A multi-level optimization scheduling method and device for energy storage equipment in source-grid-load-storage scenario

Abstract

The present application provides a multi-level optimization scheduling method and device for energy storage equipment in a source-grid-load-storage scenario, the method comprising: extracting real data from the power generation side and the load side, calculating the energy storage equipment benefit index sequence; quantifying the supporting role of the energy storage equipment on the index, forming a source-grid-load-storage operation index state matrix; calculating the expected output index sequence of the energy storage equipment; calculating the actual output index sequence of the energy storage equipment; calculating the support effect scoring sequence of the energy storage equipment; calculating the scheduling effect scoring sequence of the energy storage equipment; calculating the comprehensive scoring sequence of the energy storage equipment scheduling situation, and correcting the daily charging and discharging behavior of the energy storage equipment. The advantage of the present application is that it fully considers the supporting capacity of the energy storage equipment for the source-grid-load-storage power grid, the economic benefits of the energy storage equipment itself, and the supporting performance of the energy storage equipment for the power grid scheduling needs in the scheduling of the power grid, realizes a comprehensive evaluation, and realizes the optimal scheduling of the energy storage equipment.

Description

Technical Field

[0001] The present application belongs to the technical field of energy storage equipment optimization and scheduling, and specifically relates to a multi-level optimization and scheduling method and device for energy storage equipment in a source-grid-load-storage scenario. Background Art

[0002] At present, the proportion of new energy power generation access to the source-grid-load-storage power grid is increasing. Due to its advantages of high power generation efficiency, environmental friendliness, and low cost, it is widely used in power generation, frequency modulation and other fields. However, due to the randomness and volatility of new energy power generation, the power generation and efficiency are affected by factors such as weather conditions and light intensity. Therefore, new energy power generation has a certain impact on the diversified operating efficiency indicators in the source-grid-load-storage power grid. It is difficult to cope with this impact by simply relying on the grid's own regulation capabilities.

[0003] For the problem of high randomness in the power grid, a common method is to install energy storage equipment in the source-grid-load-storage network. Energy storage is a new type of energy storage device in recent years. It converts electrical energy into chemical energy or mechanical energy by means of electrochemical or mechanical energy storage, and can reduce the power generation peak or load peak to a certain extent within a certain period of time. However, after the energy storage equipment is built in the source-grid-load-storage network, the dispatch instructions for the energy storage equipment have affected the operating efficiency of the energy storage equipment and the ability of the energy storage equipment to support the diversified indicators of the power grid to a certain extent.

[0004] The optimal scheduling research of energy storage equipment requires targeted solution design to ensure that the energy storage equipment can effectively support key indicators such as voltage fluctuations and load shedding in the power grid, and at the same time, the energy storage operators can also obtain satisfactory profitability. The existing optimal scheduling methods for energy storage equipment generally only consider their own economic efficiency or the single-target support effect on the power grid. Such scheduling methods can solve the single-target scheduling solution design, but cannot comprehensively consider the various needs faced by energy storage equipment, and cannot achieve the optimal scheduling of energy storage equipment. Summary of the invention

[0005] The purpose of this application is to overcome the defects of the prior art that it is unable to comprehensively consider the various demands faced by energy storage equipment and cannot achieve optimal scheduling of energy storage equipment.

[0006] In order to achieve the above objectives, this application proposes a multi-level optimization scheduling method for energy storage equipment in a source-grid-load-storage scenario, including:

[0007] Step 1: Use clustering algorithm to extract the real data features of the power generation side and load side for a period of time to form the corresponding typical scenario. Use the time series scenario generation method to sample the source and load side data of 24 hours in a day, combine the source and load side data and arrange them according to the 24-hour time series to obtain the source and load side status samples of 24 hours in a day;

[0008] Step 2: Calculate the economic benefit indicators of energy storage equipment in each hour of the 24 hours of the day, consider the investment and operation and maintenance costs of energy storage equipment, and the economic benefits obtained by energy storage equipment through charging and discharging, and form a sequence of energy storage equipment benefit indicators as the first level of optimization; quantify the supporting role of energy storage equipment in the source-grid-load-storage power grid for various indicators, and form a source-grid-load-storage operation indicator state matrix; calculate the redundant power resources or load shedding indicators in the power grid, and form a sequence of expected output indicators of energy storage equipment; calculate the actual output of energy storage equipment based on the flow calculation results and form a sequence of actual output indicators of energy storage equipment;

[0009] Step 3: Evaluate the source-grid-load-storage operation indicator state matrix, and quantify the evaluation results into a storage equipment support effect scoring sequence as the second-level optimization; evaluate the energy storage equipment expected output indicator sequence and the energy storage equipment actual output indicator sequence to form a storage equipment dispatch effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid as the third-level optimization;

[0010] Step 4: Based on the energy storage device dispatch effect scoring sequence, the comprehensive scoring sequence of the energy storage device dispatch situation is calculated, and on this basis, the daily charging and discharging behavior of the energy storage device itself is corrected.

[0011] As an improvement of the above method, the source and load state samples P of the day for 24 hours all It is expressed as:

[0012]

[0013] in, It represents the actual amount of electricity generated by the m1th thermal power generating unit in the first time period; It represents the actual amount of electricity generated by the m2-th photovoltaic generator set in the first time period; represents the actual amount of electricity generated by the m3th wind turbine generator set in the first time period; P ld,r,n,1 It represents the actual amount of electricity used by the nth load node in the first time period; the subscript r represents the actual amount of electricity; m1 represents the number of thermal power units; m2 represents the number of wind power units; m3 represents the number of photovoltaic power units; and n represents the number of load nodes.

