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

Abstract

The invention relates to a multi-time-scale energy storage credible capacity measuring and calculating method based on dynamic programming, and belongs to the field of energy storage operation optimization. The method comprises the following steps: respectively constructing an hour-level short-time energy storage model and a day-level long-time energy storage model based on energy storage operation parameters, wherein each energy storage model respectively comprises a plurality of operation constraint conditions and an operation cost minimum objective function of each piece of energy storage; constructing a power balance constraint of the power grid system based on the output of each energy of the power grid system; the hour-level short-time energy storage model and the day-level long-time energy storage model are jointly solved based on power balance constraints of a power grid system, and a multi-time-scale energy storage charging and discharging operation scheme is obtained; based on the multi-time-scale energy storage charging and discharging operation scheme, the credible capacity of each energy storage is obtained through calculation; the credible capacity comprises effective load capacity of hour-level short-time energy storage and day-level long-time energy storage. According to the method disclosed by the invention, the energy storage operation plan is solved with high accuracy and high efficiency, and the credible capacity of the energy storage is accurately measured and calculated before the operation scheme is implemented.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage operation optimization, and in particular relates to a multi-time scale energy storage reliable capacity calculation method and system based on dynamic programming. Background Art

[0002] Driven by the "dual carbon" goal of achieving "carbon neutrality", the penetration rate of new energy in the power grid continues to increase, and new energy power generation represented by wind power and photovoltaic power generation is constantly replacing the share of traditional thermal power generation. However, the access of new energy has significantly reduced the synchronous inertia of the power grid system, the frequency security of the system is facing challenges, and the demand for flexible regulation has also increased significantly. Traditional flexibility resources are no longer sufficient to meet the flexible regulation needs of the new power system.

[0003] In response to the challenge of increasing imbalance in power and electricity, energy storage is gradually becoming the main flexible regulation resource for future power systems. Energy storage is divided into short-term energy storage and long-term energy storage. Short-term energy storage uses power-type energy storage devices, represented by electrochemical energy storage. The main function of this type of energy storage is to regulate peak and frequency within the day, and alleviate the problem of mismatch between the output of traditional generators, the output of new energy and the real-time load power on a short time scale. Long-term energy storage uses capacity-type energy storage devices, represented by pumped storage, which can realize long-term energy transfer, alleviate power fluctuations above the daily scale, and solve the problem of seasonal power supply and demand mismatch. Therefore, the coordination of long and short-term energy storage provides a solution to the problem of imbalance between output and load demand on different time scales. The multi-time scale energy storage dynamic programming model aims to improve the operating efficiency and stability of the power system by rationally planning energy storage resources.

[0004] However, in the existing technology, the operation plan of long-term and short-term energy storage is collaboratively optimized with hourly resolution, which has the problems of computational redundancy, excessive scale, and the operation plan with hourly resolution is not suitable for the actual operation of long-term energy storage during implementation. On the other hand, the existing trusted capacity measurement is mostly based on historical operation data for energy storage, which has lags and cannot make reasonable risk assessments on the energy storage operation plan of the power grid system, affecting the safe operation of the power grid. Summary of the invention

[0005] In view of the above analysis, the present invention aims to provide a multi-time-scale energy storage credible capacity calculation method and system based on dynamic programming, which plans hourly short-term energy storage and daily long-term energy storage respectively at different time scales while considering the minimum operating cost, solves and obtains a multi-time-scale energy storage operation plan, and calculates the effective load capacity of hourly short-term energy storage and daily long-term energy storage based on the multi-time-scale energy storage operation plan and the probability distribution model of the remaining charge of the energy storage system.

