Energy storage scheduling operation scheme evaluation method and system

Abstract

The invention provides an energy storage scheduling operation scheme evaluation method and system, and belongs to the technical field of energy storage scheduling evaluation. According to the invention, benefits brought to the power system and costs paid by the power grid after the energy storage power station responds to the power grid demand are quantified from the perspective of the power grid, and the difference of attention degrees of different evaluation indexes of energy storage safety, economy and the like under different level scheduling of the power system is considered; firstly, a multi-dimensional energy storage scheduling evaluation index system including an economic benefit index, a new energy index, a power purchase cost index and a power utilization quality index is established from the perspective of a power grid, differentiated hierarchical weights are introduced in the scheduling scheme evaluation process, and a fuzzy analytic hierarchy process and a TOPSIS method are used for evaluating a scheduling scheme. According to the invention, the energy storage scheduling scheme which most accords with the power grid benefit can be screened.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage scheduling evaluation, and in particular to an energy storage scheduling operation plan evaluation method and system. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Energy storage dispatch refers to integrating energy storage equipment (such as battery energy storage, supercapacitor energy storage, etc.) into the power system through reasonable charging and discharging strategies to achieve supply and demand balance of the power system and improve the flexibility and reliability of the power grid. Energy storage dispatch needs to consider multiple factors such as the real-time load situation of the power system, the volatility of renewable energy generation, and the electricity price mechanism in order to formulate the optimal charging and discharging plan.

[0004] Previous studies on the evaluation methods of new energy storage systems have established evaluation indicators from the perspective of energy storage power stations or energy storage operators, focusing on evaluating the economic or social benefits obtained by energy storage power stations after participating in system auxiliary services, power markets or responding to grid dispatch instructions, and charging and discharging according to the curve planned by the dispatching agency. Few studies have quantified the benefits brought to the power system and the costs paid by the grid after the energy storage power station responds to grid demand from the perspective of the grid. Power system dispatching has the characteristics of multi-level dispatching, and different levels of dispatching have different degrees of emphasis on different evaluation indicators such as energy storage safety and economy. However, existing research mainly focuses on the evaluation of various control strategies for new energy storage, and the classification is also multi-level control within the control strategy. These control strategies are mostly limited to the control level, and few consider the evaluation differences of different energy storage control schemes at multiple grid dispatching levels. Summary of the invention

[0005] In order to address the deficiencies of the prior art, the present invention provides a method and system for evaluating an energy storage scheduling operation plan, which focuses on quantitatively analyzing the effect of energy storage responding to the support needs of the power grid from the perspective of the power grid, and evaluates the energy storage scheduling plan through the fuzzy analytic hierarchy process and TOPSIS method in combination with the differences in the importance of evaluation indicators at different scheduling levels, which is more suitable for the power grid to select energy storage scheduling plans.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for evaluating an energy storage scheduling operation plan.

[0008] A method for evaluating an energy storage dispatching operation plan includes the following steps:

[0009] An evaluation index matrix is ​​obtained according to the established energy storage dispatch evaluation index system, and the evaluation index matrix is ​​normalized, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0010] For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0011] Construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0012] According to the weighted normalized fuzzy decision matrix, the TOPSIS method is used to calculate the positive ideal solution and negative ideal solution of the evaluation index b, and further calculate the positive ideal solution and negative ideal solution of each evaluation index. For each scheduling scheme, the sum of the distances between this scheduling scheme and each positive ideal solution is calculated as the first distance, and the sum of the distances between this scheduling scheme and each negative ideal solution is calculated as the second distance;

[0013] For each scheduling scheme, the proximity coefficient is calculated according to the first distance and the second distance, and the scheduling scheme with the highest proximity coefficient is the scheduling scheme that best meets expectations.

[0014] As a further limitation of the first aspect of the present invention, for any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight thereof in combination with the hierarchical weight factor, including:

[0015] The fuzzy comparison sum of evaluation index b and all other indexes is calculated, the inverse of the fuzzy comparison sum is calculated, and the fuzzy comparison sum is multiplied by the inverse of the fuzzy comparison sum to obtain the fuzzy comprehensive degree of evaluation index b;

[0016] The fuzzy comprehensive degree is converted into a normalized weight, the normalized weight is adjusted according to the priority of the evaluation index to obtain the priority weight, and the fuzzy decision matrix weight is obtained according to the product of the priority weight and the hierarchical weight factor.

