Intelligent energy storage configuration optimization method and system for micro-grid scene

Abstract

The invention provides a micro-grid scene-oriented energy storage configuration optimization method and system, and the method comprises the steps: dividing micro-grid application scene types according to load characteristics and operation targets; constructing a comprehensive performance evaluation model of the energy storage system, and taking an energy storage configuration value effectiveness index as an optimization target; based on the energy storage configuration value efficiency index, performing iterative optimization on configuration parameters of the energy storage system until the configuration parameters meet a preset threshold condition; and outputting an energy storage configuration scheme. According to the method, technical factors such as battery characteristics, an energy conversion device, operation maintenance, system life, battery attenuation and replacement are considered in modeling; multiple technical benefits of peak load shifting, renewable energy consumption, power grid capacity expansion delay, power supply reliability improvement, market interaction, carbon emission reduction and the like are covered in the aspect of performance analysis; in the aspect of application scenarios, multiple types of micro-grids are included, systematic configuration optimization of micro-grid energy storage in different scenarios is achieved, and the reliability and adaptability of energy storage technology application are improved.

Description

Technical Field

[0001] This invention belongs to the field of microgrid energy storage technology, and in particular relates to an intelligent energy storage configuration optimization method and system for microgrid scenarios. Background Technology

[0002] With the large-scale integration of distributed and renewable energy sources, microgrids have become widely used as an important form of local energy consumption and flexible dispatch. Microgrids typically integrate photovoltaic, wind power, conventional power sources, and energy storage devices, enabling flexible operation in both grid-connected and islanded modes. They provide safe and reliable power supply for industrial park microgrids, data center microgrids, community / rural microgrids, and island microgrids. Energy storage systems play key technical roles in microgrids, including peak shaving, valley filling, fluctuation smoothing, and reliability improvement. However, different types of microgrids differ significantly in their operational objectives, load characteristics, energy structure, and investment entities, requiring customized energy storage configurations based on specific scenario characteristics. For example, industrial park microgrids primarily focus on peak shaving and valley filling and electricity price arbitrage; data center microgrids emphasize power supply reliability and backup capacity guarantees; community or rural microgrids prioritize basic energy supply and cost affordability; and island microgrids emphasize independent operation capabilities and renewable energy consumption levels. Therefore, there is an urgent need to establish a set of energy storage configuration optimization methods that can be combined with the characteristics of microgrid application scenarios. Through multi-scenario modeling and technical indicator evaluation, this approach can improve the technical applicability and operational efficiency of energy storage systems and provide a reference for investment decisions and policy formulation. Summary of the Invention

[0003] This invention provides an intelligent energy storage configuration optimization method for microgrid scenarios. Through technical parameter classification, energy storage function analysis, configuration model construction, technical indicator evaluation and scenario verification, it realizes the scientific configuration and efficient utilization of energy storage, while improving the level of renewable energy consumption, enhancing power supply reliability, reducing grid expansion pressure, and promoting the safe, economical and sustainable development of microgrids.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: An energy storage configuration optimization method for microgrid scenarios includes the following steps: Microgrid application scenarios are classified according to load characteristics and operational objectives; Construct a comprehensive performance evaluation model for energy storage systems, with the energy storage configuration value efficiency index as the optimization target; Based on the energy storage configuration value efficiency index, the configuration parameters of the energy storage system are iteratively optimized until they meet the preset threshold conditions. Output an energy storage configuration scheme that meets the threshold condition.

[0005] Furthermore, the microgrid application scenarios include industrial park microgrids, data center microgrids, community / rural microgrids, and island microgrids.

[0006] Furthermore, the formula for calculating the energy storage configuration value efficiency index is as follows:

[0007] in, These correspond to: 1. Industrial park microgrid, 2. Data center microgrid, 3. Community / rural microgrid, and 4. Island microgrid, respectively. For the first i Value efficiency index of energy storage configuration in microgrid-like scenarios; Indicates the first The comprehensive annualized value of microgrid-like scenarios. For the first Net present value of total cost over the entire lifecycle of energy storage projects in microgrid-like scenarios.

