This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.

In power system operation, the economic dispatch problem (EDP) aims to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient push-sum method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. This paper shows that the proposed algorithm is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time-varying delays on communication links. Numerical simulations are used to illustrate and validate the proposed algorithm.

This paper proposes methodologies to estimate the electric energy and power consumption by light-duty plug-in electric vehicles (PEVs). Using the travel patterns of light-duty vehicles in the U.S. obtained from the 2009 National Household Travel Survey, the PEVs' energy and power consumption are estimated for two uncontrolled charging scenarios.

Demand response and energy storage play a profound role in the smart grid. The focus of this paper is to evaluate benefits of coordinating flexible loads and energy storage to provide power grid and end user services. We present a generalized battery model (GBM) to describe the flexibility of building loads and energy storage. An optimization-based approach is proposed to characterize the parameters (power and energy limits) of the GBM for flexible building loads. We then develop optimal coordination algorithms to provide power grid and end user services such as energy arbitrage, frequency regulation, spinning reserve, as well as energy cost and demand charge reduction. Several case studies have been performed to demonstrate the efficacy of the GBM and coordination algorithms, and evaluate the benefits of using their flexibility for power grid and end user services. We show that optimal coordination yields significant cost savings and revenue. Moreover, the best option for power grid services is to provide energy arbitrage and frequency regulation. Furthermore, when coordinating flexible loads with energy storage to provide end user services, it is recommended to consider demand charge in addition to time-of-use price in order to flatten the aggregate power profile.

Microgrids can be operated in island mode during utility grid outages to support service restoration and improve system resilience. To schedule and dispatch distributed energy resources (DERs) in an islanded microgrid, conventional model-based methods rely on accurate distribution network models and lack generalization and adaptability. Data-driven methods are promising for DER coordination but face practical challenges such as potential hazards to microgrids during online training and insufficient online training opportunities due to low outage rates. This paper presents a novel two-stage learning framework to identify an optimal restoration strategy. The proposed framework builds on the deep deterministic policy gradient from demonstrations, which is a dataset that contains a trajectory of states and the associated expert actions. At the pre-training stage, imitation learning is applied to equip the control agent with expert experiences to guarantee acceptable initial performance. At the online training stage, action clipping, reward shaping, and expert demonstrations are leveraged to ensure safe exploration while accelerating the training process. The proposed method is illustrated using the IEEE 123-node system and compared with a representative model-based method and the standard deep deterministic policy gradient method to prove solution accuracy and demonstrate increased computational efficiency.