[0014] As an improvement of the above method, step 2 comprises:

[0015] For the source-grid-load grid state samples, the power flow calculation method is used to obtain the voltage fluctuation index, new energy consumption index, power user load shedding index and grid line loss index in the grid, forming the source-grid-load-storage operation index state matrix;

[0016] Calculate the economic benefit index of energy storage equipment at each hour of the 24 hours in a day, taking into account the investment and operation and maintenance costs of energy storage equipment, as well as the economic benefits obtained by energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indexes;

[0017] Calculate the redundant power resources or load shedding indicators in the power grid to form the expected output indicator sequence of the energy storage equipment; calculate the actual output of the energy storage equipment based on the power flow calculation results and form the actual output indicator sequence of the energy storage equipment.

[0018] As an improvement of the above method, step 3 comprises:

[0019] The evaluation is conducted based on the source-grid-load-storage operation indicator state matrix, and the evaluation results are quantified into a scoring sequence of the energy storage equipment support effect:

[0020] Normalize the elements in the source-grid-load-storage operation indicator state matrix, convert all data to [0,1], and obtain the normalized source-grid-load-storage operation indicator state matrix D N :

[0021]

[0022] Among them, ΔU N,1 represents the normalized source-grid-load node voltage variation sequence in the first time period of the day; P pv,r,f,N,1 represents the actual power generation of the fth photovoltaic power station in the first time period after normalization; P wd,r,i,N,1 represents the actual power generation of the i-th wind farm in the first time period after normalization; P ld,c,g,N,1 It represents the load shedding power of the g-th load node in the first time period after normalization;

[0023] The normalized source-grid-load-storage operation indicator state matrix is ​​averaged by column to obtain the energy storage equipment support effect score sequence E su,N :

[0024] E su,N =(E ave,N,1 E ave,N,2 … E ave,N,24 )

[0025]

[0026] Among them, E ave,N,1 Indicates the support effect score of the energy storage equipment in the first time period;

[0027] The expected output index sequence of energy storage equipment and the actual output index sequence of energy storage equipment are evaluated to form a scoring sequence of energy storage equipment dispatching effect for redundant or missing resources in the source-grid-load-storage grid, and normalize it:

[0028] Normalized energy storage equipment dispatch effect score sequence P ES,e,N The calculation formula is:

[0029]

[0030]

[0031] Among them, P ES,e,max and P ES,e,min They represent the energy storage equipment scheduling effect scoring sequence P ES,e The maximum and minimum values ​​of P ES,e,1 It represents the difference between the actual output index and the expected output index of the energy storage device in the first time period; P ES,r,1 Indicates the actual output index of the energy storage equipment in the first time period; P ES,a,1 Indicates the expected output index of the energy storage equipment in the first time period.

[0032] As an improvement of the above method, step 4 includes:

[0033] Scoring sequence E for the support effect of energy storage equipment su,N , Energy storage equipment benefit index sequence P ES,b,N and the energy storage equipment dispatch effect scoring sequence P ES,e,N Perform comprehensive scoring to obtain the comprehensive scoring sequence of energy storage equipment scheduling:

[0034] The effect scoring sequence E supported by energy storage equipment su,N , Energy storage equipment benefit index sequence P ES,b,N and the energy storage equipment dispatch effect scoring sequence P ES,e,N Composition based on energy storage scheduling impact factor matrix D ES,ef :

[0035]

[0036] Among them, P ES,e,N,1 represents the normalized energy storage equipment scheduling effect score sequence P ES,e,N The first column of

[0037] P ES,b,N,1 Represents the energy storage equipment benefit index sequence P ES,b,N The first column of

[0038] Energy storage equipment benefit index sequence P ES,b,N The calculation formula is as follows:

[0039]

[0040] Among them, P ES,b,max and P ES,b,minThey represent the maximum and minimum values ​​of the 24-hour total return indicator sequence of the energy storage equipment respectively;

[0041]

[0042] Among them, P ES,b,h,t , P ES,c,h,t and P ES,r,h,t They represent the charging and discharging income, operation and maintenance cost, and operation compensation of the h-th energy storage device in the t-th time period; n es Indicates the number of energy storage devices;

[0043] Calculate the comprehensive scoring sequence S of the energy storage equipment dispatch situation:

[0044]

[0045] Among them, d max and min They represent the optimal solution vector and the worst solution vector of energy storage scheduling effect respectively:

[0046]

[0047] Among them, D ES,ef,max,1 and D ES,ef,min,1 They represent the energy storage dispatch influencing factor matrix D ES,ef The maximum and minimum values ​​of the first column;

[0048] The energy storage equipment scheduling plan is optimized based on the comprehensive scoring sequence of the energy storage equipment scheduling situation:

[0049] Normalize the comprehensive score sequence S of the energy storage equipment scheduling situation and return the score to the interval [0,2]:

[0050]

[0051] Among them, S max and S min Respectively represent the maximum and minimum values ​​in the comprehensive score sequence of the energy storage equipment scheduling situation;

[0052] The optimization formula for the scheduling scheme of energy storage equipment is as follows:

[0053] P ES,r,o =(P ES,r,1 ·S N,1 P ES,r,2 ·S N,2 ... P ES,r,24 ·S N,24 )

[0054] Among them, P ES,r,o Represents the optimized energy storage equipment scheduling plan.

