Method and system for calculating multi-time scale energy storage credible capacity based on dynamic programming

Abstract

The present application relates to a kind of multi-time scale energy storage reliable capacity measurement method based on dynamic programming, belong to the field of energy storage operation optimization.Method includes: based on the operating parameters of energy storage respectively constructs hour-level short-time energy storage model and day-level long-time energy storage model, each energy storage model respectively includes multiple operating constraints of each energy storage and operating cost minimum objective function;Based on the output of each energy of power grid system, power grid system power balance constraint is constructed;Based on power grid system power balance constraint, hour-level short-time energy storage model and day-level long-time energy storage model are jointly solved, and multi-time scale energy storage charge-discharge operation scheme is obtained;Based on multi-time scale energy storage charge-discharge operation scheme, the reliable capacity of each energy storage is calculated respectively;Reliable capacity includes the effective load carrying capacity of hour-level short-time energy storage and day-level long-time energy storage.The present application method realizes high accuracy and high efficient solution energy storage operation planning, and realizes the accurate measurement of reliable capacity of energy storage before implementing operation scheme.

Description

Technical Field

[0001] This invention belongs to the field of energy storage operation optimization, and in particular relates to a method and system for calculating the reliable capacity of energy storage across multiple time scales based on dynamic programming. Background Technology

[0002] Driven by the dual-carbon goal of achieving carbon neutrality, the penetration rate of new energy sources in the power grid is constantly increasing, with new energy power generation, represented by wind power and photovoltaic power generation, continuously replacing the share of traditional thermal power generation. However, the integration of new energy sources significantly reduces the synchronous inertia of the power grid system, posing challenges to the system's frequency security and greatly increasing the demand for flexible adjustment. Traditional flexibility resources are no longer sufficient to meet the flexible adjustment needs of the new power system.

[0003] To address the growing challenge of power imbalance, energy storage is gradually becoming a primary resource for flexibility regulation in future power systems. Energy storage is categorized into short-term and long-term storage. Short-term storage utilizes power-type energy storage devices, such as electrochemical energy storage. Its main function is intraday peak shaving and frequency regulation, alleviating the mismatch between traditional generator output, renewable energy output, and real-time load power on short timescales. Long-term storage employs capacity-type energy storage devices, such as pumped hydro storage, enabling energy transfer over longer timescales, mitigating power fluctuations on daily or larger scales, and resolving seasonal power supply-demand mismatches. Therefore, the combination of short-term and long-term energy storage provides a solution to the imbalance between power output and load demand at different timescales. Multi-timescale energy storage dynamic programming models aim to improve the operational efficiency and stability of the power system through rational planning of energy storage resources.

[0004] However, existing technologies all use hourly resolution to collaboratively optimize the operation planning of long-term and short-term energy storage, which has problems such as computational redundancy, excessive scale, and the fact that hourly resolution operation planning is not suitable for the actual operation of long-term energy storage. On the other hand, existing reliable capacity calculations are mostly based on historical operation data to calculate the reliable capacity of energy storage, which has a lag and cannot make a reasonable risk assessment of the energy storage operation planning of the power grid system, thus affecting the safe operation of the power grid. Summary of the Invention

[0005] Based on the above analysis, this invention aims to provide a method and system for calculating the reliable capacity of energy storage across multiple time scales based on dynamic programming. While considering the minimum operating cost, different time scales are used for planning hourly short-term energy storage and daily long-term energy storage, respectively, to obtain the multi-time-scale energy storage operation scheme. Based on the multi-time-scale energy storage operation scheme and the probability distribution model of the remaining load of the energy storage system, the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage is calculated respectively.

[0006] On the one hand, this invention provides a method for calculating the reliable capacity of energy storage across multiple time scales based on dynamic programming, specifically including the following steps:

[0007] Based on the operating parameters of energy storage, hourly short-term energy storage models and daily long-term energy storage models are constructed respectively. Each energy storage model includes multiple operating constraints and an objective function for minimizing operating costs for each energy storage.

[0008] Power balance constraints of the power grid system are constructed based on the output of thermal power units, the output of new energy units, the system load, the hourly short-term energy storage charging and discharging power and the daily long-term energy storage charging and discharging power at different time periods.

[0009] Based on the power balance constraints of the power grid system, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain a multi-timescale energy storage charging and discharging operation scheme.

[0010] The reliable capacity is calculated based on a multi-timescale energy storage charging and discharging operation scheme; the reliable capacity includes the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage.

[0011] Furthermore, the reliable capacity calculated based on the multi-timescale energy storage charging and discharging operation scheme includes:

[0012] Probability distribution models of the remaining charge of the first and second energy storage systems corresponding to hourly short-term energy storage and daily long-term energy storage are constructed respectively. The probability distribution models of the remaining charge of the first and second energy storage systems are solved based on the multi-timescale energy storage charging and discharging operation scheme.

[0013] The expected values ​​of the first and second load shedding corresponding to hourly short-term energy storage and daily long-term energy storage are calculated based on the solutions of the probability distribution models of the remaining load of the first and second energy storage systems, respectively.