[0006] On the one hand, the present invention provides a method for calculating the reliable capacity of multi-time-scale energy storage based on dynamic programming, which specifically includes the following steps:

[0007] Based on the operating parameters of the energy storage, an hourly short-term energy storage model and a daily long-term energy storage model are respectively constructed, wherein each of the energy storage models includes a plurality of operating constraints of each energy storage and an operating cost minimization objective function;

[0008] The power balance constraints of the power grid system are established based on the output of thermal power units, output of new energy units, system load, hourly short-term energy storage charging and discharging power, and daily long-term energy storage charging and discharging power in each period;

[0009] Based on the power balance constraint 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 the multi-time scale energy storage charging and discharging operation scheme;

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

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

[0012] Construct the probability distribution models of the remaining charge of the first and second energy storage systems corresponding to the hourly short-term energy storage and the daily long-term energy storage, respectively, and solve the probability distribution models of the remaining charge of the first and second energy storage systems based on the multi-time scale energy storage charging and discharging operation scheme;

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

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

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

[0016] Constructing initial state constraints of each energy storage based on the power capacity of each energy storage;

[0017] Based on the power capacity of each energy storage and the charge / discharge power of each energy storage, respectively construct the charge and discharge constraints of each energy storage;

[0018] Based on the charge / discharge power of each energy storage, the power capacity of each energy storage, the continuous discharge time of each energy storage, and the remaining power of each energy storage, the capacity constraints of each energy storage are respectively constructed;

[0019] Based on the energy storage charge / discharge power, the remaining power of each energy storage, and the energy storage discharge efficiency, a minimum operating cost objective function of each energy storage is constructed;

[0020] Based on the initial state constraints of each of the energy storages, energy storage charging and discharging constraints, capacity constraints, and the minimum operating cost objective function, an hourly short-term energy storage model and a daily long-term energy storage model are respectively constructed.

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

[0022]

[0023] in, Indicates the hourly short-term energy storage power capacity, with a time scale of hours; l t1 Indicates the remaining power at the beginning of the hour-level short-term energy storage; c t and d t Respectively represent the hour-level short-term energy storage charging and discharging power; ω t is a 0 / 1 variable, 0 means that the power system has a load loss event during period t, and 1 means that the power system operates normally; t It indicates the remaining power of short-term energy storage in hours during period t; h indicates the sustainable discharge time of short-term energy storage in hours; K t (c t ,d t ,l t ,ω t ) represents the hourly short-term energy storage operation cost; π t is the real-time electricity price during period t; is the short-time energy storage discharge efficiency; is the unit power penalty for the part of the hourly short-term energy storage that is not fully discharged in period t when a load loss event occurs; T is the total number of periods;

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

[0025]

[0026] in, represents the daily long-term energy storage power capacity, with a time scale of days; l d1 Indicates the remaining power at the beginning of daily long-term energy storage; c d and d d Respectively represent the daily long-term energy storage charging and discharging power; ω dis a 0 / 1 variable, 0 means that on day d, the power system has a need to dispatch daily-level long-duration energy storage for frequency and peak regulation, and 1 means there is no dispatch demand; l d d represents the remaining power of the daily long-term energy storage; h l Indicates the sustainable discharge time of daily long-term storage; K d (c d ,d d ,l d ,ω d ) represents the daily long-term energy storage operation cost; π d is the real-time electricity price on day d; It is the daily level long-term energy storage discharge efficiency; is the unit power penalty for the part of the daily long-duration energy storage that is not fully discharged within d days when the power system has peak load 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:

[0028]

[0029] Among them, δ * represents the solved multi-time-scale energy storage charging and discharging operation scheme; Γ t+1 (y) means that in the operation scheme, t and ω t The set of pairs of numbers composed of; ζ1(y) represents the probability that the remaining power of the hourly short-term energy storage is y in the first hour; ζ t (λ) and ζ t+1 (y) represents the probability that the remaining power of the hourly short-term energy storage 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:

[0031]

[0032] Among them, Γ d+1 (y') means that in the operation scheme, d and ω d A set of number pairs composed of ζ1(y') represents the probability that the remaining power of the daily long-term energy storage is y' on the first day; ζ1(y') and ζ1(y') represent the probabilities that the remaining power of the daily long-term energy storage is λ' and y' on the d and d+1 days respectively.

