Robust expansion planning of a distribution system with electric vehicles, storage and renewable units

Abstract

The decarbonization of energy systems passes through the transition towards low- and zero-emission vehicles and the investments in efficient technologies. To this end, an adaptive robust optimization approach is proposed for the expansion planning problem of a distribution system where expansion decisions involve the construction of renewable generating units, storage units, and charging stations for electric vehicles. The problem is formulated under the perspective of a central planner that aims at determining the expansion plan that minimizes both investment and operation costs. Both short-term variability and long-term uncertainty are considered in the proposed approach and are modeled in different ways. Short-term variability of the demand, the production of stochastic units, and the price of electricity withdrawn from or injected into the transmission system is modeled using a number of representative days corresponding to different operating conditions. Long-term uncertainty in the future peak demands, the future value of electricity exchanged with the transmission grid, and the number of electric vehicles is instead modeled through confidence bounds. A case study based on a 69-node distribution network shows the effectiveness of the proposed technique and the relationship between the optimal expansions decisions, the revenues from selling electricity to the electric vehicles, the degree of independence from the transmission system, and the role played by the investment budget availability. Moreover, an ex-post decarbonization analysis is conducted to evaluate the environmental impact of the adoption of electric vehicles. Finally, the proposed approach outperforms the results of a stochastic model in terms of computational performance.