[0014] The first effective load-carrying capacity of hourly short-term energy storage and the second effective load-carrying capacity of daily long-term energy storage are calculated based on the first and second expected values ​​of load loss and the solutions of the corresponding probability distribution models of the remaining charge of the first and second energy storage systems, respectively.

[0015] Furthermore, the construction of hourly short-term energy storage models and daily long-term energy storage models based on energy storage operating parameters includes:

[0016] Initial state constraints for each energy storage system are constructed based on its power capacity.

[0017] The charging and discharging constraints of each energy storage device are constructed based on the power capacity and charging / discharging power of each energy storage device.

[0018] The capacity constraints of each energy storage device are constructed based on its charging / discharging power, its power capacity, its continuous discharge time, and its remaining power.

[0019] Based on the charging / discharging power of each energy storage, the remaining power of each energy storage, and the discharge efficiency of each energy storage, a minimum operating cost objective function is constructed for each of the energy storage systems.

[0020] Based on the initial state constraints, energy storage charging and discharging constraints, capacity constraints, and the objective function of minimizing operating costs, hourly short-term energy storage models and daily long-term energy storage models are constructed respectively.

[0021] Furthermore, the hourly short-term energy storage model is expressed as follows:

[0022]

[0023] in, This indicates short-term energy storage capacity in the hourly range, with the time scale being hours; t1 Indicates the initial remaining power of hourly short-term energy storage; c t and d t These represent the hourly short-term energy storage charging and discharging power, respectively; ω t These are 0 / 1 variables, where 0 indicates a power system load shedding event occurred during time period t, and 1 indicates the power system is operating normally; t The remaining short-term energy storage capacity is represented by t (hourly range); h represents the continuous discharge time of the short-term energy storage system (hourly range); K t (c t ,d t ,l t ,ω t ) represents the hourly short-term energy storage operating cost; π t The real-time electricity price for time period t; For short-time energy storage and discharge efficiency; This is the unit power penalty for the portion of hourly short-term energy storage that is not fully discharged during time period t when a load loss event occurs; T is the total number of time periods.

[0024] The daily-level long-term energy storage model is expressed as follows:

[0025]

[0026] in, This indicates the daily-scale long-term energy storage capacity, with a time scale of daily; d1 Indicates the initial remaining capacity of daily-level long-term energy storage; c d and d d These represent the daily long-duration energy storage charging and discharging power, respectively; ω dThe variable is 0 or 1, where 0 indicates that the power system has a need for dispatching daily-level long-term energy storage for frequency regulation and peak shaving on day d, and 1 indicates that there is no need for dispatching; l d Indicates the remaining energy capacity of the daily-level long-term energy storage on day d; h l Indicates the sustainable discharge time of a day-level long-duration storage system; K d (c d ,d d ,l d ,ω d ) represents the daily long-term energy storage operating cost; π d The real-time electricity price for day d; It achieves daily-level long-term energy storage and discharge efficiency; This is a penalty per unit power for the portion of daily long-term energy storage that is not fully discharged within d days when the power system has peak-shaving and frequency regulation needs; D is the total number of days.

[0027] Furthermore, the probability distribution model of the remaining charge of the first energy storage system is expressed as follows:

[0028]

[0029] Where, δ * The multi-timescale energy storage charging and discharging operation scheme obtained from the solution; Γ t+1 (y) indicates that in the aforementioned operating scheme, l t and ω t The set of ordered pairs; ζ1(y) represents the probability that the remaining amount of short-term energy storage in the hourly range is y in the first hour; ζ t (λ) and ζ t+1 (y) represents the probability that the remaining hourly short-term energy storage capacity is λ and y at hour t and t+1, respectively; Prob represents the probability of the event occurring.

[0030] The probability distribution model of the remaining charge of the second energy storage system is expressed as follows:

[0031]

[0032] Among them, Γ d+1 (y') indicates that in the aforementioned operating scheme, l d and ω d The set of pairs is composed of: ζ1(y') represents the probability that the remaining daily long-term energy storage capacity is y' on the first day; ζ1(y') and ζ1(y') represent the probabilities that the remaining daily long-term energy storage capacity is λ' and y' on the d and d+1 days, respectively.

[0033] Furthermore, the calculation methods for the first and second unloaded load expected values ​​are as follows:

[0034] First unload expectation value

[0035] in, G represents the expected value of the first load shedding corresponding to hourly short-term energy storage; t M represents the available generating capacity of all generator units in the power system during time period t; t This represents the load power during time period t;

[0036] Second Unload Expected Value

[0037] in, This represents the expected second load shedding value corresponding to daily-level long-term energy storage; G d M represents the available generating capacity of all generator units in the power system on day d; d This indicates the load power on day d.

