A risk-based multi-year expansion planning of active distribution networks under uncertainty

This paper presents a novel dynamic programming-based framework for the multi-year active distribution networks expansion planning under uncertainty, accounting for the true nature of a decision-maker’s attitude towards risk. It proposes a multi-objective mixed-integer linear programming (MILP) formulation based on interval Relative Distance Measure (RDM) arithmetic to generate a set of non-inferior static network expansion plans. These plans form the basis for determining the multi-year expansion plan within the presented dynamic programming framework, which aims to minimize financial risk in accordance with the decision-maker’s risk preference. Demand response and active/reactive power (P/Q) control of distributed generators, as non-network alternatives, are co-optimized with network enhancements to support flexible planning and reduce the risk of inefficient expansion decisions. The case studies demonstrate the economic and strategic advantages of the proposed approach over traditional multi-stage scenario-based approaches. The results also highlight the impact of the decision-maker’s risk preferences on the network expansion decisions, as well as the role and value of the non-network alternatives in managing uncertainties.