[0055] The present application also provides a multi-level optimization scheduling device for energy storage equipment in a source-grid-load-storage scenario, which is implemented based on the above method, and the device includes:

[0056] The module for obtaining source and load side status samples is used to extract the real data characteristics of the power generation side and the load side for a period of time using a clustering algorithm to form a corresponding typical scenario. The time series scenario generation method is used to sample the source and load side data of 24 hours in a day, and the source and load side data are combined and arranged in a 24-hour time series to obtain the source and load side status samples of 24 hours in a day;

[0057] The first-level optimization module is used to calculate the economic benefit indicators of energy storage equipment in each hour of the 24 hours of the day, taking into account the investment and operation and maintenance costs of energy storage equipment, as well as the economic benefits obtained by energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indicators as the first-level optimization; quantify the supporting role of energy storage equipment in the source-grid-load-storage power grid for various indicators, and form a source-grid-load-storage operation indicator state matrix; calculate the redundant power resources or load shedding indicators in the power grid to form an expected output indicator sequence of energy storage equipment; calculate the actual output of energy storage equipment based on the flow calculation results and form an actual output indicator sequence of energy storage equipment;

[0058] The second and third level optimization modules are used to evaluate the source-grid-load-storage operation indicator state matrix, and quantify the evaluation results into a storage device support effect scoring sequence as the second level optimization; evaluate the energy storage device expected output indicator sequence and the energy storage device actual output indicator sequence, and form a storage device dispatch effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid as the third level optimization; and

[0059] The discharge behavior correction module is used to calculate the comprehensive scoring sequence of the energy storage device scheduling situation based on the energy storage device scheduling effect scoring sequence, and on this basis correct the daily charging and discharging behavior of the energy storage device itself.

[0060] Compared with the prior art, the advantages of this application are:

[0061] This application fully considers the supporting capacity of energy storage equipment for the source-grid-load-storage power grid, the economic benefits of the energy storage equipment itself, and the supporting performance of energy storage equipment for the power dispatching needs of the power grid during dispatching, realizes a comprehensive evaluation, and guides the design of the optimal dispatching plan for energy storage equipment, thereby realizing the optimal dispatching of energy storage equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Shown is a flow chart of the multi-level optimization scheduling method for energy storage equipment in the source-grid-load-storage scenario. DETAILED DESCRIPTION

[0063] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0064] The present application provides a multi-level optimization scheduling method and device for energy storage equipment in a source-grid-load-storage scenario. By combining a clustering algorithm with a time-series scenario generation technology, the randomness and volatility existing in the source-grid-load-storage network are accurately described. The power flow distribution in the source-grid-load-storage network is obtained using power flow calculation. The method is divided into three levels, which respectively consider the support capacity of the energy storage equipment for the source-grid-load-storage network, the economic benefits of the energy storage equipment itself, and the support performance of the energy storage equipment for the power dispatching demand of the power grid in the scheduling of the energy storage equipment. A comprehensive evaluation is achieved and the design of the optimization scheduling plan for the energy storage equipment is guided, thereby achieving the optimization scheduling of the energy storage equipment.

[0065] Example 1

[0066] The present application provides a multi-level optimization scheduling method for energy storage equipment in a source-grid-load-storage scenario, comprising the following steps:

[0067] Step 1 For a source-grid-load-storage power grid including n load nodes, m generation-side nodes and e energy storage nodes, there are m1 thermal power units, m2 wind power units and m3 photovoltaic power units, and m1+m2+m3=m. Based on the real data of the generation side and load side throughout the year, the data features of each side are extracted according to the clustering algorithm and the corresponding typical scenarios are formed. The source and load side data of each hour of the day are sampled using the time series scenario generation method. The source and load side data are combined and arranged in a 24-hour time series to obtain the 24-hour source and load side state samples that are in line with the actual situation.

[0068] Step 1.1 For a power grid with a total of m+n nodes, collect the load data series of n load nodes and the power generation data series of m power plants throughout the year. Based on the data characteristics and existing research experience, use the mean clustering method to extract the state sample characteristics of the source and load sides to obtain the typical scenarios of the source and load sides. Here, take the photovoltaic power generation data sample as an example. The photovoltaic power generation data sample D pv As shown in Formula 1:

[0069]

[0070] D pv T in the table represents the number of time periods within a day, which is 24 in total; M represents the number of real data samples of photovoltaic power generation, that is, the number of days for collecting real photovoltaic data. pv Each row in is called a sample data D pv,i , as shown in Formula 2:

[0071] D pv,i =(d pv,i1 d pv,i2 …d pv,iT ) (2)

[0072] Step 1.1.1 D pv As the input of the mean clustering method. Set V as the number of cluster centers. pv Randomly extract the initial cluster center from q represents the cluster center at the qth iteration, as shown in Formula 3:

[0073] a q =(a 1,q a 2,q … a V,q ) (3)

[0074] Among them, a j,q represents the jth cluster center after q iterations, as shown in Formula 4:

[0075] a j,q =(a j,q,1 a j,q,2 … a j,q,T ) (4)

[0076] The iteration termination condition is set to the cluster center change error less than 0.001.

[0077] Step 1.1.2 D pv Each sample data in is assigned to the cluster center with the smallest distance from this sample data sequence. Here, the distance uses the Euclidean distance. Since the dimension of the input sample data sequence is 1×24, the i-th sample data sequence D pv,i In the qth iteration, with the jth cluster center a j,q Taking the Euclidean distance as an example, the Euclidean distance formula can be simplified to Formula 5:

[0078]

[0079] Step 1.1.3 Repeat step 1.1.2 for all M sample data sequences, and it is considered that one iteration is completed. Repeat the iteration until the convergence condition is met, that is, the cluster center change error is less than 0.001, all clustering is completed, and the V cluster centers after the final stop of iteration are the obtained V typical photovoltaic power generation scenarios, as shown in Formula 6:

[0080] a pv,end =(a pv,end,1 a pv,end,2 … a pv,end,V ) (6)

[0081] In V typical scenarios, since the number of sample data sequences attached to each cluster center is different after the clustering stops iterating, the number of sample data sequences attached to the cluster center and the total number of sample data sequences M can be usedpv Calculate the occurrence probability of each of V typical photovoltaic power generation scenarios, as shown in Formula 7:

[0082]

[0083] Among them, P pv represents the probability sequence of typical photovoltaic power generation scenarios, where P pv,1 Represents the probability of occurrence of the first typical photovoltaic power generation scenario. pv,1 and M pv They respectively represent the number of sample data sequences to which the first cluster center in the photovoltaic power generation clustering results is attached and the total number of photovoltaic power generation sample data sequences.

[0084] Step 1.2: Based on the typical source and load-side scenarios obtained by the clustering method, sample and obtain the 24-hour source and load-side power data within the day according to the time series scenario generation method.

[0085] Taking the generation of V typical photovoltaic power generation scenarios to obtain 24-hour source-grid-load grid data within a day as an example, the process of the time series scenario generation method is explained.

[0086] Step 1.2.1: In the time period t=1, sampling is performed according to the obtained occurrence probability sequence of the typical photovoltaic power generation scene, and a typical scene is extracted from V typical photovoltaic power generation scenes to obtain the typical photovoltaic power generation scene in the first time period.

[0087] After completing the selection of typical photovoltaic power generation scenarios within the time period t=1 in step 1.2.2, t=t+1.

[0088] Step 1.2.4 repeats steps 1.2.1 to 1.2.2 until t=24, at which time the typical scene of photovoltaic power generation within a day is a vector with a dimension of 1×24, representing the hourly power generation of the photovoltaic station in the 24-hour time of the day obtained by sampling the first photovoltaic power station, as shown in Formula 8:

[0089] P pv，1 =(P pv,r,1,1 P pv,r,1,2 … P pv,r,1,24 ) (8)

[0090] Step 1.3 repeats steps 1.1 and 1.2 for the electricity consumption data of wind power, thermal power generation, and power load, and finally obtains the generated data of photovoltaic power generation, wind power generation, thermal power generation, and power load in 24 hours a day, combines the power data on the source and load sides and the energy storage configuration data, and forms the basic data for the flow calculation of the source-grid-load-storage scenario, which provides a data basis for the subsequent use of flow calculation to obtain the support capacity of energy storage equipment in the power grid for various indicators and the economic indicators of the energy storage equipment itself.

[0091] For the source-grid-load-storage power grid with all n load nodes, m generation-side nodes and e energy storage nodes, there are m1 thermal power units, m2 wind power units and m3 photovoltaic power units, m1+m2+m3=m. Step 1 is used to generate the time series scenario and then the source-grid-load-storage power grid operation scenario is obtained after combination, as shown in Formula 9:

[0092]

[0093] Among them, P g,r,1,1 Indicates the actual amount of electricity generated by the first thermal power generating unit in the first hour; P pv,r,1,1 Indicates the actual amount of electricity generated by the first photovoltaic generator set in the first hour; P wd,r,1,1 Indicates the actual amount of electricity generated by the first wind turbine in the first hour; P ld,r,1,1 It represents the actual power used by the first load node in the first hour; the subscript r represents the actual power.

[0094] Step 2 calculates the economic benefit index of the energy storage equipment in each hour of the 24 hours of the day, taking into account the investment and operation and maintenance costs of the energy storage equipment, as well as the economic benefits obtained by the energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indexes, which is 1×24 dimensions. Calculate the redundant power resources or load shedding index in the power grid to form a sequence of expected output indexes for energy storage equipment.

[0095] Step 2.1 For the source-grid-load grid state sample P Si , use the power flow calculation method to obtain the voltage fluctuation index, new energy consumption index, power user load shedding rate index and power grid line loss index in the power grid, and form the source-grid-load-storage operation index state matrix.

[0096] Step 2.1.1 The process of calculating the voltage fluctuation index is as follows. The node voltage of m+n nodes U =[U1,U2,…U m+n ], the node voltages of m+n nodes are compared with the reference voltage U of each node N =[U N,1 ,U N,2 ,…U N,m+n}, and use formula 10 to calculate the comprehensive voltage fluctuation ΔU of the source grid and load grid:

[0097]

[0098] In the formula, ΔU represents the voltage variation sequence of the source-grid-load node within 24 hours of the day, and the dimension of this sequence is 1×24. j,1 and represent the voltage of the j-th node in the first time period and the rated voltage of the j-th node respectively.

[0099] Step 2.1.2 The process of calculating the new energy consumption index is as follows. There are m nodes in the source-grid-load power grid with power stations, including m1 thermal power units, m2 wind power units, and m3 photovoltaic power units. m1+m2+m3=m. The j-th photovoltaic power generation is used as an example to illustrate the calculation process of the new energy consumption index. The actual power generation of the j-th photovoltaic power generation is P pv,r,j That is, the 24-hour scene data generated in the day obtained by the time series scene generation in step 1, as shown in formula 11:

[0100] P pv,r,j =(P pv,r,j,1 P pv,r,j,2 ... P pv,r,j,24 ) (11)

[0101] After the flow calculation, the flow distribution in the source-grid-load power grid, as well as the grid-connected power of the generator set and the power consumption of the power load can be obtained. At this time, the grid-connected power of the jth photovoltaic power generation P pv,v,j As shown in Formula 12:

[0102] P pv,v,j =(P pv,v,j,1 P pv,v,j,2 ... P pv,v,j,24 ) (12)

[0103] At this time, the actual power generation of the j-th photovoltaic power generation P pv,r,j And the jth photovoltaic power generation grid-connected power P pv,v,i By subtracting, we can get the abandoned power P of the jth photovoltaic power station. pv,r,i,all , as shown in Formula 13:

[0104] P pv,ab,j =(P pv,r,j,1 P pv,r,j,2 ... P pv,r,j,24 ) (13)

[0105] -(P pv,v,j,1 P pv,v,j,2 … P pv,v,j,24 )

[0106] =(P pv,ab,j,1 P pv,ab,j,2 ... P pv,ab,j,24 )

[0107]

[0108] Where P pv,ab,j,all That is, the abandoned power of the jth photovoltaic power station. At this time, the abandoned power of m2 wind turbines and m3 photovoltaic generators is summed up, as shown in Formula 14:

[0109]

[0110] Where P new,ab,all It represents the sum of the total power abandoned by all wind power stations and the total power abandoned by photovoltaic power stations after 24 hours of operation, and the power abandonment rate index, which can measure the utilization rate of renewable energy power generation in the source-grid-load-storage system; i and j are temporary variables representing 24 hours in a day and the numbers of wind power stations and photovoltaic stations, respectively. Thermal power generating units are started and stopped according to the pre-established power generation plan, and power abandonment is generally rare, so this application does not consider the phenomenon of thermal power abandonment. The new energy consumption index here mainly focuses on the consumption of renewable energy power generation in the power grid.

[0111] Step 2.1.3 The process of calculating the load shedding rate index of power users is as follows. For all n load nodes, the calculation process of the load shedding rate index of power users is explained by taking the i-th load node as an example. The actual power demand of the i-th load node is expressed as P ld,r,i , as shown in Formula 15:

[0112] P ld,r,i =(P ld,r,i,1 P ld,r,i,2 ... P ld,r,i,24 ) (15)

[0113] After the power flow calculation, the purchased electricity of the i-th load node is expressed as P ld,b,i , as shown in Formula 16:

[0114] P pv,b,i =(P pv,b,i,1 P pv,b,i,2 … P pv,b,i,24 ) (16)

[0115] Where P ld,b,i That is, the amount of electricity purchased by the i-th load node. At this time, the actual amount of electricity required for the i-th load node is P ld,r,i And the amount of electricity purchased by the i-th load node P ld,b,i Subtract and sum, as shown in Formula 17:

[0116]

[0117]

[0118] In the formula, P ld,c,i,all That is, it indicates the load shedding power of the i-th load node. At this time, the load shedding power of all n load nodes is counted and summed, as shown in formula 18:

[0119]

[0120] Where P ld,c,all It represents the sum of the load shedding powers of all load nodes, i.e. the load shedding index of power users.

[0121] Step 2.1.4 The process of calculating the grid line loss index is as follows. The source grid load grid line loss index represents the electric energy lost in the distribution line. This part of the electric energy is calculated based on the source grid load grid line flow current obtained by the power flow calculation, as shown in Formula 19:

[0122]

[0123]

[0124] Where P line,d,all It represents the power consumed by the source grid and load grid in 24 hours. z,1 and r z They respectively represent the flowing current and line impedance on line z in the tth time period, z is a temporary variable, indicating the line number; k represents the number of lines.

[0125] Step 2.2: The values ​​of node voltage fluctuation index, wind power load shedding index, photovoltaic power generation curtailment index, and power load shedding index within 24 hours are combined into a source-grid-load-storage operation index state matrix, as shown in Formula 20.

[0126]

[0127] Step 2.3 quantifies the supporting role of energy storage equipment on various indicators in the source-grid-load-storage power grid, forms a source-grid-load-storage operation indicator status matrix, and provides theoretical support for subsequent multi-level evaluation of energy storage equipment and the formation of optimized scheduling plans.

[0128] Step 2.4 In level one, calculate the economic benefit indicators of the energy storage equipment for each hour of the 24 hours of the day, taking into account the investment and operation and maintenance costs of the energy storage equipment, as well as the economic benefits obtained by the energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indicators with a dimension of 1×24, as shown in Formula 21.

[0129]

[0130] P ES,b =(P ES,b,1 P ES,b,2 … P ES,b,24 )

[0131]

[0132] Where P ES,b,t and PES,b They represent the revenue index of the energy storage equipment in the tth time period and the total revenue index sequence of the energy storage equipment within 24 hours. The dimension of this sequence is 1×24. ES,b,N represents the normalized energy storage equipment benefit index sequence; P ES,b,max and P ES,b,min They represent the maximum and minimum values ​​of the total return index sequence of energy storage equipment within 24 hours; P ES,b,j,t , P ES,c,j,t and P ES,r,j,t They represent the charging and discharging income, operation and maintenance cost, and operation compensation of the j-th energy storage device in time period t; n es Indicates the number of energy storage devices.

[0133] Step 2.5 Calculate the redundant power resources or load shedding index in the power grid to form the expected output index sequence P of the energy storage device ES,a , the dimension of the sequence is 1×24; the actual output of the energy storage device is calculated based on the power flow calculation results and the actual output index sequence of the energy storage device P is formed ES,r , the sequence dimension is 1×24, the energy storage equipment expected output index sequence P ES,a And the actual output index sequence P of energy storage equipment ES,r As shown in Formula 22.

[0134]

[0135] In the formula, ΔP ES It indicates the expected correction sequence of the energy storage device, which means the direction in which the source, grid and load grid expect the energy storage device to correct its output.

[0136] Step 3: Based on the obtained source-grid-load-storage operation indicator state matrix, in level 2, this indicator matrix is ​​evaluated, and the evaluation results are quantified into a storage equipment support effect scoring sequence, and the dimension of this sequence is 1×24. In level 3, the energy storage equipment expected output indicator sequence and the energy storage equipment actual output indicator sequence are evaluated to form a storage equipment scheduling effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid, and the dimension of this sequence is 1×24. Based on the obtained multi-level evaluation results, the energy storage equipment optimization scheduling correction sequence is formed.