[0038] Furthermore, the calculation methods for the first effective load-carrying capacity and the second effective load-carrying capacity are as follows:

[0039] The first effective load-carrying capacity is calculated based on the following formula:

[0040]

[0041] The second effective load-carrying capacity is calculated based on the following formula:

[0042]

[0043] Wherein, M1 and M2 represent the first effective load-carrying capacity corresponding to hourly short-term energy storage and the second effective load-carrying capacity corresponding to daily long-term energy storage, respectively.

[0044] Furthermore, the power balance constraint is expressed as:

[0045]

[0046] Where, N g g represents the number of thermal power units. i Numbering of thermal power units N represents the output of the i-th thermal power unit during the t-hour period on day d; w For the number of new energy generating units, w j Numbering of new energy generating units c represents the power output rate of the j-th new energy unit during the t-hour period on day d; t,d This represents the hourly short-term energy storage charging power during the d-day t-period period; d t,d This refers to the hourly short-term energy storage discharge power during the d-day t-period period. The power received by the power system from the energy storage device during the d-day t-period period; This represents the daily power contribution of the long-duration energy storage system to the power system for each time period; M t,d This indicates the current total load power of the power system.

[0047] Furthermore, after obtaining the reliable capacity, the method also includes evaluating the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage based on the reliable capacity. The following evaluation indicators are used to evaluate the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage, respectively:

[0048]

[0049]

[0050] Wherein, σ1 and σ2 represent the evaluation indicators for hourly short-term energy storage and daily long-term energy storage, respectively, and the larger the value, the stronger the effective load-carrying capacity.

[0051] On the other hand, the present invention also provides a multi-timescale reliable capacity measurement system for energy storage based on dynamic programming, comprising:

[0052] The operation scheme solution module is used to construct hourly short-term energy storage models and daily long-term energy storage models based on the operating parameters of the energy storage. Each energy storage model includes multiple operating constraints and a minimum operating cost objective function for each energy storage. Based on the output of thermal power units, the output of new energy units, the system load, the hourly short-term energy storage charging and discharging power, and the daily long-term energy storage charging and discharging power for each time period, the power balance constraints of the power grid system are constructed. Based on the power balance constraints of the power grid system, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain multi-timescale energy storage charging and discharging operation schemes.

[0053] The effective load-carrying capacity calculation module is used to calculate the reliable capacity based on the multi-timescale energy storage charging and discharging operation schemes respectively; the reliable capacity includes the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage.

[0054] The present invention can achieve at least one of the following beneficial effects:

[0055] This embodiment discloses a reliable capacity calculation method for multi-timescale energy storage based on dynamic programming. By constructing hourly short-term energy storage models and daily long-term energy storage models respectively, considering the minimum operating cost, and planning the hourly short-term energy storage models and daily long-term energy storage models using different time scales, and establishing a coupling relationship between the two using the power balance constraints of the power grid system, a multi-timescale energy storage operation scheme is obtained. This solves the problems of redundant calculation data and excessive calculation volume when coordinating the planning of long-term and short-term energy storage in current technologies. Moreover, the obtained multi-timescale energy storage operation scheme is more realistic in implementation and has good practicality.

[0056] By solving the multi-timescale energy storage operation scheme and the probability distribution model of the remaining load of the energy storage system, the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage can be calculated respectively. This allows for accurate calculation of the reliable capacity of energy storage before the implementation of the operation scheme. It helps to rationally evaluate and utilize the capacity value of energy storage resources in power system planning and operation, optimize resource allocation, and improve the reliability and economy of the power system.

[0057] Other features and advantages of the invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained from what is particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0058] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0059] Figure 1 This is a flowchart of the reliable capacity calculation method for multi-timescale energy storage based on dynamic programming according to the present invention. Detailed Implementation

[0060] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0061] Method Implementation Examples

[0062] A specific embodiment of the present invention discloses a method for calculating the reliable capacity of energy storage across multiple time scales based on dynamic programming, specifically including steps S01-S04.

[0063] Step S01: Construct hourly short-term energy storage models and daily long-term energy storage models based on the operating parameters of the energy storage. Each energy storage model includes multiple operating constraints and a minimum operating cost objective function for each energy storage.

[0064] Energy storage includes hourly short-term energy storage and daily long-term energy storage.

[0065] The operating parameters of each energy storage device include its power capacity, charging / discharging power, continuous discharge time, remaining power, and discharge efficiency.

[0066] Specifically, based on the operating parameters of energy storage, hourly short-term energy storage models and daily long-term energy storage models are constructed, including:

[0067] Initial state constraints for each energy storage system are constructed based on its power capacity.

[0068] The charging and discharging constraints of each energy storage device are constructed based on the power capacity and charging / discharging power of each energy storage device.

[0069] The capacity constraints of each energy storage device are constructed based on its charging / discharging power, its power capacity, its continuous discharge time, and its remaining power.

[0070] Based on the charging / discharging power of each energy storage, the remaining power of each energy storage, and the discharge efficiency of each energy storage, an operating cost model for each energy storage is constructed.

