A method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks

Abstract

This application provides a multi-timescale capacity configuration method for energy storage in flexible interconnected distribution networks. After obtaining typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, the method can determine the multi-scenario demands of the flexible interconnected distribution network at different time scales and the output control strategy of the energy storage system in the flexible interconnected distribution network in response to these multi-scenario demands based on the typical data. In this way, a time-series simulation model can be built based on the multi-scenario demands and the output control strategy. This time-series simulation model can be used to simulate the charging and discharging power and capacity consumption of the energy storage system after responding to multi-scenario demands according to the output control strategy under different pre-configured energy storage capacity schemes. Then, the objective function with the goal of optimal economic efficiency and the corresponding constraints are determined. After optimizing the objective function under these constraints, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system can be determined.

Description

Technical Field

[0001] This application relates to the field of energy storage configuration planning technology for flexible interconnected distribution networks, and in particular to a multi-timescale capacity configuration method for energy storage in flexible interconnected distribution networks. Background Technology

[0002] Currently, building a new power system with new energy sources as the mainstay is not only an inevitable requirement for energy and power transformation, but also a key path to achieving the "dual carbon" goal. With the large-scale integration of distributed power sources into distribution networks, the intermittency and variability of highly permeable renewable energy sources bring new challenges to the operation of flexible interconnected distribution networks. Therefore, seeking stable and efficient methods for integrating new energy sources into the grid is an urgent problem to be solved. Energy storage systems have characteristics such as energy time-shifting, rapid response, and flexible deployment. With the rapid development of energy storage technology, the new structure of "new energy + energy storage" is widely used and developed. The coupling of energy storage and new energy can not only improve the level of new energy absorption, but also provide ancillary services such as power prediction compensation and frequency regulation, thereby reducing the negative impact of new energy grid integration on flexible interconnected distribution networks and improving the network-friendliness and economic efficiency of new energy integration.

[0003] However, on the one hand, the scale of new energy access is constantly expanding, and flexible interconnected distribution network systems have multiple requirements for renewable energy grid connection and active support. Existing energy storage systems cannot meet these multiple requirements. On the other hand, existing energy storage systems have high energy storage costs and low profitability. How to optimize the capacity of energy storage systems to reduce energy storage costs and increase profitability while meeting the multiple requirements of flexible interconnected distribution network systems for renewable energy grid connection and active support is also an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency that existing energy storage systems not only fail to meet the multiple requirements of flexible interconnected distribution network systems for renewable energy grid connection and active support, but also fail to optimize the capacity configuration of energy storage systems while meeting these multiple requirements.

[0005] This application provides a method for configuring energy storage capacity across multiple time scales for flexible interconnected distribution networks, the method comprising:

[0006] Acquire typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, and determine the multi-scenario demand of the flexible interconnected distribution network at different time scales based on the typical data, as well as the output control strategy of the energy storage system in the flexible interconnected distribution network when responding to the multi-scenario demand.

[0007] Based on the multi-scenario requirements and the output control strategy, a time-series simulation model is built. Using the time-series simulation model, the charging and discharging power and capacity consumption of the energy storage system in response to the multi-scenario requirements are simulated under different pre-configured energy storage capacity schemes, according to the output control strategy.

[0008] A target function with optimal economic efficiency and corresponding constraints are determined. Under these constraints, the target function is optimized based on the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes, and the solution results of the target function under different energy storage capacity schemes are obtained.

[0009] Based on the solution results of the objective function under different energy storage capacity schemes, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system is determined.

[0010] Optionally, the typical data includes the rated frequency, real-time frequency, frequency regulation coefficient, and inertia coefficient of the flexible interconnected distribution network; the rated power, predicted power, and actual output power of renewable energy grid connection; and the grid connection peak shaving line and grid connection valley filling line of the system nodes; the multi-scenario requirements include frequency regulation requirements, power prediction compensation requirements, and renewable energy consumption requirements.

[0011] The determination of the multi-scenario requirements of the flexible interconnected distribution network at different time scales based on the typical data includes:

[0012] Based on the rated frequency, real-time frequency, frequency regulation coefficient, inertia coefficient of the flexible interconnected distribution network, and the rated power of new energy grid connection, the frequency regulation requirements of the flexible interconnected distribution network under different time scales are determined.

[0013] Based on the predicted power and actual output power of new energy grid connection in the flexible interconnected distribution network, the power prediction compensation requirements of the flexible interconnected distribution network at different time scales are determined.

[0014] Based on the grid-connected peak shaving line and grid-connected valley filling line of the system nodes in the flexible interconnected distribution network, the renewable energy consumption demand of the flexible interconnected distribution network under different time scales is determined.

[0015] Optionally, the energy storage system is a dual-battery structure, and each battery in the dual-battery structure has a different charging and discharging state. When any battery is fully charged or fully discharged, the charging and discharging states of the two battery groups are switched.

[0016] The determination of the output control strategy for the energy storage system in the flexible interconnected distribution network in response to the multi-scenario demands includes:

[0017] The actual output value of renewable energy in the flexible interconnected distribution network under the renewable energy consumption demand is obtained, and the actual output value of renewable energy is compared with the preset peak shaving and valley filling line of the energy storage system.

[0018] If the actual output value of the new energy source is outside the preset peak shaving and valley filling line, the real-time frequency of the flexible interconnected distribution network under the frequency regulation demand is obtained, and the real-time frequency is compared with the preset frequency adjustment dead zone of the energy storage system.

[0019] If the real-time frequency is not within the preset frequency adjustment dead zone, the energy storage system is activated to participate in the primary frequency regulation of the flexible interconnected distribution network.

[0020] If the real-time frequency is within the preset frequency adjustment dead zone, the energy storage system is used to absorb new energy in the flexible interconnected distribution network.

[0021] If the actual output value of the new energy source is between the preset peak shaving and valley filling lines, and the real-time frequency is not within the preset frequency adjustment dead zone, then the energy storage system is activated to participate in the primary frequency regulation of the flexible interconnected distribution network.

[0022] If the actual output value of the new energy is between the preset peak shaving and valley filling lines, and the real-time frequency is within the preset frequency adjustment dead zone, then the actual output power of the new energy grid connection of the flexible interconnected distribution network under the power prediction compensation requirement is obtained, and the actual output power is compared with the preset power prediction error band of the energy storage system.

