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

Abstract

The invention relates to a multi-type energy storage system capacity configuration method suitable for peak regulation auxiliary service, and the method gives full play to the characteristics of high flexibility and wide application range of multi-type energy storage through the application of multi-type energy storage in multiple time scales. Factors such as economy, safety and peak regulation effect are considered to perform multi-target optimization analysis on the multi-type energy storage configuration, and the economic cost of the multi-type energy storage configuration is reduced while the peak regulation effect is ensured; and scheduling calculation is carried out through an actual application scene, so that the accuracy and credibility of multi-type energy storage capacity configuration are improved, and the method has important guiding significance and engineering application value for multi-type energy storage power capacity configuration for peak regulation.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system configuration, and in particular relates to a capacity configuration method for multi-type energy storage systems suitable for multi-time scale peak regulation. Background Art

[0002] Renewable energy includes solar energy, hydropower, wind energy, biomass energy, geothermal energy, etc., among which photovoltaic power generation and wind power generation are widely used. However, photovoltaic power generation is easily affected by environmental factors such as weather and light intensity, resulting in fluctuations; wind power generation is affected by climate and geographical factors, and both have certain randomness and instability. Under the background of large-scale grid connection of renewable energy such as wind power and photovoltaic power, the peak-to-valley difference of the power system continues to increase, bringing difficulties to the stable operation of the power grid.

[0003] Peak shaving mainly refers to storing excess electricity during the low-consumption period and releasing it during the peak-consumption period to meet the matching between the power generation end and the load. For peak shaving auxiliary services, traditional peak shaving methods such as hydropower and thermal power peak shaving have large hysteresis and cannot respond quickly to frequent fluctuations; on the other hand, during the peak shaving process, the peak shaving units need to frequently enter deep peak shaving, which poses a hidden danger to the safe operation of the system. Energy storage has the characteristics of fast response speed, flexible power capacity configuration, bidirectional output, and most importantly, the ability to store electricity, which can effectively solve the peak shaving problem.

[0004] Selecting appropriate energy storage and making reasonable configurations can ensure the economical, safe and efficient operation of renewable energy power systems. Multi-type energy storage technology is more flexible, economical and reliable than single energy storage because it couples multiple energy storage systems with different power density, energy density, response time and unit cost. When applied to peak-shaving auxiliary services, it can ensure high economic efficiency while ensuring its peak-shaving effect. However, due to the large number of energy storage technologies involved, how to provide a multi-type energy storage system capacity configuration method suitable for multi-time scale peak-shaving in the application and peak-shaving service of multi-type energy storage systems is a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a capacity configuration method for a multi-type energy storage system suitable for multi-time scale peak regulation. The method considers the control strategy, configuration scheme and scheduling scheme of multi-type energy storage including electricity storage, heat storage and gas storage in multi-time scale peak regulation application scenarios, so as to solve the power capacity configuration problem of multi-type energy storage in participating in peak regulation services, thereby making the entire peak regulation process more economical and efficient.

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

[0007] A method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation comprises the following steps:

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

[0009] S2: Based on the application scenario constructed in S1, a multi-type energy storage planning model is constructed by combining peak load distribution based on wavelet packet transform and energy storage operation control strategy.

[0010] S3: Based on the planning model constructed in S2, multiple types of energy storage systems are optimized and calculated based on multiple indicators and preliminary results of energy storage configuration are obtained; the multiple indicators are economy, safety and peak regulation effect.

[0011] S4: Based on the preliminary configuration results obtained in S3, the actual operating parameters are used to simulate the power flow scheduling, and the final result of the energy storage configuration is obtained through iterative optimization.

[0012] Among them, the multi-type energy storage system described in the present invention can be divided into mechanical energy storage, thermal mass storage, electrochemical energy storage and electromagnetic energy storage according to physical properties, and can also be divided into energy storage and power storage according to use. The energy storage types it constitutes include but are not limited to the following energy storage types: lithium battery energy storage, all-vanadium liquid flow battery energy storage, compressed air energy storage, pumped storage, flywheel energy storage, heat storage, hydrogen energy storage, superconducting magnetic energy storage, supercapacitors, etc.

