Unit Commitment Incorporating Active Distribution Grids with Learning-Based Power Flow Constraints

Traditional unit commitment (UC) does not consider distribution grid constraints, while common transmission-distribution coordination methods require substantial computational costs. As the distributed energy resources (DERs) in distribution grids continue to grow, traditional distribution grids are gradually transforming into active distribution grids (ADGs), when solving UC, it is necessary to consider the constraints of the distribution grid to ensure the operational safety of the transmission and distribution grids. This paper proposes a UC approach with ADGs by using learning-based power flow constraints substitution which can accelerate the computation and promote the integration of DERs while ensuring the safe operation of the distribution grid. We trained two multi-layer perceptrons (MLPs) and converted them into binary variable linear constraints equivalent to substitute for power flow constraints. The experiment confirms that the suggested strategy performs better in power scheduling when based on the IEEE118-bus system as the transmission grid, and ten IEEE33-bus systems as distribution girds.