balance constraints, micro gas turbine output constraints, power exchange constraints between the microgrid and the main grid, and energy storage constraints.

6. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 1, characterized in that, The refined battery life loss model based on the depth of discharge and equivalent cycle theory transforms the battery life loss during the i-th irregular discharge process into cost loss by using the proportion of the loss during the i-th discharge to the effective discharge ampere-hours of the battery's entire life cycle and the initial investment cost of the battery.

7. The capacity optimization configuration and scheduling method for a photovoltaic-storage microgrid hybrid energy storage system according to claim 6, characterized in that, The loss of the battery during the i-th discharge consists of the total amount of electricity actually released during the i-th discharge, the current ratio coefficient of the i-th irregular discharge process, and the cycle life ratio coefficient of the i-th irregular discharge process.

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