Parameter configuration method, device and equipment for whole life cycle of energy storage system and medium

Abstract

The application discloses a parameter configuration method, device and equipment of a full life cycle of an energy storage system and a medium. The method comprises the following steps: obtaining energy storage basic data corresponding to an energy storage system to be configured. Based on a pre-constructed full life cycle net present value cost function of the energy storage system, all candidate parameter combinations of power parameters and capacity parameters are traversed according to the energy storage basic data, and the full life cycle net present value cost of all candidate parameter combinations is calculated, wherein the full life cycle net present value cost function of the energy storage system is at least composed of candidate power parameters and candidate capacity parameters. The full life cycle cost of the full life cycle net present value cost of each candidate parameter combination is evaluated uniformly, and an optimal parameter combination scheme is determined. The application can realize joint solving of the rated power and the rated capacity of the energy storage system, so that an optimal capacity configuration result considering economy and operation performance is obtained.

Description

Technical Field

[0001] This invention relates to the field of energy storage system configuration technology, and in particular to parameter configuration methods, devices, equipment and media for the entire life cycle of energy storage systems. Background Technology

[0002] As the penetration rate of distributed photovoltaic, wind power and flexible loads in the power system continues to increase, the volatility of the power supply side and the load side has significantly increased. Due to its key role in peak shaving and valley filling, power smoothing and renewable energy consumption, energy storage system has become an indispensable part of the new power system.

[0003] However, existing energy storage capacity configuration methods mostly rely on empirical rules or a single optimization objective, such as roughly selecting based on the proportion of photovoltaic installations, or calculating only with the goal of minimizing initial investment and maximizing peak-valley arbitrage profits, lacking a comprehensive consideration of the system's full life cycle operation characteristics.

[0004] Energy storage systems exhibit typical full lifecycle characteristics. During long-term charging and discharging, batteries experience capacity decay and increased internal resistance, leading to a gradual decrease in effective capacity year by year. Simultaneously, the PCS energy storage converter, thermal management system, and battery clusters require phased maintenance and replacement of key components. Designing capacity solely based on initial static investment will result in either overcapacity leading to wasted investment or undercapacity causing insufficient operational performance, making it difficult to achieve a balance between economy and reliability. Summary of the Invention

[0005] This invention provides a method, device, equipment, and medium for parameter configuration throughout the entire life cycle of an energy storage system. It can jointly solve for the rated power and rated capacity of the energy storage system, thereby obtaining the optimal capacity configuration result that balances economy and operational performance.

[0006] According to one aspect of the present invention, a parameter configuration method for the entire lifecycle of an energy storage system is provided. The method includes:

[0007] Obtain the basic energy storage data corresponding to the energy storage system to be configured. The basic energy storage data includes, but is not limited to, historical load curves, renewable energy output curves, time-of-use electricity price series, grid connection electricity price series, demand electricity price coefficient, unit power investment cost of energy storage system, unit capacity investment cost of energy storage system, annual operation and maintenance cost coefficient, discount rate, planned lifespan, and system module replacement cost.

[0008] Based on the pre-constructed net present value cost function for the entire life cycle of the energy storage system, and according to the basic energy storage data, all candidate parameter combinations of power parameters and capacity parameters are traversed to obtain the net present value cost for the entire life cycle of all candidate parameter combinations. The net present value cost function for the entire life cycle of the energy storage system is composed of at least candidate power parameters and candidate capacity parameters.

[0009] A unified evaluation of the life-cycle net present value cost for each candidate parameter combination is conducted to determine the optimal parameter combination scheme.

[0010] According to another aspect of the present invention, a parameter configuration device for the entire life cycle of an energy storage system is provided. The device includes:

[0011] The energy storage basic data acquisition module is used to acquire the energy storage basic data corresponding to the energy storage system to be configured. The energy storage basic data includes, but is not limited to, load history curves, new energy output curves, time-of-use electricity price series, grid connection electricity price series, demand electricity price coefficient, unit power investment cost of energy storage system, unit capacity investment cost of energy storage system, annual operation and maintenance cost coefficient, discount rate, planned lifespan, and system module replacement cost.

