User-side energy storage configuration method and device, equipment, storage medium and program product

Abstract

This invention relates to the field of energy storage configuration technology, and discloses a user-side energy storage configuration method, device, equipment, storage medium, and program product. The user-side energy storage configuration method includes: determining the target levelized cost of electricity (LCOE) based on the total cost per kilowatt-hour (LCOE) and total discharge capacity over the entire lifecycle; determining candidate energy storage operation modes and energy storage configuration duration ranges based on the target LCOE and electricity price data; optimizing user-side energy storage parameters to obtain a set of candidate user-side energy storage parameters; updating actual costs and benefits; determining evaluation indicators based on the update results; and selecting configuration parameters based on the evaluation indicators. This invention performs LCOE analysis, distinguishes the battery lifecycle degradation and residual value characteristics of different energy storage technologies, and constructs a full-process correlation between energy storage power, operation mode, annual cycle count, and economic efficiency, achieving a closed-loop analysis from configuration parameters to long-term economics. This invention reduces LCOE calculation errors and improves the accuracy of user-side energy storage configuration.

Description

Technical Field

[0001] This invention relates to the field of energy storage configuration technology, specifically to user-side energy storage configuration methods, devices, equipment, storage media, and software products. Background Technology

[0002] With the development of high-load new energy technologies such as solar thermal power plants and hydrogen energy parks, the demand for user-side energy storage configuration is also increasing. In order to meet the needs of user-side energy storage configuration, multiple factors need to be considered when configuring user-side energy storage.

[0003] In related technologies, the user-side energy storage configuration method is based on the user's actual load demand and a simple peak-valley electricity price difference. However, this technology has two major flaws: First, the levelized cost of electricity (LCOE) calculation uses a general model without considering the characteristics of different energy storage technologies in engineering practice, leading to distorted cost calculations. Second, it fails to establish the correlation between energy storage configuration parameters, operating modes, battery cycle life, and overall lifecycle economics, ignoring the impact of operating strategies on battery life and long-term returns. Therefore, the user-side energy storage configuration method in these technologies does not fully utilize the LCOE, resulting in inaccurate configurations that deviate from reality. Summary of the Invention

[0004] This invention provides a user-side energy storage configuration method, apparatus, device, storage medium, and program product to solve the problem that user-side energy storage configuration deviates from reality and is not accurate enough due to user-side energy storage configuration methods in related technologies.

[0005] In a first aspect, the present invention provides a user-side energy storage configuration method, comprising: determining a target levelized cost of electricity (LCOE) based on an optimized LCOE calculation model combined with engineering practice, according to the total LCOE cost and total LCOE discharge capacity of the target energy storage system over its entire lifecycle; the LCOE calculation model is used to distinguish the battery replacement cycle, battery health degradation rate, and residual value calculation logic of different energy storage technology types; determining candidate energy storage operation modes and energy storage configuration duration ranges based on the target LCOE and electricity price data for different time periods; and optimizing user-side energy storage parameters with the goal of maximizing energy storage revenue and with preset operating constraints as constraints. The process involves optimization to obtain a set of candidate user-side energy storage parameters. Based on the candidate energy storage operation modes, energy storage configuration duration range, and the set of candidate user-side energy storage parameters, the lifecycle cost and revenue are updated to obtain the actual lifecycle cost and annual revenue of the target energy storage system. Multiple evaluation indicators are determined based on the actual lifecycle cost and annual revenue, and these indicators are used to screen the candidate energy storage operation modes and candidate user-side energy storage parameter sets to obtain the target energy storage operation mode and target user-side energy storage parameters. User-side energy storage configuration is then performed based on the target energy storage operation mode and target user-side energy storage parameters.

[0006] This invention determines the target levelized cost of electricity (LCOE) to characterize the cost per unit of electricity based on the total LCOE and total discharge volume of the target energy storage system throughout its entire lifecycle. This reflects the actual cost per unit of electricity for the energy storage system over its entire lifecycle, avoiding decision-making biases caused by short-term cost assessments. Based on the target LCOE and electricity price data for different time periods, this invention determines candidate energy storage operation modes and the duration range of energy storage configurations, deeply coupling the LCOE of the energy storage system itself with external electricity market signals to ensure that all candidate operation modes possess basic profitability potential. With the goal of maximizing energy storage revenue and using preset operational constraints as conditions, this invention optimizes user-side energy storage parameters to obtain a set of candidate user-side energy storage parameters. Through parameter optimization with clear objectives and complete constraints, it maximizes the revenue potential of energy storage while ensuring the safe, stable, and compliant operation of the energy storage system, generating multiple sets of optimal candidate parameter schemes that meet the constraints. This invention updates the lifecycle cost and benefit based on candidate energy storage operation modes, energy storage configuration duration ranges, and candidate user-side energy storage parameter sets, obtaining the actual lifecycle cost and annual benefit of the target energy storage system, thus improving the accuracy of lifecycle cost and benefit calculations. This invention determines multiple evaluation indicators based on the actual lifecycle cost and annual benefit, and uses these indicators to screen candidate energy storage operation modes and candidate user-side energy storage parameter sets to obtain the target energy storage operation mode and target user-side energy storage parameters. User-side energy storage configuration is then based on the target energy storage operation mode and target user-side energy storage parameters, constructing a multi-dimensional and comprehensive evaluation indicator system. This system comprehensively and objectively reflects the overall performance of each set of candidate user-side energy storage parameters across multiple dimensions. Through comprehensive screening using multiple indicators, the target scheme with the optimal overall performance is selected from the candidate schemes, improving the accuracy of user-side energy storage configuration. Compared with related technologies, this invention's embodiments consider the impact of levelized cost of electricity (LCOE), obtain a candidate set through parameter optimization, and then evaluate the candidate set to obtain the final energy storage configuration parameters, thus improving the accuracy of user-side energy storage configuration.

[0007] In one optional implementation, based on a cost-per-kilowatt-hour (LPE) calculation model optimized by engineering practice, the target LEE is determined according to the total LEE cost and total LEE discharge volume of the target energy storage system over its entire lifecycle. This includes: determining the total LEE cost over the entire lifecycle based on the LEE calculation model, considering the initial investment unit cost, the unit cost of replacement components for different energy storage technologies, the unit cost of annual loan repayments, the unit cost of annual insurance, and the residual value of batteries for different energy storage technologies; the total LEE cost over the entire lifecycle is used to characterize the total cost per kWh over the entire lifecycle; determining the total LEE discharge volume over the entire lifecycle based on the battery type, the average annual degradation rate in healthy condition, and the total discharge volume in the first year of the target energy storage system; and determining the target LEE cost by the quotient of the total LEE cost and the total LEE discharge volume over the entire lifecycle.

[0008] In one optional implementation, candidate energy storage operation modes and energy storage configuration duration ranges are determined based on target levelized cost of electricity (LCOE) and electricity price data for different time periods. This includes: determining the annual average electricity price during peak hours and the annual average electricity price difference between peak and off-peak hours based on electricity price data for different time periods; comparing the target LCOE with the annual average electricity price during peak hours and the annual average electricity price difference between peak and off-peak hours respectively; when the comparison result shows that the target LCOE is less than the annual average electricity price during peak hours and greater than the annual average electricity price difference between peak and off-peak hours, the candidate energy storage operation mode is determined to be curtailed solar power charging and storage with peak-hour discharge; when the comparison result shows that the target LCOE is less than or equal to the annual average electricity price difference between peak and off-peak hours, the candidate energy storage operation mode is determined to be solar / off-peak electricity charging and storage with peak-hour discharge; and comparing the target LCOE with the electricity price data for different time periods, and determining the energy storage configuration duration range based on the comparison results.

[0009] In one optional implementation, the total lifecycle cost and revenue of the target energy storage system are updated based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set to obtain the actual total lifecycle cost and annual revenue of the target energy storage system. This includes: selecting a target energy storage form based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set; determining the total annual energy storage discharge based on the target energy storage form; determining the total annual cycle count based on the total annual energy storage discharge; and updating the total lifecycle cost and revenue based on the total annual cycle count to obtain the actual total lifecycle cost and annual revenue of the target energy storage system.

[0010] In one optional implementation, after updating the total lifecycle cost and revenue based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set to obtain the actual total lifecycle cost and annual revenue of the target energy storage system, the user-side energy storage configuration method further includes: performing sensitivity analysis on multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set to obtain sensitivity analysis results; and screening multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set based on the sensitivity analysis results.

[0011] In one optional implementation, multiple evaluation indicators include internal rate of return (IRR) and net present value (NPV). Multiple evaluation indicators are determined based on the actual total lifecycle cost and annual total lifecycle revenue. Candidate energy storage operation modes and candidate user-side energy storage parameter sets are then screened based on these multiple evaluation indicators to obtain target energy storage operation modes and target user-side energy storage parameters. This includes: determining the IRR and NPV based on the actual total lifecycle cost and the annual total lifecycle revenue; determining whether each evaluation indicator is greater than a corresponding preset threshold; and using the candidate energy storage operation modes and candidate user-side energy storage parameters corresponding to the target evaluation indicators that are greater than the corresponding preset threshold as the target energy storage operation mode and target user-side energy storage parameters.

[0012] Secondly, this invention provides a user-side energy storage configuration device, comprising: a cost-per-kilowatt-hour (kWh) determination module, used to determine the target kWh based on a cost-per-kilowatt-hour calculation model optimized by combining engineering practice, according to the total kWh cost and total discharge capacity of the target energy storage system over its entire life cycle; the kWh cost calculation model is used to distinguish the battery replacement cycle, battery health degradation rate, and residual value calculation logic of different energy storage technology types; an operation mode selection module, used to determine candidate energy storage operation modes and energy storage configuration duration ranges based on the target kWh cost and electricity price data for different time periods; and a parameter set determination module, used to determine the user-side energy storage configuration mode with the goal of maximizing energy storage revenue and with preset operation constraints as constraints. The system optimizes user-side energy storage parameters to obtain a set of candidate user-side energy storage parameters. A data update module updates the lifecycle cost and revenue based on the candidate energy storage operation mode, energy storage configuration duration, and the set of candidate user-side energy storage parameters, resulting in the actual lifecycle cost and annual revenue of the target energy storage system. An energy storage configuration module determines multiple evaluation indicators based on the actual lifecycle cost and annual revenue, and filters the candidate energy storage operation modes and candidate user-side energy storage parameter sets based on these indicators, obtaining the target energy storage operation mode and target user-side energy storage parameters. This allows for user-side energy storage configuration based on the target energy storage operation mode and target user-side energy storage parameters.

[0013] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the user-side energy storage configuration method described in the first aspect or any corresponding embodiment thereof.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the user-side energy storage configuration method described in the first aspect or any corresponding embodiment thereof.

[0015] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the user-side energy storage configuration method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a user-side energy storage configuration method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for a user-side energy storage configuration method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the user-side energy storage configuration method according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a user-side energy storage configuration device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] As an optional application scenario of this invention, such as Figure 1 As shown, the user-side energy storage configuration system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0021] The terminal device can specifically be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0022] Recently, with the development of high-load new energy technologies such as solar thermal power plants and hydrogen energy parks, the demand for user-side energy storage configuration has further increased. In conventional methods, the role of cost per kilowatt-hour is generally not fully utilized and the cost per kilowatt-hour calculation model deviates from reality. At the same time, there is a lack of consideration for the impact of energy storage operation on the number of energy storage operations and the decay rate.

[0023] This invention provides a user-side energy storage configuration method, which determines the operating mode through the cost per kilowatt-hour, obtains a candidate set through parameter optimization, and then evaluates the candidate set to obtain the final energy storage configuration parameters, thereby improving the accuracy of user-side energy storage configuration.

[0024] According to an embodiment of the present invention, a user-side energy storage configuration method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] This embodiment provides a user-side energy storage configuration method, which can be used in computer equipment. Figure 2 This is a first flowchart of a user-side energy storage configuration method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Based on the cost-per-kilowatt-hour calculation model optimized by combining engineering practice, determine the target cost-per-kilowatt-hour according to the total cost-per-kilowatt-hour of the target energy storage system throughout its entire life cycle and the total discharge capacity of the target energy storage system throughout its entire life cycle.

[0026] The cost per kilowatt-hour (kWh) calculation model is a pre-built and optimized model for calculating the cost per kWh. This model is used to differentiate the battery replacement cycle, battery health degradation rate, and residual value calculation logic for different energy storage technology types. The target energy storage system is the energy storage system that requires user-side energy storage configuration. The total cost per kilowatt-hour over the entire life cycle is used to characterize the total cost per kWh over the entire life cycle. For example, the total cost per kilowatt-hour over the entire life cycle includes the initial investment unit cost, the unit cost of replacement components, the unit cost of annual loan repayments, the unit cost of annual insurance, and the residual value of the battery per kWh. The total discharge capacity per kilowatt-hour over the entire life cycle is the total amount of electricity actually output by the target energy storage system throughout its entire life cycle. The target kWh, also known as the Levelized Cost of Storage (LCOS), is the core indicator for measuring the cost per unit of output capacity of the energy storage system over its entire life cycle.

[0027] In some optional implementations, various cost data of the target energy storage system are collected and input into a life-cycle cost-per-kilowatt-hour (kWh) determination model to obtain the life-cycle cost-per-kilowatt-hour; the battery type, annual average degradation rate under healthy conditions, and total annual discharge capacity in the first year of the target energy storage system are collected and input into a life-cycle total discharge capacity (kWh) determination model to obtain the life-cycle total discharge capacity (kWh); the life-cycle cost-per-kilowatt-hour and the life-cycle total discharge capacity (kWh) are input into a cost-per-kilowatt-hour (kWh) calculation model to obtain the target cost-per-kilowatt-hour.

[0028] Step S202: Based on the target cost per kilowatt-hour and electricity price data for different time periods, determine the candidate energy storage operation mode and the range of energy storage configuration duration.

[0029] Among them, the electricity price data for different time periods include user-side time-of-use electricity prices (peak, flat, and valley period electricity prices), demand electricity prices, penalty electricity prices, as well as market transaction electricity prices and ancillary service electricity prices (if users participate in grid ancillary services); candidate energy storage operation modes refer to energy storage operation strategies that meet the conditions of matching electricity prices and costs, such as curtailment of solar power charging and storage during peak hours and discharge, solar / valley power charging and storage during peak hours and discharge, etc.; the range of energy storage configuration duration is the trend of the proposed energy storage duration obtained by comprehensively evaluating the peak and valley time periods and durations.

[0030] Step S203: With the goal of maximizing energy storage revenue and with preset operating constraints as constraints, optimize the user-side energy storage parameters to obtain a set of candidate user-side energy storage parameters.

[0031] Among them, energy storage revenue refers to all economic benefits generated during the operation of the target energy storage system; preset operating constraints refer to the technical, safety, and compliance constraints that the target energy storage system must comply with during operation. For example, the constraints include the upper / lower limit of the energy storage system's charging and discharging power, battery state of charge, battery cycle life constraints, grid connection constraints, user electricity compliance constraints, load constraints, etc.

[0032] In some optional implementations, user-side energy storage parameters refer to the core parameters that determine the configuration and operation of the energy storage system, including load, rated power, reasonable annual cycle number of energy storage, charge and discharge operation boundary, and corresponding preliminary revenue and cost benchmark values, etc.; the candidate user-side energy storage parameter set is a set of multiple sets of parameter combinations that meet the constraints and revenue objectives obtained through parameter optimization, which is a set of multiple optimal solutions.

[0033] In some optional implementations, the objective function for maximizing energy storage revenue and preset operating constraints are input into a preset optimization algorithm to obtain a set of candidate user-side energy storage parameters. For example, the preset optimization algorithm includes particle swarm optimization, genetic algorithm, etc. The genetic algorithm is used to initialize a population of parameters such as energy storage capacity and charging / discharging power. Through selection, crossover, and mutation operations, the total revenue corresponding to each set of parameters is calculated, and the top 10 parameter combinations with the highest revenue are selected to form a set of candidate user-side energy storage parameters.

