New energy storage system capacity configuration method based on whole life cycle cost optimization

Abstract

The present application proposes a new energy storage system capacity configuration method based on full life cycle cost optimization, relates to the technical field of energy storage capacity configuration optimization, and establishes an annualized net income objective function by respectively constructing an annualized investment cost model, an annualized operation and maintenance cost model and an annualized total income model related to the capacity configuration of the energy storage system. Based on the minimum energy capacity constraint, the actual operation efficiency constraint and the annual throughput energy constraint required to meet the fluctuation suppression of new energy output of the energy storage system, the annualized net income objective function is converted to obtain the converted annualized net income objective function; based on the converted annualized net income objective function, the unit capacity annual net income function is obtained, and the energy storage capacity configuration is carried out based on the unit capacity annual net income objective function to obtain the optimal capacity configuration result. The present application fully considers the implicit influence of the full life cycle of energy storage, improves the scientificity and reliability of energy storage capacity configuration, and further improves the construction quality of new energy.

Description

Technical Field

[0001] This invention relates to the technical field of energy storage capacity configuration optimization, and more specifically, to a method for capacity configuration of new energy storage systems based on full life cycle cost optimization. Background Technology

[0002] With the accelerated transformation of the global energy structure and the continuous expansion of the grid connection scale of large-scale renewable energy (such as wind power and photovoltaics), the capacity configuration optimization of energy storage systems, as a core supporting technology for improving grid stability and economy, has become a key bottleneck in the industry.

[0003] Current research on energy storage capacity configuration generally focuses solely on initial investment costs as the optimization objective, neglecting the full lifecycle costs of energy storage operation and maintenance, cycle degradation, and decommissioning. This approach leads to an underestimation of actual operating costs, distorting the economic assessment of energy storage configuration and causing investment decisions to deviate from actual returns. Traditional energy storage capacity optimization models also fail to consider efficiency losses during charging and discharging, typically employing constant efficiency or linear degradation assumptions. However, in actual operation, energy storage efficiency declines non-linearly with the number of cycles, and the effective capacity of the energy storage system decreases with increasing cycle count. This error results in significant capacity configuration deviations, potentially leading to overcharging / under-distribution risks. Furthermore, existing methods require parameter recalibration for different regional climates (e.g., cold regions vs. tropical regions) and power source types (e.g., photovoltaic vs. wind power), severely hindering the large-scale application of energy storage. Summary of the Invention

[0004] To address the problem that existing technologies do not fully consider the full life cycle cost in energy storage system capacity configuration, resulting in poor scientificity and low reliability of capacity configuration, this invention proposes a new energy storage system capacity configuration method based on full life cycle cost optimization. This method aims to fully consider the implicit impact of energy storage throughout its entire life cycle, improve the scientificity and reliability of energy storage capacity configuration, and further enhance the construction quality of new energy.

[0005] To achieve the above-mentioned technical effects, the technical solution of the present invention is as follows: Firstly, this application proposes a capacity configuration method for new energy storage systems based on full life-cycle cost optimization, including the following steps: S1: Based on the acquired energy storage data, construct annualized investment cost model, annualized operation and maintenance cost model and annualized total revenue model related to the capacity configuration of the energy storage system; S2: Based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model, establish an annualized net revenue objective function; S3: Based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth the fluctuation of new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. S4: Based on the transformed annualized net income objective function, obtain the annual net income function per unit capacity, and configure the energy storage capacity based on the annual net income function per unit capacity to obtain the optimal capacity configuration result.

[0006] Preferably, the acquired energy storage data includes energy storage installed capacity. New energy hourly output sequence Unit capacity investment cost residual value coefficient Design life Annual maintenance cost per unit capacity Effective round-trip efficiency of energy storage system Peak-valley electricity price difference Annual effective operating days Demand response subsidies Electricity demand reduction and the adjustment ratio that conventional power supplies can handle. .