[0012] The net present value cost determination module is used to traverse all candidate parameter combinations of power parameters and capacity parameters based on the pre-built net present value cost function of the energy storage system throughout its entire life cycle, and to obtain the net present value cost of all candidate parameter combinations throughout their entire life cycle based on the energy storage basic data. The net present value cost function of the energy storage system throughout its entire life cycle is composed of at least candidate power parameters and candidate capacity parameters.

[0013] The optimal parameter combination determination module is used to uniformly evaluate the life-cycle net present value cost for each candidate parameter combination and determine the optimal parameter combination scheme.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the parameter configuration method for the entire life cycle of the energy storage system according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the parameter configuration method for the entire life cycle of the energy storage system according to any embodiment of the present invention.

[0019] The technical solution of this invention constructs a full life cycle cost-benefit model for energy storage systems, a set of typical operating scenarios, a dynamic model of energy storage state of charge, a lifespan degradation model, and a capacity configuration optimization model. Under the premise of satisfying power balance, SOC boundary, power boundary, rate boundary, load peak shaving target, and equipment lifespan constraints, it jointly solves the rated power and rated capacity of energy storage systems. This solves the problems in existing technologies such as energy storage capacity configuration only considering local costs, ignoring lifespan degradation, insufficient power and capacity coordination, and weak engineering feasibility, thereby obtaining the optimal capacity configuration result that balances economy and operational performance.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a parameter configuration method for the entire life cycle of an energy storage system according to an embodiment of the present invention;

[0023] Figure 2 This is a structural diagram of a parameter configuration device for the entire life cycle of an energy storage system provided according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the parameter configuration method for the entire life cycle of the energy storage system according to embodiments of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Figure 1 This is a flowchart illustrating a parameter configuration method for the entire lifecycle of an energy storage system, provided by an embodiment of the present invention. This embodiment is applicable to configuring the operating parameters of an energy storage system. The method can be executed by a parameter configuration device for the entire lifecycle of the energy storage system. This device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0028] S101. Obtain the basic energy storage data corresponding to the energy storage system to be configured.

[0029] Collect basic energy storage data for the energy storage system to be configured. This basic data includes, but is not limited to, historical load curves, renewable energy output curves, time-of-use tariff sequences, feed-in tariff sequences, demand tariff coefficients, unit power investment cost of the energy storage system, unit capacity investment cost of the energy storage system, annual operation and maintenance cost coefficients, discount rates, planned lifespan, and system module replacement costs. System module replacement costs are broadly categorized into PCS replacement costs, thermal management system replacement costs, battery replacement costs, and residual value rates.

[0030] Where time t represents the discrete scheduling time interval, and the time step is denoted as . The timeframe can be 15 minutes, 30 minutes, or 1 hour. Based on historical load and renewable energy data, a representative set of operating scenarios is constructed according to dimensions such as season, weekday / non-weekday, extreme high temperature day, extreme low temperature day, and peak price / high load day. And assign weights to each scenario. ,satisfy:

[0031] ;

[0032] By using the above method, the 8760 hours of operation characteristics throughout the year are compressed into a set of typical scenarios that can be used for optimization and solution, thereby improving the solution efficiency while ensuring accuracy.

[0033] S102. Based on the pre-constructed net present value cost function for the entire life cycle of the energy storage system, and according to the energy storage basic data, traverse all candidate parameter combinations of power parameters and capacity parameters to obtain the net present value cost for the entire life cycle of all candidate parameter combinations.

[0034] The net present value cost function of the energy storage system over its entire life cycle consists of at least candidate power parameters and candidate capacity parameters.

[0035] Let the candidate power parameters of the energy storage system be... The candidate capacity parameter is The net present value cost function for the entire lifecycle of an energy storage system is defined as:

[0036] ;

[0037] in, This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the initial construction investment cost; This represents the present value of the total lifecycle maintenance costs. Present value of the cost of replacing critical components; The present value of the cost discounted for battery life degradation; The present value of the cost of purchasing electricity to charge energy storage; The present value of the additional costs caused by system losses; The present value of peak-valley arbitrage profits; The present value of demand management revenue; For backup, the present value of demand response or ancillary service revenue; It represents the present value of the residual value at the end of the lifespan.

