# A Fuzzy-ANP Approach for Comprehensive Benefit Evaluation of Grid-Side Commercial Storage Project

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## Abstract

**:**

## 1. Introduction

- (1)
- A comprehensive benefit evaluation index system has been established, which fully takes into account the actual operation status of grid-side energy storage projects and has more practical and theoretical value than financial evaluation. Refer to existing researches and literatures [17,18,19,20,21], take energy efficiency, economic benefit, social benefit, and environmental benefit as the four dimensions of the comprehensive benefit evaluation index system.
- (2)
- Summarized the current grid-side energy storage business modes in China. Consider the differences among modes, different indicators in the evaluation index system for specific business mode are selected to evaluate the comprehensive benefits, which can avoid the ambiguity of the evaluation process and ensure the accuracy of evaluation results.
- (3)
- Considering that the energy storage industry is in a rapid and unstable stage, the Analytic Network Process (ANP) and comprehensive fuzzy evaluation methods are combined to apply the comprehensive benefits evaluation of grid-side energy storage projects.
- (4)
- Through the empirical analysis of 100-megawatt storage project, the key influencing factors of comprehensive benefits are extracted. It would help promote the innovation and breakthrough of energy storage policy mechanism and ensure the orderly and sustainable development of energy storage.

## 2. Materials and Methods

#### 2.1. Comprehensive Benefit Evaluation Index System of Grid-Side Commercial Storage Project

#### 2.1.1. Comprehensive Benefit Evaluation Index System

#### Energy Efficiency

#### Economic Benefits

#### Social Benefits

#### Environmental Benefits

#### 2.1.2. Benefit Evaluation System of Large-Scale Energy Storage Projects under Different Business Modes

#### 2.2. Fuzzy-ANP Evaluation Method of Grid-Side Commercial Storage Project

_{s}. Since there are m B

_{s}criteria in the control layer, there are m hypermatrices similar to the above criteria. The sub-block W

_{ij}of each hypermatrix is column normalized, but the entire hypermatrix W is not column normalized. Therefore, the hypermatrix needs to be weighted, using B

_{s}as the criterion, and then C

_{j}as the sub-criterion. Under B

_{s}, the relative importance of each element group and C

_{j}is compared one by one. Finally, N comparison matrices are formed, and the weighting matrix A is obtained as shown in Formula (2) [48].

_{ij}Wij. The number of such weighted hypermatrices is m.

- (1)
- Unqualified function$${f}_{1}(x)=\{\begin{array}{l}1,x\in [a,\frac{a+c}{2}]\\ \frac{2d-2x}{2d-a-c},x\in (\frac{a+c}{2},d]\\ \\ 0,x\in (d,b]\end{array}$$
- (2)
- Qualified function$${f}_{2}(x)=\{\begin{array}{l}\frac{2x-2a}{e-a},x\in [a,\frac{a+e}{2}]\\ \frac{2e-2x}{e-a},x\in (\frac{a+e}{2},e]\\ \\ 0,x\in (e,b]\end{array}$$
- (3)
- Good function$${f}_{3}(x)=\{\begin{array}{l}0,x\in [a,c]\\ \\ \frac{2x-2c}{b-c},x\in (c,\frac{c+b}{2}]\\ \frac{2b-2x}{b-c},x\in [\frac{c+b}{2},b)\end{array}$$
- (4)
- Excellent function$${f}_{4}(x)=\{\begin{array}{l}0,x\in [a,d]\\ \frac{x-d}{b+e-2d},x\in (d,\frac{e+b}{2}]\\ 1,x\in (\frac{e+b}{2},b]\end{array}$$

_{i}= { r

_{i1}, r

_{i2}, …, r

_{im}} can be constructed. r

_{ij}represents the membership degree of the evaluation factor e

_{i}to the graded fuzzy subset V

_{j}. Combine the fuzzy subsets corresponding to each single benefit indicator to form an evaluation matrix:

_{j}is the membership degree of a large-scale energy storage project belonging to the j-th comment V

_{j}when comprehensively considering the impact of all benefit indicators. B is a fuzzy set on the comment set V, and ○ represents the M(·,⊕) operator.

