A charging station light storage optimization configuration method and system

Abstract

This disclosure relates to the field of energy supply and comprehensive optimization and control technology, and in particular to a method and system for optimizing the configuration of photovoltaic and energy storage in charging stations. This disclosure utilizes a multi-stage optimization algorithm, combining charging load characteristics, photovoltaic resource conditions, energy storage technical parameters, and economic evaluation indicators, to achieve precise matching of photovoltaic installed capacity, energy storage capacity, and power. Simultaneously, it optimizes the daily charging and discharging strategy of energy storage, achieving the goal of minimizing the levelized cost of electricity (LCOE) and maximizing economic benefits throughout the entire lifecycle. Using the actual load curve of the charging station as the core input, and following the principle of load-based power generation, this disclosure employs a four-stage optimization algorithm to iteratively calculate the optimal capacity combination of photovoltaic and energy storage under constraints of site space, technical performance, and economic benefits. Simultaneously, it formulates an energy storage charging and discharging strategy that balances photovoltaic absorption and peak-valley arbitrage, ultimately selecting the photovoltaic-energy storage configuration scheme with the optimal LCOE throughout the entire lifecycle, achieving safe, efficient, and economical operation of the charging station's energy supply.

Description

Technical Field

[0001] This disclosure relates to the field of energy supply and comprehensive optimization and control technology, and in particular to a method and system for optimizing the configuration of photovoltaic and energy storage in charging stations. Background Technology

[0002] With the rapid popularization of electric vehicles, charging stations have become an important part of urban infrastructure, and their charging load demand continues to grow. However, traditional charging stations mainly rely on the power grid for power supply, which can easily cause excessive grid load pressure during peak electricity consumption periods, and also suffers from problems such as insufficient power supply stability and high electricity costs. Photovoltaics, as a clean and renewable energy source, can effectively alleviate grid pressure, reduce electricity costs, and promote the consumption of new energy when combined with energy storage systems in charging stations. However, current photovoltaic-energy storage configuration schemes mostly adopt empirical designs, lacking a comprehensive consideration of charging load characteristics, photovoltaic output fluctuations, energy storage technology constraints, and economic benefits, resulting in problems such as high photovoltaic curtailment rates, insufficient energy storage utilization, and poor economic efficiency throughout the entire life cycle. In response to the above situation, this invention proposes a scientific and reasonable photovoltaic-energy storage optimization configuration method to achieve safe, efficient, and economical operation of energy supply for charging stations.

[0003] Existing technologies often focus solely on a single economic benefit objective, failing to establish a quantitative system for the levelized cost of electricity (LCOE) across the entire lifecycle, thus hindering accurate measurement of long-term economic viability. They also lack dynamic optimization of energy storage charging and discharging strategies, and fail to balance "PV integration with peak-valley arbitrage." Constraints only cover basic operational requirements, neglecting key factors for project implementation such as site utilization and energy storage degradation rates. Furthermore, their optimization dimensions are singular, focusing only on static matching of energy storage capacity without coordinating the optimization of PV installed capacity and energy storage power. The lack of multi-strategy charging and discharging simulations makes them unable to cope with varying load characteristics and fluctuations in solar resources. The solution process relies on curve fitting, lacking hourly operational simulations and dual technical and economic verification, resulting in slow convergence and difficulty in adapting to complex engineering implementation needs.

[0004] In summary, there is an urgent need for a technology to address the problems of low matching between photovoltaic and load, unreasonable energy storage configuration, and poor economic benefits in existing photovoltaic-energy storage configuration methods. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure provides a method and system for optimizing the configuration of photovoltaic and energy storage in charging stations. This method solves problems such as high photovoltaic curtailment rates, insufficient energy storage utilization, and poor economic efficiency throughout the entire life cycle caused by unreasonable existing photovoltaic and energy storage configurations. It improves the photovoltaic absorption rate, energy storage utilization rate, and overall economic benefits of charging stations, thereby promoting the large-scale application of new energy in charging stations.

[0006] Firstly, a method for optimizing the configuration of photovoltaic and energy storage in charging stations includes: The system acquires load data, solar resource data, site constraint data, and economic evaluation parameters of charging stations. Combining site physical constraints and load adaptation constraints, it calculates the upper and lower limits of photovoltaic installed capacity and generates a candidate set of photovoltaic installed capacity. For each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set, the upper limit of energy storage capacity is calculated based on promoting photovoltaic consumption and realizing peak-valley arbitrage. The maximum value of the two is taken to determine the feasible range of energy storage capacity, and the range of energy storage charging and discharging power is calculated based on the feasible range of energy storage capacity. Enumerate the preset charging and discharging strategies, and for each photovoltaic candidate capacity, energy storage capacity and energy storage charging and discharging power combination, perform hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculate the present value of total cost and the present value of total effective power generation of each combination throughout the entire life cycle. The levelized cost of electricity (LCOE) over the entire lifecycle is calculated by the ratio of the present value of total cost to the present value of total effective power generation. The LCOE of each combination is calculated, and the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy are selected by combining technical feasibility and economic benefit constraints.

[0007] Furthermore, the calculation of the upper limit of photovoltaic installed capacity includes: Calculate the upper limit of physical constraints on site area based on the total available site area and the area occupied per unit power of photovoltaic arrays; Based on the requirement that the actual output of photovoltaic power during peak irradiance periods must be able to cover the user's daily peak load, the instantaneous maximum output constraint of photovoltaic power is calculated; based on the user's total annual load and the annual effective utilization hours of photovoltaic power, the full load coverage constraint of annual photovoltaic power generation is calculated; the minimum value between the instantaneous maximum output constraint of photovoltaic power and the full load coverage constraint of annual photovoltaic power generation is taken as the upper limit of the load adaptation constraint. The minimum value between the upper limit of the physical constraint on site area and the upper limit of the load adaptation constraint shall be taken as the upper limit of photovoltaic installed capacity.

[0008] Furthermore, the calculation of the lower limit of photovoltaic installed capacity includes: Based on the requirement that the annual effective power generation of photovoltaic power needs to cover a certain proportion of the total annual electricity consumption of the load, calculate the annual load coverage constraint; Based on the requirement that photovoltaic power generation can cover part of the peak load during peak output periods, calculate the daily peak load coverage constraint; The maximum value between the annual load coverage constraint and the daily peak load coverage constraint is taken as the lower limit for calculating photovoltaic installed capacity.

[0009] Furthermore, determine the feasible range of energy storage capacity, including: Based on the maximum daily power curtailment corresponding to the photovoltaic candidate capacity, and combined with the energy storage attenuation rate, load fluctuation coefficient and charge-discharge cycle efficiency, the upper limit of energy storage capacity to promote photovoltaic consumption is calculated. Based on the difference between the maximum total load during peak load periods and the minimum coverage load of photovoltaic power during peak periods, the upper limit of energy storage capacity for achieving peak-valley arbitrage is calculated. The maximum value between the upper limit of energy storage capacity for promoting photovoltaic consumption and the upper limit of energy storage capacity for realizing peak-valley arbitrage is taken as the total upper limit of energy storage capacity, and the feasible range of energy storage capacity is determined as [0, total upper limit of energy storage capacity].

[0010] Furthermore, the calculation of the energy storage charging and discharging power range includes: The charging power needs to match the demand for fully charging the corresponding capacity during the peak period of photovoltaic curtailment, and the boundary is derived by combining the capacity range. Minimum charging power is used to ensure that the minimum effective capacity is fully charged during periods of power curtailment. Maximum charging power is used to ensure that the capacity is fully charged during the curtailment period, while not exceeding the maximum curtailment power of photovoltaic power.