[0038] The initial construction investment cost is represented as follows:

[0039] ;

[0040] in, This refers to the initial construction investment cost; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the cost of installation, commissioning, grid connection, civil engineering, and monitoring systems.

[0041] The present value of the annual operation and maintenance costs is represented as follows:

[0042] ;

[0043] in, It refers to the present value of the total life-cycle operation and maintenance costs; This refers to the planned lifespan of the energy storage system; This refers to the annual maintenance ratio; This refers to the investment cost per unit power of an energy storage system; This refers to candidate power parameters; This refers to the investment cost per unit capacity of an energy storage system; This refers to the candidate capacity parameter; It refers to the discount rate.

[0044] The present value of the replacement cost of the key component is represented as follows:

[0045] ;

[0046] in, This refers to the present value of the cost of replacing key components; This refers to the total number of events that were changed; It refers to the first Cost of replacing the corresponding parts; It refers to the discount rate.

[0047] The present value of the end-of-life residual value is represented as follows:

[0048] ;

[0049] in, It refers to the present value of the residual value at the end of the life; This refers to the residual value rate; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

[0050] For example, for any typical scenario At any time t, let the energy storage charging power be... The discharge power is Then the energy storage state of charge The dynamic equation is:

[0051] ;

[0052] in, It refers to time t+1. Energy storage state of charge in the scenario; It refers to time t Energy storage state of charge in the scenario; This refers to charging efficiency; This refers to charging power; This refers to the time step; This refers to the candidate capacity parameter; This refers to discharge power; This refers to discharge efficiency.

[0053] In this invention, the SOC boundary constraint is:

[0054] ;

[0055] The power boundary constraints are:

[0056] ;

[0057] To avoid simultaneous charging and discharging, a mutually exclusive variable is introduced. ,satisfy:

[0058] ;

[0059] The net power exchange between energy storage and load, and between energy sources, meets the following requirements:

[0060] ;

[0061] in, Indicates net exchange power; Represents time t Local load power in the scenario; Represents time t The power generation capacity of new energy sources (such as photovoltaics and wind power) in the given scenario; This refers to charging power; This refers to discharge power.

[0062] Peak-valley arbitrage profits are represented as follows:

[0063] ;

[0064] in, This refers to the present value of peak-valley arbitrage profits; This refers to the planned lifespan of the energy storage system; This refers to the weight of each operating scenario; This refers to the electricity price for electricity substitution or the electricity price for equivalent revenue. This refers to the electricity purchase substitution price; This refers to discharge power; This refers to the time step; It refers to the discount rate.

[0065] The demand management revenue is represented as follows:

[0066] ;

[0067] in, This refers to the present value of demand management revenue; This refers to the demand-based electricity price coefficient; This refers to the first time when no energy storage is configured. The average monthly maximum demand or assessment demand for the year; This indicates the corresponding demand value after configuring energy storage; It refers to the discount rate.

[0068] Furthermore, considering the calendar aging and cycle aging of energy storage systems, the first... Available system capacity at the end of the year If so, then:

[0069] ;

[0070] in, Calendar aging factor. The unit equivalent cyclic aging coefficient, For the first The equivalent number of full-load cycles per year This refers to the candidate capacity parameter.

[0071] No. The equivalent number of cycles per year can be calculated from the throughput:

[0072] ;

[0073] To ensure the project meets the minimum available capacity requirements during the planning period, the lower limit of the capacity retention rate at the end of its life is set as follows: Then it should satisfy:

[0074] ;

[0075] in, For the first Available system capacity at the end of the year This refers to the candidate capacity parameter.

[0076] When the If the capacity decays below the threshold by the end of the year, battery cluster replacement or capacity expansion is required. The corresponding degradation cost can be expressed as:

[0077] ;

[0078] in, The present value of the cost discounted for battery life degradation; This refers to the unit capacity degradation cost; Indicates the first Annual increase in degraded capacity, , Indicates the first Available system capacity at the end of the year; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

[0079] The power-to-capacity configuration ratio constraint can be expressed as:

[0080] ;

[0081] and These represent the minimum and maximum allowed energy storage durations, respectively, such as 1 hour and 4 hours, or can be customized according to the scenario.

