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A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids

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A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids. / Ma, Jie; Ma, Xiandong.
In: Systems Science and Control Engineering, Vol. 6, No. 1, 06.2018, p. 237-248.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Ma J, Ma X. A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids. Systems Science and Control Engineering. 2018 Jun;6(1):237-248. Epub 2018 Jun 1. doi: 10.1080/21642583.2018.1480979

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Ma, Jie ; Ma, Xiandong. / A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids. In: Systems Science and Control Engineering. 2018 ; Vol. 6, No. 1. pp. 237-248.

Bibtex

@article{993b6a28de4a44dea0d7399160310a5c,
title = "A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids",
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.",
keywords = "Microgrid, power generation forecasting, load demand forecasting, optimal energy management, centralized controller, consensus protocol",
author = "Jie Ma and Xiandong Ma",
year = "2018",
month = jun,
doi = "10.1080/21642583.2018.1480979",
language = "English",
volume = "6",
pages = "237--248",
journal = "Systems Science and Control Engineering",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids

AU - Ma, Jie

AU - Ma, Xiandong

PY - 2018/6

Y1 - 2018/6

N2 - 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.

AB - 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.

KW - Microgrid

KW - power generation forecasting

KW - load demand forecasting

KW - optimal energy management

KW - centralized controller

KW - consensus protocol

U2 - 10.1080/21642583.2018.1480979

DO - 10.1080/21642583.2018.1480979

M3 - Journal article

VL - 6

SP - 237

EP - 248

JO - Systems Science and Control Engineering

JF - Systems Science and Control Engineering

IS - 1

ER -