| Description |
TITLE: DECENTRALIZED SCHEDULING OF EV ENERGY AND REGULATION RESERVE SERVICES IN DISTRIBUTION NETWORK MARKETS
ABSTRACT: The electricity transmission and distribution grid is undergoing a paradigm shift as renewable generation explodes while flexible,
storage-like loads are being massively adopted. We address the intermittency issues of renewable resources in connection with spatiotemporal distribution location marginal-cost-based prices (DLMPs) that guide flexible loads to utilize their significant degrees of freedom for the purpose of providing valuable services to the grid including demand response, energy arbitrage and regulation reserves. Dynamic DLMPs can induce socially optimal energy and reserve schedules to be adopted by flexible load. To this end,
existing transmission markets must be extended to distribution network connected participants. Since the inclusion of complex preferences of many flexible loads renders familiar centralized transmission market designs intractable, we propose tractable decentralized market designs with Electric Vehicles (EVs) as the representative flexible load.
<P>We address equilibrium existence, uniqueness, and efficiency issues that arise with decentralized market designs, using game theory techniques. We investigate various multi-hour, energy and reserves market designs including EV self-scheduling under distribution network information aware/unaware conditions, and load aggregator(s) scheduling groups of EVs. We investigate the role of network information in enabling price anticipating EVs to self-schedule in order to achieve individual benefits at the expense of
social welfare. Our contribution is the proof of uniqueness of decentralized market equilibria, and comparative analysis.
We then depart from the ideal battery assumption, employing a realistic model. We develop a novel Markovian Decision Process (MDP) application to estimate the hourly regulation tracking error incurred by an EV optimally responding to the regulation signal which is reset every four seconds by the system operator. The hourly tracking error increases when the EV promises higher regulation reserves while sustaining a high average charging rate. We solve the MDP repeatedly to capture the impact of average charging rate and regulation reserves promised at the beginning of an hour on the hourly regulation tracking error. We then estimate a convex closed form relationship mapping hourly charging rate and regulation reserve offerings to the hourly tracking cost. These convex cost functions provide input to the hourly energy bids and regulation reserve offers made by EVs to the Day Ahead market.
COMMITTEE: ADVISOR/CHAIR Michael Caramanis, SE, ME; Yannis Paschalidis, SE, ECE, BME; Pirooz Vakili, SE, ME; Pablo Ruiz, ME; Chair: James R. Perkins, SE, ME |