[0082] Specifically, to obtain the total life-cycle net present value cost for all candidate parameter combinations and facilitate engineering implementation, this invention can employ the following two methods for solving the problem:

[0083] Method 1: Using the candidate power parameter and the candidate capacity parameter as outer decision variables, perform capacity search using enumeration search, particle swarm optimization, genetic algorithm or Bayesian optimization, and use inner linear programming or mixed integer linear programming to obtain multiple combinations of candidate parameters within a given range.

[0084] Method 2: After the profit model and constraints are linearized, a single-layer mixed integer programming model is directly constructed for joint solution to determine multiple sets of candidate parameter combinations.

[0085] S103. Conduct a unified evaluation of the life-cycle net present value cost for each candidate parameter combination to determine the optimal parameter combination scheme.

[0086] This invention aims to minimize the net present value cost of an energy storage system throughout its entire lifecycle. It identifies the candidate parameter combination that minimizes the net present value cost throughout the entire lifecycle as the optimal parameter combination scheme.

[0087] If there are multiple candidate parameter combinations that minimize the total life-cycle net present value cost, then the optimal parameter combination scheme is determined based on the comprehensive economics of the multiple candidate parameter combinations.

[0088] After obtaining the optimal parameter combination scheme, the following indicators are further output: optimal rated power. Optimal rated capacity Optimal energy storage duration Net present value cost over the entire life cycle Dynamic investment payback period Internal rate of return Peak-valley arbitrage contribution rate, demand management contribution rate, and ancillary service contribution rate; capacity retention rate and replacement frequency at the end of the lifecycle.

[0089] The dynamic payback period can be determined based on the year in which the cumulative discounted net cash flow first turns positive; the internal rate of return is obtained by setting the project's net present value to zero.

[0090] The technical solution of this invention constructs a full life cycle cost-benefit model for energy storage systems, a set of typical operating scenarios, a dynamic model of energy storage state of charge, a lifespan degradation model, and a capacity configuration optimization model. Under the premise of satisfying power balance, SOC boundary, power boundary, rate boundary, load peak shaving target, and equipment lifespan constraints, it jointly solves the rated power and rated capacity of energy storage systems. This solves the problems in existing technologies such as energy storage capacity configuration only considering local costs, ignoring lifespan degradation, insufficient power and capacity coordination, and weak engineering feasibility, thereby obtaining the optimal capacity configuration result that balances economy and operational performance.

[0091] This embodiment selects a user-side energy storage configuration scenario in an industrial and commercial park to further describe the present invention in detail, but the scope of protection of the present invention is not limited thereto.

[0092] 1. Application Scenario Description

[0093] This example selects a user-side energy storage configuration scenario in an industrial and commercial park. The park has a 6MW distributed photovoltaic system, a maximum load of 9.2MW, and an annual electricity consumption of approximately 36.5 million kWh. It adopts a time-of-use pricing mechanism and charges a basic electricity fee based on maximum demand. The park plans to construct a lithium-ion battery energy storage system to achieve peak-valley arbitrage, reduce the maximum monthly demand, and increase the local consumption rate of photovoltaic power.

[0094] The main parameters set in this embodiment are as follows:

[0095] Candidate rated power range: MW, with a search step size of 0.5MW;

[0096] Candidate rated capacity range: MWh, with a search step size of 1MWh;

[0097] Unit power investment cost: Yuan / kW;

[0098] Unit capacity investment cost: Yuan / kWh;

[0099] Installation and related costs: Ten thousand yuan;

[0100] Charging efficiency: ;

[0101] Discharge efficiency: ;

[0102] SOC operating range: ;

[0103] Planned lifespan: Year;

[0104] Discount rate: ;

[0105] Annual maintenance ratio: ;

[0106] Lower limit of capacity retention: ;

[0107] Calendar aging factor: ;

[0108] Unit equivalent cyclic aging coefficient: ;

[0109] Residual value rate: ;

[0110] The PCS is replaced every 6 years, with a replacement cost of 800,000 yuan.