[0071] Based on the initial state constraints, charging and discharging constraints, capacity constraints, and operating cost models of each energy storage system, hourly short-term energy storage models and daily long-term energy storage models are constructed respectively.

[0072] Furthermore, hourly short-term energy storage models include:

[0073] Initial state constraints:

[0074] Charge and discharge constraints:

[0075] Capacity constraints:

[0076] Minimize operating cost objective function:

[0077]

[0078] in, This represents the power capacity of short-term energy storage on an hourly scale, and is a known quantitative quantity; t1 This indicates the initial remaining energy capacity of hourly short-term energy storage; c t and d t These represent the charging and discharging power of short-term energy storage at the hour level, respectively; ω t These are 0 / 1 variables, where 0 indicates a power system load shedding event occurred during time period t, and 1 indicates the power system is operating normally; t t represents the remaining capacity of hourly short-term energy storage; h represents the sustainable discharge time of hourly short-term energy storage, i.e., it can be charged or discharged at full power for h hours, which is a known quantity; K t (c t ,d t ,l t ,ω t ) represents the operating cost of hourly short-term energy storage; π tThe real-time electricity price for time period t is determined based on the electricity price curve; Let be the discharge efficiency of short-time energy storage, and be a known quantitative value. When the discharge is d... t At that time, the actual amount of electricity supplied to the power grid system was The unit power penalty imposed by the power grid system on the portion of hourly short-term energy storage that is not fully discharged during time period t when a load loss event occurs is a known quantitative value; T is the total number of time periods, which is 24 time periods within a day.

[0079] Furthermore, the daily-scale long-term energy storage model includes:

[0080] Initial state constraints:

[0081] Charge and discharge constraints:

[0082] Capacity constraints:

[0083] Minimize operating cost objective function:

[0084]

[0085] in, This represents the power capacity of daily-scale long-term energy storage, with a time scale of days, and is a known quantitative quantity; d1 This indicates the initial remaining capacity of daily-level long-term energy storage; c d and d d These represent the charging and discharging power of daily-level long-term energy storage, respectively; ω d The variable is 0 or 1, where 0 indicates that the power system has a need for dispatching daily-level long-term energy storage for frequency regulation and peak shaving on day d, and 1 indicates that there is no need for dispatching; l d Indicates the remaining energy capacity of the daily-level long-term energy storage (d-day); h l This indicates the sustainable discharge time of daily-level long-term energy storage, that is, the duration of continuous charging or discharging at full power (in hours). l Day, a known quantity; K d (c d ,d d ,l d ,ω d ) represents the operating cost of daily-level long-term energy storage; π d The real-time electricity price for day d is determined based on the electricity price curve; The daily long-term energy storage discharge efficiency is a known quantitative value. When the discharge is d... d At that time, the actual amount of electricity supplied to the power grid system was The penalty per unit power for the portion of daily long-term energy storage that is not fully discharged within d days when the power system has peak-shaving and frequency regulation needs is a known quantitative value; D is the total number of days, which is the total number of days in a year, 365 / 366.

[0086] The principle of the objective function for hourly short-term energy storage operation costs is explained below:

[0087] Operating cost K for hourly short-term energy storage t This includes the revenue generated from hourly short-term energy storage by utilizing electricity price differences at different times for charging and discharging. And the penalty costs incurred in the event of a loss-of-load event due to failure to discharge at full power. Right now:

[0088]

[0089] Similarly, the operating cost K of daily-scale long-term energy storage d This includes the revenue generated from daily-level long-term energy storage by utilizing electricity price differences at different times for charging and discharging. And the unit power penalty cost for the portion of a daily long-duration energy storage system that does not discharge at full power within 24 hours when the power system has peak shaving and frequency regulation needs. Right now:

[0090]

[0091] Step S02: Construct power balance constraints for the power grid system based on the output of thermal power units, the output of new energy units, the system load, the charging and discharging power of hourly short-term energy storage, and the charging and discharging power of daily long-term energy storage for each time period.

[0092] Specifically, the power balance constraint of the power grid system is expressed as:

[0093]

[0094] Where, N g g represents the number of thermal power units. i Numbering of thermal power units N represents the output of the i-th thermal power unit during the t-hour period on day d; w For the number of new energy generating units, w j Numbering of new energy generating units This indicates that the j-th renewable energy unit outputs power during the t-hour period on day d; c t,d This represents the charging power of hourly short-term energy storage during the d-day t-period period; d t,d The discharge power of short-term energy storage in the hourly range during the d-day t-period period. This represents the actual energy storage output received by the power system during the d-day t-period period; This represents the power contribution of the daily-level long-duration energy storage system to the power system for each time period. It should be noted that this invention assumes the power of the daily-level long-duration energy storage system remains constant during a single charge-discharge cycle; M t,d This indicates the current total load power of the power system.

[0095] Step S03: Based on the power balance constraints of the power grid system, jointly solve the hourly short-term energy storage model and the daily long-term energy storage model to obtain a multi-timescale energy storage charging and discharging operation scheme.

