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Functional Combination Based Comprehensive Benefit Evaluation of Energy Storage Projects under Source-grid-load Scenarios via Super Efficiency DEA

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02 April 2024

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04 April 2024

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Abstract
As an important support for power system with high penetration of renewable energy, energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption, and its installed capacity under the source-grid-load scenario is rising year by year, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under the source-grid-load scenarios, and proposes a comprehensive benefit evaluation method of energy storage projects based on fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super efficiency data envelopment analysis (DEA) model, which considers the perspective of economic and social environment. Firstly, the functional requirements of energy storage on the source-grid-load scenarios are explored respectively, and the characteristics of various functions are analyzed to form functional combination schemes. Secondly, index modeling is carried out from three aspects: the whole life cycle cost, functional combination benefit and social environmental benefit, and the comprehensive benefit evaluation index system of energy storage project is proposed, which includes both economic and social environment. Then, the intuitionistic trapezoidal fuzzy number is combined with DEMATEL to form an effective analysis method for the input-output relationship of the indexes, and the comprehensive evaluation is realized based on the super efficiency DEA model. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios respectively.
Keywords: 
Subject: Engineering  -   Electrical and Electronic Engineering

1. Introduction

As a kind of flexible regulation resource, energy storage technology can provide a variety of auxiliary services required by power system operation, including load-frequency regulation and backup power supply services [1]. These characteristics make it an important tool to improve the flexibility, economy and safety of new power system. However, the current application scenario of energy storage technology is single and the utilization rate is low. In 2022, the China Electricity Council released the "Research Report on the Operation of New Energy Distribution Energy storage", which shows that the average equivalent utilization coefficient of electrochemical energy storage projects in China in 2022 is 12.2%, while the utilization rate of new energy distribution energy storage system (ESS) is only 6.1% [2]. The data above indicates that the application and potential benefits of multiple functions of energy storage technology in different scenarios haven’t been excavated completely. Moreover, in the first half of 2023, the low utilization rate of the ESS still exists, the average daily operation of the energy storage projects configured by the new energy power stations is 2.05 hours, which only reaches 27% of the average design utilization hours of the power station [3]. The current energy storage projects only consider part of the functions, or only focus on the application of the power grid side, which only consider individual or limited benefits, failing to fully consider the full benefits of the ESS in various application scenarios. Therefore, taking into account multiple functional combinations and considering multiple application scenarios of energy storage on source-grid-load sides for energy storage operation planning can be used as a way to make full use of energy storage and improve project benefits.
In order to verify the role of functional combination in the benefit improvement of energy storage projects, a scientific comprehensive benefit evaluation can be carried out from the aspects of economy, society and environment. In terms of economic benefits, the planning method was used to establish the cost calculation model of energy storage power station and the income calculation model including source-grid-load sides in [4,5,6,7]. In terms of index system, a hierarchical evaluation system of battery energy storage technology was constructed from three aspects in [8,9]: technology, economy and environment, but its economic analysis was relatively simple. In terms of evaluation algorithm, the intuitive trapezoidal fuzzy number was combined with TOPSIS to form a comprehensive evaluation method in [10]. A profit coefficient method and an equivalent cost method were proposed to carry out benefit-cost analysis of multifunctional indicators of hierarchical ESS for comprehensive evaluation in [11]. Data envelopment analysis (DEA) is a multivariate analysis tool used to evaluate the relative effectiveness of multiple decision-making units (DMUS) of the same type [12,13]. When there are multiple simultaneous effective DMUS, the super efficiency DEA model can be adopted to realize further ordering of effective DMUS [14]. A comprehensive evaluation model based on fuzzy DEMATEL-super efficiency DEA was established in [15], which realized the effective construction of input-output evaluation index system, but it still could be further optimized in the quantitative processing of expert fuzzy language.
In order to improve the utilization rate of energy storage equipment and expand the benefits of energy storage projects, this study analyzes the energy storage schemes based on functional combination under source-grid-load scenarios, and establishes the comprehensive benefit evaluation model of energy storage projects based on intuitionistic trapezoidal fuzzy-DEMATEL and super efficiency DEA for verification. Compared with the existing research work, the innovations and contributions of this study are as follows:
1) Various functional characteristics of source-grid-load sides are analyzed and eight functional combination schemes are formed. Based on the profit coefficient method and equivalent cost method, the functional combination benefit index is established, which is combined with the whole life cycle cost and social and environmental benefits to form the comprehensive benefit evaluation index system of energy storage projects;
2) The intuitionistic trapezoidal fuzzy number is combined with the traditional DEMATEL method to analyze the correlation and causality among the indexes., and an input-output evaluation index system based on the analysis results of the indicators is built; Finally, a comprehensive benefit evaluation method of energy storage projects based on super efficiency DEA was formed.

2. Functional Combination Analysis of Energy Storage

According to the installation position in power system, the application scenario of energy storage can be divided into three categories: source, grid and load side. This study firstly studies the various functions that energy storage can achieve in different application scenarios; Then, considering the mutual exclusion among different kinds of functions in charge and discharge state, working period and required capacity, the functional combination schemes of energy storage project are proposed.

2.1. Functional Analysis

Figure 1 shows the main function of ESS under source-grid-load scenario. The following mainly summarize the ESS’s function under different scenarios, so as to find the functional combination scheme of ESS in each scenario.

2.1.1. Power Source Side

1) Reduce new energy grid-connected assessment: new energy grid-connected consumption has an impact on the stable operation of the system, and ESS can assist grid-connected [16].
2) Reduce wind and light abandonment: abandoned power of new energy is stored and transferred to other time periods for grid connection. The characteristics of new energy generation make this function appear more frequently [17,18].
3) Black start: After the failure or power outage of the power system, the power system can be restarted by using the ESS as the backup power supply.
4) Cold start: ESS can quickly release energy during cold start by storing a large amount of electrical energy, providing the high power-output required for start-up.
5) Rotary backup: When ESS is used as rotary backup, it can provide the rapid adjustment ability and stability support of the power system in short term.
6) Delaying power generation equipment upgrade investment: through the adjustment capacity of ESS and the absorption of new energy, the additional load demand can be met, the frequency and voltage support can be involved, and the demand for new power generation equipment and upgrade investment can be delayed.
7) Frequency modulation auxiliary services: the response speed of ESS for frequency modulation auxiliary service is fast, which can be used as a kind of good frequency modulation resource.

