Sustainability Intelligent Evaluation of Regional Microgrid Interconnection System Based on Combination Entropy Weight Rank Order-TOPSIS and NILA-KELM

Abstract

Sustainability evaluation of regional microgrid interconnection system is conducive to a profound and comprehensive understanding of the impact of interconnection system projects. In order to realize the comprehensive and scientific intelligent evaluation of the system, this paper proposes an evaluation model based on combination entropy weight rank order-technique for order preference by similarity to an ideal solution (TOPSIS) and Niche Immune Lion Algorithm-Extreme Learning Machine with Kernel (NILA-KELM). Firstly, the sustainability evaluation indicator system of the regional microgrid interconnection system is constructed from four aspects of economic, environmental, social, and technical characteristics, and the evaluation indicators are explained. Then, the classical evaluation model based on TOPSIS is constructed, and the entropy weight method and rank order method (RO) are coupled to obtain the indicator weight. The niche immune algorithm is used to improve the lion algorithm, and the improved lion algorithm is used to optimize the parameters of KELM, and the intelligent evaluation model based on NILA-KELM is obtained to realize fast real-time calculation. Finally, the scientificity and accuracy of the model proposed in this paper are verified. The model proposed in this paper has the lowest RMSE, MAE and RE values, indicating that its intelligent evaluation results are the most accurate. This study is conducive to the horizontal comparison of the overall performance of regional microgrid interconnection system projects, helps investors to choose the most promising project scheme, and helps the government to find feasible project.

Evaluation of the effect of ultrasonic pretreatment on vacuum far‐infrared drying characteristics and quality of Angelica sinensis based on entropy weight‐coefficient of variation method

“…The smaller the measured factor entropy, the higher the discrete level. Entropy can be calculated by the following formula (Sushma et al., 2022; Wang, Fan, et al., 2022; Wang, Zhao, et al., 2021; Zhang, Wang, et al., 2022): $$$$\begin{equation}{e}_j = \frac{{ - 1}}{{\ln n}}\sum_{i = 1}^n {{P}_{ij}} \ln \left( {{P}_{ij}} \right),\end{equation}$$$$where e j is the entropy value of the j th indicator.…”

“…The smaller the measured factor entropy, the higher the discrete level. Entropy can be calculated by the following formula (Sushma et al, 2022;Wang, Fan, et al, 2022;Zhang, Wang, et al, 2022):…”