[0017] As a further limitation of the first aspect of the present invention, the fuzzy comprehensive degree S i Transformed into normalized weight w i ,include:

[0018]

[0019] Among them, n is the total number of evaluation indicators, S i is the fuzzy comprehensive degree of the i-th evaluation index.

[0020] As a further limitation of the first aspect of the present invention, the fuzzy comprehensive degree S i for:

[0021] in, L ij is the lower bound of the fuzzy number of evaluation indexes i and j, M ij is the middle value (most likely or most preferred value) of the fuzzy numbers of evaluation indicators i and j, U ij is the upper bound of the fuzzy number of evaluation indexes i and j, L kj is the lower bound of the fuzzy number of evaluation indexes k and j, M kj is the middle value (most likely or most preferred value) of the fuzzy numbers of evaluation indicators k and j, U kj is the upper bound of the fuzzy number of evaluation indicators k and j.

[0022] As a further limitation of the first aspect of the present invention, the weighted normalized fuzzy decision matrix for: in, is the fuzzy decision matrix weight of evaluation index b, is the score of scheduling scheme a in evaluation index b;

[0023] For the revenue indicator:

[0024] For cost indicators:

[0025] in, are the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the minimum values ​​of the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the lower bound, median and upper bound of the fuzzy score of scheduling scheme a on evaluation index b;

[0026] Calculate the proximity coefficient based on the first distance and the second distance, including:

[0027]

[0028] Among them, C a is the proximity coefficient of scheduling scheme a, is the first distance between scheduling solution a and the positive ideal solution, D a - is the second distance between scheduling scheme a and the negative ideal solution.

[0029] In a second aspect, the present invention provides an energy storage scheduling operation plan evaluation system.

[0030] An energy storage dispatching operation scheme evaluation system, comprising:

[0031] The evaluation index matrix normalization unit is configured to: obtain an evaluation index matrix according to the established energy storage dispatch evaluation index system, and normalize the evaluation index matrix, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0032] The fuzzy decision matrix weight calculation unit is configured to: for any evaluation index b, use the fuzzy hierarchical analysis method to calculate its fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0033] The weighted normalized fuzzy decision matrix calculation unit is configured to: construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0034] The positive and negative ideal solution calculation unit is configured to: calculate the positive ideal solution and the negative ideal solution of the evaluation index b by using the TOPSIS method according to the weighted normalized fuzzy decision matrix, further calculate the positive ideal solution and the negative ideal solution of each evaluation index, and for each scheduling scheme, calculate the sum of the distances between the scheduling scheme and each positive ideal solution as the first distance, and calculate the sum of the distances between the scheduling scheme and each negative ideal solution as the second distance;

[0035] The scheduling strategy evaluation result generating unit is configured to: for each scheduling scheme, calculate the proximity coefficient according to the first distance and the second distance, and take the scheduling scheme with the highest proximity coefficient as the most expected scheduling scheme.

[0036] As a further limitation of the second aspect of the present invention, the weighted normalized fuzzy decision matrix for: in, is the fuzzy decision matrix weight of evaluation index b, is the score of scheduling scheme a in evaluation index b;

[0037] For the revenue indicator:

[0038] For cost indicators:

[0039] in, are the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the minimum values ​​of the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the lower bound, median and upper bound of the fuzzy score of scheduling scheme a on evaluation index b;

[0040] Calculate the proximity coefficient based on the first distance and the second distance, including:

[0041]

[0042] Among them, C a is the proximity coefficient of scheduling scheme a, is the first distance between scheduling solution a and the positive ideal solution, D a - is the second distance between scheduling scheme a and the negative ideal solution.

[0043] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium;

[0044] a processor adapted to execute a computer program;

[0045] A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the energy storage scheduling operation scheme evaluation method as described in the first aspect of the present invention is implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the energy storage scheduling operation plan evaluation method as described in the first aspect of the present invention.

