An optimal integrated planning method for supporting growing penetration of electric vehicles in distribution systems. (1st May 2017) - Zeng, Bo; Feng, Jiahuan; Zhang, Jianhua; Liu, Zongqi
Abstract: This paper proposes a multi-year expansion planning method for enabling distribution systems to support growing penetrations of plug-in electric vehicles. As distinct from the existing studies, the temporal characteristics of charging loads and their reliability impacts are especially focused in our work. To achieve this, a novel dual-stage optimization framework is developed. The proposed method considers the capacity reinforcement of distribution systems in conjunction with their operation decisions and coordinates them under the same frame so as to minimize the total system costs for accommodating electric vehicles. The uncertainties associated with renewable energy generation, charging behaviors, and conventional load demand are represented by multiple probabilistic scenarios. To fully reveal the impacts of electric vehicle integration, both uncontrolled and coordinated charging schemes are considered in our analysis. Furthermore, as charging loads bring about extra demand to the grid, the reliability criteria is also taken into account in the proposed model. Using a heuristic algorithm combined with reliability analysis, the optimal solution for the concerned problem can be determined, which involves the best timing, locations, and capacities for installation of distributed generation units and network components. The effectiveness of the proposed framework is examined based on a 38-bus test system and the obtained results verify the performance of the approach. Highlights: The temporal characteristic of electric vehicle loads is considered. We proposed a novel dual-stage optimization model to formulate the problem. We take into account the reliability requirements of system in the planning process. A heuristic algorithm embedded with reliability analysis is used to solve the model. We used a scenario-based method to deal with the uncertainties that involved.