_{j}in fuzzy set B, take the level corresponding to the largest b

_{j}value, and determine the (excellent, good, qualified, unqualified) level corresponding to the large-scale energy storage project.

## 3. Results

#### 3.1. Evaluation Indicators Values of Zhenjiang Storage Project

#### 3.1.1. Energy Efficiency Indicators Values

#### 3.1.2. Economic Indicators Values

#### 3.1.3. Social and Environmental Indicators Values

_{1}and C

_{2}are −0.028 × 10

^{−5}and 2.79 × 10

^{−7}.

_{3}) is 50 times that of the thermal power unit of the same capacity as shown in Figure 6.

#### 3.2. Comprehensive Benefit Evaluation of Zhenjiang Storage Project

#### 3.2.1. Index System Weight Determination

#### 3.2.2. Fuzzy Comprehensive Benefit Evaluation

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**The hierarchical structure of the comprehensive benefit evaluation network of energy storage.

**Figure 6.**Contribution comparison of frequency regulation between energy storage and conventional unit.

First Level Indicator | Second Level Indicator | Third Level Indicator | Indicator Preference | Explanation |
---|---|---|---|---|

Energy efficiency (A) | Technology | Cell voltage (A_{1}) | + | Standard voltage of each battery |

Energy Density (A_{2}) | + | Effective storage capacity of per unit mass of material | ||

Power density (A_{3}) | + | Effective storage power of per unit mass of material | ||

Self-discharge rate (A_{4}) | − | The retain ability when the battery with open circuit state | ||

Application | Cycle life (A_{5}) | + | The maximum cycle number that the system could withstand | |

Charge and discharge efficiency (A_{6}) | + | The ratio of the released energy to the initial energy | ||

Stability (A_{7}) | + | Ability to maintain stable operation under external influence | ||

Responsiveness (A_{8}) | + | Required time for system response | ||

Economic benefits (B) | Cost | Construction cost (B_{1}) | − | Construction cost of project |

Capacity cost (B_{2}) | − | Cost of configuring battery system | ||

Power cost (B_{3}) | − | Cost of conversion equipment and other facilities | ||

Operation and maintenance cost (B_{4}) | − | Operation & maintenance costs and rental fees of storage systems | ||

Profit | Peak-to-valley price spread (B_{5}) | + | Profit from peak shaving and valley filling with storage system | |

Saving investment (B_{6}) | + | Saving investment of grid equipment due to storage system | ||

Government subsidy (B_{7}) | + | Policy subsidy rewards of energy storage systems | ||

Network loss reduction (B_{8}) | + | Annual revenue from reducing line loss due to storage system | ||

Recycle revenue (B_{9}) | + | Recyclable value at the end of the energy storage system life | ||

Social benefits (C) | Reliability | Change value of power shortage rate (C_{1}) | + | =LOLP _{with storage} − LOLP _{without storage}LOLP: loss of load probability |

Change value of power available rate (C_{2}) | + | =ASAI _{with storage} − ASAI _{without storage}ASAI: average service availability index | ||

Frequency regulation benefit | Frequency regulation multiple (C_{3}) | + | = σf_{with storage}/σf_{without storage}σf: frequency standard deviation | |

Frequency regulation contribution rate (C _{4}) | + | =(CPS_{with storage} − CPS_{without storage})/CPS_{without storage}CPS: control performance standard | ||

Environmental benefits (D) | Clean consumption | Change rate of clean consumption (D _{1}) | + | =(NC_{with storage} − NC_{without storage})/NC_{without storage}NC: regional new energy consumption amount |

Low carbon reduction | Emission reduction revenue (D _{2}) | + | =Emission cost of thermal power unit × (Storage charge quantity + NC_{with storage} − NC_{without storage}) |

**Table 2.**Comprehensive benefit evaluation index system of energy storage projects under different business modes.

Indicators | A | B_{1} | B_{2} | B_{3} | B_{4} | B_{5} | B_{6} | B_{7} | B_{8} | B_{9} | C_{1} | C_{2} | C_{3} | C_{4} | D_{1} | D_{2} |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Mode A | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||

