User side energy storage configuration comprehensive evaluation method considering comprehensive market service and investment income risk

Abstract

The invention discloses a user-side energy storage configuration comprehensive evaluation method considering comprehensive market service and investment income risks. The method comprises the steps of constructing a user-side optimal configuration model considering the comprehensive market service; introducing a risk theory, and on the basis of fully considering the comprehensive market service, constructing a user side energy storage full life cycle optimization configuration model considering the comprehensive market service and the investment income risk; solving the user-side energy storage full-life-cycle optimal configuration model under different investment income risks by using an improved genetic algorithm, and calculating net income, investment cost, return on investment and an investment income risk value in the user-side energy storage full-life cycle, and taking the net income, the investment cost, the return on investment and the investment income risk value as evaluation indexes; and giving an optimal energy storage configuration comprehensive evaluation result based on a subjective and objective weight algorithm combining an improved AHP method and a CRITIC method. According to the method provided by the invention, the risk faced by the user investment income is reduced while the energy storage configuration benefit of the user side is fully ensured, and reference is provided for commercial popularization and market service application of energy storage.

Description

Technical Field

[0001] The present invention relates to a comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks, belonging to the technical field of energy storage. Background Art

[0002] With the development of the power market and the increasing requirements of users for power supply reliability and economy, user-side energy storage has been more and more widely used. Reasonable configuration of user-side energy storage can help users reduce electricity costs and improve power supply reliability, and also contribute to the stable operation of the power system. However, at present, in the process of user-side energy storage configuration, there is a lack of a comprehensive evaluation method that comprehensively considers various uncertain factors and risks.

[0003] Most of the existing energy storage configuration evaluation methods only focus on the technical performance and economic cost of energy storage, while ignoring the impact of risk factors such as power market price fluctuations, load forecasting errors, and energy storage equipment failures on the energy storage configuration effect. This may result in the configured energy storage not meeting the actual needs of users, or being economically unreasonable and unable to achieve the expected benefits. Therefore, there is an urgent need for an evaluation method for user-side energy storage configuration that can comprehensively consider various factors and risks to improve the scientificity and rationality of energy storage configuration. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art, and thus propose a comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks.

[0005] A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks includes the following steps:

[0006] Step 1: Fully consider the full life cycle cost of user-side energy storage and the market services that can participate in profit-making, and construct an optimized configuration model for user-side energy storage considering comprehensive market services;

[0007] Step 2: On the basis of the optimized configuration model for user-side energy storage considering comprehensive market services, further introduce the risk theory, focus on the risk factors faced by investment returns, and construct an optimized configuration model for the full life cycle of user-side energy storage considering comprehensive market services and investment return risks;

[0008] Step 3: Use an improved genetic algorithm to solve the optimized configuration model for the full life cycle of user-side energy storage considering comprehensive market services and investment return risks under different investment return risks, and obtain the net income, investment cost, investment return rate, and investment return risk value within the full life cycle of user-side energy storage based on the solution results of the optimized configuration model for the full life cycle of user-side energy storage;

[0009] Step 4: Taking the net income, investment cost, return on investment, and investment income risk value of the user-side energy storage over its entire life cycle as evaluation indicators, an optimal energy storage configuration comprehensive evaluation result is given based on the subjective and objective weight algorithms of the improved Analytic Hierarchy Process (AHP) and CRITIC method.

[0010] In Step 1, the objective function of the user-side energy storage optimization configuration model considering comprehensive market services is as follows:

[0011] F 1 = f 1 + f 2 + f 3 - C 1 - C 2 + C 3

[0012] Among them, F 1 represents the net income of the user-side energy storage over its entire life cycle, f 1 represents the income from the energy storage participating in the energy arbitrage market, f 2 represents the income from the energy storage participating in the reserve ancillary service market, f 3 represents the reduction income of the demand-side electricity bill from the energy storage participating in the demand response market, C 1 represents the initial investment cost of the user-side energy storage, C 2 represents the operation and maintenance cost of the user-side energy storage over its entire life cycle, C 3 represents the recovery cost of the user-side energy storage.

[0013] The calculation method of the income f 1 from the energy storage participating in the energy arbitrage market is as follows:

[0014]

[0015] P ch,t 、P dis,t 、U ch,t 、U dis,t represent the charging power, discharging power, charging state, and discharging state of the energy storage at time t; T represents 24 time periods in a day, p t represents the electricity price at time t; Y represents the service life of the energy storage system; D represents the annual operating days of the energy storage system; i r represents the inflation rate; d r represents the discount rate;

[0016] The calculation method of the income f 2 from the energy storage participating in the reserve ancillary service market is as follows:

[0017]

[0018] p as,t represents the ancillary service price at time t; pc,t Denote the energy purchase cost during period t;

[0019] The demand charge reduction benefit f of the energy storage participating in the demand response market 3 is calculated as follows:

[0020]

[0021] P cap Denote the benefit brought by the reduction of unit demand charge; P rate Denote the reduced electricity demand per month.

