Multi-objective unit commitment under hybrid uncertainties: A data-driven approach

摘要

In recent years, the growing penetration of renewable energy has increased the level of uncertainty in power systems, which brings challenges to modern unit commitment. This paper develops a data-driven unit commitment model with multi-objectives under wind power and load uncertainties. In particular, the distribution of the above uncertainties are estimated by a non-parameter kernel density method whose bandwidth is optimized to get more reliable and cost-effective UC solutions. To solve the complicated model, a reinforcement learning-based multi-objective particle swarm optimization algorithm is proposed. Finally, several experiments were carried out to demonstrate the effectiveness of this research.