Mode B | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||

Mode C | √ | √ | √ | √ | √ | √ | ||||||||||

Mode D | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |

Indicator | A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 |
---|---|---|---|---|---|---|---|---|

Value | 3.6 | 160 | 1200 | 1% | 10,000 | 95% | 7 | 9 |

Scenario | LOLP (%) | EENS (MWh × a^{−1}) | BPECI (h × a^{−1}) | ASAI (%) |
---|---|---|---|---|

1 | 3.2035 × 10^{−5} | 34.5100 | 0.28063 | 99.9967965 |

2 | 3.2315 × 10^{−5} | 34.8110 | 0.28308 | 99.9967686 |

Year | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|

CPS | 143.70 | 145.33 | 143.58 | 154.72 |

A_{1} | A_{2} | A_{3} | A_{4} | A_{5} | A_{6} | A_{7} | A_{8} | B_{4} | B_{5} | B_{6} | B_{7} | B_{8} | C_{1} | C_{2} | C_{3} | C_{4} | D_{1} | D_{2} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

A_{1} | |||||||||||||||||||

A_{2} | |||||||||||||||||||

A_{3} | |||||||||||||||||||

A_{4} | |||||||||||||||||||

A_{5} | √ | ||||||||||||||||||

A_{6} | √ | ||||||||||||||||||

A_{7} | √ | √ | √ | √ | |||||||||||||||

A_{8} | |||||||||||||||||||

B_{4} | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||||

B_{5} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||

B_{6} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||

B_{7} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||

B_{8} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||||

C_{1} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||

C_{2} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||

C_{3} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | |||||||||

C_{4} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||

D_{1} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | ||||||||

D_{2} | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |

A_{1} | A_{2} | A_{3} | A_{4} | A_{5} | A_{6} | A_{7} | A_{8} | |
---|---|---|---|---|---|---|---|---|

A_{1} | 0 | 0 | 0 | 0.18434 | 0 | 0 | 0 | 0 |

A_{2} | 0.10917 | 0 | 0.05911 | 0.0742 | 0.13172 | 0.04176 | 0 | 0 |

A_{3} | 0.16706 | 0.0477 | 0 | 0.03882 | 0.10387 | 0.05295 | 0 | 0 |

A_{4} | 0.03874 | 0.02207 | 0.09557 | 0 | 0.06994 | 0.07936 | 0 | 0 |

A_{5} | 0.03874 | 0.0163 | 0.03692 | 0.03882 | 0 | 0.11245 | 0.11682 | 0 |

A_{6} | 0.03874 | 0.11602 | 0.026 | 0.05756 | 0.04935 | 0 | 0.35049 | 0 |

A_{7} | 0.03874 | 0.13261 | 0.22391 | 0.03258 | 0.05621 | 0.09039 | 0 | 0 |

A_{8} | 0.03612 | 0.13261 | 0.02579 | 0.04098 | 0.05621 | 0.09039 | 0 | 0 |

B_{4} | 0.05168 | 0.03591 | 0.06221 | 0.05941 | 0.01352 | 0 | 0.04323 | 0.02853 |

B_{5} | 0.03917 | 0.05664 | 0.03617 | 0.03839 | 0.0213 | 0.08035 | 0.01644 | 0.07141 |

B_{6} | 0.02969 | 0.02884 | 0.02504 | 0.02761 | 0.03397 | 0.01342 | 0.0303 | 0.05393 |

B_{7} | 0.0225 | 0.02196 | 0.01988 | 0.02 | 0.06553 | 0.0275 | 0.06029 | 0.03778 |

B_{8} | 0.01705 | 0.01672 | 0.01678 | 0.01467 | 0.02577 | 0.03882 | 0.00983 | 0.10886 |

C_{1} | 0.03827 | 0.05509 | 0.12704 | 0.07094 | 0.09103 | 0.05813 | 0.08401 | 0.07205 |

