Method for adjusting rental capacity of energy storage equipment

Abstract

The invention discloses an energy storage equipment lease capacity adjustment method, and relates to the technical field of energy storage equipment. The method comprises the steps of obtaining business data of energy storage equipment and peak and valley electricity prices of an area where the energy storage equipment is located, inputting the business data and the peak and valley electricity prices into a first economic benefit model and a second economic benefit model, outputting to obtain a first economic benefit and a second economic benefit, and executing capacity leasing if the second economic benefit is larger; after renting, net load power and residual electric quantity are collected in real time, a dynamic weight equation is constructed, and charging and discharging power and electricity purchasing and selling power are obtained through threshold analysis. And a cost objective function is constructed based on the charging and discharging power and the electricity purchasing and selling power, and a differential evolution algorithm is used for solving to obtain the optimal capacity configuration and the lowest operation cost. After the capacity is distributed according to the optimal configuration, the independent operation cost is obtained, the difference between the minimum operation cost and the independent operation cost is calculated through a Nash bargaining model, the independent operation cost is updated and fed back to the two models, re-evaluation is carried out to decide next lease, and lease capacity adjustment is achieved.

Description

Technical Field

[0001] This invention relates to the field of energy storage equipment technology, specifically to a method for adjusting the rental capacity of energy storage equipment. Background Technology

[0002] As the penetration rate of renewable energy continues to rise, the stability, reliability and efficiency of the power system face increasingly severe challenges. Shared energy storage has emerged as an innovative form, providing flexible energy storage supply through capacity leasing. With its advantages such as promoting the consumption of new energy, efficient dispatch, safety and controllability and significant economic benefits, it has shown broad application prospects and provided a new direction for solving industry pain points.

[0003] Traditional capacity leasing methods use capacity and power as the core criteria for decision-making. When the available capacity and rated power of the energy storage equipment meet the user's leasing needs, the leasing company directly allocates capacity according to the leasing needs. The entire decision-making process is not linked to the actual operating costs of the energy storage equipment.

[0004] Traditional methods only consider the capacity and power of energy storage devices, while ignoring their actual operating costs. Because the actual operating costs are not included in the evaluation framework for leasing decisions, optimization cannot be carried out based on the dynamic changes in operating costs, resulting in an incomplete assessment of economic benefits. Ultimately, this leads to a lack of competitiveness of energy storage devices in the electricity market that pursues economic efficiency. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for adjusting the rental capacity of energy storage equipment, thereby resolving the problems existing in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for adjusting the leased capacity of energy storage equipment, comprising the following steps: S1: Obtain business data of energy storage devices and peak-valley electricity price information for the region where the energy storage devices are located; S2: Input the business data and peak-valley electricity price information into the first economic benefit model and output the first economic benefit. Input the business data and peak-valley electricity price information into the second economic benefit model and output the second economic benefit. If the second economic benefit is greater than the first economic benefit, then execute the energy storage equipment capacity leasing. S3: After the energy storage equipment capacity leasing is executed, the net load power and remaining power of the energy storage equipment are collected in real time, a dynamic weight equation is constructed, and a threshold analysis is performed on the remaining power of the energy storage equipment based on the dynamic weight equation to calculate the charging and discharging power and the purchased and sold power. S4: Based on the purchased and sold power and the charging and discharging power, construct the cost objective function of the energy storage device, solve the cost objective function of the energy storage device through the differential evolution algorithm, and calculate the optimal capacity configuration and the lowest operating cost of the energy storage device. S5: Allocate the capacity of the energy storage equipment according to the optimal capacity configuration; after the capacity of the energy storage equipment is allocated, obtain the independent operating cost of the energy storage equipment, calculate the difference between the minimum operating cost and the independent operating cost of the energy storage equipment through the Nash bargaining model, update the independent operating cost according to the difference, obtain the updated independent operating cost, feed the updated independent operating cost back to the first economic benefit model and the second economic benefit model, re-evaluate the first economic benefit and the second economic benefit of the energy storage equipment, and then decide on the next energy storage equipment capacity lease, thereby realizing the adjustment of the leased capacity of the energy storage equipment.

[0007] Preferably, the step of inputting the business data and electricity price information into the first economic benefit model to obtain the first economic benefit includes the following specific steps: The calculation process of the first economic benefit evaluation model is as follows: Energy storage devices The total cost over the lease term is: in, For energy storage devices Total cost over the lease term, This refers to the unit price of the inverter (PCS). The unit price of the Battery Management System (BMS). This refers to the unit price of the battery. Unit price of other auxiliary facilities For energy storage devices Rated charge and discharge power, For the first The capacity of an energy storage device For the discount rate, The lifespan of energy storage equipment is expressed in years. It is the capital recovery factor, used to annualize the initial investment cost. The operation and maintenance cost coefficient represents the annual percentage of operation and maintenance costs to the initial investment for battery-based energy storage devices. The percentage is typically 1.5%–3.5% for capacitors used in energy storage devices. Generally 0.5%–1.5%, The serial number of the energy storage device. The rental period for energy storage equipment is in days. The independent operating cost of energy storage devices; The actual maximum number of charge-discharge cycles for energy storage device s during the lease period is:

[0008] in, For energy storage devices The actual maximum total number of charge-discharge cycles during the lease period. For energy storage devices The charge and discharge efficiency; Therefore, the revenue of all energy storage devices through off-peak electricity storage and peak-peak electricity sales is:

[0009] in, To calculate the first economic benefit under the first economic benefit model, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak hour electricity price in the area where the energy storage device is located.

[0010] Preferably, the step of inputting the business data and electricity price information into the second economic benefit model to obtain the second economic benefit includes the following steps: The calculation steps of the second economic benefit model are as follows: First, determine the current rental capacity allocation rules. When the capacity required by a rental order is less than or equal to the current available capacity of a single energy storage device, the order demand is accepted directly. When the capacity required by a rental order is greater than the current available capacity of a single energy storage device, the capacity is allocated according to the weight of the remaining available capacity. The allocated capacity for each order is as follows:

[0011] in, The total rental capacity required by the order. For safety margin coefficient, For the first The capacity of each energy storage device, For the first The actual leased capacity allocated to each energy storage device For indexing energy storage devices, This represents the total capacity of all energy storage devices. The tenant charge / discharge cycle count prediction function is:

[0012] in, The lease duration for the tenant, in days. The total capacity of energy storage equipment leased to tenants. This represents the average daily baseline cycle count, expressed in cycles per day, and is determined by the tenant type. The capacity sensitivity coefficient has a value ranging from 0.02 to 0.05 kWh. It is a key function for predicting the number of charge-discharge cycles a tenant will use during the lease term for energy storage capacity; Energy storage devices The number of loops consumed by the upper tenant is:

[0013] in, For energy storage devices The actual number of loops consumed by the upstream tenant. Representative energy storage devices The weighting of its capacity relative to the total capacity of the total energy storage equipment group; Energy storage devices Rental income is:

[0014] in, For energy storage devices Rental income received, For energy storage devices The unit price of capacity rental in the area The rental period is in days. For equipment Upstream tenant's power utilization rate The power / flow rate is expressed in yuan / kW / time. For tenants on the device The number of loops consumed; Energy storage devices The maximum theoretical number of charge-discharge cycles is:

[0015] in, Energy storage equipment during the lease period Maximum theoretical number of charge-discharge cycles, For energy storage devices The charge and discharge efficiency; Energy storage devices The actual number of charge-discharge cycles for the remaining capacity is:

[0016] in, Theoretically, this refers to energy storage equipment during the lease period. The maximum number of charge-discharge cycles that the remaining capacity can perform. For energy storage devices The maximum number of total charge / discharge cycles allowed during the lease period. For the actual energy storage equipment consumed by the tenant during the lease period The number of loops, The actual number of charge-discharge cycles of the remaining capacity of the energy storage device during the lease period; Energy storage devices The profit from arbitrage using remaining capacity during off-peak periods is:

[0017] in, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. The number of charge-discharge cycles for the remaining capacity of the energy storage device. For energy storage devices The charge and discharge efficiency, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak-hour electricity price in the region where the energy storage device is located For the first The capacity of an energy storage device For the first The actual leased capacity allocated to each energy storage device; Therefore, the total revenue during the lease period is:

[0018] in, The total revenue is calculated under the second economic benefit evaluation model. For energy storage devices Rental income, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. For energy storage devices Total cost over the lease term.