[0111] The time-of-use electricity pricing parameters are set as follows: off-peak electricity price is 0.38 yuan / kWh, normal electricity price is 0.72 yuan / kWh, and peak electricity price is 1.08 yuan / kWh; the demand electricity price is calculated at 42 yuan / kW / month.

[0112] 2. Construction of representative days

[0113] Based on the park's load and photovoltaic data over the past year, eight typical day scenarios were extracted, including weekdays and rest days in spring, summer, autumn, and winter. Two extreme peak load scenarios were also added, forming ten representative days. The scenario weights were calculated based on the frequency of occurrence throughout the year.

[0114] For example, in a summer weekday scenario, photovoltaic output is higher from 11:00 to 14:00, resulting in a decrease in electricity purchase load, while a peak load occurs from 18:00 to 21:00, coinciding with peak electricity prices. Therefore, energy storage can prioritize absorbing the remaining photovoltaic power and low-priced electricity during midday, releasing it during the evening peak period to simultaneously achieve peak-valley arbitrage and demand reduction.

[0115] 3. Substitute the whole life cycle cost model

[0116] Candidate solutions MW Taking MWh as an example, its initial investment cost is:

[0117]

[0118]

[0119] The initial investment cost was 10.48 million yuan.

[0120] The present value of annual maintenance costs is:

[0121]

[0122] Calculations show that:

[0123]

[0124] The present value of the PCS replacement cost in year 6 is:

[0125]

[0126] The present value of the residual value is:

[0127]

[0128] 4. Run simulation and degradation calculation

[0129] For candidate solutions MW After typical daily scheduling optimization, the equivalent number of full-charge and full-discharge cycles per year is approximately 210. Therefore, the available capacity at the end of year 10 is:

[0130]

[0131]

[0132] Therefore, the end-of-life capacity retention rate is:

[0133]

[0134] The result is 70% higher than the set lower limit, thus meeting the lifespan constraint requirements.

[0135] Regarding operating revenue, after being converted to annual returns, the following is obtained:

[0136] The average annual peak-valley arbitrage profit is approximately 1.78 million yuan;

[0137] The average annual revenue from demand management is approximately 960,000 yuan;

[0138] The average annual increase in photovoltaic consumption and additional income is approximately RMB 210,000.

[0139] After discounting, the present value of the total income over 10 years is approximately RMB 18.76 million.

[0140] If the cost of battery degradation is estimated at 650 yuan / kWh per unit of degradation capacity, the present value of the degradation cost over 10 years is approximately 820,000 yuan, and the present value of the additional costs of charging, electricity purchase, and losses is approximately 2.11 million yuan.

[0141] 5. Comparison of optimization results

[0142] All candidate capacity combinations are traversed, and the total lifecycle net present value cost is calculated based on the inner-layer runtime optimization model. Some results are shown below:

[0143] A 2.0 4 2.0 312.6 7.9 B 3.0 6 2.0 184.3 6.4 C 4.0 8 2.0 71.8 4.8 D 4.0 12 3.0 126.5 5.9 E 5.0 10 2.0 96.2 5.1 F 6.0 18 3.0 268.7 7.2

[0144] As can be seen from the table above, Scheme C is... MW and The candidate parameter combination for MWh offers the lowest total lifecycle net present value cost, the shortest dynamic payback period, and the best overall economic performance. The main reasons for this are:

[0145] The proposed solution provides 2 hours of energy storage, effectively covering the main peak-valley price difference window in the industrial park; its power level is sufficient to reduce the park's maximum monthly demand, resulting in a significant contribution to demand revenue; the capacity is not over-configured, thus avoiding additional investment and low utilization issues; and it still meets the capacity retention requirements at the end of its lifespan, eliminating the need for large-scale battery cluster replenishment. Therefore, the optimal parameter combination for this embodiment is determined as follows: .

