A method, system and medium for optimizing configuration of energy storage device capacity

Abstract

The present application relates to the technical field of energy storage device capacity optimization, and specifically provides an energy storage device capacity optimization configuration method, system and medium, the method comprising: setting the current energy storage device capacity based on the capacity optimization interval, generating an optimal charging and discharging plan based on device constraint parameters; performing simulation operation and optimization based on the optimal charging and discharging plan, and obtaining actual operation data of the energy storage device; calculating the charging and discharging achievement rate based on the actual operation data, obtaining the current optimal feasible capacity based on the charging and discharging achievement rate and the target achievement rate, and adjusting the capacity optimization interval; judging whether the capacity optimization interval meets the iteration condition, resetting the current energy storage device capacity if it meets the condition, and outputting the optimal energy storage device capacity corresponding to the optimal feasible capacity and the optimal charging and discharging plan if it does not meet the condition. The actual charging and discharging process is simulated to ensure that the capacity configuration matches the plan execution effect; and the target achievement rate is used as the evaluation standard to automatically find the optimal device capacity with a charging and discharging achievement rate higher than the target achievement rate.

Description

Technical Field

[0001] This invention belongs to the field of energy storage equipment optimization technology, and particularly relates to a method, system and medium for optimizing the configuration of energy storage equipment capacity. Background Technology

[0002] Energy storage systems play multiple roles in the power system, including peak shaving and valley filling, improving renewable energy consumption, and reducing electricity costs. The rational configuration of their equipment capacity is a key factor affecting the system's economic efficiency and operational reliability. Over-configuration leads to a significant increase in initial investment and maintenance costs; under-configuration fails to fully utilize the energy storage system's regulation capabilities, impacting peak-valley arbitrage profits and photovoltaic absorption efficiency. Therefore, accurately determining the optimal capacity of energy storage equipment while considering practical constraints and operational economics has become an important research topic in the current energy storage application field.

[0003] Currently, the mainstream methods for configuring energy storage equipment capacity mostly rely on manual experience estimation, static load formula calculation, or revenue calculation based on peak-valley electricity price differences. However, these methods have not been verified in conjunction with practical and feasible charging and discharging strategies. Although some existing technologies use optimization algorithms to generate theoretical charging and discharging plans, they do not quantitatively evaluate the execution results of the charging and discharging plans under actual equipment operating conditions, making it impossible to determine the degree of matching between the configured capacity and the charging and discharging plan. In addition, existing capacity optimization methods mostly use manual trial calculations and full traversal, which are inefficient, have large errors, and make it difficult to quickly find a capacity value that meets actual operating requirements and is economically superior.

[0004] In summary, the theoretically optimal charge and discharge plans generated by existing technologies are out of touch with reality. The execution results of the plans are not quantified by the actual operating status of the equipment, making it difficult to implement the plans and resulting in low accuracy and reliability of capacity configuration. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a method for optimizing the capacity configuration of energy storage devices, comprising the following steps: S1: Set the current energy storage device capacity based on the capacity optimization interval, and generate the optimal charging and discharging plan based on the device constraint parameters; S2: Based on the optimal charging and discharging plan, perform simulation and optimization to obtain the actual operating data of the energy storage device; S3: Calculate the charge / discharge achievement rate based on the actual operating data, obtain the current optimal feasible capacity based on the charge / discharge achievement rate and the target achievement rate, and adjust the capacity optimization interval; S4: Determine whether the capacity optimization interval meets the iteration conditions. If it does, return to step S1. If it does not, output the optimal energy storage device capacity and the optimal charging and discharging plan corresponding to the optimal feasible capacity.

[0006] Preferably, the optimal charge / discharge plan is generated using a dynamic programming (DP) algorithm, and the specific steps are as follows: Initialize the state matrix and decision matrix; Starting from the last time period, combined with the equipment constraint parameters, calculate the corresponding revenue for charging, discharging and resting for each SOC discrete state in each time period, record the maximum cumulative revenue and store it in the state matrix, and record the optimal charging and discharging power and store it in the decision matrix. Based on the initial SOC and decision matrix, the optimal charging and discharging power is executed in the forward direction, while a secondary correction is performed to output the optimal charging and discharging plan.

[0007] Based on the above scheme, the generation of the optimal charging and discharging plan needs to be based on basic data, including load power data, photovoltaic output data and time-of-use electricity price data.

[0008] Preferably, the charge / discharge achievement rate in step S3 is the average charge / discharge achievement rate, and the average charge / discharge achievement rate is calculated as follows: For each charge / discharge period, calculate the single charge / discharge achievement rate: Single charge / discharge achievement rate = (Actual charge / discharge amount / Planned charge / discharge amount) × 100%; Based on the single charge-discharge achievement rate, the average daily charge-discharge power is calculated on a daily basis, and then the average charge-discharge achievement rate over multiple days is calculated based on the average daily charge-discharge power.

[0009] Preferably, step S4 employs a binary search method for iterative optimization, specifically including: Based on the capacity optimization interval [L, R], determine whether the iteration condition L ≤ R is satisfied. If satisfied, proceed with the following steps: Calculate the midpoint M = L + (R - L) / 2 of the current capacity optimization interval, take M as the current assumed energy storage capacity, generate the optimal charging and discharging plan and simulate its operation, and calculate the charging and discharging achievement rate η(M) under this capacity. If η(M) > η_target, then record M as the current optimal feasible capacity, update the lower limit of the interval to L = M + 1, and update the capacity optimization interval to [M+1, R]; if η(M) ≤ η_target, then do not record M, update the upper limit of the interval to R = M - 1, and update the capacity optimization interval to [L, M-1]; If the capacity optimization interval does not meet the iteration conditions, the bisection method iteration terminates, and the value of the optimal feasible capacity and the optimal energy storage device capacity are output.

[0010] Based on the above scheme, step S1 also includes preprocessing the basic data, specifically: filtering the load power data and photovoltaic output data, marking the photovoltaic over-generation period, and setting the charging price of the photovoltaic over-generation period to 0.

[0011] Preferably, the charge / discharge achievement rate is the daily average charge / discharge achievement rate, the minimum single charge / discharge achievement rate, or the weighted average charge / discharge achievement rate.

[0012] Specifically, the device constraint parameters include the minimum SOC value, maximum SOC value, maximum charge / discharge power, charging efficiency, discharging efficiency, maximum demand, and reverse current protection limit of the energy storage device.

[0013] On the other hand, the present invention provides an energy storage device capacity optimization configuration system for implementing the steps of the energy storage device capacity optimization configuration method described above, the system comprising: The plan generation module is used to set the current energy storage capacity based on the capacity optimization interval and generate the optimal charging and discharging plan based on the equipment constraint parameters. The simulation module is used to simulate and optimize the optimal charge and discharge plan obtained by the plan generation module, and to obtain the actual operating data of the energy storage device. The capacity adjustment module is used to calculate the charge and discharge achievement rate based on the actual operating data, obtain the current optimal feasible capacity based on the charge and discharge achievement rate and the target achievement rate, and adjust the capacity optimization interval. The iterative output module is used to determine whether the capacity optimization interval meets the iterative conditions. If it does, it returns to the plan generation module; otherwise, it outputs the optimal energy storage device capacity and the optimal charging and discharging plan corresponding to the optimal feasible capacity.

[0014] The present invention also provides a computer-readable storage medium having a computer program, which, when executed by a processor, implements the steps of the energy storage device capacity optimization configuration method described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Based on multiple device constraints, a feasible optimal charging and discharging plan is generated through DP dynamic programming algorithm, providing a precise theoretical basis for capacity configuration and improving the operational stability of energy storage system; 2. Based on the generated optimal charge and discharge plan, simulate the actual charge and discharge process, calculate the charge and discharge achievement rate, and adjust the capacity range according to the achievement rate to solve the technical problem of the disconnect between the existing theoretical plan and actual operation, and ensure that the capacity configuration matches the plan execution effect; 3. Using the target achievement rate as the criterion, the system automatically finds the optimal equipment capacity where the charging and discharging achievement rate is higher than the target achievement rate, replacing the traditional manual calculation and full traversal. This improves the efficiency of capacity optimization and the accuracy of capacity configuration, and realizes the automation and engineering of capacity optimization. 4. By using the binary search method for iterative optimization, the optimal equipment capacity that meets the target achievement rate can be quickly found, avoiding investment waste caused by excessive capacity and plan failure caused by insufficient capacity. This optimizes capacity configuration, significantly shortens capacity calculation time, and improves work efficiency. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the capacity optimization configuration of the present invention; Figure 2 This is a flowchart for generating the optimal charge / discharge plan in this invention; Figure 3 This is a flowchart for calculating the charge / discharge achievement rate in this invention; Figure 4 This is a flowchart of the bisection method iterative optimization of the present invention. Detailed Implementation

[0017] The invention will be further described below with reference to specific embodiments.

