# Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area

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## Abstract

**:**

## 1. Introduction

## 2. Construction of an Operational Efficiency Evaluation System for Microgrids in Port Areas

## 3. Quantification of Operational Efficiency Evaluation Indicators for Microgrids in Port Areas

#### 3.1. Microgrid Economic Performance Indicators

- Percentage of Distributed Generation (DG) Unit Generation Costs

_{a,i}represents the unit capacity purchase cost of the i-th unit in the system, measured in CNY per kW; P

_{i}represents the installed capacity of the i-th unit in the system; C

_{n}represents the total generation cost of the system.

- 2.
- Annual Operation and Maintenance Cost Ratio

_{a,i}represents the unit capacity variable cost of the i-th unit in the system, measured in CNY per kW·h; T

_{i}represents the annual average operating time of the equipment; C

_{b,i}represents the unit capacity fixed cost of the i-th unit in the system, measured in CNY per kW.

- 3.
- Electricity Cost Savings Rate

_{g}represents the electricity cost savings achieved by all users in response to dynamic pricing during a specific time period, while P

_{g}represents the electricity cost incurred by all users who do not respond to dynamic pricing during the same time period.

#### 3.2. Microgrid Energy Efficiency Indicators

- Primary Energy Utilization Efficiency

- 2.
- Energy Consumption per Unit Throughput

_{stec}represents the energy consumption for production, which can be considered as the amount of purchased electricity and natural gas converted to standard coal equivalent. ${{\displaystyle \sum}}_{j}{T}_{b}$ represents the throughput of the port. ${E}_{pur}^{ele}$ and ${E}_{pur}^{gas}$ are the annual consumption of electricity and natural gas for the port’s energy system, respectively. ${\eta}_{ce}^{ele}$ and ${\eta}_{ce}^{gas}$ are the conversion coefficients for electricity and natural gas to standard coal equivalent, which are 0.4040 kgce/kW h and 1.2143 kgce/m

^{3}, respectively.

- 3.
- Equipment Energy Feedback Rate

_{re}represents the energy feedback amount, and E

_{use}represents the actual consumed energy.

- 4.
- Abandonment Rate

_{d,t,w}represents the curtailed power of renewable energy in the system during different time periods, while P

_{d,t,g}represents the actual power of renewable energy consumed by the system in various time periods.

#### 3.3. Microgrid Environmental Performance Indicators

- New Energy Penetration Rate

_{re}represents the installed capacity of renewable energy in the system, and E

_{total}represents the total capacity for renewable energy generation.

- 2.
- Shore Power Berth Allocation Rate

_{clean}represents the number of shore power berths in use, and N

_{tot}represents the total number of berths at the port.

- 3.
- Carbon Dioxide Emissions per Unit Throughput

_{co}

_{2}represents the CO

_{2}emissions from the port’s energy system, and C

_{33}is the ratio of emissions to port throughput. Where ε

^{CO}

_{2}represents the CO

_{2}emission coefficient after converting purchased electricity and natural gas into standard coal, with a value of 2.54 t CO

_{2}/tec. When calculating the unit throughput CO

_{2}emissions for the port’s production, the value of port throughput used is consistent with the port throughput value used for calculating the comprehensive energy consumption of the port’s production per unit throughput.

#### 3.4. Microgrid Reliability Indicators

_{E}represents the load demand, and ΔW

_{E}represents the electricity supply shortfall.

#### 3.5. Microgrid Safety Indicators

_{12}as the critical value for the safety domain, denoting it as the safety indicator.

## 4. Evaluation Method for the Operational Efficiency of Port Microgrids

#### 4.1. Architecture of the Port Microgrid Operational Efficiency Evaluation Model

#### 4.2. Port Operational Efficiency Evaluation Based on Improved CRITIC-TOPSIS

#### 4.2.1. Objective Weighting Method Based on Improved CRITIC

_{1}–C

_{12}, providing the necessary input parameters for the comprehensive evaluation model of port operational efficiency.