[0137] Step 3.1 Based on the obtained source-grid-load-storage operation indicator state matrix, at level two, this indicator matrix is ​​evaluated and the evaluation results are quantified into a storage equipment support effect score sequence with a sequence dimension of 1×24.

[0138] Step 3.1.1 Normalize the source-grid-load-storage operation index state matrix and convert all data to [0,1]. Take the voltage fluctuation index as an example to illustrate the conversion process, as shown in Formula 23:

[0139]

[0140] In the formula, ΔU N It represents the normalized voltage variation sequence of the source-grid-load node within 24 hours of the day, ΔU max and ΔU min They respectively represent the maximum and minimum values ​​in the source-grid-load node voltage change sequence within 24 hours of the day.

[0141] Step 3.1.2 Repeat step 3.1.1 for wind power curtailment, photovoltaic power curtailment and load shedding indicators to obtain the normalized source-grid-load-storage operation indicator state matrix, as shown in formula 24:

[0142]

[0143] Step 3.1.3 Calculate the average of the normalized source-grid-load-storage operation indicator state matrix by column to obtain the energy storage equipment support effect score sequence E su,N , as shown in Formula 25:

[0144]

[0145] In the formula, E su,N represents the energy storage equipment support effect scoring sequence, E ave,N,1 Indicates the support effect score of the energy storage equipment in the first time period.

[0146] Step 3.2 In level 3, the energy storage equipment expected output index sequence P obtained in step 2.5 is ES,a And the actual output index sequence P of energy storage equipment ES,r An evaluation is performed to form a scoring sequence of the dispatching effect of energy storage equipment for redundant or missing resources in the source-grid-load-storage grid and normalize it. The dimension of this sequence is 1×24, as shown in Formula 26:

[0147]

[0148] Where P ES,e represents the energy storage equipment scheduling effect scoring sequence, P ES,e,N represents the normalized energy storage equipment dispatch effect score sequence, P ES,e,max and P ES,e,min They represent the energy storage equipment scheduling effect scoring sequence P ES,e The maximum and minimum values ​​of .

[0149] Step 4 calculates the energy storage device optimization scheduling correction sequence based on the obtained normalized energy storage device scheduling effect score sequence, and on this basis corrects the energy storage device's own daily charging and discharging behavior. A positive optimization scheduling correction sequence indicates that the energy storage device's daily charging and discharging behavior needs to be increased, and vice versa, it indicates that the energy storage device's daily charging and discharging behavior needs to be reduced.

[0150] In the multi-level evaluation results obtained in step 4.1, the energy storage equipment support effect scoring sequence E su,N Represents the ability of energy storage equipment to support voltage fluctuation index, new energy consumption index, power user load shedding rate index and power grid line loss index in the source-grid-load-storage power grid. The energy storage equipment benefit index sequence P ES,b,N Represents the economic benefit of the energy storage equipment scheduling results to the energy storage equipment itself, and the energy storage equipment scheduling effect scoring sequence P ES,e,N It represents the matching of the dispatching results of the energy storage equipment with the electric energy in the source-grid-load-storage grid, and whether the energy storage equipment can make full use of the redundant energy of the grid, and minimize the load shedding phenomenon in the source-grid-load-storage grid to ensure reliable power supply of the grid. The above multi-level energy storage equipment dispatching influencing factors are comprehensively scored, and finally a comprehensive scoring sequence of the energy storage equipment dispatching situation is obtained, and the dimension of this sequence is 1×24.

[0151] First, based on the energy storage equipment support effect scoring sequence E su,N , Energy storage equipment benefit index sequence P ES,b,N And the energy storage equipment scheduling effect scoring sequence P ES,e,N Form the energy storage dispatch influencing factor matrix D ES,ef , as shown in Formula 27:

[0152]

[0153] Based on the energy storage dispatch influencing factor matrix D ES,ef To evaluate the dispatching effect of energy storage equipment, first calculate the energy storage dispatching influence factor matrix D ES,ef The maximum value D of each column ES,ef,max and minimum value D ES,ef,min , and then calculate the distance between each column of data and the maximum and minimum values ​​of this column. The calculation process is shown in formula 28:

[0154]

[0155] Where, d max and min They represent the optimal solution vector and the worst solution vector of the energy storage scheduling effect respectively. At this time, the comprehensive score S of the energy storage equipment scheduling is shown in formula 29:

[0156]

[0157] Step 4.2 optimizes the energy storage equipment scheduling scheme based on the obtained comprehensive score S of the energy storage equipment scheduling. First, the comprehensive score S of the energy storage equipment scheduling is reduced to the interval [0,2], as shown in Formula 30:

[0158]

[0159] In the formula, S N represents the normalized score after reduction. N The period greater than 1 indicates that the energy storage equipment is in good operating condition during this period and has the potential for further improvement. The period less than 1 indicates that the energy storage equipment is in poor operating condition during this period and the output of the energy storage equipment needs to be reduced. Therefore, the scheduling scheme optimization of the energy storage equipment is shown in formula 31:

[0160]

[0161] Where P ES,r Represents the actual output index sequence of energy storage equipment, P ES,r,o It represents the optimized scheduling plan for energy storage equipment. At this time, the optimized scheduling plan for energy storage equipment comprehensively considers the support status of energy storage equipment for the source grid, load and storage network, the economic benefits of the energy storage equipment itself, and the demand for electric energy regulation of the source grid, load and storage network, forming a more comprehensive and more widely applicable optimized scheduling plan for energy storage equipment.

