Coordinated energy management for inter-community imbalance minimization. (March 2016)

• Record Type: Journal Article
• Authors: Verschae, Rodrigo; Kawashima, Hiroaki; Kato, Takekazu; Matsuyama, Takashi
• Is Part Of: Renewable energy. Volume 87:Part 2(2016)
• Page Start: 922
• Page End: 935
• Publication Date: 2016-03
• Subjects: Coordinated energy management -- Imbalance minimization -- Power balancing -- Demand response -- Optimization -- Distributed coordination; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.07.039
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: With the increase in demand flexibility and the rapid introduction of uncontrollable renewable power sources, effective schemes for managing the power usage of end-users are required. In this line, we propose an energy management framework for coordinating the power usage of communities of networked agents. More specifically, communities (groups of loads) coordinate to minimize their aggregated power imbalance, while taking into account each community's objectives and constraints, as well as the preferred power usage pattern of each end-user. For having a robust coordination that can work under unexpected events, we propose to assign the agents to communities using a measure of the flexibility of sets of agents. The coordination framework builds on the alternating directions method of multipliers (ADMM), algorithm that is used to implement a distributed coordination using a hierarchical architecture. While the distributed coordination allows to manage the power usage of each end-user, the hierarchical architecture enables the integration, in a single framework, of energy management problems that would be otherwise handled independently. We illustrate and analyze the coordination framework using three simulated scenarios. Highlights: A coordination framework for inter-community imbalance minimization is proposed. End-users implement the coordination of power usage in a distributed optimization. It takes into account objectives for each end-user, and for communities of end-users. The end-users are assigned to communities considering their power usage flexibility. The coordination framework is analyzed and illustrated in three simulated scenarios.