Distribution System Planning in Smart Grids to Accommodate Distributed Energy Resources and Electric Vehicles

Abstract

Major changes in planning paradigms have taken place in power systems in recent years because of deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation of the grid to intelligent systems, referred to as the smart grid. These changes will continue to drive the distribution systems planning function to evolve in the coming years. It is therefore important to develop effective planning strategies to identify the qualities, capabilities, and attributes that are necessary for the future distribution grid. Demand response (DR), distributed generation (DG), energy storage systems (ESS), and plug-in electric vehicles (PEV) are expected to be a part of the solution of these distribution system planning challenges. However, very little of the present research on distribution system planning have considered these options simultaneously. Moreover, traditional planning options such as substation expansion, new feeder connections and capacitor placements should also be simultaneously considered. Such a coordinated planning can help evaluate the alternatives to provide maximum benefits to the network owner and customers. With the increase in gas prices driven by a foreseeable fossil fuel depletion in the future, development in the automotive sector, and environmental concerns, penetration of PEVs has been increasing in recent times. The charging load of PEVs will definitely impact the distribution grid. To mitigate these effects, the local distribution companies (LDCs) need to adopt the right actions and policies, and develop associated infrastructure. In the current context of smart grids, the LDCs need to control PEV charging demand while also considering customer preferences, which can lead to benefits such as deferment of the decisions on reinforcement and other investments, and maximize the use of existing infrastructure. In addition, LDCs need to establish rate structures that incentivize the use of smart charging and increase the adoption and use of PEVs, which can benefit both the LDCs and the customer. This research focuses on developing models to investigate and address the problem of distribution system planning in the presence of PEV charging loads. First, a comprehensive long-term distribution planning framework from the perspective of LDCs is proposed considering 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 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. Second, a generic and novel framework is proposed to assess the Distribution System Loading Margin (DSLM) to accommodate uncontrolled and smart PEV charging loads without the need for any additional investments or upgrades in the distribution system. The model determines what percentage of the fleet can be served by uncontrolled charging and smart charging, respectively. Monte Carlo simulation has been carried out to simulate the uncertainty of demand, drivers' behaviour, market share of PEV class, and charging level. The maximum allowable penetration of uncontrolled and smart charging loads are determined based on the current available market data pertaining to PEV type and charging level, considering different charging scenarios. Finally, a PEV smart charging approach is proposed where the charging loads are incentivized by the LDC for every unit of energy controlled. A novel framework is proposed to determine the optimal participation of PEVs in the smart charging program and optimal incentives paid by the LDC to PEV customers, such that both parties are economically benefited. The proposed framework models the relationship between customers' participation and incentives offered by the LDC. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well. Monte Carlo simulation is carried out to simulate the uncertainty of demand, electricity market price, drivers' behaviour, PEV market share, and charging level.