Energy storage technology full life cycle cost evaluation method suitable for multi-dimensional scene

Abstract

The invention belongs to the technical field of energy storage economic benefit evaluation, and aims to provide support for a novel energy storage investment strategy of a power grid long-range form. The invention specifically provides an energy storage technology full life cycle cost evaluation method suitable for a multi-dimensional scene, and the economical efficiency of different energy storage technologies is comprehensively evaluated. According to the method, multiple life cycle cost factors such as capital expenditure, operation cost, end-of-life cost and charging cost are considered, and the uncertainty of cost parameters is simulated and quantified by utilizing a Monte Carlo prediction model in combination with multiple factor conditions such as an energy-to-power ratio (E / P), terrain fitness and different power application scenes; and predicting the future change trend of the energy storage technology cost. And finally, visual evaluation is carried out through refined cost analysis and a constructed superposition visualization method. The technology 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 invention belongs to the technical field of energy storage economic benefit evaluation, and in particular relates to a full life cycle cost evaluation method for energy storage technology applicable to multi-dimensional scenarios. Background Art

[0002] With the continuous development of energy systems, the research and development of energy storage technology is becoming more and more important. Energy storage technology is crucial to promoting the development of economic benefits and provides more flexibility and regulation capabilities in the power market and energy management. The existing economic analysis of power energy storage technology mostly adopts a single scenario or a short time span, lacks long-term systematic research on cost evolution, and leads to high uncertainty in the prediction results. With the increasing proportion of renewable energy generation, the instability of power supply has become an urgent problem to be solved. Traditional fossil fuel power generation has relatively little demand for large-scale energy storage in history due to its flexible dispatchability. Through the continuous innovation of new energy storage technologies, such as electrochemical 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 a clean, low-carbon, safe and efficient modern energy system, the construction and optimization planning and operation of energy storage systems require comprehensive benefit analysis and evaluation of energy storage systems. In terms of economic benefit evaluation, the cost-economic benefit evaluation of energy storage systems on the new energy side is an important topic. At the same time, the future cost competitiveness of different energy storage technologies in different power applications also varies. This shows that the economic benefits of energy storage systems are closely related to the price mechanism of the electricity market and the applicability of energy storage technology scenarios. Summary of the invention

[0003] The importance of energy storage technology is increasing day by day. Especially in the context of building a new power system, the development of efficient and economical energy storage technology has become a key direction for power companies. The present invention aims to propose a new energy storage investment strategy and an economic evaluation method for energy storage technology for the future power system form. By comprehensively considering factors such as technological progress, cost changes, performance degradation and power market price fluctuations, a more accurate and comprehensive economic evaluation is provided for investment decisions of energy storage technology. This method can be applied to the power market environment and policy background, and provide a scientific basis for power system planning and energy storage technology development. By optimizing the energy storage investment strategy, the present invention 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 promotion and application of energy storage technology, and support the power grid to maintain stable and efficient operation in response to future changes in power demand and fluctuations in electricity prices.

[0004] The present invention provides a method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios, comprising the following steps:

[0005] Step 1: Consider the cost structure of energy storage technology in each life cycle and the annual decline rate parameters of energy storage technology, and establish an economic model for energy storage cost equilibrium analysis;

[0006] Step 2: Construct an energy-to-power ratio matrix that characterizes the energy storage discharge capacity, substitute it into the economic model of the energy storage cost equilibrium analysis, and calculate the multi-parameter combination economic indicators of different energy storage technologies in combination with terrain adaptability to obtain the evaluation results of the lowest, second lowest cost, and most substitutable energy storage technologies in the current power system that can be configured under different terrain conditions, that is, the balanced cost values ​​of different energy storage technologies;

[0007] Step 3: Based on the key technical characteristics of the energy storage system, including system size, discharge duration and response time, the applicability of energy storage technologies for different power application scenarios of power generation, transmission, distribution and user end is screened, and three energy storage technologies suitable for each power application scenario are selected. Based on each power application scenario, the specific variables and their standard deviations of energy storage technologies in different years are determined, and the rate of change of variables over time is derived using an experience curve model that includes market growth forecasts;

[0008] Step 4: For specific variables in the above energy storage cost equilibrium analysis economic model, the Monte Carlo prediction model is used to simulate the standard deviation range of specific variables multiple times to predict the parameter values ​​of the three energy storage technologies that are suitable for different scenarios and change over time. The economic model of energy storage cost equilibrium analysis is then used to calculate the probability range of their future costs in turn. Finally, the calculated equilibrium cost value is used to perform the minimum cost probability analysis to obtain the probability ratio, and quantitatively compare the competitiveness of different energy storage technologies in various power application scenarios.