2.1.2. Grid Side

1) Power auxiliary peak regulation: ESS can absorb electric energy in the off-peak period of power load and release electric energy in the peak period of power load to alleviate the contradiction between unbalanced power supply and demand caused by the large difference between peak and valley.
2) Improve the reliability of the power grid: ESS enhances the stability and reliability of the power grid by providing backup capacity and rapid adjustment ability.
3) Delay the capacity expansion of transmission and distribution equipment: the power load is less than or close to the rated load in most of the time in a year, and energy storage is used to deal with the insufficient capacity of the grid during peak hours, so as to alleviate the investment pressure of expansion construction [19].
4) Reactive power support: When the reactive power in the power system is unbalanced, ESS realizes reactive power compensation adjustment to the power grid by rapidly adjusting reactive power output [20].
5) Voltage support: By installing ESS on the transmission and distribution lines, reactive power can be absorbed or injected to adjust the transmission voltage and maintain the stable operation of the transmission and distribution lines.
6) Reduce network loss: ESS is used as the load to store electric energy during the valley, and as the power source to release electric energy during the peak, so as to reduce the current on the line during the peak load and reduce the network loss.

2.1.3. Load Side

1) Peak-valley spread arbitrage: in the period of low electricity price, the electricity is purchased and stored by ESS. In the peak period, the stored electricity is used and the price difference is used to obtain income.
2) Capacity cost management: ESS is used to store electric energy during the valley period of power consumption, and discharge during the peak period of power consumption, which can replace part of the power supply of the power grid, thereby reducing the cost of capacity management.
3) Improve user power quality: ESS can reduce problems such as voltage rise and frequency fluctuation to reduce the loss caused by power quality events [21].
4) Demand response: ESS responds to market price signals, incentive mechanisms or instructions issued by operators to change its short- or long-term operation strategies [22].
5) Backup power supply: In some clients with high requirements for reliability, ESS can provide continuous power supply in the event of power failure or failure.

2.1.4. Source-Grid-Load System as a Whole

Whether on the power source side, the power grid side or the load side, after the energy storage equipment participates in the system operation, it reduces the output of high-emission units such as coal-fired power stations and gas-fired power stations, thereby reducing pollution and carbon emissions, saving coal resources; On the other hand, due to the emerging nature of ESS, the profit model and market mechanism are not mature enough, for the purpose of incentive and protection, the construction and operation of energy storage projects at the regional level can get the corresponding subsidies. The above technical and economic benefits presented in overall system can be regarded as additional benefits to achieve scenario functions, including coal saving benefits [23], carbon emission reduction benefits [24]and government subsidies [25].

2.2. Functional Combination Schemes

1) Functional combination on power source side
With the promotion of the double-carbon policy, large-scale new energy access to the power generation side, because of the randomness and intermittently of wind power and photovoltaic output, the absorption of new energy in the power generation side has become a major problem and an important indicator of the power generation side assessment. While reducing wind and light abandonment, energy storage equipment also reduces the assessment cost of new energy grid connection, so those two effects can be regarded as the combined income in the process of promoting the consumption of new energy. ESS on power source side often makes output adjustment due to the demand of the power grid side. The technical characteristics of charge and discharge make it have great flexibility in the functional response of the power source side, and therefore has good income prospects. While serving the functional demands of rotary backup and frequency modulation, it can also slow down the pressure of the unit itself and delay the investment in power generation equipment upgrading. The complementarity of the three functions can be seen as the auxiliary income combination of the energy storage on power source side. In the case of failure, in order to help the restart and normal operation of the generator set, the combination of the two functions of the energy storage black start and cold start can realize the combination of start-up benefits under the failure.
2) Functional combination on power grid side
When ESS on power grid side responds to the grid dispatching demand instructions, it can realize the role of auxiliary peak load balancing, delaying the expansion of transmission and distribution equipment, and reducing network loss. The complementarity of the three functions can be seen as the combination of auxiliary peak regulation benefits of energy storage on the power grid side. While responding to reactive power support, energy storage on the power grid side will also play a supporting role in node voltage, thereby improving the reliability of power transmission and distribution. The complementarity of the three functions can be regarded as the combination of grid support benefits of ESS on power grid side.
3) Functional combination on load side
Under normal operation, the energy storage on the load side mainly uses the peak-valley price difference to make profits with the higher power grid operators. While realizing the arbitrage of the peak-valley price difference, it can realize the functional requirements of demand response and capacity cost management, and the three functions are used as a combination of low storage and high discharge functions. According to the scheduling needs of emergency situations, ESS can be used as backup power supply for continuous power supply of important loads, while improving the power quality of users. Those two functions can be combined as user auxiliary functions.
4) Additional function combination
After the energy storage equipment is configured in system, whether it is on power source side, power grid side or load side, ESS participates in the system operation, partially reducing the output of high-emission units such as coal-fired power stations and gas-fired power stations, thereby reducing pollution and carbon emissions, saving coal resources; On the other hand, due to its emerging technology, ESS is not mature enough in terms of profit model and market mechanism. For the purpose of incentive and protection, the implementation and operation of energy storage projects are subsidized at the regional level. Therefore, after the configuration of energy storage projects in new energy power system, it can get government subsidies, carbon emission reduction benefits and coal saving benefits at the same time, the above three as the additional income function combination of energy storage projects.
Based on the above analysis, eight functional combination schemes of ESS in different application scenarios are obtained, as shown in Table 1.

3. Establishment of Comprehensive Evaluation Index System

In order to realize the evaluation of energy storage projects based on functional combination, the comprehensive benefit evaluation index system is constructed from multiple levels including economy, society and environment, as shown in Figure 2.
From the economic perspective, this study chooses the whole life cycle cost and functional combination benefits as constituent indicators [26]. The life cycle cost of ESS includes the initial investment cost, operation and maintenance cost, replacement cost and decommissioning recovery cost.
In the process of realizing various functional combinations, ESS produces explicit or implicit economic benefits, but also needs to pay the initial investment, operation and maintenance costs and other costs. Taking into account the mutual exclusion of different kinds of functions in charge and discharge status, working period and required capacity, simply adding up the benefits brought by different functions in the combination will cause large errors in the technical and economic analysis of energy storage projects. In order to avoid the problem of repeated calculation of some components in the total revenue assessment, two methods are used to measure the benefits of energy storage projects in the power system: profit coefficient method and equivalent cost method [11].
Index calculation based on the profit coefficient method and the equivalent cost method can reflect the diversity of the functions and control objectives of the ESS, ensure the completeness of the benefit types. In addition, the classification and integration of the income of different functions also enables the relative values of various indicators to reflect the degree of functional requirements and the degree of demand satisfaction. The benefit evaluation indexes of energy storage based on functional combination proposed in this study are as follows.
1) Power source side
① New energy consumption benefits
When ESS on power source side plays the function of reducing the abandonment of wind and light, it can also reduce the cost of new energy grid connected assessment at the same time. The cost of reducing the construction of conventional power sources in other ways is used to represent the benefits of promoting new energy consumption, which is calculated by the profit coefficient method and recorded as indicator P1.
P 1 = κ D E C B / C i ¯
where κ is the reduction coefficient of new energy assessment cost compared with previous years； C B , C i ¯ are the ESS cost and the average cost of typical conventional power supply; D E is the variance value of the wind-power output obtained according to the Beta probability density distribution shown in equations (2, 3).
f ( P ) = Γ ( α + β ) Γ ( α ) Γ ( β ) ( P P max ) α − 1 ( 1 − P P max ) β − 1
D E = α β α + β 2 ( α + β + 1 ) P max 2
② Unit ancillary benefits
In this study, ESS is compared with hydropower units, gas units and coal fired units commonly used as scheduling power supply. Considering the investment cost per unit capacity, scheduling response performance and unit upgrade delay time, the response performance is expressed by T={climbing ability, response time, response accuracy, adjustment amplitude}, which is calculated by equivalent substitution method and recorded as indicator P2.
P 2 = n c ∑ i = 1 4 T b i C B T ¯ i C ¯ i
where T b i , T ¯ i are the response performance indicators of the ESS and other power supplies, n c is delayed investment in upgrading power generation equipment.
③Start-up benefits
Considering that the failure probability of the power source side unit is small, but the importance of the smooth start of the unit in the process of power grid, fault start-up benefit is calculated by the profit coefficient method and recorded as indicator P3.
P 3 = n b s I / C B
where nbs the number of failure startups in a year, I is start-up income [27].
2) Power grid side
① Peak regulation benefits
Benefits such as power auxiliary peak regulating, delaying the expansion of transmission and distribution equipment, and reducing network loss are generated by the energy storage device transferring a certain amount of electricity within a specified period, which can be calculated by the profit coefficient method and recorded as indicator P4.
P 4 = ∑ E i / [ c p + c w / ( L • D O D ) + c m ]
where c p 、 c w 、 c m are the unit power cost, energy cost and maintenance cost of the ESS; DOD and L are respectively equivalent discharge depth and life span; ∑ E i represents a collection of benefits such as reduced net loss.
② Grid support benefits
Reactive power support, voltage support, reliability improvement of the grid, the three functions are directly related, but the voltage support benefits are difficult to quantify. Considering the effect of voltage support, the profit coefficient method is used to establish the index P5
P 5 = η ∑ E i / [ c p + c w / ( L • D O D ) + c m ]
where ∑ E i is a collection of benefits to support reactive power and improve grid reliability; η is the improvement coefficient of voltage deviation.
3) Load side
① Low storage high discharge benefits
Peak valley spread arbitrage, capacity cost management and demand response use the time-of-use electricity price mechanism to obtain benefits while responding to grid scheduling, which can be calculated by the profit coefficient method and recorded as indicator P6
P 6 = ∑ E i / [ c p + c w / ( L • D O D ) + c m ]
where ∑ E i represents a collection of gains such as peak-valley spread arbitrage.
② User support benefits
All kinds of loads, especially important loads, often have less frequent but necessary targeted demand for power supply, at this time, energy storage is generated as the functional requirements of backup power supply and power quality improvement. These user-assisted benefits are calculated by the equivalent substitution method and recorded as indicator P7
P 7 = n z C B C ¯ i
where is the single-year frequency of participation in user assistance services.
4) Additional benefits
The additional benefits of the system include coal saving benefits, carbon emission reduction benefits and government subsidies. Such benefits are related to the discharge capacity of energy storage equipment, which are calculated by the equivalent substitution method and recorded as indicator P8.
P 8 = P E C B P i C ¯ i
where P E and P i are respectively the ESS annual discharge capacity and load value.
In terms of social and environmental benefits, investment in energy storage projects can promote regional economy and stimulate regional economic growth, which can be expressed by the GDP growth rate. As for environmental benefits, traditional thermal power plants not only produce high carbon emissions but also release solid wastes such as dust particles, pulverized coal ash and slag. The operation of energy storage projects helps to reduce such environmental impacts. The efficiency of energy storage projects is expressed by reducing carbon emissions and solid waste emissions, which are calculated as follows:
1) The growth rate of gross domestic product is expressed as in (11):
Δ α = α − α 0
where the added value of Δα gross product growth rate; α has the gross product growth rate after the energy storage project; α0 gross product growth rate without the energy storage project.
2) Reduced carbon emissions
Carbon emissions can be calculated by (12), (13) and (14).
D c = λ c C
λ c = α c γ c
C = k c b c Δ N 10 − 7
where Dc is the carbon emission that can be reduced (10,000 tons); λc is the carbon emission reduction coefficient; C is can reduce coal consumption (10,000 tons); αc is the carbon content; γc is the carbon release rate; kc is the coefficient of converting standard coal into coal; bc is the coal consumption used to produce one degree of electricity (kg/kWh); ΔN is the amount of electricity on the generating side that can be saved (kWh).
3) Reduced solid waste emissions
Solid waste generated by thermal power plants mainly includes dust particles and pulverized coal ash. Both of them can cause environmental pollution and even damage human health in severe cases, which can be calculated by (15) and (16):
D G = λ G C
λ G = α G γ G
where DG is the solid waste emission can be reduced (10,000 tons); λG is the emission reduction coefficient of solid waste; αG is the solid waste content rate of coal consumed; γG is the solid waste release rate.