_{pq}for the q-th evaluation indicator of the p-th evaluation scenario.

_{q}for the efficiency indicators of the port area as follows:

#### 4.2.2. Efficiency Comprehensive Evaluation Method Based on TOPSIS

## 5. Case Study Analysis

#### 5.1. Analysis of Original Data for Port Microgrid Indicators

#### 5.2. Comprehensive Efficiency Evaluation of Port Microgrid

#### 5.3. Results Validation and Discussion

## 6. Conclusions

- (1)
- The constructed evaluation index system for the operational efficiency of port area microgrids in this paper allows for the evaluation of both the construction and operational results of microgrid projects. The evaluation method presented can provide reference insights for the comprehensive energy system design of microgrids in industrial parks, including equipment configuration and safety performance. It identifies the weaknesses within port area microgrids and provides a more rigorous framework with independent indicators. This system offers a more objective assessment of the strengths and weaknesses of different configurations and deployment schemes for port area microgrids. It serves as scientific guidance and aids in decision-making for improving the operation and future planning of port area microgrids.
- (2)
- The proposed operational efficiency evaluation model, based on the improved CRITIC-TOPSIS method, enables the objective quantitative assessment of various port area microgrid operational efficiencies. This method is free from subjective factors and introduces a new, simple, and scientifically effective engineering evaluation approach for assessing the operational efficiency of port area microgrids. It effectively fills the gap in the evaluation techniques for port area microgrid project operation efficiency and holds practical value and applicability.
- (3)
- By incorporating the improved CRITIC method with variation coefficients, this approach scientifically and reasonably determines the weights of different indicators. It considers the interrelationships between indicators while ensuring the objectivity of the weightings, thus enhancing the scientific accuracy of the optimization results.
- (4)
- The paper studied the operational efficiency indicator system of port microgrids. However, factors affecting the safety indicators of port microgrids should not only include voltage stability but also frequency stability, harmonics, flicker values, and other safety indicators. The next step should involve comprehensive efficiency evaluations that take all these factors into account. At the same time, the model and method proposed in this paper also have their limitations, such as overlooking the importance of subjective knowledge and experience. Therefore, further research is needed on how to simultaneously consider objective data and expert experience when determining indicator weights.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

CRITIC | Criteria Importance Through Intercriteria Correlation |

TOPSIS | Technique for Order of Preference by Similarity to Ideal Solution |

AHP | Analytic Hierarchy Process |

DG | Distributed Generation |

VSM | Voltage Security Margin |

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**Figure 2.**Efficiency assessment model for port area microgrid systems based on the enhanced CRITIC-TOPSIS evaluation model.

Criterion Layer | Indicator Layer | Data Requirement | Indicator Attribute |
---|---|---|---|

Economic (B1) | Percentage of DG Unit Generation Costs (C1) | Number of power generation units such as wind turbines, photovoltaic panels, etc. in the port area. Purchase cost per unit capacity; installed capacity; | − |

number of energy storage unit groups in the port area; purchase cost per unit capacity; installed capacity. | |||

Annual Operation and Maintenance Cost Ratio (C2) | The variable cost per unit capacity; fixed cost per unit capacity; and average operational time of power generation equipment such as wind turbines and photovoltaic units on a daily, monthly, and annual basis. | − | |

The variable cost per unit capacity; fixed cost per unit capacity; and average operational time of energy storage equipment units on a daily, monthly, and annual basis. | |||

Electricity Cost Savings Rate (C3) | The purchase price of electricity from the main grid and the total purchased electricity by the system. | − | |

Energy efficiency (B2) | Primary Energy Utilization Efficiency (C4) | Total annual load in the port area. | + |

Energy Consumption per Unit Throughput (C5) | Annual electricity consumption of the port’s energy system. | + | |

Port throughput. | |||

Equipment Energy Feedback Rate (C6) | Energy feedback amount and actual consumption. | + | |