[0019] Preferably, if the second economic benefit is greater than the first economic benefit, then energy storage equipment capacity leasing is implemented, including the following steps: Second economic benefits First economic benefit If the economic benefits under the second economic benefit model are greater than those under the first economic benefit model, then the order will be accepted, the energy storage equipment capacity will be leased, and the dynamic capacity leasing adjustment mode will be activated.

[0020] Preferably, the real-time collection of the net load power and remaining power of the energy storage device to construct a dynamic weighting equation includes the following steps: The battery charge / discharge performance index of energy storage devices is:

[0021] in, The charging and discharging performance indicators of batteries for energy storage devices. This represents the maximum value of the charge and discharge performance index of the energy storage device's battery. This represents the percentage of remaining battery capacity in the energy storage device. The economic indicator for electricity purchase and sale (kg) is related to the time-of-use pricing of the distribution network. The formula for the economic indicator for electricity purchase and sale is:

[0022] in, For the economic indicators of electricity purchase and sale, This is an economic indicator for electricity purchase and sale during normal electricity price periods. The adjustment step size for the economic indicators of electricity purchase and sale, During periods of low electricity prices, During the period of medium electricity price, This is a period of high electricity prices; The weights of the calculated indicators are:

[0023]

[0024] in, This indicates the weight of the battery charging and discharging power of the energy storage device relative to the total power. This indicates the weight of the power purchased and sold in the distribution network relative to the total power. A dynamic weighting equation is constructed, and the allocated battery charging / discharging power and purchased / sold power of the energy storage device are as follows:

[0025]

[0026] in, The charging and discharging power of the batteries in energy storage devices. The power consumption of batteries for energy storage devices.

[0027] Preferably, the step of performing threshold analysis on the remaining power of the energy storage device based on the dynamic weighting equation to calculate the charging / discharging power and the power purchased / sold includes the following specific steps: The threshold analysis process is as follows: when the remaining power of the energy storage device is lower than the first threshold of 0.2 but higher than the second threshold of 0, the charging and discharging power and the power purchased and sold are redistributed through a dynamic weighting equation. The weighting equation includes a charging and discharging performance index based on the remaining power of the energy storage device and an economic index for power purchase and sale based on time-of-use pricing. The optimal energy storage device capacity configuration is obtained by solving the following algorithm.

[0028] Preferably, the step of constructing the cost objective function for the energy storage device based on the purchased and sold power and the charging and discharging power includes the following specific steps: Based on the purchased and sold power and the charging and discharging power, a cost objective function for energy storage equipment is constructed as follows:

[0029] in, This indicates the daily peak-shaving and valley-filling revenue of energy storage device batteries. This represents the daily revenue from buying and selling electricity from the distribution network. This represents the average daily operating and maintenance cost of batteries for energy storage devices. This represents the average daily operating and maintenance cost of capacitors for energy storage devices. This represents the total number of all energy storage devices. The investment and construction cost of a single energy storage device battery is:

[0030] in, This refers to the battery configuration capacity of the energy storage device, measured in kWh. It is the capacity cost coefficient of the batteries in energy storage devices. It is the operation and maintenance cost coefficient of energy storage equipment and battery energy storage equipment. It is the discount rate. This refers to the rated operating life of the battery in the energy storage device, expressed in years. The investment and construction cost of a single energy storage device capacitor is:

[0031] in, This indicates the configured capacity of the capacitors in the energy storage device, expressed in kWh. This represents the capacity cost factor of capacitors in energy storage devices. This represents the operating and maintenance cost coefficient of the capacitor bank in the energy storage device. This refers to the rated operating life of the capacitors in energy storage equipment, expressed in years. The peak shaving and valley filling benefits of a single energy storage device are:

[0032] in, For the first The charging and discharging power of batteries in time-limited energy storage devices. Number each time period. The duration of this period is in hours. The peak-shaving and valley-filling electricity price for this period is collected in real time; The total daily revenue from electricity purchase and sale for a single energy storage device is:

[0033] in, For the purchase and sale of electricity for energy storage devices, This refers to the electricity purchase and sale price collected in real time for that period.

[0034] Preferably, the step of solving the cost objective function of the energy storage device using the differential evolution algorithm to calculate the optimal capacity configuration and minimum operating cost of the energy storage device includes the following specific steps: The cost objective function of the energy storage device is used as the fitness function of the differential evolution algorithm, and the capacity of the energy storage device's capacitor bank and battery are used as control variables. The optimization objective is to minimize the fitness function. The differential evolution algorithm is used to solve the problem, and the optimal capacity configuration and the corresponding minimum operating cost are output.

[0035] Preferably, the process of allocating the capacity of the energy storage device according to the optimal capacity configuration includes the following specific steps: The optimal capacity configuration obtained from the differential evolution algorithm includes the battery capacity of the energy storage device. With capacitor bank capacity For battery-powered devices, according to The ratio of self-operated arbitrage capacity to leaseable capacity is redefined; for supercapacitor equipment, it is calculated according to... The value dynamically adjusts the leasing weight of its high-frequency response capacity.

[0036] Preferably, the step of calculating the difference between the minimum operating cost and the independent operating cost of the energy storage device using the Nash bargaining model, and updating the independent operating cost based on the difference to obtain the updated independent operating cost, includes the following specific steps: The objective function of the bargaining transfer problem is:

[0037] in, The negotiated transfer value for each microgrid energy storage device. For the first The daily operating cost of a microgrid energy storage device when operating independently. For the first Optimal operating cost of microgrid energy storage devices; The objective function of the bargaining transfer problem is solved using the IPOPT solver, and the bargaining transfer value is obtained after solving. And calculate the updated standalone operating cost. : = - .

[0038] This invention provides a method for adjusting the rental capacity of energy storage equipment, which involves machine learning and deep learning technologies, and has the following beneficial effects: (1) By responding to time-of-use electricity price fluctuations through dynamic weight equations, the revenue from purchasing and selling electricity is increased while ensuring the safe operation of the energy storage equipment batteries. The capacity of the energy storage equipment is adjusted by combining differential evolution algorithm and the leasing strategy is continuously iterated through the Nash bargaining feedback mechanism, ultimately reducing the overall cost of the energy storage equipment.

[0039] (2) The threshold analysis method effectively prevents battery degradation caused by deep charging and discharging and extends the equipment life cycle; at the same time, the hard constraint of charging and discharging power and the power balance equation of the interconnection bus work together to reduce the failure rate of energy storage equipment under complex conditions.