[0023] If the actual output power is outside the upper and lower limits of the preset power prediction error band, the energy storage system is used to perform power prediction compensation on the flexible interconnected distribution network.

[0024] If the actual output power is within the upper and lower limits of the preset power prediction error band, then the state of charge imbalance of the energy storage system at the current moment is determined, and the state of charge imbalance is compared with the preset imbalance range.

[0025] If the state of charge imbalance is outside the upper and lower limits of the preset imbalance range, the energy storage system is controlled to perform adaptive charging and discharging.

[0026] If the state of charge imbalance is within the upper and lower limits of the preset imbalance range, then there is no need to control the energy storage system to output power at the current moment.

[0027] Optionally, the formula for calculating the state-of-charge imbalance of the energy storage system at the current moment is:

[0028] A(t) = 2 × S soc (t)-(Ssocmax +S socmin )

[0029] Where A(t) represents the state-of-charge imbalance of the energy storage system at time t, and S SOC (t) represents the state of charge of the energy storage system at time t, S soc,min It is the lower limit of the state of charge of the energy storage system, S soc,max This represents the upper limit of the state of charge of the energy storage system.

[0030] The energy storage system outputs P during adaptive charging and discharging. bess for:

[0031] P bess =A(t)×Er

[0032] Er represents the rated capacity of the battery in the energy storage system.

[0033] Optionally, the step of using the time-series simulation model to simulate the charging and discharging power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes, according to the output control strategy, in response to the multi-scenario demands, includes:

[0034] Different pre-configured energy storage capacity schemes are input into the time-series simulation model, and relevant parameters of the flexible interconnected distribution network under multiple scenario requirements and simulation cycles corresponding to different energy storage capacity schemes are configured in the time-series simulation model.

[0035] For each energy storage capacity scheme, the time-series simulation model is used to simulate the charging and discharging power and capacity consumption of the energy storage system in response to the multi-scenario demands according to the output control strategy within the corresponding simulation period.

[0036] Optionally, the objective function aimed at achieving optimal economic efficiency is formulated as follows:

[0037] f1 = max(S) x +S y +S f -C bess )

[0038] Among them, S x For the benefits of energy storage systems participating in the consumption of new energy sources, S y For the benefits of energy storage systems participating in power prediction compensation, S f For the benefits of energy storage systems participating in frequency regulation, C bess The cost of energy storage batteries and the revenue S from the energy storage system's participation in the consumption of new energy sources. x It consists of two parts, one part being the peak shaving and valley filling revenue S. x1 The other part is the revenue from selling electricity. x2The calculation formulas are as follows:

[0039] S x1 =K b Q xian

[0040] S x2 =S dianjia ×Q binwang

[0041] Among them, K b Q is the compensation coefficient per unit of electricity. xian S is used to store electricity for peak shaving and valley filling. dianjia The grid-connected electricity price per unit of energy, Q binwang It is used for energy storage to participate in peak shaving.

[0042] Optionally, the energy storage system participates in power prediction compensation, and the resulting benefit S... y It consists of two parts: automatic power control service compensation and electricity sales revenue. The calculation formula for the automatic power control service compensation R is as follows.

[0043] R = Ks × D × [ln(K pd )+1]×YAPC

[0044] Among them, YAPC is the automatic power control regulation performance compensation standard, K pd D represents the unit's regulation performance index for the day, and D represents the regulation depth.

[0045] Optionally, the benefit S of the energy storage system participating in frequency regulation f It consists of two parts, one part being frequency modulation mileage compensation S. f1 The other part is frequency modulation capacity compensation S f2 The calculation formulas are as follows:

[0046]

[0047]

[0048] Where N is the total number of trading sessions for the day, and D i,t The adjustment mileage of frequency modulation unit i during trading period t. B represents the comprehensive frequency modulation performance index of frequency modulation unit i during trading period t. t The clearing price for frequency regulation mileage during trading session t. C is the adjustment coefficient for frequency modulation unit i. i,t B represents the winning frequency modulation capacity of frequency modulation unit i during trading period t. Cp This is the price for compensation of frequency modulation capacity.

[0049] Optionally, the cost C of the energy storage batterybess It consists of three parts, namely the device cost C of the energy storage battery. bsys_p Operation and maintenance costs C yw And the cost of life loss C loss The calculation formulas are as follows:

[0050]

[0051] C yw =c pyw C bsys_p

[0052]

[0053] Among them, C bsys_p For the device cost of energy storage batteries, C E η is the unit capacity cost coefficient for energy storage batteries, t is the configuration time of energy storage batteries, and η is the unit capacity cost coefficient. b For power conversion efficiency, C P P is the unit power cost coefficient for energy storage batteries. rat Where i is the rated power of the energy storage battery, i is the discount rate, and N is the service life; C yw For the operation and maintenance costs of energy storage batteries, c pyw N is the operation and maintenance coefficient of the unit investment cost of energy storage batteries. BE The maximum cycle life of energy storage provided to energy storage battery manufacturers; C loss The cost of energy storage battery lifespan is denoted by C, where n is the total number of charge / discharge cycles over the battery's entire lifespan. s,k This represents the lifetime loss cost during the k-th charge-discharge cycle.

[0054] Optionally, the constraints include the charging and discharging power of the energy storage system at different times, and the battery capacity in the energy storage system;

[0055] The formulas for the constraints corresponding to the objective function aimed at achieving optimal economic efficiency are as follows:

[0056] The relationship between the charging and discharging power of the energy storage system and the battery capacity in the energy storage system is as follows:

[0057]

[0058] Among them, E BAT,n Let E be the battery charge in the energy storage system at time n. BAT,0 P represents the initial charge of the battery in the energy storage system. BAT,n The charging and discharging power of the energy storage system;

[0059] The energy level of a battery in an energy storage system is expressed using the state of charge (SOC) as follows:

[0060]

[0061] Among them, S SOC,n Let n be the state of charge of the battery in the energy storage system at time n. The rated capacity of the battery in the energy storage system;

[0062] The battery capacity constraint in the energy storage system is:

[0063] S soc,min ≤S SOC,n ≤S soc,max

[0064] Among them, S soc,min S represents the lower limit of the battery's state of charge. soc,max This represents the upper limit of the battery's state of charge.

[0065] The charging and discharging power constraints of the energy storage system are:

[0066]

[0067] Among them, P pcs This represents the maximum charging and discharging power of the energy storage system.