Abandonment Rate (C7) | Proportion of unused electrical energy in the total electricity generation in the power system. | - | |

Environmental (B3) | New Energy Penetration Rate (C8) | Installed capacity of new energy sources and total electricity generation. | + |

Shore Power Berth Allocation Rate (C9) | Number of shore power berths replaced by clean energy and total number of port berths. | + | |

Carbon Dioxide Emissions per Unit Throughput (C10) | CO_{2} emissions. | − | |

Reliability (B4) | Port microgrid system outage rate (C11) | Power supply shortfall. | − |

Safety (B5) | Microgrid Safety Indicators (C12) | Voltage at node i at time t. | − |

Indicator Layer | Indicator Values | Rankings | ||||
---|---|---|---|---|---|---|

Scenario 1 | Scenario 2 | Scenario 3 | Scenario 1 | Scenario 2 | Scenario 3 | |

C_{1} | 0.210 | 0.160 | 0.190 | 1 | 3 | 2 |

C_{2} | 0.018 | 0.014 | 0.016 | 1 | 3 | 2 |

C_{3} | 0.00050 | 0.00056 | 0.00052 | 3 | 1 | 2 |

C_{4} | 0.456 | 0.523 | 0.676 | 3 | 2 | 1 |

C_{5} | 4.387 | 4.33 | 4.24 | 1 | 2 | 3 |

C_{6} | 0.531 | 0.679 | 0.666 | 3 | 1 | 2 |

C_{7} | 0.027 | 0.049 | 0.038 | 1 | 3 | 2 |

C_{8} | 0.490 | 0.540 | 0.500 | 3 | 1 | 2 |

C_{9} | 0.54 | 0.35 | 0.48 | 1 | 3 | 2 |

C_{10} | 0.05077 | 0.04096 | 0.03728 | 1 | 2 | 3 |

C_{11} | 0.0025 | 0.0022 | 0.0020 | 3 | 2 | 1 |

C_{12} | 0.200 | 0.210 | 0.160 | 2 | 3 | 1 |

Indicators | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} |
---|---|---|---|---|---|---|

coefficient of variation | 1.0785 | 0.8542 | 0.9165 | 0.9070 | 1.0899 | 1.5271 |

Indicators | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} |

coefficient of variation | 0.7455 | 0.8819 | 1.1655 | 0.8978 | 0.9437 | 1.3229 |

Indicators | C_{1} | C_{2} | C_{3} | C_{4} | C_{5} | C_{6} |
---|---|---|---|---|---|---|

ω | 0.0726 | 0.0615 | 0.0664 | 0.1031 | 0.1114 | 0.0786 |

Indicators | C_{7} | C_{8} | C_{9} | C_{10} | C_{11} | C_{12} |

ω | 0.0615 | 0.0736 | 0.0861 | 0.0572 | 0.0708 | 0.1572 |

Indicators | Microgrid Scenario 1 | Microgrid Scenario 2 | Microgrid Scenario 3 |
---|---|---|---|

$\mathrm{Comprehensive}\mathrm{Evaluation}\mathrm{Value}{Z}_{i}$ | 0.5297 | 0.4315 | 0.5825 |

Positive Ideal Distance D+ | 0.0712 | 0.0756 | 0.0569 |

Negative ideal distance D− | 0.0801 | 0.0574 | 0.0794 |

rank | 2 | 3 | 1 |

Criterion Layer | Indicator Layer | ω | AHP-Expert Scoring | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Expert Scoring Rules: Excellent: 9–10; Good: 8–9; Fairly Good: 6.5–8; Average: 5.5–6.5; Fair: 3.5–5.5; Poor: 1.5–3.5; Very Poor: 0–1.5 | ||||||||||||

Microgrid Scenario 1 | Microgrid Scenario 2 | Microgrid Scenario 3 | ||||||||||

Expert 1 | Expert 2 | Expert 3 | Expert 1 | Expert 2 | Expert 3 | Expert 1 | Expert 2 | Expert 3 | ||||