[0007] Preferably, in S1, the annualized investment cost model, annualized operation and maintenance cost model, and annualized total revenue model related to the energy storage system capacity configuration are constructed as follows: Based on unit capacity investment cost residual value coefficient and design life The initial investment in energy storage is converted into an equivalent annualized investment cost using the annualized capital recovery factor (ACRF), and an annualized investment cost model is constructed. Satisfies the expression:

[0008] in, For the initial investment in energy storage, For the rated energy capacity of energy storage, The discount rate; Based on annual maintenance cost per unit capacity Construct an annualized operation and maintenance cost model Satisfies the expression:

[0009] in, This refers to the rated energy capacity of the energy storage. Based on the effective round-trip efficiency of energy storage systems Peak-valley electricity price difference Annual effective operating days Calculate annual peak-valley arbitrage returns The expression is:

[0010] in, This refers to the net discharge amount per cycle. This represents the annual arbitrage return coefficient per unit capacity. This refers to the rated energy capacity of the energy storage. Demand response subsidies Electricity demand reduction and annual peak-valley arbitrage income Construct an annualized total return model Satisfies the expression:

[0011] in, This represents the annual comprehensive return coefficient per unit capacity.

[0012] Preferably, in S2, an annualized net income objective function is established based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model. Satisfies the expression: .

[0013] Preferably, in S3, based on the minimum energy capacity constraint, actual operating efficiency constraint, and annual throughput energy constraint that the energy storage system needs to satisfy to smooth out fluctuations in new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. The process is as follows: S31: Model the fluctuations in new energy output and obtain the volatility. and duration of fluctuation ; S32: Based on the adjustment ratio that a conventional power supply can handle. Volatility and duration of fluctuation Calculate the energy that needs to be smoothed for energy storage. The expression is:

[0014] in, This indicates the proportion that energy storage needs to bear. This indicates the energy required to smooth out a single high-fluctuation event. , This represents the corrected capacity factor. , Represents the capacity factor. Represents the capacity factor. Indicates the energy type efficiency factor; S33: Based on the energy that needs to be mitigated in the aforementioned energy storage The minimum capacity requirement is calculated by considering the efficiency loss of the energy storage system during charging and discharging. The expression is:

[0015] in, This represents the initial round-trip efficiency. This represents the efficiency degradation correction factor; S34: Energy needs to be mitigated based on energy storage requirements Calculating the energy fluctuation smoothing required for energy storage The expression is:

[0016] in, This indicates the number of highly volatile events that occur in a year; S35: Calculate the annual throughput energy for arbitrage, the expression is: ,in, This indicates the number of complete cycles per day for the energy storage system; S36: Based on annual throughput energy of arbitrage Energy storage needs to smooth out energy fluctuations. Calculate annual throughput energy The expression is:

[0017] S37: Based on annual throughput energy Calculate the actual operating efficiency The expression is:

[0018] S38: Based on minimum capacity requirements Actual operating efficiency Energy intake and output Arbitrage return coefficient per unit capacity Perform the transformation to satisfy the expression:

[0019] The transformed annualized total return model Satisfying the expression:

[0020] S39: Based on the transformed annualized total return model The annualized net return objective function is transformed, and the expression for the transformed annualized net return objective function is as follows:

[0021] in, This represents the annual comprehensive return coefficient per unit capacity after conversion.

[0022] Preferably, in S31, the fluctuation of new energy power output is modeled to obtain the fluctuation rate. and duration of fluctuation The process is as follows: S311: Based on the hourly output sequence of new energy sources The volatility of new energy power output is standardized to obtain the volatility. ; S312: Use the Weibull distribution to fit all data exceeding the fluctuation threshold. volatility Satisfies the expression:

[0023] in, For shape parameters, and ; For scale parameters, and ; S313: For the Weibull distribution Seasonally adjusted quantile volatility yields the seasonally adjusted... Quantile volatility Satisfies the expression:

[0024] in, Before seasonal adjustment Quantile volatility Seasonal volatility factor; S314: Define high volatility events, perform continuous segment detection on the high volatility events, and obtain the duration sequence of the high volatility events. ,in, i Indicates order; S315: Based on the duration sequence of high volatility events and seasonal volatility factors The seasonally corrected duration of the fluctuation was calculated. The expression is:

[0025] in, Indicates the duration of fluctuations before seasonal correction. This indicates the expectation value.