[0068] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0069] This application provides a multi-timescale capacity configuration method for energy storage in flexible interconnected distribution networks. After obtaining typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, the method can determine the multi-scenario demands of the flexible interconnected distribution network at different time scales, and the output control strategy of the energy storage system in the flexible interconnected distribution network in response to these multi-scenario demands. This allows for the construction of a time-series simulation model based on the multi-scenario demands and output control strategy. This model is then used to simulate the charging and discharging power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes, responding to multi-scenario demands according to the output control strategy. Next, this application can determine the objective function and corresponding constraints with the goal of optimal economic efficiency. Under these constraints, the method then calculates the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes. By optimizing the objective function based on discharge power and capacity consumption, the solution results of the objective function under different energy storage capacity schemes are obtained. Finally, based on the solution results of the objective function under different energy storage capacity schemes, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system can be determined. This allows existing energy storage systems to meet the multiple needs of flexible interconnected distribution network systems for renewable energy grid connection and active support. It also allows for the construction of a multi-functional, multi-timescale capacity configuration model for energy storage based on time-series simulation models, based on multi-scenario needs and output control strategies, and the optimization of energy storage capacity with the goal of optimal economic efficiency. This not only improves the overall economic efficiency of energy storage equipment and ensures that the energy storage system has the ability to adapt to complex scenarios, but also guarantees the return on investment even when energy storage costs are still high. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 A flowchart illustrating a multi-timescale capacity configuration method for energy storage in a flexible interconnected distribution network, provided as an embodiment of this application;

[0072] Figure 2 This application provides a topology diagram of a flexible interconnected distribution network with dual-battery energy storage.

[0073] Figure 3 A comparison chart of net benefits of an energy storage system in a flexible interconnected distribution network under different energy storage capacity configuration schemes, provided in an embodiment of this application. Detailed Implementation

[0074] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0075] Currently, on the one hand, the scale of new energy access is constantly expanding, and flexible interconnected distribution network systems have multiple requirements for renewable energy grid connection and active support. Existing energy storage systems cannot meet these multiple requirements. On the other hand, existing energy storage systems have high energy storage costs and low profitability. How to optimize the capacity of energy storage systems to reduce energy storage costs and increase profitability while meeting the multiple requirements of flexible interconnected distribution network systems for renewable energy grid connection and active support is also an urgent problem to be solved.

[0076] Based on this, this application proposes the following technical solution, as detailed below:

[0077] In one embodiment, such as Figure 1 As shown, Figure 1 This application provides a flowchart illustrating a multi-timescale capacity configuration method for energy storage in flexible interconnected distribution networks. The method may include:

[0078] S110: Obtain typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, and determine the multi-scenario demand of the flexible interconnected distribution network at different time scales based on the typical data, as well as the output control strategy of the energy storage system in the flexible interconnected distribution network when responding to multi-scenario demand.

[0079] In this step, when optimizing the capacity of the energy storage system in the flexible interconnected distribution network to meet the multiple needs of the flexible interconnected distribution network system for renewable energy grid connection and active support, typical data of the flexible interconnected distribution network connected to new energy sources can be obtained first within a preset historical period. Then, based on the typical data, the multi-scenario needs of the flexible interconnected distribution network at different time scales and the output control strategy of the energy storage system in response to multi-scenario needs can be determined.

[0080] It is understandable that, in order to optimize the capacity configuration of the energy storage system, this application can first determine the multi-functional and multi-scenario requirements of the flexible interconnected distribution network, and then determine the output control strategy of the energy storage system in response to the multi-functional and multi-scenario requirements. In this way, a corresponding time-series simulation model can be built, and the output of the energy storage system under different energy storage capacity schemes can be simulated through the time-series simulation model to determine the optimal configuration capacity.

[0081] Based on this, this application can obtain and analyze typical data from the past one or two years of flexible interconnected distribution networks connected to new energy sources to determine the multi-scenario demands of the flexible interconnected distribution network at different time scales. For example, this application can obtain annual frequency data, power output data of new energy power plants, day-ahead forecast data, and load peak shaving and valley filling data of the flexible interconnected distribution network system, and use these to calculate the multi-scenario demands of the flexible interconnected distribution network, such as frequency regulation demand, power forecasting and compensation demand, and new energy consumption demand.

[0082] Next, in order to build a time-series simulation model to simulate the output of the energy storage system in response to multiple scenario demands, this application can formulate an output control strategy for the energy storage system in response to multiple scenario demands based on the flexible interconnected distribution network at different time scales. This output control strategy can be a control strategy for the energy storage system in response to any one demand, or a control strategy for the energy storage system in response to multiple scenario demands. The control strategy can be the energy storage system participating in primary frequency regulation, the energy storage system consuming new energy sources, the energy storage system performing power prediction compensation, or the energy storage system performing adaptive charging and discharging, etc. The specific settings can be determined according to the actual scenario requirements, and no restrictions are imposed here.

[0083] S120: Based on multi-scenario requirements and output control strategies, a time-series simulation model is built. Using the time-series simulation model, the charging and discharging power and capacity consumption of the energy storage system in response to multi-scenario requirements are simulated under different pre-configured energy storage capacity schemes, according to the output control strategy.

[0084] In this step, after determining the multi-scenario demands of the flexible interconnected distribution network at different time scales through S110, and the output control strategy of the energy storage system in the flexible interconnected distribution network in response to the multi-scenario demands, this application can build a time-series simulation model based on the multi-scenario demands and the corresponding output control strategy. The input of the time-series simulation model can be the relevant parameters of the flexible interconnected distribution network under different scenario demands and the pre-configured different energy storage capacity schemes. The time-series simulation model can use the pre-configured output control strategy of the energy storage system to simulate the charging and discharging power and capacity consumption of the energy storage system after responding to the multi-scenario demands according to the output control strategy under different energy storage capacity schemes. In this way, the optimal configuration capacity can be selected based on the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes.

[0085] S130: Determine the objective function and corresponding constraints with the goal of achieving optimal economic efficiency. Under the constraints, optimize the objective function based on the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes, and obtain the solution results of the objective function under different energy storage capacity schemes.

[0086] In this step, when determining the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system, we can first construct an objective function with optimal economic efficiency and corresponding constraints. Then, the charging and discharging power and capacity consumption obtained by the time-series simulation model of the energy storage system responding to multi-scenario demands according to the output control strategy under different energy storage capacity schemes are input into the objective function. Under the constraints, the objective function is optimized and solved. In this way, the solution results of the objective function under different energy storage capacity schemes can be obtained. The solution results show the net energy storage benefit of the energy storage system under different energy storage capacity schemes. Therefore, by solving the objective function, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system can be determined.

