A multi-type energy storage system capacity configuration method suitable for multi-time scale peak regulation

Abstract

The present application relates to a kind of multi-type energy storage system capacity configuration method suitable for peak shaving auxiliary service, by applying multi-type energy storage in multiple time scales, give full play to the characteristics of multi-type energy storage strong flexibility, wide application range;Consider economic efficiency, safety, peak shaving effect and other factors to carry out multi-objective optimization analysis on multi-type energy storage configuration, reduce the economic cost of multi-type energy storage configuration while ensuring the effect of peak shaving;And through the scheduling calculation of actual application scene, improve the accuracy and reliability of multi-type energy storage capacity configuration, and have important guiding significance and engineering application value for the power capacity configuration of multi-type energy storage used for peak shaving.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system configuration technology, and in particular relates to a capacity configuration method for multiple types of energy storage systems suitable for peak shaving at multiple time scales. Background Technology

[0002] Renewable energy sources include solar, hydro, wind, biomass, and geothermal energy, among which photovoltaic (PV) and wind power generation are widely used. However, PV power generation is susceptible to fluctuations due to environmental factors such as weather and sunlight intensity; wind power generation is affected by climate and geographical factors, and both exhibit a degree of randomness and instability. With the large-scale grid connection of renewable energy sources such as wind and PV, the peak-to-valley difference in the power system is continuously widening, posing challenges to the stable operation of the power grid.

[0003] Peak shaving primarily refers to storing excess electricity during periods of low demand and releasing it during peak demand to meet the matching needs between power generation and load. For peak shaving ancillary services, traditional methods such as hydropower and thermal power peak shaving have two main drawbacks: firstly, they exhibit significant lag and cannot respond quickly to frequent fluctuations; secondly, peak shaving units frequently need to enter deep peak shaving phases, posing a risk to the safe operation of the system. Energy storage, on the other hand, offers advantages such as fast response time, flexible power capacity configuration, bidirectional power output, and, most importantly, electricity storage, effectively solving the peak shaving problem.

[0004] Selecting appropriate energy storage and configuring it rationally can ensure the economical, safe, and efficient operation of renewable energy power systems. Multi-type energy storage technologies, by coupling various energy storage systems with different power densities, energy densities, response times, and unit costs, are more flexible, economical, and reliable than single-type energy storage. When applied to peak-shaving ancillary services, they can ensure high economic efficiency while maintaining peak-shaving effectiveness. However, due to the large number of energy storage technologies involved, how to provide a capacity configuration method for multi-type energy storage systems suitable for peak-shaving across multiple time scales is a technical problem that urgently needs to be solved by those skilled in the art in the application of multi-type energy storage systems and peak-shaving services. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales. This method considers control strategies, configuration schemes, and scheduling schemes for multiple types of energy storage, including electricity storage, thermal storage, and gas storage, in peak shaving application scenarios at multiple time scales, in order to solve the power capacity configuration problem of multiple types of energy storage participating in peak shaving services, thereby making the entire peak shaving process more economical and efficient.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for configuring the capacity of various types of energy storage systems suitable for peak shaving across multiple time scales includes the following steps:

[0008] S1: Consider building multiple types of energy storage peak-shaving auxiliary service application scenarios across multiple time scales.

[0009] S2: Based on the application scenarios constructed in S1, construct multi-type energy storage planning models by combining peak-shaving power allocation and energy storage operation control strategies based on wavelet packet transform.

[0010] S3: Based on the planning model constructed in S2, perform optimization calculations on multiple types of energy storage systems based on various indicators and obtain preliminary results of energy storage configuration; the various indicators are economy, safety and peak-shaving effect.

[0011] S4: Based on the preliminary configuration results obtained from S3, power flow scheduling simulation is performed using actual operating parameters, and the final energy storage configuration result is obtained through iterative optimization.

[0012] The various types of energy storage systems described in this invention can be categorized based on physical characteristics into mechanical energy storage, thermo-mass energy storage, electrochemical energy storage, and electromagnetic energy storage, and can also be categorized based on application into energy-type energy storage and power-type energy storage. The types of energy storage systems they comprise include, but are not limited to, the following: lithium battery energy storage, vanadium redox flow battery energy storage, compressed air energy storage, pumped hydro storage, flywheel energy storage, thermal energy storage, hydrogen energy storage, superconducting magnetic energy storage, and supercapacitors.

