Multi-objective electricity generation expansion planning towards renewable energy policy objectives under uncertainties
Qiao Peng, Weilong Liu, Yufeng Shi, Yuanyuan Dai, Kunjie Yu, Byron Graham
Abstract
Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches . The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts , and incorporate changing policy objectives into the constraints to meet policy makers’ expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system . The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers . Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made.