[0087] Specifically, when determining the objective function and corresponding constraints for optimal economic efficiency, this application needs to consider not only the benefits of the energy storage system participating in different scenarios of flexible interconnected distribution networks, but also the losses and costs of the energy storage system itself, as well as the charging and discharging power of the energy storage system at different times and the battery capacity in the energy storage system. Under these circumstances, an objective function and corresponding constraints for optimal economic efficiency are constructed. In this way, once the charging and discharging power and capacity consumption of the energy storage system are obtained, the full life cycle cost and auxiliary frequency regulation benefits under the current energy storage capacity can be calculated through the objective function and constraints, and the net energy storage benefit value under the current combination can be output.

[0088] Furthermore, this application can either determine the objective function and corresponding constraints with the goal of optimal economic efficiency after building the time-series simulation model, and then use the objective function to solve for the net energy storage benefit value of the energy storage system after simulating the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes and responding to multi-scenario demands according to the output control strategy, using the time-series simulation model; or determine the objective function and corresponding constraints with the goal of optimal economic efficiency at the same time as building the time-series simulation model, or before building the time-series simulation model, so that the objective function and corresponding constraints can be included when building the time-series simulation model, so that the final time-series simulation model can directly output the charging and discharging power and capacity consumption of the energy storage system after responding to multi-scenario demands under different energy storage capacity schemes.

[0089] S140: Based on the solution results of the objective function under different energy storage capacity schemes, determine the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system.

[0090] In this step, after obtaining the solution results of the objective function under different energy storage capacity schemes through S130, this application can determine the optimal configuration capacity that maximizes the net energy storage benefit value of the energy storage system based on the solution results of the objective function under different energy storage capacity schemes.

[0091] Specifically, after obtaining the solution results under different energy storage capacity schemes, this application can determine the net energy storage benefit value of the energy storage system under different energy storage capacity schemes. Then, this application can compare the net energy storage benefit value of the energy storage system under each energy storage capacity scheme to determine the energy storage capacity scheme that maximizes the net energy storage benefit value of the energy storage system, and take this energy storage capacity scheme as the optimal configuration capacity.

[0092] In the above embodiments, after obtaining typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, the multi-scenario demands of the flexible interconnected distribution network at different time scales and the output control strategy of the energy storage system in the flexible interconnected distribution network in response to these multi-scenario demands can be determined based on the typical data. This allows for the construction of a time-series simulation model based on the multi-scenario demands and output control strategy. This time-series simulation model can then be used to simulate the charging and discharging power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes, based on the output control strategy in response to the multi-scenario demands. Next, this application can determine the objective function and corresponding constraints with optimal economic efficiency as the goal. Under these constraints, based on the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes, the objective function can be optimized. The function is optimized and solved to obtain the solution results of the objective function under different energy storage capacity schemes. Finally, based on the solution results of the objective function under different energy storage capacity schemes, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system can be determined. This allows the existing energy storage system to meet the multiple needs of flexible interconnected distribution network systems for renewable energy grid connection and active support. It also allows for the construction of a multi-functional, multi-time-scale capacity configuration model for energy storage based on time-series simulation models, based on multi-scenario needs and output control strategies, and the optimization of energy storage capacity with the goal of optimal economic efficiency. This not only improves the overall economic efficiency of energy storage equipment and ensures that the energy storage system has the ability to adapt to complex scenarios, but also guarantees the return on investment even when energy storage costs are still high.

[0093] In one embodiment, the typical data includes the rated frequency, real-time frequency, frequency regulation coefficient, and inertia coefficient of the flexible interconnected distribution network; the rated power, predicted power, and actual output power of new energy grid connection; and the grid connection peak shaving line and grid connection valley filling line of the system nodes; the multi-scenario requirements include frequency regulation requirements, power prediction compensation requirements, and new energy consumption requirements.

[0094] S110, based on the typical data, determines the multi-scenario requirements of the flexible interconnected distribution network at different time scales, which may include:

[0095] S1110: Based on the rated frequency, real-time frequency, frequency regulation coefficient, inertia coefficient, and rated power of new energy grid connection of the flexible interconnected distribution network, determine the frequency regulation requirements of the flexible interconnected distribution network at different time scales.

[0096] S1111: Based on the predicted power and actual output power of new energy grid connection in the flexible interconnected distribution network, determine the power prediction compensation requirements of the flexible interconnected distribution network at different time scales.

[0097] S1112: Based on the grid-connected peak shaving line and grid-connected valley filling line of the system nodes in the flexible interconnected distribution network, determine the renewable energy consumption demand of the flexible interconnected distribution network at different time scales.

[0098] In this embodiment, when determining the multi-scenario requirements of the flexible interconnected distribution network at different time scales, different data from typical data can be selected for calculation based on different scenario requirements.

[0099] Specifically, in calculating the frequency regulation demand P of the flexible interconnected distribution network at different time scales... f At that time, it can be based on the rated frequency f of the flexible interconnected distribution network. N Real-time frequency f pll Frequency modulation coefficient K f Inertia coefficient T j and the rated power P of new energy grid connection N The calculation is performed using the following formula:

[0100]

[0101] Calculating the power prediction and compensation demand P of flexible interconnected distribution networks at different time scales yu At that time, the predicted power P of new energy grid connection in the flexible interconnected distribution network can be used as a basis. yuce and actual output power P shiji The calculation is performed using the following formula:

[0102] P yu =P shiji -P yuce

[0103] Calculating the renewable energy consumption demand P of flexible interconnected distribution networks at different time scales xiaona At that time, the peak shaving line P of the system node in the flexible interconnected distribution network can be used as a reference. shangxian And the valley filling line P xiaona The calculation is performed using the following formula:

[0104]

[0105] The above calculation formula can be used to calculate the demand of flexible interconnected distribution networks under different time scales in multiple scenarios, and then the output control strategy of the energy storage system in response to the demand of multiple scenarios can be determined based on the demand of multiple scenarios.

[0106] In one embodiment, such as Figure 2 As shown, Figure 2 This is a topology diagram of a flexible interconnected distribution network with dual-battery energy storage provided in an embodiment of this application; the energy storage system is a dual-battery structure, and the charging and discharging states of each battery group in the dual-battery structure are different. When any battery group is fully charged or fully discharged, the charging and discharging states of the two battery groups are switched.

