Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies
This study proposes a model predictive control (MPC)-based home energy management system for residential microgrid (RM) in which all related information such as the time-varying information of the load demand, electricity price and renewable energy generations, are all taken into account. A novel fi...
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| Vydané v: | IET generation, transmission & distribution Ročník 10; číslo 10; s. 2367 - 2378 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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The Institution of Engineering and Technology
07.07.2016
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | This study proposes a model predictive control (MPC)-based home energy management system for residential microgrid (RM) in which all related information such as the time-varying information of the load demand, electricity price and renewable energy generations, are all taken into account. A novel finite-horizon mixed-integer linear programming problem is iteratively formulated to investigate the optimal control actions of the RM under an MPC framework. Three case studies are conducted to discuss the technical and economic impacts of the responsive electrical and thermal loads, plug-in hybrid electric vehicles, and electrical and thermal energy storage units. Moreover, a sensitivity analysis is performed to demonstrate the superiority of the proposed approach when forecasts of related information are imperfect. |
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| AbstractList | This study proposes a model predictive control (MPC)‐based home energy management system for residential microgrid (RM) in which all related information such as the time‐varying information of the load demand, electricity price and renewable energy generations, are all taken into account. A novel finite‐horizon mixed‐integer linear programming problem is iteratively formulated to investigate the optimal control actions of the RM under an MPC framework. Three case studies are conducted to discuss the technical and economic impacts of the responsive electrical and thermal loads, plug‐in hybrid electric vehicles, and electrical and thermal energy storage units. Moreover, a sensitivity analysis is performed to demonstrate the superiority of the proposed approach when forecasts of related information are imperfect. |
| Author | Guo, Bo Zhang, Yan Liu, Yajie Wang, Rui Zhang, Tao |
| Author_xml | – sequence: 1 givenname: Yan surname: Zhang fullname: Zhang, Yan email: zy331214534@126.com organization: College of Information System and Management, National University of Defense Technology, Changsha 410073, People's Republic of China – sequence: 2 givenname: Rui surname: Wang fullname: Wang, Rui organization: College of Information System and Management, National University of Defense Technology, Changsha 410073, People's Republic of China – sequence: 3 givenname: Tao surname: Zhang fullname: Zhang, Tao organization: College of Information System and Management, National University of Defense Technology, Changsha 410073, People's Republic of China – sequence: 4 givenname: Yajie surname: Liu fullname: Liu, Yajie organization: College of Information System and Management, National University of Defense Technology, Changsha 410073, People's Republic of China – sequence: 5 givenname: Bo surname: Guo fullname: Guo, Bo organization: College of Information System and Management, National University of Defense Technology, Changsha 410073, People's Republic of China |
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| Copyright | The Institution of Engineering and Technology 2016 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | iterative methods integer programming sensitivity analysis plug-in hybrid electric vehicle power generation control linear programming renewable energy generation distributed power generation model predictive control-based operation management electrical energy storage unit thermal energy storage power generation economics responsive electrical load power grids demand forecasting renewable energy sources electricity price RM optimal control action optimal control finite-horizon mixed-integer linear programming problem thermal energy storage unit MPC-based home energy management system responsive thermal load forecast uncertainty energy management systems demand response strategy load demand time-varying information residential microgrid hybrid electric vehicles demand side management predictive control pricing iterative formulation |
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| SubjectTerms | demand forecasting demand response strategy demand side management Distributed generation distributed power generation Economic forecasting electrical energy storage unit electricity price Electricity pricing energy management systems finite‐horizon mixed‐integer linear programming problem forecast uncertainty hybrid electric vehicles Hybrid vehicles integer programming iterative formulation iterative methods linear programming load demand time‐varying information Mathematical models model predictive control‐based operation management MPC‐based home energy management system optimal control plug‐in hybrid electric vehicle power generation control power generation economics power grids predictive control pricing renewable energy generation renewable energy sources Residential energy residential microgrid responsive electrical load responsive thermal load RM optimal control action Sensitivity analysis thermal energy storage thermal energy storage unit Transmissions (automotive) |
| Title | Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.1127 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-gtd.2015.1127 https://www.proquest.com/docview/1845803738 |
| Volume | 10 |
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