[0022] The initial investment cost C of the user-side energy storage 1 is calculated as follows:

[0023] C 1 = C p P + C e E

[0024] In the formula: C p , C e respectively denote the unit charge / discharge power construction cost and the unit capacity construction cost of the user-side energy storage; P denotes the rated power of the user-side energy storage; E denotes the rated capacity of the user-side energy storage;

[0025] The operation and maintenance cost C of the user-side energy storage over its entire life cycle 2 is calculated as follows:

[0026]

[0027] In the formula: f m denotes the annual operation and maintenance cost per unit charge / discharge power of the user-side energy storage;

[0028] The recovery cost C of the user-side energy storage 3 is calculated as follows:

[0029]

[0030] In the formula: γ is the recovery value coefficient of the user-side energy storage, usually taking 3% - 5%.

[0031] In step one, the optimization configuration model of the user-side energy storage considering comprehensive market services also includes the constraint conditions for the operation of the energy storage. The constraint conditions are:

[0032] S min ≤ S t ≤ S max

[0033] U cha,t + U dis,t <= 1

[0034] 0 ≤ P dis,t ≤ U dis,t P

[0035] 0 ≤ P cha,t ≤ U cha,t P

[0036]

[0037] In the formula: S t represents the state of charge of the energy storage in the t time period; S min and S max are the minimum and maximum values of the state of charge of the energy storage respectively; η represents the charge-discharge efficiency of the energy storage;

[0038] Considering the demand response benefit, after the operation of the energy storage, the equivalent load should not be greater than 1.05 times the maximum value of the optimized demand, that is:

[0039]

[0040] In the formula: P laod,t is the load value used by the user in the t time period, is the maximum load power value of the user before installing the energy storage.

[0041] In step two, the objective function of the life-cycle optimal configuration model of the user-side energy storage considering comprehensive market services and investment income risks is:

[0042] max F 1 =(1 - σ 1 )f 1 +(1 - σ 2 )f 2 +(1 - σ 3 )f 3 - C 1 - C 2 + C 3

[0043] max F 1 represents the maximum net income during the life cycle of the user-side energy storage, σ 1 represents the risk value of the energy storage participating in the energy arbitrage market, σ 2 represents the risk value of the energy storage participating in the reserve ancillary service market, σ 3 represents the risk value of the energy storage participating in the demand response market, σ 1 , σ 2 , σ 3 The value range is in [0, 1].

[0044] In step 3, the improved genetic algorithm is used to solve the user-side energy storage life-cycle optimization allocation model considering comprehensive market services and investment return risks under different investment return risks, including the following steps:

[0045] Step 31: Encode the problem

[0046] Encode the capacity, power, and charge-discharge strategy parameters in the user-side energy storage life-cycle optimization allocation model considering comprehensive market services and investment return risks under different investment return risks to form the gene sequence of an individual, and each individual represents a possible user-side energy storage configuration plan;

[0047] Step 32: Initialize the population and parameters

[0048] Input the encoded capacity, power, and charge-discharge strategy parameters into the established user-side energy storage life-cycle optimization allocation model considering comprehensive market services and investment return risks under different investment return risks, and use the method of random reverse learning to generate the initial population, so that the generated individuals are randomly and evenly distributed in the solution space, accelerating the convergence speed of the algorithm and quickly finding the best area of the solution;

[0049] p f = C min +(C max - C min )rand i

[0050] p r = C min +(C max - p f )rand i

[0051] C min 、C max are the lower and upper limit vectors of the optimization variables respectively; rand i is the random number vector of the i-th individual, whose dimension is the same as the dimension of the optimization space, and the value of each element is a random number in [0,1], p f is the forward population; p r is the reverse population generated through the random reverse learning mechanism. Combine the forward population with the reverse population generated through the random reverse learning mechanism, calculate the fitness value, and take out the half of the particles with high fitness values in the combined new population as the initial population of the algorithm

[0052]

[0053] Among them, is the initial concentration vector of the i-th individual in the combined new population;

[0054] Step 33: Design the fitness function

[0055] Take the objective function of the user-side energy storage full-life cycle optimization configuration model considering comprehensive market services and investment return risks as the fitness function F x ;

[0056] Step 34: Perform the selection operation

[0057] Adopt a hybrid selection method of roulette wheel and elite retention strategy to ensure that the optimal individual in the current population directly enters the next generation without crossover and mutation operations, improving the convergence speed of the genetic algorithm;

[0058] Step 35: Calculate the crossover and mutation probabilities and perform crossover and mutation operations

[0059] The formula for calculating the crossover probability is:

[0060]

[0061] In the formula: P c is the adaptive crossover probability; t is the current iteration number; is the maximum crossover probability; is the minimum crossover probability; T is the maximum iteration number; f′ is the larger fitness in the upcoming crossover operation; f ave is the average fitness of all individuals in the current iteration number, k 1 is a natural number between [0, 1]; f max represents the set maximum iteration number.

[0062] The formula for calculating the mutation probability is:

[0063]

[0064] In the formula: P m is the adaptive mutation probability; t is the current iteration number; is the maximum mutation probability; is the minimum crossover probability; T is the maximum iteration number; f is the fitness value of the individual to be mutated; f ave is the average fitness of all individuals in the current iteration number, k 2 is a natural number between [0, 1]; f max represents the set maximum iteration number;

[0065] Step 36: Judge the termination condition

[0066] When the maximum iteration number f is reached maxWhen the algorithm stops running, the individual with the highest fitness in the current population is output as the optimal solution, that is, the optimal allocation scheme for the whole life cycle of the user-side energy storage under different investment returns and risks; otherwise, return to steps 3 and 4 to continue operations such as selection, crossover, and mutation.