[0107] S110 may include determining the output control strategy of the energy storage system in the flexible interconnected distribution network when responding to the multi-scenario demands, which may include:

[0108] S1113: Obtain the actual output value of the new energy in the flexible interconnected distribution network under the new energy consumption demand, and compare the actual output value of the new energy with the preset peak shaving and valley filling line of the energy storage system.

[0109] S1114: If the actual output value of the new energy source is outside the preset peak shaving and valley filling line, then obtain the real-time frequency of the flexible interconnected distribution network under the frequency regulation demand, and compare the real-time frequency with the preset frequency adjustment dead zone of the energy storage system.

[0110] S1115: If the real-time frequency is not within the preset frequency adjustment dead zone, then the energy storage system is activated to participate in the primary frequency regulation of the flexible interconnected distribution network.

[0111] S1116: If the real-time frequency is within the preset frequency adjustment dead zone, then the energy storage system is used to absorb new energy from the flexible interconnected distribution network.

[0112] S1117: If the actual output value of the new energy source is between the preset peak shaving and valley filling lines, and the real-time frequency is not within the preset frequency adjustment dead zone, then the energy storage system is started to participate in the primary frequency regulation of the flexible interconnected distribution network.

[0113] S1118: If the actual output value of the new energy is between the preset peak shaving and valley filling lines, and the real-time frequency is within the preset frequency adjustment dead zone, then the actual output power of the new energy grid connection of the flexible interconnected distribution network under the power prediction compensation requirement is obtained, and the actual output power is compared with the preset power prediction error band of the energy storage system.

[0114] S1119: If the actual output power is outside the upper and lower limits of the preset power prediction error band, then the energy storage system is used to perform power prediction compensation on the flexible interconnected distribution network.

[0115] S1120: If the actual output power is within the upper and lower limits of the preset power prediction error band, then determine the state of charge imbalance of the energy storage system at the current moment, and compare the state of charge imbalance with the preset imbalance range.

[0116] S1121: If the state of charge imbalance is outside the upper and lower limits of the preset imbalance range, the energy storage system is controlled to perform adaptive charging and discharging.

[0117] S1122: If the state of charge imbalance is within the upper and lower limits of the preset imbalance range, then there is no need to control the energy storage system to output power at the current moment.

[0118] In this embodiment, as Figure 2 As shown, the energy storage system in the flexible interconnected distribution network of this application can be a dual-battery structure. Furthermore, to avoid frequent battery switching, the energy storage system can be divided into two groups with different charging and discharging states. When either group of batteries is fully charged or fully discharged, the charging and discharging states of the two groups are switched. This not only enables functions such as frequency regulation, power prediction compensation, and renewable energy consumption, thereby meeting the multiple needs of the flexible internet across multiple time scales and improving the utilization rate of energy storage devices, but also considers the control strategy and charge imbalance of the energy storage batteries. After participating in various scenarios, the battery can automatically recover its state of charge, keeping it in an intermediate state as much as possible to improve the lifespan of the energy storage and better respond to the multiple needs of the flexible interconnected distribution network. Moreover, the designed dual-battery structure can prevent frequent switching of the battery's charging and discharging state, further extending the battery's lifespan.

[0119] Based on this, this application designs an output control strategy for an energy storage system, which can assist the energy storage system in responding to the multi-scenario needs of flexible interconnected distribution networks.

[0120] Specifically, this application can pre-set corresponding adjustment parameters according to multiple scenarios of the flexible interconnected distribution network, such as preset frequency regulation dead zone, preset power prediction error band, and preset peak shaving and valley filling line; then, according to the actual operation needs of the flexible interconnected distribution network, the actual output value of new energy is divided into two operating conditions: operating condition one is when the actual output value of new energy is outside the peak shaving and valley filling line, and operating condition two is when the actual output value of new energy is between the peak shaving and valley filling lines; in operating condition one, when the real-time frequency of the system is not within the preset frequency regulation dead zone, the energy storage system starts to participate in primary frequency regulation; when the real-time frequency of the system is within the preset frequency regulation dead zone, the energy storage system performs new energy consumption; in operating condition two, when... If the system's real-time frequency is outside the preset frequency regulation dead zone, the energy storage system will initiate primary frequency regulation. When the system's real-time frequency is within the preset frequency regulation dead zone, power prediction compensation will be performed. Specifically, if the actual grid-connected power of the renewable energy source is outside the upper and lower limits of the preset power prediction error band, the energy storage system will perform power prediction compensation. If the actual grid-connected power of the renewable energy source is within the upper and lower limits of the preset power prediction error band, the current state of charge (SOC) imbalance of the energy storage system will be calculated. If the SOC imbalance occurs, the energy storage system will perform adaptive charging and discharging. If the SOC balance occurs, the energy storage system will not require output control at the current moment and will proceed to the next cycle. This is how the output control strategy of the energy storage system is implemented.

[0121] In one embodiment, the formula for calculating the state-of-charge imbalance of the energy storage system at the current moment is:

[0122] A(t) = 2 × S soc (t)-(S socmax +S socmin )

[0123] Where A(t) represents the state-of-charge imbalance of the energy storage system at time t, and S SOC (t) represents the state of charge of the energy storage system at time t, S soc,min It is the lower limit of the state of charge of the energy storage system, S soc,max This represents the upper limit of the state of charge of the energy storage system.

[0124] The energy storage system outputs P during adaptive charging and discharging. bess for:

[0125] P bess =A(t)×Er

[0126] Er represents the rated capacity of the battery in the energy storage system.

[0127] In one embodiment, S120 utilizes the time-series simulation model to simulate the charging and discharging power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes, based on the output control strategy, in response to the multi-scenario demands. This may include:

[0128] S121: Input the pre-configured different energy storage capacity schemes into the time-series simulation model, and configure the relevant parameters of the flexible interconnected distribution network under multiple scenario requirements, as well as the simulation period corresponding to different energy storage capacity schemes, in the time-series simulation model.

[0129] S122: For each energy storage capacity scheme, the time-series simulation model is used to simulate the charging and discharging power and capacity consumption of the energy storage system in response to the multi-scenario demand according to the output control strategy within the corresponding simulation period.