[0011] Furthermore, calculating the energy storage charge and discharge power range also includes: The discharge power needs to match the requirement of discharging the corresponding capacity during peak load periods, and the boundary is derived by combining the capacity range. Minimum discharge power is used to ensure that the minimum effective capacity is discharged during peak periods; Maximum discharge power is used to ensure that the upper limit of capacity is discharged during peak hours, while not exceeding the peak load gap.

[0012] Furthermore, the preset charging and discharging strategy includes at least: Prioritize photovoltaic power consumption strategy: when photovoltaic output exceeds load, prioritize energy storage charging and prioritize discharge during peak load periods. Prioritize peak-valley arbitrage strategy, force charging during off-peak hours and force discharging during peak hours; The dynamic balancing strategy calculates in real time the cost of photovoltaic curtailment losses and off-peak charging costs, as well as the cost of peak electricity purchases and energy storage discharge losses, and makes dynamic decisions on charging and discharging behavior based on the cost comparison results.

[0013] Furthermore, the full lifecycle hourly simulation includes: Considering the photovoltaic power decay rate and the energy storage capacity decay rate, the photovoltaic output and energy storage charging and discharging process are simulated on an annual iterative basis and 24-hour daily basis. The present value of total cost and the present value of total effective power generation are calculated over the entire life cycle. The present value of total cost includes initial investment, operation and maintenance costs, curtailment losses and energy storage replacement costs. The present value of total effective power generation includes direct photovoltaic power consumption, energy storage for the reuse of curtailed photovoltaic power, and energy storage for peak-valley arbitrage.

[0014] Furthermore, a screening process is conducted considering both technical feasibility and economic constraints, including: Calculate the levelized cost of electricity (LCOE) for each combination throughout its lifecycle; Eliminate combinations of excessive curtailment rate, excessive energy storage cycle count, or strategy execution deviation greater than the preset threshold; Calculate the payback period and internal rate of return, and eliminate portfolios that fail to meet the economic benchmark target; Sort the remaining combinations by LCOE from smallest to largest, verify the stability of the strategy, and determine the scheme with the lowest LCOE.

[0015] Secondly, a photovoltaic-storage optimization configuration system for charging stations includes: The unit includes a capacity candidate set calculation unit, an energy storage capacity and power calculation unit, a simulation unit, and an optimization scheme determination unit. The capacity candidate set calculation unit is used to acquire load data, solar resource data, site constraint data and economic evaluation parameters of charging stations, and calculate the upper and lower limits of photovoltaic installed capacity by combining site physical constraints and load adaptation constraints, and generate a photovoltaic installed capacity candidate set. The energy storage capacity and power calculation unit is used to calculate the upper limit of energy storage capacity for each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set, based on promoting photovoltaic consumption and realizing peak-valley arbitrage, respectively. The maximum value of the two is taken to determine the feasible range of energy storage capacity, and the energy storage charging and discharging power range is calculated based on the feasible range of energy storage capacity. The simulation unit is used to enumerate the preset charging and discharging strategies. For each photovoltaic candidate capacity, energy storage capacity, and energy storage charging and discharging power combination, it performs hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculates the present value of total cost and the present value of total effective power generation of each combination over the entire life cycle. The optimization scheme determination unit calculates the levelized cost of electricity (LCOE) over the entire life cycle based on the ratio of the present value of total cost to the present value of total effective power generation. It calculates the LCOE of each combination and selects the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy based on technical feasibility and economic constraints.

[0016] This disclosure includes at least the following beneficial effects: This disclosure utilizes a multi-stage optimization algorithm, combined with charging load characteristics, photovoltaic resource conditions, energy storage technology parameters, and economic evaluation indicators, to achieve precise matching of photovoltaic installed capacity, energy storage capacity, and power. At the same time, it optimizes the daily charging and discharging strategy of energy storage, aiming to achieve the goal of minimizing the levelized cost of electricity and maximizing economic benefits throughout the entire life cycle.

[0017] This disclosure uses the actual load curve of the charging station as the core input and follows the principle of "determining the source based on the load". Under the constraints of multiple dimensions such as site space, technical performance, and economic benefits, it uses a four-stage optimization algorithm to iteratively calculate the optimal capacity combination of photovoltaic and energy storage. At the same time, it formulates an energy storage charging and discharging strategy that takes into account both photovoltaic absorption and peak-valley arbitrage. Finally, it selects the photovoltaic and energy storage configuration scheme with the optimal levelized cost of electricity (LCOE) over the entire life cycle, so as to achieve safe, efficient and economical operation of energy supply for charging stations.

[0018] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the optimized configuration method according to an embodiment of the present disclosure; Figure 2 A schematic diagram of the optimized configuration system architecture for embodiments of this disclosure. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0022] The key parameters disclosed herein are shown in Table 1: Table 1

[0023] like Figure 1 As shown, a method for optimizing the configuration of photovoltaic and energy storage in charging stations includes: S101: Obtain load data, solar resource data, site constraint data, and economic evaluation parameters of the charging station; combine site physical constraints and load adaptation constraints to calculate the upper and lower limits of photovoltaic installed capacity and generate a candidate set of photovoltaic installed capacity. S102, for each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set, calculate the upper limit of energy storage capacity based on promoting photovoltaic consumption and realizing peak-valley arbitrage, take the maximum value of the two to determine the feasible range of energy storage capacity, and calculate the range of energy storage charging and discharging power based on the feasible range of energy storage capacity. S103, enumerate the preset charging and discharging strategies, and for each photovoltaic candidate capacity, energy storage capacity and energy storage charging and discharging power combination, perform hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculate the present value of total cost and the present value of total effective power generation of each combination in the entire life cycle. S104. The levelized cost of electricity (LCOE) over the entire life cycle is calculated based on the ratio of the present value of the total cost to the present value of the total effective power generation. The LCOE of each combination is calculated, and the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy are selected in combination with technical feasibility and economic benefit constraints.

[0024] The specific implementation details are as follows: The formula for the levelized cost of electricity (LCOE) of a photovoltaic-storage configuration is:

[0025] in: Numerator: The sum of the present value of all costs throughout the entire life cycle (initial investment + operation and maintenance costs + power curtailment losses, etc.); Denominator: The sum of the present value of all effective power generation during the entire life cycle (direct photovoltaic consumption + energy storage discharge). y :years( y =0 represents the initial year. y =1 to y = n (for the year of operation). n System economic life (photovoltaics typically last 25 years, energy storage typically lasts 10-15 years; take the shorter of the two or adjust according to the energy storage replacement cycle). r Discount rate (reflects the time value of money, usually taken as 5%-8%).

[0026] Numerator: Present value of total life cycle cost (total investment). Total investment includes initial investment in photovoltaic and energy storage, operation and maintenance costs, curtailment losses, equipment replacement costs (if any), etc., and needs to be discounted to the initial year on an annual basis.

[0027] (1) Initial investment ( , (0 o'clock) Initial investment in photovoltaics: ( Photovoltaic installed capacity (kW). The cost per unit of photovoltaic power (RMB / kW) includes modules, inverters, installation, etc. Initial investment in energy storage: ( Energy storage capacity (kWh) Cost per unit capacity (RMB / kWh); Energy storage capacity (kW). Cost per unit power (RMB / kW), including battery, BMS, inverter, etc. Initial total investment: .