[0067] In step 3,

[0068] The calculation formula for the investment cost is:

[0069] C = C 1 + C 2

[0070] In the formula: C represents the investment cost of the energy storage;

[0071] The calculation formula for the net income is the objective function of the user-side energy storage optimal allocation model:

[0072] F 1 = f 1 + f 2 + f 3 - C 1 - C 2 + C 3

[0073] The calculation formula for the investment return rate K t is:

[0074]

[0075] The calculation formula for the risk value is:

[0076] σ sp = σ 1 + σ 3 + σ 3 .

[0077] Step 4 specifically includes:

[0078] Step 41: Determine the subjective weight based on the improved analytic hierarchy process

[0079] (1) Determine the hierarchical structure of each evaluation index according to the expert scoring, sort them according to the importance degree. Assume there are n indicators from x 1 - x n to x 1 ≥ x 2 ≥.....≥ x n ;

[0080] (2) Compare the importance degrees between each indicator in turn to determine the corresponding scale value t i; A scale value of 1 indicates that two elements are compared and have the same importance; a scale value of 1.2 indicates that two elements are compared and the former element is slightly more important than the latter element; a scale value of 1.4 indicates that two elements are compared and the former element is significantly more important than the latter element; a scale value of 1.6 indicates that two elements are compared and the former element is strongly more important than the latter element; a scale value of 1.8 indicates that two elements are compared and the former element is extremely more important than the latter element;

[0081] (3) Construct the judgment matrix as follows:

[0082]

[0083] Where: n is the number of evaluation indicators to be evaluated; based on the determined scale value, calculate the values of each element of the judgment matrix;

[0084] (4) Calculate the comprehensive subjective weight score of each indicator based on the judgment matrix. The formula is:

[0085]

[0086] s i That is the comprehensive subjective weight score corresponding to the evaluation indicator, a ij is each element in the judgment matrix, n is the number of evaluation indicators to be evaluated, and j represents the corresponding evaluation indicator;

[0087] Step 42: Use the CRITIC method to determine the objective weight. The specific steps are as follows:

[0088] (1) Index normalization

[0089] Construct a normalized evaluation matrix. For reverse indicators, use the following formula for normalization calculation;

[0090]

[0091] For positive indicators, use the following formula for normalization calculation;

[0092]

[0093] (2) Calculate the index comparison intensity CI

[0094] CI = S j

[0095] In the formula, S j is the standard deviation of the jth indicator;

[0096] (3) Calculate the index conflict CT

[0097]

[0098] Wherein, n is the index number, r ij is the correlation coefficient between indexes i and j, and its calculation formula is as follows:

[0099]

[0100] Wherein, x i,k represents the k-th data of index i, and x j,k represents the k-th data of index j, and m is the number of samples; are respectively the average values of the two index values;

[0101] (4) Calculation of information amount Gj

[0102] By calculating the comparison intensity and conflict of indexes, the calculation formula of the information amount contained in each index can be obtained as follows:

[0103]

[0104] (5) Calculation of objective weight:

[0105] The objective weight of the j-th evaluation index can be expressed as:

[0106]

[0107] Calculate the comprehensive score q of the objective weights of each index i as:

[0108]

[0109] Step 43: Calculate the comprehensive weight score of the evaluation index by the subjective and objective weight algorithm based on the improved AHP and CRITIC methods as:

[0110] β = l 1 s i + l 2 q i

[0111] Wherein: l 1 and l 2 are respectively the weight coefficients of the subjective and objective weights, which can be set by the user according to needs and satisfy l 1 + l 2 = 1, and l 1 , l 2 ≥ 0.

[0112] The present invention has the following beneficial effects:

[0113] (1) The present invention introduces the risk theory. On the basis of fully considering the comprehensive market service, it further focuses on the risk factors faced by investment returns, and constructs an optimal allocation model for the whole life cycle of user-side energy storage considering the comprehensive market service and investment return risks, which can help energy storage users select the optimal energy storage allocation plan and comprehensive market service scenario, ensuring the benefits while reducing the possible investment risks faced.

[0114] (2) The present invention uses an improved genetic algorithm to solve the optimal allocation model for the whole life cycle of user-side energy storage under different investment return risks. By optimizing the genetic process through the improved algorithm, the global optimization ability of the algorithm is improved, and the obtained results are more accurate.

[0115] (3) The present invention adopts an improved analytic hierarchy process. When obtaining the subjective weight, it is not necessary to conduct a consistency test, which greatly reduces the calculation amount and makes the weight determination process more concise and intuitive. Based on the CRITIC method, the differences and correlations of each index are comprehensively considered, and the information contained in the data to be evaluated can be more comprehensively mined. The energy storage configuration results obtained by the subjective and objective weight algorithm based on the improved AHP and CRITIC methods are more reasonable and accurate. Description of the Drawings

[0116] Figure 1 It is the overall flowchart of the comprehensive evaluation of user-side energy storage configuration considering the comprehensive market service and investment return risks;

[0117] Figure 2 It is the flowchart of the solution optimization model of the improved genetic algorithm;

[0118] Figure 3 It is the comprehensive evaluation system of user-side energy storage configuration considering the comprehensive market service and investment return risks. Detailed Embodiment

[0119] The present invention will be described below with reference to the drawings.