[0130] In this embodiment, when simulating the energy storage system responding to multiple scenario demands, the time-series simulation model can first input different pre-configured energy storage capacity schemes into the time-series simulation model, and configure the relevant parameters of the flexible interconnected distribution network under multiple scenario demands, as well as the simulation period corresponding to different energy storage capacity schemes in the time-series simulation model. In this way, for each energy storage capacity scheme, the time-series simulation model can be used to simulate the charging and discharging power and capacity consumption of the energy storage system after responding to multiple scenario demands according to the output control strategy within the corresponding simulation period.

[0131] Specifically, when building the time-series simulation model, this application can combine the objective function and constraints. In this way, when using the time-series simulation model to simulate the output of the energy storage system in response to the needs of multiple scenarios, the real-time frequency data of the flexible interconnected distribution network, the energy storage life cycle cost data, the auxiliary revenue data, the rated power, and other data can be input into the time-series simulation model. Then, the parameters of the time-series simulation model are initialized. According to the current energy storage capacity scheme, the upper and lower limits of the state of charge of the energy storage battery, the charge-discharge conversion efficiency are set, the upper and lower boundaries and step size of the energy storage power and reasonable duration are selected, the rated power of the energy storage system converter and the rated capacity of the battery are set, the simulation time T is set, the simulation start time t is set to 1, and the initial energy storage capacity of the energy storage battery pack is set. Once the parameters are initialized, the time-series simulation model can be used to simulate the energy storage system's output in response to multiple scenarios, and update the charging and discharging power and real-time capacity of the batteries in the energy storage system at the current moment. Then, it is determined whether the above steps have been completed at the current moment. If completed, let t = t + 1, calculate the capacity loss of the energy storage battery, and update the rated capacity of the energy storage battery. If not completed, continue to complete the above steps. Then, it can be determined whether the simulation cycle has ended. If the simulation cycle has not been completed, the time-series simulation model can be used to continue simulating the energy storage system's output in response to multiple scenarios, and update the charging and discharging power and real-time capacity of the batteries in the energy storage system at the current moment, until all are completed. If the simulation cycle has been completed, the life cycle cost and auxiliary frequency regulation benefits under the current energy storage capacity can be calculated, and then the net energy storage benefit value under the current combination can be output. Then, the parameters of the next energy storage capacity scheme can be configured, and finally the optimal configuration capacity corresponding to the maximum net energy storage benefit can be output.

[0132] In one specific implementation, this application can select typical data from one year of a flexible interconnected distribution network connected to new energy sources for analysis. The relevant economic and technical parameters are shown in Table 1.

[0133]

[0134] Table 1 shows the parameter indicators for the calculation example.

[0135] Next, the capacity can be configured according to the following steps:

[0136] Step 1: Import relevant new energy data; determine relevant calculation parameters for the energy storage system; import the actual annual output data, day-ahead forecast data, real-time frequency data, and peak-valley data of the new energy source; import the economic parameters of the energy storage system's entire life cycle cost, error band upper and lower limits, etc.

[0137] Step 2: Parameter initialization; set the upper and lower limits of the state of charge of the energy storage battery; select the power configuration boundary of the energy storage converter as 12MW-24MW, with a step size of 3MW; set the energy storage configuration duration boundary as 0.5h-2h, with a step size of 0.5 hours;

[0138] Step 3: Set the rated power of the inverter and the rated capacity of the battery in the energy storage system. Set the simulation time T; initialize the simulation operation time by setting t=1;

[0139] Step 4: Calculate the available charging and discharging power of energy storage at time t; calculate the charging and discharging power of energy storage using a multi-scenario collaborative operation strategy;

[0140] Step 5: Update the battery pack's capacity for the current cycle;

[0141] Step 7: Determine whether the above steps have completed a charge-discharge simulation cycle. Let t = t + 1, calculate the energy storage battery capacity loss, and continue to execute Step 4.

[0142] Step 8: Determine if the simulation cycle has ended; if the simulation cycle has not yet been completed, continue with Step 4 until it is completed; if the cycle is completed, calculate the total life cycle cost and all benefits under the current energy storage capacity; output the net energy storage benefit value under the current combination; continue with Step 2.

[0143] Step 9: Determine if all solutions have been traversed. If not, continue with Step 2. If completed, output the optimal energy storage configuration capacity corresponding to the maximum net benefit.

[0144] By performing the above calculations, the optimal configuration capacity that maximizes the net energy storage revenue of the energy storage system in this application can be obtained.

[0145] In one embodiment, the objective function aimed at achieving optimal economic efficiency is formulated as follows:

[0146] f1 = max(S) x +S y +S f -C bess )

[0147] Among them, S x For the benefits of energy storage systems participating in the consumption of new energy sources, S y For the benefits of energy storage systems participating in power prediction compensation, S f For the benefits of energy storage systems participating in frequency regulation, C bess The cost of energy storage batteries and the revenue S from the energy storage system's participation in the consumption of new energy sources. x It consists of two parts, one part being the peak shaving and valley filling revenue S. x1 The other part is the revenue from selling electricity. x2 The calculation formulas are as follows:

[0148] S x1 =K b Q xian

[0149] S x2 =S dianjia ×Q binwang

[0150] Among them, K b Q is the compensation coefficient per unit of electricity. xian S is used to store electricity for peak shaving and valley filling. dianjia The grid-connected electricity price per unit of energy, Q binwang It is used for energy storage to participate in peak shaving.

[0151] Optionally, the energy storage system participates in power prediction compensation, and the resulting benefit S... y It consists of two parts: automatic power control service compensation and electricity sales revenue. The calculation formula for the automatic power control service compensation R is as follows.

[0152] R = Ks × D × [ln(K pd )+1]×YAPC

[0153] Among them, YAPC is the automatic power control regulation performance compensation standard, K pd D represents the unit's regulation performance index for the day, and D represents the regulation depth.

[0154] In this embodiment, after establishing an objective function with economic efficiency as the goal and corresponding constraints, this application incorporates the objective function and constraints as part of the time-series simulation model. In this way, the time-series simulation model can directly calculate the net energy storage revenue value under each energy storage capacity scheme based on the objective function and constraints.

[0155] Indicatively, such as Figure 3 As shown, Figure 3 This is a comparison chart of the net benefits of an energy storage system in a flexible interconnected distribution network under different energy storage capacity configurations, provided in an embodiment of this application. Figure 3 Option 12 corresponds to the highest net benefit and the best economic performance, making it the optimal solution. At this point, the energy storage capacity is 18 MWh, and the charging / discharging power is 9 MW. Furthermore, as shown in Table 2, a comparative analysis of the profitability of energy storage in single-function and multi-function scenarios reveals that the overall profitability of multi-function energy storage systems is significantly higher than that of single-function energy storage systems.