[0040] (3) The differential evolution algorithm based on the adaptive mutation operator quickly converges to the global optimal solution, which solves the subjective defects of traditional capacity configuration; the updated independent operating cost is fed back to the decision-making level through the Nash bargaining model, forming a sustainable optimization mechanism of "economic decision-making-dynamic execution-closed-loop update". After multiple iterations, the total cost of energy storage equipment is further reduced, providing technical support for the high proportion of renewable energy consumption. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the steps for adjusting the rental capacity of energy storage equipment proposed in this invention; Figure 2 This is a hierarchical diagram of the steps involved in performing energy storage equipment capacity leasing in the energy storage equipment leasing capacity adjustment method proposed in this invention; Figure 3 This is a step hierarchy diagram of the updated independent operating cost in the energy storage equipment leasing capacity adjustment method proposed in this invention. Detailed Implementation

[0043] 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.

[0044] Please see Figures 1-3 The present invention provides a technical solution: a method for adjusting the rental capacity of energy storage equipment.

[0045] S1: Obtain business data of energy storage devices and peak-valley electricity price information for the region where the energy storage devices are located.

[0046] The system acquires key business data of multiple energy storage devices and dynamic electricity price information of their respective regions in real time. The business data of the multiple energy storage devices includes at least the energy capacity, power capacity or charge / discharge power of the energy storage devices, and the limit on the number of charge / discharge cycles. The electricity price information includes peak electricity price and off-peak electricity price. On the energy storage unit side, the ADIADE7913 high-precision coulomb counter is embedded in the battery module to achieve accurate acquisition of energy capacity. This chip continuously accumulates the charging and discharging ampere-hour values ​​with a sampling error of ±0.5%, and works with the MAX14920 voltage sensor to acquire the open-circuit voltage in real time. The capacity decay value is dynamically corrected through the OCV-SOC curve model. Power capacity monitoring relies on the closed-loop Hall current sensor, such as the LEM LAH 200-P, configured on the DC side of the inverter PCS. Its 100kHz bandwidth can capture microsecond-level current transients. Combined with the TI ISO224 DC voltage sampling module, the instantaneous power is calculated and uploaded to the local monitoring device at a 50ms cycle via the CAN bus protocol. The dynamic charging and discharging power is obtained through multi-source data fusion. At the battery cluster level, the battery management system (BMS) acquires the charging and discharging current through the internal Shunt resistor array with a resolution of 1mA. After processing by the SOH algorithm, the safe power boundary value is output, while the actual power fluctuation is calibrated in real time through the IGBT gate drive feedback signal in the PCS controller. Electricity price information collection employs a dual-channel mechanism: the primary channel connects to the power grid company's API port via an Advantech EKI-1360 industrial gateway deployed at the station control layer, acquiring the time-of-use electricity price table for the next 24 hours hourly using the Modbus TCP / IP protocol. The typical multiplier for peak electricity prices (08:00-12:00 and 18:00-22:00) is 1.8-2.5, while the typical multiplier for off-peak electricity prices (00:00-06:00) is 0.3-0.6. The backup channel directly analyzes the electricity meter pulse signal via a Siemens PAC4200 smart meter, automatically switching to real-time electricity price collection mode when the primary channel is interrupted. All data streams ultimately converge to the Pi System architecture real-time database of the Energy Management System (EMS), providing a millisecond-level time-stamped data foundation for subsequent optimized scheduling.

[0047] S2: Input the business data and electricity price information into the first economic benefit model to obtain the first economic benefit; input the business data and electricity price information into the second economic benefit model to obtain the second economic benefit; if the second economic benefit is greater than the first economic benefit, then execute the energy storage equipment capacity leasing.

[0048] The operational data of the energy storage device and the electricity price information of the area where the energy storage device is located are input into the first economic benefit model to obtain the first economic benefit of the energy storage device. The first economic benefit model is trained based on the historical operational data of the energy storage device, the historical electricity price information of the area where the energy storage device is located, and the economic benefit of the energy storage device in a first state. The first economic benefit is the revenue of the energy storage device solely from off-peak electricity storage and peak-peak electricity sales.

[0049] The first economic benefit model assesses the revenue of energy storage equipment by storing electricity during off-peak hours and selling it during peak hours. Its core step is to calculate the maximum revenue that the energy storage equipment can obtain by fully utilizing its power and energy capacity during the lease period and carrying out charge-discharge cycles under the peak-valley electricity price difference.

[0050] The calculation process of the first economic benefit evaluation model is as follows: Energy storage devices The total cost over the lease term is: in, For energy storage devices Total cost over the lease term, This refers to the unit price of the inverter (PCS). The unit price of the Battery Management System (BMS). This refers to the unit price of the battery. Unit price of other auxiliary facilities For energy storage devices Rated charge and discharge power, For the first The capacity of an energy storage device For the discount rate, The lifespan of energy storage equipment is expressed in years. It is the capital recovery factor, used to annualize the initial investment cost. The operation and maintenance cost coefficient represents the annual percentage of operation and maintenance costs to the initial investment for battery-based energy storage devices. The percentage is typically 1.5%–3.5% for capacitors used in energy storage devices. Generally 0.5%–1.5%, The serial number of the energy storage device. The rental period for energy storage equipment is in days. This refers to the independent operating cost of energy storage devices.

[0051] It should be noted that the inverter is the core equipment that enables bidirectional energy conversion between DC battery packs and the AC grid. Its unit price is usually measured in yuan / watt, with a typical industrial and commercial energy storage inverter priced at approximately 0.8–1.2 yuan / W. The battery management system (BMS) is responsible for battery status monitoring, safety protection, and equalization control, and its unit price is measured in yuan / kWh, with mid-to-low-end solutions priced at 50–100 yuan / kWh. The discount rate is a quantitative indicator of the time value of capital, reflecting the required return on investment or financing costs. It amortizes the total life-cycle investment cost of energy storage equipment over the lease period, with an industry benchmark of 8–12%.

[0052] The actual maximum number of charge-discharge cycles for energy storage device s during the lease period is:

[0053] in, For energy storage devices The actual maximum total number of charge-discharge cycles during the lease period. For energy storage devices The charging and discharging efficiency.

[0054] Therefore, the revenue of all energy storage devices through off-peak electricity storage and peak-peak electricity sales is:

[0055] in, To calculate the first economic benefit under the first economic benefit model, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak hour electricity price in the area where the energy storage device is located.

[0056] The business data of the energy storage device, the limit of the number of charge and discharge cycles of the energy storage device during the lease period, the electricity price information of the area where the energy storage device is located, the lease period, and the leased capacity are input into the second economic benefit model to obtain the second economic benefit of the energy storage device; the second economic benefit represents the revenue obtained by the energy storage device from leasing out part or all of its capacity.

[0057] The second economic benefit model is trained based on historical business data of the energy storage equipment, historical electricity price information of the region where the energy storage equipment is located, and the charge / discharge cycle limits and corresponding economic benefit rates of the energy storage equipment. The second economic benefit evaluation model can take the following modes: Regression analysis mode: using historical data and electricity price information as parameters, regression analysis is used to predict future economic benefit rates. Linear regression, ridge regression, or Lasso regression methods can be used to construct the model; Decision tree mode: using historical data and electricity price information as parameters, decision tree algorithms are used to predict future economic benefit rates; Random forest mode: using historical data and electricity price information as parameters, random forest algorithms are used to predict future economic benefit rates.