[0013] In embodiments of the present invention, the peak-shaving auxiliary service of S1 needs to allocate multiple types of energy storage systems according to different time scales to ensure that peak shaving and valley filling effects are achieved while achieving optimal economic efficiency, wherein:

[0014] Long-term peak shaving uses a weekly (e.g., 1 week) cycle and an hourly (e.g., 1 hour) time period. Based on long-term renewable energy output forecasts and load forecasts, long-term peak shaving planning is carried out for various types of energy storage systems. It is suitable for long-term energy storage systems with low unit energy cost and unit power cost (e.g., capacity range of MW or above) and high energy storage level (e.g., monthly or above).

[0015] Day-ahead peak shaving uses a daily (e.g., 1 day) cycle and a minute (e.g., 15 minutes) time period. Based on the day-ahead renewable energy output forecast and load forecast, day-ahead peak shaving planning is carried out for various types of energy storage systems. It is suitable for medium and short-term energy storage systems that can provide power support capacity, have good economic efficiency, and can meet peak shaving needs.

[0016] Intraday peak shaving uses hours (e.g., 1 hour) as the cycle and minutes (e.g., 15 minutes) as the time period. It implements intraday peak shaving control for various types of energy storage based on the day-ahead peak shaving plan and intraday forecast results. It is suitable for short-term energy storage systems with fast response speed (e.g., seconds) and good economic efficiency.

[0017] Real-time peak shaving controls the output of various types of energy storage based on the daily peak shaving plan and the real-time output of renewable energy and load demand. It is suitable for short-term energy storage systems with fast response speed (e.g., less than seconds), high safety performance, and the ability to be frequently charged and discharged.

[0018] In an embodiment of the present invention, S2 performs peak power allocation based on wavelet packet transform according to different time scales, including:

[0019] ① Perform initial power decomposition using wavelet packet transform: Obtain the predicted output power and load prediction values ​​of renewable energy sources such as wind farms and photovoltaics, calculate the net load power, and decompose the net load power layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the obtained power sequence satisfies the constraint of the maximum change in grid-connected power. Take the current decomposition layer as the optimal layer.

[0020] ② Configure the capacity of the long-term peak-shaving energy storage section: Based on the low-frequency part of the power allocation, generate the long-term energy storage section configuration power that requires the low-frequency part of the power, and calculate the long-term energy storage section configuration capacity.

[0021] ③ Perform wavelet packet transform for secondary power decomposition: Update the predicted output power and load of renewable energy sources such as wind farms and photovoltaics. Based on the allocated output power for long-term peak shaving, calculate the net load power required to meet peak shaving. Decompose the net load power in this step layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the obtained power sequence meets the constraint of the maximum change in grid-connected power. Take the current decomposition level as the optimal level.

[0022] ④ Configure the day-ahead peak-shaving energy storage capacity: Based on the low-frequency part of the power allocation, generate the day-ahead energy storage configuration power for the required medium-frequency part of the power, and calculate the day-ahead energy storage configuration capacity.

[0023] ⑤ Configure the intraday peak-shaving energy storage capacity: Based on the high-frequency part of the power allocation, generate the intraday energy storage configuration power that requires the high-frequency part of the power, and calculate the intraday energy storage configuration capacity.

[0024] ⑥ Based on the allocated power output of long-term peak shaving, day-ahead peak shaving, and intraday peak shaving, as well as the real-time power output of renewable energy and loads, power allocation is carried out to calculate the power output required for real-time peak shaving.

[0025] In an embodiment of the present invention, the S2 energy storage operation control strategy is specifically a multi-type energy storage system charging and discharging simulation and control strategy. The multi-type energy storage system charging and discharging simulation includes the process of simulating the charging and discharging of the energy storage system, energy storage power constraints, and energy storage SOC upper and lower limit constraints.

[0026] The charging and discharging control strategies for various types of energy storage systems include methods such as the energy storage SOC classification method and the energy storage charging and discharging priority control method using fuzzy rules to control the charging and discharging of various types of energy storage systems.

[0027] By establishing fuzzy rules to determine the relationship between input and output quantities, the priority order of various types of energy storage in charge and discharge control can be obtained.

[0028] In an embodiment of the present invention, S3 specifically includes calculating a multi-type energy storage system index system that includes economy, safety, and peak-shaving effect:

[0029] First, calculate multiple indicators, as follows:

[0030] ① Economic efficiency, including but not limited to the calculation methods of life cycle cost or levelized cost of electricity, and the calculation methods of economic benefits including but not limited to the calculation methods of peak-valley arbitrage benefits and peak-shaving benefits.

[0031] ② Safety, the calculation methods for safety indicators include, but are not limited to, the calculation methods for safety accident rate and failure rate.