[0033] Furthermore, the calculation method of the first and second load loss expected values ​​is:

[0034] First load loss expected value

[0035] in, G represents the first load loss expectation value corresponding to the hourly short-term energy storage; t represents the available generating capacity of all generating units in the power system in period t; M t Indicates the load power size in the t period;

[0036] Second load loss expectation

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

[0038] Furthermore, the calculation method of the first effective belt load capacity and the second effective belt load capacity is:

[0039] Solve the first effective belt load capacity based on the following formula:

[0040]

[0041] Solve the second effective belt load capacity based on the following formula:

[0042]

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

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

[0045]

[0046] Among them, N g is the number of thermal power units, g i The thermal power unit number, N represents the output of the i-th thermal power unit in the d-day t-period period; w is the number of new energy units, w j Number the new energy unit. represents the output rate of the j-th renewable energy unit in the d-day t period; c t,d represents the hourly short-term energy storage charging power during the period t in day d; t,d is the hourly short-term energy storage discharge power at time t on day d, The power generated by the energy storage device received by the power system during the period t on day d; Indicates the power contribution of the daily long-term energy storage system to the power system in each period; M t,d Indicates the current total load power of the power system.

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

[0048]

[0049]

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

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

[0052] An operation scheme solving module is used to construct an hourly short-term energy storage model and a daily long-term energy storage model based on the operation parameters of the energy storage, each of which includes multiple operation constraints and an operation cost minimum objective function of each energy storage; based on the output of thermal power units, the output of new energy units, system load, hourly short-term energy storage charging and discharging power and daily long-term energy storage charging and discharging power in each time period, a power grid system power balance constraint is constructed; based on the power grid system power balance constraint, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain a multi-time scale energy storage charging and discharging operation scheme;

[0053] The effective load calculation module is used to calculate the reliable capacity based on the multi-time scale energy storage charging and discharging operation scheme; the reliable capacity includes the effective load capacity of hour-level short-term energy storage and day-level long-term energy storage.

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

[0055] The present embodiment discloses a multi-time-scale energy storage credible capacity calculation based on dynamic programming. By respectively constructing an hourly short-term energy storage model and a daily long-term energy storage model, considering the minimum operating cost and planning the hourly short-term energy storage model and the daily long-term energy storage model at different time scales, a coupling relationship is established between the two using the power balance constraint of the power grid system, and a multi-time-scale energy storage operation plan is obtained by solving the problem of redundant calculation data and excessive calculation amount when performing collaborative planning of long-term and short-term energy storage in the existing technology. The obtained multi-time-scale energy storage operation plan is more practical in implementation and has good practicality.

[0056] By solving the multi-time-scale energy storage operation plan and the probability distribution model of the remaining charge of the energy storage system, the effective load capacity of the hourly short-term energy storage and the daily long-term energy storage are calculated respectively. The reliable capacity of the energy storage can be accurately calculated before the implementation of the operation plan, which is helpful to reasonably evaluate and utilize the capacity value of energy storage resources in the planning and operation of the power system, optimize resource allocation, and improve the reliability and economy of the power system.

[0057] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0059] Figure 1 The present invention is a flow chart of a method for calculating the credible capacity of multi-time-scale energy storage based on dynamic programming. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a 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 used to limit the scope of the present invention.

[0061] Method Embodiment

[0062] A specific embodiment of the present invention discloses a method for calculating the reliable capacity of multi-time-scale energy storage based on dynamic programming, which specifically includes steps S01 to S04.

[0063] Step S01: constructing an hourly short-term energy storage model and a daily long-term energy storage model based on the operating parameters of the energy storage, wherein each of the energy storage models includes a plurality of operating constraints of each energy storage and an operating cost minimization objective function.

[0064] Energy storage includes short-term energy storage at the hour level and long-term energy storage at the day level.

