Multi-Stage Real-Time Operation of a Multi-Energy Microgrid With Electrical and Thermal Energy Storage Assets: A Data-Driven MPC-ADP Approach

This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage...

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Published in:	IEEE transactions on smart grid Vol. 13; no. 1; pp. 213 - 226
Main Authors:	Li, Zhengmao, Wu, Lei, Xu, Yan, Moazeni, Somayeh, Tang, Zao
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active-reactive power Costs Distributed generation Dynamic programming Electrical loads Energy storage heterogeneous energy storage Hybrid model predictive control-approximate dynamic programming Linear functions Microgrids multi-energy microgrid Predictive control Real time operation Real-time systems Renewable energy sources stochastic operation Stochastic processes Thermal energy Uncertainty
ISSN:	1949-3053, 1949-3061
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Abstract	This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman's equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.
AbstractList	This paper studies the multi-stage real-time stochastic operation of grid-tied multi-energy microgrids (MEMGs) via the hybrid model predictive control (MPC) and approximate dynamic programming (ADP) approach. In the MEMG, practical power and thermal network constraints, heterogeneous energy storage devices, and distributed generations are involved. Given the relatively large thermal inertia and slow thermal energy fluctuation, only uncertainties of renewable energy sources and active/reactive power loads are considered. Then, historical data are adopted as training scenarios for the MPC-ADP method to acquire empirical knowledge for dealing with all the diverse uncertainties. Further, piecewise linear functions are used to approximate value functions with respect to the operation status of energy storage assets, which enables sequentially solving the Bellman's equation forward through time to minimize MEMG operation cost. Finally, numerical case studies are conducted to illustrate the effectiveness and superiority of the proposed MPC-ADP approach. Simulation results indicate that with sufficient information embedded, the MPC-ADP approach could obtain good-enough real-time operation solutions with the successively updated forecast. Further, it outperforms alternative real-time operation benchmarks in terms of optimality and convergence for various application scenarios.
Author	Xu, Yan Tang, Zao Li, Zhengmao Wu, Lei Moazeni, Somayeh
Author_xml	– sequence: 1 givenname: Zhengmao surname: Li fullname: Li, Zhengmao email: zli161@stevens.edu organization: ECE Department, Stevens Institute of Technology, Hoboken, NJ, USA – sequence: 2 givenname: Lei orcidid: 0000-0002-0722-5769 surname: Wu fullname: Wu, Lei email: lei.wu@stevens.edu organization: ECE Department, Stevens Institute of Technology, Hoboken, NJ, USA – sequence: 3 givenname: Yan orcidid: 0000-0002-0503-183X surname: Xu fullname: Xu, Yan organization: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore – sequence: 4 givenname: Somayeh orcidid: 0000-0003-3631-563X surname: Moazeni fullname: Moazeni, Somayeh organization: School of Business, Stevens Institute of Technology, Hoboken, NJ, USA – sequence: 5 givenname: Zao surname: Tang fullname: Tang, Zao email: ztang17@stevens.edu organization: ECE Department, Stevens Institute of Technology, Hoboken, NJ, USA
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SubjectTerms	Active-reactive power Costs Distributed generation Dynamic programming Electrical loads Energy storage heterogeneous energy storage Hybrid model predictive control-approximate dynamic programming Linear functions Microgrids multi-energy microgrid Predictive control Real time operation Real-time systems Renewable energy sources stochastic operation Stochastic processes Thermal energy Uncertainty
Title	Multi-Stage Real-Time Operation of a Multi-Energy Microgrid With Electrical and Thermal Energy Storage Assets: A Data-Driven MPC-ADP Approach
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