[0047] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the energy storage scheduling operation plan evaluation method as described in the first aspect of the present invention.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention quantifies the benefits that energy storage power stations bring to the power system and the costs paid by the power grid after responding to power grid demand from the perspective of the power grid, and considers the differences in the degree of emphasis on different evaluation indicators such as energy storage safety and economy under different levels of scheduling of the power system, and proposes a method for evaluating energy storage scheduling schemes. Firstly, a multi-dimensional energy storage scheduling evaluation index system including economic benefit indicators, new energy indicators, electricity purchase cost indicators, and electricity quality indicators is established from the perspective of the power grid, and differentiated hierarchical weights are introduced in the process of scheduling scheme evaluation. The scheduling scheme is evaluated using the fuzzy hierarchical analysis method and the TOPSIS method, so that the energy storage scheduling scheme that best meets the benefits of the power grid can be selected.

[0050] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0052] Figure 1 Schematic diagram of the process of the energy storage scheduling operation plan evaluation method provided in Example 1 of the present invention Figure 1 ;

[0053] Figure 2 Schematic diagram of the process of the energy storage scheduling operation plan evaluation method provided in Example 1 of the present invention Figure 2 ;

[0054] Figure 3 A schematic diagram of an energy storage scheduling operation plan evaluation system provided in Example 2 of the present invention;

[0055] Figure 4 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0057] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0058] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0059] Embodiment 1:

[0060] This implementation proposes a method for evaluating energy storage scheduling and operation plans, such as Figure 1 and Figure 2 As shown in the figure, firstly, from the perspective of the power grid, an energy storage dispatch evaluation index system including economic benefit indicators, new energy indicators, electricity purchase cost indicators, and electricity quality indicators is established and the indicators are quantified. Secondly, the different levels of importance attached to different types of evaluation indicators at different dispatching levels of the power grid are considered, and the differentiated hierarchical weights are calculated by the fuzzy analytic hierarchy process (FAHP) method. Finally, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is used to evaluate the dispatching scheme, calculate the proximity coefficients of each dispatching scheme, and select the energy storage dispatching scheme that best meets the benefits of the power grid through sorting.

[0061] More specifically, the process includes the following:

[0062] S1: Establish an energy storage dispatch evaluation index system.

[0063] The evaluation indicators are divided into two categories: benefit indicators and cost indicators. The benefit indicators include economic benefit indicators and new energy indicators. The cost indicators include electricity purchase cost indicators and electricity quality indicators.

[0064] (1) Economic benefit indicators.

[0065] (1.1) Economic benefits brought by the reduction of line losses after energy storage is connected:

[0066] S de =ΔP loss ×C e ×T S1 (1);

[0067] Among them, ΔP loss is the difference in line loss before and after energy storage is connected, unit: kW; C e is the electricity price, unit: yuan / kWh; T S1 The time of the evaluation period, in hours.

[0068] (1.2) Revenue from electricity sales during peak hours:

[0069] S tl =P peak ×T peak ×(C peak -C offpeak )(2);

[0070] Among them, P peak The power released by energy storage during peak hours, unit: kW; T peakis the duration of the peak period, in hours; C peak is the electricity price during peak hours, unit: yuan / kWh; C offpeak It is the electricity price during the energy storage charging period, unit: Yuan / kWh.

[0071] (1.3) Indirect economic benefits of energy storage on improving grid stability:

[0072] S w =(ΔP t ×T t )×C t (3);

[0073] ΔP t The power outage reduction before and after energy storage is connected can be obtained through historical data analysis, unit: kW; T t The power outage time reduced before and after energy storage is connected can be obtained through historical data analysis, unit: hour; C t The economic loss caused by power outage is a fixed value obtained from different loads, unit: Yuan / kWh.

[0074] (2) New energy indicators.

[0075] (2.1) Benefits of reducing wind and solar power curtailment:

[0076] R xn =ΔE renew ×C renew (4);

[0077] Where, ΔE renew The amount of wind and solar power that is reduced by energy storage during the dispatching period, unit: kWh; C renew It is the on-grid electricity price of new energy, unit: Yuan / kWh.

[0078] (2.2) Improvement of comprehensive utilization efficiency.