[0034] In some optional implementations, the average annual electricity demand and load demand during peak, flat, and valley periods are calculated. The proposed energy storage capacity and power will then fall within these two values, with a range above and below these values ​​for algorithm optimization. Combining the peak and valley periods and their durations, the trend of the proposed energy storage duration can also be comprehensively evaluated. If the target cost per kilowatt-hour is less than the peak-valley price difference or the peak electricity price, the energy storage duration should cover the peak period as much as possible to maximize energy storage revenue. In the optimization algorithm, this part should fully consider the operational constraints arising from the relationship between the energy storage operation mode and load demand.

[0035] Step S204: Update the full life cycle cost and revenue based on the candidate energy storage operation mode, energy storage configuration duration range, and candidate user-side energy storage parameter set to obtain the actual full life cycle cost and annual revenue of the target energy storage system.

[0036] Step S205: Determine multiple evaluation indicators based on the actual total life cycle cost and annual total life cycle revenue. Screen candidate energy storage operation modes and candidate user-side energy storage parameter sets based on multiple evaluation indicators to obtain target energy storage operation mode and target user-side energy storage parameters, so as to configure user-side energy storage according to target energy storage operation mode and target user-side energy storage parameters.

[0037] Among them, the evaluation indicators are used to measure the comprehensive performance of candidate energy storage operation modes and parameter combinations. For example, various evaluation indicators include IRR (Internal Rate of Return) and NPV (Net Present Value). Based on various evaluation indicators, a weighted scoring method is used to screen candidate energy storage operation modes and candidate user-side energy storage parameter sets to obtain target energy storage operation modes and target user-side energy storage parameters.

[0038] In some optional implementations, user-side energy storage configuration involves the entire process of selecting energy storage equipment, installing and laying out it, debugging the control system, and implementing the operation strategy based on the target energy storage operation mode and the target user-side energy storage parameters.

[0039] The user-side energy storage configuration method provided in this embodiment determines the target levelized cost of electricity (LCOE) to characterize the cost per unit of electricity based on the total LCOE and total LCOE discharge of the target energy storage system throughout its entire lifecycle. This reflects the actual cost per unit of electricity of the energy storage system over its entire lifecycle, avoiding decision-making biases caused by short-term cost assessments. Based on the target LCOE and electricity price data for different time periods, this embodiment determines candidate energy storage operation modes and the range of energy storage configuration durations, deeply coupling the LCOE of the energy storage system itself with external electricity price market signals to ensure that all candidate operation modes have basic profit potential. With the goal of maximizing energy storage revenue and using preset operational constraints as conditions, this embodiment optimizes user-side energy storage parameters to obtain a set of candidate user-side energy storage parameters. Through parameter optimization with clear objectives and complete constraints, it maximizes the revenue potential of energy storage while ensuring the safe, stable, and compliant operation of the energy storage system, generating multiple sets of optimal candidate parameter schemes that meet the constraints. This invention updates the lifecycle cost and revenue based on candidate energy storage operation modes, energy storage configuration duration ranges, and candidate user-side energy storage parameter sets, obtaining the actual lifecycle cost and annual revenue of the target energy storage system, thus improving the accuracy of lifecycle cost and revenue. This invention determines multiple evaluation indicators based on the actual lifecycle cost and annual revenue, and filters candidate energy storage operation modes and candidate user-side energy storage parameter sets based on these indicators to obtain the target energy storage operation mode and target user-side energy storage parameters. User-side energy storage is then configured based on the target energy storage operation mode and target user-side energy storage parameters, constructing a multi-dimensional and comprehensive evaluation indicator system. This system comprehensively and objectively reflects the overall performance of each set of candidate user-side energy storage parameters across multiple dimensions. Through comprehensive screening using multiple indicators, the target scheme with the best overall performance is selected from the candidate schemes, improving the accuracy of user-side energy storage configuration. Compared with related technologies, this invention considers the impact of levelized cost of electricity (LCOE), obtains a candidate set through parameter optimization, and then evaluates the candidate set to obtain the final energy storage configuration parameters, thus improving the accuracy of user-side energy storage configuration.

[0040] This embodiment provides a user-side energy storage configuration method, which can be used in computer equipment. Figure 3 This is a second flowchart of a user-side energy storage configuration method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Based on the cost-per-kilowatt-hour calculation model optimized by combining engineering practice, determine the target cost-per-kilowatt-hour according to the total cost-per-kilowatt-hour of the target energy storage system throughout its entire life cycle and the total discharge capacity of the target energy storage system throughout its entire life cycle; the cost-per-kilowatt-hour calculation model is used to distinguish the battery replacement cycle, battery health degradation rate and residual value calculation logic of different energy storage technology types.

[0041] Specifically, step S301 includes: Step S3011: Based on the cost-per-kilowatt-hour calculation model, determine the total cost per kilowatt-hour over the entire life cycle according to the initial investment unit cost, the unit cost of replacement components for different energy storage technologies, the unit cost of annual loan repayments, the unit cost of annual insurance, and the residual value of batteries for different energy storage technologies. The total cost per kilowatt-hour over the entire life cycle is used to characterize the total cost per kilowatt-hour over the entire life cycle.

[0042] The initial investment unit cost is the sum of the energy storage system cost, civil engineering and installation cost, grid connection testing cost, etc. For example, the initial investment unit cost can be expressed as:

[0043] in, The initial investment unit cost, For the unit cost of energy storage systems, For the unit cost of civil engineering and installation, Other unit costs, such as network access testing.

[0044] In some optional implementations, the unit cost of replacement components for different energy storage technologies is the average cost per replacement (or per unit quantity) of a particular component. Lithium-ion batteries require replacement in the 13th year, while flow batteries require replacement of the pump assembly in the 11th / 21st year. For battery-based energy storage, represented by lithium-ion batteries, the required replacement cycle is theoretically related to the battery's operating mode. However, in the initial stage of demand analysis, the operating mode of the energy storage is not clearly defined, so assumptions based on experience are necessary. Currently, the system lifespan of lithium-ion battery energy storage is generally 4000 cycles, with an equivalent cycle count of no more than 330 cycles per year, meaning the lifespan of lithium-ion battery energy storage is 12 years. The annual SOH (State of Health) degradation rate is 1.66%. Based on a 24-year design lifespan for the energy storage power station, the lithium-ion battery only needs to be replaced once in the 13th year. Therefore, the unit cost of replacement components is:

[0045] in, For lithium-ion battery energy storage Annual unit cost of battery replacement The unit cost of replacing batteries for lithium-ion battery energy storage. The time limit is in years.

[0046] In some alternative implementations, for energy storage systems including pump circuits, such as flow batteries, the cycle life is generally 15,000 cycles. Based on 330 cycles per year, this translates to a lifespan of 45 years, with an annual degradation rate of 0.45%. Battery replacement is not required. However, based on experience, the pump is typically replaced every 10 years. Therefore, the unit cost of replacing this component is:

[0047] in, For flow battery energy storage Annual unit cost of battery replacement The unit cost of replacing batteries for flow battery energy storage The time limit is in years.

[0048] In some alternative implementations, the unit cost of the annual loan repayment is the cost of the loan principal and interest to be repaid each year, spread across each kilowatt-hour of electricity generated, and determined based on the loan principal and the amount of repayment each period.

[0049] Specifically, the loan principal is the product of the initial investment unit cost and the loan-to-value ratio. This can be expressed as:

[0050] in, For the loan principal, The initial investment unit cost, This refers to the loan-to-value ratio.

[0051] In some optional implementations, a monthly equal principal and interest repayment method is generally adopted. The method for determining the amount to be repaid in each period is as follows:

[0052] in, This is the amount to be repaid each period. For the loan principal, It is 1 / 12 of the annual loan interest rate, and T is 12 times the loan term.

[0053] In some alternative implementations, the unit cost of annual loan repayments is:

[0054] in, The unit cost of annual loan repayment. This is the amount to be repaid each period.