[0120] Please refer to Figure 1 , the comprehensive evaluation method for user-side energy storage configuration considering the comprehensive market service and investment return risks of the present invention includes the following steps:

[0121] Step 1: Fully consider the full life cycle cost of user-side energy storage and the market services that can participate in making a profit, and construct an optimal allocation model for user-side considering the comprehensive market service;

[0122] Step 2: Introduce the risk theory. On the basis of fully considering the comprehensive market service, further focus on the risk factors faced by investment returns, and construct an optimal allocation model for the whole life cycle of user-side energy storage considering the comprehensive market service and investment return risks;

[0123] Step 3: Use the improved genetic algorithm to solve the user-side energy storage full-life cycle optimization configuration model considering comprehensive market services and investment return risks under different investment return risks, and obtain the net income, investment cost, investment return rate, and investment return risk value within the full life cycle of the user-side energy storage based on the solution results of the user-side energy storage full-life cycle optimization configuration model, and use these as evaluation indicators;

[0124] Step 4: Take the net income, investment cost, investment return rate, and investment return risk value within the full life cycle of the obtained user-side energy storage as evaluation indicators, and give the comprehensive evaluation result of the optimal energy storage configuration plan based on the subjective and objective weight algorithms of the improved analytic hierarchy process AHP and CRITIC method.

[0125] In Step 1, since energy storage benefits from its charging and discharging flexibility and can be applied to multiple power markets to obtain corresponding benefits, the present invention considers that energy storage participates in three power markets, namely the energy arbitrage market, the reserve auxiliary service market, and the demand response market, and constructs a user-side optimization configuration model considering comprehensive market services. The objective function is shown as follows:

[0126] F 1 =f 1 +f 2 +f 3 -C 1 -C 2 +C 3

[0127] Among them, F 1 represents the net income within the full life cycle of the user-side energy storage, f 1 represents the income from the energy storage participating in the energy arbitrage market, f 2 represents the income from the energy storage participating in the reserve auxiliary service market, f 3 represents the reduction in demand-side electricity charges from the energy storage participating in the demand response market, C 1 represents the initial investment cost of the energy storage, C 2 represents the operation and maintenance cost of the energy storage throughout its life cycle, C 3 represents the recovery cost of the energy storage.

[0128] (1) Income from the energy arbitrage market

[0129] The energy storage realizes arbitrage by charging at low electricity prices and discharging at high electricity prices. The income f 1 from the energy storage participating in the energy arbitrage market can be expressed as:

[0130]

[0131] P ch,t 、P dis,t 、U ch,t 、U dis,trepresents the charging power, discharging power, charging state, and discharging state of the energy storage in time period t; T represents 24 time periods in a day, p t represents the electricity price in period t; Y represents the service life of the energy storage system; D represents the annual operating days of the energy storage system; i r represents the inflation rate; d r Represents the discount rate.

[0132] (2) Reserve ancillary service market revenue

[0133] Similar to the profit model of energy storage in the energy arbitrage market, the revenue of energy storage participating in the backup ancillary service market comes from the difference between the price of providing this application and the energy cost. 2 It can be expressed as:

[0134]

[0135] p as,t represents the ancillary service price during period t; p c,t Represents the energy purchase cost during period t.

[0136] (3) Demand management benefits

[0137] Users can reduce electricity charges and demand charges by configuring energy storage. The reduction in electricity charges is reflected in the demand management benefits. The demand charge reduction benefits of energy storage participating in the demand management market are f 3 It can be expressed as:

[0138]

[0139] P cap P represents the benefit brought by the reduction of unit demand electricity charge; rate Indicates the monthly reduction in electricity demand.

[0140] (4) Initial investment cost of energy storage

[0141] The initial investment cost of energy storage refers to the fixed capital invested in the initial stage of energy storage construction. The initial investment cost is mainly determined by the rated power and rated capacity of the energy storage system itself. The initial investment cost of energy storage on the user side C 1 It can be expressed as:

[0142] C 1 =C p P+C e E

[0143] Where: C 1 Represents the initial investment cost of user-side energy storage; C p , C erespectively represent the unit charge / discharge power cost and the unit capacity cost of the user-side energy storage; P represents the rated power of the user-side energy storage; E represents the rated capacity of the user-side energy storage.

[0144] (5) Operation and maintenance cost

[0145] The total life cycle operation and maintenance cost of the user-side energy storage mainly consists of two parts: the daily operation consumption cost of the energy storage and the daily maintenance and management cost, which is mainly related to the rated power of the energy storage. The operation and maintenance cost C of the user-side energy storage over its entire life cycle 2 can be expressed as:

[0146]

[0147] In the formula: f m represents the annual operation and maintenance cost per unit charge / discharge power of the user-side energy storage.

[0148] (6) Energy storage investment recovery cost

[0149] The energy storage investment recovery cost of the user-side energy storage refers to the recovery cost C of the user-side energy storage after T years of operation in its life cycle 3 , which is expressed as:

[0150]

[0151] In the formula: γ is the energy storage recovery value coefficient of the user-side energy storage, usually taking 3% - 5%.

[0152] As a preferred embodiment of the present invention, the step of constructing an optimal allocation model for the entire life cycle of the user-side energy storage considering comprehensive market services further includes the constraint conditions for the operation of the energy storage, and the constraint conditions are:

[0153] S min ≤ S t ≤ S max

[0154] U cha,t + U dis,t <= 1

[0155] 0 ≤ P dis,t ≤ U dis,t P

[0156] 0 ≤ P cha,t ≤ U cha,t P

[0157]

[0158] In the formula: S t represents the state of charge of the energy storage at time t; S min and S maxare the minimum and maximum values of the energy storage state of charge, respectively; η represents the charge and discharge efficiency of the energy storage.