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

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

[0067] Constructing initial state constraints of each energy storage based on the power capacity of each energy storage;

[0068] Based on the power capacity of each energy storage and the charge / discharge power of each energy storage, respectively construct the charge and discharge constraints of each energy storage;

[0069] Based on the charge / discharge power of each energy storage, the power capacity of each energy storage, the continuous discharge time of each energy storage, and the remaining power of each energy storage, the capacity constraints of each energy storage are respectively constructed;

[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 operation cost model of each energy storage is constructed respectively;

[0071] Based on the energy storage initial state constraints, the energy storage charging and discharging constraints, the energy storage capacity constraints, and the energy storage operation cost models, an hourly short-term energy storage model and a daily long-term energy storage model are respectively constructed.

[0072] Furthermore, the hourly short-term energy storage model includes:

[0073] Initial state constraints:

[0074] Charge and discharge constraints:

[0075] Capacity constraints:

[0076] Minimize the operating cost objective function:

[0077]

[0078] in, It represents the power capacity of short-term energy storage at the hour level, with a time scale of hours and a known quantity; l t1 Indicates the initial remaining power of the hour-level short-term energy storage; c t and d t Respectively represent the charging and discharging power of short-term energy storage at the hour level; ω t is a 0 / 1 variable, 0 means that the power system has a load loss event during period t, and 1 means that the power system operates normally; t represents the remaining power of the hourly short-term energy storage in period t; h represents the sustainable discharge time of the hourly short-term energy storage, that is, charging or discharging at full power can last 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 tis the real-time electricity price in period t, determined according to the electricity price curve; is the discharge efficiency of short-term energy storage, which is a known quantity. When the discharge is d t When , the actual amount of electricity supplied to the power grid system is When a load loss event occurs, the power grid system imposes a unit power penalty on the hourly short-term energy storage for the part of the power discharge that is not full in period t, which is a known quantity; T is the total number of periods, which is 24 periods in a day.

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

[0080] Initial state constraints:

[0081] Charge and discharge constraints:

[0082] Capacity constraints:

[0083] Minimize the operating cost objective function:

[0084]

[0085] in, It represents the power capacity of daily long-term energy storage, with a time scale of days and a known quantity; l d1 Indicates the initial remaining power of the daily long-term energy storage; c d and d d Respectively represent the charging and discharging power of daily long-term energy storage; ω d is a 0 / 1 variable, 0 means that on day d, the power system has a need to dispatch daily-level long-duration energy storage for frequency and peak regulation, and 1 means there is no dispatch demand; l d d represents the remaining power of the daily long-term energy storage; h l Indicates the sustainable discharge time of long-term energy storage at the daily level, that is, charging or discharging at full power can last for hours l Day, is a known quantity; K d (c d ,d d ,l d ,ω d ) represents the operating cost of daily long-term energy storage; π d is the real-time electricity price on day d, determined according to the electricity price curve; is the daily long-term energy storage discharge efficiency, which is a known quantity. When the discharge is d d When , the actual amount of electricity supplied to the power grid system is is the unit power penalty for the part of the daily long-duration energy storage that is not fully discharged within d days when the power system has peak load and frequency regulation needs, which is a known quantity; D is the total number of days, which is 365 / 366 of the total number of days in a year.

[0086] The following is an explanation of the principle of the hourly short-term energy storage operation cost objective function:

[0087] Operating cost of hourly short-term energy storage K t Including hour-level short-term energy storage, which uses the difference in electricity prices in different time periods to charge and discharge to gain benefits and the penalty costs for not discharging at full power when a load loss event occurs Right now:

[0088]

[0089] Similarly, the operating cost of daily long-term energy storage is K d Including daily long-term energy storage, which uses the difference in electricity prices in different time periods to charge and discharge. When the power system has peak load and frequency regulation requirements, the unit power penalty cost for the part of the daily long-duration energy storage system that is not fully discharged within 24 hours Right now:

[0090]

[0091] Step S02: constructing a power balance constraint of 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 in each time period.