[0009] Step 5: By superimposing terrain heat maps and stacked bar charts, the balanced cost values ​​of different energy storage technologies under multi-parameter adaptation and their predicted probability ratios in the future are intuitively displayed and compared, so as to select the energy storage technology with the best future technical performance in different terrains and various power application scenarios, realize the cost prediction analysis of energy storage, and improve the economic analysis of different energy storage.

[0010] Furthermore, in step 1, a storage cost equilibrium analysis model is established, and the levelized cost of storage (LCOS) is as follows:

[0011]

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

[0013] Furthermore, the total energy release can be used to measure the total amount of energy that the energy storage system can provide throughout its entire life cycle, and further consider the annual decay rate of energy storage to optimize the method of judging the efficiency and economy of the energy storage system; the total energy release expression is as follows:

[0014]

[0015] Among them, Y cycles Indicates the number of annual cycles, DoD indicates 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 cyclical recession rate, D t Represents the annual decline rate.

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

[0017] Capital expenditures are the costs associated with the initial construction of an energy storage system, including unit power cost, unit capacity cost, and replacement cost, and are determined by summing them up as follows:

[0018]

[0019] Among them, C p represents the cost per unit of power, P cap Indicates 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 system life, and C p-r represents the replacement cost per unit power, m represents the current replacement, t r Indicates the replacement time interval;

[0020] (2) Operating costs (C OPEX )

[0021] The operating cost involves the expenses required for the daily operation of the energy storage facility, including maintenance and operation costs. Taking into account the annual decline rate, the sum of the present values ​​of the operation and maintenance costs in all years is equal to the sum of the present values ​​of the costs based on power and capacity and the performance degradation caused by the cycle. The expression is as follows:

[0022]

[0023] Among them, C p-OM represents the operating cost per unit power, C e-OM represents the operating cost per unit capacity, D cyc represents the cyclical recession rate, D t represents the annual decline rate;

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

[0025] When a storage technology reaches the end of its designed useful life, it may incur costs for removal, cleanup or reuse. These costs may be increased by the reuse of the technology, as expressed as follows:

[0026]

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

[0028] (4) Charging cost (C charge ): Charging cost involves the present value of the charging cost of the energy storage system due to electricity prices during its service life. First, calculate the annual charging cost, 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; then add up the annual charging costs to get the total charging cost of the energy storage system over its entire service life, as shown below:

[0029]

[0030] Among them, C Charge_last represents the charging cost in the last year; P buy Indicates the electricity purchase price; t idx Represents the construction period of the energy storage system.

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

[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, 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 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 in the previous article, we can deduce its C CAPEX The future value of p , C e , C P-OM , C e-OM Through its basic standard deviation, the Monte Carlo simulation predicts the value at future moments; the central estimate and standard deviation depend on the historical data of the initial year. Finally, the simulation iteration calculation of the balanced energy storage cost LCOS is performed n times for different energy storage technologies under specific applications; the probability ratio of the LCOS value generated by each technology is calculated in the Monte Carlo simulation iteration, and the probability value of the lowest LCOS is obtained through n 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 datasets {a1; a2; …; a​n},{b1; b2; ...; b n}, and {c1; c2; ...; c n} represents, N is the number of technologies being compared. The obtained probability values ​​are then used for subsequent visual drawing analysis.

[0038] Further, based on the in-depth discussion of the above analysis, we turned to the visualization of empirical data using the designed code. The code designed in this scheme reads the energy storage technology parameter data, combines the annual cycle number and energy-to-power ratio, calculates the economic indicators of each energy storage technology, and generates a heat map for visualization. First, the necessary parameters are initialized and the energy storage technology characteristics are loaded from the external file. Then, the core calculation function is called through a nested loop to calculate the economic indicators of each energy storage technology under different parameter combinations point by point to form a two-dimensional matrix; then, the economic indicators of each technology are compared by defining the optimal screening function, and the lowest cost energy storage technology and the second lowest cost technology are extracted. At the same time, their cost differences are calculated to dynamically adjust the transparency of the heat map; then, color coding is used to represent the optimality of different energy storage technologies, and the differences between technologies are highlighted by transparency changes. The combination of color mapping and transparency is used to achieve color coverage in the heat map. These images comprehensively show the cost performance of energy storage technologies under different operating conditions and terrain restrictions, and provide intuitive visualization tools to support the selection and optimization analysis of energy storage technologies.