[0096] Specifically, during the solution process, historical data with a time span of one year is selected and clustered to obtain multiple typical daily scenarios. Based on these typical days, the load curve and the output curve of thermal power units / new energy units within one year are determined. The load curve, the output curve of thermal power units, the output curve of new energy units, and the corresponding historical electricity price curve are used as inputs to jointly solve the hourly short-term energy storage model and the daily long-term energy storage model.

[0097] Furthermore, when jointly solving the hourly short-term energy storage model and the daily long-term energy storage model based on the power balance constraints of the power grid system, a dynamic programming algorithm is used for solving the problem because the decisions made in each time period will affect the decisions made in the next time period. For example, in implementation, solvers such as CPLEX or Gurobi can be used.

[0098] Furthermore, the solution results include the charging or discharging power of hourly short-term energy storage and daily long-term energy storage for each day and time period.

[0099] Step S04: Calculate the reliable capacity based on the multi-timescale energy storage charging and discharging operation scheme; the reliable capacity includes the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage.

[0100] Specifically, the reliable capacity calculated based on the multi-timescale energy storage charging and discharging operation scheme includes:

[0101] Probability distribution models of remaining charge capacity for the first and second energy storage systems, corresponding to hourly short-term energy storage and daily long-term energy storage, are constructed respectively. Based on the multi-timescale energy storage charging and discharging operation scheme, the probability distribution models of remaining charge capacity for the first and second energy storage systems are solved respectively.

[0102] The expected values ​​of the first and second load shedding corresponding to hourly short-term energy storage and daily long-term energy storage are calculated based on the solutions of the probability distribution models of the remaining load of the first and second energy storage systems, respectively.

[0103] The first effective load-carrying capacity of hourly short-term energy storage and the second effective load-carrying capacity of daily long-term energy storage are calculated based on the first and second expected values ​​of load loss and the solutions of the corresponding probability distribution models of the remaining charge of the first and second energy storage systems, respectively.

[0104] Furthermore, the probability distribution model of the remaining charge of the first energy storage system is expressed as follows:

[0105]

[0106] Where, δ * The multi-timescale energy storage charging and discharging operation scheme obtained from the solution; Γ t+1 (y) indicates that in the aforementioned operating scheme, l t and ω t The set of ordered pairs; ζ1(y) represents the probability that the remaining amount of short-term energy storage in the first hour is y; ζ t (λ) and ζ t+1 (y) represents the probability that the remaining electricity of hourly short-term energy storage is λ and y at hour t and t+1, respectively; Prob represents the probability of the event occurring; it should be noted that λ and y have the same meaning, but in order to distinguish the remaining electricity at hour t and the remaining electricity at hour t+1, two different variables are chosen to represent them.

[0107] The probability distribution model of the remaining charge of the second energy storage system is expressed as follows:

[0108]

[0109] Among them, Γ d+1 (y') indicates that in the aforementioned operating scheme, l d and ω d The set of pairs is composed of: ζ1(y') represents the probability that the remaining daily long-term energy storage capacity is y' on the first day; ζ1(y') and ζ1(y') represent the probabilities that the remaining daily long-term energy storage capacity is λ' and y' on the d and d+1 days, respectively.

[0110] Furthermore, when solving the probability distribution model of the remaining charge of the first and second energy storage systems based on the multi-timescale energy storage charging and discharging operation scheme, the remaining charge in the previous hour and whether a load shedding event has occurred are uncertain. Moreover, the continuous distribution of the remaining charge in the previous hour makes solving the model difficult. Therefore, the remaining charge in the previous hour is discretized, with each case forming a pair (λ, ω). The desired remaining energy charge at time t+1 is y, while the energy charge at time t is not constant. The discretized remaining charge can be represented as λ1, λ2, ..., λ n Therefore, it is necessary to calculate the probability that the remaining energy storage capacity in the next time period is y under different remaining energy storage capacities during time period t. The probability of the remaining energy storage capacity in the next time period is obtained by weighted summation of the different cases (i.e., the third formula in the probability distribution model of the remaining energy storage capacity).

[0111] Furthermore, the calculation methods for the first and second unloaded load expected values ​​are as follows:

[0112] First unloaded expected value

[0113] in, G represents the expected value of the first load shedding corresponding to hourly short-term energy storage; t M represents the available generating capacity of all generator units in the power system during time period t; t This represents the load power during time period t;

[0114] Second Unload Expected Value

[0115] in, This represents the expected second load shedding value corresponding to daily-level long-term energy storage; G d M represents the available generating capacity of all generator units in the power system on day d; d This indicates the load power on day d.

[0116] The following explains the principle behind calculating the expected value of load shedding:

[0117] In power system reliability assessment, expected load loss and probability of load loss are commonly used for evaluation. Probability of load loss p t The calculation method is as follows: p t =Prob{G t <M t}, where G t M represents the available generating capacity of all generators in hour t. t This represents the load power at hour t. The method for calculating the expected load is as follows: T represents the number of time periods for which the calculation is performed. Therefore, in this invention, the expected values ​​of the first and second load shedding can be calculated based on the solutions of the probability distribution models of the remaining charge of the first and second energy storage systems.