[0162] Example 2

[0163] The present application also provides a multi-level optimization scheduling device for energy storage equipment in a source-grid-load-storage scenario, which is implemented based on the above method, and the device includes:

[0164] The module for obtaining source and load side status samples is used to extract the real data characteristics of the power generation side and the load side for a period of time using a clustering algorithm to form a corresponding typical scenario. The time series scenario generation method is used to sample the source and load side data of 24 hours in a day, and the source and load side data are combined and arranged in a 24-hour time series to obtain the source and load side status samples of 24 hours in a day;

[0165] The first-level optimization module is used to calculate the economic benefit indicators of energy storage equipment in each hour of the 24 hours of the day, taking into account the investment and operation and maintenance costs of energy storage equipment, as well as the economic benefits obtained by energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indicators as the first-level optimization; quantify the supporting role of energy storage equipment in the source-grid-load-storage power grid for various indicators, and form a source-grid-load-storage operation indicator state matrix; calculate the redundant power resources or load shedding indicators in the power grid to form an expected output indicator sequence of energy storage equipment; calculate the actual output of energy storage equipment based on the flow calculation results and form an actual output indicator sequence of energy storage equipment;

[0166] The second and third level optimization modules are used to evaluate the source-grid-load-storage operation indicator state matrix, and quantify the evaluation results into a storage device support effect scoring sequence as the second level optimization; evaluate the energy storage device expected output indicator sequence and the energy storage device actual output indicator sequence, and form a storage device dispatch effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid as the third level optimization; and

[0167] The discharge behavior correction module is used to calculate the comprehensive scoring sequence of the energy storage device scheduling situation based on the energy storage device scheduling effect scoring sequence, and on this basis correct the daily charging and discharging behavior of the energy storage device itself.

[0168] The present application may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0169] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0170] It is understood that the memory in the disclosed embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0171] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0172] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0173] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0174] Execute the steps of the above method.

[0175] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above-disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the above-disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0176] It is understood that the embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0177] For software implementation, the technology of the present application can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0178] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A multi-level optimization scheduling method for energy storage equipment in a source-grid-load-storage scenario, comprising: Step 1: Use clustering algorithm to extract the real data features of the power generation side and load side for a period of time to form the corresponding typical scenario. Use the time series scenario generation method to sample the source and load side data of 24 hours in a day, combine the source and load side data and arrange them according to the 24-hour time series to obtain the source and load side status samples of 24 hours in a day; Step 2: Calculate the economic benefit indicators of energy storage equipment in each hour of the 24 hours of the day, consider the investment and operation and maintenance costs of energy storage equipment, and the economic benefits obtained by energy storage equipment through charging and discharging, and form a sequence of energy storage equipment benefit indicators as the first level of optimization; quantify the supporting role of energy storage equipment in the source-grid-load-storage power grid for various indicators, and form a source-grid-load-storage operation indicator state matrix; calculate the redundant power resources or load shedding indicators in the power grid, and form a sequence of expected output indicators of energy storage equipment; calculate the actual output of energy storage equipment based on the flow calculation results and form a sequence of actual output indicators of energy storage equipment; Step 3: Evaluate the source-grid-load-storage operation indicator state matrix, and quantify the evaluation results into a storage equipment support effect scoring sequence as the second-level optimization; evaluate the energy storage equipment expected output indicator sequence and the energy storage equipment actual output indicator sequence to form a storage equipment dispatch effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid as the third-level optimization; Step 4: Based on the energy storage equipment dispatch effect scoring sequence, a comprehensive scoring sequence of the energy storage equipment dispatch situation is calculated, and on this basis, the daily charging and discharging behavior of the energy storage equipment itself is corrected; The step 2 comprises: For the source-grid-load grid state samples, the power flow calculation method is used to obtain the voltage fluctuation index, new energy consumption index, power user load shedding index and grid line loss index in the grid, forming the source-grid-load-storage operation index state matrix; Calculate the economic benefit index of energy storage equipment at each hour of the 24 hours in a day, taking into account the investment and operation and maintenance costs of energy storage equipment, as well as the economic benefits obtained by energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indexes; Calculate the redundant power resources or load shedding indicators in the power grid to form the expected output indicator sequence of the energy storage equipment; calculate the actual output of the energy storage equipment based on the power flow calculation results and form the actual output indicator sequence of the energy storage equipment.

2. The multi-level optimization scheduling method for energy storage equipment in the source-grid-load-storage scenario according to claim 1 is characterized in that: The source and load status samples P for 24 hours in the day all It is expressed as: in, It represents the actual amount of electricity generated by the m1th thermal power generating unit in the first time period; It represents the actual amount of electricity generated by the m2-th photovoltaic generator set in the first time period; represents the actual amount of electricity generated by the m3th wind turbine generator set in the first time period; P ld,r,n,1 It represents the actual amount of electricity used by the nth load node in the first time period; the subscript r represents the actual amount of electricity; m1 represents the number of thermal power units; m2 represents the number of wind power units; m3 represents the number of photovoltaic power units; and n represents the number of load nodes.