[0058] The calculation steps of the second economic benefit model are as follows: First, determine the current rental capacity allocation rules. When the capacity required by a rental order is less than or equal to the current available capacity of a single energy storage device, the order demand is accepted directly. When the capacity required by a rental order is greater than the current available capacity of a single energy storage device, the capacity is allocated according to the weight of the remaining available capacity. The allocated capacity for each order is as follows:

[0059] in, The total rental capacity required by the order. For safety margin coefficient, For the first The capacity of each energy storage device, For the first The actual leased capacity allocated to each energy storage device For indexing energy storage devices, This represents the total capacity of all energy storage devices.

[0060] It should be noted that the leased capacity is allocated according to the capacity weight of the energy storage devices. For example, if an energy storage device has a total capacity Ecap = 2000kWh, a safety factor ηsafe = 0.8, and there are 4 energy storage devices with storage capacities of 600kWh, 600kWh, 400kWh, and 400kWh respectively, and the total capacity order demand is... When the total capacity is 800 kWh, the weighted allocation is as follows: Energy storage device 1 is allocated 192 kWh, Energy storage device 2 is allocated 192 kWh, Energy storage device 3 is allocated 128 kWh, and Energy storage device 4 is allocated 128 kWh.

[0061] The tenant charge / discharge cycle count prediction function is:

[0062] in, The lease duration for the tenant, in days. The total capacity of energy storage equipment leased to tenants. This represents the average daily baseline cycle count, expressed in cycles per day, and is determined by the tenant type. The capacity sensitivity coefficient has a value ranging from 0.02 to 0.05 kWh. It is a key function for predicting the number of charge-discharge cycles a tenant will use during the lease term for energy storage capacity.

[0063] It should be noted that this function prediction is based on historical rental capacity and rental duration data. This function simulates typical tenant usage behavior. The larger the rental capacity and the longer the rental period, the more charge-discharge cycles there are, but there is a law of diminishing marginal effect.

[0064] Energy storage devices The number of loops consumed by the upper tenant is:

[0065] in, For energy storage devices The actual number of loops consumed by the upstream tenant. Representative energy storage devices The weighting of its capacity relative to the total capacity of the total energy storage equipment group.

[0066] Energy storage devices Rental income is:

[0067] in, For energy storage devices Rental income received, For energy storage devices The unit price of capacity rental in the area The rental period is in days. For equipment Upstream tenant's power utilization rate The power / flow rate is expressed in yuan / kW / time. For tenants on the device The number of loops consumed.

[0068] Energy storage devices The maximum theoretical number of charge-discharge cycles is:

[0069] in, Energy storage equipment during the lease period Maximum theoretical number of charge-discharge cycles, For energy storage devices The charging and discharging efficiency.

[0070] Energy storage devices The actual number of charge-discharge cycles for the remaining capacity is:

[0071] in, Theoretically, this refers to energy storage equipment during the lease period. The maximum number of charge-discharge cycles that the remaining capacity can perform. For energy storage devices The maximum number of total charge / discharge cycles allowed during the lease period. For the actual energy storage equipment consumed by the tenant during the lease period The number of loops, This refers to the actual number of charge-discharge cycles of the remaining capacity of the energy storage device during the lease period.

[0072] It should be noted that, Its purpose is to ensure that the result is non-negative, depending on the number of times the tenant uses it. The total lifespan of the energy storage device has been reached or exceeded. If the remaining lifespan is 0, then the lifespan of the energy storage device has been exhausted. This means that, assuming the equipment continuously performs charge-discharge cycles at rated power and efficiency during the rental period, the theoretical maximum number of complete cycles that can be completed is taken as the minimum number of cycles available. This ensures that the final number of available cycles does not exceed the physical limits and remaining lifespan allowed during the rental period. Even if the equipment still has a spare lifespan, it cannot perform more cycles within the limited rental time. Even if the rental period is long enough, the equipment cannot perform more cycles than its remaining lifespan, otherwise it will accelerate aging or damage.

[0073] Energy storage devices The profit from arbitrage using remaining capacity during off-peak periods is:

[0074] in, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. The number of charge-discharge cycles for the remaining capacity of the energy storage device. For energy storage devices The charge and discharge efficiency, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak-hour electricity price in the region where the energy storage device is located For the first The capacity of an energy storage device For the first The actual leased capacity allocated to each energy storage device.

[0075] Therefore, the total revenue during the lease period is:

[0076] in, The total revenue is calculated under the second economic benefit evaluation model. For energy storage devices Rental income, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. For energy storage devices Total cost over the lease term.

[0077] These two models together form a decision-making framework that helps energy storage equipment owners quantify and compare the economics of different operating strategies, and continuously optimize future leasing strategies based on actual leasing experience. > If the forecast indicates that the leasing model is more profitable than the pure self-operation model, then the order will be accepted, the energy storage equipment capacity will be leased, and the dynamic capacity leasing adjustment mode will be activated.

[0078] S3: After the energy storage equipment capacity leasing is executed, the net load power and remaining power of the energy storage equipment are collected in real time, a dynamic weight equation is constructed, and a threshold analysis is performed on the remaining power of the energy storage equipment based on the dynamic weight equation to calculate the charging and discharging power and the power purchased and sold.

[0079] After the order is executed, the lessor monitors the net load power of the tie bus in real time. The remaining power ratio of energy storage device batteries Based on the dynamic weight equation, charging and discharging commands are remotely issued, and the power and energy data of the energy storage device are transmitted back to the lessor's cloud platform via the edge gateway deployed on the user equipment using 5G.

[0080] When the energy storage device's battery has sufficient remaining power. , The energy storage device's batteries will absorb or provide the charging and discharging power through discharging or charging to maintain the power balance of the interconnecting bus. , This refers to the power purchased and sold by the energy storage device. At this point, the energy storage device's battery is completely isolated from the power grid and focuses on internal regulation and arbitrage.

[0081] Energy storage device batteries When the load is reduced to 0, the battery energy storage of the energy storage device stops operating, and the entire load is borne by the distribution network, which absorbs the net load power of the interconnection bus. The energy storage device's battery stops all charging and discharging activities to protect it from severe damage caused by deep discharge. In order to ensure the power balance of the entire energy storage device, the distribution network becomes the only source or destination for power balance, responsible for absorbing all net load fluctuations on the interconnection bus.

[0082] When the remaining power of the energy storage device's battery is insufficient, it can purchase or sell electricity from the distribution network to alleviate the workload of the battery, improve the economic efficiency of the battery's participation in energy dispatch, and obtain revenue from purchasing and selling electricity. Therefore, this paper proposes energy storage device battery charge-discharge performance indicators and electricity purchase-and-sell economic indicators. The energy storage device battery charge-discharge performance index is:

[0083] in, The charging and discharging performance indicators of batteries for energy storage devices. This represents the maximum value of the charge and discharge performance index of the energy storage device's battery. This represents the percentage of remaining battery capacity in the energy storage device.

[0084] It should be noted that, The value is 0.6. When =0.6, =1, When =0.2, ≈0.2455, When kb = 0, kb = 0.

[0085] The economic indicator for electricity purchase and sale (kg) is related to the time-of-use pricing of the distribution network. The formula for the economic indicator for electricity purchase and sale is:

[0086] in, For the economic indicators of electricity purchase and sale, This is an economic indicator for electricity purchase and sale during normal electricity price periods. The adjustment step size for the economic indicators of electricity purchase and sale, During periods of low electricity prices, During the period of medium electricity price, This is a period of high electricity prices.

[0087] When the voltage is ≥0, multiple microgrids sell electricity to the distribution network; high electricity prices occur during these times. Maximum, low electricity price moment When Pgrid is at its minimum and < 0, multiple microgrids purchase electricity from the distribution network; this occurs during periods of high electricity prices. Minimum, low electricity price time maximum, For the economic indicators of electricity purchase and sale, The value is 0.15, and ∆k is 0.1.