[0079] Characterize the improvement of comprehensive utilization efficiency of new energy power generation and energy storage:

[0080]

[0081] The percentage of comprehensive utilization efficiency improvement after combining energy storage and new energy power generation is calculated, where E co It is the effective power after the combination of renewable energy power generation and energy storage, unit: kWh; E re It is the independent renewable energy power generation, unit: kWh; E es The amount of energy stored, unit: kWh.

[0082] (2.3) Prediction error compensation.

[0083] Characterize the improvement in power system operation stability brought about by energy storage compensating for the forecast error of renewable energy power generation:

[0084]

[0085] Where, ΔE er The predicted error of renewable energy power generation compensated by energy storage, unit: kWh; E er It is the original prediction error of renewable energy power generation, unit: kWh.

[0086] (3) Electricity purchasing cost indicator.

[0087] (3.1) Electricity purchase balance.

[0088] Represents the higher cost of purchasing electricity from energy storage than from thermal power:

[0089] G d =E es (P es -P the )(7);

[0090] Among them, E es P is the amount of energy purchased for energy storage, unit: kWh; es P is the price of electricity purchased by energy storage, unit: yuan / kWh; the It is the purchase price of thermal power, unit: Yuan / kWh.

[0091] (4) Electricity quality indicators.

[0092] (4.1) Frequency of exceeding the limit.

[0093] Characterizes the frequency of voltage and frequency exceeding the limit at the energy storage access node:

[0094]

[0095] λ U A 0-1 value to determine whether the voltage or frequency exceeds the limit. If it exceeds the limit, it is 1, otherwise it is 0; T y It is the scheduling running time, unit: hour.

[0096] (4.2) Power quality.

[0097] Characterize the harmonic impact caused by energy storage access:

[0098]

[0099] Among them, H g (t) is the total harmonic distortion (THD) of the g-th harmonic, which changes with time (unit: %); C h,gis the cost per unit of harmonic distortion (unit: yuan / THD); T h,g is the duration of the harmonic distortion event (unit: hour); T h Total calculation time (unit: hour); M is the harmonic number.

[0100] (4.3) Response bias indicator.

[0101] Quantify the losses to the power grid caused by insufficient energy storage output, excessive output, and insufficient output duration:

[0102] C p =C ex +C du (10);

[0103] Among them, C ex is the output deviation cost; C du is the duration deviation cost.

[0104] When the actual output of energy storage deviates greatly from the dispatch curve specified by the power grid, the power grid may need to perform additional dispatch and incur additional costs:

[0105]

[0106] Among them, P de is the energy storage output required by the grid at hour t, unit: MW; P ac is the actual output of energy storage at hour t, unit: MW; C dis is the unit cost of power dispatch at the tth hour, unit: yuan; J is the number of energy storage participating in the grid support scenario; E is the number of energy storage types; T ex is the total number of hours in the scheduling cycle;

[0107] is the indicator function.

[0108] When the actual output time of energy storage is less than the grid dispatch instruction, the cost of additional dispatch or power purchase may be:

[0109]

[0110] Among them, T de T is the energy storage output duration required by the grid during the dispatching period, unit: hour; ac C is the actual output duration of energy storage within the dispatching period; gen is the unit cost of additional electricity purchase or standby power generation, unit: yuan / MWh; J is the number of energy storage participating in grid support scenarios; E is the number of energy storage types.

[0111] There are four evaluation indicators: A, B, C, and D, of which A and B are benefit indicators, and C and D are cost indicators;

[0112] Sub-indicator: A 1 , A 2 , ...; B 1 , B 2 ,……;C 1 , C 2 ,……;D 1 , D 2 ,……;

[0113] For scheduling plan X 1 , X 2 , …, X a , …, X q ,It is necessary to normalize the indicators to facilitate expert scoring. For the convenience of evaluation, the higher the cost, the lower the evaluation, and the cost indicator takes a negative value.

[0114]

[0115] Among them, q is the number of scheduling schemes, p is the number of sub-indicators under the evaluation index, and a represents the ath scheduling scheme.

[0116] Based on the evaluation index A 1 For example:

[0117] Calculate the schedule X 1 , X 2 , …, X a , …, X q Medium evaluation index A 1 The mean of :

[0118]

[0119] Calculate the standard deviation:

[0120]

[0121] Calculate the Z value:

[0122]

[0123] Adjust the Z value:

[0124]

[0125] Map the Z value to the interval [0, 10]:

[0126]

[0127] Get the evaluation index A 1 The normalized evaluation index matrix:

[0128]

[0129] The calculation methods for other indicators are the same.