[0039] (2) In order to evaluate the economic competitiveness of different energy storage technologies, this study uses probability stacked bar charts to show the economic performance and market competitiveness of different energy storage technologies in a specific year. By using the calculated probability of the lowest balanced cost for each technology, it is converted into the cost probability ratio of each technology. The height of the different colored parts inside each bar in the figure is proportional to the proportion of the lowest cost probability of different energy storage technologies. The relative advantages of different technologies in economic performance are intuitively demonstrated. The average balanced cost change trend of the best energy storage technology is displayed by the double Y-axis. The cost-effectiveness and market potential of the energy storage technology are evaluated by the relative size of each part in the stacked bar chart and the trend over time. By constructing a predicted probability stacked bar chart, we can have a more comprehensive understanding of the feasibility and sustainability of the energy storage system, thereby optimizing the economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0042] Figure 3Thermal maps of suboptimal LCOS energy storage technologies under different terrain conditions;

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

[0044] Figure 5 LCOS value range bar chart;

[0045] Figure 6 LCOS value probability distribution diagram;

[0046] Figure 7 LCOS forecast for various energy storage technologies (2020-2055) stacked bar chart;

[0047] Figure 8 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0048] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0049] refer to Figure 1 , this patent provides a method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios. The solution considers the impact of the annual decay rate and uses the method of equalizing the energy storage cost to calculate the cost of different energy storage technologies, so as to achieve a comprehensive economic evaluation of factors. Secondly, the patent constructs a model of fixed electricity prices and proposes to analyze energy storage technologies 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-effectiveness under a specific discharge cycle. Then, a cycle duration matrix is ​​established based on E / P and the annual cycle number to quantify the cost per unit of discharged electricity over the entire life cycle of a specific energy storage technology. Finally, the patent visualizes the economics of energy storage technology, displays the equalized energy storage costs of different energy storage technologies through color coding, and converts complex calculation results into intuitive visual information by generating separate charts for different terrain conditions, which can more efficiently display the economics of different energy storage technologies under different conditions.

[0050] Based on the comprehensive analysis of multiple scenarios, this study adopts the experience curve analysis framework to predict the future evolution trend of energy storage technology costs. The study first systematically collects and organizes the historical cost data of various energy storage technologies such as lithium-ion batteries, sodium-sulfur batteries, pumped storage, etc. in various application scenarios such as energy arbitrage, time-of-use pricing, and energy time shifting, laying a solid empirical foundation for subsequent forecasting work. Subsequently, for each energy storage technology, the study further analyzes its full life cycle cost in various application scenarios, and uses probability analysis methods to accurately calculate the probability of the technology cost reaching the optimal level and the corresponding LCOS value. In order to further improve the accuracy and robustness of the energy storage technology cost prediction results, this patent combines Monte Carlo simulation technology with the experience curve model for comprehensive evaluation. Specifically, for technologies that lack direct experience curve references, this study uses analogy reasoning based on the historical data and industry trends of the relevant technology experience curve data set, referring to the development trends of similar technologies or similar fields, and scientifically predicts its future cost reduction trends. In addition, the Monte Carlo simulation technology is introduced to simulate a large number of random combinations within the 80% confidence interval of the normal distribution of parameters, and multiple LCOS estimates are performed for each technology and its application scenario to fully consider the uncertainty inherent in the technology cost. In-depth exploration of the costs of various technologies is achieved. The application of this technology has greatly enhanced the precision of this forecast. By depicting the possibility of various technologies showing the lowest LCOS in different application scenarios, a detailed probabilistic analysis of the economic efficiency of energy storage is achieved, which clearly provides detailed data support and scientific decision-making references for the future strategic deployment and long-term development of power storage technology. The specific plan is as follows:

[0051] Step 1: Consider the cost structure of energy storage technology in each life cycle and the annual decline rate parameters of energy storage technology, and establish an economic model for energy storage cost equilibrium analysis;

[0052] Step 2: Construct an energy-to-power ratio matrix that characterizes the energy storage discharge capacity, substitute it into the economic model of the energy storage cost equilibrium analysis, and calculate the multi-parameter combination economic indicators of different energy storage technologies in combination with terrain adaptability to obtain the evaluation results of the lowest, second lowest cost, and most substitutable energy storage technologies in the current power system that can be configured under different terrain conditions, that is, the balanced cost values ​​of different energy storage technologies;