C_{2} | 0.05412 | 0.16603 | 0.08014 | 0.15484 | 0.04663 | 0.16571 | 0.13497 | 0.03877 |

C_{3} | 0.07654 | 0.03736 | 0.04803 | 0.03397 | 0.1063 | 0.03558 | 0.03865 | 0.27637 |

C_{4} | 0.10824 | 0.0187 | 0.02196 | 0.01743 | 0.03322 | 0.01776 | 0.01955 | 0.13313 |

D_{1} | 0.06362 | 0.06362 | 0.06362 | 0.03181 | 0.07158 | 0.02386 | 0.01909 | 0.14332 |

D_{2} | 0.03181 | 0.03181 | 0.03181 | 0.06362 | 0.02386 | 0.07158 | 0.07635 | 0.03583 |

Indicator | D_{2} | C_{3} | C_{4} | B_{4} | B_{7} | B_{8} | A_{6} | A_{7} | A_{8} |
---|---|---|---|---|---|---|---|---|---|

Limiting | 0.13744 | 0.007347 | 0.006773 | 0.007902 | 0.149637 | 0.081315 | 0.001145 | 0.00432 | 0.00011 |

Indicator | Excellent | Good | Qualified | Unqualified | Value |
---|---|---|---|---|---|

A_{1} | {3.25; 4} | {2.5; 3.25} | {1.75; 2.5} | {1; 1.75} | 3.6 |

A_{2} | {200; 250} | {150; 200} | {100; 150} | {50; 100} | 160 |

A_{3} | {1040; 1340} | {740; 1040} | {440; 740} | {140; 440} | 1200 |

A_{4} | {0; 5%} | {5%; 10%} | {10%; 15%} | {15%; 20%} | 1% |

A_{5} | {12,000; 16,000} | {8000; 12,000} | {4000; 8000} | {0; 4000} | 13,000 |

A_{6} | {95%; 100%} | {90%; 95%} | {85%; 90%} | {80%; 85%} | 95% |

A_{7} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 7 |

A_{8} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 9 |

B_{4} | {0; 326} | {326; 651} | {651; 977} | {977; 1304} | 460.7 |

B_{5} | {1617.5; 2156.6} | {1078.3; 1617.5} | {539.2; 1078.3} | {0; 539.2} | 1831.3 |

B_{6} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 6 |

B_{7} | {3954; 5272} | {2636; 3954} | {1318; 2636} | {0; 1318} | 3753.2 |

B_{8} | {6.93–9.24} | {4.62–6.93} | {2.31; 4.62} | {0; 2.31} | 6.97 |

C_{1} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 7 |

C_{2} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 8 |

C_{3} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 6 |

C_{4} | {7.5; 10} | {5; 7.5} | {2.5; 5} | {0; 2.5} | 9 |

D_{1} | {0.45; 0.6} | {0.3; 0.45} | {0.15; 0.3} | {0; 0.15} | 0.453 |

D_{2} | {246.6; 328.8} | {164.4; 246.6} | {82.2; 164.4} | {0; 82.2} | 294.3 |

Indicator | Value (unit: million yuan) | |
---|---|---|

Cost | B_{4} | 4.61 |

Profit | B_{5} | 18.31 |

B_{6} | 6.27 | |

B_{7} | 37.53 | |

B_{8} | 0.07 | |

D_{2} | 2.94 | |

Total revenue | 60.52 |

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## Share and Cite

**MDPI and ACS Style**

Yang, H.; Fan, W.; Qin, G.; Zhao, Z.
A Fuzzy-ANP Approach for Comprehensive Benefit Evaluation of Grid-Side Commercial Storage Project. *Energies* **2021**, *14*, 1129.
https://doi.org/10.3390/en14041129

**AMA Style**

Yang H, Fan W, Qin G, Zhao Z.
A Fuzzy-ANP Approach for Comprehensive Benefit Evaluation of Grid-Side Commercial Storage Project. *Energies*. 2021; 14(4):1129.
https://doi.org/10.3390/en14041129

**Chicago/Turabian Style**

Yang, Huijia, Weiguang Fan, Guangyu Qin, and Zhenyu Zhao.
2021. "A Fuzzy-ANP Approach for Comprehensive Benefit Evaluation of Grid-Side Commercial Storage Project" *Energies* 14, no. 4: 1129.
https://doi.org/10.3390/en14041129