A review of forecasting algorithms and energy management strategies for microgrids. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Ma, Jie; Ma, Xiandong
• Is Part Of: Systems science & control engineering. Volume 6:Issue 1(2018)
• Page Start: 237
• Page End: 248
• Publication Date: 2018-01-01
• Subjects: Microgrid -- power generation forecasting -- load demand forecasting -- optimal energy management -- centralized controller -- consensus protocol; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2018.1480979
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed.