Abstract
With deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation to smart grid, the distribution planning paradigm has gone through significant changes in recent years. Concurrently, with increase in gas prices, driven by a foreseeable fossil fuel depletion in the future, developments in the automotive sector, and environmental concerns, penetration of plug-in electric vehicles (PEVs) has been increasing. These changes will continue to drive the distribution planning problem to evolve in the coming years. This paper presents a comprehensive long-term distribution planning framework from the perspective of local distribution companies (LDCs) considering distributed generation (DG), substations, capacitors, and feeders. Apart from considering the usual demand profile, the proposed framework considers uncontrolled and controlled (smart) PEV charging demand, as well as demand response (DR) options. Based on a back-propagation algorithm combined with cost-benefit analysis, a novel approach is proposed to determine the optimal upgrade plan, allocation, and sizing of the selected components in distribution systems, to minimize the total capital and operating cost. A new iterative method is proposed which involves post-processing the plan decisions to guarantee acceptable adequacy levels for each year of the planning horizon. The performance of the proposed framework is examined considering several case studies on the 33-bus and 69-bus test systems. It is noted from the studies that the presence of uncontrolled PEV charging loads results in much higher plan costs as compared to the case without PEVs, impacts the distribution plan significantly, and hence these loads indeed need be considered in the planning process. On the other hand, smart charging of PEV5 have a much reduced impact on the plan cost, and helps alleviate some investments which were needed with uncontrolled charging PEVs. Therefore, the LDCs and policy makers need to encourage customers to adapt these options in the long-run. (C) 2016 Elsevier B.V. All rights reserved.