[0053] Step 3: Based on the key technical characteristics of the energy storage system, including system size, discharge duration and response time, the applicability of energy storage technologies for different power application scenarios of power generation, transmission, distribution and user end is screened, and three energy storage technologies suitable for each power application scenario are selected. Based on each power application scenario, the specific variables and their standard deviations of energy storage technologies in different years are determined, and the rate of change of variables over time is derived using an experience curve model that includes market growth forecasts;

[0054] Step 4: For specific variables in the above energy storage cost equilibrium analysis economic model, the Monte Carlo prediction model is used to simulate the standard deviation range of specific variables multiple times to predict the parameter values ​​of the three energy storage technologies that are suitable for different scenarios and change over time. The economic model of energy storage cost equilibrium analysis is then used to calculate the probability range of their future costs in turn. Finally, the calculated equilibrium cost value is used to perform the minimum cost probability analysis to obtain the probability ratio, and quantitatively compare the competitiveness of different energy storage technologies in various power application scenarios.

[0055] Step 5: By superimposing terrain heat maps and stacked bar charts, the balanced cost values ​​of different energy storage technologies under multi-parameter adaptation and their predicted probability ratios in the future are intuitively displayed and compared, so as to select the energy storage technology with the best future technical performance in different terrains and various power application scenarios, realize the cost prediction analysis of energy storage, and improve the economic analysis of different energy storage.

[0056] Specifically,

[0057] This solution calculates the energy storage costs of various energy storage technologies by establishing an energy storage cost equilibrium analysis model. Based on the analysis of the whole life cycle concept, it is necessary to consider each life cycle part when calculating the energy storage cost. Therefore, LCOS can be determined by comparing the sum of the present values ​​of various costs with the sum of the present values ​​of the total energy release. The expression is as follows:

[0058]

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

[0060] The total energy release 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 decay rate of energy storage, the method of optimizing the efficiency and economy of the energy storage system can be used. The total energy release expression is as follows:

[0061]

[0062] Among them, Y cycles Indicates the number of annual cycles, DoD indicates the depth of discharge, E cap represents the energy capacity, η RT represents the round-trip efficiency, η selfrepresents the self-discharge rate per cycle, t c represents the construction time in years, D cyc represents the cyclical recession rate, D t Represents the annual decline rate.

[0063] 3. Analysis of various cost expenditures

[0064] In this step, the life cycle costs of energy storage technologies are broken down and analyzed.

[0065] (1) Capital expenditure CAPEX )

[0066] Capital expenditures are the costs associated with the initial construction of an energy storage system, including unit power cost, unit capacity cost, and replacement cost, and are determined by summing them up as follows:

[0067]

[0068] Among them, C p represents the cost per unit of power, P cap Indicates 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 system life, and C p-r represents the replacement cost per unit power, m represents the current replacement, t r Indicates the replacement time interval;

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

[0070] The operating cost involves the expenses required for the daily operation of the energy storage facility, including maintenance and operation costs. Taking into account the annual decline rate, the sum of the present values ​​of the operation and maintenance costs in all years is equal to the sum of the present values ​​of the costs based on power and capacity and the performance degradation caused by the cycle. The expression is as follows:

[0071]

[0072] Among them, C p-OM represents the operating cost per unit power, C e-OM represents the operating cost per unit capacity, D cyc represents the cyclical recession rate, D t represents the annual decline rate;

[0073] (3) End of life cost (C EoL ):

[0074] When a storage technology reaches the end of its designed useful life, it may incur costs for removal, cleanup or reuse. These costs may be increased by the reuse of the technology, as expressed as follows:

[0075]

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

[0077] (4) Charging cost (C charge ): Charging cost involves the present value of the charging cost of the energy storage system due to electricity prices during its service life. First, calculate the annual charging cost, 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; then add up the annual charging costs to get the total charging cost of the energy storage system over its entire service life, as shown below:

[0078]

[0079] Among them, C Charge_last represents the charging cost in the last year; P buy Indicates the electricity purchase price; t idx Represents the construction period of the energy storage system.

[0080] This patent proposes the energy-to-power ratio (E / P) to characterize the energy storage discharge capacity, which measures the time the energy storage system can continue to supply power when it is discharged at full power. Among them, E and P are the rated capacity (E / P) in formula (3) cap )、Rated power(P cap Specifically, a higher E / P ratio means that the energy storage system can provide electricity for a longer period of time, which is suitable for long-term energy regulation and power supply, while at 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, this patent defines several different terrain scenarios, including "all sites", "plains", "caves", "mountains", "coal transformation" and "natural gas transformation", and uses Boolean arrays to screen out energy storage technologies suitable for specific terrain scenarios (as shown in Table 1).