B1 | C_{1} | 0.15 | 8.0 | 7.9 | 8.8 | 8.6 | 8.4 | 6.5 | 8.5 | 7.7 | 7.0 | |

C_{2} | 0.10 | 9.6 | 7.5 | 7.9 | 8.2 | 7.0 | 8.5 | 8.0 | 8.5 | 7.5 | ||

C_{3} | 0.05 | 8.2 | 7.0 | 6.8 | 7.5 | 8.4 | 8.2 | 8.0 | 8.6 | 8.6 | ||

B2 | C_{4} | 0.07 | 8.3 | 7.9 | 7.4 | 7.9 | 8.6 | 8.3 | 7.4 | 8.4 | 8.4 | |

C_{5} | 0.12 | 8.5 | 7.1 | 6.8 | 8.2 | 8.4 | 9.6 | 8.0 | 7.0 | 7.0 | ||

C_{6} | 0.05 | 8.6 | 8.3 | 8.4 | 7.7 | 7.0 | 7.1 | 8.3 | 6.8 | 6.8 | ||

C_{7} | 0.13 | 8.4 | 8.6 | 6.8 | 8.9 | 6.8 | 6.8 | 8.0 | 7.9 | 6.8 | ||

B3 | C_{8} | 0.08 | 7.0 | 8.5 | 7.0 | 8.2 | 8.4 | 6.8 | 8.7 | 8.4 | 7.9 | |

C_{9} | 0.10 | 6.8 | 8.2 | 8.4 | 8.5 | 8.5 | 7.0 | 8.5 | 8.6 | 8.2 | ||

C_{10} | 0.05 | 8.4 | 8.3 | 6.8 | 7.0 | 7.0 | 6.8 | 8.0 | 8.4 | 8.3 | ||

B4 | C_{11} | 0.05 | 7.5 | 9.6 | 6.8 | 8.0 | 7.9 | 6.8 | 8.0 | 7.0 | 9.6 | |

B5 | C_{12} | 0.05 | 7.0 | 8.0 | 7.1 | 7.0 | 8.4 | 8.3 | 8.3 | 6.8 | 9.5 | |

Total/Mean | 1.00 | 8.228 | 8.018 | 7.523 | 8.170 | 7.911 | 7.546 | 8.169 | 7.872 | 7.704 |

Methodology | Microgrid Scenario 1 | Microgrid Scenario 2 | Microgrid Scenario 3 | |
---|---|---|---|---|

Subjective analysis | AHP-Expert Scoring | 0.7923 | 0.7857 | 0.7915 |

Rank | (1) | (3) | (2) | |

Entropy-Weighted TOPSIS | 0.3426 | 0.6352 | 0.4437 | |

Rank | (3) | (1) | (2) | |

Improved CRITIC-TOPSIS | 0.5528 | 0.4423 | 0.5850 | |

Rank | (2) | (3) | (1) | |

Objective analysis | Entropy-Weighted TOPSIS | 0.4897 | 0.4235 | 0.5483 |

rank | (2) | (3) | (1) | |

Entropy-Weighted TOPSIS | 0.5297 | 0.4315 | 0.5825 | |

rank | (2) | (3) | (1) |

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## Share and Cite

**MDPI and ACS Style**

Xu, X.; Wang, K.; Lu, Y.; Tian, Y.; Hu, L.; Zhong, M.
Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area. *Sustainability* **2023**, *15*, 15019.
https://doi.org/10.3390/su152015019

**AMA Style**

Xu X, Wang K, Lu Y, Tian Y, Hu L, Zhong M.
Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area. *Sustainability*. 2023; 15(20):15019.
https://doi.org/10.3390/su152015019

**Chicago/Turabian Style**

Xu, Xianfeng, Ke Wang, Yong Lu, Yunbo Tian, Liqun Hu, and Ming Zhong.
2023. "Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area" *Sustainability* 15, no. 20: 15019.
https://doi.org/10.3390/su152015019