[0130] S2: Fuzzy hierarchical weighting of evaluation indicators.

[0131] Firstly, the fuzzy pairwise comparison matrix is ​​constructed for fuzzy weight calculation by using the differences in the importance attached to evaluation indicators and sub-indicators by the three-level dispatching of the power grid.

[0132]

[0133] in, is the fuzzy comparison value of evaluation indexes i and j, L is the lower bound (minimum value) of the fuzzy number, M is the middle value (most likely or preferred value) of the fuzzy number, and U is the upper bound (maximum value) of the fuzzy number. For example: If evaluation index A is more important than B, experts may rate it as This means that the fuzzy comparison value of A and B is between 2 and 4, with the most likely value being 3.

[0134] Secondly, in order to determine the weights of evaluation indicators and sub-indicators, fuzzy comprehensive range analysis is used to calculate fuzzy weights to obtain the fuzzy weights of evaluation indicators and sub-indicators.

[0135] For each metric i, calculate the sum of its fuzzy comparisons with all other metrics:

[0136]

[0137] Among them, n is the number of evaluation indicators. If there are four evaluation indicators A, B, C, and D, then n=4.

[0138] Aggregate the inverse of the fuzzy comparison value and calculate the inverse of the fuzzy comparison sum of each sub-index k:

[0139]

[0140] Where k is the number of sub-indicators contained in the evaluation indicator j. For example, for A(A 1 , A 2 , A 3 , A 4 , A 5 ), k=5.

[0141] The fuzzy weight of evaluation index i is calculated by multiplying the sum of fuzzy comparisons by the inverse of aggregate comparisons through comprehensive pairwise comparisons. The fuzzy comprehensive degree S of index i is i for:

[0142]

[0143] Then, the fuzzy weights are normalized and converted into normalized weights using formula (24).

[0144]

[0145] Among them, w i is the index weight of index i.

[0146] Then, adjust the priority level weight and adjust the index fuzzy weight according to the priority of the evaluation index.

[0147]

[0148] Among them, the priority level is: A>B=C>D.

[0149] Finally, the weights are adjusted to highlight the focus of the level. According to the obtained weights of the priority levels and the focus of the specific level, the weight of the specific level is determined for use in step S3 to calculate the weighted normalized fuzzy decision matrix.

[0150]

[0151] Among them, the weight of a specific level is:

[0152] S3: Fuzzy TOPSIS evaluation of scheduling schemes.

[0153] First, construct the fuzzy decision matrix m experts evaluate the performance of q scheduling schemes for each sub-indicator, b represents the b-th sub-indicator, and the scores of experts 1 to m are:

[0154]

[0155] in, They are the lower bound, median and upper bound of the fuzzy score of scheduling scheme a on sub-indicator b respectively.

[0156] Then the fuzzy decision matrix Fuzzy score of element scheduling scheme a on sub-indicator b for:

[0157]

[0158] The fuzzy evaluation matrix is ​​normalized to ensure the comparability of different evaluation indicators.

[0159] For the revenue indicator:

[0160]

[0161] For cost indicators:

[0162]

[0163] in, are the maximum values ​​of the lower bound, median and upper bound of the fuzzy evaluation of sub-indicator b in all scheduling schemes respectively; The minimum values ​​of the lower bound, median, and upper bound of the fuzzy score, respectively.

[0164] Substitute the normalized fuzzy decision matrix from the previous step and the level-specific weights from step 2 into the normalized decision matrix to obtain the weighted normalized fuzzy decision matrix:

[0165]

[0166] in, is the weight of sub-indicator b in the specific standard.

[0167] According to the weighted normalized fuzzy decision matrix, the positive ideal solution (FPIS) and negative ideal solution (FNIS) of each evaluation index are calculated.