[0028] (2) Operating costs ( , 1 to n): Operation and maintenance costs: Photovoltaic operation and maintenance costs: ( The annual operation and maintenance fee for photovoltaic systems is typically 0.5%-1%. Energy storage operation and maintenance costs: ( The annual operation and maintenance cost for energy storage is typically 1%-2%, higher than that for photovoltaics due to the frequent charging and discharging. Annual maintenance costs: .

[0029] (3) Loss of abandoned electricity: If solar power curtailment is permitted, the resulting cost losses must be factored in: For the first y Annual solar power curtailment (kWh) The cost per kilowatt-hour of photovoltaic power (RMB / kWh) represents the cost of losing 1 kWh of photovoltaic power for every 1 kWh of power wasted.

[0030] (4) Energy storage replacement cost (if the energy storage life is shorter than the total system life): If the energy storage life is (e.g., 10 years), total lifespan n =25 years, then it needs to be in y Replace energy storage at 10: ( (This refers to the residual value rate, typically 5%-10%). Total annual operating cost: (If the year is changed, add) ).

[0031] (5) Present value of total cost: Discount the costs of each year to the initial year using the discount rate:

[0032] Denominator: Present value of effective power generation over the entire life cycle (total output). Total effective power is the actual power provided by the photovoltaic-storage system to the load, including direct photovoltaic consumption, photovoltaic power reuse stored in energy storage, and peak-valley arbitrage power from energy storage, etc. It needs to be discounted to the initial year by year.

[0033] (1) Direct photovoltaic power consumption ( ): No. Annual electricity directly absorbed by loads from photovoltaic power:

[0034] Actual photovoltaic power output in year y (considering degradation) ), (The decay rate is 2% in the first year and 0.5% thereafter). Average annual effective hours of sunshine (h); Photovoltaic conversion efficiency; : Matching rate of photovoltaic power generation and load in year y (overlap ratio of power output and load).

[0035] (2) The amount of photovoltaic power reused in energy storage ( ): The amount of abandoned photovoltaic power stored and discharged in year y:

[0036] : High-value abandoned electricity in year y (only the portion stored by energy storage); Energy storage charge-discharge cycle efficiency (typically 0.7-0.85, i.e., charging efficiency) Discharge efficiency).

[0037] (3) Energy storage peak-valley arbitrage electricity ( ): The effective discharge volume of energy storage through peak-valley price arbitrage in year y:

[0038] :Dedicated energy storage capacity for arbitrage in year y (considering degradation); : Number of charge / discharge cycles in year y (usually 1-2 times / day).

[0039] (4) Present value of total effective energy: Discount the effective electricity volume of each year to the initial year using the discount rate:

[0040] The calculation logic is as follows: 1. Cost side: It needs to cover the "initial investment + annual operation and maintenance + curtailment loss + possible replacement cost" of the entire life cycle of photovoltaic and energy storage, and discount it according to the time value of money; 2. Electricity side: Only "effective electricity that actually supplies power to the load" is calculated (electricity directly used by photovoltaic power, electricity stored in energy storage for reuse, and electricity from energy storage arbitrage), which is also discounted using the time value of money; The above formula can quantify the economics of different photovoltaic and energy storage configuration schemes, providing a core basis for the optimal selection of "photovoltaic installation + energy storage capacity".

[0041] Phase 1: Design Logic of Photovoltaic Installed Capacity Optimization Algorithm: Optimization of photovoltaic installed capacity is the "fundamental link" in the overall optimization of photovoltaic and energy storage. Its core positioning is to initially screen out a set of technically feasible photovoltaic installed capacity candidates under the dual constraints of "site physical constraints" and "user load adaptation" based on the principle of "load-based source". This provides the basic input for subsequent energy storage capacity / power optimization and full life cycle LCOE calculation.

[0042] Step 1: Calculate the upper limit of photovoltaic installation under dual constraints ; The upper limit for photovoltaic installations must simultaneously meet both the "user full load coverage boundary" and the "physical limitation of site area," ultimately taking the minimum of the two (i.e., the "short board value under dual constraints") to avoid problems such as "installed capacity exceeding site capacity" or "installed capacity far exceeding load leading to uncontrolled power curtailment." The formula logic is as follows:

[0043] Site area is a hard constraint for photovoltaic installations, and the requirement of "physical space capacity" must be prioritized, as shown in the following formula:

[0044] Site utilization coefficient (usually 0.8-0.9, with reserved space for passageways and equipment maintenance; adjustments need to be made based on site shape); Total usable area of ​​the site; The area occupied per unit power of a photovoltaic array (e.g., approximately 10-15㎡ / kW for fixed bracket photovoltaic arrays and approximately 15-20㎡ / kW for tracking bracket photovoltaic arrays, depending on the project's installation method).

[0045] Based on "load baseline data" and "photovoltaic resource data," with "the maximum photovoltaic output can cover the user's highest daily load" as the core constraint, and considering the photovoltaic-load matching degree (to avoid ineffective installations caused by output-load mismatch), the formula is derived as follows: (1) Constraints on instantaneous maximum output of photovoltaic power: The actual output of photovoltaic power during peak irradiance periods (such as 12:00 in summer) must be able to cover the user's highest daily load. Considering photovoltaic conversion efficiency With respect to system losses, the formula is:

[0046] User's daily peak load (kW); Peak photovoltaic irradiance (kWh / m²) 2 ); Photovoltaic conversion efficiency; Photovoltaic-load matching degree.

[0047] (2) Constraints on full load coverage of annual photovoltaic power generation:

[0048] Total annual load of users (kWh, derived from "daily load curve") (Accumulated calculation); : Annual effective utilization hours of photovoltaic power (h).

[0049] (3) Determine the upper limit of the total user load: Take the minimum value between the instantaneous maximum output constraint and the annual power generation constraint (to ensure that the load can be covered throughout the entire time period and cycle, without over-installation):

[0050] Step 2: Calculate the lower limit of photovoltaic installation capacity under load demand constraints : Based on the core logic of "supply source determined by load," photovoltaic installations must meet the "basic load coverage capacity" to avoid "economic ineffectiveness of photovoltaic-storage systems" due to insufficient installation. The lower limit is determined through two dimensions: Annual load coverage constraints: The annual effective power generation of photovoltaic power must cover a certain proportion of the total annual electricity consumption of the load, as shown in the following formula:

[0051] Total annual electricity consumption (load) , (for hourly loads) The expected percentage of the load electricity to be covered by photovoltaic power (set according to the project's economic objectives, such as 30%-50%). Photovoltaic-load matching degree (e.g., 0.6 in summer, 0.3 in winter, using annual average or weighted value, correcting for invalid electricity generation due to "output and load not overlapping"). Intraday peak load coverage constraints: During peak power output periods (such as midday), photovoltaic systems need to cover part of the peak load to avoid "no contribution from photovoltaic systems during peak hours." The formula is as follows:

[0052] Daily peak load; The percentage of load that photovoltaic systems are expected to cover during peak hours (e.g., 20%-40%, to avoid complete reliance on the grid or energy storage during peak periods); The final lower limit has been determined: Take the larger value from the two dimensions to ensure that both "annual electricity consumption coverage" and "daily peak coverage" are satisfied: , ) Step 3: Generate a candidate set of photovoltaic installed capacity: Within the range after screening by "site-load" constraints, candidate values ​​are selected at fixed step sizes to form the "photovoltaic installation candidate set" output in stage 1.