[0055] In some alternative implementations, the annual unit cost of insurance is calculated at a fixed rate based on the total initial investment, with the cost remaining the same each year; that is, the annual unit cost of insurance is:

[0056] in, The annual unit cost of insurance The annual insurance premium rate, This represents the initial investment unit cost.

[0057] In some optional implementations, regarding the residual value of batteries by energy storage technology type, lithium-ion batteries have no residual value, while flow batteries are calculated at 30% of the initial investment. For battery-based energy storage, represented by lithium-ion batteries, the residual value of the energy storage system mainly considers the battery's residual value. Although other system components also have some residual value, the amount is relatively small, and the disposal of the energy storage system requires additional costs. It is assumed that these disposal costs offset the residual value of other system components, so these two factors are not considered. The battery residual value is mainly related to the State of Health (SOH) of the battery at the end of the energy storage station's lifespan. Since the battery lifespan is 12 years, the SOH will reach the end of the lifespan again after 24 years. Considering these two factors, it can be considered that lithium-ion battery energy storage has no residual value per kilowatt-hour. For energy storage systems including pump circuits, represented by flow batteries, the residual value is generally calculated as a certain percentage of the initial investment cost. The formula for determining the residual value per kilowatt-hour is:

[0058] in, The residual value of a battery per kilowatt-hour. The initial investment unit cost, The residual value of the energy storage system can be taken as 30%.

[0059] In some alternative implementations, the formula for determining the total cost per kilowatt-hour over the entire lifecycle is:

[0060] in, The total cost per kilowatt-hour over the entire lifecycle, The initial investment unit cost, The target energy storage system is the total service life. For the first Annual unit cost of operation and maintenance For the first Annual loan repayment amount per unit cost For the first Annual unit cost of battery replacement For the first Annual unit cost of insurance The residual value of a battery per kilowatt-hour. This is the discount rate.

[0061] Step S3012: Determine the total discharge capacity per kilowatt-hour over the entire life cycle based on the battery type, average annual degradation rate under healthy conditions, and total annual discharge capacity in the first year of the target energy storage system.

[0062] In some alternative implementations, the annual discharge capacity of the target energy storage system is related to its operating mode, but in the absence of this information, assumptions can only be made based on experience. The rated energy of an energy storage power station generally refers to the discharge capacity under the actual depth of charge and discharge during operation. Therefore, the depth of charge and discharge and energy conversion efficiency have no impact on the annual discharge capacity; their main impact is on the over-allocation ratio, i.e., the initial investment cost. Furthermore, considering factors such as energy storage maintenance and insufficient market participation, the equivalent cycle time for energy storage is generally 330 times per year. Therefore, the charge and discharge capacity in the first year is generally 330 kWh.

[0063] In some alternative implementations, if lithium-ion batteries are used for energy storage, since the batteries need to be replaced once and the average annual SOH decay rate is 1.66%, the total annual discharge capacity of the energy storage system is:

[0064] in, For the first The total discharge capacity of lithium-ion battery energy storage systems in a year. This refers to the total annual discharge volume in the first year. For the year.

[0065] In some alternative implementations, if a flow battery is used for energy storage, since it does not require battery replacement and the average annual SOH decay rate is 0.45%, the total annual discharge capacity of the energy storage system is:

[0066] in, For the first The total discharge capacity of the flow battery energy storage system in a year. This refers to the total annual discharge volume in the first year. For the year.

[0067] In some optional implementations, the total discharge capacity per kilowatt-hour over the entire lifecycle is:

[0068] in, The total discharge capacity per kilowatt-hour over the entire lifecycle. For the first The total annual discharge capacity of the energy storage system This is the discount rate.

[0069] Step S3013: Determine the target cost per kilowatt-hour based on the quotient of the total cost per kilowatt-hour over the entire lifecycle and the total discharge capacity per kilowatt-hour over the entire lifecycle.

[0070] The formula for determining the target cost per kilowatt-hour is:

[0071] in, The total cost per kilowatt-hour over the entire lifecycle, The total discharge capacity per kilowatt-hour over the entire lifecycle. The target cost per kilowatt-hour.

[0072] In some alternative implementations, the cost per kilowatt-hour is a core indicator for measuring the economics of power generation / energy storage projects. The calculation logic is to spread all costs (initial investment, operation and maintenance costs, etc.) throughout the project's entire life cycle onto the total power generation throughout the project's entire life cycle to obtain the unit cost of electricity, which is usually expressed in yuan / kWh, and is used to measure the unit energy storage cost of the energy storage system.

[0073] Step S302: Based on the target cost per kilowatt-hour and electricity price data for different time periods, determine the candidate energy storage operation mode and the range of energy storage configuration duration.

[0074] Specifically, step S302 includes: Step S3021: Determine the annual average electricity price during peak hours and the difference between the annual average electricity price during peak and off-peak hours based on the electricity price data for different time periods.

[0075] Specifically, based on electricity price data for different time periods, the annual average electricity price during peak hours is obtained, and the annual average electricity price difference between peak and valley hours is obtained based on the difference between the annual average electricity price during peak hours and the annual average electricity price during valley hours.

[0076] Step S3022: Compare the target cost per kilowatt-hour with the annual average electricity price during peak hours and the difference between the annual average electricity price during peak and off-peak hours.

[0077] Step S3023: When the comparison result shows that the target cost per kilowatt-hour is less than the average annual electricity price during peak hours and the target cost per kilowatt-hour is greater than the difference between the average annual electricity price during peak and valley hours, the candidate energy storage operation mode is determined to be curtailment of solar power charging and storage during peak hours and discharge.

[0078] Among them, when the comparison result shows that the target cost per kilowatt-hour is less than the annual average electricity price during peak hours and the target cost per kilowatt-hour is greater than the annual average electricity price difference between peak and valley hours, the loss risk of the peak-valley arbitrage model is identified, and the candidate energy storage operation mode is determined to be curtailment of solar power charging and storage during peak hours and discharge.

[0079] Step S3024: When the comparison result shows that the target cost per kilowatt-hour is less than or equal to the annual average electricity price difference during peak and valley periods, the candidate energy storage operation mode is determined to be photovoltaic / valley electricity charging and storage with peak discharge.

[0080] Step S3025: Compare the target cost per kilowatt-hour with the electricity price data for different time periods, and determine the energy storage configuration duration range based on the comparison results.

[0081] Among them, when the comparison result shows that the target cost per kilowatt-hour is less than or equal to the annual average electricity price difference during peak and valley periods, both peak-valley arbitrage and curtailment of solar power for energy storage modes are opened simultaneously to maximize the full-scenario revenue potential of the energy storage system.

[0082] In some alternative implementations, when the comparison result shows that the target cost per kilowatt-hour is greater than the annual average electricity price difference during peak and off-peak periods, it is not economically viable and no candidate energy storage operation mode is selected.

[0083] In some alternative implementations, if peak-valley regulation is not economically viable, the form of building new distributed photovoltaic and energy storage can be considered. This is because the target cost per kilowatt-hour of photovoltaic is generally lower than the off-peak electricity price, meaning that distributed photovoltaic is generally economical. Building energy storage can further maximize economic benefits. However, considering the high cost of energy storage, it is generally not advisable to build long-term energy storage in this case, and 2-hour energy storage is more appropriate.

[0084] In some optional implementations, for hybrid energy storage, if the comparative analysis results of the target levelized cost of electricity of the two types of energy storage are the same, then the hybrid energy storage will also have the same analysis results. If the comparative analysis results of the target levelized cost of electricity of the two types of energy storage are different, then it is necessary to proceed to detailed optimization configuration analysis and economic indicator calculation.

[0085] In some alternative implementations, if the target cost per kilowatt-hour is less than the peak-to-valley difference or the valley-to-peak difference, energy storage should prioritize peak-to-valley adjustment based on duration, and then adjust peak-to-valley or valley-to-peak adjustment in order of price difference. However, since the target cost per kilowatt-hour for energy storage is currently high and the peak-to-valley difference has decreased in most provinces, such a situation is unlikely to occur in the short term.

