Stochastic Optimization of Grid to Vehicle Frequency Regulation Capacity Bids

Abstract

This paper investigates the application of stochastic dynamic programming to the optimization of charging and frequency regulation capacity bids of an electric vehicle (EV) in a smart electric grid environment. We formulate a Markov decision problem to minimize an EV's expected cost over a fixed charging horizon. We account for both Markov random prices and a Markov random regulation signal. We also propose an enhancement to the classical discrete stochastic dynamic programming method. This enhancement allows optimization over a continuous space of decision variables via linear programming at each state. Simple stochastic process models are built from real data and used to simulate the implementation of the proposed method. The proposed method is shown to outperform deterministic model predictive control in terms of average EV charging cost.

Sustainable Transportation with Electric Vehicles

“…The first thread is opportune charging and/or discharging and example works include [36,37,38,39,40,41]. In general, these works charge and/or discharge vehicles at some opportune time.…”

Coordinated electric vehicle charging solutions using renewable energy sources

“…Constraint (8) and (9) ensures the lower and upper bounds on charging rates respectively. The upper bound on power drawn from grid is given by constraint (10).…”

Optimal Bidding in Performance-Based Regulation Markets: An MPEC Analysis With System Dynamics

“…The second group seeks profitable operation of regulation resources in non-performance-based [9], [10] or performancebased [11]- [13] regulation markets. Our work is different from the existing studies in this group in at least three key aspects.…”