[0008] Furthermore, the first The comprehensive annualized value of microgrid-like scenarios is:

[0009] in, Indicates the first The value of expansion and replacement in microgrid-like scenarios Indicates the first The lifecycle operational efficiency of energy storage systems in microgrid-like scenarios is achieved through optimized charging and discharging strategies. Indicates the first The equivalent green value of the annual discharge of energy storage systems in microgrid-like scenarios. For the first The annualized value of energy storage in microgrid-like scenarios to reduce grid outage losses. Indicates the first The value of annual line loss savings in microgrid-like scenarios; The discount rate; N This represents the operational lifespan of an energy storage project. n This indicates the nth year of operation for the energy storage project.

[0010] Furthermore, the first The net present value of the total lifecycle cost of energy storage projects in microgrid-like scenarios is:

[0011] in, The discount rate; Indicates the first Initial investment cost of energy storage power stations in microgrid-like scenarios For the first The annual operation and maintenance costs for microgrid-like scenarios. Indicates the first The first microgrid-like scenario Annual financial expenses For the first Battery replacement costs in microgrid-like scenarios For the first Depreciation impacts costs in microgrid-like scenarios. For the first time during the operating life of the energy storage system Year.

[0012] Furthermore, the first The value of capacity expansion and replacement in microgrid-like scenarios is calculated based on the present value analysis of capacity expansion delay or the system value calculation based on the equivalent replacement of capacity expansion with energy storage. The present value analysis calculation formula based on capacity expansion delay is as follows:

[0013] This represents the total investment for expansion. This is the time required for the load to reach its maximum capacity without energy storage. The time required for the load to reach its maximum capacity when energy storage is configured; The system value calculation formula based on energy storage equivalent replacement capacity expansion is as follows:

[0014] in, For energy storage systems in the first The average utilization efficiency coefficient for peak shaving in actual operation in microgrid-like scenarios; Indicates the first The unit capacity expansion cost in microgrid-like scenarios; Energy storage systems have the ability to reduce peak loads on the power grid.

[0015] Furthermore, the first The full lifecycle operational efficiency of energy storage systems in microgrid-like scenarios, achieved through optimized charging and discharging strategies, is as follows:

[0016] No. The equivalent green value of the annual discharge of an energy storage system in a microgrid-like scenario is:

[0017] No. In microgrid-like scenarios, the annualized value of energy storage in reducing grid outage losses is:

[0018] Indicates the first The value of annual line loss savings in microgrid-like scenarios is:

[0019] in, and These represent the peak and off-peak electricity prices, respectively. and They represent the first Annual discharge and charging volume; The market price of a unit of green certificate; This indicates the average annual power outage rate; Indicates the assessed value of the user's losses due to power outage; Indicates the electrical status of the energy storage device; Indicates the rated power of the energy storage system; Indicates the duration that can be supported; Indicates annual power savings due to line loss; This refers to the marginal electricity purchase price.

[0020] Furthermore, the first The initial investment cost of an energy storage power station in a microgrid-like scenario is: ; No. The annual operation and maintenance costs for microgrid-like scenarios are:

[0021] No. The first microgrid-like scenario The annual financial expenses are:

[0022] No. The battery replacement cost in microgrid-like scenarios is:

[0023] No. The depreciation impact cost in microgrid-like scenarios is as follows:

[0024] in, Indicates battery cost, Indicates the cost of the energy conversion device. Indicates the cost of auxiliary facilities, Other costs; This refers to the rated power of the energy storage system. This indicates the initial investment cost of an energy storage power station. This indicates the percentage of annual maintenance costs to initial investment. For income tax rates; Indicates the loan-to-value ratio. Indicates loan interest; This indicates the annual rate at which the cost of energy storage batteries decreases. For the lifespan of energy storage batteries; The number of times an energy storage battery can be replaced during its lifespan. Y represents the battery cost; Y represents the depreciation period of the energy storage power station. It is the residual value rate.

[0025] Furthermore, the iterative optimization process includes: By calculating the energy storage configuration value efficiency index under different capacity and power combinations and comparing it with a preset threshold, when the calculated energy storage configuration value efficiency index reaches the set threshold, it is determined to be a feasible capacity configuration scheme; if it does not reach the threshold, the energy storage capacity is further iteratively optimized until a feasible scheme that meets the threshold requirements is obtained.