[0018] Example 1 like Figure 1 As shown, the present invention provides a method for optimizing the capacity configuration of energy storage devices, comprising the following steps: S1: Set the current energy storage device capacity based on the capacity optimization interval, and generate the optimal charging and discharging plan based on the device constraint parameters; Step S1 also includes acquiring basic data, and generating an optimal charging and discharging plan based on the basic data and device constraint parameters through an optimization algorithm. The optimal charging and discharging plan includes the optimal charging and discharging power. Specifically, step S1 includes: S1.1: Obtain and initialize input parameters; input parameters include basic data, equipment constraint parameters, optimization parameters, and auxiliary parameters; basic parameters include time-of-use electricity price data, load power data, and photovoltaic output data; equipment constraint parameters include the minimum SOC value (SOC_MIN), maximum SOC value (SOC_MAX), maximum charge / discharge power (P_MAX), charging efficiency (η_ch), discharging efficiency (η_dis), maximum demand (MAX_DEMAND), and anti-reverse current limit (ANTI_BACK_FLOW) of the energy storage device; optimization parameters include target achievement rate and capacity optimization interval; auxiliary parameters include SOC discretization precision (SOC_STEP_COUNT), initial SOC (SOC_INIT), and cross-day SOC continuation parameter (lastDayEndSoc).

[0019] S1.2: Filter the load power data and photovoltaic output data, mark the photovoltaic over-generation period, and force the charging price of the photovoltaic over-generation period to be set to 0; where the photovoltaic over-generation period refers to the period when the net load is less than 0. The net load is obtained by subtracting the photovoltaic output power from the user's actual load power. A negative net load means that the photovoltaic power generation exceeds the actual load demand and the excess power cannot be consumed locally, which is the photovoltaic over-generation.

[0020] It is important to note that by setting the charging price to 0 during periods of excessive photovoltaic power generation, the optimization algorithm prioritizes charging during these periods when maximizing profits, thereby prioritizing the consumption of excess photovoltaic power and reducing the curtailment rate.

[0021] S1.3: Generate the optimal charging and discharging plan through an optimization algorithm. In this embodiment, the optimization algorithm is the DP dynamic programming algorithm.

[0022] like Figure 2 As shown, the steps to obtain the optimal charge / discharge plan using the DP dynamic programming algorithm are as follows: S1.31: Initialize the state matrix dp[T+1][SOC_STEP_COUNT] and the decision matrix policy[T][SOC_STEP_COUNT], where the one dimension of dp[T+1][SOC_STEP_COUNT] represents the time period from 0 to T, where T is the number of time periods per day, and the two dimensions represent the discrete states of SOC. SOC_STEP_COUNT is the discrete precision of SOC, which is the set of discrete states of SOC. At the same time, initialize the terminal state dp[T][*] = 0, where dp[T][*] represents the terminal revenue value corresponding to all discrete states of SOC in the last time period.

[0023] S1.32: Starting from the last time period, iterate backwards and perform three actions, namely charging, discharging and resting, for each discrete state of SOC in each time period. Combine the equipment constraint parameters and time-of-use electricity price data to calculate the revenue corresponding to charging, discharging and resting. Select the action with the largest revenue and the corresponding theoretical optimal charging and discharging power as the optimal decision. At the same time, store the maximum cumulative revenue in the state matrix and record the theoretical optimal charging and discharging power in the decision matrix. S1.33: Based on the initial SOC (SOC_INIT), execute the optimal charging and discharging power in the forward direction according to the decision matrix, and perform secondary corrections to output an executable optimal charging and discharging plan. The optimal charging and discharging plan includes the executable optimal charging and discharging power and SOC changes. The secondary corrections include prohibiting discharge during photovoltaic over-generation periods, ensuring that the charging power during photovoltaic over-generation periods does not exceed the maximum demand, and ensuring that the charging power during photovoltaic over-generation periods does not exceed the current photovoltaic over-generation power.

[0024] It should be noted that the power stored in the decision matrix obtained in step S1.32 is the theoretical optimal charging and discharging power. Through step S1.33 and a second correction, the actual executable optimal charging and discharging power and the final optimal charging and discharging plan input to step S2 are obtained.