[0013] In the embodiment of the present invention, the peak load auxiliary service of S1 needs to allocate multiple types of energy storage systems according to different time scales to ensure the peak load shaving and valley filling effect while achieving the best economic efficiency, where:

[0014] Long-term peak-shaving is based on a week (e.g. 1 week) as a cycle and an hour (e.g. 1 hour) as a period. According to the long-term renewable energy output forecast and load forecast, long-term peak-shaving planning of multiple types of energy storage systems is carried out. It is suitable for long-term energy storage systems with low unit energy cost and unit power cost (e.g. capacity range above MW level) and high energy storage energy level (e.g. above monthly level).

[0015] The day-ahead peak-shaving is based on a cycle of one day (e.g. 1 day) and a period of one minute (e.g. 15 minutes). Based on the day-ahead renewable energy output forecast and load forecast, a day-ahead peak-shaving plan is carried out for multiple types of energy storage systems. This is suitable for medium- and short-term energy storage systems that can provide power support capabilities, have good economic efficiency, and can meet peak-shaving needs.

[0016] Intraday peak-shaving is based on hours (e.g. 1 hour) as a cycle and minutes (e.g. 15 minutes) as a time period. According to the day-ahead peak-shaving plan and intraday forecast results, multi-type energy storage intraday peak-shaving control is performed. It is suitable for short-term energy storage systems with fast response speed (e.g. seconds) and good economy.

[0017] Real-time peak-shaving controls the output of multiple types of energy storage according to the daily peak-shaving plan and the real-time renewable energy output 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 frequent charging and discharging.

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

[0019] ① Perform initial power decomposition by wavelet packet transform: obtain the predicted output power and load predicted value of renewable energy such as wind farms and photovoltaics, calculate the net load power, and decompose the net load power layer by layer by wavelet packet transform until the reconstruction of the low-frequency part of the power sequence meets the constraint of the maximum change in grid-connected power, and take the current number of decomposition layers as the optimal number of layers.

[0020] ② Configure the capacity of the long-term peak-shaving energy storage part: According to the low-frequency part in the power distribution, generate the long-term energy storage part configuration power that requires the low-frequency part power, and calculate the long-term energy storage part configuration capacity.

[0021] ③ Perform secondary power decomposition by wavelet packet transform: Update the predicted output power and load forecast values ​​of renewable energy sources such as wind farms and photovoltaics, calculate the net load power required for peak regulation based on the allocated output power for long-term peak regulation, and decompose the net load power of this step layer by layer by wavelet packet transform until the reconstruction of the low-frequency part of the power sequence obtains satisfies the constraint of the maximum change in grid-connected power, and take the current number of decomposition layers as the optimal number of layers.

[0022] ④ Perform capacity configuration of the day-ahead peak-shaving energy storage part: According to the low-frequency part in the power allocation, generate the day-ahead energy storage part configuration power that requires the medium-frequency part power, and calculate the day-ahead energy storage part configuration capacity;

[0023] ⑤ Perform capacity configuration of the intraday peak-shaving energy storage part: According to the high-frequency part in the power distribution, generate the intraday energy storage part configuration power that requires the high-frequency part power, and calculate the intraday energy storage part configuration capacity;

[0024] ⑥ Power allocation is carried out according to the allocated output power of long-term peak regulation, day-ahead peak regulation, and intra-day peak regulation, as well as the real-time power conditions of renewable energy and loads, and the output required for real-time peak regulation is calculated.

[0025] In an embodiment of the present invention, the S2 energy storage operation control strategy is specifically a multi-type energy storage system charge and discharge simulation and control strategy, wherein the multi-type energy storage system charge and discharge simulation includes energy storage charge and discharge process simulation, energy storage power constraints, and energy storage SOC upper and lower limit constraints, etc., which simulate the charge and discharge of multiple types of energy storage systems.