[0118] Furthermore, the calculation methods for the first effective load-carrying capacity and the second effective load-carrying capacity are as follows:

[0119] The first effective load-carrying capacity is calculated based on the following formula:

[0120]

[0121] The second effective load-carrying capacity is calculated based on the following formula:

[0122]

[0123] Wherein, M1 and M2 represent the first effective load-carrying capacity corresponding to hourly short-term energy storage and the second effective load-carrying capacity corresponding to daily long-term energy storage, respectively.

[0124] Furthermore, after obtaining the reliable capacity, the method also includes evaluating the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage based on the reliable capacity. The following evaluation indicators are used to evaluate the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage, respectively:

[0125]

[0126]

[0127] Wherein, σ1 and σ2 represent the evaluation indicators for hourly short-term energy storage and daily long-term energy storage, respectively, and the larger the value, the stronger the effective load-carrying capacity.

[0128] It should be noted that in practical applications, the effective load-carrying capacity is related to the unit power penalty in the objective function of minimizing operating costs and the maximum sustainable discharge time of energy storage. This is because these parameters will profoundly affect the energy storage's decision-making at each time point, thereby changing the probability distribution of the remaining energy charge. The remaining energy charge is the factor that determines the effective load-carrying capacity. Therefore, the evaluation indicators σ1 and σ2 for the effective load-carrying capacity M1 and M2 need to be further determined with more detailed evaluation criteria based on the specific circumstances of the actual application.

[0129] This embodiment discloses a reliable capacity calculation method for multi-timescale energy storage based on dynamic programming. By constructing hourly short-term energy storage models and daily long-term energy storage models respectively, considering the minimum operating cost, and planning the hourly short-term and daily long-term energy storage models using different time scales, and establishing a coupling relationship between the two models using the power balance constraints of the power grid system, a multi-timescale energy storage operation scheme is obtained. This solves the problems of redundant calculation data and excessive calculation volume when coordinating the planning of short-term and long-term energy storage in current technologies. Moreover, the obtained multi-timescale energy storage operation scheme is more realistic in implementation and has good practicality. Hourly short-term energy storage can ensure sufficient power supply within the day and reduce the probability of load shedding events. Daily long-term energy storage can be coordinated with annual load forecasting and power supply plans to perform peak shaving and frequency regulation on monthly and annual time scales.

[0130] By solving the multi-timescale energy storage operation scheme and the probability distribution model of the remaining load of the energy storage system, the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage can be calculated respectively. This allows for accurate calculation of the reliable capacity of energy storage before the implementation of the operation scheme, which helps to rationally assess and utilize the capacity value of energy storage resources in power system planning and operation. By using evaluation indicators to evaluate the strength of the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage, experience can be accumulated during implementation, resource allocation can be optimized, and the reliability and economy of the power system can be improved.

[0131] System Implementation Examples

[0132] A specific embodiment of the present invention discloses a multi-timescale reliable capacity measurement system for energy storage based on dynamic programming, comprising:

[0133] The operation scheme solution module is used to construct hourly short-term energy storage models and daily long-term energy storage models based on the operating parameters of the energy storage. Each energy storage model includes multiple operating constraints and a minimum operating cost objective function for each energy storage. Based on the output of thermal power units, the output of new energy units, the system load, the hourly short-term energy storage charging and discharging power, and the daily long-term energy storage charging and discharging power for each time period, the power balance constraints of the power grid system are constructed. Based on the power balance constraints of the power grid system, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain multi-timescale energy storage charging and discharging operation schemes.

[0134] The effective load-carrying capacity calculation module is used to calculate the reliable capacity based on the multi-timescale energy storage charging and discharging operation schemes respectively; the reliable capacity includes the effective load-carrying capacity of hourly short-term energy storage and daily long-term energy storage.

[0135] Compared with the prior art, the beneficial effects of the multi-timescale reliable capacity calculation system for energy storage based on dynamic programming provided in this embodiment are basically the same as those provided in the method embodiment, and will not be described in detail here.

[0136] It should be noted that the above embodiments are based on the same inventive concept, and the parts not described repeatedly can be used as references to each other.

