A method for evaluating the full life cycle cost of energy storage technology suitable for multi-dimensional scenarios

Abstract

The present application belongs to the technical field of energy storage economic benefit evaluation, and aims to provide support for new energy storage investment strategies for the future form of power grids. Specifically, a whole life cycle cost evaluation method of energy storage technology suitable for multi-dimensional scenarios is provided, which comprehensively evaluates the economy of different energy storage technologies. The method considers multiple life cycle cost factors such as capital expenditure, operating cost, end-of-life cost and charging cost, and combines energy power ratio (E / P), terrain adaptability and different power application scenarios to simulate and quantify the uncertainty of cost parameters using a Monte Carlo prediction model, and predict the future trend of energy storage technology cost. Finally, intuitive evaluation is performed through detailed cost analysis and the constructed superimposed visualization method. The present application provides scientific decision support for power system planning and energy storage technology investment, and has significant practical value and theoretical significance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of energy storage economic benefit evaluation, and particularly relates to a method for evaluating the whole life cycle cost of energy storage technology suitable for multi-dimensional scenarios. BACKGROUND

[0002] With the continuous development of energy systems, the research and development of energy storage technology are becoming more and more important. Energy storage technology is crucial for promoting economic benefits and provides more flexibility and regulation capacity in power markets and energy management. The existing economic analysis of power storage technology mostly uses single scenario or short time span, lacks long-term systematic research on cost evolution, and leads to high uncertainty of prediction results. With the increasing proportion of renewable energy generation, the instability of power supply has become a problem to be solved. Traditional fossil fuel power generation has relatively less demand for large-scale energy storage due to its flexible scheduling. Through continuous innovation of new energy storage technologies, such as lithium-ion batteries, flow batteries, lead-acid batteries, and sodium-ion batteries, and mechanical energy storage technologies such as compressed air energy storage, the efficient utilization and sustainable development of new energy can be better achieved. Especially in the construction of clean, low-carbon, safe and efficient modern energy system, the construction, optimization and operation planning of energy storage system require comprehensive benefit analysis and evaluation of the energy storage system. In terms of economic benefit evaluation, the cost and economic benefit evaluation of new energy side energy storage system is an important topic. At the same time, the future competitiveness of different energy storage technologies in different power applications is different, which shows that the economic benefit of energy storage system is closely related to the price mechanism of power market and the scenario applicability of energy storage technology. SUMMARY

[0003] The importance of energy storage technology is increasing, especially in the context of building a new type of power system, developing efficient and economic energy storage technology has become the focus of power enterprises. The present application aims to propose a new energy storage investment strategy and an economic evaluation method for future power system form of energy storage technology, by comprehensively considering factors such as technological progress, cost changes, performance degradation and power market price fluctuations, to provide more accurate and comprehensive economic evaluation for energy storage technology investment decision-making. This method can be applied to the power market environment and policy background, providing scientific basis for power system planning and energy storage technology development. By optimizing the energy storage investment strategy, the present application strives to improve the accuracy and adaptability of energy storage technology investment, and ensure the scientificity and feasibility of subsequent economic benefit evaluation. Ultimately, this research will provide a solid economic foundation for the popularization and application of energy storage technology, supporting the stable and efficient operation of power grid in response to future changes in power demand and price fluctuations.

[0004] The present application provides a method for evaluating the whole life cycle cost of energy storage technology suitable for multi-dimensional scenarios, comprising the following steps,

[0005] Step 1: Considering the cost composition in the life cycle of energy storage technology and the annual degradation rate parameter of energy storage technology, an economic model for energy storage cost balance analysis is established;

[0006] Step 2: An energy-to-power ratio matrix representing the discharge capacity of energy storage is constructed, which is substituted into the above-mentioned economic model for energy storage cost balance analysis. The multi-parameter combination economic indicators of different energy storage technologies are calculated respectively in combination with the terrain adaptability to obtain the evaluation results of the lowest, the second lowest cost and the stronger replaceability of the energy storage technology that can be configured in the current power system under different terrain conditions, i.e. the balanced cost value of different energy storage technologies;

[0007] Step 3: According to the key technical characteristics of the energy storage system, including system size, discharge duration and response time, the adaptability of energy storage technology for different power application scenarios of power generation, power transmission, power distribution and user side is screened, and three kinds of energy storage technologies suitable for each power application scenario are selected. Based on each power application scenario, the specific variables of energy storage technology in different years and their standard deviations are determined, and the variable change rate changing with time is derived using an empirical curve model containing market growth prediction;

[0008] Step 4: For the specific variables in the above-mentioned economic model for energy storage cost balance analysis, the Monte Carlo prediction model is used to simulate the value of the standard deviation range of the specific variable multiple times to predict the parameter value of the appropriate three kinds of energy storage technology changing with time under different scenarios. Then, the economic model for energy storage cost balance analysis is used to calculate the probability range of the future cost in turn; Finally, the probability ratio is obtained by the lowest cost probability analysis using the balanced cost value, which quantitatively compares the competitiveness of different energy storage technologies in each power application scenario;

[0009] Step 5: The balanced cost value of different energy storage technologies and the probability ratio of the predicted future probability are displayed and compared in the form of superimposed terrain thermal map and stacked column chart to select the energy storage technology that may perform best in different terrains and each power application scenario in the future, realize the cost prediction analysis of energy storage, and perfect the economic analysis of different energy storage.