[0026] The charging and discharging control strategies for multi-type energy storage systems include energy storage SOC grading methods and energy storage charging and discharging priority control methods using fuzzy rules, etc., which are methods for controlling the charging and discharging of multi-type energy storage systems.

[0027] By establishing fuzzy rules to determine the relationship between input and output, the priority order of multiple types of energy storage in charging and discharging control is obtained.

[0028] In the embodiment of the present invention, S3 performs calculation of a multi-type energy storage system index system including economy, safety and peak load regulation effect, specifically including:

[0029] First, calculate multiple indicators as follows:

[0030] ① Economicity: economic cost calculation methods include but are not limited to the calculation methods of full life cycle cost or levelized cost per kilowatt-hour; economic benefits include but are not limited to the calculation methods of peak-valley arbitrage benefits and peak-shaving benefits.

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

[0032] ③ Peak-shaving effect. Peak-shaving effect indicators include but are not limited to the calculation method of system peak-shaving shortage and the calculation method of system wind and solar power abandonment.

[0033] Then, we can optimize the configuration of multiple types of energy storage based on multiple indicators, that is, optimize the calculation based on multiple indicators. The implementation method is as follows:

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

[0035] Finally, the capacity of energy storage involving multiple time scales is optimized, which can be achieved by optimizing the same type of energy storage involving multiple time scales as follows:

[0036] Multi-time domain charging and discharging superposition is performed on the same type of energy storage involving multiple time scales, that is, the charging and discharging simulations at different time scales are superimposed at the same time, so as to reduce costs without affecting the peak-shaving effect, redefine the power capacity range of this type of energy storage, and thus obtain preliminary results of multi-type energy storage configuration.

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

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

[0039] The energy storage power constraint conditions and energy storage SOC constraint conditions are as shown in S2, and then the scheduling operation result is obtained, and the scheduling simulation result is returned to S3 for iterative optimization until the configuration specification requirements are met, thereby obtaining the final result of the energy storage configuration.

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

[0041] The present invention provides a method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation: first, the requirements in the peak regulation application scenario of the multi-type energy storage system are classified according to multiple time scales and suitable multi-type energy storage types are provided, giving full play to the characteristics of strong flexibility and wide application range of multi-type energy storage; secondly, according to different time scales and the characteristics of different technical parameters in the multi-type energy storage, multi-objective optimization analysis is performed, which can reduce the economic cost while ensuring the peak regulation effect; finally, the obtained preliminary results are put into the actual application scenario for iterative calculation to obtain the final energy storage configuration result, thereby improving the accuracy and reliability of the energy storage capacity configuration calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the process flow for configuring multiple types of energy storage capacity.

[0043] Figure 2 Schematic diagram of multi-time scale peak regulation scenario.

[0044] Figure 3 Schematic diagram of wavelet packet transform power allocation.

[0045] Figure 4 It is the fuzzy control membership function diagram.

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

[0047] Figure 6 Flowchart for configuring energy storage capacity involving multiple time scales.

[0048] Figure 7 Simulate structures for multiple types of energy storage systems. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the embodiments described in this section are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, any other embodiments obtained by ordinary persons in the art under the premise of non-creative work should fall within the protection scope of the present invention. The specific implementation methods are described in detail as follows:

[0050] A capacity configuration method for multi-type energy storage systems suitable for multi-time scale peak regulation, such as Figure 1 As shown, the following steps are included:

[0051] 1) Consider multiple time scales to build multiple types of energy storage peak load auxiliary service application scenarios, such as Figure 2 As shown:

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

[0053] Long-term peak-shaving is based on a cycle of one week and a period of one hour. Based on the long-term renewable energy output forecast and load forecast, long-term peak-shaving planning of multiple types of energy storage systems is carried out. It is suitable for long-term energy storage systems with a capacity range of more than MW and a storage time of more than one month.

[0054] The day-ahead peak-shaving is based on a one-day cycle and a 15-minute period. Based on the day-ahead renewable energy output forecast and load forecast, a day-ahead peak-shaving plan for multiple types of energy storage systems is carried out. This plan is applicable to energy storage systems that can provide power support capabilities and meet peak-shaving needs.