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

[0093]

[0094] Among them, N g is the number of thermal power units, g i The thermal power unit number, N represents the output of the i-th thermal power unit in the d-day t-period period; w is the number of new energy units, w j Number the new energy unit. represents the output of the j-th renewable energy unit at time t on day d; c t,d represents the charging power of short-term energy storage at the hour level during the period of t day; d t,d is the discharge power of the hourly short-term energy storage at the time period t on day d, is the energy storage output actually received by the power system during period t on day d; M represents the power contribution of the daily long-term energy storage system to the power system in each time period. It should be noted that the present invention assumes that the power of the daily long-term energy storage system is constant during a single charge and discharge period; t,d Indicates the current total load power of the power system.

[0095] Step S03: based on the power balance constraint 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-time scale energy storage charging and discharging operation plan.

[0096] Specifically, when solving the problem, historical data with a time length of one year is selected for clustering to obtain multiple typical day scenarios, and the load curve and output curve of thermal power units / new energy units within a year are determined based on multiple typical days; the load curve, thermal power unit output curve, new energy output curve and the corresponding historical electricity price curve are used as input 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 constraint of the power grid system, since the decision of each period will affect the decision of the next period, a dynamic programming algorithm is used for solving. Exemplarily, during implementation, a solver such as CPLEX or Gurobi can be used for solving.

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

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

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

[0101] Construct the probability distribution models of the remaining charge of the first and second energy storage systems corresponding to the hourly short-term energy storage and the daily long-term energy storage, respectively, and solve the probability distribution models of the remaining charge of the first and second energy storage systems based on the multi-time scale energy storage charging and discharging operation scheme;

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

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

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

[0105]

[0106] Among them, δ * represents the solved multi-time-scale energy storage charging and discharging operation scheme; Γ t+1 (y) means that in the operation scheme, t and ω t ζ1(y) represents the probability that the remaining power of the hourly short-term energy storage is y in the first hour; ζ t (λ) and ζ t+1 (y) represents the probability that the remaining power of the hourly short-term energy storage is λ and y at the t hour and t+1 hour respectively; Prob represents the probability of the event occurring; it should be noted that λ and y have the same meaning. In order to distinguish the remaining power at t hour from the remaining power at t+1 hour, two different variables are chosen to be used for representation;

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

[0108]

[0109] Among them, Γ d+1 (y') means that in the operation scheme, d and ω d A set of number pairs composed of ζ1(y') represents the probability that the remaining power of the daily long-term energy storage is y' on the first day; ζ1(y') and ζ1(y') represent the probabilities that the remaining power of the daily long-term energy storage 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-time scale energy storage charging and discharging operation scheme, since the remaining charge of the previous hour and whether the load loss event occurred are uncertain, and the continuous distribution of the remaining charge of the previous hour makes it difficult to solve the model, the remaining charge of the previous hour is discretized, and each case constitutes a number pair (λ, ω). The remaining energy storage in the t+1 period is expected to be y, while the storage capacity in the t period is not certain. The remaining energy after discretization can be expressed as λ1, λ2, ..., λ n We need to calculate the probability that the remaining energy storage capacity in the next period will be y under different remaining energy storage capacity in period t. We weighted sum different situations (i.e. the third formula of the probability distribution model of remaining charge of energy storage system), and the result of the sum is the probability that the remaining energy storage capacity in the next period will be y.

[0111] Furthermore, the calculation method of the first and second load loss expected values ​​is:

[0112] First load loss expected value

[0113] in, G represents the first load loss expectation value corresponding to the hourly short-term energy storage; t represents the available generating capacity of all generating units in the power system in period t; M t Indicates the load power size in the t period;

[0114] Second load loss expectation

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

[0116] The following is an explanation of the principle of calculating the expected value of load loss:

[0117] In the reliability assessment of power systems, the load loss expectation and load loss probability are usually used for assessment. t The calculation method is: t =Prob{G t <M t}, where G t represents the available generating capacity of all generators at hour t, M t Indicates the load power at hour t. The calculation method of base load expectation is: T represents the number of time periods for calculation. Therefore, in the present invention, the first and second load loss expected values ​​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 method of the first effective belt load capacity and the second effective belt load capacity is:

[0119] Solve the first effective belt load capacity based on the following formula:

[0120]

[0121] Solve the second effective belt load capacity based on the following formula:

[0122]

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

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

[0125]

[0126]

[0127] Among them, σ1 and σ2 represent the evaluation indicators of hourly short-term energy storage and daily long-term energy storage respectively. 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, because these parameters will profoundly affect the decision-making of energy storage at each time node, thereby changing the probability distribution of the remaining charge of energy storage, and the remaining charge is the factor that determines the effective load-carrying capacity. Therefore, the evaluation indicators σ1 and σ2 of the effective load-carrying capacity M1 and M2 need to be further determined in combination with the specific circumstances of actual applications. More detailed evaluation criteria.

[0129] The present embodiment discloses a multi-time-scale energy storage credible capacity calculation based on dynamic programming. By respectively constructing an hourly short-term energy storage model and a daily long-term energy storage model, considering the minimum operating cost and adopting different time scales to plan the hourly short-term energy storage model and the daily long-term energy storage model, a coupling relationship is established between the two by using the power balance constraint of the power grid system, and a multi-time-scale energy storage operation plan is obtained by solving. The problems of redundant calculation data and excessive calculation amount in the coordinated planning of long and short-term energy storage in the existing technology are solved, and the obtained multi-time-scale energy storage operation plan is more practical in implementation and has good practicality. The hourly short-term energy storage can ensure the daily power supply margin and reduce the probability of load loss events. The daily long-term energy storage can be coordinated with the annual load forecast and power supply plan to perform peak and frequency regulation on the monthly and annual time scales.

[0130] By solving the multi-time scale energy storage operation plan and the probability distribution model of the remaining charge of the energy storage system, the effective load-carrying capacity of the hourly short-term energy storage and the daily long-term energy storage are calculated respectively. The reliable capacity of the energy storage can be accurately calculated before the implementation of the operation plan, which is helpful to reasonably evaluate and utilize the capacity value of energy storage resources in the planning and operation of the power system; by using evaluation indicators to evaluate the strength of the effective load-carrying capacity of the hourly short-term energy storage and the daily long-term energy storage, it can be known that 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 Example

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

[0133] An operation scheme solving module is used to construct an hourly short-term energy storage model and a daily long-term energy storage model based on the operation parameters of the energy storage, each of which includes multiple operation constraints and an operation cost minimum objective function of each energy storage; based on the output of thermal power units, the output of new energy units, system load, hourly short-term energy storage charging and discharging power and daily long-term energy storage charging and discharging power in each time period, a power grid system power balance constraint is constructed; based on the power grid system power balance constraint, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain a multi-time scale energy storage charging and discharging operation scheme;

[0134] The effective load calculation module is used to calculate the reliable capacity based on the multi-time scale energy storage charging and discharging operation scheme; the reliable capacity includes the effective load capacity of hour-level short-term energy storage and day-level long-term energy storage.

[0135] Compared with the prior art, the beneficial effects of the multi-time-scale energy storage reliable capacity calculation system based on dynamic programming provided in this embodiment are basically the same as the beneficial effects provided by the method embodiment, which will not be repeated here.

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

[0137] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for calculating the reliable capacity of multi-time-scale energy storage based on dynamic programming, characterized in that: The steps include: Based on the operating parameters of the energy storage, an hourly short-term energy storage model and a daily long-term energy storage model are respectively constructed, wherein each of the energy storage models includes a plurality of operating constraints of each energy storage and an operating cost minimization objective function; The power balance constraints of the power grid system are established based on the output of thermal power units, output of new energy units, system load, hourly short-term energy storage charging and discharging power, and daily long-term energy storage charging and discharging power in each period; Based on the power balance constraint 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 the multi-time scale energy storage charging and discharging operation scheme; The credible capacity is calculated based on a multi-time scale energy storage charging and discharging operation scheme; the credible capacity includes the effective load carrying capacity of hourly short-term energy storage and daily long-term energy storage.