[0137] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic programming-based multi-time scale energy storage credible capacity estimation method, characterized in that, Comprising the following steps: Based on the operating parameters of the energy storage, an hourly short-time energy storage model and a daily long-time energy storage model are respectively constructed, each of the energy storage models comprising a plurality of operating constraint conditions of each energy storage and a minimum operating cost objective function; Based on the output of thermal power units, the output of new energy units, system load, the charging and discharging power of the hourly short-time energy storage, and the charging and discharging power of the daily long-time energy storage, a power balance constraint of the power grid system is constructed; Based on the power balance constraint of the power grid system, the hourly short-time energy storage model and the daily long-time energy storage model are jointly solved to obtain a multi-time scale energy storage charging and discharging operation scheme; The trusted capacity is calculated based on the multi-time scale energy storage charging and discharging operation scheme, including: constructing first and second energy storage system remaining state of charge probability distribution models corresponding to the hour-level short-time energy storage and the day-level long-time energy storage respectively, and solving the first and second energy storage system remaining state of charge probability distribution models based on the multi-time scale energy storage charging and discharging operation scheme; calculating first and second loss load expectation values corresponding to the hour-level short-time energy storage and the day-level long-time energy storage respectively based on the solutions of the first and second energy storage system remaining state of charge probability distribution models; calculating first and second effective load carrying capacities of the hour-level short-time energy storage and the day-level long-time energy storage respectively based on the first and second loss load expectation values and the solutions of the first and second energy storage system remaining state of charge probability distribution models; the trusted capacity includes the effective load carrying capacities of the hour-level short-time energy storage and the day-level long-time energy storage; the calculation method of the first and second effective load carrying capacities is: solving the first effective load carrying capacity based on the following formula: ; solving the second effective load carrying capacity based on the following formula: ; wherein, and respectively represent the first effective load carrying capacity corresponding to the hour-level short-time energy storage and the second effective load carrying capacity corresponding to the day-level long-time energy storage; represents the first loss load expectation value corresponding to the hour-level short-time energy storage; represents the second loss load expectation value corresponding to the day-level long-time energy storage; is the total number of time periods; is the total number of days; represents the event occurrence probability; represents the probability that the hour-level short-time energy storage remaining state of charge is at the tth hour; represents the probability that the day-level long-time energy storage remaining state of charge is at the dth day; represents the available generation capacity of all generators at the tth hour; represents the available generation capacity of all generators of the power system at the dth day; represents the load power size at the tth hour; represents the load power size at the dth day; represents the multi-time scale energy storage charging and discharging operation scheme solved.

2. The energy storage credible capacity estimation method according to claim 1, characterized in that, The construction of the hourly short-time energy storage model and the daily long-time energy storage model based on the operating parameters of the energy storage comprises: Based on the power capacity of each of the energy storages, an initial state constraint of each of the energy storages is constructed; Based on the power capacity of each of the energy storages and the charging / discharging power of each of the energy storages, a charging and discharging constraint of each of the energy storages is constructed; Based on the charging / discharging power of each of the energy storages, the power capacity of each of the energy storages, the continuous discharging time of each of the energy storages, and the residual power of each of the energy storages, a capacity constraint of each of the energy storages is constructed; Based on the charging / discharging power of each of the energy storages, the residual power of each of the energy storages, and the discharging efficiency of each of the energy storages, a minimum operating cost objective function of each of the energy storages is constructed; Based on the initial state constraint, the charging and discharging constraint, the capacity constraint, and the minimum operating cost objective function of each of the energy storages, the hourly short-time energy storage model and the daily long-time energy storage model are constructed.

3. The energy storage reliable capacity estimation method of claim 2, wherein, The hourly short-time energy storage model is represented as: ； wherein, represents the short-time energy storage power capacity at the hour level, with a time scale of hours; represents the initial remaining energy of the short-time energy storage at the hour level; and respectively represent the charging and discharging power of the short-time energy storage at the hour level; is a 0 / 1 variable, 0 represents that a loss-of-load event occurs in the power system at time period t, and 1 represents that the power system operates normally; represents the remaining energy of the short-time energy storage at the hour level at time period t; represents the sustainable discharging time of the short-time energy storage at the hour level; represents the operating cost of the short-time energy storage at the hour level; is the real-time electricity price at time period t; is the discharging efficiency of the short-time energy storage; is the unit power penalty for the part of the short-time energy storage at the hour level that does not meet the power at time period t when a loss-of-load event occurs; is the total number of time periods; The daily long-time energy storage model is represented as: ； wherein, represents the daily long-duration energy storage power capacity, with a time scale of day; represents the initial remaining energy of the daily long-duration energy storage at the beginning of the day; and respectively represent the daily long-duration energy storage charging and discharging power; is a 0 / 1 variable, 0 represents that there is a dispatching demand for the daily long-duration energy storage to participate in frequency modulation and peak shaving in the power system on day d, and 1 represents that there is no dispatching demand; represents the remaining energy of the daily long-duration energy storage on day d; represents the sustainable discharging time of the daily long-duration energy storage; represents the operation cost of the daily long-duration energy storage; is the real-time electricity price on day d; is the discharging efficiency of the daily long-duration energy storage; is the unit power penalty for the part of the daily long-duration energy storage that is not fully discharged on day d when the power system has a demand for peak shaving and frequency modulation; and D is the total number of days.

4. The method according to claim 3, characterized in that, The first energy storage system residual state of charge probability distribution model is represented as: ； wherein, represents the obtained multi-time scale energy storage charging and discharging operation scheme; represents the probability of the remaining energy of the short-time energy storage at the hour level being y in the operation scheme; and a pair set composed of and ; represents the probability of the remaining energy of the short-time energy storage at the hour level being y in the first hour; and respectively represent the probability of the remaining energy of the short-time energy storage at the hour level being y in the tth and (t+1)th hours; and y in the tth and (t+1)th hours; represents the probability of the event occurring; The second energy storage system residual state of charge probability distribution model is represented as: ； wherein, denotes the probability that the energy storage has a remaining energy of and on the first day, when the day length is denotes the probability that the energy storage has a remaining energy of and on the dth and (d+1)th day, when the day length is denotes the probability that the energy storage has a remaining energy of​​ 5. The energy storage credible capacity estimation method according to claim 4, characterized in that, The calculation method of the first and second loss of load expectation values is: first off-load expectation value ; wherein, represents the first loss-of-load expectation value corresponding to the short-time energy storage at the hour level; represents the available generation capacity of all generator units of the power system in the tth period; represents the load power size in the tth period; Second off-load expectation value ; wherein, represents the second loss-of-load expectation value corresponding to the daily energy storage; represents the available generation capacity of all power generators of the power system on the dth day; represents the load power size on the dth day.

6. The energy storage credible capacity estimation method according to any one of claims 3-5, characterized in that, The power balance constraint is represented as: ； wherein, is the number of thermal power units, is the number of thermal power units, represents the output of the i-th thermal power unit at the t period of the d day; is the number of new energy units, is the number of new energy units, represents the output rate of the j-th new energy unit at the t period of the d day; represents the charging power of the short-time energy storage at the t period of the d day; is the discharging power of the short-time energy storage at the t period of the d day, is the power emitted by the energy storage device received by the power system at the t period of the d day; represents the power contribution of the long-time energy storage system to the power system at each period; represents the total load power size of the power system at present.

7. The energy storage credible capacity estimation method according to claim 6, characterized in that, After obtaining the credible capacity, an evaluation of the strong and weak effective load carrying capacity of the hourly short-time energy storage and the daily long-time energy storage based on the credible capacity is further included, wherein the following evaluation indexes are used to evaluate the strong and weak effective load carrying capacity of the hourly short-time energy storage and the daily long-time energy storage, respectively: ； ； wherein, and respectively represent evaluation indexes of short-time energy storage at the hour level and long-time energy storage at the day level, and the greater the value is, the stronger the effective load carrying capacity is.

8. A multi-time scale energy storage credible capacity estimation system based on dynamic programming, characterized in that, Comprising: An operation scheme solving module is configured to construct an hourly short-time energy storage model and a daily long-time energy storage model based on the operating parameters of the energy storage, each of the energy storage models comprising a plurality of operating constraint conditions of each energy storage and a minimum operating cost objective function; construct a power balance constraint of the power grid system based on the output of thermal power units, the output of new energy units, system load, the charging and discharging power of the hourly short-time energy storage, and the charging and discharging power of the daily long-time energy storage; and jointly solve the hourly short-time energy storage model and the daily long-time energy storage model based on the power balance constraint of the power grid system to obtain a multi-time scale energy storage charging and discharging operation scheme. The effective load carrying capacity calculation module is used for calculating the trusted capacity based on the multi-time scale energy storage charging and discharging operation scheme, and comprises the following steps: constructing a first and a second energy storage system remaining state of charge probability distribution model corresponding to the hour-level short-time energy storage and the day-level long-time energy storage respectively, and solving the first and the second energy storage system remaining state of charge probability distribution model based on the multi-time scale energy storage charging and discharging operation scheme; calculating a first and a second loss load expectation value corresponding to the hour-level short-time energy storage and the day-level long-time energy storage based on the solution of the first and the second energy storage system remaining state of charge probability distribution model respectively; calculating a first effective load carrying capacity of the hour-level short-time energy storage and a second effective load carrying capacity of the day-level long-time energy storage based on the first and the second loss load expectation value and the solution of the first and the second energy storage system remaining state of charge probability distribution model respectively; the trusted capacity comprises the effective load carrying capacity of the hour-level short-time energy storage and the day-level long-time energy storage; the calculation method of the first and the second effective load carrying capacity is as follows: the first effective load carrying capacity is solved based on the following formula: ; the second effective load carrying capacity is solved based on the following formula: ; wherein, and respectively represent the first effective load carrying capacity corresponding to the hour-level short-time energy storage and the second effective load carrying capacity corresponding to the day-level long-time energy storage; represents the first loss load expectation value corresponding to the hour-level short-time energy storage; represents the second loss load expectation value corresponding to the day-level long-time energy storage; is the total number of time periods; is the total number of days; represents the event occurrence probability; represents the probability that the hour-level short-time energy storage remaining state of charge is in the tth hour; represents the probability that the day-level long-time energy storage remaining state of charge is in the dth day; represents the available power generation capacity of all generators in the tth hour; represents the available power generation capacity of all generators in the dth day; represents the load power size in the tth hour; represents the load power size in the dth day; represents the multi-time scale energy storage charging and discharging operation scheme solved.

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