[0055] The intraday peak load regulation is based on a cycle of 1 hour and a period of 15 minutes. According to the day-ahead peak load regulation plan and intraday forecast results, multiple types of energy storage are used for intraday peak load regulation control. It is suitable for short-term energy storage systems with a response speed of about seconds.

[0056] Real-time peak load regulation controls the output of multiple types of energy storage according to the daily peak load regulation plan and the real-time renewable energy output and load demand. It is suitable for short-term energy storage systems with a response speed of less than seconds and can be charged and discharged frequently. 2) Based on wavelet packet transform, power is allocated according to different time scales, such as Figure 3 As shown:

[0057] Perform initial power decomposition by wavelet packet transform: obtain the predicted output power and load predicted value of renewable energy such as wind farms and photovoltaics, calculate the net load power, and decompose the net load power layer by layer by wavelet packet transform until the reconstruction of the low-frequency part of the power sequence of the decomposed power meets the constraint of the maximum change amount of grid-connected power, and take the current number of decomposition layers as the optimal number of layers;

[0058] Wavelet packet decomposition is based on wavelet decomposition and is more finely divided. Both high-frequency and low-frequency signals are decomposed to obtain a complete binary tree structure. The decomposition algorithm is:

[0059]

[0060] The reconstruction algorithm is:

[0061]

[0062] Performing capacity configuration of the long-term peak-shaving energy storage part: generating the long-term energy storage part configuration power that requires the low-frequency part power according to the low-frequency part in the power distribution, and calculating the long-term energy storage part configuration capacity;

[0063] Perform secondary power decomposition using wavelet packet transform: Update the predicted output power and load predicted value of renewable energy sources such as wind farms and photovoltaics, calculate the net load power required to meet the peak load according to the output power allocated for long-term peak load regulation, and decompose the net load power layer by layer using wavelet packet transform until the reconstruction of the low-frequency part of the power sequence from the decomposed power meets the constraint of the maximum change in grid-connected power, and take the current number of decomposition layers as the optimal number of layers;

[0064] Performing day-ahead peak-shaving energy storage capacity configuration: generating day-ahead energy storage configuration power that requires medium-frequency power according to the low-frequency part of the power allocation, and calculating day-ahead energy storage configuration capacity;

[0065] Performing capacity configuration of the intraday peak-shaving energy storage part: generating the intraday energy storage part configuration power that requires the high-frequency part power according to the high-frequency part in the power allocation, and calculating the intraday energy storage part configuration capacity;

[0066] Power allocation is carried out based on the allocated output power of long-term peak regulation, day-ahead peak regulation, and intra-day peak regulation, as well as the real-time power conditions of renewable energy and loads, and the output required for real-time peak regulation is calculated.

[0067] 3) Simulation and control strategy of charging and discharging process of multiple types of energy storage systems:

[0068] Energy storage charging and discharging process simulation and energy storage charging and discharging constraints establishment;

[0069] The charging process is:

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

[0071] The discharge process is:

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

[0073] The energy storage power constraint is:

[0074] |PES (t)|≤|P ES,lim (t)|

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

[0076] The energy storage SOC constraint is:

[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 is the maximum charging power value determined by the characteristics of various energy storage systems; P ES,dmax It is the maximum discharge power value determined by the characteristics of various energy storage systems; SOC ES,max It is the upper limit of SOC constraint for various energy storage systems; SOC ES,min It is the lower limit of SOC constraint for various energy storage systems.