[0146] Figure 2 This is a structural schematic diagram of a parameter configuration device for the entire lifecycle of an energy storage system, provided as an embodiment of the present invention. Figure 2 As shown, the device includes:

[0147] The energy storage basic data acquisition module 301 is used to acquire the energy storage basic data corresponding to the energy storage system to be configured. The energy storage basic data includes, but is not limited to, load history curves, new energy output curves, time-of-use electricity price series, grid connection electricity price series, demand electricity price coefficient, unit power investment cost of energy storage system, unit capacity investment cost of energy storage system, annual operation and maintenance cost coefficient, discount rate, planned lifespan, and system module replacement cost.

[0148] The net present value cost determination module 302 is used to calculate the net present value cost of all candidate parameter combinations based on the pre-built net present value cost function of the energy storage system throughout its entire life cycle, according to the energy storage basic data, by traversing all candidate parameter combinations of power parameters and capacity parameters. The net present value cost function of the energy storage system throughout its entire life cycle is composed of at least candidate power parameters and candidate capacity parameters.

[0149] The optimal parameter combination determination module 303 is used to uniformly evaluate the life-cycle net present value cost for each candidate parameter combination and determine the optimal parameter combination scheme.

[0150] Optionally, the net present value cost function for the entire lifecycle of the energy storage system is as follows:

[0151] ;

[0152] in, This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the initial construction investment cost; This represents the present value of the total lifecycle maintenance costs. Present value of the cost of replacing critical components; The present value of the cost discounted for battery life degradation; The present value of the cost of purchasing electricity to charge energy storage; The present value of the additional costs caused by system losses; The present value of peak-valley arbitrage profits; The present value of demand management revenue; For backup, the present value of demand response or ancillary service revenue; It represents the present value of the residual value at the end of the lifespan.

[0153] The initial construction investment cost is represented as follows:

[0154] ;

[0155] in, This refers to the initial construction investment cost; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the cost of installation, commissioning, grid connection, civil engineering, and monitoring systems.

[0156] The present value of the annual operation and maintenance costs is represented as follows:

[0157] ;

[0158] in, It refers to the present value of the total life-cycle operation and maintenance costs; This refers to the planned lifespan of the energy storage system; This refers to the annual maintenance ratio; This refers to the investment cost per unit power of an energy storage system; This refers to candidate power parameters; This refers to the investment cost per unit capacity of an energy storage system; This refers to the candidate capacity parameter; This refers to the discount rate;

[0159] The present value of the replacement cost of the key component is represented as follows:

[0160] ;

[0161] in, This refers to the present value of the cost of replacing key components; This refers to the total number of events that were changed; It refers to the first Cost of replacing the corresponding parts; This refers to the discount rate;

[0162] The degradation cost is expressed as follows:

[0163] ;

[0164] in, The present value of the cost discounted for battery life degradation; This refers to the unit capacity degradation cost; Indicates the first Annual increase in degraded capacity, , Indicates the first Available system capacity at the end of the year; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

[0165] The peak-valley arbitrage profit is represented as follows:

[0166] ;

[0167] in, This refers to the present value of peak-valley arbitrage profits; This refers to the planned lifespan of the energy storage system; This refers to the weight of each operating scenario; This refers to the electricity price for electricity substitution or the electricity price for equivalent revenue. This refers to the electricity purchase substitution price; This refers to discharge power; This refers to the time step; This refers to the discount rate;

[0168] The demand management revenue is represented as follows:

[0169] ;

[0170] in, This refers to the present value of demand management revenue; This refers to the demand-based electricity price coefficient; This refers to the first time when no energy storage is configured. The average monthly maximum demand or assessment demand for the year; This indicates the corresponding demand value after configuring energy storage; This refers to the discount rate;

[0171] The present value of the end-of-life residual value is represented as follows:

[0172] ;

[0173] in, It refers to the present value of the residual value at the end of the life; This refers to the residual value rate; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

[0174] During the operation of the energy storage system, the dynamic equation of the energy storage state of charge is:

[0175] ;

[0176] in, It refers to time t+1. Energy storage state of charge in the scenario; It refers to time t Energy storage state of charge in the scenario; This refers to charging efficiency; This refers to charging power; This refers to the time step; This refers to the candidate capacity parameter; This refers to discharge power; This refers to discharge efficiency.