[0026] Preferably, based on the transformed annualized net return objective function, the annual net return function per unit capacity is obtained, and the process is as follows: Based on the transformed annualized net income objective function The function π, representing the annual net income per unit capacity, is expressed as: .

[0027] Preferably, the optimal capacity allocation result is obtained by configuring energy storage capacity based on the objective function of annual net income per unit capacity. The process is as follows: Based on the annual net return per unit capacity, the optimal energy storage capacity is determined. Satisfies the expression:

[0028] If the annual net revenue per unit capacity π > 0, meaning the revenue exceeds the cost, then the capacity limit will be adjusted. As the optimal capacity allocation; if the annual net revenue per unit capacity π < 0, i.e., the revenue is less than the cost, then the lower limit of the capacity will be set. As the optimal configuration capacity.

[0029] Preferably, if the annual net income per unit capacity π = 0, that is, the income equals the cost, then any The capacity value is taken as the optimal configuration capacity.

[0030] Secondly, this application proposes a system for configuring the capacity of a new energy storage system based on full life-cycle cost optimization, used to implement the method, including: The cost model building module is used to build annualized investment cost models, annualized operation and maintenance cost models, and annualized total revenue models related to the capacity configuration of energy storage systems, based on the acquired energy storage data. The net income function construction module is used to establish an annualized net income objective function based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total income model. The revenue correction module is used to transform the annualized net revenue objective function based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth out the fluctuations in new energy output, so as to obtain the transformed annualized net revenue objective function. The capacity configuration module is used to obtain the annual net income function per unit capacity based on the converted annualized net income objective function, and to configure the energy storage capacity based on the annual net income objective function per unit capacity to obtain the optimal capacity configuration result.

[0031] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: This invention proposes a capacity configuration method for new energy storage systems based on full lifecycle cost optimization. It establishes an annualized net profit objective function by constructing annualized investment cost models, annualized operation and maintenance cost models, and annualized total revenue models related to energy storage system capacity configuration. Based on the minimum energy capacity constraints, actual operating efficiency constraints, and annual throughput energy constraints required for the energy storage system to mitigate fluctuations in new energy output, the method unifies and quantifies the costs across the entire chain, including investment, operation and maintenance, and efficiency degradation, with returns such as arbitrage annual throughput energy. This transforms the annualized net profit objective function, resulting in a transformed annualized net profit objective function. Based on this transformed annualized net profit objective function, a unit capacity annual net profit function is obtained. Finally, energy storage capacity is configured based on this unit capacity annual net profit objective function to obtain the optimal capacity configuration result. This invention can significantly improve the economic rationality of configuration schemes and the scientific nature of investment decisions, avoiding the configuration trap of "low price in the early stage and high consumption in the later stage". At the same time, it establishes intuitive decision-making rules based on the annual net income per unit capacity, which can reduce the technical threshold, facilitate engineers and investors to make quick decisions, and quantify the impact of attenuation on economics in the planning stage, avoid the operation and maintenance risk of "meeting standards in the early stage and failing in the later stage", and improve the reliability of the project throughout its entire life cycle and the construction quality of the new energy power system. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating the capacity configuration method for new energy storage systems based on full life-cycle cost optimization proposed in this embodiment of the invention. Figure 2 A diagram illustrating the composition of the annualized net income objective function proposed in this embodiment of the invention; Figure 3 This diagram illustrates the system composition of the new energy storage system capacity configuration based on full life cycle cost optimization proposed in this embodiment of the invention. Detailed Implementation

[0033] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts of the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions; It is understandable to those skilled in the art that some well-known details may be omitted from the accompanying drawings.

