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This paper deals with the service restoration problem in renewable-powered microgrids that are driven islanded by an unscheduled breakdown from the main grid. The objective is to determine the maximum of the expected restorative loads by choosing the best arrangement of the power network configurations immediately from the beginning of the breakdown all the way to the end of the island mode. The intermittency nature of the renewable power, as well as the uncertainty of the duration of the breakdown pose new challenges to this classic optimization scheduling task. The proposed two scenario-splitting methods can be solved in a two-step solving procedure, in which a Lagrangian technique and dynamic programming are utilized to provide an analytical sub-optimal yet efficient solution to the original problem. Simulation results demonstrate that both methods can find solutions very close to optima effectively; the scheduling plan should be adjusted when the time evolves, especially when the renewable power generation takes a large portion in the power supply; and the energy storage system plays a significant role to reduce the risk of unreliability in the wind power forecasting, even with a small amount of capacity. Finally, the proposed approach can be applied to radially configured systems with other types of distributed generators. [ABSTRACT FROM PUBLISHER]

This paper investigates a pricing strategy to maximize the revenue in future plug-in hybrid electric vehicles' market for battery charging services. Service models are developed for a monopoly market with two typical service classes. The unique characteristics of battery charging result in a piecewise linear quality of service (QoS) model. By resorting to the concept of superdifferential, some theoretical results, including the existence and uniqueness of the subscriber equilibrium and the convergence of the corresponding subscriber dynamics, are established. In the course of developing revenue-maximizing pricing strategies for both service classes, a general tradeoff has been identified between monetization and customer acquisition. [ABSTRACT FROM PUBLISHER]

This paper considers the operation scheduling problem in renewable-powered microgrids, which is used to determine the least-cost unit commitment (UC) and the associated dispatch, while meeting load, environmental, and system operating requirements. The intermittency nature of the renewable energy sources, as well as microgrid’s capacity to operate either in parallel with, or autonomously of, the traditional power grid, pose new challenges to this classic optimization task. A probability-based concept, probability of self-sufficiency (PSS), is introduced to indicate the probability that the microgrid is capable of meeting local demand in a self-sufficient manner. Furthermore, to the best of our knowledge, we make the first attempt in approaching the mixed-integer UC problem from a convex optimization perspective, which leads to an analytical closed-form characterization of the optimal commitment and dispatch solutions. The simulation results show that 1) the proposed method achieves an efficient performance that incurs no loss of optimality with lower complexity than existing algorithms; 2) an energy storage system (ESS) with suitable capacity contributes to the self-sufficiency target of a microgrid, and the stored energy varies less remarkably as the microgrid tends to operate more independently; 3) the proposed method provides guidelines in deciding the ESS size to achieve a desired PSS. [ABSTRACT FROM PUBLISHER]