[0026] On the other hand, the present invention provides an intelligent energy storage configuration optimization system for microgrid scenarios, comprising: The scenario segmentation module is used to classify microgrid application scenario types based on load characteristics and operational objectives. The model building module is used to build a comprehensive performance evaluation model for energy storage systems and calculate the energy storage configuration value efficiency index. The optimization module is used to iteratively optimize the configuration parameters of the energy storage system based on the energy storage configuration value efficiency index until it meets the preset threshold conditions. The scheme output module is used to output energy storage configuration schemes that meet the threshold conditions.

[0027] Compared with the prior art, the present invention has the following beneficial effects: (1) By classifying different types of microgrid application scenarios and analyzing the energy storage mechanism, this invention can specifically identify the functional value of energy storage in industrial park microgrids, data center microgrids, community / rural microgrids and island microgrids.

[0028] (2) By constructing a refined technical model and evaluation system, this invention realizes a multi-dimensional technical evaluation of microgrid energy storage configuration schemes, effectively improves the scientific nature and operational efficiency of energy storage system planning, and provides core algorithm support for the optimized design of smart energy storage.

[0029] (3) The present invention has been applied in typical microgrid scenarios to verify the applicability and effectiveness of the method under different operating conditions, providing a scientific basis for energy storage investment decisions and microgrid construction. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2 This is a value efficiency index diagram of energy storage configuration under different types of microgrid scenarios in embodiments of the present invention. Detailed Implementation

[0032] 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.

[0033] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0034] Example 1 like Figure 1 As shown, this embodiment provides an energy storage configuration optimization method for microgrid scenarios, including the following steps: Step 1. Classify microgrid application scenario types based on load characteristics and operational objectives; Step 2. Construct a comprehensive performance evaluation model for the energy storage system, using the energy storage configuration value efficiency index as the optimization objective; Step 3. Based on the energy storage configuration value efficiency index, iteratively optimize the configuration parameters of the energy storage system until they meet the preset threshold conditions; Step 4. Output the energy storage configuration scheme that meets the threshold conditions.

[0035] In one specific embodiment, step 1 includes analyzing the role of energy storage in the microgrid, including peak shaving and valley filling, optimizing power load, mitigating renewable energy fluctuations, enhancing new energy absorption capacity, improving power supply reliability, and optimizing grid investment and reducing grid expansion needs. Based on the load characteristics of the microgrid, it is divided into industrial park microgrids, data center microgrids, community / rural microgrids, and island microgrids.

[0036] In one specific embodiment, step 2 involves constructing a comprehensive performance evaluation model for the energy storage system, introducing an energy storage configuration value efficiency index as an optimization objective, which includes a comprehensive quantitative expression of multiple annualized benefits such as peak shaving value, reliability improvement value, incremental absorption value, and grid investment substitution value.

[0037] Energy storage systems can generate multi-dimensional direct and indirect value during their operating cycle. Introducing the energy storage configuration value efficiency index as an optimization objective, its expression can be written as:

[0038] in These correspond to: 1. Industrial park microgrid, 2. Data center microgrid, 3. Community / rural microgrid, and 4. Island microgrid. For the first i Value efficiency index of energy storage configuration in microgrid-like scenarios; Indicates the first The comprehensive annualized value of microgrid-like scenarios. It is the net present value of the total cost over the entire life cycle of an energy storage project.

[0039] Furthermore, the first The comprehensive annualized value of microgrid-like scenarios is:

[0040] in, Indicates the first The value of expansion and replacement in microgrid-like scenarios Indicates the first The lifecycle operational efficiency of energy storage systems in microgrid-like scenarios is achieved through optimized charging and discharging strategies. Indicates the first The equivalent green value of the annual discharge of energy storage systems in microgrid-like scenarios. For the first The annualized value of energy storage in microgrid-like scenarios to reduce grid outage losses. Indicates the first The value of annual line loss savings in microgrid-like scenarios; The discount rate; N This represents the operational lifespan of an energy storage project.n This indicates the nth year of operation for the energy storage project.