[0082] The multi-type energy storage charge and discharge control method includes the energy storage SOC classification method, the energy storage charge and discharge priority control method using fuzzy rules, and other methods for controlling the charge and discharge of multi-type energy storage systems:

[0083] In order to effectively utilize multiple types of energy storage systems and extend their service life, an energy storage SOC grading method is adopted, specifically: the current SOC level of the energy storage is divided into a prohibited charging zone a [SOC ES,max,1], more discharge less charge area b[SOC ES,high ,SOC ES,max ]、Normal charge and discharge area c[SOC ES,low ,SOC ES,high ]、More charge and less discharge area d[SOC ES,min ,SOC ES,low ]、Discharge prohibited area e[0,SOC ES,min ];

[0084] Fuzzy control is a control method that uses the basic ideas and theories of fuzzy mathematics. It uses fuzzification and fuzzy reasoning to simplify the nonlinear characteristics and complex models of input information or controlled objects, thereby achieving effective control of the controlled object. In the configuration process of multi-type energy storage systems, fuzzy methods can be used to determine when multi-type energy storage systems need to be charged and discharged and which energy storage should be charged and discharged first;

[0085] In the fuzzy process, the characteristics of the energy storage system and the need for charge and discharge control are considered, and the state of charge SOC of the i-th energy storage is set. i And energy storage power P ES is the fuzzy input value. Take the SOC of the i-th energy storage i The basic domain of P is [0,1], ES The basic domain of is [-3,3]. The SOC of the i-th energy storage i The domain of the energy storage system is divided into five levels according to the actual parameters of different types of energy storage, {ZL, LM, MM, HM, OH}, corresponding to low, lower, moderate, higher, and high respectively; P ES The domain 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 by P ctr,i Define its discrete domain as [-1,1], control subset as [N,Z,P], corresponding to negative, zero, and positive respectively, and select membership function, such as Figure 4 As shown;

[0086] By establishing fuzzy rules to determine the relationship between input and output, the priority order of multiple types of energy storage in charging and discharging control is obtained. In this embodiment, two different types of energy storage participate in peak load auxiliary services in the same time scale. Assume that 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 multi-type energy storage charging and discharging

[0088]

[0089]

[0090]

[0091] 4) The calculation of the multi-type energy storage system index system including economy, safety and peak load regulation effect includes:

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

[0093] The calculation method of the life cycle cost is:

[0094]

[0095] The calculation method of levelized cost of electricity is:

[0096]

[0097] Among them, C inv is the initial investment cost; C OM is the annual maintenance and operation cost; C R is the replacement cost; C C is the charging cost; C Rec is the recovery cost; N is the life span of the entire cycle; r is the discount rate; Q E is the capacity of the energy storage system; η is the system energy efficiency; θ DOD is the energy storage cycle depth; n c is the number of cycles.

[0098] Furthermore, the discharge depth of the battery is calculated by the rain flow counting method, and the equivalent cycle life of the battery is calculated based on the corresponding relationship between the discharge depth of the battery and the cycle life.

[0099] The calculation method of peak-to-valley arbitrage profit is:

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

[0101] The calculation method of peak load regulation benefit is:

[0102]

[0103] Among them, P t dis is the energy storage discharge power; P tcha is the energy storage charging power; η dis is the discharge efficiency; q e is the time-of-use electricity price; Δt d is the discharge time; q ps The peak price; To store energy for peak load regulation.

[0104] The safety index calculation method calculated in the multi-type energy storage system index system includes but is not limited to the safety accident rate calculation method and the failure rate calculation method;

[0105] The calculation method of safety accident rate is:

[0106]

[0107] The failure rate is calculated as:

[0108]

[0109] Among them, Z is the number of accidents, M is the number of failures, T is the statistical operating period, and the data related to the safety accident rate comes from the recently published information from relevant departments and journal papers.

[0110] The peak-shaving effect indicators calculated in the multi-type energy storage system indicator system include but are not limited to the calculation method of the system peak-shaving shortage and the calculation method of the system wind and solar power abandonment;

[0111] The calculation method of insufficient peak load regulation of the system is:

[0112]

[0113] The calculation method of the system abandoned wind and solar power is:

[0114]

[0115] Among them, u t is the variable of insufficient peak load regulation occurring at time t; P lack,t is the peak load shortage at time t, is the maximum active power output of wind power, P w,t is the actual output of the wind turbine at time t, is the maximum active power output of photovoltaic, P s,t is the actual output of photovoltaic at time t.