[0159] Considering the demand management benefit, after the operation of the energy storage, the equivalent load should not be greater than 1.05 times the maximum value of the optimized demand, that is:

[0160]

[0161] In the formula: P laod,t is the load value used by the user in the t-th period, is the maximum load power value of the user before installing the energy storage.

[0162] In step 2, although the economic benefit is an important indicator to measure the development and application of energy storage, however, while the energy storage makes a profit, it also has to bear the risks from different markets, and the market with high profits is often accompanied by high risks. Therefore, it is necessary to evaluate the risk-benefit of the energy storage operation. Assuming that the energy storage owner will give an investment risk value for each market portfolio according to the revenue risk situation, the higher the score, the greater the risk of this portfolio. Introducing the risk theory, on the basis of fully considering the comprehensive market service, further focusing on the risk factors faced by the investment income, the user-side energy storage full-life cycle optimization allocation model considering the comprehensive market service and investment income risk is constructed as shown in the following formula:

[0163] maxF 1 =(1 - σ 1 )f 1 +(1 - σ 2 )f 2 +(1 - σ 3 )f 3 -C 1 -C 2 +C 3

[0164] maxF 1 represents the maximum net income within the full-life cycle of the user-side energy storage. σ 1 , σ 2 , σ 3 are the investment risk values for the user-side to participate in different market services. σ 1 represents the risk value of the energy storage participating in the energy arbitrage market. σ 2 represents the risk value of the energy storage participating in the reserve ancillary service market. σ 3 represents the risk value of the energy storage participating in the demand management market. σ 1 , σ 2 , σ 3 The value range is in [0, 1]. The larger the value, the greater the investment risk. If there is no risk value for participating in the market service, the maximum risk value is 1.

[0165] Please refer toFigure 2 In step 3, an improved genetic algorithm is used to solve the user-side energy storage full-life cycle optimization allocation model considering comprehensive market services and investment return risks under different investment return risks; the genetic process is optimized through the initial population generated by the reverse learning generation algorithm, the elite retention strategy, the adaptive changes of the crossover probability and the mutation probability, so as to improve the global optimization ability of the algorithm. The algorithm solving steps are as follows:

[0166] Step 31: Conduct problem encoding

[0167] Encode the capacity, power, and charge and discharge strategy parameters in the user-side energy storage full-life cycle optimization allocation model considering comprehensive market services and investment return risks under different investment return risks, and use real number encoding to convert them into corresponding encoding forms to form the gene sequence of an individual. Each individual represents a possible user-side energy storage configuration scheme.

[0168] Step 32: Initialize the population and parameters

[0169] Input the encoded capacity, power, and charge and discharge strategy parameters into the established user-side energy storage full-life cycle optimization allocation model considering comprehensive market services and investment return risks, and use the method of random reverse learning to generate the initial population, so that the generated individuals are randomly and evenly distributed in the solution space, which can accelerate the convergence speed of the algorithm and quickly find the best area of the solution:

[0170] p f = C min +(C max - C min )rand i

[0171] p r = C min +(C max - p f )rand i

[0172] C min 、C max are the lower and upper limit vectors of the optimization variables respectively; rand i is the random number vector of the i-th individual, and its dimension is the same as the dimension of the optimization space. Each element value is a random number in [0,1]. p f is the forward population; p r is the reverse population generated through the random reverse learning mechanism. Combine the two populations, calculate the fitness value, and take out the half of the particles with high fitness value in the combined new population as the initial population of the algorithm

[0173]

[0174] Among them, is the initial concentration vector of the i-th individual in the merged new population.

[0175] Step 33: Design the fitness function

[0176] Take the objective function of the user-side energy storage full-life cycle optimization configuration model considering comprehensive market services and investment return risks as the fitness function F x .

[0177] Step 34: Perform the selection operation

[0178] Adopt a hybrid selection method of roulette wheel and elite retention strategy to ensure that the optimal individual in the current population directly enters the next generation without crossover and mutation operations, improving the convergence speed of the genetic algorithm.

[0179] Step 35: Calculate the crossover and mutation probabilities, and perform crossover and mutation operations

[0180] The requirements for crossover probability and mutation probability of individuals in the population should be different at different evolutionary stages. In addition, different crossover probabilities and mutation probabilities should also be given according to the quality of individuals. Therefore, when dynamically adjusting the parameters in the present invention, both the individual fitness value and the number of evolutionary generations are considered, and the following adjustment mechanism is set:

[0181]

[0182] In the formula: P c is the adaptive crossover probability; t is the current iteration number; is the maximum crossover probability; is the minimum crossover probability; T is the maximum iteration number; f′ is the larger fitness in the upcoming crossover operation; f ave is the average fitness of all individuals in the current iteration number, k 1 is a natural number between [0, 1].

[0183]

[0184] In the formula: P m is the adaptive mutation probability; t is the current iteration number; is the maximum mutation probability; is the minimum crossover probability; T is the maximum iteration number; f is the fitness value of the individual to be mutated; f ave is the average fitness of all individuals in the current iteration number, k 2 is a natural number between [0, 1].