[0032] ③ Peak shaving effect, the peak shaving effect indicators include, but are not limited to, the calculation method of insufficient peak shaving in the system and the calculation method of wind and solar curtailment in the system.

[0033] Then, multi-type energy storage optimization configuration is performed based on multiple indicators, that is, optimization calculation based on multiple indicators. The implementation method is as follows:

[0034] A multi-objective optimization model is established using optimization algorithms to calculate the rated power capacity range of energy storage systems and optimize the configuration of various types of energy storage systems at different time scales.

[0035] Finally, capacity optimization is performed for energy storage involving multiple time scales. Specifically, this can be achieved by optimizing the same type of energy storage involving multiple time scales as follows:

[0036] Multi-time-domain charge-discharge superposition calculations are performed on the same type of energy storage involving multiple time scales. That is, charge-discharge simulations at different time scales are superimposed simultaneously to reduce costs without affecting peak-shaving performance, redetermine the power capacity range of this type of energy storage, and thus obtain preliminary results of multiple types of energy storage configurations.

[0037] In an embodiment of the present invention, S4 uses actual operating parameters to perform power flow scheduling simulation, and the implementation method is as follows:

[0038] Power system modeling mainly includes node power balance constraints, branch power flow constraints, energy storage output constraints, and energy storage SOC constraints. Since energy storage devices mainly regulate active power, DC power flow is used to model the power network.

[0039] The energy storage power constraints and energy storage SOC constraints are shown in S2. The scheduling operation results are then obtained. The scheduling simulation results are returned to S3 for iterative optimization until the configuration specification requirements are met, thus obtaining the final energy storage configuration result.

[0040] The advantages and positive effects of this invention are:

[0041] This invention provides a method for configuring the capacity of multiple types of energy storage systems suitable for peak shaving across multiple time scales: First, the peak shaving requirements of multiple types of energy storage systems are classified according to multiple time scales, and suitable types of energy storage are provided, fully leveraging the flexibility and wide applicability of multiple types of energy storage; Second, multi-objective optimization analysis is performed according to different time scales and the characteristics of different technical parameters in multiple types of energy storage, which can reduce economic costs while ensuring peak shaving effects; Finally, the preliminary results are placed into actual application scenarios for iterative calculation to obtain the final energy storage configuration result, improving the accuracy and reliability of energy storage capacity configuration calculation. Attached Figure Description

[0042] Figure 1 A schematic diagram illustrating the configuration process for various types of energy storage capacity.

[0043] Figure 2 This is a schematic diagram of a multi-timescale peak shaving scenario.

[0044] Figure 3 This is a schematic diagram of wavelet packet transform power allocation.

[0045] Figure 4 This is a graph of the membership function for fuzzy control.

[0046] Figure 5 Flowchart of the optimization solution steps for the NSGA-II algorithm.

[0047] Figure 6 This is a flowchart for configuring energy storage capacity across multiple time scales.

[0048] Figure 7 Simulation structures for various types of energy storage systems. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the embodiments described in this section are only some, not all, of the embodiments of the present invention. Any other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the protection scope of the present invention. The specific embodiments are described in detail below:

[0050] A capacity configuration method for multiple types of energy storage systems suitable for peak shaving across multiple time scales, such as Figure 1 As shown, it includes the following steps:

[0051] 1) Consider constructing various types of energy storage peak-shaving ancillary service application scenarios across multiple time scales, such as... Figure 2 As shown:

[0052] In peak shaving ancillary services, multiple types of energy storage systems need to be allocated according to different time scales to ensure that peak shaving and valley filling effects can be achieved while achieving optimal economic efficiency.

[0053] Long-term peak shaving is based on a 1-week cycle and a 1-hour time period. It is used to plan long-term peak shaving for various types of energy storage systems based on long-term renewable energy output forecasts and load forecasts. It is applicable to long-term energy storage systems with a capacity of MW or more and a storage time of more than a month.

[0054] Day-ahead peak shaving uses a 1-day cycle and 15-minute time intervals. Based on the day-ahead renewable energy output forecast and load forecast, day-ahead peak shaving planning is carried out for various types of energy storage systems. It is applicable to energy storage systems that can provide power support and meet peak shaving needs.

[0055] Intraday peak shaving uses a 1-hour cycle and 15-minute time intervals. It implements intraday peak shaving control for various types of energy storage based on the day-ahead peak shaving plan and intraday forecast results. It is suitable for short-term energy storage systems with a response speed of around seconds.