[0082] Pumped Storage Compressed air energy storage Lithium-ion battery Hydrogen Energy Vanadium flow battery Sodium-sulfur battery Lead-acid batteries All Sites √ √ √ √ √ √ √ Plains √ √ √ √ cave √ √ √ Mountain √ √ Coal transformation √ √ √ √ √ Natural gas transformation √ √ √ √

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

[0084] In Table 1, the energy storage technology applicable to each terrain scenario is indicated by a “√” check mark.

[0085] On this basis, this patent constructs a series of products with different energy-to-power ratios and annual cycles (Y cycles ) is a two-dimensional array used to calculate the LCOS value of each energy storage technology at different energy levels and usage 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-to-power ratios and annual cycle times, thereby evaluating the economic performance of various energy storage technologies under different terrain and infrastructure conditions.

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

[0087] The comparison table of the three energy storage technologies with the best application effects is shown in Table 2, which predicts and evaluates the adaptive energy storage in power application scenarios and analyzes its future benefits; quantitatively evaluates the economic feasibility of energy storage technology, introduces LCOS as an important indicator, considers the full life cycle cost of energy storage technology, and calculates the LCOS of each technology in a specific application using formula (1).

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

[0089] Pumped Storage Compressed air energy storage Lithium-ion battery Hydrogen Energy Vanadium flow battery Sodium-sulfur battery Lead-acid batteries Time-of-use electricity price √ √ √ Black Start √ √ √ Relieve grid congestion √ √ √ Energy Arbitrage √ √ √ Peak reduction and valley filling √ √ √ Power quality √ √ √ Power supply reliability √ √ √ One response √ √ √ Energy Time Shift √ √ √ Secondary Response √ √ √ Grid upgrades delayed √ √ √ Three responses √ √ √

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

[0091] In Table 2, the energy storage technology applicable to each application scenario is indicated by a “√” check mark.

[0092] However, the uncertainty of future costs poses a challenge to the evaluation of technical economics. The solution of LCOS requires the technical input parameter C in formula (1) CAPEX , C p , C e , C P-OM , C e-OM Simulating its uncertainty, for C varying with time CAPEX The C is derived by using the experience curve model including market growth forecast and the combined standard deviation and Monte Carlo simulation. CAPEX The future value of C is the parameter whose value does not change significantly over time. p , C e , C P-OM , C e-OM The uncertainty of their central estimates and standard deviations at future moments is simulated by Monte Carlo only through their basic standard deviations, depending on the data of the initial year.

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

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

[0095] Table 3 Energy storage technology C CAPEX Parameter year-on-year change rate

[0096] Furthermore, the future time C is derived using the following formula (6): CAPEXT value, and C at time T CAPEXT C is the initial moment CAPEX0 Multiply the rate of change C% at time T and use this as the formula (8) in the following text to calculate C CAPEX The central estimate (μ) at :

[0097] C CAPEXT =C CAPEX0 ×C% (8)

[0098] In order to evaluate the comprehensive uncertainty of investment cost reduction, the uncertainty of cost estimation and cost reduction is combined, and the standard deviation (σ) is used to characterize the uncertainty of investment cost parameters, which is used as the formula (8) in the following text to calculate C CAPEX The standard deviation (σ) when : The formula is as follows:

[0099]

[0100] where x Parameter That is, the investment cost C in 2020 CAPEX0 ,y ％Reduction is the percentage of relative investment cost reduction from 2020 to 2055, i.e. the rate of change C%, σ Parameter is the standard deviation of investment cost, σ ％Reduction is the standard deviation of the percentage reduction of relative investment cost. The combined standard deviation is shown in Table 4.