3. The multi-level optimization scheduling method for energy storage equipment in the source-grid-load-storage scenario according to claim 1 is characterized in that: The step 3 comprises: The evaluation is conducted based on the source-grid-load-storage operation indicator state matrix, and the evaluation results are quantified into a scoring sequence of the energy storage equipment support effect: Normalize the elements in the source-grid-load-storage operation indicator state matrix, convert all data to [0,1], and obtain the normalized source-grid-load-storage operation indicator state matrix D N : Among them, ΔU N,1 represents the normalized source-grid-load node voltage variation sequence in the first time period of the day; P pv,r,f,N,1 represents the actual power generation of the fth photovoltaic power station in the first time period after normalization; P wd,r,i,N,1 represents the actual power generation of the i-th wind farm in the first time period after normalization; P ld,c,g,N,1 It represents the load shedding power of the g-th load node in the first time period after normalization; The normalized source-grid-load-storage operation indicator state matrix is ​​averaged by column to obtain the energy storage equipment support effect score sequence E su,N : AND su,N =(And ave,N,1 AND ave,N,2 …AND ave,N,24 ) Among them, E ave,N,1 Indicates the support effect score of the energy storage equipment in the first time period; The expected output index sequence of energy storage equipment and the actual output index sequence of energy storage equipment are evaluated to form a scoring sequence of energy storage equipment dispatching effect for redundant or missing resources in the source-grid-load-storage grid, and normalize it: Normalized energy storage equipment dispatch effect score sequence P ES,e,N The calculation formula is: P ES,e =(P ES,e,1 P ES,e,2 …P ES,e,24 ) =(P ES,r,1 -P ES,a,1 P ES,r,2 -P ES,a,2 …P ES,r,24 -P ES,a,24 ) Among them, P ES,e,max and P ES,e,min They represent the energy storage equipment scheduling effect scoring sequence P ES,e The maximum and minimum values ​​of P ES,e,1 It represents the difference between the actual output index and the expected output index of the energy storage device in the first time period; P ES,r,1 Indicates the actual output index of the energy storage equipment in the first time period; P ES,a,1 Indicates the expected output index of the energy storage equipment in the first time period.

4. The multi-level optimization scheduling method for energy storage equipment in the source-grid-load-storage scenario according to claim 3 is characterized in that: The step 4 comprises: Scoring sequence E for the support effect of energy storage equipment su,N , Energy storage equipment benefit index sequence P ES,b,N and the energy storage equipment dispatch effect scoring sequence P ES,e,N Perform comprehensive scoring to obtain the comprehensive scoring sequence of energy storage equipment scheduling: The effect scoring sequence E supported by energy storage equipment su,N , Energy storage equipment benefit index sequence P ES,b,N and the energy storage equipment dispatch effect scoring sequence P ES,e,N Composition based on energy storage scheduling impact factor matrix D ES,ef : Among them, P ES,e,N,1 represents the normalized energy storage equipment scheduling effect score sequence P ES,e,N The first column of P ES,b,N,1 Represents the energy storage equipment benefit index sequence P ES,b,N The first column of Energy storage equipment benefit index sequence P ES,b,N The calculation formula is as follows: Among them, P ES,b,max and P ES,b,min They represent the maximum and minimum values ​​of the 24-hour total return indicator sequence of the energy storage equipment respectively; Among them, P ES,b,h,t , P ES,c,h,t and P ES,r,h,t They represent the charging and discharging income, operation and maintenance cost, and operation compensation of the h-th energy storage device in the t-th time period; n es Indicates the number of energy storage devices; Calculate the comprehensive scoring sequence S of the energy storage equipment dispatch situation: Among them, d max and min They represent the optimal solution vector and the worst solution vector of energy storage scheduling effect respectively: Among them, D ES,ef,max,1 and D ES,ef,min,1 They represent the energy storage dispatch influencing factor matrix D ES,ef The maximum and minimum values ​​of the first column; The energy storage equipment scheduling plan is optimized based on the comprehensive scoring sequence of the energy storage equipment scheduling situation: Normalize the comprehensive score sequence S of the energy storage equipment scheduling situation and return the score to the interval [0,2]: Among them, S max and S min Respectively represent the maximum and minimum values ​​in the comprehensive score sequence of the energy storage equipment scheduling situation; The optimization formula for the scheduling scheme of energy storage equipment is as follows: P ES,r,o =(P ES,r,1 ·S N,1 P ES,r,2 ·S N,2 …P ES,r,24 ·S N,24 ) Among them, P ES,r,o Represents the optimized energy storage equipment scheduling plan.

5. A multi-level optimization scheduling device for energy storage equipment in a source-grid-load-storage scenario, implemented based on the method described in any one of claims 1-4, characterized in that: The device comprises: The module for obtaining source and load side status samples is used to extract the real data characteristics of the power generation side and the load side for a period of time using a clustering algorithm to form a corresponding typical scenario. The time series scenario generation method is used to sample the source and load side data of 24 hours in a day, and the source and load side data are combined and arranged in a 24-hour time series to obtain the source and load side status samples of 24 hours in a day; The first-level optimization module is used to calculate the economic benefit indicators of energy storage equipment in each hour of the 24 hours of the day, taking into account the investment and operation and maintenance costs of energy storage equipment, as well as the economic benefits obtained by energy storage equipment through charging and discharging, to form a sequence of energy storage equipment benefit indicators as the first-level optimization; quantify the supporting role of energy storage equipment in the source-grid-load-storage power grid for various indicators, and form a source-grid-load-storage operation indicator state matrix; calculate the redundant power resources or load shedding indicators in the power grid to form an expected output indicator sequence of energy storage equipment; calculate the actual output of energy storage equipment based on the flow calculation results and form an actual output indicator sequence of energy storage equipment; The second and third-layer optimization modules are used to evaluate the source-grid-load-storage operation indicator state matrix, and quantify the evaluation results into a storage equipment support effect scoring sequence as the second-level optimization; evaluate the energy storage equipment expected output indicator sequence and the energy storage equipment actual output indicator sequence, and form a storage equipment dispatch effect scoring sequence for redundant or missing resources in the source-grid-load-storage power grid as the third-level optimization; The discharge behavior correction module is used to calculate the comprehensive scoring sequence of the energy storage device scheduling situation based on the energy storage device scheduling effect scoring sequence, and on this basis correct the daily charging and discharging behavior of the energy storage device itself.

Images