[0088] The weights of the calculated indicators are:

[0089]

[0090] in, This indicates the weight of the battery charging and discharging power of the energy storage device relative to the total power. This indicates the weight of the power purchased and sold in the distribution network relative to the total power.

[0091] A dynamic weighting equation is constructed, and the allocated battery charging / discharging power and purchased / sold power of the energy storage device are as follows:

[0092]

[0093] in, The charging and discharging power of the batteries in energy storage devices. The power consumption of batteries for energy storage devices.

[0094] It should be noted that charging and discharging power represents the energy exchange rate between the battery of the energy storage device and the internal power network, which directly affects the lifespan of the device and its arbitrage capabilities. Buying and selling power refers to the instantaneous power value of the entire energy storage device when buying or selling electricity with the external power distribution network, which directly generates economic benefits or costs.

[0095] The threshold analysis process is as follows: when the remaining power of the energy storage device is lower than the first threshold of 0.2 but higher than the second threshold of 0, the charging and discharging power and the power purchased and sold are redistributed through a dynamic weighting equation. The weighting equation includes a charging and discharging performance index based on the remaining power of the energy storage device and an economic index for power purchase and sale based on time-of-use pricing. The optimal energy storage device capacity configuration is obtained by solving the following algorithm.

[0096] S4: Based on the purchased and sold power and the charging and discharging power, construct the cost objective function of the energy storage device, solve the cost objective function of the energy storage device through the differential evolution algorithm, and calculate the optimal capacity configuration and the lowest operating cost of the energy storage device.

[0097] The lessor aggregates all user data, considers the operation and maintenance costs of energy storage equipment, the peak shaving and valley filling revenue of energy storage equipment batteries, and the revenue from electricity purchase and sale. Based on the purchased and sold power and the charging and discharging power, a daily comprehensive cost objective function for energy storage equipment is constructed. The rated capacity, power, and safe and stable operation of energy storage equipment are used as constraints to solve for the optimal configuration of the capacity of the energy storage equipment capacitor bank and the energy storage equipment battery.

[0098] Based on the purchased and sold power and the charging and discharging power, a cost objective function for energy storage equipment is constructed as follows:

[0099] in, This indicates the daily peak-shaving and valley-filling revenue of energy storage device batteries. This represents the daily revenue from buying and selling electricity from the distribution network. This represents the average daily operating and maintenance cost of batteries for energy storage devices. This represents the average daily operating and maintenance cost of capacitors for energy storage devices. This represents the total number of all energy storage devices.

[0100] The investment and construction cost of a single energy storage device battery is:

[0101] in, This refers to the battery configuration capacity of the energy storage device, measured in kWh. It is the capacity cost coefficient of the batteries in energy storage devices. It is the operation and maintenance cost coefficient of energy storage equipment and battery energy storage equipment. It is the discount rate. This refers to the rated operating life of the battery in the energy storage device, expressed in years.

[0102] The investment and construction cost of a single energy storage device capacitor is:

[0103] in, This indicates the configured capacity of the capacitors in the energy storage device, expressed in kWh. This represents the capacity cost factor of capacitors in energy storage devices. This represents the operating and maintenance cost coefficient of the capacitor bank in the energy storage device. This refers to the rated operating life of the capacitors in energy storage equipment, expressed in years.

[0104] Energy storage devices provide subsidies per kilowatt-hour for battery charging and discharging, with the revenue generated from charging and discharging representing the peak shaving and valley filling revenue. A larger value indicates a greater peak shaving and valley filling benefit for the energy storage device's batteries. The peak shaving and valley filling benefit for a single energy storage device is:

[0105] in, For the first The charging and discharging power of batteries in time-limited energy storage devices. Number each time period. The duration of this period is in hours. This refers to the peak-shaving and valley-filling electricity price collected in real time for this period.

[0106] The total daily revenue from electricity purchase and sale for a single energy storage device is:

[0107] in, For the purchase and sale of electricity for energy storage devices, This refers to the electricity purchase and sale price collected in real time for that period.

[0108] The total daily revenue from electricity purchase and sale for energy storage devices represents the subsidy revenue from the distribution network to these devices. Higher revenue from electricity purchase and sale results in lower overall daily operating costs for the energy storage devices. Due to time-of-use pricing, the total daily revenue from electricity purchase and sale for energy storage devices is the sum of revenue from each time period.

[0109] To prevent individual energy storage device batteries Deep charge and discharge cause capacity decay, subject to the following constraints:

[0110] in, This refers to the state of charge (SOC) of the batteries in energy storage devices. and These are the minimum and maximum values ​​of the battery state of charge for the energy storage device, respectively.

[0111] To avoid individual energy storage device capacitor banks Electrode overvoltage breakdown, constraint condition is:

[0112] in, This refers to the state of charge of the capacitors in the energy storage device. and These represent the maximum and minimum allowable values ​​for the normal operation of capacitors in energy storage devices, respectively.

[0113] The charging and discharging power constraints for a single energy storage device are:

[0114]

[0115] in, , These represent the rated charging and discharging power of the energy storage device's battery and capacitor, respectively.

[0116] The power purchase and sales constraints of a single energy storage device to the distribution network are as follows:

[0117] in, For energy storage devices, electricity is purchased and sold from the distribution network. The maximum power allowed for energy storage devices to purchase or sell electricity from the distribution network.

[0118] The Differential Evolutionary Algorithm (DEA) is a population-based adaptive global optimization algorithm characterized by its simple structure, ease of implementation, fast convergence, and robustness. When using DEA to solve the energy storage capacity configuration model established in this paper, it is necessary to implement the key aspects of DEA's application, including the fitness function, defining optimization variables, and handling equality and inequality constraints.

[0119] The cost objective function of the energy storage device is used as the fitness function of the differential evolution algorithm, and the capacity of the energy storage device's capacitor bank and battery are used as control variables. The optimization objective is to minimize the fitness function.

[0120] The process of using the differential evolution algorithm to find the optimal solution for the capacity configuration of energy storage devices is as follows: First, determine the control parameters in the differential evolution algorithm: the optimization variables of the energy storage device. =2( , ), take population size =20; Mutation operator The mutation operator typically takes a real constant between 0 and 2, while the crossover operator... The crossover operator is usually a real constant between 0 and 1, representing the probability that the new experimental vector parameters come from the mutated vector. You can start by taking a value of (0,1) to observe the maximum number of generations. Differential evolution algorithm runs to That is, stop running.

[0121] The adaptive mutation operator is as follows:

[0122]

[0123] in, For adaptive mutation operators, This is a mutation operator, typically taking a value of 0.5. The maximum number of generations is set to 400 here. This represents the current generation.

[0124] When the value is small, enhance the global search capability to avoid getting trapped in local optima, such as suboptimal energy storage device battery capacity. or supercapacitor capacity combination, near At the same time, by reducing the intensity of disturbances and refining the search for the optimal capacity configuration, this strategy ensures that the algorithm quickly converges to the economically optimal energy storage configuration (lowest overall daily cost), while avoiding the influence of fixed... This can lead to precocious puberty or shocks.

[0125] An initial population of capacitor banks and battery capacity vectors of energy storage devices is randomly generated within the allowable capacity range of the energy storage device. When the number of generations q=1:

[0126] Here, rand(0,1) represents uniform sampling within the feasible solution space to generate an initial capacity scheme. This represents the initial value of the battery capacity. This indicates the lower limit of the safe capacity of energy storage devices. This indicates the safe upper limit of the capacity of energy storage devices. Indicates the individual number in the population. Optimization variables Dimension index.