[0101] Pumped Storage 0％ 0％ 1％ 3％ 6％ 8％ 10％ 12％ Compressed air energy storage 0％ 0％ 1％ 3％ 6％ 8％ 10％ 12％ Lithium-ion battery 0％ 12％ 14％ 13％ 12％ 10％ 10％ 9％ Hydrogen Energy 0％ 3％ 6％ 8％ 10％ 11％ 10％ 10％ Vanadium flow battery 0％ 15％ 16％ 14％ 12％ 11％ 10％ 9％ Lead-acid batteries 0％ 5％ 6％ 5％ 5％ 4％ 4％ 5％ Sodium-sulfur battery 0％ 3％ 6％ 8％ 10％ 11％ 10％ 10％

[0102] Table 4 Energy storage technology C CAPEX Combined standard deviation

[0103] For technologies that lack experience curve data and corresponding cost forecasts, their relative cost reduction and standard deviation mainly come from indirect inference of the cost reduction trend and uncertainty (i.e., standard deviation) of the target technology by identifying and analyzing related technologies or similar technologies that have similar technical characteristics, market positioning, or application prospects as the target technology. The application of this method is based on the similarity or correlation between these related technologies or similar technologies in terms of cost reduction potential and technologies that lack experience curve data, thus providing a reasonable basis for cost reduction and standard deviation forecasts for the latter to a certain extent.

[0104] Then, the technical input parameter C CAPEX , C p , C e , C P-OM , C e-OM Set as technical parameter (x) and use Monte Carlo simulation method to simulate its uncertainty. CAPEX The value is combined with the central estimate (μ) and standard deviation (σ) obtained by formula (8) and (9) above, and the value is simulated to determine the change of the input parameter within the standard deviation range assuming a normal distribution. Parameter C p , C e , C P-OM , C e-OM The uncertainty of future moments is simulated by Monte Carlo only through its basic standard deviation. 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 article is substituted into the LCOS calculation formula (1), and n LCOS simulation iterations are performed for different energy storage under specific applications. 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 set as A, B, and C, and their LCOS are respectively determined by the data set {a1; a2; ...; a n},{b1; b2; ...; b n}, and {c1; c2; ...; c n Formula (11) indicates that for the i-th simulation of technology A, its LCOS value a i is the probability of the minimum value in a single simulation. This probability is calculated by a i Smaller than the LCOS value b of all other technologies 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 smallest in all n simulations, and then dividing it by the total number of simulations n. N Here, N is the number of technologies being compared. The summation symbol in the formula means summing the number of times the LCOS value of technology A is the smallest in all n simulations and then dividing by n. N To obtain the final probability. 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 k ,k∈[1;n])·P(a i <c k ,k∈[1;n])·… (11)

[0108]

[0109] Visual display part:

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

[0111] Figure 1 ​The following table shows the energy storage technologies with the best LCOS under different combinations of energy-to-power ratio and annual cycle number conditions in different terrain scenarios based on a fixed electricity price (100 yuan / MWh) and a specific return on investment (ROI = 0.08). The six figures on the left show the economic trends of different energy storage technologies under various terrain conditions. The horizontal axis is the annual cycle number, that is, the number of times the energy storage system is used each year, and the vertical axis is the energy-to-power ratio. The heat map on the right shows the comparison between the sub-economical energy storage technology and the most economical energy storage technology, and the relative differences of these energy storage technologies compared with the most economical technology are reflected according to the changes in color depth.

[0112] (2) Draw a heat map of the LCOS value of the most economical energy storage technology under different terrain conditions.

[0113] Figure 2 The LCOS values ​​of the most economical energy storage technology under different terrain conditions, different energy-to-power ratios and annual cycle times are shown based on a fixed electricity price of 100 yuan / MWh and an 8% return on investment. As the annual cycle times increase, the LCOS shows a significant downward trend, and short-term and high-frequency energy storage technology applications can achieve lower LCOS in various terrain and technical scenarios.

[0114] (3) Draw thermal maps of suboptimal LCOS energy storage technology under different terrain conditions.

[0115] Figure 3 The following table shows the energy storage technologies with suboptimal LCOS under different combinations of energy-to-power ratio and annual cycle number conditions in different terrain scenarios based on a fixed electricity price (100 yuan / MWh) and a specific return on investment (ROI = 0.08). The six figures on the left show the changing trends of suboptimal energy storage technologies under various terrain conditions, with the horizontal axis being the annual cycle number, i.e. the number of times the energy storage system is used each year, and the vertical axis being the energy-to-power ratio. The heat map on the right shows the names of energy storage technologies corresponding to different colors.

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

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

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

[0119] Figure 5 The "LCOS Forecast (2020-2055) for Different Application Scenarios" shows in detail the forecast range of LCOS for the three most competitive energy storage technologies in specific application scenarios in the form of a bar chart. The chart covers the time range from 2020 to 2055 and distinguishes 12 different application scenarios (only three of them are used as examples in the chart). In addition, Figure 5 Through the Monte Carlo simulation method, uncertainty ranges are provided for the LCOS predictions of each technology, thereby more comprehensively reflecting the volatility and reliability of future cost forecasts.