[0010] Further, in step 1, the energy storage cost balance analysis model is established, and the balanced energy storage cost (Levelized Cost of Storage, LCOS) is as follows:

[0011]

[0012] Wherein, C CAPEX represents capital expenditure, n represents the number of years, N represents the system life, r represents the discount rate, C OPEX represents operating cost expenditure, Ccharge represents the cost of charging, C EoL represents the end-of-life cost, E discharged represents the total amount of energy released.

[0013] Further, the total amount of energy released can be used to measure the total amount of energy that the energy storage system can provide throughout its life cycle, further considering the annual degradation rate of the energy storage, thereby optimizing the method of judging the efficiency and economy of the energy storage system; the expression of the total amount of energy released is as follows:

[0014]

[0015] wherein Y cycles represents the number of annual cycles, DoD represents the depth of discharge, F cap represents the energy capacity, η RT represents the round-trip efficiency, η self represents the self-discharge rate per cycle, t c represents the construction time in years, D cyc represents the periodic degradation rate, D t represents the annual degradation rate.

[0016] Further, the (1) capital expenditure (C CAPEX )

[0017] The capital expenditure is the cost related to the initial construction of the energy storage system, including the cost per unit power, the cost per unit capacity, and the replacement cost, and is determined by summation, the expression is as follows:

[0018]

[0019] wherein C p represents the cost per unit power, P cap represents the rated power, C e represents the cost per unit capacity, E cap represents the rated capacity, M represents the number of replacements required during the life of the system, C p-r represents the replacement cost per unit power, m represents the current replacement, t r represents the time interval of replacement;

[0020] (2) operating cost (C OPEX )

[0021] The operating cost involves the expenses required for the daily operation of the energy storage facility, including maintenance and operation costs, and considering the annual degradation rate, the present value of the sum of the operating and maintenance costs of all years is equal to the present value of the sum of the costs based on power and capacity and the performance degradation caused by cycles, the expression is as follows:

[0022]

[0023] where C p-OM represents the operating cost per unit power, C e-OM represents the operating cost per unit capacity, D cyc represents the periodic degradation rate, D t represents the annual degradation rate;

[0024] (3) End-of-life cost (C EoL ):

[0025] When an energy storage technology reaches the end of its designed useful life, costs for decommissioning, cleanup, or recycling can arise, which can increase as the technology is reused, expressed as follows:

[0026]

[0027] where EoL p represents the end-of-life cost per unit power, EoL e represents the end-of-life related cost per unit capacity;

[0028] (4) Charging cost (C charge ): The charging cost involves the present value summation of the charging cost of the energy storage system over its useful life due to electricity prices. First, the annual charging cost is calculated, which involves the cost of the energy storage system to purchase electricity, multiplied by the number of cycles per year, and then divided by the present value factor per year; then the annual charging cost is accumulated to obtain the total charging cost of the energy storage system over its entire useful life, expressed as follows:

[0029]

[0030] where C Charge_last represents the charging cost of the last year; P buy represents the electricity purchase price; t idx represents the construction period of the energy storage system.

[0031] Further, in step 2, the energy-to-power ratio (E / P) representing the discharge capacity of the energy storage is proposed, and according to the definition of different terrain scenarios, the energy storage technology suitable for a specific terrain scenario is screened out through a Boolean array, and further combined with the requirements of different terrain conditions, the economic model of the energy storage cost balance analysis calculates the economic indicators of various energy storage technologies under different energy-to-power ratios and annual cycle times.

[0032] Furthermore, in the method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios, in step 3, the application scenarios of the power system include time-of-use electricity prices, black start, relief of grid congestion, energy arbitrage, peak shaving and valley filling, power quality, power supply reliability, primary response, energy time shifting, secondary response, grid upgrade delay and tertiary response.

[0033] Furthermore, the application scenarios of the power system include time-of-use electricity prices, black start, grid congestion relief, energy arbitrage, peak shaving and valley filling, power quality, power supply reliability, primary response, energy time shifting, secondary response, grid upgrade delay and tertiary response. When solving the levelized energy storage cost LCOS of the corresponding energy storage technology for each of the above power application scenarios, it is necessary to calculate the specific parameter C in formula (1) that changes with time. CAPEX The rate of change over time is derived through an experience curve model that includes market growth forecasts for use in subsequent calculations.

[0034] Furthermore, the solution of the LCOS requires the technical input parameter C in formula (1) CAPEX , D p , C e , C P-OM , C e-OM etc. to simulate its uncertainty, for a specific parameter C that varies with time CAPEX By combining the change rate obtained above, we can deduce its C CAPEX The future value of parameter C p , C e , C P-OM , C e-OM Monte Carlo simulations are used to predict future values ​​based on their basic standard deviations. The central estimate and standard deviation depend on historical data from the initial year. Finally, n iterations of simulations are performed to calculate the levelized energy storage cost (LCOS) for different energy storage technologies under specific applications. The probability of the LCOS values ​​generated by each technology is calculated during the Monte Carlo simulation iterations, and the lowest LCOS probability value is obtained through n iterations of calculations.

[0035] P(a i =minLCOS) = P(a i k ,k∈[1;n])·P(a i <c k ,k∈[1;n])·… (8)

[0036]

[0037] In this framework, the three most competitive energy storage technologies selected for each specific power scenario are set as A, B, and C, and their LCOS are respectively represented by the dataset {a1; a2; ...; a​n}, and {c1; c2;... ; c n}, and {c1; c2;... ; c n} represent, N is the number of technologies being compared. The obtained probability values are then used for subsequent visualization plot analysis.