[0034] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments; The positional relationships depicted in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0035] Example 1 This embodiment provides a method for configuring the capacity of a new energy storage system based on full lifecycle cost optimization. The flowchart of this method can be found in [link to flowchart]. Figure 1This includes the following steps: S1: Based on the acquired energy storage data, construct annualized investment cost model, annualized operation and maintenance cost model and annualized total revenue model related to the capacity configuration of the energy storage system; S2: Based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model, establish an annualized net revenue objective function; S3: Based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth the fluctuation of new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. S4: Based on the transformed annualized net income objective function, obtain the annual net income function per unit capacity, and configure the energy storage capacity based on the annual net income objective function per unit capacity to obtain the optimal capacity configuration result.

[0036] In this embodiment, an annualized net return objective function is established by constructing annualized investment cost models, annualized operation and maintenance cost models, and annualized total revenue models related to the energy storage system capacity configuration. Based on the minimum energy capacity constraints, actual operating efficiency constraints, and annual throughput energy constraints required for the energy storage system to mitigate fluctuations in renewable energy output, the entire chain of costs, including investment, operation and maintenance, and efficiency degradation, as well as revenues such as peak-valley arbitrage, are uniformly quantified. The annualized net return objective function is then transformed to obtain a transformed annualized net return objective function, significantly improving the economic rationality of the configuration scheme and the scientific nature of investment decisions, avoiding the configuration trap of "low initial price, high later consumption." Subsequently, based on the transformed annualized net return objective function, the annual net return function per unit capacity is obtained, and energy storage capacity is configured based on the annual net return objective function per unit capacity to obtain the optimal capacity configuration result. The method proposed in this embodiment establishes intuitive decision-making rules based on the annual net income per unit capacity, which lowers the technical threshold, facilitates quick decision-making by engineers and investors, and quantifies the impact of attenuation on economics during the planning stage. This avoids the operation and maintenance risk of "initial compliance, later failure" and improves the reliability of the project throughout its entire life cycle and the construction quality of the new energy power system.

[0037] Example 2 In this embodiment, the acquired energy storage data includes energy storage installed capacity. New energy hourly output sequence Unit capacity investment cost residual value coefficient Design life Annual maintenance cost per unit capacity Effective round-trip efficiency of energy storage system Peak-valley electricity price difference Annual effective operating days Demand response subsidies Electricity demand reduction and the adjustment ratio that conventional power supplies can handle. .

[0038] Specifically, in this implementation, the energy storage installed capacity can be obtained from the actual engineering situation. Annual maintenance cost per unit capacity is obtained through battery manufacturers and statistics. Effective round-trip efficiency of energy storage system Peak-valley electricity price difference Annual effective operating days Demand response subsidies Electricity demand reduction and the adjustment ratio that conventional power supplies can handle. In this embodiment, the objective function for annualized net income is composed as follows: Figure 2 As shown, see Figure 2 The annualized net return objective function can include project costs and annualized total revenue. Project costs refer to the hard investment costs considered in the energy storage system investment project. Annualized total revenue, after the energy storage system is put into operation, considers other ancillary revenues, such as demand response subsidies, demand tariff reductions, and annual peak-valley arbitrage revenue. Based on the minimum energy capacity constraints, actual operating efficiency constraints, and annual throughput energy constraints that the energy storage system needs to meet to smooth out fluctuations in renewable energy output, the entire chain of costs, including investment, operation and maintenance, and efficiency degradation, along with peak-valley arbitrage revenues, are uniformly quantified. Specifically: In S1, the annualized investment cost model, annualized operation and maintenance cost model, and annualized total revenue model related to the energy storage system capacity configuration are constructed as follows: Based on unit capacity investment cost residual value coefficient and design life The initial investment in energy storage is converted into an equivalent annualized investment cost using the annualized capital recovery factor (ACRF), and an annualized investment cost model is constructed. Satisfies the expression:

[0039] in, For the initial investment in energy storage, For the rated energy capacity of energy storage, The discount rate; Based on annual maintenance cost per unit capacity Construct an annualized operation and maintenance cost model Satisfies the expression:

[0040] in, This refers to the rated energy capacity of the energy storage. Based on the effective round-trip efficiency of energy storage systems Peak-valley electricity price difference Annual effective operating days Calculate annual peak-valley arbitrage returns The expression is:

[0041] in, This refers to the net discharge amount per cycle. This represents the annual arbitrage return coefficient per unit capacity. This refers to the rated energy capacity of the energy storage. Demand response subsidies Electricity demand reduction and annual peak-valley arbitrage income Construct an annualized total return model Satisfies the expression:

[0042] in, This represents the annual comprehensive return coefficient per unit capacity.

[0043] Specifically, the initial investment in energy storage is converted into an equivalent annualized investment cost using the Annual Capital Recovery Factor (ACRF), satisfying the expression: In this embodiment, it is assumed that a complete charge-discharge cycle is performed once per day. This represents the net discharge amount per cycle, of which... The effective round-trip efficiency of an energy storage system includes not only the efficiency of the battery itself, but also the losses of the power conversion system, line losses, and the effects of cycle degradation.

[0044] In S2, based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model, an annualized net revenue objective function is established. Satisfies the expression: .

[0045] Specifically, it can be observed that the objective function for annualized net income is related to capacity. If a function is a linear function, then its optimal solution (i.e., the maximum annualized net return) depends on the sign of the slope.

[0046] In S3, based on the minimum energy capacity constraint, actual operating efficiency constraint, and annual throughput energy constraint that the energy storage system needs to satisfy to smooth out fluctuations in new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. The process is as follows: S31: Model the fluctuations in new energy output and obtain the volatility. and duration of fluctuation ; S32: Adjustment ratio based on the capacity of a conventional power supply Volatility and duration of fluctuation Calculate the energy that needs to be smoothed for energy storage. The expression is:

[0047] in, This indicates the proportion that energy storage needs to bear. This indicates the energy required to smooth out a single high-fluctuation event. This represents the corrected capacity factor. Represents the capacity factor. Indicates the energy type efficiency factor; S33: Based on the energy that needs to be mitigated in the aforementioned energy storage The minimum capacity requirement is calculated by considering the efficiency loss of the energy storage system during charging and discharging. The expression is:

[0048] in, This represents the initial round-trip efficiency. This represents the efficiency degradation correction factor; S34: Energy needs to be mitigated based on energy storage Calculating the energy fluctuation smoothing required for energy storage The expression is:

[0049] in, This indicates the number of highly volatile events that occur in a year; S35: Calculate the annual throughput energy for arbitrage, the expression is: ,in, This indicates the number of complete cycles per day for the energy storage system; S36: Based on annual throughput energy of arbitrage Energy storage needs to smooth out energy fluctuations. Calculate annual throughput energy The expression is:

[0050] S37: Based on annual throughput energy Calculate the actual operating efficiency The expression is:

[0051] S38: Based on minimum capacity requirements Actual operating efficiency Energy intake and output Arbitrage return coefficient per unit capacity Perform the transformation to satisfy the expression:

[0052] The transformed annualized total return model Satisfying the expression:

[0053] S39: Based on the transformed annualized total return model The annualized net return objective function is transformed, and the expression for the transformed annualized net return objective function is as follows:

[0054] in, This represents the annual comprehensive return coefficient per unit capacity after conversion.

[0055] Specifically, the output of new energy sources may fluctuate drastically in a short period of time. This fluctuation requires energy storage to "absorb" or "compensate" it. Therefore, this embodiment uses minimum energy capacity constraints, actual operating efficiency constraints, and annual throughput energy constraints to smooth out fluctuations in new energy output. In S33, the energy storage system experiences efficiency losses during charging and discharging. Furthermore, the effective capacity of the energy storage system decreases with the increase in the number of cycles. Therefore, the actual required energy storage capacity should be greater than the theoretical energy smoothing capacity, which means the actual required energy storage capacity should be greater than the minimum capacity requirement. .