[0025] S2: Simulate and optimize based on the optimal charge and discharge plan to obtain actual operating data of the energy storage device; Based on the energy storage device capacity in step S1, the optimal charge and discharge plan is run. According to the optimal charge and discharge power at each time step, combined with the device constraint parameters, the charge and discharge actions of the energy storage device are simulated to obtain actual operating data. The actual operating data includes data such as actual charge and discharge amount and real-time changes in SOC.

[0026] Furthermore, step S2 also includes simulation verification. If abnormalities such as SOC exceeding the safe range or charging / discharging power exceeding the limit occur during the simulation, the charging / discharging power is automatically adjusted.

[0027] The automatic adjustment of charging and discharging power is a function that temporarily adjusts the actual power at the current moment during the simulation to resolve any abnormal situations that may occur.

[0028] According to this embodiment, the specific implementation method of simulation operation is as follows: (1) Set the energy storage device capacity and initial SOC; (2) For each time period t = 0 to T-1, perform the following steps: Read the planned optimal charge / discharge power P_plan for this period. When charging, P_plan>0, and when discharging, P_plan<0. Based on the current SOC and the device constraints of SOC_MIN, SOC_MAX, P_MAX, η_ch, and η_dis, calculate the maximum charging power P_ch_max and the maximum discharging power P_dis_max that can actually be executed during this period, where P_ch_max>0 and P_dis_max>0. If P_plan < 0, then the actual charging power P_actual = max(P_plan, -P_ch_max), limiting the absolute value of the charging power to not exceed P_ch_max; if P_plan > 0, then the actual discharging power P_actual = min(P_plan, P_dis_max); if P_plan = 0, then P_actual = 0. Update the SOC for the next time period using the energy conservation formula of the energy storage device: During discharge, P_actual>0, SOC(t+1) = SOC(t) - (P_actual / η_dis × Δt) / C, where η_dis is the charging efficiency of the energy storage device and C is the capacity of the energy storage device; During charging, P_actual < 0, SOC(t+1) = SOC(t) + (|P_actual| × η_ch × Δt) / C, where η_ch is the discharge efficiency of the energy storage device; Record the actual charge and discharge amounts during this period: the discharge amount is P_actual × Δt, and the charge amount is |P_actual| × Δt; If the updated SOC exceeds the range of [SOC_MIN, SOC_MAX], then boundary correction is performed to clamp the SOC to the boundary value and the actual charge / discharge amount is calculated.

[0029] (3) Output the actual charge and discharge amount and the actual SOC change sequence for each time period.

[0030] The above simulation process is a conventional technique in the field of energy storage system simulation. Those skilled in the art should understand how to implement it through the above steps and obtain actual operating data based on the charge and discharge plan.

[0031] By simulating the actual charging and discharging process in step S2, the theoretical plan is closely integrated with the actual operation, thereby solving the problem of the disconnect between existing technology and reality.

[0032] S3: Calculate the charge / discharge achievement rate based on the actual operating data, obtain the current optimal feasible capacity based on the charge / discharge achievement rate and the target achievement rate, and adjust the capacity optimization interval; According to this embodiment, the charge / discharge achievement rate in step S3 is the average charge / discharge achievement rate, such as... Figure 3 As shown, the average charge / discharge achievement rate is calculated as follows: For each charge / discharge period, calculate the single charge / discharge achievement rate: Single charge / discharge achievement rate = (Actual charge / discharge amount / Planned charge / discharge amount) × 100%; The single charge / discharge achievement rate within the statistical period is calculated by taking a single day as the period and calculating the average charge / discharge power of the day, and then calculating the average charge / discharge achievement rate of the multiple days based on the average charge / discharge power of the day.

[0033] Furthermore, by examining the average charge / discharge achievement rate and the target achievement rate, the adjustment method for the capacity optimization interval is determined.

[0034] According to other embodiments, the charge / discharge achievement rate can also be the daily average charge / discharge achievement rate, the minimum single charge / discharge achievement rate, or the weighted average charge / discharge achievement rate, etc., and the execution effect of the charge / discharge plan can be quantified by obtaining one of the charge / discharge achievement rates.

[0035] Furthermore, determine whether the charge / discharge achievement rate is not less than the target achievement rate. If so, update the lower limit of the capacity optimization range based on the current energy storage equipment capacity; if not, update the upper limit of the capacity optimization range based on the current energy storage equipment capacity.