[0041] In the above embodiments, deploying energy storage in a power system can effectively alleviate capacity pressure on substations or transmission lines, thereby optimizing system expansion planning. Depending on the evaluation perspective, the first... The value of capacity expansion in microgrid-like scenarios can be quantified in two ways: present value analysis based on capacity expansion delay or system value calculation based on energy storage equivalent replacement capacity expansion. Method 1: Present Value Analysis Based on Capacity Expansion Delay: The initial maximum load of the system is The maximum capacity of a substation is The annual load growth rate is Without energy storage, the time required for the load to reach its capacity limit is... for:

[0042] If an energy storage system is configured, it can provide power during peak hours. The load reduction capability is equivalent to increasing the system's "equivalent capacity" to [a certain level]. The expansion was postponed to the following point in time. for:

[0043]

[0044] Efficiency is usually expressed as a parameter, and in energy storage systems, it refers to either charging efficiency or discharging efficiency. Rated power of energy storage system This represents the depth of discharge coefficient.

[0045] The delay period for capacity expansion is expressed as follows:

[0046] The value of capacity expansion investment The discount difference can be calculated as follows:

[0047] in, This represents the total investment amount for the expansion. The system value calculation formula based on energy storage equivalent replacement capacity expansion is as follows: The calculation method is as follows:

[0048] In the formula, This represents the cost per unit of capacity expansion (RMB 10,000 / MW).

[0049] Method 2: System value assessment based on energy storage equivalent replacement and capacity expansion From the perspective of system capacity planning or value assessment, if an energy storage system can stably provide peak shaving capabilities during peak load periods, it can be considered a functional substitute for some of the transmission and transformation capacity expansion needs. In this case, its "equivalent substitution value" can be expressed as:

[0050] in, For energy storage systems in the first The average utilization efficiency coefficient for peak shaving in actual operation in microgrid-like scenarios; Indicates the first The unit capacity expansion cost in microgrid-like scenarios.

[0051] When the power grid fails or power supply is interrupted, energy storage can serve as a backup power source (emergency power, islanded operation, microgrid support) to continue supplying power, thereby reducing the direct value loss to users caused by power outages (production interruption, equipment damage, service interruption, etc.).

[0052] In the above embodiments, when a time-of-use pricing mechanism is adopted, the electricity price charged during peak and off-peak hours differs. The power grid configures an energy storage system, charging it during off-peak hours and discharging it during peak hours, thus profiting from the peak-valley price difference through low-charge-high-discharge. The mathematical model for this operational strategy is shown below:

[0053] in, Indicates the first The full life-cycle operational efficiency of energy storage systems in microgrid-like scenarios, achieved through optimized charging and discharging strategies, is expressed in yuan. and These represent the peak and off-peak electricity prices, respectively, in yuan / kWh. and They represent the first The annual discharge and charge capacity, both in kWh, are closely related to the system's technical parameters.

[0054]

[0055]

[0056] The annual grid-connected electricity generation of an energy storage system is related to its technical characteristics, such as capacity, self-discharge rate, cycle degradation rate, and cycle life. These factors all affect the annual grid-connected electricity generation. In the formula, This indicates the total annual discharge of the energy storage system. Indicates the self-discharge rate. This indicates the rate of capacity degradation of an energy storage battery after one cycle. It refers to charging efficiency. This represents the depth of discharge coefficient, which measures the ratio between the actual discharge capacity of an energy storage battery and its rated capacity. Energy storage capacity (kW) Daily discharge duration (hours). This represents the number of operating days per year (e.g., 300 days). In summary, the annual efficiency under the low-storage, high-incidence strategy can be simplified as follows:

[0057] Energy storage systems significantly enhance the ability of microgrids to absorb renewable energy by smoothing and transferring renewable energy output. This technological contribution can be quantified through green value, which can be modeled as the equivalent green value of the annual discharge of the energy storage system.

[0058] in, Indicates the first The equivalent green value of the annual discharge of energy storage systems in microgrid-like scenarios. The market price of a unit of green certificate. and The calculation method is the same as described above.

[0059] When the power grid fails or power supply is interrupted, energy storage can serve as a backup power source (emergency power, islanded operation, microgrid support) to continue supplying power, thereby reducing the direct value loss to users caused by power outages (production interruption, equipment damage, service interruption, etc.).