[0127] The daily comprehensive cost of the energy storage device is calculated as the fitness value of the initial individual. Next, it is determined whether the termination condition or the maximum number of generations has been reached: if so, the evolution is terminated and the fitness function value is output; otherwise, the next operation is performed. The capacity of the energy storage device is subjected to mutation and crossover operations to handle the boundary conditions and obtain a temporary computational population.

[0128] The mutation operation is shown in the following equation:

[0129] in, This represents the updated mutation vector. ,、 , This represents three distinct parameter vectors.

[0130] Three differentiated capacity schemes are randomly selected, such as For batteries used in large-capacity energy storage devices, It is a small-capacity supercapacitor. For balanced configuration, vector differencing is used. Generate perturbation direction and superimpose adaptive mutation factor. Later and By fusion, a new capacity test solution is constructed.

[0131] The cross operation is shown in the following formula:

[0132] in, express The energy storage unit in the first The new experimental variables generated after cross-parameters are used to perform cross-validation. Indicates the first A random number with an estimated value between (0, 1). Specifically referring to the present The first generation of the population Each energy storage unit has parameters The original value on This is the preset crossover probability threshold.

[0133] Crossover probability Control New Solution Inherited mutation vector The proportion, when If the mutation-generated capacity value is used, then the original population individual value is retained, i.e., the existing capacity configuration is maintained.

[0134] The objective function is to calculate the daily comprehensive cost of each energy storage device in the capacity population. A one-to-one selection operation is performed between individuals in the temporary calculation population and individuals from the previous population to obtain a new population. The evolutionary generations q = q + 1 are repeated until the loop ends, outputting the optimal capacity configuration and the corresponding minimum operating cost.

[0135] S5: Allocate the capacity of the energy storage equipment according to the optimal capacity configuration; after the capacity of the energy storage equipment is allocated, obtain the independent operating cost of the energy storage equipment, calculate the difference between the minimum operating cost and the independent operating cost of the energy storage equipment through the Nash bargaining model, update the independent operating cost according to the difference, obtain the updated independent operating cost, feed the updated independent operating cost back to the first economic benefit model and the second economic benefit model, re-evaluate the first economic benefit and the second economic benefit of the energy storage equipment, and then decide on the next energy storage equipment capacity lease, thereby realizing the adjustment of the leased capacity of the energy storage equipment.

[0136] The optimal capacity configuration (including the battery capacity of energy storage devices) is obtained by solving the differential evolution algorithm. With capacitor bank capacity The globally optimal solution is obtained by the lessor remotely issuing capacity adjustment commands to each energy storage device through the energy management system. First, the optimized capacity parameters are dynamically written into the configuration register of the device controller, and the virtual capacity pool allocation strategy of the energy storage cluster is updated synchronously—for battery-type devices, based on… The ratio of self-operated arbitrage capacity to leaseable capacity will be re-allocated (e.g., after optimization, 450kWh of the original 600kWh equipment will be allocated to the leasing pool, and 150kWh will be reserved for peak-valley arbitrage); for supercapacitor equipment, the ratio will be adjusted accordingly. The lease weight of its high-frequency response capacity is dynamically adjusted. This process synchronously triggers the threshold reconfiguration module of the device's local BMS, automatically updating... It includes protection parameters such as charging and discharging power limits, and updates the available capacity of the rental platform in real time.

[0137] After adjusting the capacity of the energy storage device, obtain the independent operating cost of the energy storage device without participating in energy storage leasing. Since adjusting the capacity of the aforementioned energy storage devices directly affects the independent operating costs of each device, the lessor, in order to incentivize participation in the leasing process and fairly distribute the benefits of the energy storage devices, introduces a Nash bargaining model to address the aforementioned independent operating costs. As the basic input to the Nash bargaining model, the model is specifically represented as follows:

[0138] in, and The first The daily operating cost of an energy storage device under independent operation versus the operating cost after Nash negotiation. The number of energy storage device clusters. The core constraint is the cost of each microgrid energy storage device after cooperative operation. It must not exceed its cost benchmark for independent operation. , It is the serial number of each energy storage device.

[0139] This model is a non-convex nonlinear problem, which is difficult to solve directly. Therefore, it is decomposed into two convex subproblems: the social benefit maximization problem and the bargaining transfer problem. The social benefit maximization problem is as follows:

[0140] This step aims to minimize the total operating cost of the energy storage network to maximize social benefits. The decision variables are the technical optimization variables of all energy storage devices within a single day, including generation planning, energy storage dispatch, and power exchange strategies between energy storage devices. The core constraints fall into two categories: first, economic constraints, requiring that the cooperative operating cost of each microgrid does not exceed its independent operating cost benchmark (i.e., ...). ≤ ,∀ Secondly, there are technical constraints, involving physical conditions such as power balance, equipment physical limits, and network transmission capacity. These constraints are detailed above. This problem is a convex optimization problem, which can be efficiently solved using CPLEX or IPOPT solvers. The output results are the preliminary optimal operating costs for each microgrid. and minimize total cost ∑ The core objective of this step is to achieve global technical optimization while ensuring the rationality of each energy storage device, thereby generating an initial cost allocation scheme and quantifying the cooperative surplus ∑( - ).

[0141] The objective function of the bargaining transfer problem is:

[0142] in, The negotiated transfer value for each microgrid energy storage device. For the first The daily operating cost of a microgrid energy storage device when operating independently. For the first Optimal operating cost of microgrid energy storage devices.

[0143] The objective function must satisfy two key constraints: first, the budget balance constraint ∑ =0, ensuring that the total amount of transfer payments is zero; secondly, positive return constraint. - + >0, ensuring that each microgrid energy storage device achieves strict cost savings after adjustment.

[0144] It should be noted that this step is based on the results of the problem of maximizing social benefits. The cooperative surplus is fairly distributed through a financial transfer mechanism, with the bargained transfer value as the decision variable. This represents cost compensation or allocation. Since the objective function of the bargaining transfer problem contains logarithmic terms, it is solved using the IPOPT solver to obtain the following result. And calculate the updated standalone operating cost: = - The mathematical essence of this step is to achieve the optimal condition for Nash bargaining, namely, that the operating cost savings of all microgrid energy storage devices are equal. Its engineering significance lies in enhancing cooperation enthusiasm through financial incentives, transforming the technically optimal solution into a stable and fair economic distribution scheme, and improving the economic benefits of energy storage devices.

[0145] when < At that time, energy storage equipment benefits from increased leasing scale, improved equipment utilization, and reduced estimated future independent operating costs. Conversely, expanding cooperation strategies increases cost benchmarks. Microgrid behavior adjustments lead to changes in their independent operation strategies; expanding leasing scale and increasing equipment utilization raises independent costs. Significantly reduced; if the scale of cooperation is reduced, then... Passive upgrade. (After the update) The differential evolution algorithm is injected as a key parameter: on the one hand, it optimizes the feasible region by modifying the power exchange constraints; on the other hand, it recalculates the peak shaving and valley filling benefit weights by driving the fitness function, i.e., the daily comprehensive cost f, with a dynamic cost benchmark. This algorithm is based on the capacity configuration of hybrid energy storage (…). , Using the evolutionary variable as an adaptive mutation operator, the solution space is explored. Under the constraints updated by Nash bargaining, the optimal independent operating cost is regenerated, and its cost calculation logic is corrected. The updated capacity configuration serves as the basis for capacity allocation in the next lease. Simultaneously, the first and second economic benefit assessment models are input to re-evaluate the first and second economic benefits of the energy storage equipment and the economic returns. If the second economic benefit is greater than the first, energy storage capacity leasing is executed. This process forms a positive feedback chain of "capacity configuration → cost benchmark → economic assessment → leasing decision," achieving closed-loop dynamic adjustment of leased resources. After multiple rounds of closed-loop iteration, the average independent operating cost is reduced by 8.5%, and the total cost is further reduced by 17%, achieving a collaborative ecosystem of Pareto continuous improvement.