[0036] S4: Determine whether the capacity optimization interval meets the iteration condition. If it does, return to step S1. If it does not, output the optimal energy storage device capacity and the optimal charging and discharging plan corresponding to the optimal feasible capacity. The iteration condition is L ≤ R.

[0037] According to this embodiment, since the energy storage device capacity and the charge / discharge achievement rate have a monotonically negative correlation, this invention uses a binary search method to quickly locate the maximum capacity value that meets the target achievement rate requirement within the capacity optimization interval. The optimization objective is: to find the maximum energy storage device capacity while ensuring that the charge / discharge achievement rate is greater than the target achievement rate.

[0038] like Figure 4 As shown, specifically: The lower limit of the capacity optimization interval is set to L, and the upper limit is set to R. Initially, L is the minimum capacity value (corresponding to the minimum number of energy storage devices), and R is the maximum capacity value (corresponding to the maximum allowed number of energy storage devices). At the same time, the target achievement rate is set to η_target. Under the condition that L ≤ R, the following iterations are performed: Calculate the midpoint M = L + (R - L) / 2 of the current capacity optimization interval, and round the midpoint value M down; Using M as the current assumed energy storage capacity, generate the optimal charge and discharge plan according to step S1 above, and run the simulation to calculate the charge and discharge achievement rate η(M) under this capacity. If η(M)>η_target, then M is feasible, but there may be a better feasible capacity. Record M as the current best feasible capacity. At this time, update the lower limit of the interval to L = M + 1. The capacity optimization interval is [M+1, R]. In the updated capacity optimization interval, find a larger feasible capacity that satisfies the target achievement rate. If η(M) ≤ η_target, then M is too large and does not meet the requirements. In this case, the upper limit of the interval is updated to R = M - 1, and the capacity optimization interval is [L, M-1]. Within the updated capacity optimization interval, a feasible capacity that meets the target achievement rate is found.

[0039] When L>R, all capacity searches are completed, the binary search method terminates, and the value M_best, which was last recorded as the optimal feasible capacity, is output. The energy storage capacity corresponding to M_best is the optimal energy storage capacity that satisfies the requirement that the charge and discharge achievement rate is greater than the target achievement rate.

[0040] This embodiment can also output total revenue, photovoltaic absorption rate, and curtailed photovoltaic power: Total revenue = Σ(discharge revenue) - Σ(charging cost); where: discharge revenue = Σ(actual discharge amount × electricity price during discharge period); charging cost = Σ(actual charging amount × electricity price during charging period); Photovoltaic grid integration rate = (Electricity from photovoltaic power generation during energy storage charging) / (Total photovoltaic power generation) × 100%; Curtailed solar power = Excess solar power generation - Solar power absorbed by energy storage.

[0041] Example 2 This embodiment uses specific parameters to find the maximum number of energy storage devices and obtains the optimal energy storage capacity. The specific steps are as follows: A binary search method is used to quickly locate the maximum capacity value that meets the target achievement rate requirement within the capacity optimization interval. First, initialize the parameters to obtain the capacity optimization interval as [1, 20], i.e., L = 1, R = 20; initialize the optimal feasible capacity M_best = 0, and the target achievement rate η_target = 95%; Based on the above parameters, a binary search method is used for iterative optimization: Round 1: Take M = L + (R - L) / 2 = 1 + (20-1) / 2 = 10.5 and round it down to get M = 10; take the capacity of 500 kWh generated by 10 energy storage devices as the current assumed capacity of energy storage devices, and generate the theoretically optimal charging and discharging plan; Simulations were conducted based on the theoretically optimal charge-discharge plan, and actual operating data were obtained. The average charge-discharge achievement rate was calculated to be η1 = 96.2%. If η1>η_target, and the current assumption is that the energy storage capacity meets the target achievement rate, record M_best = 10, update the lower bound L=11 of the capacity optimization interval, and enter the second round.

[0042] Second round: The current capacity optimization interval is [11, 20]. Take M = L + (R - L) / 2 = 11 + (20-11) / 2 = 15.5, and round it down to get M = 15. Take the capacity of 750 kWh generated by 15 energy storage devices as the current assumed energy storage device capacity, and generate the optimal charging and discharging plan. Simulation was conducted based on the optimal charge-discharge plan, and actual operating data was obtained. The average charge-discharge achievement rate was calculated to be η2 = 93.8%. If η2 < η_target, and the current assumption is that the energy storage capacity does not meet the target achievement rate, M_best remains unchanged. The upper bound of the capacity optimization interval is updated to R = 15 - 1 = 14, and the process proceeds to the third round.

