Load Scheduling and Power Trading in Systems With High Penetration of Renewable Energy Resources

• Published in: IEEE transactions on smart grid Vol. 7; no. 4; pp. 1802 - 1812
• Main Authors: Samadi, Pedram, Wong, Vincent W. S., Schober, Robert
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximate dynamic programming; demand side management (DSM); Demand-side management; Dynamic programming; Dynamical systems; Dynamics; Electric utilities; Home appliances; load scheduling; Penetration; Power consumption; Power markets; power trading; Renewable energy; Revenues; Scheduling; Approximate dynamic programming; power trading; demand side management (DSM); load scheduling
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2015.2435708

In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.