[0086] In some optional implementations, the target cost per kilowatt-hour is compared with the electricity price data for different time periods, and the energy storage configuration duration range is determined based on the comparison results. This includes: if the LCOS is less than the peak-valley price difference or the peak electricity price, the energy storage duration should cover the peak period as much as possible to maximize energy storage revenue.

[0087] In some optional implementations, the average annual peak, flat, and valley periods' average electricity demand and average load demand are calculated. The proposed energy storage capacity and power will then fall around these two values, with a range above and below these values ​​for algorithm optimization. Combining the peak and valley periods and their durations, the trend of the proposed energy storage duration can also be comprehensively evaluated. If the LCOS is less than the peak-valley price difference or the peak electricity price, the energy storage duration should cover the peak period as much as possible to maximize energy storage revenue. In the optimization algorithm, this part should fully consider the operational constraints arising from the relationship between energy storage operation mode and load demand, thereby constructing the underlying algorithm logic of energy storage power optimization variables, energy storage operation mode, energy storage cycle count, and energy storage economics.

[0088] Step S303: With the goal of maximizing energy storage revenue and using preset operational constraints as conditions, the user-side energy storage parameters are optimized to obtain a set of candidate user-side energy storage parameters. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0089] Step S304: Update the full life cycle cost and revenue based on the candidate energy storage operation mode, energy storage configuration duration range, and candidate user-side energy storage parameter set to obtain the actual full life cycle cost and annual full life cycle revenue of the target energy storage system.

[0090] Specifically, step S304 includes: Step S3041: Select the target energy storage form based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set.

[0091] First, based on the determined energy storage configuration duration range (short-term 1-4h / long-term ≥4h) and the candidate energy storage operation modes (peak-valley regulation / high-power discharge / waste power consumption, etc.), the core characteristics (cycle life, adaptation time, response speed, etc.) of energy storage technologies such as lithium batteries, flow batteries, and sodium batteries are matched for preliminary technical screening. Then, combined with the economic results of the previous LCOS analysis, a second screening of candidate types is conducted, prioritizing those with lower LCOS under corresponding operating conditions and compatibility with charging / discharging strategies. Finally, considering the actual conditions such as project site, load stability, and full lifecycle operation and maintenance requirements, the target energy storage form is determined to ensure technical compatibility, economic rationality, and feasibility.

[0092] Step S3042: Determine the total annual energy storage discharge based on the target energy storage form, determine the total annual cycle count based on the total annual energy storage discharge, update the total life cycle cost and revenue based on the total annual cycle count, and obtain the actual total life cycle cost and annual revenue of the target energy storage system.

[0093] In some alternative implementations, different types of energy storage demand correspond to different energy storage operation modes, and thus different operational constraints. Peak-valley regulation of user-side energy storage is the main application scenario and requirement. Assume the rated energy storage power is P, the rated hours are H, and the rated energy is E. During energy storage power optimization, if the rated energy storage power is greater than the average power demanded during peak hours, then discharge is performed according to the average power demanded during peak hours; if the rated energy storage power is less than or equal to the average power demanded during peak hours, then discharge is performed according to the rated energy storage power. The average daily power of peak energy storage discharge is:

[0094] in, This represents the average daily peak discharge power of the energy storage system. To minimize the value, P is the rated power of the energy storage. This represents the average daily load demand during peak hours.

[0095] In some optional implementations, the total daily discharge of the energy storage peak discharge is:

[0096] in, This represents the total daily discharge amount during peak energy storage discharge. This represents the average daily peak discharge power of the energy storage system. This refers to the rated operating hours of the energy storage power station.

[0097] In some alternative implementations, the total daily charging amount for peak energy storage discharge is:

[0098] in, This represents the total daily charging amount during peak discharge of energy storage. This represents the average daily peak discharge power of the energy storage system. The rated hours of the energy storage power station. This refers to the energy conversion efficiency of an energy storage power station.

[0099] In some optional implementations, the total monthly peak discharge of energy storage is as follows:

[0100] in, For the first The total monthly discharge of peak energy storage over a month. This refers to the total discharge capacity of the energy storage system during peak hours. This represents the number of days in each month.

[0101] In some optional implementations, the total monthly charge amount for peak energy storage discharge is as follows:

[0102] in, For the first Total monthly charging amount during peak discharge of energy storage in a month. This represents the total daily charging capacity of the energy storage system. This represents the number of days in each month.

[0103] In some alternative implementations, the total annual energy storage discharge is:

[0104] in, For the first Total annual energy storage discharge. For the first The total monthly discharge of energy storage peak discharge over a month.

[0105] In some alternative implementations, the total annual energy storage charging capacity is:

[0106] in, For the first Total annual energy storage charging capacity For the first The total monthly charging amount for peak energy storage discharge over a month.

[0107] In some alternative implementations, if there are already large-scale photovoltaic power plants with a high curtailment rate, the curtailed photovoltaic power can be used as a charging source for energy storage. It can be assumed that the charging capacity and power are sufficient and the cost is zero. That is, there is no need to consider the energy storage charging capacity and corresponding costs, only the energy storage to discharge during peak power periods and obtain corresponding revenue.

[0108] In some alternative implementations, considering that energy storage power stations undergo maintenance annually for various reasons, and that energy storage typically involves one charge and one discharge cycle per day, the number of cycles lost due to downtime for maintenance is:

[0109] in, This refers to the number of cycles lost due to downtime for maintenance. This represents the average number of days of power plant shutdown for maintenance per year for energy storage.

[0110] In some alternative implementations, the total number of annual cycles for the energy storage system is:

[0111] in, This represents the total number of cycles per year for the energy storage system. This represents the total monthly discharge of the energy storage system. The rated energy of the energy storage power station This represents the number of cycles lost due to downtime for maintenance.

[0112] In some optional implementations, this invention, for the first time, constructs a closed-loop correlation logic for the entire process of "energy storage power - operating mode - annual cycle count - economic efficiency": the rated energy storage power determines the peak discharge capacity; the peak discharge capacity, combined with load constraints, determines the daily charge / discharge volume; the daily charge / discharge volume, combined with the number of operating days, determines the annual cycle count; the annual cycle count determines the battery replacement cycle and the total life cycle cost; and the total life cycle cost ultimately determines the project's economic efficiency. In related technologies, the annual cycle count is usually based on a fixed empirical value (e.g., 300 times / year), which is not correlated with the actual configured power and operating mode, resulting in a cost calculation deviation exceeding 30%.

[0113] In some optional implementations, when determining the annual revenue over the entire life cycle based on the candidate energy storage operation mode and the candidate user-side energy storage parameter set, the energy storage should be discharged during the peak electricity price period, so the discharge price is the peak electricity price; the discharge amount should fully consider the comprehensive impact of factors such as the relationship between the energy storage configuration power and the load demand, the energy storage charge and discharge depth, the energy storage energy conversion efficiency, the life decay of the energy storage over the entire life cycle, and the replacement of energy storage cells.

[0114] In some alternative implementations, energy storage is used for peak-valley regulation, charging during low-electricity-price periods or photovoltaic charging, and discharging during high-electricity-price periods. The monthly revenue is then:

[0115] in, For the first Monthly income, For the first Total discharge of the energy storage system in one month. For the first Average peak-hour electricity price over the past month For the first Total monthly charging volume of the energy storage system over the past month. No. Average monthly electricity price during off-peak hours for one month.

[0116] In some alternative implementations, the discharge revenue in the first year is:

[0117] in, For the first year's discharge revenue, For the first Total discharge of the energy storage system in one month. For the first Average peak-hour electricity price over the past month For the first Total monthly charging volume of the energy storage system over the past month. No. Average monthly electricity price during off-peak hours for one month.

[0118] In some alternative implementations, if solar power curtailment is used for charging, the charging cost is zero, and revenue is only considered from peak discharge.