[0038] Further, on the basis of in-depth analysis of the above-mentioned analysis, turn to the use of the designed code empirical data visualization. The code designed in this scheme reads the energy storage technology parameter data, combines the different conditions of annual cycle times and energy power ratio, calculates the economic indicators of each energy storage technology, and generates a heat map for visualization. First, initialize the necessary parameters and load the energy storage technology characteristics from the external file, then call the core calculation function through nested loop to calculate the economic indicators of each energy storage technology under different parameter combinations point by point, forming a two-dimensional matrix; then, by defining the optimal screening function, the economic indicators of each technology are compared, and the lowest cost energy storage technology and the next lowest cost technology are extracted, and their cost difference is calculated to dynamically adjust the transparency of the heat map; then, use color coding to represent the optimality of different energy storage technologies, and highlight the differences between technologies through transparency changes, use the combination of color mapping and transparency to realize color coverage in the heat map. These images comprehensively show the cost performance of energy storage technologies under different operating conditions and terrain restrictions, providing an intuitive visualization tool to support the selection and optimization analysis of energy storage technologies.

[0039] (2) To evaluate the economic competitiveness of different energy storage technologies, this study uses probability stacked column chart to show the economic performance and market competitiveness of different energy storage technologies in a specific year. By using the calculated probability of each technology's lowest equalization cost, it is converted into the cost probability proportion of each technology. The different color parts inside each column chart in the figure are proportional to the proportion of the lowest cost probability of different energy storage technologies. It intuitively shows the relative advantages of different technologies in economic performance. By displaying the average equalization cost trend of the best energy storage technology through double Y-axis, through the relative size of each part in the stacked column chart, and the trend of change over time, the cost-effectiveness and market potential of energy storage technology are evaluated. By constructing the probability stacked column chart after prediction, a more comprehensive understanding of the feasibility and sustainability of energy storage systems can be obtained, so as to optimize the economic benefits. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 Heat map of optimal LCOS energy storage technology under different terrain conditions;

[0041] Figure 2 Heat map of the most economical energy storage technology LCOS value under different terrain conditions;

[0042] Figure 3Thermograph with sub-optimal LCOS energy storage technology under different terrain conditions;

[0043] Figure 4 Electric energy storage technology experience curve graph;

[0044] Figure 5 LCOS value range bar graph;

[0045] Figure 6 LCOS value probability distribution graph;

[0046] Figure 7 Multiple energy storage technology LCOS prediction (2020-2055) stacked column chart;

[0047] Figure 8 Flowchart of the present application. DETAILED DESCRIPTION

[0048] The principles and features of the present application are described below in conjunction with the accompanying drawings, which are only used to explain the present application and are not intended to limit the scope of the present application.

[0049] Reference Figure 1 The present patent provides a method for evaluating the full life cycle cost of energy storage technology suitable for multi-dimensional scenarios. The method takes into account the impact of annual degradation rate and uses a method to balance the cost of different energy storage technologies to calculate the cost of different energy storage technologies, achieving comprehensive economic evaluation. Secondly, the patent constructs a fixed price model and proposes to analyze the energy storage technology under different annual cycle times and E / P conditions. By analyzing E / P, it can be determined whether the energy storage technology can provide cost benefits under a specific discharge cycle. Then, according to the E / P and annual cycle time, a cycle duration discharge time matrix is established, thereby quantifying the cost per unit of discharge power in the entire life cycle of a specific energy storage technology. Finally, the patent visualizes the economic performance of energy storage technology, displays the balanced energy storage cost of different energy storage technologies through color coding, and generates separate charts for different terrain conditions, converting complex calculation results into intuitive visual information, which can more efficiently display the economic performance of different energy storage technologies under different conditions.

[0050] On the basis of comprehensive analysis of various scenarios, the present study adopts the experience curve analysis framework to predict the future evolution trend of energy storage technology cost. The study first systematically collects and organizes historical cost data of various energy storage technologies such as lithium-ion batteries, sodium-sulfur batteries, pumped hydro storage, etc. in diversified application scenarios such as energy arbitrage, time-of-use pricing, energy time shifting, etc. to lay a solid empirical foundation for subsequent prediction work. Subsequently, for each energy storage technology, the study further analyzes its full life cycle cost in each application scenario, and uses probability analysis method to accurately calculate the probability of the technology cost reaching the optimal level and the corresponding LCOS value. To further improve the accuracy and robustness of the prediction results of energy storage technology cost, the present patent integrates Monte Carlo simulation technology and experience curve model for comprehensive evaluation. Specifically, for technologies lacking direct experience curve reference, the study uses analogy reasoning method based on historical data and industry trends of related technology experience curve data sets to predict the future cost reduction trend by referring to the development trend of similar technologies or similar fields. In addition, Monte Carlo simulation technology is introduced to simulate a large number of random combinations within the 80% confidence interval of normal distribution of parameters, and to perform LCOS estimation for each technology and its application scenario multiple times to comprehensively consider the uncertainty contained in the technology cost. The cost of each technology is thoroughly explored. The use of this technology greatly enhances the precision of the prediction, and by depicting the probability map of various technologies exhibiting the lowest LCOS in different application scenarios, a fine probability analysis of energy storage economy is achieved, providing detailed data support and scientific decision-making reference for the future strategic deployment and substantial development of power energy storage technology. The specific scheme is as follows:

[0051] Step 1: Considering the cost composition of each life cycle of energy storage technology and the annual depreciation rate parameter of energy storage technology, an economic model for energy storage cost balance analysis is established;