[0053]

[0054] Step 4: Stage 1 Output Results (Providing Input for Stage 2 Energy Storage Optimization): Without considering energy storage and consumption, we simulate the hourly matching of photovoltaic output and load to calculate the daily power curtailment: 1. Hourly photovoltaic power output simulation: (Hourly irradiance, such as 0.8 kWh / m² at 12:00 in summer). 2. Hourly power curtailment calculation: (Power curtailment occurs when photovoltaic output exceeds load). 3. Daily power wastage summary: ; The output must include "candidate capacity + key parameters" to ensure that subsequent energy storage optimization can accurately match photovoltaic characteristics. The specific output items are shown in Table 2. Table 2

[0055] The core value of Phase 1 is to "narrow down the scope and anchor the direction for subsequent optimizations," and its output needs to form a strong logical connection with subsequent phases: 1. Candidate set This will be directly used as input for Phase 2 "Energy Storage Capacity / Power Optimization," and energy storage needs to be tailored to different... Design charging and discharging strategies based on output characteristics (such as high power) Larger energy storage is needed to absorb abandoned electricity, and lower energy consumption is required. Energy storage needs to focus more on peak-valley arbitrage. 2. The output "curtailment rate" and "photovoltaic power generation" will serve as the basic data for Phase 3 "hourly operation simulation" to accurately calculate the benefits and costs of photovoltaic and energy storage synergistic operation; 3. All final candidates In Phase 4, the optimal photovoltaic installed capacity that matches energy storage will be determined through the "lowest LCOE" target screening.

[0056] Phase Two: Design Logic of Energy Storage Capacity and Power Optimization Algorithm (Coordination between Photovoltaics and Load): This phase, based on each photovoltaic candidate capacity output from Phase 1, uses "user load characteristics" and "PV curtailment" as criteria, focusing on the two main functions of energy storage: "promoting PV consumption" and "achieving peak-valley arbitrage," and considering energy storage technology constraints, to determine the feasible range of energy storage capacity. This provides parameter boundaries for the hourly operation simulation in Phase 3, and the optimal configuration is ultimately determined in Phase 4 through LCOE calculations.

[0057] For each photovoltaic candidate capacity output in Phase 1 Perform the following steps to derive the energy storage capacity range where the minimum value is 0 and only the maximum value needs to be determined: Step 1: Derive the upper limit of capacity based on "promoting photovoltaic consumption" : This upper limit is designed solely to "fully absorb the maximum curtailment of photovoltaic power," and is not related to peak-valley arbitrage demand. Its core purpose is to ensure that the maximum curtailment of photovoltaic power throughout its entire lifecycle can be fully absorbed by energy storage (curtailment rate ≤ 0), as shown in the following formula:

[0058] The maximum daily power curtailment corresponding to the photovoltaic candidate capacity in Phase 1; Lower limit of energy storage battery degradation rate; Load fluctuation coefficient: This factor addresses the temporary increase in power curtailment caused by sudden load surges. Minimum depth of charge / discharge protects battery life; Upper limit of energy storage charge-discharge cycle efficiency; Step 2: Derive the capacity limit based on "achieving peak-valley arbitrage" : This upper limit is designed solely to "fully match the peak-valley load gap" and is not related to the demand for photovoltaic curtailment. The core is to ensure that energy storage can be fully charged during off-peak hours and fully discharged during peak hours, completely covering the maximum peak-valley load gap, as shown in the formula below:

[0059] Maximum total load during peak load periods; The minimum coverage load power of this photovoltaic candidate capacity during peak hours ( This refers to the minimum number of hours of illumination during peak hours. (Photovoltaic conversion efficiency); Step 3: Integrate the total feasible range of energy storage capacity [0, ]: Since energy storage can achieve "photovoltaic consumption" or "peak-valley arbitrage" independently, or simultaneously, the total capacity limit is the maximum of the two function limits (ensuring that both single and dual functions can be covered without intersection restrictions). The final range is "minimum value 0, maximum value is the maximum of the two," as shown in the following formula: ) Feasible range of energy storage capacity = [0, ] Step 4: Derive the feasible range of energy storage charging and discharging power: The power range needs to be linked to the capacity range, matching the "PV abandoned power charging speed" and the "peak load discharge speed," and allocating charging power accordingly. With discharge power Derivation is performed separately.

[0060] Charging power range [ , ]: The charging power needs to match the requirement of "fully charging the corresponding capacity during the concentrated period of photovoltaic curtailment," and the boundary is derived based on the capacity range: Minimum charging power : Ensure that the minimum effective capacity is fully charged during the power curtailment period (take 10% of the maximum capacity as the minimum effective capacity to avoid insufficient power causing power curtailment to be unable to be stored in time), formula:

[0061] Maximum charging power Ensure that the capacity is fully charged during the curtailment period, while not exceeding the maximum curtailment power of photovoltaic power (to avoid power redundancy). Formula:

[0062] Discharge power range [ , ]: The discharge power needs to match the requirement of "discharging the corresponding capacity during peak load periods," and the boundary is derived based on the capacity range: Minimum discharge power To ensure that the minimum effective capacity is fully utilized during peak hours, the formula is:

[0063] Maximum discharge power Ensure that the maximum capacity is utilized during peak hours, while not exceeding the peak load gap (to avoid power redundancy). Formula:

[0064] (Maximum load gap during peak periods, such as...) ) Ensure compliance with the linkage logic between capacity and power: Power-capacity ratio verification: The ratio of charging / discharging power to the upper limit of capacity should be between 0.2 and 1.0 (industry standard, to avoid "large capacity with small power" or "small capacity with large power"). Time period matching verification: The charging and discharging power must be ensured to be within the corresponding time period ( Complete capacity charging and discharging within ) Key logical connections in the algorithm: 1. Alignment with Phase 1: Strictly derive the upper limit based on the curtailment characteristics of each photovoltaic candidate capacity to ensure the technical compatibility of "photovoltaic capacity - energy storage capacity" and avoid deviating from the energy storage range designed for photovoltaic output; 2. Connection with Stage 3: Output [0, The range provides parameter sampling boundaries for hourly simulation. In stage 3, multiple sample points (such as 0kWh, 50kWh, 150kWh, and 248kWh) can be selected within the range to simulate technical indicators such as power curtailment rate and peak-valley arbitrage power at each sample point. 3. Connection with Stage 4: After Stage 3 outputs the full life cycle cost (based on the cost item in the LCOE formula) and effective power generation for each sample point, Stage 4 can use LCOE to screen the energy storage capacity that meets the "technical standards + optimal economics" within this range.

[0065] Phase Three: Hourly Simulation and Profit Calculation Algorithm Design Logic (Incorporating Daily Charge / Discharge Strategy Combination): This stage is the "technical and economic quantification phase" of the photovoltaic-storage configuration scheme. Its core focus is on further enumerating "daily charge / discharge strategies" sub-combinations for each "PV candidate capacity - energy storage capacity - energy storage power" combination output from Stage 2. Based on the core logic of "load-based power allocation" and "optimal LCOE," hourly operational simulations are used to accurately calculate the full lifecycle technical indicators (curtailment rate, charge / discharge efficiency) and economic indicators (revenue, cost) for each "PV-storage configuration + charge / discharge strategy" sub-combination. This provides Stage 4 with fundamental LCOE data across two dimensions: "PV-storage configuration + charge / discharge strategy," ensuring that the final optimal solution simultaneously satisfies both reasonable capacity and power configuration and optimal daily operation strategies.