[0185] Step 36: Judge the termination condition

[0186] When the maximum number of iterations is reached, the algorithm stops running, and the individual with the highest fitness in the current population is output as the optimal solution, that is, the optimal allocation scheme for the whole life cycle of the user-side energy storage under different investment returns and risks; otherwise, return to step 34 to continue operations such as selection, crossover, and mutation.

[0187] In step 3, calculate the net income, investment cost, investment return rate, and investment return risk value of the user-side energy storage during its whole life cycle, and use these as evaluation indicators. The selection of indicators in this invention mainly considers both economic and risk aspects, and uses the net income, investment cost, investment return rate of the user-side energy storage during its whole life cycle, and the investment return risk value of the user-side energy storage considering comprehensive market services as evaluation indicators. The expressions are as follows:

[0188] The calculation formula for the investment cost is:

[0189] C = C 1 + C 2

[0190] In the formula: C represents the investment cost of the energy storage;

[0191] The calculation formula for the net income is the objective function of the user-side energy storage optimization configuration model:

[0192] F 1 = f 1 + f 2 + f 3 - C 1 - C 2 + C 3

[0193] The investment return rate is the economic return value obtained by the user's investment in the energy storage and can be expressed as:

[0194]

[0195] The calculation formula for the risk value is:

[0196] σ sp = σ 1 + σ 3 + σ 3

[0197] Please refer to Figure 3 , in step 4, based on the subjective and objective weight algorithm of the improved AHP and CRITIC methods, give the comprehensive evaluation result of the optimal energy storage configuration scheme. When comprehensively evaluating the energy storage configuration result, it is necessary to consider economy and investment return risk to meet the user's investment and construction requirements. This invention uses the subjective and objective weight algorithm combining the improved AHP and CRITIC methods to solve, making the weight result more in line with the actual situation.

[0198] Step 4 includes the following steps:

[0199] Step 41: Determine the subjective weight by using the improved analytic hierarchy process. When obtaining the subjective weight, there is no need to conduct a consistency test, which greatly reduces the computational complexity and makes the weight determination process more concise and intuitive. The specific implementation steps are as follows:

[0200] (1) Determine the hierarchical structure of each evaluation index according to the expert scoring suggestions, and sort them according to the degree of importance. Assume there are x 1 -x n A total of n indicators, which can be sorted according to the degree of importance as x 1 ≥x 2 ≥.....≥x n ;

[0201] (2) The scale t i represents the comparison relationship between two adjacent elements. Compare the importance of each indicator in turn, and determine the corresponding scale t according to Table 1-1 i ;

[0202] Table 1-1 Meanings of each scale in the judgment matrix

[0203]

[0204] (3) Construct the judgment matrix as follows:

[0205]

[0206] Where: n is the number of indicators to be evaluated; based on the determined scale value, calculate the values of each element in the judgment matrix;

[0207] (4) Calculate the comprehensive score of the subjective weight of each indicator based on the judgment matrix. The formula is:

[0208]

[0209] s i is the comprehensive score of the subjective weight corresponding to the evaluation index, a ij is each element in the judgment matrix, n is the number of indicators to be evaluated, and j represents the corresponding evaluation index;.

[0210] Step 42: Determine the objective weight by using the CRITIC method; as an objective weighting method, the CRITIC method calculates the objective weight of the evaluation object by introducing the index comparison intensity and conflict. It comprehensively considers the differences and correlations of each index, and can more comprehensively explore the information contained in the data to be evaluated. The specific implementation steps are as follows:

[0211] (1) Index normalization processing

[0212] Construct a normalized evaluation matrix. For reverse indicators (i.e., the smaller the value, the better), the following formula is used for calculation;

[0213]

[0214] For positive indicators (i.e., the larger the value, the better), the following formula is used for normalization calculation;

[0215]

[0216] (2) Calculation of index comparison intensity (CI)

[0217] The index comparison intensity is expressed by the standard deviation of each index in the sample. The larger the standard deviation, the greater the difference in the values of the index, the greater the amount of information it contains, and the greater the weight value should be.

[0218] CI = S j

[0219] In the formula, S j is the standard deviation of the j-th index.

[0220] (3) Calculation of index conflict (CT)

[0221] The index conflict is expressed by the correlation coefficient of each index. The smaller the correlation coefficient of a certain index with other indexes, the greater the conflict between the indexes, the more different information it contains, and the greater the weight value assigned should be.

[0222]

[0223] In the formula, n is the number of indexes, r ij is the correlation coefficient between indexes i and j, and its calculation formula is as follows:

[0224]

[0225] In the formula, x i,k represents the j-th data of index i, x j,k represents the k-th data of index j, and m is the number of samples.

[0226] (4) Calculation of information amount (Gj)

[0227] By calculating the comparison intensity and conflict of the indexes, the calculation formula for the information amount contained in each index is as follows:

[0228]

[0229] (5) Calculation of objective weight:

[0230] The objective weight of the j-th evaluation index can be expressed as:

[0231]

[0232] Calculate the comprehensive score q of the objective weights of each index i as follows:

[0233]

[0234] Step 43: Calculate the comprehensive weight of the evaluation index based on the subjective and objective weight algorithm of the improved AHP and CRITIC methods as follows:

[0235] β = l 1 s i + l 2 q i

[0236] In the formula: l 1 and l 2 are the weight coefficients of the subjective and objective weights respectively, which can be set by the user according to needs and satisfy l 1 + l 2 = 1, and l 1 , l 2 ≥ 0.