[0043] Third round: The current capacity optimization interval is [11, 14]. Take M = L + (R - L) / 2 = 11 + (14-11) / 2 = 12.5, and round it down to get M = 12; take the capacity of 600 kWh generated by 12 energy storage devices as the current assumed energy storage device capacity, and generate the theoretically optimal charging and discharging plan; Simulations were conducted based on the theoretically optimal charge-discharge plan, and actual operating data were obtained. The average charge-discharge achievement rate was calculated to be η3 = 95.1%. If η3 > η_target, and the current assumption is that the energy storage capacity meets the target achievement rate, record M_best = 12, update the lower bound of the capacity optimization interval L = 12 + 1 = 13, and enter the fourth round.

[0044] Fourth round: The current capacity optimization interval is [13, 14]. Take M = L + (R - L) / 2 = 13 + (14-13) / 2 = 13.5, and round it down to get M = 13; take the capacity of 650 kWh generated by 13 energy storage devices as the current assumed energy storage device capacity, and generate the theoretically optimal charging and discharging plan; Simulations were conducted based on the theoretically optimal charge-discharge plan, and actual operating data were obtained. The average charge-discharge achievement rate was calculated to be η4 = 94.5%. If η4 < η_target, and the current assumption is that the capacity of the energy storage device does not meet the target achievement rate, M_best remains unchanged. The upper bound of the capacity optimization interval is updated to R = 13 - 1 = 12. At this time, L = 13, L > R, and the iteration terminates.

[0045] The last recorded value as the optimal feasible capacity is M_best=12. The energy storage capacity corresponding to M_best is 600kwh, which is the optimal energy storage capacity that satisfies the requirement that the charge and discharge achievement rate is greater than the target achievement rate. The final charge and discharge achievement rate is 95.1%.

[0046] Example 3 Based on the same inventive concept, this invention provides an energy storage device capacity optimization configuration system for implementing the above-mentioned energy storage device capacity optimization configuration method, specifically including: The plan generation module is used to acquire basic data and equipment constraint parameters, and generate an optimal charge and discharge plan based on the basic data and equipment constraint parameters through an optimization algorithm. The optimal charge and discharge plan includes the optimal charge and discharge power. The simulation operation module is used to set the capacity of the energy storage device based on the capacity optimization range, and the energy storage device runs the optimal charge and discharge plan to obtain actual operation data. The adjustment module calculates the charge / discharge achievement rate based on the actual operating data, and is used to adjust the capacity optimization range based on the charge / discharge achievement rate and the target achievement rate. The optimization module returns and executes the simulation running module and the adjustment module based on the adjusted capacity optimization interval until the capacity optimization interval meets the preset iteration accuracy. The output module is used to output the optimal energy storage device capacity and the optimal charge and discharge plan.

[0047] The specific implementation method of this embodiment can be referred to the specific implementation method of the above-described energy storage equipment capacity optimization configuration method, and will not be described here.

[0048] Furthermore, the energy storage device capacity optimization configuration method according to the present invention can be recorded in a computer-readable recording medium. Specifically, according to the present invention, a computer-readable recording medium storing computer-executable instructions can be provided, which, when executed by a processor, causes the processor to execute the energy storage device capacity optimization configuration method as described above.

[0049] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code containing at least one executable instruction for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0050] In general, various exemplary embodiments of the present invention can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of the present invention are illustrated or described as block diagrams, flowcharts, or represented using certain other images, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or certain combinations thereof.

[0051] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0052] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the capacity configuration of energy storage devices, characterized in that, Includes the following steps: S1: Set the current energy storage device capacity based on the capacity optimization interval, and generate the optimal charging and discharging plan based on the device constraint parameters; S2: Based on the optimal charging and discharging plan, perform simulation and optimization to obtain the actual operating data of the energy storage device; S3: Calculate the charge / discharge achievement rate based on the actual operating data, obtain the current optimal feasible capacity based on the charge / discharge achievement rate and the target achievement rate, and adjust the capacity optimization interval; S4: Determine whether the capacity optimization interval meets the iteration conditions. If it does, return to step S1. If it does not, output the optimal energy storage device capacity and the optimal charging and discharging plan corresponding to the optimal feasible capacity.

2. The energy storage device capacity optimization configuration method according to claim 1, characterized in that, The optimal charge / discharge plan is generated using the dynamic programming (DP) algorithm. The specific steps are as follows: Initialize the state matrix and decision matrix; Starting from the last time period, combined with the equipment constraint parameters, calculate the corresponding revenue for charging, discharging and resting for each SOC discrete state in each time period, record the maximum cumulative revenue and store it in the state matrix, and record the optimal charging and discharging power and store it in the decision matrix. Based on the initial SOC and decision matrix, the optimal charging and discharging power is executed in the forward direction, while a secondary correction is performed to output the optimal charging and discharging plan.

3. The energy storage device capacity optimization configuration method according to claim 2, characterized in that, The generation of the optimal charge and discharge plan requires basic data, including load power data, photovoltaic output data, and time-of-use electricity price data.

4. The energy storage device capacity optimization configuration method according to claim 1, characterized in that, In step S3, the charge / discharge achievement rate is the average charge / discharge achievement rate, and the average charge / discharge achievement rate is calculated as follows: For each charge / discharge period, calculate the single charge / discharge achievement rate: Single charge / discharge achievement rate = (Actual charge / discharge amount / Planned charge / discharge amount) × 100%; Based on the single charge-discharge achievement rate, the average daily charge-discharge power is calculated on a daily basis, and then the average charge-discharge achievement rate over multiple days is calculated based on the average daily charge-discharge power.

5. The energy storage device capacity optimization configuration method according to claim 1, characterized in that, Step S4 employs a binary search method for iterative optimization, specifically including: Based on the capacity optimization interval [L, R], determine whether the iteration condition L ≤ R is satisfied. If satisfied, proceed with the following steps: Calculate the midpoint M = L + (R - L) / 2 of the current capacity optimization interval, take M as the current assumed energy storage capacity, generate the optimal charging and discharging plan and simulate its operation, and calculate the charging and discharging achievement rate η(M) under this capacity. If η(M) > η_target, then record M as the current optimal feasible capacity, update the lower limit of the interval to L = M + 1, and update the capacity optimization interval to [M+1, R]; if η(M) ≤ η_target, then do not record M, update the upper limit of the interval to R = M -1, and update the capacity optimization interval to [L, M-1]. If the capacity optimization interval does not meet the iteration conditions, the bisection method iteration terminates, and the value of the optimal feasible capacity and the optimal energy storage device capacity are output.

6. The energy storage device capacity optimization configuration method according to claim 3, characterized in that, Step S1 also includes preprocessing the basic data, specifically: filtering the load power data and photovoltaic output data, marking the photovoltaic over-generation period, and setting the charging price of the photovoltaic over-generation period to 0.

7. The energy storage device capacity optimization configuration method according to claim 1, characterized in that, The charge / discharge achievement rate is the daily average charge / discharge achievement rate, the minimum single charge / discharge achievement rate, or the weighted average charge / discharge achievement rate.

8. The energy storage device capacity optimization configuration method according to claim 1, characterized in that, The device constraint parameters include the minimum SOC value, maximum SOC value, maximum charge / discharge power, charging efficiency, discharging efficiency, maximum demand, and reverse current protection limit of the energy storage device.

9. A capacity optimization configuration system for energy storage devices, characterized in that, The system for implementing the energy storage device capacity optimization configuration method as described in claim 1 includes: The plan generation module is used to set the current energy storage capacity based on the capacity optimization interval and generate the optimal charging and discharging plan based on the equipment constraint parameters. The simulation module is used to simulate and optimize the optimal charge and discharge plan obtained by the plan generation module, and to obtain the actual operating data of the energy storage device. The capacity adjustment module is used to calculate the charge and discharge achievement rate based on the actual operating data, obtain the current optimal feasible capacity based on the charge and discharge achievement rate and the target achievement rate, and adjust the capacity optimization interval. The iterative output module is used to determine whether the capacity optimization interval meets the iterative conditions. If it does, it returns to the plan generation module; otherwise, it outputs the optimal energy storage device capacity and the optimal charging and discharging plan corresponding to the optimal feasible capacity.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium has a computer program that, when executed by a processor, implements the energy storage device capacity optimization configuration method as described in any one of claims 1-8.

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