[0168] FPIS:

[0169]

[0170] FNIS:

[0171]

[0172] Calculate the distance between each scheduling solution and FPIS and FNIS,

[0173] Distance from FPIS:

[0174]

[0175] Distance from FNIS:

[0176]

[0177] in, They are the distances between the weighted normalized fuzzy value of the scheduling scheme a and the positive ideal solution and negative ideal solution of the sub-index b calculated using the triangular fuzzy number distance measurement method, respectively. The calculation method is:

[0178]

[0179]

[0180] Among them, for the income indicator:

[0181] is the maximum fuzzy score lower limit of all scheduling schemes under the evaluation sub-indicator b;

[0182] is the maximum median value of the fuzzy scores of all scheduling schemes evaluating sub-indicator b;

[0183] is the maximum fuzzy score upper limit of all scheduling schemes for evaluating sub-indicator b;

[0184] is the minimum fuzzy score lower limit of all scheduling schemes under the evaluation sub-indicator b;

[0185] is the minimum median value of the fuzzy scores of all scheduling schemes evaluating sub-indicator b;

[0186] It is the minimum fuzzy score upper limit of all scheduling schemes for evaluating sub-indicator b.

[0187] For cost indicators:

[0188] is the minimum fuzzy score lower limit of all scheduling schemes under the evaluation sub-indicator b;

[0189] is the minimum fuzzy score median value of all scheduling schemes under the evaluation sub-indicator b;

[0190] is the minimum fuzzy score upper limit of all scheduling schemes under the evaluation sub-indicator b;

[0191] is the maximum fuzzy score lower limit of all scheduling schemes under the evaluation sub-indicator b;

[0192] is the maximum median value of the fuzzy scores of all scheduling schemes evaluating sub-indicator b;

[0193] It is the maximum fuzzy score upper limit of all scheduling schemes for evaluation sub-indicator b.

[0194] Calculate the proximity coefficient C of scheduling scheme a a , determine the relative closeness of each scheduling scheme to FPIS:

[0195]

[0196] The proximity coefficients C of each scheduling scheme are sorted, and the one with the highest value is the scheduling strategy that best meets expectations. The scheduling strategy evaluation is completed.

[0197] Embodiment 2:

[0198] like Figure 3 As shown, this implementation provides an energy storage scheduling operation plan evaluation system, including:

[0199] The evaluation index matrix normalization unit is configured to: obtain an evaluation index matrix according to the established energy storage dispatch evaluation index system, and normalize the evaluation index matrix, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0200] The fuzzy decision matrix weight calculation unit is configured to: for any evaluation index b, use the fuzzy hierarchical analysis method to calculate its fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0201] The weighted normalized fuzzy decision matrix calculation unit is configured to: construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0202] The positive and negative ideal solution calculation unit is configured to: calculate the positive ideal solution and the negative ideal solution of the evaluation index b by using the TOPSIS method according to the weighted normalized fuzzy decision matrix, further calculate the positive ideal solution and the negative ideal solution of each evaluation index, and for each scheduling scheme, calculate the sum of the distances between the scheduling scheme and each positive ideal solution as the first distance, and calculate the sum of the distances between the scheduling scheme and each negative ideal solution as the second distance;

[0203] The scheduling strategy evaluation result generating unit is configured to: for each scheduling scheme, calculate the proximity coefficient according to the first distance and the second distance, and take the scheduling scheme with the highest proximity coefficient as the most expected scheduling scheme.

[0204] The specific working details of each unit are as described in Example 1, which will not be repeated here;

[0205] It can be understood that each of the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the energy storage scheduling operation plan evaluation system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0206] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the energy storage scheduling operation scheme evaluation method of the embodiment of the present application can be implemented by running a computer program (including program code) capable of executing each step involved in the corresponding method described in Example 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0207] Embodiment 3:

[0208] like Figure 4 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0209] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0210] Processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0211] The processor 1001 is configured to execute the following process:

[0212] An evaluation index matrix is ​​obtained according to the established energy storage dispatch evaluation index system, and the evaluation index matrix is ​​normalized, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0213] For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0214] Construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0215] According to the weighted normalized fuzzy decision matrix, the TOPSIS method is used to calculate the positive ideal solution and negative ideal solution of the evaluation index b, and further calculate the positive ideal solution and negative ideal solution of each evaluation index. For each scheduling scheme, the sum of the distances between this scheduling scheme and each positive ideal solution is calculated as the first distance, and the sum of the distances between this scheduling scheme and each negative ideal solution is calculated as the second distance;

[0216] For each scheduling scheme, the proximity coefficient is calculated according to the first distance and the second distance, and the scheduling scheme with the highest proximity coefficient is the scheduling scheme that best meets expectations.

