Seyedeh Elham Akhavan Rezai
New Energy Management System to incorporate Smart Parking Lots into Demand Response
Ehab El-Saadany and Fakhreddine Karray
Electric transportation has attracted a great deal of interest within the transport sector because of its notable potential to become a low-carbon substitute for conventional combustion engine vehicles. However, widespread use of this form of transportation, such as plug-in electric vehicles (PEVs), will constitute a significant draw on power grids, especially when associated with uncontrolled charging schemes. In fact, electric utilities are unable to control individual PEVs in order to manage their charging and avoid negative consequences for distribution lines. However, a control strategy could be directed at a single vehicle or group of vehicles. One effective approach could be to build on a supervisory control system, similar to a SCADA system, that manages the aggregation of PEVs, a role that could be filled by aggregators that exchange data and information among individual PEVs and energy service providers. An additional consideration is that advances in intelligent technologies and expert systems have introduced a range of flexible control strategies, which make smart grid implementation more attractive and viable for the power industry. These developments have been accompanied by the initiation of a new paradigm for controllable PEV loads based on a number of advantages associated with a smart grid context. One of the established goals related to smart grids is to build on their ability to take advantage of all available energy resources through efficient, decentralized management. To this end, utilities worldwide are using IT, communication, and sensors to provide enhanced incorporation of operational tools and thus create a more robust and interactive environment able to handle generation-demand dynamics and uncertainties. One of these tools is demand response (DR), a feature that adjusts customers’ electricity usage through the offer of incentive payments.
Motivated by this background, the goal of the work presented in this thesis was to introduce new operational algorithms that facilitate the charging of PEVs and the employment of their batteries for short-term grid support. To allow both public parking lots and small residential garages to benefit from smart charging for end-user DR, a framework has been developed in which the aggregator handles decision-making through real-time interactions with PEV owners. A multi-stage decision-making approach with two-round interaction is proposed. Real-time interaction provides owners with an appropriate scheme for contributing to DR, while avoiding the inconvenience of pre-signed long-term contracts. A new stochastic model predicts future PEV arrivals and their energy demand through a combination of an artificial neural network (ANN) and a Markov chain.
Simulation results derived from 38-bus distribution test systems have verified the efficacy of the proposed methods. Through case-study comparisons, the inefficiency of conventional charging regimes has been confirmed and the effectiveness of real-time interactions with vehicle owners through DR has been demonstrated. Although the most promising performance seen in larger parking lots with more vehicle transactions. Yet, the proposed solution can be applied in any size of lots.