[0052] Step 2: An energy-to-power ratio matrix representing the discharge capacity of energy storage is constructed, which is substituted into the above-mentioned economic model for energy storage cost balance analysis. Combined with terrain adaptability, the multi-parameter combination economic indicators of different energy storage technologies are calculated to obtain the evaluation results of the lowest, second lowest cost and stronger replaceability of energy storage technologies in the current power system under different terrain conditions, i.e. the balanced cost values of different energy storage technologies;

[0053] Step 3: According to the key technical characteristics of energy storage systems, including system size, discharge duration and response time, the suitability of energy storage technologies for different power application scenarios of power generation, transmission, distribution and user side is selected, and three energy storage technologies suitable for each power application scenario are selected. Based on each power application scenario, the specific variables and standard deviations of energy storage technologies in different years are determined, and the variable change rate changing with time is derived using the experience curve model containing market growth prediction;

[0054] Step 4: For specific variables in the economic model of the above energy storage cost balance analysis, the Monte Carlo prediction model is cited to simulate multiple values of the standard deviation range of the specific variables, predict the parameter values of the three appropriate energy storage technologies changing over time under different scenarios, and then use the economic model of the energy storage cost balance analysis to calculate the probability range of the future cost in turn; Finally, the probability ratio is obtained by the lowest cost probability analysis of the calculated equalized cost value, and the competitiveness of different energy storage technologies in each power application scenario is quantitatively compared;

[0055] Step 5: The equalized cost value and the probability ratio of the future possible occurrence of different energy storage technologies under multi-parameter adaptation are intuitively displayed and compared in the form of superimposed terrain thermal maps and stacked column charts to select the energy storage technology that may perform best in different terrains and each power application scenario in the future, realize the cost prediction analysis of energy storage, and perfect the economic analysis of different energy storage.

[0056] Specifically,

[0057] The present scheme calculates the energy storage cost of various energy storage technologies by establishing an energy storage cost balance analysis model. Based on the analysis of the whole life cycle, the energy storage cost needs to consider each life cycle part when calculating, therefore, LCOS can be determined by comparing the sum of the present value of various costs with the sum of the present value of the total energy release. The expression is as follows:

[0058]

[0059] Wherein, C CAPEX represents capital expenditure, n represents the number of years, N represents the system life, r represents the discount rate, C OPEX represents operating cost expenditure, C charge represents the cost of charging, C EoL represents the end-of-life cost, E discharged represents the total energy release. The parameter data involved in the model calculation is the basic data for subsequent calculation.

[0060] The total energy release can be used to measure the total amount of energy that the energy storage system can provide in the whole life cycle, and further consider the annual depreciation rate of the energy storage, so as to optimize the method of judging the efficiency and economy of the energy storage system. The total energy release expression is as follows:

[0061]

[0062] Wherein, Y cycles represents the number of annual cycles, DoD represents the depth of discharge, E cap represents the energy capacity, η RT represents the round-trip efficiency, η selfSelf-discharge rate per cycle, t c Construction time in years, D cyc Periodic degradation rate, D t Annual degradation rate.

[0063] 3. Analysis of various cost expenditures

[0064] In this step, the various life cycle costs of energy storage technologies are broken down for analysis.

[0065] (1) Capital Expenditure (C CAPEX )

[0066] Capital expenditure is the cost associated with the initial construction of the energy storage system, including the cost per unit of power, the cost per unit of capacity, and the replacement cost, and is determined by summation, as follows:

[0067]

[0068] where C p is the cost per unit of power, P cap is the rated power, C e is the cost per unit of capacity, E cap is the rated capacity, M is the number of replacements required over the lifetime of the system, C p-r is the replacement cost per unit of power, m is the current replacement, and t r is the time interval between replacements.

[0069] (2) Operating Expenditure (C OPEX )

[0070] Operating expenditure involves the expenses required for the daily operation of the energy storage facility, including maintenance and operational costs, and takes into account the annual degradation rate, such that the present value of the sum of the operating and maintenance costs over all years is equal to the present value of the costs based on power and capacity, as well as the performance degradation due to cycling, as follows:

[0071]

[0072] where C p-OM is the operating cost per unit of power, C e-OM is the operating cost per unit of capacity, D cyc is the periodic degradation rate, D t is the annual degradation rate.

[0073] (3) End-of-Life Cost (C EoL ):

[0074] When an energy storage technology reaches the end of its designed useful life, there can be costs associated with decommissioning, cleanup, or repurposing, which can increase as the technology is repurposed, expressed as follows:

[0075]

[0076] where EoL p represents the end-of-life cost per unit of power, EoL e represents the end-of-life related cost per unit of capacity;

[0077] (4) Charging cost (C charge ): The charging cost involves the present value sum of the charging cost of the energy storage system over its useful life due to electricity prices. First, the annual charging cost is calculated, which involves the cost of purchasing electricity by the energy storage system, multiplied by the number of cycles per year, and then divided by the present value factor per year; then the annual charging cost is accumulated to obtain the total charging cost of the energy storage system over its entire useful life, expressed as follows:

[0078]

[0079] where C Charge_last represents the charging cost of the last year; P buy represents the electricity purchase price; t idx represents the construction period of the energy storage system.

[0080] The patent proposes an energy-to-power ratio (E / P) to characterize the discharge capacity of energy storage, which measures the time the energy storage system can continue to supply power at full power. Where E and P are the rated capacity (E cap ) and rated power (P cap ) in formula (3), respectively. Specifically, the higher the E / P ratio, the longer the energy storage system can provide power, suitable for long-term energy regulation and power supply, while in the case of a lower E / P ratio, the energy storage system is suitable for short-term peak load regulation and rapid response.