[0066] For each "" output of phase 2 The process involves first enumerating the sub-combinations of "daily charge / discharge strategy," and then performing simulations and calculations for each one. Step 1: Enumerate the "Daily Charge / Discharge Strategy" sub-combinations (based on dual-objective balancing logic) Focusing on the two core functions of "promoting photovoltaic consumption" and "realizing peak-valley arbitrage", three typical daily charge-discharge strategies are defined (each strategy is a sub-combination), covering different balance logics as shown in Table 3: Table 3

[0067] Step 2: Perform a full lifecycle time-by-time simulation for each "optical storage configuration + charging / discharging strategy" sub-combination; Iterates on a yearly basis (covering a 25-year lifespan for photovoltaics and a 10-15 year lifespan for energy storage), simulating operational status 24 hours a day for 365 days each year. The core simulation module is adjusted around "strategy differentiation": Hourly photovoltaic power output simulation (considering power degradation): Based on the "photovoltaic power attenuation rate", the first Year( Hourly photovoltaic actual output formula:

[0068] Photovoltaic degradation coefficient in year y; Photovoltaic conversion efficiency (e.g., a conventional value of 0.8).

[0069] Hourly energy storage charge and discharge simulation (executed according to strategy differentiation): Based on "energy storage capacity decay" and "charge / discharge power / depth constraints", the charge / discharge logic is adjusted according to different strategies. Taking year y as an example: (1) Actual usable energy storage capacity in year y (calculated uniformly): (Capacity in year y = Initial capacity) ) (2) Differentiated charging logic (executed according to strategy): Strategy 1 (Prioritize Solar Power Consumption):

[0070]

[0071] Strategy 2 (Prioritizing Peak-Valley Arbitrage):

[0072]

[0073] Strategy 3 (Dynamic Equilibrium):

[0074] in: , This represents the state of charge in the previous time period. The cost per kilowatt-hour of photovoltaic electricity ( ); (3) Differentiated discharge logic (executed according to strategy): Strategy 1 (Prioritizing Solar Power Consumption):

[0075] Strategy 2 (Prioritizing Peak-Valley Arbitrage):

[0076] Strategy 3 (Dynamic Equilibrium):

[0077] (4) SOC Update (Unified Logic):

[0078] Hourly key indicator statistics (output by sub-combination): For each sub-combination of "photovoltaic-storage configuration + charge / discharge strategy", the cumulative indicators for year y are as follows: Technical Specifications: (Annual solar power curtailment) (Annual charge-discharge cycles of energy storage) (Average annual curtailment rate); Economic indicators: (Annual direct photovoltaic power consumption) (Annual peak-valley arbitrage electricity from energy storage) (Annual electricity purchase by the power grid).

[0079] Step 3: Calculate the present value of the total lifetime revenue and costs for each sub-combination (supporting LCOE): Following the definitions of numerator and denominator in the LCOE formula in Part III, the calculation is performed based on sub-combinations: Present value of total lifecycle income :

[0080] (Revenue from self-consumption of photovoltaic power); (Energy storage arbitrage profits).

[0081] Present value of total life cycle cost :

[0082] (Initial investment); (Annual maintenance costs); (Gross curtailment loss, curtailment = loss of photovoltaic power generation cost); :No. Annual energy storage replacement cost (if) , (Residual value rate).

[0083] Present value of total effective power generation over the entire life cycle :

[0084] Annual storage capacity for reuse of abandoned photovoltaic power ( ); Step 4: Output of Stage 3 (providing two-dimensional sub-combination data for Stage 4): For each "photovoltaic-storage configuration + charging / discharging strategy" sub-combination, output complete "technical-economic" data, in the format shown in Table 4: Table 4

[0085] Key logic connections in the algorithm 1. Alignment with core objectives: By enumerating the sub-combinations of "daily charge and discharge strategies", the logic of "promoting photovoltaic consumption and peak-valley arbitrage balance" is visualized into computable strategy rules, ensuring that LCOE calculation covers the optimal possibilities in both "configuration + operation" dimensions; 2. Seamless integration with previous stages: Strategy constraints (such as charging and discharging power and capacity) strictly adhere to the output range of Stage 2, photovoltaic power output simulation is based on the candidate capacity of Stage 1, and the data link is fully connected; 3. Connection with Phase 4: The output "sub-combination identifier + LCOE basic data" provides direct input for Phase 4 to filter the optimal "configuration + strategy" based on "lowest LCOE".

[0086] Phase Four: Algorithm Design Logic for Selecting the Lowest LCOE and Optimal Efficiency Solution (Integrating Daily Charge-Discharge Strategy Combination) This stage is the "final decision-making closed loop" of the photovoltaic and energy storage optimization configuration. Its core positioning is based on the two-dimensional sub-combination data of "photovoltaic and energy storage configuration + charging and discharging strategy" output from stage 3. Following the core objective function of "lowest LCOE", and combined with technical feasibility and economic benefit constraints, the unique optimal combination of "photovoltaic and energy storage capacity and power configuration + daily charging and discharging strategy" is selected to achieve the optimal levelized cost of electricity and maximize economic benefits throughout the project's entire life cycle.

[0087] Step 1: Calculate the LCOE for each "photovoltaic-storage configuration + charge / discharge strategy" sub-combination: Using the Phase 3 sub-combination data as input, the formula is as follows:

[0088] Step 2: Technical feasibility verification (eliminating sub-combinations where strategy implementation fails to meet standards): Based on technical constraints and engineering implementation requirements, the "LCOE Preliminary Ranking Table" was verified one by one to ensure that the sub-assemblies meet the technical requirements for daily operation: 1. Power curtailment rate verification: If the sub-combination's If the percentage is 8%, then the goal of "promoting photovoltaic consumption" cannot be achieved, and it should be excluded. 2. Energy storage cycle verification: If the sub-combination's If it is used twice a day, the battery life will degrade too quickly (violating the "energy storage degradation rate" constraint), and it will be removed. 3. Strategy execution verification: If the "actual number of charge and discharge cycles deviates from the strategy rules by more than 10%" (e.g., Strategy 2 requires charging during off-peak hours but the actual charging rate is less than 80%), then the strategy has poor implementation and is removed. Step 3: Economic Benefit Constraint Verification (Screening of Qualified Sub-combinations): Based on the full life-cycle economic objectives, the "list of technically feasible sub-combinations" is further validated to ensure that the solutions have investment value: Investment recovery period ( Calculation and verification: The payback period is the time required to recover the initial investment.

[0089] :No. Total annual return (including arbitrage / absorption gains from the strategy, Stage 3 output); Verification rules: If If the recycling period is 8 years, the recycling requirement is met and the product is retained; otherwise, it is discarded (violating the goal of "maximizing economic benefits").

[0090] Internal rate of return (IRR) Calculation and verification: The internal rate of return (IRR) is the discount rate that makes the net present value (NPV) over the entire life cycle equal to 0. The formula is based on the discount logic of the objective function.

[0091] Solving by iterative method (as in trial calculation) hour Ten thousand yuan, hour Ten thousand yuan, worth inserting ; Verification rules: If If the percentage is 10%, the profit target is met and the product is retained; otherwise, it is removed.