[0217] The specific working details are described in Example 1 and will not be repeated here.

[0218] Embodiment 4:

[0219] This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0220] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0221] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0222] An evaluation index matrix is ​​obtained according to the established energy storage dispatch evaluation index system, and the evaluation index matrix is ​​normalized, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0223] For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0224] Construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0225] According to the weighted normalized fuzzy decision matrix, the TOPSIS method is used to calculate the positive ideal solution and negative ideal solution of the evaluation index b, and further calculate the positive ideal solution and negative ideal solution of each evaluation index. For each scheduling scheme, the sum of the distances between this scheduling scheme and each positive ideal solution is calculated as the first distance, and the sum of the distances between this scheduling scheme and each negative ideal solution is calculated as the second distance;

[0226] For each scheduling scheme, the proximity coefficient is calculated according to the first distance and the second distance, and the scheduling scheme with the highest proximity coefficient is the scheduling scheme that best meets expectations.

[0227] The specific working details are described in Example 1 and will not be repeated here.

[0228] Embodiment 5:

[0229] The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process:

[0230] An evaluation index matrix is ​​obtained according to the established energy storage dispatch evaluation index system, and the evaluation index matrix is ​​normalized, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes;

[0231] For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index;

[0232] Construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix;

[0233] According to the weighted normalized fuzzy decision matrix, the TOPSIS method is used to calculate the positive ideal solution and negative ideal solution of the evaluation index b, and further calculate the positive ideal solution and negative ideal solution of each evaluation index. For each scheduling scheme, the sum of the distances between this scheduling scheme and each positive ideal solution is calculated as the first distance, and the sum of the distances between this scheduling scheme and each negative ideal solution is calculated as the second distance;

[0234] For each scheduling scheme, the proximity coefficient is calculated according to the first distance and the second distance, and the scheduling scheme with the highest proximity coefficient is the scheduling scheme that best meets expectations.

[0235] The specific working details are described in Example 1 and will not be repeated here.

[0236] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0237] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instruction can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instruction can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (for example, coaxial cable, optical fiber, digital line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server, a data center, etc. that contains one or more available media integration. Available media can be magnetic media (for example, floppy disk, hard disk, tape), optical media (for example, DVD), or semiconductor media (for example, solid-state drive (Solid State Disk, SSD)) and the like.

[0238] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating energy storage scheduling and operation scheme, characterized in that: The process includes: An evaluation index matrix is ​​obtained according to the established energy storage dispatch evaluation index system, and the evaluation index matrix is ​​normalized, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes; For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index; Construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix; According to the weighted normalized fuzzy decision matrix, the TOPSIS method is used to calculate the positive ideal solution and negative ideal solution of the evaluation index b, and further calculate the positive ideal solution and negative ideal solution of each evaluation index. For each scheduling scheme, the sum of the distances between this scheduling scheme and each positive ideal solution is calculated as the first distance, and the sum of the distances between this scheduling scheme and each negative ideal solution is calculated as the second distance; For each scheduling scheme, the proximity coefficient is calculated according to the first distance and the second distance, and the scheduling scheme with the highest proximity coefficient is the scheduling scheme that best meets expectations.

2. The energy storage dispatching operation scheme evaluation method according to claim 1, characterized in that: For any evaluation index b, the fuzzy analytic hierarchy process is used to calculate the fuzzy decision matrix weight in combination with the hierarchical weight factors, including: The fuzzy comparison sum of evaluation index b and all other indexes is calculated, the inverse of the fuzzy comparison sum is calculated, and the fuzzy comparison sum is multiplied by the inverse of the fuzzy comparison sum to obtain the fuzzy comprehensive degree of evaluation index b; The fuzzy comprehensive degree is converted into a normalized weight, the normalized weight is adjusted according to the priority of the evaluation index to obtain the priority weight, and the fuzzy decision matrix weight is obtained according to the product of the priority weight and the hierarchical weight factor.

3. The energy storage dispatching operation scheme evaluation method according to claim 2, characterized in that: The fuzzy comprehensive degree S i Transformed into normalized weights, including: Among them, n is the total number of evaluation indicators.