4. Fuzzy DEMATEL- Super Efficiency DEA Model

4.1. Intuitive Trapezoidal Fuzzy Number

It is supposed that A is an intuitive trapezoidal fuzzy number [28] on a real number set R, A = a 1 , a 2 , a 3 , a 4 , b 1 , b 2 , b 3 , b 4 ; μ A , ν A , parameter range is b 1 ⩽ a 1 ⩽ b 2 ⩽ a 2 ⩽ a 3 ⩽ b 3 ⩽ a 4 ⩽ b 4 , its membership and non-membership functions can be expressed as in (17) and (18):
μ A ( x ) = x − a 1 a 2 − a 1 μ A a 1 ≤ x < a 2 μ A a 2 ≤ x ≤ a 3 a 4 − x a 4 − a 3 μ A a 3 < x ≤ a 4 0 e l s e
ν A x = b − x + ν A x − a 1 b − a 1 a 1 ≤ x < b ν A b < x ≤ c x − c + ν A d 1 − x d 1 − c c < x ≤ d 1 0 e l s e
Defining the degree of hesitation as πA(x)=1−μA(x)−vA(x).
Set A1=<(a11,a12,a13,a14),(b11,b12,b13,b14)>,A2=<(a21,a22,a23,a24),(b21,b22,b23,b24)> as two intuitive trapezoidal fuzzy numbers, and the distance between them is:
d = 1 12 ( ∑ i = 1 4 a 2 i − a 1 i 2 + ∑ i = 1 4 a 2 i − a 1 i 2 + a 21 − a 11 ( a 22 − a 12 ) + ( a 23 − a 23 ) ( a 24 − a 14 ) + b 21 − b 11 ( b 22 − b 12 ) + ( a 23 − a 13 ) ( a 24 − a 14 ) ) 1 2
A new kind of trapezoidal fuzzy number [ a , b , c , d ] is added on the basis of the traditional intuitive fuzzy set. By extending its domain from discrete set to continuous set, membership and non-membership are not limited to the uncertain information such as “good” or “bad”. Compared with traditional intuitionistic fuzzy numbers, they express different dimensions of evaluation information, but can truly reflect evaluation information.

4.2. Fuzzy DEMATEL Method

DEMATEL is a method to analyze the influential factors of complex systems. The important influential factors, the degree of influence and the types of factors are clarified Through the calculation of relevant indicators.
In this study, the hesitant fuzzy linguistic term set (HFLTs) [29] is used to collect the evaluation information of decision makers, and a five-level evaluation semantic term is constructed. Table 2 is used to realize the conversion of evaluation semantic term to intuitionistic trapezoidal fuzzy number.
It is supposed that there are m evaluation objects, and each object has n index attribute values. Language evaluation matrix of each index is obtained by Delphi method, and then the language evaluation matrix is transformed into the intuitive trapezoid fuzzy number evaluation matrix by using the intuitive trapezoid fuzzy number.
(1) Establish the direct influence matrix. The expert evaluation group is composed of L experts and is denoted as D = ( D 1 , D 2 , … … , D L ) . Each expert independently constructs a semantic direct impact matrix based on the direction and degree of interaction between indicators. And combined with Table 1, it is converted into an intuitive trapezoidal fuzzy direct influence matrix Ak, finally, the total direct influence matrix A is obtained by combining expert weights.
A k = 0 a 12 k ⋯ a 1 n k a 21 k 0 ⋯ a 2 n k ⋮ ⋮ ⋱ ⋮ a n 1 k a n 2 k ⋯ 0
a i j = ∑ k = 1 L ω k a i j k
A = 0 a 12 ⋯ a 1 n a 21 0 ⋯ a 2 n ⋮ ⋮ ⋱ ⋮ a n 1 a n 2 ⋯ 0
where aijk represents the evaluation value of the kth expert on the degree of direct influence of indicator i on indicator j; n indicates the number of indicators; ωk represents the weight of expert k.
(2) Calculation criteria direct influence matrix. Firstly, the elements in the total direct influence matrix are de-fuzzized according to (17), and the de-fuzzified direct influence matrix E is obtained. Then the direct influence matrix is normalized according to formulas (18)-(19), and the standard direct influence matrix X is obtained.
E = 0 a 12 ⋯ a 1 n a 21 0 ⋯ a 2 n ⋮ ⋮ ⋱ ⋮ a n 1 a n 2 ⋯ 0
s = min { 1 max 1 ≤ j ≤ n ∑ i = 1 n a i j , 1 max 1 ≤ i ≤ n ∑ j = 1 n a i j }
X = [ x i j ] n × n = s ⋅ E
(3) Calculate the comprehensive influence matrix.
T = lim k → ∞ ( X + X 2 + X 3 + ⋯ + X k ) = X ( I - X ) − 1
where, I is the identity matrix of order n.
(4) Calculate the influence degree and affected degree. The calculation formulas of influence degree P and affected degree Q are shown in (27) and (28).
P = [ P i ] n × 1 = [ ∑ j = 1 n x i j ] n × 1
Q = [ Q j ] 1 × n = [ ∑ i = 1 n x i j ] 1 × n
(5) Calculate the centrality degree and cause degree.
M i = P i + Q i U i = P i − Q i
where, Mi is the centrality degree of the indicator, and Ui is the cause degree of the indicator. The centrality of the index reflects the position and importance of the index in the index system, and the cause of the index reflects the influence of the index on the system. If the cause degree is greater than 0, it indicates that the influence of the indicator on other indicators is greater than that of other indicators on it, which is called the cause indicator. If the cause degree is less than 0, it indicates that the influence of this indicator on other indicators is less than that of other indicators on it, which is called the result indicator. A cause degree equal to 0 indicates that the influence of this indicator on other indicators is equal to the influence of other indicators on it, and this indicator can be eliminated. From the perspective of economic management of input-output relationship, cause index and result index can be understood as input index and output index respectively.
Label the overall benefit indicators in Figure 2 from left to right as R1、R2、R3、R4、R5、R6、R7、R8、R9、R10、R11、R12、R13、R14、R15. Five experts in related fields are invited to construct the semantic direct influence matrix by pairwise comparison, assuming that the evaluation weights of each expert are the same. Combined with Table 2, it is converted into an intuitionistic trapezoidal fuzzy direct impact matrix, and the comprehensive impact matrix is calculated. The influence degree, affected degree, centrality degree and cause degree of each indicator are calculated from (27)-(29), as shown in Table A1 of the Appendix, and the centrality-causality scatter plot was drawn, as shown in Figure 3.
In Figure 3, the indicators falling in the cause factor and result factor areas are classified as input and output indicators that affect the comprehensive benefit of energy storage projects based on functional combination. That is, the initial investment cost, operation and maintenance cost, replacement cost and decommissioning recovery cost of the whole life cycle cost constitute the input index system, respectively marked as X1、X2、X3、X4; Eleven items, including functional combination benefits, social benefits and environmental benefits, constitute the output index system and are marked as Y1~Y8、Y9、Y10 and Y11, among them, Y1~Y8 does not include dimension in the comprehensive benefit evaluation.
Input-output relationship based on intuitionistic trapezoidal fuzzy-DEMATEL is analyzed. The input-output index is composed of the whole life cycle cost, and the power and capacity allocation values are represented by the power cost and capacity cost, which together constitute the initial cost. In the functional combination benefits, power source side, power grid side and load side benefits are dimensionless output indicators based on the profit coefficient method and equivalent cost method, and the value reflects the function output degree of various functional combinations under a certain power and capacity. During the whole life cycle of construction, operation, maintenance and decommissioning of energy storage projects, it will have a certain impact on the external society and environment. Driving regional economic growth and reducing carbon emissions and solid waste emissions are all output effects in the whole life cycle process.