[0056] Real-time peak shaving controls the output of various types of energy storage systems based on daily peak shaving plans and real-time renewable energy output and load demand. It is suitable for short-term energy storage systems with response times below seconds and capable of frequent charging and discharging. 2) Power is allocated according to different time scales based on wavelet packet transform, such as... Figure 3 As shown:

[0057] Initial power decomposition using wavelet packet transform: Obtain the predicted output power and load prediction values ​​of renewable energy sources such as wind farms and photovoltaics, calculate the net load power, and decompose the net load power layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the power sequence obtained from the decomposed power meets the constraint of the maximum change in grid-connected power. Take the current decomposition layer as the optimal layer.

[0058] Wavelet packet decomposition, based on wavelet decomposition, performs a more refined partitioning, decomposing both high- and low-frequency signals to obtain a complete binary tree structure. Its decomposition algorithm is as follows:

[0059]

[0060] The reconstruction algorithm is as follows:

[0061]

[0062] Configure the capacity of the long-term peak-shaving energy storage section: Based on the low-frequency part of the power allocation, generate the long-term energy storage section configuration power that requires the low-frequency part power, and calculate the long-term energy storage section configuration capacity.

[0063] Perform wavelet packet transform for secondary power decomposition: update the predicted output power and load of renewable energy sources such as wind farms and photovoltaics. Based on the allocated output power for long-term peak shaving, calculate the net load power required to meet peak shaving. Decompose the net load power layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the power sequence obtained from the decomposed power meets the constraint of the maximum change in grid-connected power. Take the current decomposition layer as the optimal layer.

[0064] Configure the day-ahead peak-shaving energy storage capacity: Based on the low-frequency portion of the power allocation, generate the day-ahead energy storage configuration power corresponding to the required medium-frequency power, and calculate the day-ahead energy storage configuration capacity.

[0065] Configure the intraday peak-shaving energy storage capacity: Based on the high-frequency part of the power allocation, generate the intraday energy storage configuration power that requires the high-frequency part power, and calculate the intraday energy storage configuration capacity.

[0066] Based on the allocated power output for long-term peak shaving, day-ahead peak shaving, and intraday peak shaving, as well as the real-time power output of renewable energy and loads, power allocation is performed to calculate the power output required for real-time peak shaving.

[0067] 3) Simulation and control strategies for charging and discharging processes of various types of energy storage systems:

[0068] Simulation of energy storage charging and discharging process and establishment of energy storage charging and discharging constraints;

[0069] The charging process is as follows:

[0070] SOC ES (t)=SOC ES (t-Δt)+P ES (t)Δt·η ES,c / C ES

[0071] The discharge process is as follows:

[0072] SOC ES (t)=SOC ES (t-Δt)+P ES (t)Δt / (η ES,d ·C ES )

[0073] The energy storage power constraints are as follows:

[0074] |PES (t)|≤|P ES,lim (t)|

[0075] Among them, SOC ES (t) represents the change in SOC for each energy storage system; Δt is the sampling interval; η ES,c The charging efficiency of the energy storage system; η ES,d C represents the discharge efficiency of the energy storage system. ES P represents the capacity of each energy storage system. ES (t) represents the input / output power of each type of energy storage at time t; |P ES,lim (t) represents the maximum allowable charging power value for each energy storage system at time t.

[0076] The energy storage SOC constraints are:

[0077] Charging process:

[0078] |P ES,lim (t)|=min{P ES,cmax C ES [SOC ES,max -SOC ES [(t-Δt)] / (Δt·η] ES,c )}

[0079] Discharge process:

[0080] |P ES,lim (t)|=min{P ES,dmax C ES [SOC ES (t-Δt)-SOC ES,max ]·η ES,d / Δt}

[0081] Among them, P ES,cmax P represents the maximum charging power value determined by the inherent characteristics of various energy storage systems. ES,dmax The maximum discharge power value is determined by the characteristics of each energy storage system; SOC ES,max The upper limit of SOC constraint for various energy storage systems; SOC ES,min This represents the lower limit of the SOC constraint for various energy storage systems.

[0082] Multi-type energy storage charging and discharging control methods include energy storage SOC grading methods and energy storage charging and discharging priority control methods using fuzzy rules, etc., to control the charging and discharging of multi-type energy storage systems.