[0119] In some optional implementations, the average annual growth rate of the peak-valley price difference is introduced to reasonably adjust for future price difference gains, resulting in the following annual return over the entire lifecycle:

[0120] in, For the first Annual revenue throughout the entire life cycle For the first year's discharge revenue, This represents the average annual growth rate of the peak-valley price difference.

[0121] In some optional implementations, the actual lifecycle cost of the target energy storage system is determined based on the candidate energy storage operation mode and the candidate user-side energy storage parameter set. The actual lifecycle cost consists of the initial actual investment cost, the actual cost of replacing components, the actual annual operation and maintenance cost, the actual annual loan repayment amount, the actual annual insurance cost, and the actual residual value of the battery. The formula for determining the actual lifecycle cost is as follows:

[0122] in, For the first The actual total lifecycle cost per year, This represents the initial actual investment cost. The actual annual maintenance cost For the first The actual annual repayment amount for the year. For the first The actual annual insurance cost for the year For the first The actual cost of replacing parts per year. This represents the actual residual value of the battery.

[0123] Specifically, the initial actual investment cost is the sum of the energy storage system cost, civil engineering and installation cost, grid connection testing cost, etc., which can be expressed as:

[0124] in, This represents the initial actual investment cost. For the cost of energy storage systems, For civil engineering and installation costs, Costs such as network access testing.

[0125] In some alternative implementations, the initial actual investment cost can also be expressed as:

[0126] in, This represents the initial actual investment cost. For the unit cost of energy storage systems, For the unit cost of civil engineering and installation, For network access testing and other unit costs, This refers to the rated energy of the energy storage power station.

[0127] In some alternative implementations, the initial actual investment cost can also be expressed as:

[0128] in, This represents the initial actual investment cost. The initial investment unit cost, This refers to the rated energy of the energy storage power station.

[0129] In some alternative implementations, for battery-based energy storage systems, such as lithium-ion batteries, the required battery replacement period is:

[0130] in, This indicates the number of years at which the battery needs to be replaced. The number of cycles for the energy storage system. This represents the total number of cycles per year for the energy storage system. To round down.

[0131] In some alternative implementations, the number of times the battery needs to be replaced is:

[0132] in, This refers to the number of times the battery needs to be replaced. To round up, For the entire life cycle of the energy storage power station / loan term, This indicates the number of years at which the battery needs to be replaced.

[0133] In some alternative implementations, the actual cost of replacing components should be added to the cost cash flow for the years in which battery replacement is required. If the years in which battery replacement is required are greater than or equal to the entire life cycle of the energy storage power station, then the actual cost of replacing components is not required. The actual cost of replacing components can be expressed as:

[0134] in, For lithium-ion battery energy storage The actual cost of replacing parts per year. To cover battery replacement costs, This indicates the number of years at which the battery needs to be replaced. This indicates the number of times the battery needs to be replaced.

[0135] In some alternative implementations, the battery is typically replaced no more than once during the entire lifespan of the energy storage system. However, in exceptional circumstances where the battery needs to be replaced two or more times, then... Integer multiples of the service life correspond to an increase in the actual cost of replacing parts.

[0136] In some alternative implementations, for energy storage systems including pump circuits, such as flow batteries, experience suggests that the pump is typically replaced every 10 years. The actual cost of replacing the component can be expressed as:

[0137] in, For flow battery energy storage The actual cost of replacing parts per year. Cost of replacing the battery.

[0138] In some alternative implementations, the actual annual maintenance cost mainly consists of labor costs, which remain the same every year. Calculated based on the actual capacity for that year, the actual annual maintenance cost can be expressed as:

[0139] in, The actual annual maintenance cost Annual maintenance cost This refers to the rated energy of the energy storage power station.

[0140] In some alternative implementations, the actual annual loan repayment amount can be expressed as:

[0141] in, This represents the actual amount repaid on the loan for the year. This is the amount to be repaid each period.

[0142] In some alternative implementations, the actual annual insurance cost can be expressed as:

[0143] in, The actual cost of annual insurance. The annual insurance premium rate, This represents the initial actual investment cost.

[0144] In some optional implementations, for battery-based energy storage, represented by lithium-ion batteries, the residual value of the energy storage system mainly considers the residual value of the batteries. Although other system components also have some residual value, the amount is relatively small, and the disposal of the energy storage system requires additional costs. It is assumed that these disposal costs offset the residual value of other system components, therefore these two factors are not considered. The battery residual value is mainly related to the State of Health (SOH) of the battery at the end of the energy storage power station's lifespan. Firstly, it is necessary to construct... The functional relationship between the battery's state of harmonics (SOH) and the energy storage station's lifespan is as follows: Assuming the latest batch of batteries has cycled b times at the end of the energy storage station's lifespan, then:

[0145] in, For battery health status, This refers to the cycle count of the latest batch of batteries at the end of the energy storage power station's lifespan. The number of cycles for the energy storage system. This represents the total number of cycles per year for the energy storage system. For the entire life cycle of the energy storage power station / loan term, This indicates the number of times the battery needs to be replaced.

[0146] After formula conversion, we can obtain:

[0147] Eliminating b and further rearranging, we get:

[0148] This allows us to calculate the actual residual value of the battery at the end of the energy storage power station's lifespan:

[0149] in, The actual residual value of the battery at the end of the lifespan of an energy storage power station using lithium-ion batteries. To cover battery replacement costs, For battery health status, For battery residual value correction system, The value is generally between 0.5 and 1.

[0150] In some alternative implementations, for energy storage systems including pump circuits, such as flow batteries, the actual residual value of the battery is:

[0151] in, This represents the actual residual value of the battery at the end of the lifespan of the flow battery energy storage power station. This represents the initial actual investment cost. This represents the residual value ratio of the energy storage system for vanadium redox flow battery energy storage. Generally, 30% is taken.

[0152] In some alternative implementations, for hybrid energy storage systems consisting of both types A and B, the cost model can be obtained by weighted averaging the cost models of individual energy storage systems to obtain the life-cycle cost model of hybrid energy storage:

[0153] in, The actual lifecycle cost of a hybrid energy storage system. The actual lifecycle cost of type A energy storage is... The actual lifecycle cost of type B energy storage Weighted by the total lifecycle cost.

[0154] Step S305: Determine multiple evaluation indicators based on the actual total life cycle cost and annual total life cycle revenue. Screen candidate energy storage operation modes and candidate user-side energy storage parameter sets based on multiple evaluation indicators to obtain target energy storage operation mode and target user-side energy storage parameters, so as to configure user-side energy storage according to target energy storage operation mode and target user-side energy storage parameters.

[0155] Specifically, step S305 includes: Step S3051: Determine the internal rate of return and the net present value based on the actual total life cycle cost and the annual total life cycle revenue.

[0156] In some optional implementations, the Internal Rate of Return (IRR) is the discount rate at which the cumulative present value of net cash flows of an energy storage project equals zero over the calculation period. A project is considered financially viable when the IRR is greater than the benchmark rate of return. The IRR is a positive indicator; a higher IRR indicates stronger profitability. For example, the expression for the IRR is:

[0157] in, Internal rate of return, For the first The actual total lifecycle cost per year, For the first Annual revenue throughout the entire lifecycle.

[0158] In some alternative implementations, the benchmark rate of return can be 5.6%, meaning that if the internal rate of return of an energy storage project is less than 5.6%, the energy storage project is not economically viable.

[0159] In some alternative implementations, Net Present Value (NPV) is the sum of the present values ​​of the net cash flows of each year within the project's calculation period, discounted to the beginning of the construction period using a benchmark rate of return. NPV reflects the financial profitability of a project and is a core indicator of economic performance. Financial NPV is a positive indicator; the higher the value, the stronger the project's profitability. The unit is yuan. The discount rate is typically taken as 5%. For example, the expression for net present value is:

[0160] in, Net present value, For the first The actual total lifecycle cost per year, For the first Annual revenue throughout the entire life cycle is the discount rate.

[0161] Step S3052: Determine whether each evaluation index is greater than the corresponding preset threshold, and take the candidate energy storage operation mode and candidate user-side energy storage parameters corresponding to the target evaluation index that is greater than the corresponding preset threshold as the target energy storage operation mode and target user-side energy storage parameters.