[0060] No. In microgrid-like scenarios, the annualized value of energy storage in reducing grid outage losses is:

[0061] in, Annualized value of energy storage to reduce grid outage losses (unit: yuan). This represents the average annual power outage rate, measured in outages per year. N represents the service life. The assessed value of power outage losses for users (unit: yuan / MWh). The value of user losses during a power outage varies depending on the region and power grid. This value is usually based on GDP or electricity load-related assessments. This indicates the state of charge (SOC) of an energy storage device, which is the remaining energy capacity of the energy storage system. It is usually expressed as a percentage (e.g., 80%). Rated power (unit: MW) of the energy storage system, which is the maximum output power of the energy storage system. This refers to the continuous support time of the energy storage system during a power outage, expressed as capacity / power (in hours). In other words, it represents the duration for which the energy storage system can provide power to the grid. To support the duration.

[0062] The grid loss reduction benefits brought by energy storage systems constitute an important part of their indirect configuration value. Especially when energy storage devices are deployed on the user side or at the end of the distribution network, the transmission pressure on the main power grid can be significantly reduced and active power loss during long-distance power transmission can be reduced through local consumption and peak shaving and valley filling mechanisms.

[0063] The active power loss in a transmission line can be described by the following formula:

[0064] in, Active power loss of the line (unit: kW). Current in a power transmission line (unit: A). Equivalent resistance of transmission lines (unit: ).

[0065] In a three-phase AC system, the relationship between active power and current, voltage, and power factor is as follows:

[0066] In the formula, The actual transmission power of a power transmission line during a specific time period (unit: kW). Line voltage level (unit: kV). Power factor is dimensionless. Therefore, the expression for current can be derived as follows:

[0067] Substituting it into the line loss formula, we get:

[0068] Considering the impact of introducing an energy storage system on power grid flow, assume the maximum line load power before the energy storage system is operational is... The energy storage discharge power is The line loss without energy storage is:

[0069] Energy storage discharge power (rated power) is After its introduction, the line power becomes The corresponding line loss is:

[0070] The difference between the two is the power saving in line loss:

[0071] in, Load power during energy storage operation (unit: kW). Discharge power of energy storage system (unit: kW). Line loss coefficient (unit: kW / kW²). Substitute into... The expression, when taken to its final form, is:

[0072] If the energy storage system discharges continuously every day Hours, number of operating days per year Therefore, its annual line loss saving power is:

[0073] This allows us to estimate the value of annual line loss savings:

[0074] in, The marginal electricity purchase price (unit: yuan / kWh).

[0075] In this embodiment, the first The net present value of the total lifecycle cost of energy storage projects in microgrid-like scenarios is:

[0076] in, The discount rate; Indicates the first Initial investment cost of energy storage power stations in microgrid-like scenarios For the first The annual operation and maintenance costs for microgrid-like scenarios. Indicates the first The first microgrid-like scenario Annual financial expenses For the first Battery replacement costs in microgrid-like scenarios For the first Depreciation impacts costs in microgrid-like scenarios; For the first time during the operating life of the energy storage system Year.

[0077] Furthermore, the first The initial investment cost of an energy storage power station in a microgrid-like scenario is: ; This indicates the initial investment cost of an energy storage power station. Indicates battery cost, Indicates the cost of the energy conversion device. Indicates the cost of auxiliary facilities, This indicates other costs. Rated power of the energy storage system (unit: kW or MW). The expression is as follows:

[0078] Total energy capacity of the energy storage system (unit: kWh or MWh). The continuous discharge time (in hours) of an energy storage system is also called "configuration duration" or "duration ratio".

[0079] For energy storage equipment, regular maintenance is required after it is put into operation. Maintenance costs are related to the initial installed capacity of the energy storage system; therefore, the calculation method involves estimating the operation and maintenance costs based on a certain percentage of the initial configuration. The calculation formula is as follows:

[0080] For annual maintenance costs, This indicates the initial investment cost of an energy storage power station. This indicates the percentage of annual maintenance costs to initial investment. This refers to the income tax rate.

[0081] No. The first microgrid-like scenario The annual financial expenses are:

[0082] In the formula: Indicates the first Annual financial expenses This indicates the initial investment cost of an energy storage power station. Indicates the loan-to-value ratio. This indicates loan interest.