2. The method for calculating the energy storage reliable capacity according to claim 1, characterized in that: The reliable capacity calculated based on the multi-time scale energy storage charging and discharging operation scheme includes: Construct the probability distribution models of the remaining charge of the first and second energy storage systems corresponding to the hourly short-term energy storage and the daily long-term energy storage, respectively, and solve the probability distribution models of the remaining charge of the first and second energy storage systems based on the multi-time scale energy storage charging and discharging operation scheme; The first and second load loss expectation values ​​corresponding to the hourly short-term energy storage and the daily long-term energy storage are calculated based on the solutions of the probability distribution models of the remaining charge of the first and second energy storage systems respectively; The first effective load capacity of the hourly short-term energy storage and the second effective load capacity of the daily long-term energy storage are calculated based on the first and second load loss expected values ​​and the corresponding solutions of the first and second energy storage system residual charge probability distribution models.

3. The method for calculating the energy storage reliable capacity according to claim 2, characterized in that: The construction of the hourly short-term energy storage model and the daily long-term energy storage model based on the energy storage operation parameters includes: Constructing initial state constraints of each energy storage based on the power capacity of each energy storage; Based on the power capacity of each energy storage and the charge / discharge power of each energy storage, respectively construct the charge and discharge constraints of each energy storage; Based on the charge / discharge power of each energy storage, the power capacity of each energy storage, the continuous discharge time of each energy storage, and the remaining power of each energy storage, the capacity constraints of each energy storage are respectively constructed; Based on the energy storage charge / discharge power, the remaining power of each energy storage, and the energy storage discharge efficiency, a minimum operating cost objective function of each energy storage is constructed; Based on the initial state constraints of each of the energy storages, energy storage charging and discharging constraints, capacity constraints, and the minimum operating cost objective function, an hourly short-term energy storage model and a daily long-term energy storage model are respectively constructed.

4. The method for calculating the energy storage reliable capacity according to claim 3 is characterized in that: The hourly short-term energy storage model is expressed as: in, Indicates the hourly short-term energy storage power capacity, with a time scale of hours; l t1 Indicates the remaining power at the beginning of the hour-level short-term energy storage; c t and d t Respectively represent the hour-level short-term energy storage charging and discharging power; ω t is a 0 / 1 variable, 0 means that the power system has a load loss event during period t, and 1 means that the power system operates normally; t It indicates the remaining power of short-term energy storage in hours during period t; h indicates the sustainable discharge time of short-term energy storage in hours; K t (c t ,d t ,l t ,ω t ) represents the hourly short-term energy storage operation cost; π t is the real-time electricity price during period t; is the short-time energy storage discharge efficiency; is the unit power penalty for the part of the hourly short-term energy storage that is not fully discharged in period t when a load loss event occurs; T is the total number of periods; The daily long-term energy storage model is expressed as: in, represents the daily long-term energy storage power capacity, with a time scale of days; l d1 Indicates the remaining power at the beginning of daily long-term energy storage; c d and d d Respectively represent the daily long-term energy storage charging and discharging power; ω d is a 0 / 1 variable, 0 means that on day d, the power system has a need to dispatch daily-level long-duration energy storage for frequency and peak regulation, and 1 means there is no dispatch demand; l d d represents the remaining power of daily long-term energy storage; h l Indicates the sustainable discharge time of daily long-term storage; K d (c d ,d d ,l d ,ω d ) represents the daily long-term energy storage operation cost; π d is the real-time electricity price on day d; It is the daily level long-term energy storage discharge efficiency; is the unit power penalty for the part of the daily long-duration energy storage that is not fully discharged within d days when the power system has peak load and frequency regulation needs; D is the total number of days.