[0162] The preset threshold for each evaluation indicator can be set according to the actual situation. For example, the preset threshold for the benchmark internal rate of return of the evaluation indicator can be 5.6%.

[0163] In some optional implementations, after updating the full lifecycle cost and revenue based on the candidate energy storage operation mode, energy storage configuration duration range, and candidate user-side energy storage parameter set to obtain the actual full lifecycle cost and annual revenue of the target energy storage system, the user-side energy storage configuration method further includes: performing sensitivity analysis on multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set to obtain sensitivity analysis results; and screening multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set based on the sensitivity analysis results.

[0164] Among them, sensitivity analysis is performed on the core parameters in the candidate energy storage operation mode and candidate user-side energy storage parameter set to screen the precise parameter values ​​that optimize the internal rate of return and net present value.

[0165] In some optional implementations, the energy storage configuration power is used as the core sensitivity index. Within the initially determined power range, a ±20% gradient analysis is performed to obtain the IRR and NPV variation curves with the configuration power, and the power value with the optimal economic index is selected. For hybrid energy storage scenarios, sensitivity analysis of different energy storage unit power ratios is added.

[0166] The user-side energy storage configuration method provided in this embodiment can initially determine the selectable energy storage type, storage duration, and operation mode through levelized cost of electricity (LCOE) calculation and scenario analysis. Furthermore, it can construct a correlation between annual cycle count and economic efficiency based on operational constraints, thereby achieving more accurate and realistic optimized energy storage configuration. This embodiment optimizes the LCOE calculation model by combining engineering practice, distinguishing the replacement cycle, state-of-the-art (SOH) decay law, and residual value characteristics of different energy storage technologies such as lithium-ion batteries and flow batteries. It also constructs a full-process correlation between "energy storage power - operation mode - annual cycle count - economic efficiency," achieving closed-loop calculation from configuration parameters to long-term economics. This embodiment reduces the LCOE calculation error by 15%-20%, and controls the deviation between the actual IRR of the configuration scheme and the predicted value to within 5%, significantly improving the accuracy and engineering practicality of user-side energy storage configuration.

[0167] This embodiment provides a user-side energy storage configuration method, which can be used in computer equipment. Figure 4 This is a third flowchart of the user-side energy storage configuration method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Calculate the levelized cost of energy storage (LCOE) based on average data parameters in the energy storage industry; compare with time-of-use pricing to initially determine the LCOE duration and operating mode; formulate energy storage charging and discharging strategies based on actual load demand data for each month of the year, and calculate monthly and annual revenues sequentially based on electricity price data; calculate annual revenues over the entire lifecycle of energy storage considering future electricity price changes; comprehensively consider operation and maintenance costs, financial costs, and residual value to calculate the total annual cost and present value of energy storage over the entire lifecycle; calculate annual cash flow and cumulative net cash flow over the entire lifecycle of energy storage; calculate indicators such as IRR and NPV; identify sensitive indicators for energy storage configuration and conduct sensitivity analysis; and determine the LCOE scheme based on the sensitivity analysis results and by comprehensively considering multiple factors.

[0168] The user-side energy storage configuration method provided in this embodiment offers more accurate levelized cost of electricity (LCOE) calculation: By using an engineering-optimized LCOE model, the characteristic parameters of different energy storage technologies are distinguished, reducing the cost calculation error from 25%-30% to 5%-10% compared to a general model; the configuration scheme is more realistic: by constructing a full-process correlation, the problem of the separation between configuration parameters and operating characteristics and long-term economics in existing technologies is solved, and the deviation between the actual IRR and the predicted value of the configuration scheme is controlled within 5%; the configuration efficiency is greatly improved: by pre-comparing LCOE with electricity prices, infeasible schemes are quickly screened, and then the optimal solution is determined through parameter optimization and sensitivity analysis, shortening the engineering configuration calculation cycle by more than 40%.

[0169] This embodiment also provides a user-side energy storage configuration device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0170] This embodiment provides a user-side energy storage configuration device, such as... Figure 5 As shown, it includes: The cost per kilowatt-hour determination module 501 is used to determine the target cost per kilowatt-hour based on the cost per kilowatt-hour calculation model optimized by combining engineering practice, according to the total cost per kilowatt-hour of the target energy storage system throughout its entire life cycle and the total discharge capacity per kilowatt-hour throughout its entire life cycle; the cost per kilowatt-hour calculation model is used to distinguish the battery replacement cycle, battery health degradation rate and residual value calculation logic of different energy storage technology types.

[0171] The operation mode selection module 502 is used to determine the candidate energy storage operation mode and the range of energy storage configuration duration based on the target cost per kilowatt-hour and electricity price data for different time periods.

[0172] The parameter set determination module 503 is used to optimize the user-side energy storage parameters with the goal of maximizing energy storage revenue and with preset operating constraints as constraints, so as to obtain a candidate user-side energy storage parameter set.

[0173] The data update module 504 is used to update the full life cycle cost and revenue based on the candidate energy storage operation mode, energy storage configuration duration range and candidate user-side energy storage parameter set, so as to obtain the actual full life cycle cost and annual revenue of the target energy storage system.

[0174] The energy storage configuration module 505 is used to determine multiple evaluation indicators based on the actual total life cycle cost and annual total life cycle revenue, and to screen candidate energy storage operation modes and candidate user-side energy storage parameter sets based on the multiple evaluation indicators to obtain the target energy storage operation mode and target user-side energy storage parameters, so as to configure user-side energy storage according to the target energy storage operation mode and target user-side energy storage parameters.

[0175] In some alternative implementations, the cost-per-kilowatt-hour determination module 501 includes: The cost per kilowatt-hour determination unit is used to determine the total cost per kilowatt-hour over the entire life cycle based on the cost per kilowatt-hour calculation model, according to the initial investment unit cost, the unit cost of replacement components for different energy storage technologies, the unit cost of annual loan repayments, the unit cost of annual insurance, and the residual value of batteries for different energy storage technologies. The total cost per kilowatt-hour over the entire life cycle is used to characterize the total cost corresponding to each kilowatt-hour over the entire life cycle.

[0176] The discharge capacity determination unit is used to determine the total discharge capacity per kilowatt-hour over the entire life cycle based on the battery type, annual degradation rate under healthy conditions, and total discharge capacity in the first year of the target energy storage system.

[0177] The cost-per-kilowatt-hour determination unit is used to determine the target cost-per-kilowatt-hour based on the quotient of the total cost-per-kilowatt-hour over the entire lifecycle and the total discharge capacity over the entire lifecycle.

[0178] In some optional implementations, the operating mode selection module 502 includes: The data determination unit is used to determine the annual average electricity price during peak hours and the annual average electricity price difference between peak and off-peak hours based on electricity price data for different time periods.

[0179] The data comparison unit is used to compare the target cost per kilowatt-hour with the annual average electricity price during peak hours and the difference between the annual average electricity price during peak and off-peak hours, respectively.

[0180] The mode determination unit is used to determine the candidate energy storage operation mode as curtailment of solar power charging and storage during peak hours and discharge when the comparison result is that the target cost per kilowatt-hour is less than the annual average electricity price during peak hours and the target cost per kilowatt-hour is greater than the annual average electricity price difference during peak and valley hours; and to determine the candidate energy storage operation mode as solar / valley electricity charging and storage during peak hours and discharge when the comparison result is that the target cost per kilowatt-hour is less than or equal to the annual average electricity price difference during peak and valley hours.

[0181] The configuration duration determination unit is used to compare the target cost per kilowatt-hour with the electricity price data for different time periods, and determine the energy storage configuration duration range based on the comparison results.

[0182] In some alternative implementations, the data update module 504 includes: The energy storage form determination unit is used to select a target energy storage form based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set.

[0183] The revenue update unit is used to determine the total annual energy storage discharge based on the target energy storage form, determine the total annual cycle number based on the total annual energy storage discharge, update the total life cycle cost and revenue based on the total annual cycle number, and obtain the actual total life cycle cost and annual revenue of the target energy storage system.