[0083] To effectively utilize various types of energy storage systems and extend their service life, a SOC (State of Charge) grading method is adopted, specifically: the current SOC level of energy storage is divided into prohibited charging zones a[SOC]. ES,max,1], More discharge and less charge area b[SOC ES,high SOC ES,max Normal charge / discharge zone c[SOC] ES,low SOC ES,high [SOC] ES,min SOC ES,low ]、Discharge-prohibited region e[0,SOC ES,min ];

[0084] Fuzzy control is a control method that utilizes the basic ideas and theories of fuzzy mathematics. It simplifies the nonlinear characteristics and complex models of input information or the controlled object through fuzzification and fuzzy inference, thereby achieving effective control. In the configuration of multi-type energy storage systems, fuzzy methods can be used to determine when the systems need charging and discharging and which energy storage methods should be prioritized for charging and discharging.

[0085] During the fuzzification process, considering the characteristics of the energy storage system and the needs of charge and discharge control, the state of charge (SOC) of the i-th type of energy storage is set. i and energy storage power P ES The input value is fuzzy. The SOC of the i-th energy storage type is taken respectively. i The fundamental domain of discourse is [0,1], P ES The fundamental domain is [-3, 3]. The SOC of the i-th type of energy storage... i The domain of discussion is divided into 5 levels based on the actual parameters of different types of energy storage: {ZL,LM,MM,HM,OH}, corresponding to low, relatively low, moderate, relatively high, and high, respectively; P ES The universe of discourse is divided into 6 levels, {NB, NM, NS, PS, PM, PB}, corresponding to negative large, negative medium, negative small, positive small, positive medium, and positive large, respectively. The output value of the fuzzy controller is the i-th energy storage control value, denoted as P. ctr,i Its discrete universe of discourse is defined as [-1, 1], and the control subset is [N, Z, P], corresponding to negative, zero, and positive respectively. A membership function is selected, such as... Figure 4 As shown;

[0086] By establishing fuzzy rules to determine the relationship between input and output quantities, the priority order of multiple types of energy storage in charge and discharge control is derived. In this embodiment, two different types of energy storage participate in peak shaving auxiliary services within the same time scale, assuming P ES,1,max <P ES,2,max The fuzzy control is shown in Table 1.

[0087] Table 1: Fuzzy Control Rules for Priority Control of Charging and Discharging of Multiple Types of Energy Storage

[0088]

[0089]

[0090]

[0091] 4) The calculation of the multi-type energy storage system index system, including economic efficiency, safety, and peak-shaving effect, specifically includes:

[0092] The economic cost calculation methods in the multi-type energy storage system index system include, but are not limited to, the calculation methods for the whole life cycle cost and the levelized cost of electricity, and the economic benefits include, but are not limited to, the calculation methods for peak-valley arbitrage benefits and peak-shaving benefits.

[0093] The method for calculating total life cycle cost is as follows:

[0094]

[0095] The method for calculating the levelized cost of electricity (LCOE) is as follows:

[0096]

[0097] Among them, C inv C represents the initial investment cost; OM Annual maintenance and operating costs; C R For replacement cost; C C For charging costs; C Rec To recover costs; N is the total lifespan; r is the discount rate; Q E η represents the energy storage system capacity; η represents the system energy efficiency; θ represents the energy storage system capacity. DOD n represents the energy storage cycle depth. c This represents the number of iterations.

[0098] Furthermore, the depth of discharge of the battery is calculated using the rainflow counting method, and the equivalent cycle life of the battery is calculated based on the correspondence between the depth of discharge and the cycle life.

[0099] The calculation method for peak-valley arbitrage profits is as follows:

[0100] R e =(η dis P t dis -P t cha )q e Δt d

[0101] The method for calculating peak-shaving revenue is as follows:

[0102]

[0103] Among them, P t dis P is the energy storage discharge power; tcha Power for energy storage charging; η dis q represents the discharge efficiency. e For time-of-use pricing; Δt d q represents the discharge time; ps For peak-shaving prices; This is for energy storage and peak shaving.

[0104] The calculation methods for safety indicators in the multi-type energy storage system index system include, but are not limited to, methods for calculating safety accident rate and failure rate.

[0105] The method for calculating the accident rate is as follows:

[0106]

[0107] The failure rate is calculated as follows:

[0108]

[0109] Where Z represents the number of accidents, M represents the number of failures, and T represents the operating period within the statistics. The data on the safety accident rate are sourced from recently published information from relevant departments and journal articles.

[0110] The peak-shaving performance indicators calculated in the multi-type energy storage system index system include, but are not limited to, the calculation methods for insufficient peak-shaving capacity and the calculation methods for wind and solar power curtailment.

[0111] The calculation method for insufficient peak shaving in the system is as follows:

[0112]

[0113] The calculation method for the amount of wind and solar power curtailment in the system is as follows:

[0114]

[0115] Among them, u t Let P be the variable representing the insufficient peak shaving situation occurring at time t; lack,t This represents the insufficient peak-shaving amount at time t. For the maximum active power output of wind power, P w,t This represents the actual output of the wind turbine at time t. To maximize the active power output of photovoltaics, P s,t This represents the actual output of the photovoltaic system at time t.