4. The energy storage dispatching operation scheme evaluation method according to claim 3, characterized in that: Fuzzy comprehensive degree S i for: in, L ij is the lower bound of the fuzzy number of evaluation indexes i and j, M ij is the middle value (most likely or most preferred value) of the fuzzy numbers of evaluation indexes i and j, U ij is the upper bound of the fuzzy number of evaluation indexes i and j, L kj is the lower bound of the fuzzy number of evaluation indexes k and j, M kj is the middle value (most likely or most preferred value) of the fuzzy numbers of evaluation indicators k and j, U kj is the upper bound of the fuzzy number of evaluation indicators k and j.

5. The energy storage dispatching operation scheme evaluation method according to any one of claims 1 to 4, characterized in that: Weighted Normalized Fuzzy Decision Matrix for: in, is the fuzzy decision matrix weight of evaluation index b, is the score of scheduling scheme a in evaluation index b; For the revenue indicator: For cost indicators: in, are the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the minimum values ​​of the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. is the lower bound, median and upper bound of the fuzzy score of the scheduling scheme a on the evaluation index b; the proximity coefficient is calculated according to the first distance and the second distance, including: Among them, C a is the proximity coefficient of scheduling scheme a, is the first distance between scheduling scheme a and the positive ideal solution, is the second distance between scheduling scheme a and the negative ideal solution.

6. A system for evaluating energy storage scheduling and operation schemes, characterized in that: include: The evaluation index matrix normalization unit is configured to: obtain an evaluation index matrix according to the established energy storage dispatch evaluation index system, and normalize the evaluation index matrix, wherein the energy storage dispatch evaluation index system includes a benefit index and a cost index, the benefit index includes an economic benefit index and a new energy index, the cost index includes a power purchase cost index and a power quality index, and each of the benefit index and each of the cost index respectively corresponds to a plurality of normalized energy storage dispatch schemes; The fuzzy decision matrix weight calculation unit is configured to: for any evaluation index b, use the fuzzy hierarchical analysis method to calculate its fuzzy decision matrix weight in combination with the hierarchical weight factor, wherein the evaluation index b is one of the benefit index or the cost index; The weighted normalized fuzzy decision matrix calculation unit is configured to: construct a fuzzy decision matrix, obtain the fuzzy score of each scheduling scheme in the fuzzy decision matrix on the evaluation index b, and obtain a weighted normalized fuzzy decision matrix according to the product of the fuzzy score and the weight of the fuzzy decision matrix; The positive and negative ideal solution calculation unit is configured to: calculate the positive ideal solution and the negative ideal solution of the evaluation index b by using the TOPSIS method according to the weighted normalized fuzzy decision matrix, further calculate the positive ideal solution and the negative ideal solution of each evaluation index, and for each scheduling scheme, calculate the sum of the distances between the scheduling scheme and each positive ideal solution as the first distance, and calculate the sum of the distances between the scheduling scheme and each negative ideal solution as the second distance; The scheduling strategy evaluation result generating unit is configured to: for each scheduling scheme, calculate the proximity coefficient according to the first distance and the second distance, and take the scheduling scheme with the highest proximity coefficient as the most expected scheduling scheme.

7. The energy storage dispatching operation plan evaluation system according to claim 6, characterized in that: Weighted Normalized Fuzzy Decision Matrix for: in, is the fuzzy decision matrix weight of evaluation index b, is the score of scheduling scheme a in evaluation index b; For the revenue indicator: For cost indicators: in, are the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. are the minimum values ​​of the lower bound, median and upper bound of the fuzzy score of all scheduling schemes of evaluation index b, respectively. is the lower bound, median and upper bound of the fuzzy score of the scheduling scheme a on the evaluation index b; the proximity coefficient is calculated according to the first distance and the second distance, including: Among them, C a is the proximity coefficient of scheduling scheme a, is the first distance between scheduling scheme a and the positive ideal solution, is the second distance between scheduling scheme a and the negative ideal solution.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the energy storage scheduling operation plan evaluation method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the energy storage scheduling operation plan evaluation method according to any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the energy storage scheduling operation plan evaluation method according to any one of claims 1 to 5.

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