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

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

[0118] NSGA-II is a multi-objective genetic algorithm that uses a fast non-dominated sorting algorithm, crowding and crowding comparison operators, and uses them as the winning criteria in the same-level comparison after fast sorting, so that individuals in the quasi-Pareto domain can be expanded to the entire Pareto domain and evenly distributed, maintaining the diversity of the population;

[0119] Since the values ​​and units of the indicators are not uniform during the optimization process, it is necessary to normalize the indicators. Since the evaluation indicators in this embodiment are all negative indicators, they are established by the following formula:

[0120]

[0121] Among them, C i is the normalized result of the i-th target, r i ` is the parameter of the i-th target calculated at that time, r i,min is the minimum value of the numerical value of the i-th target, r i,max is the maximum value of the 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] Among them C mo represents the multi-objective optimization function; α1, α2, α3 respectively represent preset weight coefficients, whose values ​​should be predetermined according to the application scenario, and C1, C2, C3 respectively represent the optimization objectives.

[0125] 6) Capacity optimization of energy storage involving multiple time scales:

[0126] The charging and discharging simulations of energy storage involving multiple time scales at different time scales are simultaneously superimposed to reduce costs without affecting the peak load effect, and preliminary results of multi-type energy storage configuration are obtained. The process is as follows Figure 6 shown.

[0127] 7) Perform multi-time scale energy storage scheduling simulation based on actual operating parameters:

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

[0129] Node power balance:

[0130]

[0131] Among them, P i,t is the sum of active power injected into node i at time t; U i,t is the voltage amplitude of node i at time t; G ij is the conductance between nodes ij; B ij is the susceptance between nodes ij; θ ij is the voltage phase angle difference of 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 are the upper and lower limits of the voltage amplitude at node i respectively; θ ij,max ,θ ij,min are the upper and lower limits of the voltage phase difference at node ij at time t; P ij,max , P ij,min They are the upper and lower limits of the line active power transmission between nodes ij respectively.

[0138] The energy storage output constraint condition and the energy storage SOC constraint condition are as shown in S2;

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

[0140] The effect of this embodiment will be verified by simulation operation. For example, the simulation structure of multiple types of energy storage systems is as follows: Figure 7 As shown, the wind power installed capacity is 300MW, the photovoltaic installed capacity is 450MW, and the load peak is 295.5MW. This embodiment selects lithium-ion batteries, all-vanadium liquid flow batteries, compressed air energy storage and Carnot battery systems from multiple types of energy storage.

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

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

[0143] Time scale Power(MW) Capacity(MWh) Long-term energy storage 425.83 12339.98 Day-ahead 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, where configuration scheme 1 is obtained by adopting the multi-type energy storage method for peak-shaving auxiliary services in the claims, and configuration scheme 2 adopts a single lithium battery energy storage system for comparison.

[0145] Table 3: Comparison of configuration results.

[0146] parameter Solution 1 Solution 2 Lithium-ion battery power (MW) 313 704 All-vanadium redox flow battery power (MW) 45 0 Compressed air energy storage power (MW) 51 0 Carnot battery power (MW) 360 0 Lithium-ion battery capacity (MWh) 10140 30100 All-vanadium redox flow battery capacity (MWh) 254 0 Compressed air energy storage capacity (MWh) 689 0 Carnot battery capacity (MWh) 43368 0 Insufficient peak load regulation and total abandoned wind and solar power (MWh) 623.74 867.00 Configuration cost (10,000 yuan) 71548.47 79275.15

[0147] Comparing the calculation results of peak shaving using multiple types of energy storage and peak shaving using only lithium-ion batteries in this embodiment, the insufficient peak shaving and the total amount of abandoned wind and solar power decreased by 28.06% and the configuration cost decreased by 9.75%. It can be seen that using multiple types of energy storage for multi-time scale peak shaving can improve the peak shaving effect while reducing the configuration cost, thus improving the economic efficiency of peak shaving configuration.