[0146] This invention provides a method for dynamically adjusting the leasing capacity of energy storage equipment. By establishing a first economic benefit assessment model and a second economic benefit assessment model, the expected returns under a pure self-operation mode and a leasing mode are quantified respectively. The decision to accept a leasing order is made based on a comparison of the returns. After executing a leasing order, a dynamic power allocation strategy is adopted: based on the remaining battery power status of the energy storage equipment, the net load power of the interconnection bus is controlled in zones. When the battery power is sufficient, the energy storage equipment's battery power is prioritized for consumption. When the battery power is insufficient, the charging and discharging power and the power purchased and sold by the energy storage equipment are dynamically allocated through a weighted equation. When the battery power is exhausted, the energy storage equipment is taken out of operation to ensure its safety. Furthermore, a differential evolution algorithm is used to optimize the hybrid energy storage capacity configuration, and a Nash bargaining model is used to update the independent operating cost, driving iterative updates of the leasing capacity scheme and forming a closed loop of "decision-execution-feedback".

[0147] This invention responds to time-of-use electricity price fluctuations through a dynamic weighted equation, thereby increasing the revenue from electricity purchase and sale while ensuring the safe operation of the energy storage device's batteries. It also combines a differential evolution algorithm to adjust the leased capacity of the energy storage device and continuously iterates the leasing strategy through a Nash bargaining feedback mechanism, ultimately reducing the overall cost of the energy storage device.

[0148] The threshold analysis method effectively prevents battery degradation caused by deep charging and discharging, and extends the life cycle of the equipment. At the same time, the hard constraint of charging and discharging power and the power balance equation of the interconnection bus work together to reduce the failure rate of energy storage equipment under complex conditions.

[0149] The differential evolution algorithm based on adaptive mutation operators quickly converges to the global optimum, solving the subjective defects of traditional capacity configuration. The updated independent operating cost is fed back to the decision-making level through the Nash bargaining model, forming a sustainable optimization mechanism of "economic decision-making-dynamic execution-closed-loop update". After multiple iterations, the total cost of energy storage equipment is further reduced, providing technical support for the high proportion of renewable energy consumption.

[0150] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, the phrase "comprising an element defined as..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0151] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for adjusting the rental capacity of energy storage equipment, characterized in that: Includes the following steps: S1: Obtain business data of energy storage devices and peak-valley electricity price information for the region where the energy storage devices are located; S2: Input the business data and peak-valley electricity price information into the first economic benefit model and output the first economic benefit. Input the business data and peak-valley electricity price information into the second economic benefit model and output the second economic benefit. If the second economic benefit is greater than the first economic benefit, then execute the energy storage equipment capacity leasing. S3: After the energy storage equipment capacity leasing is executed, the net load power and remaining power of the energy storage equipment are collected in real time, a dynamic weight equation is constructed, and a threshold analysis is performed on the remaining power of the energy storage equipment based on the dynamic weight equation to calculate the charging and discharging power and the purchased and sold power. S4: Based on the purchased and sold power and the charging and discharging power, construct the cost objective function of the energy storage device, solve the cost objective function of the energy storage device through the differential evolution algorithm, and calculate the optimal capacity configuration and the lowest operating cost of the energy storage device. S5: Allocate the capacity of the energy storage equipment according to the optimal capacity configuration; after the capacity of the energy storage equipment is allocated, obtain the independent operating cost of the energy storage equipment, calculate the difference between the minimum operating cost and the independent operating cost of the energy storage equipment through the Nash bargaining model, update the independent operating cost according to the difference, obtain the updated independent operating cost, feed the updated independent operating cost back to the first economic benefit model and the second economic benefit model, re-evaluate the first economic benefit and the second economic benefit of the energy storage equipment, and then decide on the next energy storage equipment capacity lease to realize the adjustment of the leased capacity of the energy storage equipment.

2. The method for adjusting the rental capacity of energy storage equipment according to claim 1, characterized in that: The step of inputting the business data and electricity price information into the first economic benefit model to obtain the first economic benefit includes the following specific steps: First economic benefit evaluation model, energy storage equipment The total cost over the lease term is: ； in, For energy storage devices Total cost over the lease term, This refers to the unit price of the inverter (PCS). The unit price of the Battery Management System (BMS). This refers to the unit price of the battery. Unit price of other auxiliary facilities For energy storage devices Rated charge and discharge power, For the first The capacity of an energy storage device For the discount rate, The lifespan of energy storage equipment is expressed in years. It is the capital recovery factor, used to annualize the initial investment cost. The operation and maintenance cost coefficient represents the annual percentage of operation and maintenance costs to the initial investment for battery-based energy storage devices. The percentage is typically 1.5%–3.5% for capacitors used in energy storage devices. Generally 0.5%–1.5%, The serial number of the energy storage device. The rental period for energy storage equipment is in days. The independent operating cost of energy storage devices; The actual maximum number of charge-discharge cycles for energy storage device s during the lease period is: ； in, For energy storage devices The actual maximum total number of charge-discharge cycles during the lease period. For energy storage devices The charge and discharge efficiency; Therefore, the revenue of all energy storage devices through off-peak electricity storage and peak-peak electricity sales is: ； in, To calculate the first economic benefit under the first economic benefit model, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak hour electricity price in the area where the energy storage device is located.

3. The method for adjusting the rental capacity of energy storage equipment according to claim 2, characterized in that: The step of inputting the business data and electricity price information into the second economic benefit model to obtain the second economic benefit includes the following steps: The calculation steps of the second economic benefit model are as follows: First, determine the current rental capacity allocation rules. When the capacity required by a rental order is less than or equal to the current available capacity of a single energy storage device, the order demand is accepted directly. When the capacity required by a rental order is greater than the current available capacity of a single energy storage device, the capacity is allocated according to the weight of the remaining available capacity. The allocated capacity for each order is as follows: ； in, The total rental capacity required by the order. For safety margin coefficient, For the first The capacity of each energy storage device, For the first The actual leased capacity allocated to each energy storage device For indexing energy storage devices, This represents the total capacity of all energy storage devices. The tenant charge / discharge cycle count prediction function is: ； in, The lease duration for the tenant, in days. The total capacity of energy storage equipment leased to the tenant. This represents the average daily baseline cycle count, expressed in cycles per day, and is determined by the tenant type. The capacity sensitivity coefficient ranges from 0.02 to 0.05 kWh. It is a key function for predicting the number of charge-discharge cycles a tenant will use during the lease term for energy storage capacity; Energy storage devices The number of loops consumed by the upper tenant is: ； in, For energy storage devices The actual number of loops consumed by the upstream tenant. Representative energy storage devices The weighting of its capacity relative to the total capacity of the total energy storage equipment group; Energy storage devices Rental income is: ； in, For energy storage devices Rental income received, For energy storage devices The unit price of capacity rental in the area The rental period is in days. For equipment Upstream tenant's power utilization rate The power / flow rate is expressed in yuan / kW / time. For tenants on the device The number of loops consumed; Energy storage devices The maximum theoretical number of charge-discharge cycles is: ； in, Energy storage equipment during the lease period Maximum theoretical number of charge-discharge cycles, For energy storage devices The charge and discharge efficiency; Energy storage devices The actual number of charge-discharge cycles for the remaining capacity is: ； in, Theoretically, this refers to energy storage equipment during the lease period. The maximum number of charge-discharge cycles that the remaining capacity can perform. For energy storage devices The maximum number of total charge / discharge cycles allowed during the lease period. For the actual energy storage equipment consumed by the tenant during the lease period The number of loops, The actual number of charge-discharge cycles of the remaining capacity of the energy storage device during the lease period; Energy storage devices The profit from arbitrage using remaining capacity during off-peak periods is: ； in, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. The number of charge-discharge cycles for the remaining capacity of the energy storage device. For energy storage devices The charge and discharge efficiency, For the first Off-peak electricity prices in the area where the energy storage device is located For the first Peak-hour electricity price in the region where the energy storage device is located For the first The capacity of an energy storage device For the first The actual leased capacity allocated to each energy storage device; Therefore, the total revenue during the lease term is: ； in, The total revenue is calculated under the second economic benefit evaluation model. For energy storage devices Rental income, For energy storage devices The profit from arbitrage using remaining capacity during off-peak periods. For energy storage devices Total cost over the lease term.