[0081] Since different energy storage technologies have different dependencies on geographical conditions, the patent defines several different terrain scenarios, including "all sites", "plain", "cave", "mountain", "coal transformation", and "natural gas transformation", and filters out energy storage technologies suitable for specific terrain scenarios through a Boolean array (as shown in Table 1).

[0082] Pumped hydro Compressed air energy storage Lithium-ion batteries Hydrogen energy Vanadium flow batteries Sodium-sulfur batteries Lead-acid batteries All siting √ √ √ √ √ √ √ Flatlands √ √ √ √ Caverns √ √ √ Mountainous √ √ Coal repurposing √ √ √ √ √ Natural gas repurposing √ √ √ √

[0083] Table 1 Feasibility of energy storage technologies in different terrains

[0084] Among them, in Table 1, the energy storage technology applicable to each terrain scene is indicated by a tick mark.

[0085] On this basis, the EP matrix containing different energy power ratios and annual cycle times (Y cycles ) combinations is constructed, which is actually a two-dimensional array for calculating the LCOS value of each energy storage technology under different energy levels and different use frequencies. Further combined with the requirements of different terrain conditions, the matrix is substituted into formula (1) to calculate the economic indicators of various energy storage technologies under different energy power ratios and annual cycle times, so as to evaluate the economic performance of various energy storage technologies under different terrain and infrastructure conditions.

[0086] In order to deepen the economic characteristic analysis of different energy storage in specific power application scenarios, an energy storage economic prediction model is constructed. Power storage technology is applicable to multiple links of power supply chain, including power generation, power transmission, power distribution and user side. On the power generation side, energy storage technology can be used to balance the intermittency and uncertainty of renewable energy; on the power transmission side, it can be used to relieve power grid congestion and improve power transmission efficiency; on the power distribution side and user side, energy storage technology is more used for peak clipping, improving power supply reliability and power quality, etc. The technical requirements of each link are significantly different, therefore, the applicability evaluation of energy storage technology needs to be based on key technical characteristics such as system size, discharge duration and response time. In pumped storage and underground compressed air energy storage technologies, due to their relatively slow response time (usually more than 10 seconds) and large system size (usually more than 5 MW), they are more suitable for application scenarios with low response time requirements, such as large-scale power peak shaving and standby power supply, etc. For application scenarios that require fast response, such as primary response, power quality regulation and small-scale consumer applications, etc., it is more suitable to use energy storage technologies such as lithium ion batteries with fast response speed and small system size.

[0087] The application effect of the three best energy storage technologies is shown in Table 2. For the prediction and evaluation of adaptive energy storage in power application scenarios, the future benefits are analyzed, and the economic efficiency of energy storage technology is quantitatively evaluated. LCOS is introduced as an important index, considering the whole life cycle cost of energy storage technology, and the LCOS of each technology in a specific application is calculated using formula (1).

[0088] This index provides a scientific basis for technical evaluation and decision-making, and fully reflects the cost-benefit and time value of energy storage technology.

[0089] Pumped hydro Compressed air energy storage Lithium-ion batteries Hydrogen energy Vanadium flow batteries Sodium-sulfur batteries Lead-acid batteries Time-of-use pricing √ √ √ Black start √ √ √ Relief of grid congestion √ √ √ Energy arbitrage √ √ √ Peak shaving √ √ √ Power quality √ √ √ Power supply reliability √ √ √ Primary response √ √ √ Energy time shifting √ √ √ Secondary response √ √ √ Delay of grid upgrades √ √ √ Tertiary response √ √ √

[0090] Table 2 Feasibility of energy storage technology in different power application scenarios

[0091] Among them, in Table 2, the energy storage technology applicable to each application scenario is indicated by a tick mark.

[0092] Then, the evaluation of techno-economic performance is challenged by the uncertainty of future costs. The solution of LCOS requires the technical input parameters C CAPEX , C p , C e , C P-OM , C e-OM The uncertainty of C CAPEX is simulated by a curve model of experience and a combination of standard deviation, while the parameters C CAPEX , C p , C e , C P-OM , C e-OM whose values do not change significantly over time are simulated by Monte Carlo simulation of their future uncertainty only by their basic standard deviation. Their central estimate and standard deviation depend on the data of the initial year.

[0093] The curve model of experience is based on historical data, which predicts the decreasing trend of future investment costs. The curve of experience is fitted by power according to the existing historical data Figure 4 The cost reduction of various power storage technologies in the process of cumulative installed capacity growth is intuitively displayed in the form of a curve of experience. The X-axis of the graph represents the total capacity of cumulative production of each power storage technology, and the Y-axis represents the unit energy storage cost, measured in yuan per kilowatt-hour (￥ / kWh), which directly shows the economic law that C CAPEX decreases gradually over time. Each curve in the graph corresponds to a specific power storage technology. It is depicted as the dynamic change of C CAPEX of the technology over its service life. It is expressed in the form of change rate C% as shown in Table 3:

[0094] 0 5 10 15 20 25 30 35 Pumped hydro 100 100 100 100 101 101 102 102 Compressed air energy storage 100 100 100 100 101 101 102 102 Lithium-ion batteries 100 55 34 23 18 16 15 14 Hydrogen energy 100 84 66 53 44 39 36 33 Vanadium flow batteries 100 49 34 26 21 19 18 17 Lead-acid batteries 100 80 68 63 61 59 59 58 Sodium-sulfur batteries 100 84 66 53 44 39 36 33

[0095] Table 3 Energy storage technology C CAPEX Parameter annual change rate

[0096] Further, the future value of C CAPEXT at time T is derived by the following formula (6), and C CAPEXT at time T is the C CAPEX0 at the initial time multiplied by the change rate C% at time T, which is used as the central estimate (μ) of formula (8) in the following when C CAPEX is solved:

[0097] C CAPEXT = C CAPEX0 × C% (8)

[0098] To evaluate the combined uncertainty of the investment cost reduction, the uncertainty of the cost estimate and the uncertainty of the cost reduction are combined. The standard deviation (σ) is used to represent the uncertainty of the investment cost parameter, which is used as the standard deviation (σ) of equation (8) in the following section to calculate C CAPEX

[0099]

[0100] where x Parameter is the investment cost C CAPEX0 in 2020, y ％Reduction is the relative investment cost reduction percentage, i.e. the rate of change C%, σ Parameter is the standard deviation of the investment cost, and σ ％Reduction is the standard deviation of the relative investment cost reduction percentage. The combined standard deviation is calculated as shown in Table 4.

[0101] Pumped hydro 0％ 0％ 1％ 3％ 6％ 8％ 10％ 12％ Compressed air energy storage 0％ 0％ 1％ 3％ 6％ 8％ 10％ 12％ Lithium-ion batteries 0％ 12％ 14％ 13％ 12％ 10％ 10％ 9％ Hydrogen energy 0％ 3％ 6％ 8％ 10％ 11％ 10％ 10％ Vanadium flow batteries 0％ 15％ 16％ 14％ 12％ 11％ 10％ 9％ 铅 acid cell ​ 0％ 5％ 6％ 5％ 5％ 4％ 4％ 5％ 钠 sulfur cell ​ 0％ 3％ 6％ 8％ 10％ 11％ 10％ 10％

[0102] Table 4 Combined standard deviation of the investment cost of energy storage technologies C CAPEX

[0103] For technologies that lack empirical curve data and corresponding cost estimates, the relative cost reduction and the standard deviation are mainly derived from the identification and analysis of related or similar technologies that have similar technical characteristics, market positioning or application prospects to the target technology, to indirectly infer the cost reduction trend and its uncertainty (i.e. the standard deviation) of the target technology. The application of this method is based on the similarity or correlation between the cost reduction potential of these related or similar technologies and the technology that lacks empirical curve data, so as to provide a reasonable basis for the cost reduction and standard deviation prediction of the latter to some extent.

[0104] Then, the technology input parameters C CAPEX , C p , C e , C P-OM , C e-OM are set to the technology parameters (x) to simulate their uncertainty using the Monte Carlo simulation method. The value of C CAPEX is simulated within the value standard deviation range of the input parameter determined by assuming a normal distribution of the central estimate (μ) and the standard deviation (σ) obtained from equations (8) and (9) above. The parameters C p , C e , C P-OM , C e-OM are only simulated by their basic standard deviation in the future time. Their central estimate and standard deviation depend on the data of the initial year. The formula is as follows:

[0105] ​​

[0106] Finally, the LCOS value is calculated and the minimum LCOS probability analysis is performed for different energy storage under specific power application scenarios. The dependent variable data simulated in the previous section is brought into the LCOS calculation formula (1) and the n times LCOS simulation iteration calculation is performed for different energy storage under specific application. In each Monte Carlo simulation iteration, the LCOS values generated by each technology are compared to determine the energy storage technology with the lowest LCOS. Under this framework, the three most competitive energy storage technologies selected for each specific power scenario are denoted as A, B, and C, and their LCOS are represented by the data sets {a1; a2;...; a n}, {b1; b2;...; b n}, and {c1; c2;...; c n}, respectively. Formula (11) represents the probability that the LCOS value a i of the i-th simulation of technology A is the minimum value in a single simulation. This probability is obtained by calculating the product of the probabilities that a i is less than all other technology LCOS values b k , c k . Formula (12) is the probability that technology A has the lowest LCOS in all simulations. This probability is obtained by calculating the number of times the LCOS value of technology A is the minimum in all n simulations, and then dividing by the total number of simulations n N . Here, N is the number of technologies being compared. The summation symbol in the formula represents the sum of the number of times the LCOS value of technology A is the minimum in all n simulations, and then dividing by n N to obtain the final probability. This is used to evaluate the relative competitiveness of each technology in providing the minimum cost storage solution when considering the LCOS uncertainty range.

[0107] P(a i = minLCOS) = P(a i <b k , k e [1; n]) · P(a i <c k , k e [1; n]) ·... (11)

[0108]

[0109] Visualization section:

[0110] (1) Draw a heat map of the optimal LCOS energy storage technology under different terrain conditions.

[0111] Figure 1The figure on the left shows the variation trend of the sub-optimal energy storage technologies in different terrain conditions, with the horizontal axis representing the annual cycle number, i.e. the number of cycles per year of the energy storage system, and the vertical axis representing the energy power ratio. The heat map on the right shows the names of the energy storage technologies corresponding to different colors.

[0112] (2) Draw a heat map of the LCOS values of the most economical energy storage technologies in different terrain conditions.

[0113] Figure 2 The figure on the left shows the variation trend of the sub-optimal energy storage technologies in different terrain conditions, with the horizontal axis representing the annual cycle number, i.e. the number of cycles per year of the energy storage system, and the vertical axis representing the energy power ratio. The heat map on the right shows the names of the energy storage technologies corresponding to different colors.

[0114] (3) Draw a heat map of the sub-optimal LCOS energy storage technologies in different terrain conditions.