[0056] In S31, the fluctuation of new energy power output is modeled to obtain the volatility. and duration of fluctuation The process is as follows: S311: Based on the hourly output sequence of new energy sources The volatility of new energy power output is standardized to obtain the volatility. ; S312: Use the Weibull distribution to fit all data exceeding the fluctuation threshold. volatility Satisfies the expression:

[0057] in, For shape parameters, and ; For scale parameters, and ; S313: For the Weibull distribution Seasonally adjusted quantile volatility yields the seasonally adjusted... Quantile volatility Satisfies the expression:

[0058] in, Before seasonal adjustment Quantile volatility Seasonal volatility factor; S314: Define high volatility events, perform continuous segment detection on the high volatility events, and obtain the duration sequence of the high volatility events. ,in, i Indicates order; S315: Based on the duration sequence of high volatility events and seasonal volatility factors The seasonally corrected duration of the fluctuation was calculated. The expression is:

[0059] in, Indicates the duration of fluctuations before seasonal correction. This indicates the expectation value.

[0060] Specifically, in S311, based on the hourly output sequence of new energy sources... The volatility of new energy power output is standardized to obtain the volatility. The expression is:

[0061] in, Indicates hourly fluctuation. This represents the output of energy in hour t, derived from the hourly output sequence of new energy sources. Obtain from; Capacity factor; Indicates the average output of energy; This indicates the installed capacity.

[0062] Numerous empirical studies have shown that high volatility events follow a heavy-tailed distribution. Therefore, in S312, this embodiment uses a Weibull distribution to fit large volatility events, i.e., those exceeding a volatility threshold. volatility . This is a shape parameter that reflects the sharpness of the fluctuation. This is a scale parameter that reflects the intensity of the fluctuation.

[0063] Current energy storage configuration strategies mostly require repeated parameter calibration for different regional climate conditions and power types (such as wind power / solar power), lacking cross-scenario applicability. This invention introduces capacity factor normalization, energy efficiency factor and seasonal fluctuation factor to construct a universal configuration framework, enabling the same model to be directly applicable in diverse scenarios such as drought / humidity, wind power / solar power, etc., greatly shortening the scheme design cycle and lowering the threshold for engineering implementation.

[0064] In S313, Before seasonal adjustment Quantile volatility, expressed as: .

[0065] Based on the transformed annualized net income objective function, the annual net income function per unit capacity is obtained as follows: Based on the transformed annualized net income objective function The function π, representing the annual net income per unit capacity, is expressed as: .

[0066] Specifically, the simplified and transformed annualized net income objective function ,set up

[0067] At the same time, set ,but , Only includes fluctuation smoothing And the fluctuations subsided If it is unrelated to C, then It is a constant independent of C, and the coefficient A is extracted as the annual net income function per unit capacity π.

[0068] Based on the objective function of annual net income per unit capacity, energy storage capacity is configured to obtain the optimal capacity configuration result. The process is as follows: Based on the annual net return per unit capacity, the optimal energy storage capacity is determined. Satisfies the expression:

[0069] If the annual net revenue per unit capacity π > 0, meaning the revenue exceeds the cost, then the capacity limit will be adjusted. As the optimal capacity allocation; if the annual net revenue per unit capacity π < 0, i.e., the revenue is less than the cost, then the lower limit of the capacity will be set. As the optimal configuration capacity.

[0070] If the annual net income per unit capacity π = 0, that is, the income equals the cost, then any The capacity value is taken as the optimal configuration capacity.

[0071] Specifically, the maximum capacity In engineering practice, limitations such as investment budget ceilings, site space constraints, grid connection capacity limits, and policy-mandated storage ratios are imposed. Therefore, the lower limit of capacity must be determined based on the actual project conditions. The expression is shown in step S33.