[0120] (6) Probability distribution diagram of LCOS values ​​for specific application scenarios and time (taking energy arbitrage in 2020 as an example);

[0121] Figure 6 The results of LCOS (Levelized Cost of Storage) calculations based on Monte Carlo simulation techniques are presented. The simulation performs n independent LCOS calculations for a specific technology and different years. A random technique with parameter-attributed normal distribution is used to construct an 80% confidence interval. The aim is to evaluate the uncertainty of LCOS through a large number of random samplings and quantify the impact of changes in technology input parameters on the LCOS forecast value. Figure 6 It not only presents the predicted value of LCOS, but also displays the uncertainty range of the predicted results in the form of confidence intervals, providing decision makers with more comprehensive and in-depth insights, helping them make more scientific and reasonable judgments when faced with technology selection and investment decisions.

[0122] (7) Stacked bar chart of LCOS forecasts (2020-2055) for various energy storage technologies in different application scenarios;

[0123] Figure 7Detailed display of LCOS (Levelized Cost of Storage) forecast application requirements and probability distribution of different technologies providing the lowest LCOS, with special emphasis on the average LCOS of the most cost-effective technology (represented by the black line). This figure not only reflects the frequency of each technology achieving the lowest LCOS, taking into account the uncertainty range, but also comprehensively considers the lowest LCOS probability forecast for all 7 energy storage technologies and 12 application scenarios. The analysis results show that battery technology has the highest probability 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 situations with discharge time less than 4 hours and fewer than 300 cycles (such as power quality and black start). For application scenarios with longer discharge duration and higher cycle requirements (such as power supply reliability for more than 4 hours or secondary response and bill management for more than 300 cycles), vanadium flow batteries have not been the most likely option to provide the lowest LCOS, but they still maintain their competitiveness.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios, characterized in that: The following steps are included: Step 1: Consider the cost structure of energy storage technology in each life cycle and the annual decline rate parameters of energy storage technology, and establish an economic model for energy storage cost equilibrium analysis; Step 2: Construct an energy-to-power ratio matrix that characterizes the energy storage discharge capacity, substitute it into the economic model of the energy storage cost equilibrium analysis, and calculate the multi-parameter combination economic indicators of different energy storage technologies in combination with terrain adaptability to obtain the evaluation results of the lowest, second lowest cost, and most substitutable energy storage technologies in the current power system that can be configured under different terrain conditions, that is, the balanced cost values ​​of different energy storage technologies; Step 3: Based on the key technical characteristics of the energy storage system, including system size, discharge duration and response time, the applicability of energy storage technologies for different power application scenarios of power generation, transmission, distribution and user end is screened, and three energy storage technologies suitable for each power application scenario are selected. Based on each power application scenario, the specific variables and their standard deviations of energy storage technologies in different years are determined, and the rate of change of variables over time is derived using an experience curve model that includes market growth forecasts; Step 4: For specific variables in the above energy storage cost equilibrium analysis economic model, the Monte Carlo prediction model is used to simulate the standard deviation range of specific variables multiple times to predict the parameter values ​​of the three energy storage technologies that are suitable for different scenarios and change over time. The economic model of energy storage cost equilibrium analysis is then used to calculate the probability range of their future costs in turn. Finally, the calculated equilibrium cost value is used to perform the minimum cost probability analysis to obtain the probability ratio, and quantitatively compare the competitiveness of different energy storage technologies in various power application scenarios. Step 5: By superimposing terrain heat maps and stacked bar charts, the balanced cost values ​​of different energy storage technologies under multi-parameter adaptation and their predicted probability ratios in the future are intuitively displayed and compared, so as to select the energy storage technology with the best future technical performance in different terrains and various power application scenarios, realize the cost prediction analysis of energy storage, and improve the economic analysis of different energy storage.

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: The energy storage cost equilibrium analysis model is established, and the levelized cost of storage (LCOS) is shown as follows: Among them, C CAPEX represents capital expenditure, n represents the number of years, N represents the system life, r represents the discount rate, C OPEX represents the operating cost expenditure, C charge represents the charging cost, C EoL represents the end-of-life cost, E discharged Represents the total amount of energy released.