4.3. Comprehensive Evaluation Process Based on Super Efficiency DEA Method

Super efficiency DEA model can realize the objective ordering of effective DMU and non-effective DMU simultaneously. The realization method is to exclude a certain DMU from the set of DMU when evaluating it, so that the efficiency of the effective decision-making unit under the original model is greater than 1 under the super-efficiency DEA model, while the estimated efficiency value of the DMU that is invalid under the original model remains unchanged under the super efficiency DEA model, as shown in (30).
min ρ ∑ j = 1 , j ≠ o n X j λ j + S − ⩽ ρ X o ∑ j = 1 , j ≠ o n Y j λ j − S + ⩾ Y o λ j ⩾ 0 , j = 1 , 2 , ⋯ , n ( j ≠ o ) S − , S + ⩾ 0
where ρ is the efficiency value; X j = ( x l j , x 2 j , … , x m j ) , x i j is the ith input index of DMUj; Y j = ( y l j , y 2 j , … , y r j ) , y s j is the sth output indicator of DMUj; output weight and input weight are respectively α = ( α 1 , ⋯ , α r ) T , β = ( β 1 , ⋅ ⋅ ⋅ , β m ) T ; λj is the weight coefficient; S-、S+are relaxation variables of input and output respectively.
The basic premise of the application of super efficiency DEA model is to determine the input-output index system. The traditional input-output index system construction method does not analyze the internal logic and interaction between the indicators, so it is difficult to ensure the scientific and accurate. Therefore, this study introduces fuzzy DEMATEL to improve the super efficiency DEA method. The specific algorithm flow is shown in Figure 4.

5. Case Study

5.1. Example Setting

With reference to the energy storage parameters and calculation example configuration in [30], based on particle swarm optimization algorithm, lithium battery energy storage configuration was carried out on power source side, power grid side and load side, and four schemes were formed on each side. Schemes 1-3, 5-7 and 9-11 only consider a single functional combination in turn, among them, the additional benefit combination is only considered on grid side and load side that are more affected. Schemes 4, 8 and 12 consider the configuration results of multiple functional combinations. Due to the different installation locations and limited coverage area of energy storage, the functional combination benefits that can be realized are different. In order to further compare the benefit differences in different scenarios, it is assumed that the energy storage operation in a single scenario can realize all the functional benefits of the three sides, but the functional combination benefits of the energy storage project on the non-installation side are close to 0, which is represented by 1e-4. The values of input-output indicators are shown in Appendix Table A2 and Table A3.

5.2. Result Analysis

Based on the input-output index system determined by fuzzy DEMATEL, the super efficiency DEA model is used to comprehensively evaluate 12 configuration schemes based on functional combination. The evaluation results of each system are shown in Figure 5.
In the efficiency value calculation of configuration schemes 1 to 12, the value of multi-functional combination scheme 4 on the power source side is the highest among the three typical scenarios, which reaches 2.209. Based on multi-function combination, it will promote the power and capacity optimization of the three functional combinations of new energy consumption benefits, unit ancillary benefits and start-up benefits. It can realize the complementarity of multiple functions in time, so as to achieve the maximum benefit of ESS. Due to the most diverse functional service requirements on the power source side, output benefits that can be quantified after the functional response are the most, exceeding the energy storage output on grid side and load side. Among the four configuration schemes on power grid side and load side respectively, the comprehensive efficiency value of the schemes obtained by considering the multi-function combination are the highest, which are scheme 8 and scheme 12, respectively, and the efficiency values of the two schemes reach more than 1.5, which is consistent with the starting point of optimizing the configuration based on the multi-function combination. It can make full use of the state of energy storage SOC at different periods under the support of the operation strategy, meet more functional requirements, and maximize the comprehensive benefit considering cost of the whole life cycle and social and environmental benefits.

