There are currently two major developments shaping the electricity market. On the supply side, there is increasing push towards the use of renewable energy sources, which are however associated with a highly variable rate of energy supply. On the demand side, there is increasing deployment of plug-in (hybrid) electric vehicles (PEVs/PHEVs), which will not only increase the average electricity consumption significantly, but also generate very bursty demand patterns. Fortunately, PEVs also provide significant flexibility in terms of their energy consumption rates and schedules, and we believe that variability in the supply side can be partly "absorbed" using the flexibility. This constitutes the broad goal of this project, which aims at efficient PEV demand scheduling mechanisms for effective utilization of variable-rate renewable energy sources, and hedging of forecast risks by electric utilities (aggregators). These mechanisms would allow the partial transfer of risk associated with variability of energy generation/supply -- from the electricity suppliers and aggregators (and in turn the generators) to the consumers (PEV users), in exchange of a reduced price of charging. The solutions resulting from this project for the PEV charging context would also be applicable to other elastic loads that are associated with a partly predictable amount of energy consumption over a time span. The issues considered in this project are critical for the stability and profitable operation of the electric grid as it copes up with the increased deployments of PEVs, and attempts to integrate renewable energy generation at a large scale.
More specifically, this project investigates two demand-response control strategies for charging PEVs in the smart grid. In the first method, the utility (or aggregator) sets time- and location-dependent prices for PEV charging, while letting the PEVs (or the smart meters associated with PEV charging) mostly do the scheduling, based on the individual best interests and constraints of the PEV owners. In the second method, the utility offers (in the market) hard and soft contracts to charge PEVs up to certain levels within a specified interval, which the PEVs purchase based on their needs and preferences; the scheduling of the PEV charging is then done by the utility itself such that adheres to the terms of the contracts that it has sold to the PEVs. Two broad inter-related research issues will be explored in this context. The first involves the design of these contracts, as well as the associated "contract portfolio optimization" question - which involves deciding on the how many hard and soft contracts the utility should offer to attain its desired risk-return tradeoff point. The second involves the process of offering these contracts through auctions and other market clearing mechanisms that are "incentive-compatible" (i.e., invoke truthful response from the PEVs), and result in overall "social efficiency" of the resulting energy allocation. Predictive, demand-response control within these two pricing/scheduling frameworks for PEV charging brings about challenging issues that can be modeled using tools and techniques in stochastic game theory, auction mechanism design, stochastic calculus, and risk management, but must be suitably adapted and extended to consider the models, constraints and requirements of the PEV charging and renewable energy supply processes.