[0083] In battery energy storage systems, due to the inherent reaction characteristics of lithium batteries, their capacity gradually decreases during cycling. The battery replacement cost in microgrid-like scenarios is:

[0084] For battery replacement costs, This indicates the annual rate at which the cost of energy storage batteries decreases. For the lifespan of energy storage batteries; This refers to the number of times an energy storage battery can be replaced during its lifespan. , For the operational lifespan of energy storage projects; Indicates battery cost; This refers to the rated power of the energy storage system.

[0085] Although depreciation does not incur actual expenses during project construction, it can reduce income tax expenses and thus lower project costs, taking into account the impact of taxes. The calculation formula is as follows:

[0086] In the formula, Depreciation affects costs. Y represents the initial investment cost of the energy storage power station, and Y represents the depreciation period of the energy storage power station. It is the residual value rate. This refers to the income tax rate.

[0087] The technical evaluation system described in section 3 uses the Energy Storage Configuration Value Efficiency Index (ESC-VEI) as the sole optimization and judgment indicator. It calculates the ESC-VEI values ​​under different capacity and power combinations and compares them with a preset threshold to determine the effectiveness of the energy storage configuration scheme. When the calculated ESC-VEI reaches the set threshold, it is determined to be a feasible capacity configuration scheme; if it does not reach the threshold, the energy storage capacity is further iteratively optimized until a feasible scheme that meets the threshold requirements is obtained.

[0088] The ESC-VEI threshold is set based on the technical, safety, and feasibility requirements of the energy storage system in a microgrid scenario, and can be determined in the following ways: Thresholds are set based on ESC-VEI: Comprehensive annualized value Quantitative values ​​of system lifecycle investment The constructed ESC-VEI is used as an evaluation metric when:

[0089] At that time, it was considered that the energy storage configuration met the requirements in terms of the match between value and investment, and the energy storage capacity configuration was reasonable. This represents the industry's baseline level for energy storage. When the ESC-VEI determines that the energy storage configuration value efficiency index does not reach a preset threshold, the rated total energy capacity of the energy storage system, a key configuration parameter of the energy storage system, will be adjusted. Perform iterative optimization. Adjustments are made... The value of changes the effective energy scale that the system can provide, thereby affecting the value output level during the operating cycle and thus the value efficiency of energy storage configuration until it meets the requirements. The threshold requirements are used to output the final energy storage capacity configuration scheme.

[0090] Furthermore, this embodiment also includes: verifying the advantages of the energy storage configuration model in terms of technical feasibility and system adaptability through case analysis of typical microgrid scenarios.

[0091] Furthermore, this embodiment verifies the energy storage configuration optimization method based on actual application scenarios of different types of microgrids. The selected scenarios include industrial park microgrids, data center microgrids, community / rural microgrids, and island microgrids, and their main parameters are shown in the table.

[0092] In industrial park microgrids, battery costs are approximately RMB 1.2 / Wh, while converter, auxiliary facilities, and other equipment costs are RMB 0.12 / W, RMB 0.1 / W, and RMB 0.1 / W, respectively. Annual maintenance costs account for 3% of total costs, and the unit expansion cost is RMB 500 / kW. Significant peak-valley electricity price differences exist (peak price RMB 0.9952 / kWh, valley price RMB 0.2225 / kWh), with outage losses estimated at RMB 40,000 / MWh. In this scenario, the main value of energy storage optimization lies in peak shaving and valley filling, as well as electricity price arbitrage, which can significantly reduce electricity costs and improve power supply reliability. In data center microgrids, battery costs increase to RMB 1.5 / Wh, and equipment costs are generally higher than in industrial parks. Annual maintenance costs account for 4% of total costs, and the unit expansion cost is RMB 700 / kW. Peak-valley electricity price differences remain between RMB 0.9952 / kWh and RMB 0.2225 / kWh, with outage losses reaching as high as RMB 80,000 / MWh. Because data centers have extremely high requirements for power supply reliability, the main value of energy storage in this scenario lies in its backup power function and reduction of power outage losses, with economic benefits significantly outweighing those of simply relying on electricity price arbitrage. In community and rural microgrids, the battery cost is 1.2 yuan / Wh, equipment costs are relatively low (converter and auxiliary facility costs are 0.12 yuan / W and 0.1 yuan / W respectively), operation and maintenance costs account for 3%, the unit expansion cost is 500 yuan / kW, the peak price is 1.0377 yuan / kWh, the off-peak price is 0.2302 yuan / kWh, and the power outage loss is approximately 15,000 yuan / MWh. In this type of scenario, electricity demand is stable, and energy storage has significant advantages in reducing electricity purchase costs and improving the local consumption capacity of clean energy. In island microgrids, battery costs are high (1.8 yuan / Wh), equipment investment costs are significantly higher than in other scenarios (converters and auxiliary facilities are both 0.2 yuan / W), operation and maintenance costs account for 5%, unit expansion costs reach 1000 yuan / kW, peak and off-peak prices are 1.0377 yuan / kWh and 0.2302 yuan / kWh respectively, and power outage losses are approximately 50,000 yuan / MWh. Since island microgrids typically lack large-scale grid support, the core role of energy storage in this scenario is to ensure independent operation and increase the proportion of renewable energy consumption. Its optimization benefits are primarily reflected in improved power supply security and reliability. The optimized energy storage configuration value efficiency index is as follows: Figure 1 As shown.

