Robust Joint Expansion Planning of Electrical Distribution Systems and EV Charging Stations

• Main Author: Banol Arias, Nataly [UNESP]
• Publication Date: 2018
• Other Authors: Tabares, Alejandra [UNESP], Franco, John F. [UNESP], Lavorato, Marina, Romero, Ruben [UNESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1109/TSTE.2017.2764080; http://hdl.handle.net/11449/179710
• topic: Chance constraint; electric vehicle charging stations; electrical distribution systems; mixed-integer linear programming; multistage expansion planning

Electrical distribution systems (EDSs) should be prepared to cope with demand growth in order to provide a quality service. The future increase in electric vehicles (EVs) represents a challenge for the planning of the EDS due to the corresponding increase in the load. Therefore, methods to support the planning of the EDS, considering the uncertainties of conventional loads and EV demand, should be developed. This paper proposes a mixed-integer linear programming (MILP) model to solve the robust multistage joint expansion planning of EDSs and the allocation of EV charging stations (EVCSs). Chance constraints are used in the proposed robust formulation to deal with load uncertainties, guaranteeing the fulfillment of the substation capacity within a specified confidence level. The expansion planning method considers the construction/reinforcement of substations, EVCSs, and circuits, as well as the allocation of distributed generation units and capacitor banks along the different stages in which the planning horizon is divided. The proposed MILP model guarantees optimality by applying classical optimization techniques. The effectiveness and robustness of the proposed method is verified via two distribution systems with 18 and 54 nodes. Additionally, Monte Carlo simulations are carried out, aiming to verify the compliance of the proposed chance constraint.