[0177] The net present value cost determination module 302 is specifically used for:

[0178] Using the candidate power parameters and the candidate capacity parameters as outer decision variables, capacity search is performed using enumeration search, particle swarm optimization, genetic algorithm or Bayesian optimization, and multiple combinations of candidate parameters within a given range are obtained using inner linear programming or mixed integer linear programming.

[0179] Alternatively, after the benefit model and constraints have been linearized, a single-level mixed integer programming model can be directly constructed for joint solution to determine multiple sets of candidate parameter combinations.

[0180] The optimal parameter combination determination module is specifically used for:

[0181] The candidate parameter combination with the minimum net present value cost over the entire life cycle is determined as the optimal parameter combination scheme.

[0182] If there are multiple candidate parameter combinations that minimize the total life-cycle net present value cost, then the optimal parameter combination scheme is determined based on the comprehensive economics of the multiple candidate parameter combinations.

[0183] The parameter configuration device for the entire life cycle of an energy storage system provided in this embodiment of the invention can execute the parameter configuration method for the entire life cycle of an energy storage system provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0184] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0185] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0186] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0187] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as parameter configuration methods throughout the lifecycle of an energy storage system.

[0188] In some embodiments, the parameter configuration method for the entire lifecycle of the energy storage system can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the parameter configuration method for the entire lifecycle of the energy storage system described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the parameter configuration method for the entire lifecycle of the energy storage system by any other suitable means (e.g., by means of firmware).

[0189] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0190] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0191] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0192] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0193] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0194] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0195] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0196] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A parameter configuration method for the entire lifecycle of an energy storage system, characterized in that, include: Obtain the basic energy storage data corresponding to the energy storage system to be configured. The basic energy storage data includes, but is not limited to, historical load curves, renewable energy output curves, time-of-use electricity price series, grid connection electricity price series, demand electricity price coefficient, unit power investment cost of energy storage system, unit capacity investment cost of energy storage system, annual operation and maintenance cost coefficient, discount rate, planned lifespan, and system module replacement cost. Based on the pre-constructed net present value cost function for the entire life cycle of the energy storage system, and according to the basic energy storage data, all candidate parameter combinations of power parameters and capacity parameters are traversed to obtain the net present value cost for the entire life cycle of all candidate parameter combinations. The net present value cost function for the entire life cycle of the energy storage system is composed of at least candidate power parameters and candidate capacity parameters. A unified evaluation of the life-cycle net present value cost for each candidate parameter combination is conducted to determine the optimal parameter combination scheme.

2. The method according to claim 1, characterized in that, The net present value cost function for the entire lifecycle of the energy storage system is as follows: ； in, This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the initial construction investment cost; This represents the present value of the total lifecycle maintenance costs. Present value of the cost of replacing critical components; The present value of the cost discounted due to battery life degradation; The present value of the cost of purchasing electricity to charge energy storage; The present value of the additional costs caused by system losses; The present value of peak-valley arbitrage profits; The present value of demand management revenue; For backup, the present value of demand response or ancillary service revenue; It represents the present value of the residual value at the end of the lifespan.

3. The method according to claim 2, characterized in that, The initial construction investment cost is represented as follows: ； in, This refers to the initial construction investment cost; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the cost of installation, commissioning, grid connection, civil engineering, and monitoring systems. The present value of the annual operation and maintenance costs is represented as follows: ； in, It refers to the present value of the total life-cycle operation and maintenance costs; This refers to the planned lifespan of the energy storage system; This refers to the annual maintenance ratio; This refers to the investment cost per unit power of an energy storage system; This refers to candidate power parameters; This refers to the investment cost per unit capacity of an energy storage system; This refers to the candidate capacity parameter; This refers to the discount rate; The present value of the replacement cost of the key component is represented as follows: ； in, This refers to the present value of the cost of replacing key components; This refers to the total number of events that were changed; It refers to the first Cost of replacing the corresponding parts; This refers to the discount rate; The degradation cost is expressed as follows: ； in, The present value of the cost discounted due to battery life degradation; This refers to the unit capacity degradation cost; Indicates the first Annual increase in degraded capacity, , Indicates the first Available system capacity at the end of the year; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