[0092] Step 4: Determine the optimal combination of "photovoltaic-storage configuration + charge / discharge strategy": 1. Final LCOE ranking: For the sub-combinations that meet the criteria of "technically feasible + economically feasible", sort them again according to "LCOE from smallest to largest" and take the sub-combination with the lowest LCOE as the "optimal candidate combination"; 2. Strategy stability verification: For the optimal candidate combination, simulate the scenarios of "peak-valley electricity price fluctuation ±10%" and "photovoltaic irradiance fluctuation ±15%". If the LCOE fluctuation is ≤15%, the strategy has strong anti-interference ability and is determined as the final solution; if the fluctuation is too large, select the sub-combination with the second lowest LCOE but smaller fluctuation. 3. Solution Locking: Output the final solution's "photovoltaic storage capacity and power configuration + daily charge and discharge strategy" and core indicators to ensure complete matching with the objective function.

[0093] The output results must clearly define the optimal solution from both the "configuration + strategy" dimensions to meet the needs of project implementation and investment decisions, as shown in Table 5: Table 5

[0094] like Figure 2 As shown, a photovoltaic-storage optimization configuration system for charging stations includes: The unit includes a capacity candidate set calculation unit 201, an energy storage capacity and power calculation unit 202, a simulation unit 203, and an optimization scheme determination unit 204. The capacity candidate set calculation unit 201 is used to acquire the load data, solar resource data, site constraint data and economic evaluation parameters of the charging station, and calculate the upper and lower limits of photovoltaic installed capacity by combining the site physical constraints and load adaptation constraints, and generate a photovoltaic installed capacity candidate set. The energy storage capacity and power calculation unit 202 is used to calculate the upper limit of energy storage capacity for each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set based on promoting photovoltaic consumption and realizing peak-valley arbitrage, respectively, take the maximum value of the two to determine the feasible range of energy storage capacity, and calculate the range of energy storage charging and discharging power based on the feasible range of energy storage capacity. The simulation unit 203 is used to enumerate the preset charging and discharging strategies, and for each photovoltaic candidate capacity, energy storage capacity and energy storage charging and discharging power combination, it performs hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculates the present value of the total cost and the present value of the total effective power generation of each combination throughout the entire life cycle. The optimization scheme determination unit 204 calculates the levelized cost of electricity (LCOE) over the entire life cycle based on the ratio of the present value of total cost to the present value of total effective power generation. It calculates the LCOE of each combination and selects the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy in combination with technical feasibility and economic benefit constraints.

[0095] To enable those skilled in the art to better understand this disclosure, the principles of this disclosure are explained below in conjunction with the accompanying drawings: like Figure 1 As shown, using the actual load curve of the charging station as the input benchmark (load-based source determination), under multi-dimensional constraints, the combination of photovoltaic (PV) and energy storage (ESS) capacity is iteratively calculated. Simultaneously, the energy storage charging and discharging strategy is derived and clarified—this strategy must include a balance logic that promotes PV absorption and achieves peak-valley arbitrage. Finally, the optimal configuration scheme with the best levelized cost of electricity (LCOE) over the entire lifecycle is selected, achieving the goal of maximizing the economic benefits throughout the project's lifecycle. The process is as follows: We collect basic data such as daily load curves, daily peak loads, and annual total loads of charging stations, and analyze the temporal variation patterns of loads (distribution of peak and off-peak periods within the day, and seasonal load fluctuation characteristics) to provide core basis for optimizing the configuration of photovoltaic and energy storage.

[0096] Based on load characteristic data and solar resource data (daily irradiance curve, annual effective utilization hours, photovoltaic conversion efficiency), combined with site availability constraints, the upper and lower limits of photovoltaic installed capacity are calculated, a set of technically feasible photovoltaic installed capacity candidates is generated, and key parameters such as daily curtailment rate and average annual effective power generation without energy storage are output for each candidate capacity.

[0097] For each photovoltaic candidate capacity, focusing on the two core functions of energy storage—"promoting photovoltaic consumption" and "realizing peak-valley arbitrage"—and combining energy storage technology parameters (charge-discharge cycle efficiency, attenuation rate, and charge-discharge depth constraints), we deduce the feasible range of energy storage capacity and power to ensure that energy storage configuration matches photovoltaic output and load demand.

[0098] We enumerate three typical daily charging and discharging strategies (priority photovoltaic consumption strategy, priority peak-valley arbitrage strategy, and dynamic balancing strategy), define the charging and discharging decision rules for each strategy, and provide strategy support for hourly operation simulation.

[0099] Based on the Levelized Cost of Electricity (LCOE) formula, the present value of the total cost (initial investment, operation and maintenance costs, curtailment losses, and energy storage replacement costs) and the present value of the total effective power generation (direct photovoltaic power consumption, energy storage for the reuse of curtailed photovoltaic power, and energy storage for peak-valley arbitrage) of each "PV-storage-charge-discharge strategy" combination are calculated to evaluate the economics of the scheme.

[0100] Input data such as storage load basic data, optical resource data, equipment cost data, electricity price data, and economic evaluation parameters, as well as intermediate calculation results, candidate configuration scheme parameters, and final optimization results at each optimization stage, provide data support for the collaborative operation of each module.

[0101] Phase 1: Optimization Strategy for Photovoltaic Installed Capacity 1. Calculate the upper limit of photovoltaic installation under dual constraints: Simultaneously consider the physical constraints of site area (calculated by the total available area of ​​the site, the area occupied by the photovoltaic array per unit power and the site utilization coefficient) and the load adaptation constraints (including the instantaneous maximum output constraint and the annual power generation full load coverage constraint), and take the minimum value of the two as the upper limit of photovoltaic installation.

[0102]

[0103] 2. Calculate the lower limit of photovoltaic installation under load demand constraints: Based on the annual load coverage ratio and the daily peak load coverage ratio requirements, take the larger value of the two as the lower limit of photovoltaic installation.

[0104] , )

[0105]

[0106] 3. Generate a candidate set of photovoltaic installed capacity: Select candidate values ​​within the upper and lower limits with a fixed step size, simulate the hourly photovoltaic output and curtailment of each candidate capacity, and output key parameters.

[0107]

[0108] Phase Two: Energy Storage Capacity and Power Optimization Strategies 1. Deriving the upper limit of energy storage capacity based on "promoting photovoltaic consumption": Based on the maximum daily curtailment of photovoltaic candidate capacity, combined with energy storage attenuation rate, load fluctuation coefficient, minimum charge and discharge depth and charge and discharge cycle efficiency, calculate the upper limit of energy storage capacity that can completely consume photovoltaic curtailment.

[0109]

[0110] 2. Deriving the upper limit of energy storage capacity based on "realizing peak-valley arbitrage": Based on the difference between the maximum total load during peak load periods and the minimum coverage load of photovoltaic power during peak load periods, and combined with relevant technical and economic parameters, calculate the upper limit of energy storage capacity to meet the peak-valley arbitrage requirements.

[0111]

[0112] 3. Determine the total feasible range of energy storage capacity: Take the maximum value of the two upper limits mentioned above to form the feasible range of energy storage capacity [0, total upper limit].

[0113] ) Feasible range of energy storage capacity = [0, ] 4. Derive the range of energy storage charging and discharging power: Combine the range of energy storage capacity, the length of charging and discharging time, the maximum curtailment power of photovoltaic power and the peak load gap, calculate the upper and lower limits of charging power and discharging power respectively to ensure that the power and capacity are matched in compliance.