[0184] In some alternative implementations, the energy storage configuration module 505 includes: The evaluation index determination unit is used to determine the internal rate of return and the net present value based on the actual total life cycle cost and the annual total life cycle revenue.

[0185] The energy storage configuration unit is used to determine whether each evaluation index is greater than the corresponding preset threshold, and to take the candidate energy storage operation mode and candidate user-side energy storage parameters corresponding to the target evaluation index that are greater than the corresponding preset threshold as the target energy storage operation mode and target user-side energy storage parameters.

[0186] In some optional implementations, the user-side energy storage configuration method further includes: The sensitivity analysis module is used to perform sensitivity analysis on multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set and obtain the sensitivity analysis results.

[0187] The parameter filtering module is used to filter multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set based on the sensitivity analysis results.

[0188] The user-side energy storage configuration device provided in this embodiment of the invention can execute the user-side energy storage configuration method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0189] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0190] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0191] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0192] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the user-side energy storage configuration method of the embodiments of the present invention.

[0193] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0194] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the user-side energy storage configuration method shown in the above embodiments is implemented.

[0195] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0196] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A user-side energy storage configuration method, characterized in that, The method includes: Based on the optimized cost-per-kilowatt-hour calculation model that combines engineering practice, the target cost-per-kilowatt-hour is determined according to the total cost-per-kilowatt-hour and the total discharge capacity of the target energy storage system throughout its entire life cycle. The cost-per-kilowatt-hour calculation model is used to distinguish the battery replacement cycle, battery health degradation rate and residual value calculation logic of different energy storage technology types. Based on the target cost per kilowatt-hour and electricity price data for different time periods, determine the candidate energy storage operation mode and the range of energy storage configuration duration; With the goal of maximizing energy storage revenue and with preset operational constraints as the conditions, the user-side energy storage parameters are optimized to obtain a set of candidate user-side energy storage parameters. The full life cycle cost and revenue of the target energy storage system are updated based on the candidate energy storage operation mode, the energy storage configuration duration range and the candidate user-side energy storage parameter set to obtain the actual full life cycle cost and annual full life cycle revenue of the target energy storage system. Based on the actual total life cycle cost and the annual total life cycle revenue, multiple evaluation indicators are determined. The candidate energy storage operation mode and the candidate user-side energy storage parameter set are then screened based on these multiple evaluation indicators to obtain the target energy storage operation mode and the target user-side energy storage parameters. User-side energy storage is then configured based on the target energy storage operation mode and the target user-side energy storage parameters.

2. The method of claim 1, wherein, The cost-per-kilowatt-hour calculation model, optimized based on engineering practice, determines the target cost-per-kilowatt-hour based on the total cost-per-kilowatt-hour and the total discharge volume of the target energy storage system over its entire lifecycle, including: Based on the cost-per-kilowatt-hour calculation model, the total cost per kilowatt-hour over the entire life cycle is determined according to the initial investment unit cost, the unit cost of replacement components for different energy storage technologies, the unit cost of annual loan repayments, the unit cost of annual insurance, and the residual value of batteries for different energy storage technologies. The total cost per kilowatt-hour over the entire life cycle is used to characterize the total cost per kilowatt-hour over the entire life cycle. The total discharge capacity per kilowatt-hour over the entire life cycle is determined based on the battery type, average annual degradation rate under healthy conditions, and total annual discharge capacity in the first year of the target energy storage system. The target cost per kilowatt-hour is determined by the quotient of the total cost per kilowatt-hour over the entire life cycle and the total discharge volume per kilowatt-hour over the entire life cycle.

3. The method according to claim 1 or 2, characterized in that, The step of determining candidate energy storage operation modes and energy storage configuration duration ranges based on the target cost per kilowatt-hour and electricity price data for different time periods includes: Based on electricity price data for different time periods, determine the annual average electricity price during peak hours and the difference between the annual average electricity price during peak and off-peak hours; The target cost per kilowatt-hour is compared with the annual average electricity price during peak hours and the difference between the annual average electricity price during peak and off-peak hours, respectively. When the comparison result shows that the target cost per kilowatt-hour is less than the average annual electricity price during peak hours, and the target cost per kilowatt-hour is greater than the difference between the average annual electricity price during peak and valley hours, the candidate energy storage operation mode is determined to be curtailment of solar power charging and storage during peak hours and discharge. When the comparison result shows that the target cost per kilowatt-hour is less than or equal to the annual average electricity price difference during peak and valley periods, the candidate energy storage operation mode is determined to be photovoltaic / valley electricity charging and storage with peak discharge. The target cost per kilowatt-hour is compared with the electricity price data for different time periods, and the duration range of the energy storage configuration is determined based on the comparison results.

4. The method according to claim 1 or 2, characterized in that, The process of updating the full lifecycle cost and revenue based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set to obtain the actual full lifecycle cost and annual full lifecycle revenue of the target energy storage system includes: The target energy storage form is selected based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set. Based on the target energy storage form, the total annual energy storage discharge is determined. Based on the total annual energy storage discharge, the total annual cycle count is determined. Based on the total annual cycle count, the total life cycle cost and revenue are updated to obtain the actual total life cycle cost and annual revenue of the target energy storage system.

5. The method according to claim 1 or 2, characterized in that, After updating the lifecycle cost and revenue based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set to obtain the actual lifecycle cost and annual revenue of the target energy storage system, the method further includes: Sensitivity analysis was performed on multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set to obtain the sensitivity analysis results. Based on the sensitivity analysis results, multiple candidate user-side energy storage parameters in the candidate user-side energy storage parameter set are screened.

6. The method of claim 1 or 2, wherein, The various evaluation indicators include internal rate of return and net present value; the process involves determining various evaluation indicators based on the actual total lifecycle cost and the annual total lifecycle revenue, and then filtering the candidate energy storage operation modes and the candidate user-side energy storage parameter set based on these indicators to obtain the target energy storage operation mode and target user-side energy storage parameters, including: The internal rate of return and the net present value are determined based on the actual total life cycle cost and the annual total life cycle revenue. Determine whether each of the evaluation indicators is greater than the corresponding preset threshold, and use the candidate energy storage operation mode and candidate user-side energy storage parameter corresponding to the target evaluation indicator that is greater than the corresponding preset threshold as the target energy storage operation mode and the target user-side energy storage parameter.

7. A user-side energy storage configuration apparatus, characterized by, The device includes: The cost per kilowatt-hour determination module is used to determine the target cost per kilowatt-hour based on the cost per kilowatt-hour calculation model optimized by combining engineering practice, according to the total cost per kilowatt-hour and the total discharge capacity per kilowatt-hour of the target energy storage system throughout its entire life cycle; the cost per kilowatt-hour calculation model is used to distinguish the battery replacement cycle, battery health degradation rate and residual value calculation logic of different energy storage technology types; The operation mode selection module is used to determine the candidate energy storage operation mode and the energy storage configuration duration range based on the target cost per kilowatt-hour and electricity price data for different time periods. The parameter set determination module is used to optimize the user-side energy storage parameters with the goal of maximizing energy storage revenue and with preset operating constraints as constraints, so as to obtain a candidate user-side energy storage parameter set. The data update module is used to update the full life cycle cost and revenue based on the candidate energy storage operation mode, the energy storage configuration duration range, and the candidate user-side energy storage parameter set, so as to obtain the actual full life cycle cost and annual full life cycle revenue of the target energy storage system. The energy storage configuration module is used to determine multiple evaluation indicators based on the actual total life cycle cost and the annual total life cycle revenue, and to filter the candidate energy storage operation mode and the candidate user-side energy storage parameter set based on the multiple evaluation indicators to obtain the target energy storage operation mode and the target user-side energy storage parameters, so as to configure the user-side energy storage according to the target energy storage operation mode and the target user-side energy storage parameters.

8. An electronic device, comprising: include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the user-side energy storage configuration method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the user-side energy storage configuration method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the user-side energy storage configuration method according to any one of claims 1 to 6.

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