4. The method according to claim 2, characterized in that, The peak-valley arbitrage profit is represented as follows: ； in, This refers to the present value of peak-valley arbitrage profits; This refers to the planned lifespan of the energy storage system; This refers to the weight of each operating scenario; This refers to the electricity price for electricity substitution or the electricity price for equivalent revenue. This refers to the electricity purchase substitution price; This refers to discharge power; This refers to the time step; This refers to the discount rate; The revenue from demand management is represented as follows: ； in, This refers to the present value of demand management revenue; This refers to the demand-based electricity price coefficient; This refers to the first time when no energy storage is configured. The average monthly maximum demand or assessment demand for the year; This indicates the corresponding demand value after configuring energy storage; This refers to the discount rate; The present value of the end-of-life residual value is represented as follows: ； in, It refers to the present value of the residual value at the end of the life; This refers to the residual value rate; This refers to candidate power parameters; This refers to the candidate capacity parameter; This refers to the investment cost per unit power of an energy storage system; This refers to the investment cost per unit capacity of an energy storage system; This refers to the planned lifespan of the energy storage system; It refers to the discount rate.

5. The method according to claim 1, characterized in that, During the operation of the energy storage system, the dynamic equation of the energy storage state of charge is: ； in, It refers to time t+1. Energy storage state of charge in the scenario; It refers to time t Energy storage state of charge in the scenario; This refers to charging efficiency; This refers to charging power; This refers to the time step; This refers to the candidate capacity parameter; This refers to discharge power; This refers to discharge efficiency.

6. The method according to claim 1, characterized in that, The total life-cycle net present value cost of all candidate parameter combinations is calculated using the following method: Using the candidate power parameters and the candidate capacity parameters as outer decision variables, capacity search is performed using enumeration search, particle swarm optimization, genetic algorithm or Bayesian optimization, and multiple combinations of candidate parameters within a given range are obtained using inner linear programming or mixed integer linear programming. Alternatively, after the benefit model and constraints have been linearized, a single-level mixed integer programming model can be directly constructed for joint solution to determine multiple sets of candidate parameter combinations.

7. The method according to claim 1, characterized in that, The process of uniformly evaluating the life-cycle net present value cost for each candidate parameter combination to determine the optimal parameter combination includes: The candidate parameter combination with the minimum net present value cost over the entire life cycle is determined as the optimal parameter combination scheme. If there are multiple candidate parameter combinations that minimize the total life-cycle net present value cost, then the optimal parameter combination scheme is determined based on the comprehensive economics of the multiple candidate parameter combinations.

8. A parameter configuration device for the entire life cycle of an energy storage system, characterized in that, include: The energy storage basic data acquisition module is used to acquire the energy storage basic data corresponding to the energy storage system to be configured. The energy storage basic data includes, but is not limited to, load history curves, new energy output curves, time-of-use electricity price series, grid connection electricity price series, demand electricity price coefficient, unit power investment cost of energy storage system, unit capacity investment cost of energy storage system, annual operation and maintenance cost coefficient, discount rate, planned lifespan, and system module replacement cost. The net present value cost determination module is used to traverse all candidate parameter combinations of power parameters and capacity parameters based on the pre-built net present value cost function of the energy storage system throughout its entire life cycle, and to obtain the net present value cost of all candidate parameter combinations throughout their entire life cycle based on the energy storage basic data. The net present value cost function of the energy storage system throughout its entire life cycle is composed of at least candidate power parameters and candidate capacity parameters. The optimal parameter combination determination module is used to uniformly evaluate the life-cycle net present value cost for each candidate parameter combination and determine the optimal parameter combination scheme.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the parameter configuration method for the entire life cycle of the energy storage system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the parameter configuration method for the entire life cycle of the energy storage system as described in any one of claims 1-7.

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