[0093] Through application analysis of the above four typical microgrid scenarios, the method of the present invention can comprehensively evaluate the technical performance of energy storage systems by combining the differences in technical characteristics under different parameter conditions, and provide a scientific reference for the energy storage configuration and operation of different types of microgrids.

[0094] Example 2 This embodiment provides an intelligent energy storage configuration optimization system for microgrid scenarios, including: The scenario segmentation module is used to classify microgrid application scenario types based on load characteristics and operational objectives. The model building module is used to build a comprehensive performance evaluation model for energy storage systems and calculate the energy storage configuration value efficiency index. The optimization module is used to iteratively optimize the configuration parameters of the energy storage system based on the energy storage configuration value efficiency index until it meets the preset threshold conditions. The scheme output module is used to output energy storage configuration schemes that meet the threshold conditions.

[0095] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0096] It should be understood that the above description of the preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. It is neither necessary nor possible to exhaustively describe all possible implementations. Those skilled in the art, guided by this invention, can make substitutions or modifications without departing from the scope of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for optimizing energy storage configuration in microgrid scenarios, characterized in that, Includes the following steps: Microgrid application scenarios are classified according to load characteristics and operational objectives; Construct a comprehensive performance evaluation model for energy storage systems, with the energy storage configuration value efficiency index as the optimization target; Based on the energy storage configuration value efficiency index, the configuration parameters of the energy storage system are iteratively optimized until they meet the preset threshold conditions. Output an energy storage configuration scheme that meets the threshold condition.

2. The energy storage configuration optimization method for microgrid scenarios according to claim 1, characterized in that, The microgrid application scenarios include industrial park microgrids, data center microgrids, community / rural microgrids, and island microgrids.

3. The energy storage configuration optimization method for microgrid scenarios according to claim 1, characterized in that, The formula for calculating the value efficiency index of the energy storage configuration is as follows: in, These correspond to:

1. Industrial park microgrid, 2. Data center microgrid, 3. Community / rural microgrid, and 4. Island microgrid, respectively. For the first i Value efficiency index of energy storage configuration in microgrid-like scenarios; Indicates the first The comprehensive annualized value of microgrid-like scenarios. For the first Net present value of total cost over the entire lifecycle of energy storage projects in microgrid-like scenarios.

4. The energy storage configuration optimization method for microgrid scenarios according to claim 3, characterized in that, The first The comprehensive annualized value of microgrid-like scenarios is: in, Indicates the first The value of expansion and replacement in microgrid-like scenarios Indicates the first The lifecycle operational efficiency of energy storage systems in microgrid-like scenarios is achieved through optimized charging and discharging strategies. Indicates the first The equivalent green value of the annual discharge of energy storage systems in microgrid-like scenarios. For the first The annualized value of energy storage in microgrid-like scenarios to reduce grid outage losses. Indicates the first The value of annual line loss savings in microgrid-like scenarios; The discount rate; N This represents the operational lifespan of an energy storage project. n Indicates the operational phase of the energy storage project n Year.

5. The energy storage configuration optimization method for microgrid scenarios according to claim 3, characterized in that, No. The net present value of the total lifecycle cost of energy storage projects in microgrid-like scenarios is: in, The discount rate; Indicates the first Initial investment cost of energy storage power stations in microgrid-like scenarios For the first The annual operation and maintenance costs for microgrid-like scenarios. Indicates the first The first microgrid-like scenario Annual financial expenses For the first Battery replacement costs in microgrid-like scenarios For the first Depreciation impacts costs in microgrid-like scenarios; For the first time during the operating life of the energy storage system Year.

