An innovative data-driven energy planning framework for developing regions based on multi-objective optimization and multi-index comprehensive evaluation

Abstract

With the continuous development of renewable energy in developing countries, energy supply composition, and demand have become very complex. Therefore, energy planning in developing countries is essential. This paper proposes a novel data-driven long-term energy planning method for developing countries to formulate generation expansion planning. It is based on a dynamic energy model and a multi-objective evolutionary algorithm to predict the development path and uses the CRITIC method to construct a comprehensive evaluation index that incorporates system independence and excessive power generation issues as the basis for post-processing. The proposed method is applied to the 2020–2025 generation expansion planning of Hunan Province, China. The results show that Hunan cannot immediately halt the development of coal power and that when the installed capacities of coal power, photovoltaic, and wind power increase by 6340, 4360, and 8900 MW, respectively, the excess electricity production and the proportion of imported electricity will be reduced by 41.7 and 6.14% at most. In addition, Hunan needs to increase its energy storage capacity by about 21 GWh in the next 5 years. This study can help developing countries compare different energy development paths and provides a reference for formulating appropriate energy expansion schemes. At the same time, it also gives an assessment method that considers the excessive power generation of renewable energy and the energy self-sufficiency rate, which can provide a reference for local decision-makers to develop renewable energy and avoid waste and dependence.