Evaluation of New Power System Based on Entropy Weight-TOPSIS Method

Abstract

Under the background of achieving the carbon peaking and carbon neutrality goals on time in China, this study constructs a new power system evaluation index system with new energy as the main body from four aspects of “source, network, load, and storage.” This study attempts to use appropriate subjective and objective weighting methods to reasonably distribute index weights and construct an effective, reasonable, scientific, systematic, and comprehensively covered key new power system characteristic index model. From 2017 to 2020, the relevant data for the four dimensions of “source, network, load, and storage” of the power system of three provinces are selected for empirical analysis. The results show that, in the past five years, the performance of the power supply side and network side of the power system in the three provinces was the best and the performance of the load side and energy storage side was slightly insufficient. Specifically, the system outage duration, the number of intelligent sensing terminals, etc., still need to be further optimized and improved.

Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids

“…This may aid in achieving an objective, scientific, and comprehensive expression of the evaluation indicators' information. We set e i as the entropy of evaluation indicator i, with the calculation formula for e i shown in Formula ( 16) [19]. If w i is the weighted value of the entropy in evaluation indicator i, with w i ∈ [0, 1], ∑ m i=1 w i = 1, the calculation formula for w i is shown in Formula (18), where the characteristics of the values of w i can further reveal the pattern of changes in the indicators' values.…”

“…To reasonably display both the subjective preferences of decision-makers and the objective attributes of the decision criteria, this study used Formula (19) to aggregate the subjective and objective weights.…”

“…Three experts in the fields of the development of new low-carbon power systems, energy storage system research, and carbon reduction studies were invited to subjectively weight the evaluation indicator system constructed in this study and to calculate the objective weights based on the indicators' values, as shown in Figure 4. Subsequently, the results of the subjective and objective weights were aggregated using Formula (19), and then we formulated the calculation of the degree of dominance if energy storage in different application scenarios. This section exhibits the results of the dominance calculation with 20%, 50%, and 70% permeability compared with other levels of permeability under different criteria, as shown in Figures 5-7.…”

Evaluation of the Coupling Synergy Degree of Inland Ports and Industries along the Yangtze River

“…Zhang Xinfang et al studied the evolution characteristics of ports' economic development from 2000 to 2018 using a dynamic entropy weight TOPSIS method and a geographic information system (GIS) method, so as to reveal the economic development process of coastal ports in China [3]. Liu Tianshou et al conducted a case analysis of the 2015 data of nine free-trade-zone (FTZ) ports in China, including Dalian, based on the construction of a port security management maturity evaluation model using the interval number entropy weight TOPSIS method [4]. Zhang Jing et al assessed the military transport capacity of 27 ports of different sizes in different regions of China based on the entropy weight TOPSIS method [5].…”

A short-term wind power prediction method based on wind speed correction and spatial data enhancement

“…With the increase of renewable energy penetration, clean and low-carbon transformation of energy has become an inevitable trend of global energy development [1]. However, new energy power generation is limited by its own resource characteristics [2] and is characterised by intermittency and volatility, which has a huge impact on the stability of the power system [3][4]. High-precision short-term wind power prediction can effectively reduce the uncertainty brought by wind power grid integration and promote the consumption of wind power [5][6][7][8].…”