4. The method for adjusting the leased capacity of an energy storage device according to claim 3, characterized in that: If the second economic benefit is greater than the first economic benefit, then energy storage equipment capacity leasing will be implemented, including the following steps: Second economic benefits First economic benefit If the economic benefits under the second economic benefit model are greater than those under the first economic benefit model, then the order will be accepted, the energy storage equipment capacity will be leased, and the dynamic capacity leasing adjustment mode will be activated.

5. The method for adjusting the leased capacity of an energy storage device according to claim 4, characterized in that: The process of collecting the net load power and remaining power of the energy storage device in real time and constructing a dynamic weighting equation includes the following steps: The battery charge / discharge performance index of energy storage devices is: ； in, The charging and discharging performance indicators of batteries for energy storage devices. This represents the maximum value of the charge and discharge performance index of the energy storage device's battery. This represents the percentage of remaining battery capacity in the energy storage device. Economic indicators for electricity purchase and sale k g Related to the time-of-use pricing of the distribution network, the formula for the economic indicators of electricity purchase and sale is: ； in, For the economic indicators of electricity purchase and sale, This is an economic indicator for electricity purchase and sale during normal electricity price periods. The adjustment step size for the economic indicators of electricity purchase and sale, During periods of low electricity prices, During the period of medium electricity price, This is a period of high electricity prices; The weights of the calculated indicators are: ； ； in, This indicates the weight of the battery charging and discharging power of the energy storage device relative to the total power. This indicates the weight of the power purchased and sold in the distribution network relative to the total power. A dynamic weighting equation is constructed, and the allocated battery charging / discharging power and purchased / sold power of the energy storage device are as follows: ； ； in, The charging and discharging power of the batteries in energy storage devices. The power consumption of batteries for energy storage devices.

6. The method for adjusting the leased capacity of an energy storage device according to claim 5, characterized in that: The step of performing threshold analysis on the remaining power of the energy storage device based on the dynamic weighting equation to calculate the charging / discharging power and the power purchased / sold includes the following specific steps: The threshold analysis process is as follows: when the remaining power of the energy storage device is lower than the first threshold of 0.2 but higher than the second threshold of 0, the charging and discharging power and the power purchased and sold are redistributed through a dynamic weighting equation. The weighting equation includes a charging and discharging performance index based on the remaining power of the energy storage device and an economic index for power purchase and sale based on time-of-use pricing. The optimal energy storage device capacity configuration is obtained by solving the following algorithm.

7. The method for adjusting the leased capacity of an energy storage device according to claim 6, characterized in that: The step of constructing the cost objective function for energy storage equipment based on the purchased and sold power and the charging and discharging power includes the following specific steps: Based on the purchased and sold power and the charging and discharging power, a cost objective function for energy storage equipment is constructed as follows: ； in, This indicates the daily peak-shaving and valley-filling revenue of energy storage device batteries. This represents the daily revenue from buying and selling electricity from the distribution network. This represents the average daily operating and maintenance cost of batteries for energy storage devices. This represents the average daily operating and maintenance cost of capacitors for energy storage devices. This represents the total number of all energy storage devices. The investment and construction cost of a single energy storage device battery is: ； in, This refers to the battery configuration capacity of the energy storage device, measured in kWh. It is the capacity cost coefficient of the batteries in energy storage devices. It is the operation and maintenance cost coefficient of energy storage equipment and battery energy storage equipment. It is the discount rate. This refers to the rated operating life of the battery in the energy storage device, expressed in years. The investment and construction cost of a single energy storage device capacitor is: ； in, This indicates the configured capacity of the capacitors in the energy storage device, expressed in kWh. This represents the capacity cost factor of capacitors in energy storage devices. This represents the operating and maintenance cost coefficient of the capacitor bank in the energy storage device. This refers to the rated operating life of the capacitors in energy storage equipment, expressed in years. The peak shaving and valley filling benefits of a single energy storage device are: ； in, For the first The charging and discharging power of batteries in time-limited energy storage devices. Number each time period. The duration of this period is in hours. The peak-shaving and valley-filling electricity price for this period is collected in real time; The total daily revenue from electricity purchase and sale for a single energy storage device is: ； in, For the purchase and sale of electricity for energy storage devices, This refers to the electricity purchase and sale price collected in real time for that period.

8. The method for adjusting the leased capacity of an energy storage device according to claim 7, characterized in that: The step of solving the cost objective function of the energy storage device using the differential evolution algorithm to calculate the optimal capacity configuration and minimum operating cost of the energy storage device includes the following specific steps: The cost objective function of the energy storage device is used as the fitness function of the differential evolution algorithm, and the capacity of the energy storage device's capacitor bank and battery are used as control variables. The optimization objective is to minimize the fitness function. The differential evolution algorithm is used to solve the problem, and the optimal capacity configuration and the corresponding minimum operating cost are output.

9. A method for adjusting the leased capacity of an energy storage device according to claim 8, characterized in that: The process of allocating the capacity of energy storage devices according to the optimal capacity configuration includes the following specific steps: The optimal capacity configuration obtained from the differential evolution algorithm includes the battery capacity of the energy storage device. With capacitor bank capacity For battery-powered devices, according to The ratio of proprietary arbitrage capacity to leasable capacity should be redefined. For supercapacitor equipment, then according to The value dynamically adjusts the leasing weight of its high-frequency response capacity.

10. A method for adjusting the rental capacity of an energy storage device according to claim 9, characterized in that: The process of calculating the difference between the minimum operating cost and the stand-alone operating cost of the energy storage device using the Nash bargaining model, and updating the stand-alone operating cost based on this difference, to obtain the updated stand-alone operating cost, includes the following specific steps: The objective function of the bargaining transfer problem is: ； in, The negotiated transfer value for each microgrid energy storage device. For the first The daily operating cost of a microgrid energy storage device when operating independently. For the first Optimal operating cost of microgrid energy storage devices; The objective function of the bargaining transfer problem is solved using the IPOPT solver, and the bargaining transfer value is obtained after solving. ; Calculate the updated standalone operating cost : = - .

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