[0114] Charging power range [ , ]

[0115]

[0116] Discharge power range [ , ]

[0117]

[0118] Phase Three: Hourly Simulation and Revenue Calculation Strategy 1. Enumerate the sub-combinations of "daily charge and discharge strategies": including priority photovoltaic consumption strategy, priority peak-valley arbitrage strategy, and dynamic balancing strategy, and clarify the differentiated charge and discharge logic of each strategy, as shown in Table 6.

[0119] Table 6

[0120] 2. Hourly Operation Simulation: For each "PV-energy storage-power-charge-discharge strategy" combination, the PV output (considering power decay) and energy storage charge-discharge process (executed according to the strategy, considering capacity decay) are simulated on an annual iterative, 24-hour daily basis. The technical and economic indicators such as the annual curtailment rate, direct PV consumption, and energy storage arbitrage power are statistically analyzed.

[0121] 3. Lifecycle revenue and cost calculation: Calculate the present value of the lifecycle revenue (PV self-consumption revenue, energy storage arbitrage revenue) and the present value of costs (initial investment, operation and maintenance costs, curtailment losses, energy storage replacement costs) for each combination.

[0122] Phase Four: Optimal Solution Selection Strategy 1. Calculate the LCOE for each combination: Calculate the LCOE for each combination based on the present value of total life-cycle cost and the present value of total effective power generation.

[0123]

[0124] 2. Technical feasibility verification: Eliminate combinations with excessive curtailment rates, excessive energy storage cycle counts, and excessive strategy execution deviations.

[0125] 3. Economic benefit constraint verification: Calculate the investment payback period and internal rate of return, and eliminate combinations that fail to meet the economic benchmark target.

[0126]

[0127]

[0128] 4. Determine the optimal solution: For technically feasible and economically viable combinations, sort them by LCOE from smallest to largest, and combine the strategy stability verification (simulating electricity price and irradiance fluctuation scenarios) to determine the final optimal combination of "photovoltaic-storage configuration + charging and discharging strategy".

[0129] Example of running: A charging station in a certain city has a total usable area of ​​1000㎡, a photovoltaic array unit power area of ​​12㎡ / kW, and a site utilization coefficient of 0.85; a daily peak load of 200kW and an annual total load of 500,000kWh; a local annual effective sunshine hours of 1500h, a photovoltaic system efficiency of 0.8; peak and off-peak electricity prices of 1.0 yuan / kWh and 0.3 yuan / kWh respectively, and a flat-peak electricity price of 0.6 yuan / kWh; an energy storage charge-discharge cycle efficiency of 0.8, an annual degradation rate of 2%, and a minimum charge-discharge depth of 0.2.

[0130] 1. Photovoltaic configuration optimization: The upper limit of photovoltaic installation under the site constraints is (1000×0.85) / 12≈70.8kW, which is taken as 70kW; considering the local sunshine conditions and load characteristics, the lower limit of photovoltaic installation is taken as 20kW (determined according to actual coverage needs), and candidate sets of 20kW, 30kW, 40kW, 50kW, 60kW and 70kW are generated.

[0131] 2. Energy storage configuration optimization: For a 50kW photovoltaic candidate capacity, based on a typical day analysis, its maximum daily power curtailment is 80kWh. Considering the energy storage charge-discharge efficiency of 0.8, the energy storage capacity required for photovoltaic absorption is approximately 100kWh. Based on the peak-valley electricity price difference and load characteristics, the optimal energy storage capacity for peak-valley arbitrage is approximately 150kWh. The final feasible range for energy storage capacity is [0, 150kWh]. The recommended ratio of charge-discharge power to energy storage capacity is 1:4 to 1:6.

[0132] 3. Charging and Discharging Strategies and Simulation: Three charging and discharging strategies were designed. Under the dynamic balance strategy, when photovoltaic output exceeds load, energy storage is prioritized for charging, and the remaining electricity is fed into the grid. During off-peak hours, grid charging is determined based on real-time electricity prices and energy storage charging and discharging efficiency. During peak hours, energy storage is prioritized for discharging. Simulation calculations show an average annual curtailment rate of 3.2%, annual arbitrage revenue of 85,000 yuan from energy storage, and a photovoltaic self-consumption rate of 96.8%.

[0133] 4. Optimal Solution Selection: Based on parameters such as investment cost of 2500 yuan / kWp photovoltaic + 1500 yuan / kWh energy storage, operation and maintenance cost of 0.08 yuan / kWh, and discount rate of 8%, the LCOE of each combination was calculated. After comprehensive techno-economic evaluation, the 50kW photovoltaic + 120kWh energy storage + dynamic balancing strategy was determined to be the optimal solution, with an LCOE of 0.38 yuan / kWh, an investment payback period of 6.5 years, an internal rate of return of 12.3%, and an annual net income of approximately 180,000 yuan.

[0134] This disclosure constructs a multi-stage photovoltaic (PV) and energy storage (ESS) optimization framework based on load-driven energy source allocation. Through the coordinated operation of load characteristic analysis, PV configuration optimization, ESS configuration optimization, hourly operation simulation, and economic evaluation, it achieves precise matching of PV-ESS configuration with load, resources, technology, and economics. A PV installed capacity optimization method under dual constraints is proposed, simultaneously considering site physical limitations and load adaptation requirements, generating a technically feasible candidate set to lay the foundation for subsequent optimization. A coordinated optimization logic for ESS capacity and power is established, combining the dual objectives of PV integration and peak-valley arbitrage to derive a feasible range, ensuring the rationality of ESS configuration. Differentiated charging and discharging strategies are enumerated and integrated into hourly operation simulation, combined with a full life-cycle economic evaluation, to achieve optimal allocation across both configuration and strategy dimensions.

[0135] This disclosure utilizes a five-module collaborative approach—load characteristic analysis, photovoltaic (PV) configuration optimization, energy storage configuration optimization, hourly operation simulation, and economic evaluation—to determine the optimal configuration parameters for PV and energy storage. The PV installed capacity optimization method, under dual constraints, considers both site area physical constraints and load adaptation constraints (instantaneous maximum output constraint and annual power generation full load coverage constraint) to generate a candidate set of PV installed capacity. The collaborative optimization method for energy storage capacity and power, based on the dual objectives of PV integration and peak-valley arbitrage, derives the feasible range of energy storage capacity and power by combining energy storage technology parameters. An hourly operation simulation method incorporating three types of differentiated charging and discharging strategies, through full lifecycle cost-per-kilowatt-hour calculation and verification of technical and economic constraints, selects the optimal combination of "PV-energy storage configuration + charging and discharging strategy."

[0136] This disclosure improves photovoltaic (PV) power consumption and supply stability: by precisely matching PV and energy storage configurations and combining optimized charging and discharging strategies, it significantly reduces PV curtailment rates while mitigating the impact of charging load fluctuations on the power grid, thereby enhancing power supply security and stability.

[0137] This disclosure optimizes resource allocation and improves utilization: it fully considers multiple constraints such as site, technology, and economy, avoids excessive photovoltaic installations or redundant energy storage configurations, improves the utilization rate of photovoltaic and energy storage equipment, and reduces resource waste.

[0138] This disclosure aims to reduce total lifecycle costs and improve economic efficiency: with the lowest LCOE as the core objective, it comprehensively considers factors such as initial investment, operation and maintenance costs, and peak-valley arbitrage profits to maximize the economic benefits of charging stations throughout their lifecycle and reduce electricity costs.