[0156]

[0157] Table 2. Comparison of Revenue Between Multifunctional and Single-Functional Scenarios

[0158] In one embodiment, the benefit S of the energy storage system participating in frequency regulation f It consists of two parts, one part being frequency modulation mileage compensation S. f1 The other part is frequency modulation capacity compensation Sf2 The calculation formulas are as follows:

[0159]

[0160]

[0161] Where N is the total number of trading sessions for the day, and D i,t The adjustment mileage of frequency modulation unit i during trading period t. B represents the comprehensive frequency modulation performance index of frequency modulation unit i during trading period t. t The clearing price for frequency regulation mileage during trading session t. C is the adjustment coefficient for frequency modulation unit i. i,t B represents the winning frequency modulation capacity of frequency modulation unit i during trading period t. Cp This is the price for compensation of frequency modulation capacity.

[0162] In one embodiment, the cost C of the energy storage battery bess It consists of three parts, namely the device cost C of the energy storage battery. bsys_p Operation and maintenance costs C yw And the cost of life loss C loss The calculation formulas are as follows:

[0163]

[0164] C yw =c pyw C bsys_p

[0165]

[0166] Among them, C bsys_p For the device cost of energy storage batteries, C E η is the unit capacity cost coefficient for energy storage batteries, t is the configuration time of energy storage batteries, and η is the unit capacity cost coefficient. b For power conversion efficiency, C P P is the unit power cost coefficient for energy storage batteries. rat Where i is the rated power of the energy storage battery, i is the discount rate, and N is the service life; C yw For the operation and maintenance costs of energy storage batteries, c pyw N is the operation and maintenance coefficient of the unit investment cost of energy storage batteries. BE The maximum cycle life of energy storage provided to energy storage battery manufacturers; C loss The cost of energy storage battery lifespan is denoted by C, where n is the total number of charge / discharge cycles over the battery's entire lifespan. s,k This represents the lifetime loss cost during the k-th charge-discharge cycle.

[0167] In one embodiment, the constraints include the charging and discharging power of the energy storage system at different times, and the charge level of the batteries in the energy storage system.

[0168] The formulas for the constraints corresponding to the objective function aimed at achieving optimal economic efficiency are as follows:

[0169] The relationship between the charging and discharging power of the energy storage system and the battery capacity in the energy storage system is as follows:

[0170]

[0171] Among them, E BAT,n Let E be the battery charge in the energy storage system at time n. BAT,0 P represents the initial charge of the battery in the energy storage system. BAT,n The charging and discharging power of the energy storage system;

[0172] The energy level of a battery in an energy storage system is expressed using the state of charge (SOC) as follows:

[0173]

[0174] Among them, S SOC,n Let n be the state of charge of the battery in the energy storage system at time n. The rated capacity of the battery in the energy storage system;

[0175] The battery capacity constraint in the energy storage system is:

[0176] S soc,min ≤S SOC,n ≤S soc,max

[0177] Among them, S soc,min S represents the lower limit of the battery's state of charge. soc,max This represents the upper limit of the battery's state of charge.

[0178] The charging and discharging power constraints of the energy storage system are:

[0179]

[0180] Among them, P pcs This represents the maximum charging and discharging power of the energy storage system.

[0181] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0182] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0183] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring energy storage capacity across multiple time scales for flexible interconnected distribution networks, characterized in that, The method includes: Acquire typical data of the flexible interconnected distribution network connected to new energy sources within a preset historical period, and determine the multi-scenario demand of the flexible interconnected distribution network at different time scales based on the typical data, as well as the output control strategy of the energy storage system in the flexible interconnected distribution network when responding to the multi-scenario demand. Based on the multi-scenario requirements and the output control strategy, a time-series simulation model is built. Using the time-series simulation model, the charging and discharging power and capacity consumption of the energy storage system in response to the multi-scenario requirements are simulated under different pre-configured energy storage capacity schemes, according to the output control strategy. A target function with optimal economic efficiency and corresponding constraints are determined. Under these constraints, the target function is optimized based on the charging and discharging power and capacity consumption of the energy storage system under different energy storage capacity schemes, and the solution results of the target function under different energy storage capacity schemes are obtained. Based on the solution results of the objective function under different energy storage capacity schemes, the optimal configuration capacity that maximizes the net energy storage benefit of the energy storage system is determined. The typical data includes the rated frequency, real-time frequency, frequency regulation coefficient, and inertia coefficient of the flexible interconnected distribution network; the rated power, predicted power, and actual output power of new energy grid connection; and the grid connection peak shaving line and grid connection valley filling line of the system nodes. The multi-scenario requirements include frequency regulation requirements, power prediction compensation requirements, and new energy consumption requirements. The energy storage system is a dual-battery structure, and the charging and discharging states of each battery group in the dual-battery structure are different. When any battery group is fully charged or fully discharged, the charging and discharging states of the two battery groups are switched. The determination of the multi-scenario demands of the flexible interconnected distribution network at different time scales based on the typical data, and the output control strategy of the energy storage system in the flexible interconnected distribution network in response to the multi-scenario demands, includes: Based on the rated frequency, real-time frequency, frequency regulation coefficient, inertia coefficient, and rated power of new energy grid connection of the flexible interconnected distribution network, the frequency regulation requirements of the flexible interconnected distribution network under different time scales are determined. Based on the predicted power and actual output power of new energy grid connection in the flexible interconnected distribution network, the power prediction compensation requirements of the flexible interconnected distribution network at different time scales are determined. Based on the grid-connected peak shaving line and grid-connected valley filling line of the system nodes in the flexible interconnected distribution network, the renewable energy consumption demand of the flexible interconnected distribution network under different time scales is determined; The actual output value of renewable energy in the flexible interconnected distribution network under the renewable energy consumption demand is obtained, and the actual output value of renewable energy is compared with the preset peak shaving and valley filling line of the energy storage system. If the actual output value of the new energy source is outside the preset peak shaving and valley filling line, the real-time frequency of the flexible interconnected distribution network under the frequency regulation demand is obtained, and the real-time frequency is compared with the preset frequency adjustment dead zone of the energy storage system. If the real-time frequency is not within the preset frequency adjustment dead zone, the energy storage system is activated to participate in the primary frequency regulation of the flexible interconnected distribution network. If the real-time frequency is within the preset frequency adjustment dead zone, the energy storage system is used to absorb new energy in the flexible interconnected distribution network. If the actual output value of the new energy source is between the preset peak shaving and valley filling lines, and the real-time frequency is not within the preset frequency adjustment dead zone, then the energy storage system is activated to participate in the primary frequency regulation of the flexible interconnected distribution network. If the actual output value of the new energy is between the preset peak shaving and valley filling lines, and the real-time frequency is within the preset frequency adjustment dead zone, then the actual output power of the new energy grid connection of the flexible interconnected distribution network under the power prediction compensation requirement is obtained, and the actual output power is compared with the preset power prediction error band of the energy storage system. If the actual output power is outside the upper and lower limits of the preset power prediction error band, the energy storage system is used to perform power prediction compensation on the flexible interconnected distribution network. If the actual output power is within the upper and lower limits of the preset power prediction error band, then the state of charge imbalance of the energy storage system at the current moment is determined, and the state of charge imbalance is compared with the preset imbalance range. If the state of charge imbalance is outside the upper and lower limits of the preset imbalance range, the energy storage system is controlled to perform adaptive charging and discharging. If the state of charge imbalance is within the upper and lower limits of the preset imbalance range, then there is no need to control the energy storage system to output power at the current moment.

2. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 1, characterized in that, The formula for calculating the state-of-charge imbalance of the energy storage system at the current moment is: in, S represents the state-of-charge imbalance of the energy storage system at time t. SOC (t) represents the state of charge of the energy storage system at time t, S soc,min It is the lower limit of the state of charge of the energy storage system, S soc,max This represents the upper limit of the state of charge of the energy storage system. The energy storage system outputs P during adaptive charging and discharging. bess for: P bess =A(t)×Er Er represents the rated capacity of the battery in the energy storage system.

3. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 1, characterized in that, The step of using the time-series simulation model to simulate the charging and discharging power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes, based on the output control strategy, in response to the demands of multiple scenarios, includes: Different pre-configured energy storage capacity schemes are input into the time-series simulation model, and relevant parameters of the flexible interconnected distribution network under multiple scenario requirements and simulation cycles corresponding to different energy storage capacity schemes are configured in the time-series simulation model. For each energy storage capacity scheme, the time-series simulation model is used to simulate the charging and discharging power and capacity consumption of the energy storage system in response to the multi-scenario demands according to the output control strategy within the corresponding simulation period.

4. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 1, characterized in that, The formula for the objective function aimed at achieving optimal economic efficiency is as follows: Among them, S x For the benefits of energy storage systems participating in the consumption of new energy sources, S y For the benefits of energy storage systems participating in power prediction compensation, S f For the benefits of energy storage systems participating in frequency regulation, C bess The cost of energy storage batteries and the revenue S from the energy storage system's participation in the consumption of new energy sources. x It consists of two parts, one part being the peak shaving and valley filling revenue S. x1 The other part is the revenue from selling electricity. x2 The calculation formulas are as follows: Among them, K b Q is the compensation coefficient per unit of electricity. xian S is used to store electricity for peak shaving and valley filling. dianjia The grid-connected electricity price per unit of energy, Q binwang It is used for energy storage to participate in peak shaving.

5. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 4, characterized in that, The benefit S of the energy storage system participating in power prediction compensation y It consists of two parts: automatic power control service compensation and electricity sales revenue. The calculation formula for the automatic power control service compensation R is as follows. Among them, YAPC is the automatic power control regulation performance compensation standard, K pd D represents the unit's regulation performance index for the day, and D represents the regulation depth.

6. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 4, characterized in that, The benefit S of the energy storage system participating in frequency regulation f It consists of two parts, one part being frequency modulation mileage compensation S. f1 The other part is frequency modulation capacity compensation S f2 The calculation formulas are as follows: Where N is the total number of trading sessions on that day. The adjustment mileage of frequency modulation unit i during trading period t. B represents the comprehensive frequency modulation performance index of frequency modulation unit i during trading period t. t The clearing price for frequency regulation mileage during trading session t. The adjustment coefficient for frequency modulation unit i is... B represents the winning frequency modulation capacity of frequency modulation unit i during trading period t. Cp This is the price for compensation of frequency modulation capacity.

7. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 4, characterized in that, The cost C of the energy storage battery bess It consists of three parts, namely the installation cost of energy storage batteries. Operation and maintenance costs and lifespan depreciation costs The calculation formulas are as follows: in, For the device cost of energy storage batteries, C E Here, t represents the unit capacity cost coefficient for energy storage batteries, and t represents the configuration time of the energy storage batteries. For power conversion efficiency, C P P is the unit power cost coefficient for energy storage batteries. rat Where i is the rated power of the energy storage battery, i is the discount rate, and N is the service life; For the operation and maintenance costs of energy storage batteries, c pyw N is the operation and maintenance coefficient of the unit investment cost of energy storage batteries. BE Maximum cycle life of energy storage provided to energy storage battery manufacturers; The cost of energy storage battery lifespan is denoted by C, where n is the total number of charge / discharge cycles over the battery's entire lifespan. s,k This represents the lifetime loss cost during the k-th charge-discharge cycle.

8. The method for multi-timescale capacity configuration of energy storage for flexible interconnected distribution networks according to claim 1 or 4, characterized in that, The constraints include the charging and discharging power of the energy storage system at different times, and the battery capacity in the energy storage system. The formulas for the constraints corresponding to the objective function aimed at achieving optimal economic efficiency are as follows: The relationship between the charging and discharging power of the energy storage system and the battery capacity in the energy storage system is as follows: in, Let n be the battery charge in the energy storage system at time n. This represents the initial charge level of the batteries in the energy storage system. The charging and discharging power of the energy storage system; The energy level of a battery in an energy storage system is expressed using the state of charge (SOC) as follows: in, Let n be the state of charge of the battery in the energy storage system at time n. The rated capacity of the battery in the energy storage system; The battery capacity constraint in the energy storage system is: in, This represents the lower limit of the battery's state of charge. This represents the upper limit of the battery's state of charge. The charging and discharging power constraints of the energy storage system are: Among them, P pcs This represents the maximum charging and discharging power of the energy storage system.

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