Claims

1. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks, characterized in that: The following steps are involved: Step 1: Fully consider the full life cycle cost of user-side energy storage and the market services that can be profitably participated in, and build a user-side energy storage optimization configuration model that considers comprehensive market services; Step 2: Based on the user-side energy storage optimization configuration model considering comprehensive market services, risk theory is further introduced to focus on the risk factors faced by investment returns, and a user-side energy storage full life cycle optimization configuration model that considers comprehensive market services and investment return risks is constructed; Step 3: Using an improved genetic algorithm to solve the user-side energy storage full life cycle optimization configuration model that takes into account comprehensive market services and investment return risks under different investment return risks, and based on the solution results of the user-side energy storage full life cycle optimization configuration model, obtain the net income, investment cost, investment return rate and investment return risk value over the full life cycle of the user-side energy storage; Step 4: Using the net income, investment cost, investment return rate and investment return risk value obtained during the entire life cycle of the user-side energy storage as evaluation indicators, the comprehensive evaluation results of the optimal energy storage configuration are given based on the subjective and objective weight algorithm of the improved hierarchical analysis method AHP and CRITIC method.

2. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: In step 1, the objective function of the user-side energy storage optimization configuration model considering comprehensive market services is: F1=f1+f2+f3-C1-C2+C3 Among them, F1 represents the net income of user-side energy storage over its entire life cycle, f1 represents the income of energy storage participating in the energy arbitrage market, f2 represents the income of energy storage participating in the backup ancillary service market, f3 represents the income of energy storage participating in the demand charge reduction market, C1 represents the initial investment cost of user-side energy storage, C2 represents the operation and maintenance cost of user-side energy storage over its entire life cycle, and C3 represents the recovery cost of user-side energy storage.

3. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 2, characterized in that: The calculation method of energy storage participating in the energy arbitrage market profit f1 is as follows: P ch,t , P dis,t , U ch,t , U dis,t represents the charging power, discharging power, charging state, and discharging state of the energy storage in time period t; T represents 24 time periods in a day, p t represents the electricity price in period t; Y represents the service life of the energy storage system; D represents the annual operating days of the energy storage system; i r represents the inflation rate; d r represents the discount rate; The calculation method of the revenue f2 of energy storage participating in the reserve ancillary service market is as follows: p as,t represents the ancillary service price during period t; p c,t represents the energy purchase cost in period t; The calculation method of the demand charge reduction income f3 of energy storage participating in the demand management market is as follows: P cap P represents the benefit brought by the reduction of unit demand electricity charge; rate Indicates the monthly reduction in electricity demand.

4. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 2, characterized in that: The calculation method of the initial investment cost C1 of the user-side energy storage is as follows: C1=C p P+C e E Where: C p , C e They represent the unit charging / discharging power cost and unit capacity cost of the user-side energy storage respectively; P represents the rated power of the user-side energy storage; E represents the rated capacity of the user-side energy storage; The calculation method of operation and maintenance cost C2 during the whole life cycle of user-side energy storage is as follows: Where: f m Indicates the annual operation and maintenance cost per unit charge / discharge power of the user-side energy storage; The calculation method of user-side energy storage recovery cost C3 is as follows: Where: γ is the user-side energy storage recovery value coefficient, usually 3% to 5%.

5. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: In step 1, the user-side energy storage optimization configuration model considering comprehensive market services also includes energy storage operation constraints, which are: S min ≤S t ≤S max IN cha,t +U dis,t <=1 0≤P dis,t ≤U dis,t P 0≤P cha,t ≤U cha,t P Where: S t Indicates the energy storage charge state during period t; S min and S max are the minimum and maximum values ​​of the energy storage charge state, respectively; η represents the charging and discharging efficiency of the energy storage; Taking into account the benefits of demand management, after the energy storage operation, the equivalent load should not be greater than 1.05 times the maximum value of the optimized demand, that is: Where: P laod,t is the load value used by users in period t, It is the maximum load power value of the user before energy storage is installed.

6. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: In step 2, the objective function of the user-side energy storage full life cycle optimization configuration model considering comprehensive market services and investment return risks is: maxF1=(1-σ1)f1+(1-σ2)f2+(1-σ3)f3-C1-C2+C3 maxF1 indicates that the net profit of user-side energy storage is the largest during its entire life cycle, σ1 indicates the risk value of energy storage participating in the energy arbitrage market, σ2 indicates the risk value of energy storage participating in the backup ancillary service market, and σ3 indicates the risk value of energy storage participating in the demand management market. The values ​​of σ1, σ2, and σ3 are in the range of [0,1].

7. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: In step 3, an improved genetic algorithm is used to solve the user-side energy storage full life cycle optimization configuration model considering comprehensive market services and investment return risks under different investment return risks, including the following steps: Step 31: Encode the problem Encode the capacity, power, and charging and discharging strategy parameters in the user-side energy storage full life cycle optimization configuration model that considers comprehensive market services and investment return risks under different investment return risks to form an individual gene sequence, where each individual represents a possible user-side energy storage configuration solution; Step 32: Initialize population and parameters Input the encoded capacity, power, and charging and discharging strategy parameters into the user-side energy storage full life cycle optimization configuration model that considers comprehensive market services and investment return risks under different investment return risks. Use the random reverse learning method to generate the initial population so that the generated individuals are randomly and evenly distributed in the solution space, which speeds up the convergence of the algorithm and quickly finds the best solution area. p f =C min +(C max -C min )rand i p r =C min +(C max -p f )rand i C min , C max are the lower and upper bound vectors of the optimization variables; rand i is the random number vector of the ith individual, whose dimension is consistent with the dimension of the optimization space, and each element value is a random number in [0,1]. f is the positive population; p r The reverse population generated by the random reverse learning mechanism is merged with the forward population, the fitness value is calculated, and half of the particles with high fitness values ​​in the merged new population are taken out as the initial population of the algorithm. Among them, C i init is the initial concentration vector of the i-th individual in the new population after the merger; Step 33: Design the fitness function The objective function of the user-side energy storage full life cycle optimization configuration model considering comprehensive market services and investment return risks is used as the fitness function F x ; Step 34: Make a selection A hybrid selection method of roulette and elite retention strategy is adopted to ensure that the best individuals in the current population enter the next generation directly without crossover and mutation operations, thereby improving the convergence speed of the genetic algorithm. Step 35: Calculate crossover and mutation probabilities and perform crossover and mutation operations The crossover probability calculation formula is: Where: P c is the adaptive crossover probability; t is the current iteration number; is the maximum crossover probability; is the minimum crossover probability; T is the maximum number of iterations; f′ is the larger fitness in the upcoming crossover operation; f ave is the average fitness of all individuals in the current iteration, k1 is a natural number between [0, 1]; f max Indicates the maximum number of iterations set. The mutation probability calculation formula is: Where: P m is the adaptive mutation probability; t is the current iteration number; is the maximum mutation probability; is the minimum crossover probability; T is the maximum number of iterations; f is the fitness value of the individual to be mutated; f ave is the average fitness of all individuals in the current iteration, k2 is a natural number between [0, 1]; f max Indicates the maximum number of iterations set; Step 36: Termination condition judgment When the maximum number of iterations f is reached max When , the algorithm stops running and outputs the individual with the highest fitness in the current population as the optimal solution, that is, the optimal configuration plan for the entire life cycle of user-side energy storage under different investment return risks; otherwise, return to step 34 to continue the selection, crossover, mutation and other operations.

8. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: In step three, The investment cost is calculated as follows: C=C1+C2 Where: C represents the investment cost of energy storage; The calculation formula of net benefit is the objective function of the user-side energy storage optimization configuration model: F1=f1+f2+f3-C1-C2+C3 Return on investment K t The calculation formula is: The formula for calculating the risk value is: s sp =σ1+σ3+σ3.

9. A comprehensive evaluation method for user-side energy storage configuration considering comprehensive market services and investment return risks according to claim 1, characterized in that: Step 4 specifically includes: Step 41: Determine subjective weights based on improved analytic hierarchy process (1) Determine the hierarchical structure of each evaluation indicator based on the expert scores and sort them according to their importance. Suppose there is x1-x n There are n indicators in total, which can be sorted according to the important procedures as x1≥x2≥.....≥x n ; (2) Compare the importance of each indicator in turn and determine the corresponding scale value t i ; A scale value of 1 means that when two elements are compared, they are of equal importance; a scale value of 1.2 means that when two elements are compared, the first element is slightly more important than the second element; a scale value of 1.4 means that when two elements are compared, the first element is significantly more important than the second element; a scale value of 1.6 means that when two elements are compared, the first element is strongly more important than the second element; a scale value of 1.8 means that when two elements are compared, the first element is extremely more important than the second element; (3) Construct the judgment matrix as follows: Where: n is the number of indicators to be evaluated; based on the determined scale value, the value of each element of the judgment matrix is ​​calculated; (4) Based on the judgment matrix, the subjective weighted comprehensive score of each indicator is calculated as follows: s i That is, the subjective weighted comprehensive score corresponding to the evaluation index, a ij is each element in the judgment matrix, n is the number of indicators to be evaluated, and j represents the corresponding evaluation indicator; Step 42: Determine the objective weight using the CRITIC method. The specific steps are as follows: (1) Index normalization Construct a normalized evaluation matrix and use the following formula to perform normalized calculation for the reverse indicators; For positive indicators, the following formula is used for normalization calculation; (2) Calculation of index comparison strength CI CI=S j In the formula, S j is the standard deviation of the jth indicator; (3) CT calculation of conflicting indicators In the formula, n is the number of indicators, r ij is the correlation coefficient between indicators i and j, and its calculation formula is as follows: In the formula, x i,k represents the kth data of index i, x j,k represents the kth data of the j indicator, and m is the number of samples; are the average values ​​of the two indicators respectively; (4) Calculation of information volume Gj By calculating the contrast intensity and conflict of the indicators, the calculation formula for the amount of information contained in each indicator can be obtained as follows: (5) Objective weight calculation: The objective weight of the jth evaluation index can be expressed as: Calculate the objective weighted comprehensive score q of each indicator i for: Step 43: The comprehensive weight score of the evaluation index is calculated based on the subjective and objective weight algorithm of the improved AHP and CRITIC method: β=l1s i +l2q i Where: l1 and l2 are weight coefficients of subjective and objective weights respectively, which can be set according to user needs and satisfy l1+l2=1, and l1,l2≥0.

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