5.3. Comparative Analysis of Different Evaluation Methods

In order to verify the effectiveness of the evaluation method presented in this study, it is compared with the super efficiency DEA based on the traditional input-output index system [31], the super efficiency DEA based on the triangular-DEMATEL input-output index system [32], and TOPSIS based on the entropy weight method [33]. The sorting results of different evaluation methods for the comprehensive benefit of energy storage projects are shown in Appendix Table A4.
From the comparison of the comprehensive evaluation results of various methods in Table A4, it can be seen that when the intuitional trapezoidal fuzzy DEA (BCC model) is used for calculation, the efficiency values of scheme 3, 4, 6, 7, 8, 10, 11 and 12 are 1. The super efficiency DEA model can be used to further accurately evaluate those units that achieve the highest efficiency in the process of project investment and operation, that is, configuration scheme 4. Compared with the calculation results of intuitionistic trapezoidal fuzzy super efficiency DEA and traditional super efficiency DEA, the efficiency of scheme 4 is still the highest. The power supply side has the most functional service requirements in the three typical scenarios, and the number of functional responses that can be realized simultaneously in a single functional combination is more, and the demand and response frequency of different functional combinations in continuous periods are higher. The quantifiable output benefit has a higher scope and breadth compared with the input cost. Under the multi-objective optimization configuration, the utilization rate of energy storage on the power source side is higher, and its multi-functional combination benefit is higher than that on the power grid side.
The ranking of efficiency values of scheme 1 is quite different, and the scheme ranks lower in the fuzziness evaluation including triangular fuzzy DEA, but the traditional DEA method ranks higher. Scheme 1 is the result of configuration with the goal of functional combination that promoting the maximum absorption benefit of new energy. In actual operation, the utilization rate of energy storage equipment is often low due to the random characteristics of new energy. When the maximum benefit of the functional combination is considered only, the evaluation opinions of experts through intuitive trapezoidal fuzzy processing can weaken the fuzziness of some experts in the consumption benefit of new energy, so that it is closer to the reality. The results are similar to the TOPSIS evaluation results based on entropy weight, which further confirms the scientific nature of the intuitionistic trapezoid-fuzzy DEA method proposed in this paper.
In addition, the difference between the super efficiency DEA under intuitionistic trapezoidal fuzzy and triangular fuzzy is that when expert opinions are used to determine DEMATAL input-output index system, the former can describe the distribution of fuzzy sets more flexibly and model the uncertainty of fuzzy sets more accurately, because it has two fuzzy values and two non-fuzzy values and can better describe the uncertainty situation in the reality.
In the calculation of TOPSIS method based on entropy weight, the weight value of the index determines the ranking result of the evaluation object to a large extent, and it is difficult to reflect the situation where the preference difference of decision makers is large. The super efficiency DEA method proposed in this paper based on intuitionistic trapezoidal fuzzy-DEMATEL does not need to manually determine the weight of indicators, and the evaluation results depend on the characteristics and laws of the data itself. Once the indicator data is determined, the evaluation results remain unchanged, which can ensure the objectivity and stability of the evaluation results.

6. Conclusion

In this study, the super efficiency DEA evaluation method based on intuitive trapezoidal fuzzy-DEMATEL is proposed to achieve comprehensive benefit evaluation of energy storage projects, so as to achieve benefit analysis based on multi-scenario functional combination. The main conclusions are as follows:
1) The super efficiency DEA method based on intuitive trapezoidal fuzzy DEMATEL can flexibly describe the fuzziness of expert opinions and analyze the input-output relationship among indicators. Compared with conventional evaluation methods, the method proposed in this paper has certain advantages.
2) The functional combination can leverage to the fullest extent of the functionality of existing energy storage equipment and improve the efficiency of energy storage projects. This study can provide reference for the future operation planning of energy storage equipment.
In the future work, when the type of energy storage configured is hybrid energy storage, the impact on the benefit improvement of functional combination will be analyzed, and the comprehensive benefit evaluation of energy storage projects will be further improved.

Appendix

Table A1. Fuzzy DEMATEL analysis results.
Table A1. Fuzzy DEMATEL analysis results.
Index Influence Degree p Affected Degree
q 		Centrality Degree
m 		Causality Degree
n
R1 5.7137 0.7821 6.4958 4.9316
R2 4.2351 1.2213 5.4564 3.0138
R3 2.4256 1.2333 3.6589 1.1923
R4 1.9255 1.0253 2.9508 0.9002
R5 0.8561 4.7862 5.6423 -3.9301
R6 1.8544 4.2221 6.0765 -2.3677
R7 0.5644 0.9421 1.5065 -0.3777
R8 1.2546 4.8752 6.1298 -3.6206
R9 2.1251 5.2156 7.3407 -3.0905
R10 1.9542 4.2687 6.2229 -2.3145
R11 1.0121 3.2102 4.2223 -2.1981
R12 0.8292 1.5225 2.3517 -0.6933
R13 2.5214 2.8273 5.3487 -0.3059
R14 1.7324 2.5243 4.2567 -0.7919
R15 1.6891 2.5225 4.2116 -0.8334
Table A2. Input index value
Table A2. Input index value
Scheme X1
/10,000￥		 X2
/10,000￥		 X3
/10,000￥		 X4
/10,000￥
1 868.8 13.9 8.26 81.5
2 675.19 10.79 6.31 67.5
3 385.16 7.36 3.15 38.16
4 1605.52 25.62 15.8 89.2
5 461.75 47.85 4.52 42.35
6 372.23 33.35 3.56 35.52
7 190.62 20.7 1.91 18.6
8 787.02 82.5 7.86 76.3
9 702.45 32.75 6.98 70.03
10 277.8 13.8 2.71 27.67
11 171.45 8.85 1.67 17.15
12 1003.65 52.25 10.02 91.2
Table A3. Output index value table
Table A3. Output index value table
Scheme Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9
/%		 Y10
/Tons 		Y11
/Tons
1 31.2 12.5 3.6 1e-4 1e-4 1e-4 1e-4 10.1 6.1 7.9 2.7
2 23.6 11.9 3.5 1e-4 1e-4 1e-4 1e-4 9.6 6.4 7.6 2.2
3 16.5 10.8 3.2 1e-4 1e-4 1e-4 1e-4 10.2 6.5 7.7 2.5
4 85.2 26.8 3.8 1e-4 1e-4 1e-4 1e-4 11.2 6.9 8.2 2.8
5 1e-4 1e-4 1e-4 30.9 43.2 1e-4 1e-4 9.8 5.3 7.3 2.2
6 1e-4 1e-4 1e-4 20.2 26.7 1e-4 1e-4 9.5 5.7 7.4 2.1
7 1e-4 1e-4 1e-4 12.7 14.3 1e-4 1e-4 6.2 3.1 5.2 2.2
8 1e-4 1e-4 1e-4 85.3 92.6 1e-4 1e-4 19.9 20.2 19.8 4.2
9 1e-4 1e-4 1e-4 1e-4 1e-4 37.1 30.6 9.1 4.5 6.9 2.5
10 1e-4 1e-4 1e-4 1e-4 1e-4 22.5 16.9 5.2 2.6 4 3.5
11 1e-4 1e-4 1e-4 1e-4 1e-4 9.1 6.2 3.9 1.8 2.5 2.11
12 1e-4 1e-4 1e-4 1e-4 1e-4 92.9 85.1 12.5 8.1 9.6 4.12
Table A4. Comparative results of different evaluation methods.
Table A4. Comparative results of different evaluation methods.
Scheme Intuitionistic Trapezoidal Fuzzy DEA Intuitionistic Trapezoidal Fuzzy Super Efficiency DEA Super Efficiency DEA Triangular Fuzzy Super Efficiency DEA Entropy-Based TOPSIS
Efficiency No. Efficiency No. Efficiency Ranking Closeness No.
1 0.8296 0.8296 11 1.0658 8 0.7274 10 0.3007 10
2 0.8784 0.8784 10 0.8760 12 0.6851 11 0.3025 9
3 1 1.9738 2 1.1890 4 2.2738 2 0.4456 3
4 1 2.209 1 2.1171 1 2.9591 1 0.4896 2
5 1 1.0058 8 1.0549 9 1.0555 8 0.3537 6
6 0.9609 0.9609 9 1.1264 7 0.8432 9 0.3284 8
7 1 1.4946 5 1.6161 3 1.3741 4 0.4416 4
8 1 1.6738 3 2.0596 2 1.2532 5 0.4993 1
9 0.7329 0.7329 12 0.9808 11 0.6384 12 0.2663 12
10 1 1.1512 6 1.1896 5 1.1825 6 0.4212 5
11 1 1.0177 7 1.0459 10 1.0859 7 0.3527 7
12 1 1.5277 4 1.1853 6 1.9245 3 0.2831 11