[0116] 5) Optimized configuration of multiple energy storage types based on various indicators:

[0117] The NSGA-II algorithm is used to optimize the configuration of various types of energy storage systems, calculate the rated power capacity range of the energy storage systems, and optimize the configuration of various types of energy storage systems at different time scales. The principle is as follows: Figure 5 As shown;

[0118] NSGA-II is a multi-objective genetic algorithm that employs a fast non-dominated sorting algorithm, crowding degree and crowding degree comparison operators, and uses the comparison of peers after fast sorting as the winning criterion. This allows individuals in the quasi-Pareto domain to expand to the entire Pareto domain and be evenly distributed, thus maintaining the diversity of the population.

[0119] Because the values ​​and units of the various indicators are inconsistent during the optimization process, it is necessary to normalize each indicator. Since the evaluation indicators in this embodiment are all negative, they are established using the following formula:

[0120]

[0121] Among them, C i It is the normalized result of the i-th objective, r. i ` is the parameter of the i-th target in the current calculation, r i,min It is the minimum value of the i-th target, r i,max It is the maximum value of the i-th target;

[0122] The multi-objective optimization function is established by the following formula:

[0123] C mo =min[α1C1+α2C2+α3C3]

[0124] Where C mo The multi-objective optimization function is denoted as α1, α2, and α3, which represent preset weight coefficients whose values ​​should be determined in advance according to the application scenario. C1, C2, and C3 represent the optimization objectives, respectively.

[0125] 6) Optimize the capacity of energy storage involving multiple time scales:

[0126] Simultaneous superposition calculations are performed on charge-discharge simulations of energy storage involving multiple time scales at different time scales to reduce costs without affecting peak-shaving performance, and preliminary results for various types of energy storage configurations are obtained. The process is as follows: Figure 6 As shown.

[0127] 7) Conduct multi-timescale energy storage scheduling simulations based on actual operating parameters:

[0128] Power system modeling mainly includes node power balance constraints, branch power flow constraints, energy storage output constraints, and energy storage SOC constraints. Since energy storage devices mainly regulate active power, DC power flow is used to model the network.

[0129] Node power balancing:

[0130]

[0131] Among them, P i,t U is the sum of active power injected into node i at time t; i,t G represents the voltage magnitude at node i at time t; ij B represents the electrical conductance between nodes ij; ij Let θ be the susceptance between nodes ij; ij Let be the voltage phase angle difference at node ij at time t;

[0132] Node voltage constraints:

[0133] U i,min ≤U i,t ≤U i,max

[0134] Branch flow constraints:

[0135] P ij,min ≤P ij,t ≤P ij,max

[0136] θ ij,min ≤θ ij,t ≤θ ij,max

[0137] Among them, U i,max U i,min These are the upper and lower limits of the voltage amplitude at node i, respectively; θ ij,max θ ij,min P represents the upper and lower limits of the phase angle difference of the voltage at node ij at time t; ij,max P ij,min These represent the upper and lower limits of active power transmission between nodes ij and ij, respectively.

[0138] The energy storage output constraints and energy storage SOC constraints are shown in S2.

[0139] The scheduling simulation results are returned to S3 for iterative optimization until the configuration specifications are met, thus obtaining the final energy storage configuration result.

[0140] The following simulation calculations will verify the effectiveness of this embodiment. An exemplary simulation structure for multiple types of energy storage systems is shown below. Figure 7 As shown, the installed capacity of wind power is 300MW, the installed capacity of photovoltaic power is 450MW, and the peak load is 295.5MW. This embodiment selects lithium-ion batteries, vanadium redox flow batteries, compressed air energy storage, and Carnot battery systems from various energy storage options.

[0141] Table 2 shows the net power and net capacity required for participating in peak shaving applications at different time scales, calculated by wavelet packet decomposition in this embodiment.

[0142] Table 2: Power and capacity required for peak shaving applications at different time scales.

[0143] Time scale Power (MW) Capacity(MWh) Long-term energy storage 425.83 12339.98 Energy storage 99.32 2160.04 Intraday energy storage 42.81 396.83 Real-time energy storage 65.53 854.25

[0144] The capacity configuration model solved in this embodiment is shown in Table 3. Configuration scheme 1 is obtained by using the multi-type energy storage method for peak shaving auxiliary services as claimed in the claims, while configuration scheme 2 uses a single lithium battery energy storage system for comparison.

[0145] Table 3: Comparison of configuration results.

[0146] parameter Option 1 Option 2 Lithium-ion battery power (MW) 313 704 Vanadium redox flow battery power (MW) 45 0 Compressed air energy storage capacity (MW) 51 0 Carnot battery power (MW) 360 0 Lithium-ion battery capacity (MWh) 10140 30100 Vanadium redox flow battery capacity (MWh) 254 0 Compressed air energy storage capacity (MWh) 689 0 Carnot battery capacity (MWh) 43368 0 Insufficient peak shaving and total amount of wind and solar power curtailment (MWh) 623.74 867.00 Configuration cost (ten thousand yuan) 71548.47 79275.15

[0147] Comparing the calculation results of using multiple types of energy storage for peak shaving in this embodiment with those of using only lithium-ion batteries for peak shaving, the relative decrease in insufficient peak shaving and the total amount of wind and solar power curtailment is 28.06%, and the relative decrease in configuration cost is 9.75%. It can be seen that using multiple types of energy storage for peak shaving at multiple time scales can improve the peak shaving effect while reducing configuration costs and improving the economics of peak shaving configuration.

[0148] The results obtained in this implementation are then used in the MATPOWER 9-node model for simulation scheduling. The simulation results show that the power flow in this embodiment is solvable and reasonable, and no further iteration of the energy storage system power capacity configuration is required to obtain the final capacity configuration result.

[0149] In summary, the method of this invention fully leverages the flexibility and wide applicability of various energy storage types by applying them across multiple time scales. It performs multi-objective optimization analysis on the configuration of multiple energy storage types, considering factors such as economy, safety, and peak-shaving effect, thereby reducing the economic cost of multi-type energy storage configuration while ensuring peak-shaving effectiveness. Furthermore, it improves the accuracy and reliability of multi-type energy storage capacity configuration through scheduling calculations in practical application scenarios. This method has significant guiding significance and engineering application value for the configuration of multi-type energy storage power capacity used for peak shaving.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and the technical solutions of the present invention are not limited to the embodiments shown herein. Equivalent substitutions or modifications made by those skilled in the art to the technical solutions of the present invention, without departing from the spirit or scope of the present invention, should all be covered within the scope of the claims of the present invention.

Claims

1. A method for configuring the capacity of multiple types of energy storage systems suitable for peak shaving at multiple time scales, characterized in that: Includes the following steps: S1: Consider constructing multi-type energy storage peak-shaving ancillary service application scenarios across multiple time scales; specifically, in peak-shaving ancillary services, multiple types of energy storage systems are allocated according to different time scales to ensure peak shaving and valley filling effects while achieving optimal economic efficiency, including: Long-term peak shaving is based on a weekly cycle and an hourly period. It is used to plan long-term peak shaving for various types of energy storage systems based on long-term renewable energy output forecasts and load forecasts. It is applicable to long-term energy storage systems with a capacity of MW or more and a storage time of more than a month. Day-ahead peak shaving is based on a daily cycle and a minute-by-minute time period. It is used to plan day-ahead peak shaving for various types of energy storage systems based on the day-ahead renewable energy output forecast and load forecast. It is suitable for medium and short-term energy storage systems that can provide power support and meet peak shaving requirements. Intraday peak shaving is based on the hour as the cycle and the minute as the time period. It is used to control intraday peak shaving of various types of energy storage based on the day-ahead peak shaving plan and intraday forecast results. It is suitable for short-term energy storage systems with a response speed of seconds. Real-time peak shaving controls the output of various types of energy storage based on the daily peak shaving plan and the real-time output of renewable energy and load demand. It is suitable for short-term energy storage systems with response speeds of less than seconds and capable of frequent charging and discharging. S2: Based on the application scenario constructed in S1, a multi-type energy storage planning model is constructed by combining wavelet packet transform-based peak-shaving power allocation and energy storage operation control strategies; wherein, the energy storage operation control strategy is the multi-type energy storage system charging and discharging simulation and control strategy; the multi-type energy storage system charging and discharging simulation includes energy storage charging and discharging process simulation, energy storage power constraints, and energy storage SOC upper and lower limit constraints; the multi-type energy storage system charging and discharging control strategy includes an energy storage SOC grading method and an energy storage charging and discharging priority control method using fuzzy rules; S3: Based on the planning model constructed in S2, perform optimization calculations on various types of energy storage systems based on multiple indicators and obtain preliminary results of energy storage configuration; the multiple indicators are economy, safety and peak-shaving effect; S4: Based on the preliminary configuration results obtained from S3, power flow scheduling simulation is performed using actual operating parameters, and the final energy storage configuration result is obtained through iterative optimization.

2. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales as described in claim 1, characterized in that, Based on physical characteristics, the aforementioned multi-type energy storage systems include mechanical energy storage, thermo-mass energy storage, electrochemical energy storage, and electromagnetic energy storage; based on application, the aforementioned multi-type energy storage systems include energy-type energy storage and power-type energy storage.

3. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales as described in claim 1, characterized in that, S2, based on wavelet packet transform, performs peak power allocation according to different time scales, including: ① Perform initial power decomposition using wavelet packet transform: obtain the predicted value of renewable energy output power and load prediction value, calculate the net load power, and decompose the net load power layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the obtained power sequence satisfies the constraint of the maximum change in grid-connected power. Take the current decomposition layer as the optimal layer. ② Configure the capacity of the long-term peak-shaving energy storage section: Based on the low-frequency part of the power allocation, generate the long-term energy storage section configuration power that requires the low-frequency part of the power, and calculate the long-term energy storage section configuration capacity. ③ Perform wavelet packet transform for secondary power decomposition: update the predicted values ​​of renewable energy output power and load prediction. Based on the power output already allocated for long-term peak shaving, calculate the net load power required to meet peak shaving. Decompose the net load power in this step layer by layer through wavelet packet transform until the reconstruction of the low-frequency part of the obtained power sequence meets the constraint of the maximum change in grid-connected power. Take the current decomposition layer as the optimal layer. ④ Configure the day-ahead peak-shaving energy storage capacity: Based on the low-frequency part of the power allocation, generate the day-ahead energy storage configuration power for the required medium-frequency part of the power, and calculate the day-ahead energy storage configuration capacity. ⑤ Configure the intraday peak-shaving energy storage capacity: Based on the high-frequency part of the power allocation, generate the intraday energy storage configuration power that requires the high-frequency part of the power, and calculate the intraday energy storage configuration capacity. ⑥ Based on the allocated power output of long-term peak shaving, day-ahead peak shaving, and intraday peak shaving, as well as the real-time power output of renewable energy and loads, power allocation is carried out to calculate the power output required for real-time peak shaving.

4. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales as described in claim 1, characterized in that, The energy storage SOC classification method divides the current SOC level of energy storage into prohibited charging zone a, more discharge and less charge zone b, normal charge and discharge zone c, more charge and less discharge zone d, and prohibited discharge zone e. During the fuzzification process, the characteristics of the energy storage system and the needs of charge and discharge control are considered, and the first step is set. i State of charge (SOC) of energy storage i and energy storage capacity P ES For fuzzy input values, take the first value respectively. i State of charge (SOC) of energy storage i The fundamental universe of discourse is [0,1], and the energy storage power is... P ES The fundamental domain is [-3,3]; the first... i State of charge (SOC) of energy storage i The domain of discussion is divided into 5 levels based on the actual parameters of different types of energy storage: {LL, LM, MM, HM, HH}, corresponding to low, relatively low, moderate, relatively high, and high, respectively; energy storage power P ES The universe of discourse is divided into 6 levels, {NB, NM, NS, PS, PM, PB}, corresponding to negative large, negative medium, negative small, positive small, positive medium, and positive large, respectively. The output value of the fuzzy controller is the first... i A type of energy storage control value is defined with its discrete universe of discourse as [-1,1], the control subset as [N,Z,P], and a membership function is selected.

5. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales according to claim 1, characterized in that, The S3 method, based on the optimized calculation of multiple indicators, is as follows: A multi-objective optimization model is established using optimization algorithms to calculate the rated power capacity range of energy storage systems and optimize the configuration of various types of energy storage systems at different time scales.

6. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales according to claim 5, characterized in that, S3 optimizes the same type of energy storage involving multiple time scales as follows: By performing multi-time-domain charge-discharge superposition calculations on the same type of energy storage involving multiple time scales, the power capacity range of this type of energy storage is redefined, thereby obtaining preliminary results for the configuration of multiple types of energy storage.

7. The capacity configuration method for multi-type energy storage systems applicable to peak shaving at multiple time scales according to claim 1, characterized in that, S4 uses actual operating parameters to simulate power flow scheduling, and the implementation method is as follows: The power network is modeled using DC power flow. The energy storage power constraint and energy storage SOC constraint in S2 are used to obtain the scheduling operation results. The scheduling simulation results are returned to S3 for iterative optimization until the configuration specification requirements are met, thus obtaining the final energy storage configuration result.

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