6. The energy storage configuration optimization method for microgrid scenarios according to claim 4, characterized in that, No. The value of capacity expansion and replacement in microgrid-like scenarios is calculated based on the present value analysis of capacity expansion delay or the system value calculation based on the equivalent replacement of capacity expansion with energy storage. The present value analysis calculation formula based on capacity expansion delay is as follows: This represents the total investment for expansion. This is the time required for the load to reach its maximum capacity without energy storage. The time required for the load to reach its maximum capacity when energy storage is configured; The system value calculation formula based on energy storage equivalent replacement capacity expansion is as follows: in, For energy storage systems in the first The average utilization efficiency coefficient for peak shaving in actual operation in microgrid-like scenarios; Indicates the first The unit capacity expansion cost in microgrid-like scenarios; Energy storage systems have the ability to reduce peak loads on the power grid.

7. The energy storage configuration optimization method for microgrid scenarios according to claim 4, characterized in that, No. The full lifecycle operational efficiency of energy storage systems in microgrid-like scenarios, achieved through optimized charging and discharging strategies, is as follows: No. The equivalent green value of the annual discharge of an energy storage system in a microgrid-like scenario is: No. In microgrid-like scenarios, the annualized value of energy storage in reducing grid outage losses is: Indicates the first The value of annual line loss savings in microgrid-like scenarios is: in, and These represent the peak and off-peak electricity prices, respectively. and They represent the first Annual discharge and charging volume; The market price of a unit of green certificate; This indicates the average annual power outage rate; Indicates the assessed value of the user's losses due to power outage; Indicates the electrical status of the energy storage device; Indicates the rated power of the energy storage system; Indicates the duration that can be supported; Indicates annual power savings due to line loss; This refers to the marginal electricity purchase price.

8. The energy storage configuration optimization method for microgrid scenarios according to claim 4, characterized in that, No. The initial investment cost of an energy storage power station in a microgrid-like scenario is: ； No. The annual operation and maintenance costs for microgrid-like scenarios are: No. The first microgrid-like scenario The annual financial expenses are: No. The battery replacement cost in microgrid-like scenarios is: No. The depreciation impact cost in microgrid-like scenarios is as follows: in, Indicates battery cost, Indicates the cost of the energy conversion device. Indicates the cost of auxiliary facilities, Other costs; This refers to the rated power of the energy storage system. This indicates the initial investment cost of an energy storage power station. This indicates the percentage of annual maintenance costs to initial investment. For income tax rates; Indicates the loan-to-value ratio. Indicates loan interest; This indicates the annual rate at which the cost of energy storage batteries decreases. For the lifespan of energy storage batteries; The number of times an energy storage battery can be replaced during its lifespan. Y represents the battery cost; Y represents the depreciation period of the energy storage power station. It is the residual value rate.

9. The energy storage configuration optimization method for microgrid scenarios according to claim 1, characterized in that, The iterative optimization process includes: By calculating the energy storage configuration value efficiency index under different capacity and power combinations and comparing it with a preset threshold, when the calculated energy storage configuration value efficiency index reaches the set threshold, it is determined to be a feasible capacity configuration scheme; if it does not reach the threshold, the energy storage capacity is further iteratively optimized until a feasible scheme that meets the threshold requirements is obtained.

10. A smart energy storage configuration optimization system for microgrid scenarios, characterized in that, include: The scenario segmentation module is used to classify microgrid application scenario types based on load characteristics and operational objectives. The model building module is used to build a comprehensive performance evaluation model for energy storage systems and calculate the energy storage configuration value efficiency index. The optimization module is used to iteratively optimize the configuration parameters of the energy storage system based on the energy storage configuration value efficiency index until it meets the preset threshold conditions. The scheme output module is used to output an energy storage configuration scheme that meets the threshold conditions; The intelligent energy storage configuration optimization system for microgrid scenarios is used to execute the steps in the intelligent energy storage configuration optimization method for microgrid scenarios as described in any one of claims 1-9.

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