References

  1. Jianlin, L.; Yerong, J.; Suiliang, M. Application scenario and optimal configuration of distributed energy storage in new power system. High Voltage Technology, 2024, 50(01), 30-41. [CrossRef]. [CrossRef]
  2. China Electricity Council. New energy distribution and energy storage operation research report. 2022. [CrossRef].
  3. China Electricity Council. Industry statistics of electrochemical energy storage power station in the first half of 2023. 2023. [CrossRef].
  4. Xiong, X.; Rengang, Y.; Lin, Y. Economic evaluation of large-scale energy storage system on power demand side. Transactions of China Electrotechnical Society, 2013, 28(9), 224-230. [CrossRef]. [CrossRef]
  5. Suhua, L.; Lin, Y.; Yaowu, W. Battery energy storage capacity optimization based on variable life model. Transactions of China Electrotechnical Society, 2015, 30(4), 265-271. [CrossRef]. [CrossRef]
  6. Chacra, F.A.; Bastard, P.; Fleury, G. Impact of energy storage costs on economical performance in a distribution substation. IEEE Transactions on Power Systems, 2005, 20(2), 684-691.[CrossRef]. [CrossRef]
  7. Kaldellis, J. K.; Zafirakis, D.; Kavadias, K. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Renewable & Sustainable Energy Reviews, 2009, 13(2): 378-392. [CrossRef]. [CrossRef]
  8. Yi,C.; Qi, Q .; Long, Z. New energy storage technology of the Chinese patent layout analysis. Energy storage science and technology, 2024, 1-10. [CrossRef].
  9. Zhaohong, B.; Gengfeng, L.; Haipeng, X. Microgrid reliability assessment considering load and energy storage coordination optimization. Transactions of China Electrotechnical Society, 2014, 29(2), 64-73. [CrossRef]. [CrossRef]
  10. Yuying, Y.; Xiaofen, R, Jing, Z. Comprehensive evaluation of solar + biomass combined heating in villages and towns based on improved AHP-TOPSIS. Journal of Solar Energy, 2019, 45(02), 342-350. [CrossRef].
  11. Qi, L.; Shihong, M.; Baofu, G. Comprehensive evaluation of layered energy storage system based on hierarchical analysis and improved approach to ideal solution. Power System Protection and Control, 2017, 45(03), 13-19. [CrossRef].
  12. Charnes, A.; Cooper, W.W.; Rhodes, E.L. Measuring the efficiency of decision making units. European Journal of Operational Research, 1979,2 (6), 429-444. [CrossRef]
  13. Hmamed, H, Benghabrit, A, Cherrafi, A.Achieving a Sustainable Transportation System via Economic, Environmental, and Social Optimization: A Comprehensive AHP-DEA Approach from the Waste Transportation Sector. Sustainability, 2023, 15(21). [CrossRef]. [CrossRef]
  14. Banker, R.D.; Charnes, A.; Cooper, W.W. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 1984,30 (9), 1078-1092. [CrossRef]. [CrossRef]
  15. Peng, L.; Miao, W.; Cun, D. Comprehensive benefit evaluation method of renewable energy power generation technology based on fuzzy Dematel-Superefficiency DEA. Power Grid Technology,2023, 1-12. [CrossRef]. [CrossRef]
  16. Sheng, L.;,Yanan, M ,Can, Z.Static voltage stability prediction for PCCs of new energy power stations in the grid-connected system. Journal of Physics: Conference Series, 2022, 2369(1). [CrossRef].
  17. Jingming, Z.; Jieying, S.; Feng, P. Dual-objective optimization planning of distributed energy storage in active distribution network considering photovoltaic fluctuations. Renewable Energy, 2022, 40(11), 1546-1553. [CrossRef]. [CrossRef]
  18. Weijun, Z.; Zhicheng, L.Dawei C. Random Optimal Configuration and Location Method of Distributed Energy Storage System on Distribution Network Side. High Voltage Electrical Apparatus, 2023,59(07), 125-135. [CrossRef].
  19. Shiyu, D.; Wenxia, L.; Chang, L. Energy storage planning and decision-making method of distribution network considering double uncertainty and comprehensive efficiency. Energy Storage Science and Technology, 2022, 11 (1), 164-175. [CrossRef]. [CrossRef]
  20. Ruihuan, R. Research on optimal distribution of distributed energy storage in intelligent distribution network. Electrical Applications, 2021, 40 (4), 31-35. [CrossRef].
  21. Jianlin, L.; Yuliang, T.; Han, W. Optimization strategy of energy storage system configuration in distribution network and optical storage micro-network. High Voltage Technology, 2022,48 (5), 1893-1902. [CrossRef]. [CrossRef]
  22. Ting, P.; Yanbin, X.; Yuqing, W. Integrated energy system scheduling optimization considering generalized integrated demand response. Modern Power, 2023, 1-10. [CrossRef]. [CrossRef]
  23. Yan, Y.; Hongbin, W.; Da, L. Multi-dimensional value evaluation and application of energy storage system in distribution network. Zhejiang Electric Power, 2022, 41(12), 36-45. [CrossRef]. [CrossRef]