[0139] This disclosure promotes the consumption of new energy and green development: it promotes the large-scale application of renewable energy such as photovoltaics in charging stations, reduces dependence on fossil fuels, reduces carbon emissions, and helps achieve the "dual carbon" goals.

[0140] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for optimizing the configuration of photovoltaic and energy storage in charging stations, characterized in that, include: The system acquires load data, solar resource data, site constraint data, and economic evaluation parameters of charging stations. Combining site physical constraints and load adaptation constraints, it calculates the upper and lower limits of photovoltaic installed capacity and generates a candidate set of photovoltaic installed capacity. For each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set, the upper limit of energy storage capacity is calculated based on promoting photovoltaic consumption and realizing peak-valley arbitrage. The maximum value of the two is taken to determine the feasible range of energy storage capacity, and the range of energy storage charging and discharging power is calculated based on the feasible range of energy storage capacity. Enumerate the preset charging and discharging strategies, and for each photovoltaic candidate capacity, energy storage capacity and energy storage charging and discharging power combination, perform hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculate the present value of total cost and the present value of total effective power generation of each combination throughout the entire life cycle. The levelized cost of electricity (LCOE) over the entire lifecycle is calculated by the ratio of the present value of total cost to the present value of total effective power generation. The LCOE of each combination is calculated, and the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy are selected by combining technical feasibility and economic benefit constraints.

2. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, Calculating the upper limit of photovoltaic installed capacity includes: Calculate the upper limit of physical constraints on site area based on the total available site area and the area occupied per unit power of photovoltaic arrays; Based on the requirement that the actual output of photovoltaic power during peak irradiance periods must be able to cover the user's daily peak load, the instantaneous maximum output constraint of photovoltaic power is calculated; based on the user's total annual load and the annual effective utilization hours of photovoltaic power, the full load coverage constraint of annual photovoltaic power generation is calculated; the minimum value between the instantaneous maximum output constraint of photovoltaic power and the full load coverage constraint of annual photovoltaic power generation is taken as the upper limit of the load adaptation constraint. The minimum value between the upper limit of the physical constraint on site area and the upper limit of the load adaptation constraint shall be taken as the upper limit of photovoltaic installed capacity.

3. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, The calculation of the lower limit of photovoltaic installed capacity includes: Calculate the annual load coverage constraint based on the requirement that the annual effective power generation of photovoltaic power must cover a set proportion of the total annual electricity consumption of the load. Based on the requirement that photovoltaic power generation can cover part of the peak load during peak output periods, calculate the daily peak load coverage constraint; The maximum value between the annual load coverage constraint and the daily peak load coverage constraint is taken as the lower limit for calculating photovoltaic installed capacity.

4. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, Determine the feasible range of energy storage capacity, including: Based on the maximum daily power curtailment corresponding to the photovoltaic candidate capacity, and combined with the energy storage attenuation rate, load fluctuation coefficient and charge-discharge cycle efficiency, the upper limit of energy storage capacity to promote photovoltaic consumption is calculated. Based on the difference between the maximum total load during peak load periods and the minimum coverage load of photovoltaic power during peak periods, the upper limit of energy storage capacity for achieving peak-valley arbitrage is calculated. The maximum value between the upper limit of energy storage capacity for promoting photovoltaic consumption and the upper limit of energy storage capacity for realizing peak-valley arbitrage is taken as the total upper limit of energy storage capacity, and the feasible range of energy storage capacity is determined as [0, total upper limit of energy storage capacity].

5. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, Calculate the energy storage charging and discharging power range, including: The charging power needs to match the demand for fully charging the corresponding capacity during the peak period of photovoltaic curtailment, and the boundary is derived by combining the capacity range. Minimum charging power is used to ensure that the minimum effective capacity is fully charged during periods of power curtailment. Maximum charging power is used to ensure that the capacity is fully charged during the curtailment period, while not exceeding the maximum curtailment power of photovoltaic power.

6. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, Calculating the charging and discharging power range of energy storage also includes: The discharge power needs to match the requirement of discharging the corresponding capacity during peak load periods, and the boundary is derived by combining the capacity range. Minimum discharge power is used to ensure that the minimum effective capacity is discharged during peak periods; Maximum discharge power is used to ensure that the upper limit of capacity is discharged during peak hours, while not exceeding the peak load gap.

7. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, The preset charging and discharging strategies include at least: Prioritize photovoltaic power consumption strategy: when photovoltaic output exceeds load, prioritize energy storage charging and prioritize discharge during peak load periods. Prioritize peak-valley arbitrage strategy, force charging during off-peak hours and force discharging during peak hours; The dynamic balancing strategy calculates in real time the cost of photovoltaic curtailment losses and off-peak charging costs, as well as the cost of peak electricity purchases and energy storage discharge losses, and makes dynamic decisions on charging and discharging behavior based on the cost comparison results.

8. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, Hourly simulation throughout the entire lifecycle includes: Considering the photovoltaic power decay rate and the energy storage capacity decay rate, the photovoltaic output and energy storage charging and discharging process are simulated on an annual iterative basis and 24-hour daily basis. The present value of total cost and the present value of total effective power generation are calculated over the entire life cycle. The present value of total cost includes initial investment, operation and maintenance costs, curtailment losses and energy storage replacement costs. The present value of total effective power generation includes direct photovoltaic power consumption, energy storage for the reuse of curtailed photovoltaic power, and energy storage for peak-valley arbitrage.

9. The method for optimizing the configuration of photovoltaic and energy storage in a charging station according to claim 1, characterized in that, The selection process combines technical feasibility with economic constraints, including: Calculate the levelized cost of electricity (LCOE) for each combination throughout its lifecycle; Eliminate combinations of excessive curtailment rate, excessive energy storage cycle count, or strategy execution deviation greater than the preset threshold; Calculate the payback period and internal rate of return, and eliminate portfolios that fail to meet the economic benchmark target; Sort the remaining combinations by LCOE from smallest to largest, verify the stability of the strategy, and determine the scheme with the lowest LCOE.

10. A photovoltaic-storage optimization configuration system for charging stations, characterized in that, include: The unit includes a capacity candidate set calculation unit, an energy storage capacity and power calculation unit, a simulation unit, and an optimization scheme determination unit. The capacity candidate set calculation unit is used to acquire load data, solar resource data, site constraint data and economic evaluation parameters of charging stations, and calculate the upper and lower limits of photovoltaic installed capacity by combining site physical constraints and load adaptation constraints, and generate a photovoltaic installed capacity candidate set. The energy storage capacity and power calculation unit is used to calculate the upper limit of energy storage capacity for each photovoltaic candidate capacity in the photovoltaic installed capacity candidate set, based on promoting photovoltaic consumption and realizing peak-valley arbitrage, respectively. The maximum value of the two is taken to determine the feasible range of energy storage capacity, and the energy storage charging and discharging power range is calculated based on the feasible range of energy storage capacity. The simulation unit is used to enumerate the preset charging and discharging strategies. For each photovoltaic candidate capacity, energy storage capacity, and energy storage charging and discharging power combination, it performs hourly operation simulation of the entire life cycle in combination with the charging and discharging strategies, and calculates the present value of total cost and the present value of total effective power generation of each combination over the entire life cycle. The optimization scheme determination unit calculates the levelized cost of electricity (LCOE) over the entire life cycle based on the ratio of the present value of total cost to the present value of total effective power generation. It calculates the LCOE of each combination and selects the photovoltaic-storage configuration scheme with the lowest LCOE and the corresponding charging and discharging strategy based on technical feasibility and economic constraints.

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