3. A method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios as claimed in claim 2, characterized in that: The total energy release can be used to measure the total amount of energy that the energy storage system can provide throughout its life cycle. The annual decay rate of energy storage is further considered to optimize the method of judging the efficiency and economy of the energy storage system. The total energy release expression is as follows: Among them, Y cycles Indicates the number of annual cycles, DoD indicates the depth of discharge, E 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 cyclical recession rate, D t Represents the annual decline rate.

4. A method for evaluating the full life cycle cost of energy storage technology applicable to multi-dimensional scenarios as claimed in claim 2, characterized in that: (1) Capital expenditure CAPEX ) Capital expenditures are the costs associated with the initial construction of an energy storage system, including unit power cost, unit capacity cost, and replacement cost, and are determined by summing them up as follows: Among them, C p represents the cost per unit of power, P cap Indicates 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 system life, and C p-r represents the replacement cost per unit power, m represents the current replacement, t r Indicates the replacement time interval; (2) Operating costs (C OPEX ) The operating cost involves the expenses required for the daily operation of the energy storage facility, including maintenance and operation costs. Taking into account the annual decline rate, the sum of the present values ​​of the operation and maintenance costs in all years is equal to the sum of the present values ​​of the costs based on power and capacity and the performance degradation caused by the cycle. The expression is as follows: Among them, C p-OM represents the operating cost per unit power, C e-OM represents the operating cost per unit capacity, D cyc represents the cyclical recession rate, D t represents the annual decline rate; (3) End of life cost (C EoL ): When a storage technology reaches the end of its designed useful life, it may incur costs for removal, cleanup or reuse. These costs may be increased by the reuse of the technology, as expressed as follows: Among them, EoL p represents the end-of-life cost per unit power, EoL e represents the end-of-life related costs per unit of capacity; (4) Charging cost (C charge ): Charging cost involves the present value of the charging cost due to electricity prices during the service 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, multiplied by the number of cycles per year, and then divided by the annual present value factor. Then, the annual charging cost is added up to obtain the total charging cost of the energy storage system over its entire service life. The expression is as follows: Among them, C Charge_last represents the charging cost in the last year; P buy Indicates the electricity purchase price; t idx Represents 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) characterizing the energy storage discharge capacity is proposed. According to the different terrain scenarios defined, energy storage technologies suitable for specific terrain scenarios are screened out through a Boolean array. Further combined with the requirements of different terrain conditions, the economic model of energy storage cost balance analysis calculates the economic indicators of various energy storage technologies under different energy-to-power ratios and annual cycle times.

6. 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: In step 3, the application scenarios of the power system include time-of-use electricity price, 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 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.

7. 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: In step 4, the solution of LCOS requires the technical input parameter C in formula (1) CAPEX , C 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 in claim 6, it is deduced that C CAPEX The future value of p , C e , C P-OM , C e-OM Through its basic standard deviation, the Monte Carlo simulation predicts the value at the future moment; the central estimate and standard deviation depend on the historical data of the initial year; finally, the simulation iteration calculation of the levelized energy storage cost LCOS is performed n times for different energy storage technologies under specific applications; the probability ratio of the LCOS value generated by each technology is calculated in the Monte Carlo simulation iteration, and the probability value of the lowest LCOS is obtained through n calculations; P(a i =minLCOS)=P(a i <b k ,k∈[1;n])·P(a i <c k ,k∈[1;n])·… (8) 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 datasets {a1; a2; …; a n },{b1; b2; ...; b n }, and {c1; c2; ...; c n } represents; N is the number of technologies being compared; and the obtained probability values ​​are then used for subsequent visual drawing analysis.

8. 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 In step 5, the economic performance and market competitiveness of different energy storage technologies are comprehensively and intuitively evaluated through the designed graphic visualization; first, a superimposed terrain heat map is constructed by combining the balanced cost value of energy storage technology calculated by multiple parameters such as energy-to-power ratio and terrain feasibility. By initializing parameters, loading technical characteristics, calculating economic indicators point by point, and using the optimal screening function to extract the lowest cost technology, the transparency of the heat map is dynamically adjusted to intuitively observe the subtle changes in different cost values; secondly, the study uses a probability stacked bar chart to show the economic benefits of each technology in a specific year, converts the simulation data into the proportion of each technology cost, and displays the average LCOS change trend of the best technology through the double Y-axis to intuitively reflect the relative advantage of economic performance; the comprehensive graphical modeling form provides an intuitive visualization tool to support the selection and optimization analysis of energy storage technologies, while evaluating their cost-effectiveness and market potential.

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