  24. Yi, X.; Yaowang, L.; Yuliang, L. A preliminary study on a carbon-reducing user-side cloud energy storage model: operation model and benefit analysis. Proceedings of the CSEE, 1-13. [CrossRef]. [CrossRef]
  25. Yutong, W.; Yang, L.; Lixiong X. Energy trading model for community PV users based on multiple games with energy storage assistance. Electric Power Construction, 1-14. [CrossRef].
  26. Zhen, X.;Hui, Z.;Wenfeng Z. Study on the whole life cycle cost of transmission line engineering with carbon price: An analysis from the perspective of new power system. Price theory and practice, 2023 (6), 166-169. [CrossRef].
  27. Cuiping, L.; Yiming, W.; Junhui, L. Analysis of photovoltaic black-start energy storage configuration considering feasibility assessment. China Southern Power Grid Technology, 2023, 17(10), 104-112. [CrossRef]. [CrossRef]
  28. Yongsheng, Z.;Ziyuan, D.;Congbin Y. Reliability Allocation of Intuitive Trapezoidal Fuzzy Number and Entropy Weight -TOPSIS. Modular Machine Tool & Automatic Processing Technology, 2022, (01), 76-80+84. [CrossRef].
  29. Yunna, W.; Mingjuan, L.; Mengyao, H. A decision framework of low-speed wind farm projects in hilly areas based on DEMATEL-entropy-TODIM method from the sustainability perspective: A case in China. Energy, 2019, 213(15), 119014.
  30. Ting, Z. Research on optimization and optimal configuration model of hybrid energy storage scheme for new power system. North China Electric Power University (Beijing), 2022. [CrossRef]. [CrossRef]
  31. Tianqi, S.; Zuohong, Y.; Zilin Y. Optimal investment strategy of power grid enterprise technology transformation based on weighted DEA model. Microcomputer Applications, 2021, 37(02), 161-162+172. [CrossRef].
  32. Zhao, M.; Huiru, Z.; Huijuan, H. Comprehensive evaluation of user energy storage system configuration scheme based on improved triangular fuzzy VIKOR method. China Electric Power, 2022, 55(04), 185-191. [CrossRef].
  33. Yong, X.; Jun, X. Risk assessment of battery safety operation of energy storage power station based on combination weighting and TOPSIS. Energy Storage Science and Technology, 2022, 11(08), 2574-2584. [CrossRef]. [CrossRef]
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Figure 1. Energy storage function under source-grid-load scenario.
Figure 1. Energy storage function under source-grid-load scenario.
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Figure 2. Comprehensive benefit evaluation index system.
Figure 2. Comprehensive benefit evaluation index system.
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Figure 3. Cause-centrality scatter plot.
Figure 3. Cause-centrality scatter plot.
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Figure 4. Evaluation process of fuzzy DEMATEL-super-efficiency DEA.
Figure 4. Evaluation process of fuzzy DEMATEL-super-efficiency DEA.
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Figure 5. Calculated results by using super efficiency DEA.
Figure 5. Calculated results by using super efficiency DEA.
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Table 1. Functional combination schemes of energy storage in different application scenarios.
Table 1. Functional combination schemes of energy storage in different application scenarios.
Scenes Functional Combination Name The Function Performed by the Combination
Power source
side		 New energy consumption benefits Reduce new energy grid connection assessment; Reduce wasted wind and solar power
Unit ancillary benefits Rotary backup; FM auxiliary service; Delay investment in power generation equipment upgrades
Start-up benefits Black start; Cold start
Power grid
Side		 Peak regulation benefits Power auxiliary peak load; Reduce network loss; Delay the capacity expansion of transmission and distribution equipment
Grid support benefits Reactive support; Voltage support; Improve grid reliability
Load
side		 Low storage high discharge benefits Peak-valley spread arbitrage; Capacity cost management; Demand response
User support benefits Standby power supply; Improve the power quality of users
All sides Additional benefits Government subsidies; Reduce carbon emissions; Coal saving benefits
Table 2. Intuitive trapezoidal fuzzy number represents the transformation of language evaluation variables.
Table 2. Intuitive trapezoidal fuzzy number represents the transformation of language evaluation variables.
Language Variables Intuitive Trapezoidal Fuzzy Numbers
Extremely low 0.0 , 0.1 , 0.2 , 0.3 , 0.0 , 0.1 , 0.2 , 0.3
Low 0.1 , 0.2 , 0.3 , 0.4 , 0.0 , 0.2 , 0.3 , 0.5
Medium 0.3 , 0.4 , 0.5 , 0.6 , 0.2 , 0.4 , 0.5 , 0.7
High 0.5 , 0.6 , 0.7 , 0.8 , 0.4 , 0.6 , 0.7 , 0.9
Extremely high 0 .7 , 0.8 , 0.9 , 1.0 , 0.7 , 0.8 , 0.9 , 1.0
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