[0115] Figure 3 The figure on the left shows the variation trend of the sub-optimal energy storage technologies in different terrain conditions, with the horizontal axis representing the annual cycle number, i.e. the number of cycles per year of the energy storage system, and the vertical axis representing the energy power ratio. The heat map on the right shows the names of the energy storage technologies corresponding to different colors.

[0116] (4) Draw an experience curve graph based on existing data

[0117] To assess the uncertainty of future costs, an experience curve model that includes market growth forecasts is used to derive future cost ranges Figure 4 In the form of an experience curve graph, the cost reduction phenomenon of various power storage technologies in the process of cumulative installed capacity growth is presented. The X-axis of the graph represents the total capacity of each power storage technology produced, and the Y-axis represents the unit energy storage cost in yuan per kilowatt-hour (￥ / kWh) as the measurement standard, directly showing the economic law that the cost gradually decreases with the increase of cumulative capacity. Each curve in the graph corresponds to a specific power storage technology and depicts the dynamic changes of the unit cost of the technology decreasing with the increase of cumulative installed capacity in different years.

[0118] (5) LCOS prediction under different application scenarios (2020-2055) chart

[0119] Figure 5 For "LCOS prediction under different application scenarios (2020-2055) chart", the predicted range of LCOS of three most competitive energy storage technologies under specific application scenarios is displayed in bar chart form. The chart covers the time range from 2020 to 2055 and distinguishes 12 different application scenarios (only three of which are shown in the chart). In addition, Figure 5 By Monte Carlo simulation method, the uncertainty range of LCOS prediction for each technology is provided, so as to more comprehensively reflect the volatility and reliability of future cost prediction values.

[0120] (6) LCOS value probability distribution chart for specific application scenarios and time (take energy arbitrage in 2020 as an example);

[0121] Figure 6 Show the calculation results of LCOS (equalized energy storage cost) based on Monte Carlo simulation technology, which simulates n independent LCOS calculations for specific technologies and different years. A 80% confidence interval is constructed by using the random technique of parameter attribution normal distribution. The purpose is to evaluate the uncertainty of LCOS through a large number of random sampling, and to quantify the impact of changes in technology input parameters on LCOS prediction values. Figure 6 Not only the predicted value of LCOS is presented, but also the uncertainty range of the prediction result is shown in the form of confidence interval, which provides more comprehensive and in-depth insights for decision makers, and helps them make more scientific and reasonable judgments when facing technology selection and investment decisions.

[0122] (7) LCOS prediction of multiple energy storage technologies under different application scenarios (2020-2055) stacked column chart;

[0123] Figure 7The application requirements for LCOS (Levelized Cost of Storage) projections are detailed and the probability distribution of the lowest LCOS provided by different technologies is shown, with particular emphasis on the average LCOS of the most cost-effective technology (indicated by the black line). This figure not only reflects the frequency of each technology achieving the lowest LCOS, taking into account the range of uncertainty, but also integrates the probability of the lowest LCOS across all 7 storage technologies and 12 application scenarios. The results show that battery technologies exhibit the highest likelihood of providing the lowest LCOS in most applications after 2030. In particular, by 2035, lithium-ion batteries are the most cost-effective in most application scenarios, especially in cases where the discharge duration is less than 4 hours and the number of cycles is less than 300 (such as power quality and black start). For application scenarios with longer discharge durations and higher cycle requirements (such as power supply reliability for more than 4 hours or secondary response and billing management for more than 300 cycles), vanadium flow batteries, while not the most likely option to provide the lowest LCOS, remain competitive.

[0124] The above description is merely that of the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall within the protection scope of the present application.

Claims

1. A full life cycle cost assessment method for energy storage technology applicable to multi-dimensional scenarios, characterized by: The following steps are included: Step 1: Consider the cost structure of energy storage technology throughout its life cycle and the annual degradation rate parameters of energy storage technology to establish an economic model for energy storage cost equilibrium analysis; Step 2: Construct an energy-to-power ratio matrix to characterize the energy storage discharge capacity. Substitute this matrix into the aforementioned economic model for energy storage cost equilibrium analysis. Combined with terrain adaptability, calculate the multi-parameter economic performance indicators for different energy storage technologies. This evaluates the lowest, second-lowest, and most substitutable energy storage technologies that can be configured in the current power system under different terrain conditions, i.e., the balanced cost values ​​for different energy storage technologies. Step 3: Based on the key technical characteristics of energy storage systems, including system size, discharge duration, and response time, the applicability of energy storage technologies for different power application scenarios, including power generation, transmission, distribution, and user-end, was screened. Three energy storage technologies were selected for each power application scenario. Based on each power application scenario, specific variables and their standard deviations for energy storage technologies in different years were determined. The rate of change of these variables over time was derived using an experience curve model that includes market growth forecasts. Step 4: For specific variables in the energy storage cost equilibrium analysis economic model, the Monte Carlo prediction model is used to simulate the standard deviation range of the specific variables multiple times to predict the appropriate parameter values ​​of the three energy storage technologies over time in different scenarios. The energy storage cost equilibrium analysis economic model is then used to calculate the probability range of their future costs. Finally, the calculated equilibrium cost values ​​are used to perform a minimum cost probability analysis to obtain the probability percentage, and quantitatively compare the competitiveness of different energy storage technologies in various power application scenarios. Step 5: The balanced cost values ​​of different energy storage technologies under multi-parameter adaptation, along with their predicted future probability percentages, are visually compared and displayed sequentially through overlay terrain heat maps and stacked bar charts. This approach aims to select the energy storage technology with the best future performance in different terrains and power application scenarios, enabling cost forecasting and analysis, and improving the economic analysis of different energy storage technologies. Specifically, the designed graphical visualization allows for a comprehensive and intuitive assessment of the economic performance and market competitiveness of different energy storage technologies. First, an overlay terrain heat map is constructed by combining the balanced cost values ​​of energy storage technologies calculated using multiple parameters, including energy-to-power ratio and terrain feasibility. Parameters are initialized, technology characteristics are loaded, and economic indicators are calculated point by point. The lowest-cost technology is extracted using an optimal screening function. The heat map transparency is dynamically adjusted to visually observe subtle changes in different cost values. Second, a probabilistic stacked bar chart is used to display the economic benefits of each technology in a specific year. Simulated data is converted into cost percentages for each technology, and the average LCOS trend of the best technology is displayed on dual Y-axes to intuitively reflect the relative economic advantages of the best technology. The comprehensive graphical modeling approach provides an intuitive visualization tool to support the selection and optimization of energy storage technologies, while also assessing their cost-effectiveness and market potential.

2. A method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios as claimed in claim 1, characterized in that: A levelized cost of storage (LCOS) analysis model is established, and the following formula shows the levelized cost of storage (LCOS): （1） in, represents capital expenditure, n Indicates the age, N Indicates the system life, r represents the discount rate, represents operating cost expenditure, Charging costs, represents the end-of-life cost, Indicates the total amount of energy released.

3. The method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios according to claim 2, characterized in that: The total energy release is used to measure the total amount of energy that the energy storage system can provide throughout its entire life cycle. Furthermore, the annual degradation rate of energy storage is taken into account to optimize the method for determining the efficiency and economic efficiency of the energy storage system. The total energy release is expressed as follows: （2） in, Indicates the number of annual cycles, Indicates the depth of discharge, represents the energy capacity, represents the round-trip efficiency, represents the self-discharge rate per cycle, express Construction time in years, represents the cyclical recession rate, Represents the annual decline rate.

4. The method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios according to claim 3, characterized in that: (1) Capital expenditure ) Capital expenditure is the cost associated with the initial construction of the energy storage system, including unit power cost, unit capacity cost, and replacement cost, and is determined by summing them up as follows: （3） in, represents the cost per unit of power, Indicates rated power, represents the cost per unit capacity, Indicates rated capacity, M Indicates the number of replacements required during the life of the system, represents the replacement cost per unit power, m Indicates the current replacement, Indicates the replacement time interval; (2) Operating costs ( ) Operating costs involve the expenses required for the daily operation of energy storage facilities, including maintenance and operating costs. Taking into account the annual degradation rate, the sum of the present values ​​of operation and maintenance costs in all years is equal to the sum of the present values ​​of costs based on power and capacity and performance degradation due to cycling. The expression is as follows: （4） in, represents the operating cost per unit power, represents the operating cost per unit capacity, represents the cyclical recession rate, represents the annual decline rate; (3) End-of-life costs ( ): When a storage technology reaches the end of its designed useful life, it may incur costs for dismantling, cleaning up, or repurposing. These costs may be increased by the technology being repurposed, as expressed by: （5） in, represents the end-of-life cost per unit power, represents the end-of-life related costs per unit capacity; (4) Charging cost ( Charging cost involves the present value of the total charging costs due to electricity prices over the life of the energy storage system. First, the annual charging cost is calculated, which involves the cost of purchasing electricity for the energy storage system, multiplying this cost by the number of cycles per year, and then dividing it by the annual present value factor. The annual charging costs are then added up to obtain the total charging cost of the energy storage system over its entire life, as expressed as follows: （6） （7） in, represents the charging cost in the last year; Indicates the electricity purchase price; Indicates the construction period of the energy storage system.

5. The method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios according to claim 1, characterized in that: In step 2, an energy-to-power ratio (E / P) is proposed to characterize the energy storage discharge capacity. Based on the different defined terrain scenarios, energy storage technologies suitable for specific terrain scenarios are screened out through a Boolean array. Further, in combination with the requirements of different terrain conditions, an economic model for energy storage cost balance analysis is used to calculate the economic indicators of various energy storage technologies under different energy-to-power ratios and annual cycle times.

6. The method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios according to claim 2, characterized in that: In step 3, the application scenarios of the power system include time-of-use electricity prices, black start, grid congestion relief, energy arbitrage, peak shaving and valley filling, power quality, power supply reliability, primary response, energy time shifting, secondary response, grid upgrade delay and tertiary response; when solving the levelized energy storage cost LCOS of the corresponding energy storage technology for each of the above power application scenarios, it is necessary to calculate the specific parameters that change with time in formula (1) The rate of change over time is derived through an experience curve model that includes market growth forecasts for use in subsequent calculations.

7. The method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios according to claim 4, characterized in that: In step 4, the solution of LCOS requires the technical input parameters in formula (1) Modeling its uncertainty, for a specific parameter that varies over time By combining the change rate obtained in claim 6, it is deduced that The future value of The Monte Carlo simulation is used to predict the value of the future moment through its basic standard deviation; the central estimate and standard deviation depend on the historical data of the initial year; finally, different energy storage technologies under specific applications are analyzed. Iterative simulation calculation of the sub-levelized energy storage cost LCOS; in the Monte Carlo simulation iteration, the probability ratio of the LCOS value generated by each technology is calculated, and the lowest LCOS probability value is obtained through n calculations; （8） （9） In this framework, the three most competitive energy storage technologies selected for each specific power scenario are set as A, B, and C, and their LCOS are respectively determined by the dataset and express; is the number of technologies being compared; the obtained probability values ​​are then used for subsequent visual drawing analysis.

Images