[0072] Example 3 This embodiment provides a new energy storage system capacity configuration system based on full life cycle cost optimization. See [link to documentation]. Figure 3 This system is used to implement the aforementioned new energy storage system capacity configuration method based on full life cycle cost optimization, including: The cost model building module is used to build annualized investment cost models, annualized operation and maintenance cost models, and annualized total revenue models related to the capacity configuration of energy storage systems, based on the acquired energy storage data. The net income function construction module is used to establish an annualized net income objective function based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total income model. The revenue correction module is used to transform the annualized net revenue objective function based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth out the fluctuations in new energy output, so as to obtain the transformed annualized net revenue objective function. The capacity configuration module is used to obtain the annual net income function per unit capacity based on the converted annualized net income objective function, and to configure the energy storage capacity based on the annual net income objective function per unit capacity to obtain the optimal capacity configuration result.

[0073] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A capacity configuration method for new energy storage systems based on full life-cycle cost optimization, characterized in that, Includes the following steps: S1: Based on the acquired energy storage data, construct annualized investment cost model, annualized operation and maintenance cost model and annualized total revenue model related to the capacity configuration of the energy storage system; S2: Based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model, establish an annualized net revenue objective function; S3: Based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth the fluctuation of new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. S4: Based on the transformed annualized net income objective function, obtain the annual net income function per unit capacity, and configure the energy storage capacity based on the annual net income function per unit capacity to obtain the optimal capacity configuration result.

2. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 1, characterized in that, The acquired energy storage data includes energy storage installed capacity. New energy hourly output sequence Unit capacity investment cost residual value coefficient Design life Annual maintenance cost per unit capacity Effective round-trip efficiency of energy storage system Peak-valley electricity price difference Annual effective operating days Demand response subsidies Electricity demand reduction and the adjustment ratio that conventional power supplies can handle. .

3. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 2, characterized in that, In S1, the annualized investment cost model, annualized operation and maintenance cost model, and annualized total revenue model related to the energy storage system capacity configuration are constructed as follows: Based on unit capacity investment cost residual value coefficient and design life The initial investment in energy storage is converted into an equivalent annualized investment cost using the annualized capital recovery factor (ACRF), and an annualized investment cost model is constructed. Satisfies the expression: in, For the initial investment in energy storage, For the rated energy capacity of energy storage, The discount rate; Based on annual maintenance cost per unit capacity Construct an annualized operation and maintenance cost model Satisfies the expression: in, This refers to the rated energy capacity of the energy storage. Based on the effective round-trip efficiency of energy storage systems Peak-valley electricity price difference Annual effective operating days Calculate annual peak-valley arbitrage returns The expression is: in, This refers to the net discharge amount per cycle. This represents the annual arbitrage return coefficient per unit capacity. This refers to the rated energy capacity of the energy storage. Demand response subsidies Electricity demand reduction and annual peak-valley arbitrage income Construct an annualized total return model Satisfies the expression: in, This represents the annual comprehensive return coefficient per unit capacity.

4. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 3, characterized in that, In S2, based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total revenue model, an annualized net revenue objective function is established. Satisfies the expression: 。 5. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 3, characterized in that, In S3, based on the minimum energy capacity constraint, actual operating efficiency constraint, and annual throughput energy constraint that the energy storage system needs to satisfy to smooth out fluctuations in new energy output, the annualized net income objective function is transformed to obtain the transformed annualized net income objective function. The process is as follows: S31: Model the fluctuations in new energy output and obtain the volatility. and duration of fluctuation ; S32: Based on the adjustment ratio that a conventional power supply can handle. Volatility and duration of fluctuation Calculate the energy that needs to be smoothed for energy storage. The expression is: in, This indicates the proportion that energy storage needs to bear. This indicates the energy required to smooth out a single high-fluctuation event. , This represents the corrected capacity factor. , Represents the capacity factor. Indicates the energy type efficiency factor; S33: Based on the energy that needs to be mitigated in the aforementioned energy storage The minimum capacity requirement is calculated by considering the efficiency loss of the energy storage system during charging and discharging. The expression is: in, This represents the initial round-trip efficiency. This represents the efficiency degradation correction factor; S34: Energy needs to be mitigated based on energy storage requirements Calculating the energy fluctuation smoothing required for energy storage The expression is: in, This indicates the number of highly volatile events that occur in a year; S35: Calculate the annual throughput energy for arbitrage, the expression is: ,in, This indicates the number of complete cycles per day for the energy storage system; S36: Based on annual throughput energy of arbitrage Energy storage needs to smooth out energy fluctuations. Calculate annual throughput energy The expression is: S37: Based on annual throughput energy Calculate the actual operating efficiency The expression is: S38: Based on minimum capacity requirements Actual operating efficiency Energy intake and output Arbitrage return coefficient per unit capacity Perform the transformation to satisfy the expression: The transformed annualized total return model Satisfying the expression: S39: Based on the transformed annualized total return model The annualized net return objective function is transformed, and the expression for the transformed annualized net return objective function is as follows: in, This represents the annual comprehensive return coefficient per unit capacity after conversion.

6. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 5, characterized in that, In S31, the fluctuation of new energy power output is modeled to obtain the volatility. and duration of fluctuation The process is as follows: S311: Based on the hourly output sequence of new energy sources The volatility of new energy power output is standardized to obtain the volatility. ; S312: Use the Weibull distribution to fit all data exceeding the fluctuation threshold. volatility Satisfies the expression: in, For shape parameters, and ; For scale parameters, and ; S313: For the Weibull distribution Seasonally adjusted quantile volatility yields the seasonally adjusted... Quantile volatility Satisfies the expression: in, Before seasonal adjustment Quantile volatility Seasonal volatility factor; S314: Define high volatility events, perform continuous segment detection on the high volatility events, and obtain the duration sequence of the high volatility events. ,in, i Indicates order; S315: Based on the duration sequence of high volatility events and seasonal volatility factors The seasonally corrected duration of the fluctuation was calculated. The expression is: in, Indicates the duration of fluctuations before seasonal correction. This indicates the expectation value.

7. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 5, characterized in that, Based on the transformed annualized net income objective function, the annual net income function per unit capacity is obtained as follows: Based on the transformed annualized net income objective function The function π, representing the annual net income per unit capacity, is expressed as: 。 8. The capacity configuration method for new energy storage systems based on full life cycle cost optimization according to claim 7, characterized in that, Based on the objective function of annual net income per unit capacity, energy storage capacity is configured to obtain the optimal capacity configuration result. The process is as follows: Based on the annual net return per unit capacity, the optimal energy storage capacity is determined. Satisfies the expression: If the annual net revenue per unit capacity π > 0, meaning the revenue exceeds the cost, then the capacity limit will be adjusted. As the optimal configuration capacity; If the annual net revenue per unit capacity π < 0, meaning the revenue is less than the cost, then the lower limit of the capacity will be set. As the optimal configuration capacity.

9. The capacity configuration method for new energy storage systems based on full life-cycle cost optimization according to claim 8, characterized in that, If the annual net income per unit capacity π = 0, that is, the income equals the cost, then any The capacity value is taken as the optimal configuration capacity.

10. A method for configuring the capacity of a new energy storage system based on full life cycle cost optimization, used to implement the method for configuring the capacity of a new energy storage system based on full life cycle cost optimization as described in any one of claims 1-9, characterized in that, include: The cost model building module is used to build annualized investment cost models, annualized operation and maintenance cost models, and annualized total revenue models related to the capacity configuration of energy storage systems, based on the acquired energy storage data. The net income function construction module is used to establish an annualized net income objective function based on the annualized investment cost model, the annualized operation and maintenance cost model, and the annualized total income model. The revenue correction module is used to transform the annualized net revenue objective function based on the minimum energy capacity constraint, actual operating efficiency constraint and annual throughput energy constraint that the energy storage system needs to meet to smooth out the fluctuations in new energy output, so as to obtain the transformed annualized net revenue objective function. The capacity configuration module is used to obtain the annual net income function per unit capacity based on the converted annualized net income objective function, and to configure the energy storage capacity based on the annual net income function per unit capacity to obtain the optimal capacity configuration result.

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