[0148] The results of the solution in this implementation are brought into the MATPOWER 9-node model for simulation and scheduling. The simulation calculation shows that the power flow in this embodiment is solvable and reasonable, and there is no need to further iterate the power capacity configuration of the energy storage system, that is, the final capacity configuration result has been obtained.

[0149] In summary, the method of the present invention fully utilizes the characteristics of strong flexibility and wide application range of multi-type energy storage by applying multi-type energy storage in multiple time scales; multi-objective optimization analysis is performed on the configuration of multi-type energy storage considering factors such as economy, safety, and peak-shaving effect, thereby reducing the economic cost of multi-type energy storage configuration while ensuring the peak-shaving effect; and scheduling calculations are performed through actual application scenarios to improve the accuracy and reliability of multi-type energy storage capacity configuration, which has important guiding significance and engineering application value for the power capacity configuration of multi-type energy storage 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. Those skilled in the art may make equivalent replacements or modifications to the technical solutions of the present invention, which shall be included in the scope of the claims of the present invention without departing from the spirit or scope of the technical solutions of the present invention.

Claims

1. A method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation, characterized in that: The following steps are involved: S1: Consider multiple time scales to build multiple types of energy storage peak load auxiliary service application scenarios; S2: Based on the application scenario constructed in S1, a multi-type energy storage planning model is constructed in combination with peak load power allocation based on wavelet packet transform and energy storage operation control strategy; S3: Based on the planning model constructed in S2, multiple types of energy storage systems are optimized and calculated based on multiple indicators to obtain preliminary results of energy storage configuration; the multiple indicators are economy, safety and peak load regulation effect; S4: Based on the preliminary configuration results obtained in S3, the actual operating parameters are used to simulate the power flow scheduling, and the final result of the energy storage configuration is obtained through iterative optimization.

2. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: According to the physical properties, the multi-type energy storage system includes mechanical energy storage, thermal mass storage, electrochemical energy storage and electromagnetic energy storage; according to the purpose, the multi-type energy storage system includes energy type storage and power type storage.

3. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: S1, in the peak load auxiliary service, allocates multiple types of energy storage systems according to different time scales to ensure the peak load shifting effect while achieving the best economic performance, where: Long-term peak-shaving is based on a week as a cycle and an hour as a time period. According to the long-term renewable energy output forecast and load forecast, long-term peak-shaving planning of multiple types of energy storage systems is carried out. It is suitable for long-term energy storage systems with a capacity range of more than MW and a storage time of more than one month. The day-ahead peak-shaving is based on a daily cycle and a minute-long period. Based on the day-ahead renewable energy output forecast and load forecast, a day-ahead peak-shaving plan for multiple types of energy storage systems is carried out. This plan is applicable to medium- and short-term energy storage systems that can provide power support capabilities and meet peak-shaving needs. Intraday peak-shaving is based on hours as the cycle and minutes as the time period. According to the day-ahead peak-shaving plan and intraday forecast results, multi-type energy storage intraday peak-shaving control is performed. It is suitable for short-term energy storage systems with a response speed of seconds. Real-time peak-shaving controls the output of multiple types of energy storage according to the daily peak-shaving plan and real-time renewable energy output and load demand. It is suitable for short-term energy storage systems with a response speed of less than seconds and the ability to charge and discharge frequently.

4. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: S2, performing peak load power allocation according to different time scales based on wavelet packet transform, includes: ① Perform initial power decomposition by 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 by wavelet packet transform until the reconstruction of the low-frequency part of the obtained power sequence meets the constraint of the maximum change amount of grid-connected power, and take the current number of decomposition layers as the optimal number of layers; ② Perform capacity configuration of long-term peak-shaving energy storage: According to the low-frequency part in the power distribution, generate the long-term energy storage configuration power that requires the low-frequency part power, and calculate the long-term energy storage configuration capacity; ③ Perform secondary power decomposition by wavelet packet transform: Update the predicted value of renewable energy output power and load forecast value, calculate the net load power required to meet the peak load according to the output power allocated for long-term peak load regulation, and decompose the net load power of this step layer by layer by wavelet packet transform until the reconstruction of the low-frequency part of the power sequence obtained meets the constraint of the maximum change amount of grid-connected power, and take the current number of decomposition layers as the optimal number of layers; ④ Perform capacity configuration of the day-ahead peak-shaving energy storage part: According to the low-frequency part in the power allocation, generate the day-ahead energy storage part configuration power that requires the medium-frequency part power, and calculate the day-ahead energy storage part configuration capacity; ⑤ Perform capacity configuration of the intraday peak-shaving energy storage part: According to the high-frequency part in the power distribution, generate the intraday energy storage part configuration power that requires the high-frequency part power, and calculate the intraday energy storage part configuration capacity; ⑥ Power allocation is carried out according to the allocated output power of long-term peak regulation, day-ahead peak regulation, and intra-day peak regulation, as well as the real-time power conditions of renewable energy and loads, and the output required for real-time peak regulation is calculated.

5. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: S2, energy storage operation control strategy, i.e., multi-type energy storage system charge and discharge simulation and control strategy; The multi-type energy storage system charge and discharge simulation includes energy storage charge and discharge process simulation, energy storage power constraints, and energy storage SOC upper and lower limit constraints; the multi-type energy storage system charge and discharge control strategy includes an energy storage SOC grading method and an energy storage charge and discharge priority control method using fuzzy rules.

6. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: The energy storage SOC grading method divides the current SOC level of the energy storage into a prohibited charging zone a, a more-discharging and less-charging zone b, a normal charging and discharging zone c, a more-charging and less-discharging zone d, and a prohibited discharging zone e; In the fuzzy process, the characteristics of the energy storage system and the need for charge and discharge control are considered, and the state of charge SOC of the i-th energy storage is set. i And energy storage power P ES is the fuzzy input value, and the state of charge SOC of the i-th energy storage is taken respectively i The basic domain is [0,1], and the energy storage power P ES The basic domain is [-3,3]; the state of charge SOC of the i-th energy storage i The domain of energy storage is divided into five levels according to the actual parameters of different types of energy storage, {LL, LM, MM, HM, HH}, corresponding to low, lower, moderate, higher, and high respectively; the energy storage power P ES The domain is divided into 6 levels, {NB, NM, NS, PS, PM, PB}, corresponding to large negative, medium negative, small negative, small positive, medium positive, and large positive, respectively. The output value of the fuzzy controller is the i-th energy storage control value. Its discrete domain is defined as [-1, 1], the control subset is [N, Z, P], and the membership function is selected.

7. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: In S3, multiple indicators are first calculated as follows: ① Economic efficiency: the cost is calculated by the life cycle cost or the levelized cost per kilowatt-hour, and the income is calculated by the peak-valley arbitrage income or the peak-shaving income; ② Safety, using the safety accident rate calculation method or failure rate calculation method; ③ Peak-shaving effect, using the system peak-shaving deficiency calculation method or the system wind and solar power abandonment calculation method.

8. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: The S3 is based on the optimization calculation of multiple indicators, and the implementation method is as follows: A multi-objective optimization model is established using the optimization algorithm to calculate the rated power capacity range of the energy storage system and optimize the configuration of multiple types of energy storage systems within different time scales.

9. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 8, characterized in that: S3 optimizes the same energy storage involving multiple time scales as follows: By performing multi-time domain charging and discharging superposition calculations on the same type of energy storage involving multiple time scales, the power capacity range of this type of energy storage is re-determined, thereby obtaining preliminary results for multi-type energy storage configurations.

10. The method for configuring the capacity of a multi-type energy storage system suitable for multi-time scale peak regulation according to claim 1, characterized in that: S4, using actual operating parameters to simulate power flow scheduling, is implemented as follows: The power network is modeled using DC power flow, and the energy storage power constraints and energy storage SOC constraints of 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, thereby obtaining the final result of the energy storage configuration.

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