5. The method for calculating the energy storage reliable capacity according to claim 4, characterized in that: The probability distribution model of the remaining charge of the first energy storage system is expressed as: Among them, δ * represents the solved multi-time-scale energy storage charging and discharging operation scheme; Γ t+1 (y) means that in the operation scheme, t and ω t The set of pairs of numbers composed of; ζ1(y) represents the probability that the remaining power of the hourly short-term energy storage is y in the first hour; ζ t (λ) and ζ t+1 (y) represents the probability that the remaining power of the hourly short-term energy storage is λ and y at hour t and t+1 respectively; Prob represents the probability of the event occurring; The probability distribution model of the remaining charge of the second energy storage system is expressed as: Among them, Γ d+1 (y') means that in the operation scheme, d and ω d A set of number pairs composed of ζ1(y') represents the probability that the remaining power of the daily long-term energy storage is y' on the first day; ζ1(y') and ζ1(y') represent the probabilities that the remaining power of the daily long-term energy storage is λ' and y' on the d and d+1 days respectively.

6. The method for calculating the reliable capacity of energy storage according to claim 5, characterized in that: The calculation method of the first and second load loss expected values ​​is: First load loss expected value in, G represents the first load loss expectation value corresponding to the hourly short-term energy storage; t represents the available generating capacity of all generating units in the power system in period t; M t Indicates the load power size in the t period; Second load loss expectation in, G represents the expected value of the second load loss corresponding to the daily long-term energy storage; d represents the available generating capacity of all generating units in the power system on day d; M d Indicates the load power on day d.

7. The method for calculating the energy storage reliable capacity according to claim 6, characterized in that: The calculation method of the first effective belt load capacity and the second effective belt load capacity is: Solve the first effective belt load capacity based on the following formula: Solve the second effective belt load capacity based on the following formula: Among them, M1 and M2 respectively represent the first effective load capacity corresponding to the hourly short-term energy storage and the second effective load capacity corresponding to the daily long-term energy storage.

8. The method for calculating the reliable capacity of energy storage according to any one of claims 4 to 7, characterized in that: The power balance constraint is expressed as: Among them, N g is the number of thermal power units, g i The thermal power unit number, N represents the output of the i-th thermal power unit in the d-day t-period period; w is the number of new energy units, w j Number the new energy unit. represents the output rate of the j-th renewable energy unit in the d-day t period; c t,d represents the hourly short-term energy storage charging power during the period t in day d; t,d is the hourly short-term energy storage discharge power at time t on day d, The power generated by the energy storage device received by the power system during the period t on day d; Indicates the power contribution of the daily long-term energy storage system to the power system in each period; M t,d Indicates the current total load power of the power system.

9. The method for calculating the reliable capacity of energy storage according to claim 8, characterized in that: After obtaining the credible capacity, the method further includes evaluating the strength of the effective load carrying capacity of the hourly short-term energy storage and the daily long-term energy storage based on the credible capacity, wherein the following evaluation indicators are used to respectively evaluate the strength of the effective load carrying capacity of the hourly short-term energy storage and the daily long-term energy storage: Among them, σ1 and σ2 represent the evaluation indicators of hourly short-term energy storage and daily long-term energy storage respectively. The larger the value, the stronger the effective load-carrying capacity.

10. A multi-time scale energy storage reliable capacity calculation system based on dynamic programming, characterized in that: include: An operation scheme solving module is used to construct an hourly short-term energy storage model and a daily long-term energy storage model based on the operation parameters of the energy storage, each of which includes multiple operation constraints and an operation cost minimum objective function of each energy storage; based on the output of thermal power units, the output of new energy units, system load, hourly short-term energy storage charging and discharging power and daily long-term energy storage charging and discharging power in each time period, a power grid system power balance constraint is constructed; based on the power grid system power balance constraint, the hourly short-term energy storage model and the daily long-term energy storage model are jointly solved to obtain a multi-time scale energy storage charging and discharging operation scheme; The effective load calculation module is used to calculate the reliable capacity based on the multi-time scale energy storage charging and discharging operation scheme; the reliable capacity includes the effective load capacity of hour-level short-term energy storage and day-level long-term energy storage.

Images