Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state-space approximate dynamic programming

In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programm...

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Published in:	IET generation, transmission & distribution Vol. 13; no. 15; pp. 3409 - 3420
Main Authors:	Lin, Shunjiang, Wang, Yaping, Liu, Mingbo, Fan, Guansheng, Yang, Zhibin, Li, Qifeng
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 06.08.2019
Subjects:	battery storage plants concave programming convex programming diesel generators diesel‐electric power stations distributed power generation hybrid power systems integer programming Markov decision process Markov processes mixed‐integer nonlinear nonconvex programming nonlinear programming photovoltaic inverter photovoltaic power systems power generation dispatch PV inverter PV/wind/diesel/battery microgrids Research Article state‐space approximate dynamic programming algorithm stochastic MINNP model stochastic MISOCP model stochastic mixed‐integer second‐order cone programming model stochastic optimal dispatch wind power plants PV/wind/diesel/battery microgrids integer programming nonlinear programming PV inverter photovoltaic power systems convex programming stochastic MINNP model distributed power generation photovoltaic inverter state-space approximate dynamic programming algorithm power generation dispatch stochastic optimal dispatch stochastic MISOCP model diesel generators Markov processes diesel-electric power stations mixed-integer nonlinear nonconvex programming Markov decision process battery storage plants wind power plants stochastic mixed-integer second-order cone programming model concave programming hybrid power systems
ISSN:	1751-8687, 1751-8695
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Abstract	In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programming (MINNP) model is established for the optimal dispatch of MGs. In this model, the expected value of the sum of DGs’ operation as well as start-up cost, the network-loss cost and the PV inverter's auxiliary service cost, is considered as the objective function. The stochastic MINNP model is transformed into a stochastic mixed-integer second-order cone programming (MISOCP) model to reduce the computational complexity. The state-space approximate dynamic programming algorithm is adopted to solve the stochastic MISOCP model. In the algorithm, based on the approximate value functions of typical states that are computed according to the Markov decision process, solving the optimisation model of multiple periods is executed by solving each period's optimisation model one by one to improve the computational efficiency. Meanwhile, parallel computing is executed to greatly improve the efficiency of the proposed algorithm. Test results on two modified IEEE-33 bus and IEEE-123 bus islanded MGs with PV/wind/diesel/battery demonstrate the correctness and efficiency of the proposed model and algorithm.
AbstractList	In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed‐integer non‐linear non‐convex programming (MINNP) model is established for the optimal dispatch of MGs. In this model, the expected value of the sum of DGs’ operation as well as start‐up cost, the network‐loss cost and the PV inverter's auxiliary service cost, is considered as the objective function. The stochastic MINNP model is transformed into a stochastic mixed‐integer second‐order cone programming (MISOCP) model to reduce the computational complexity. The state‐space approximate dynamic programming algorithm is adopted to solve the stochastic MISOCP model. In the algorithm, based on the approximate value functions of typical states that are computed according to the Markov decision process, solving the optimisation model of multiple periods is executed by solving each period's optimisation model one by one to improve the computational efficiency. Meanwhile, parallel computing is executed to greatly improve the efficiency of the proposed algorithm. Test results on two modified IEEE‐33 bus and IEEE‐123 bus islanded MGs with PV/wind/diesel/battery demonstrate the correctness and efficiency of the proposed model and algorithm.
Author	Fan, Guansheng Liu, Mingbo Yang, Zhibin Li, Qifeng Wang, Yaping Lin, Shunjiang
Author_xml	– sequence: 1 givenname: Shunjiang surname: Lin fullname: Lin, Shunjiang email: linshj@scut.edu.cn organization: 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China – sequence: 2 givenname: Yaping surname: Wang fullname: Wang, Yaping organization: 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China – sequence: 3 givenname: Mingbo orcidid: 0000-0001-9097-9045 surname: Liu fullname: Liu, Mingbo organization: 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China – sequence: 4 givenname: Guansheng surname: Fan fullname: Fan, Guansheng organization: 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China – sequence: 5 givenname: Zhibin surname: Yang fullname: Yang, Zhibin organization: 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, People's Republic of China – sequence: 6 givenname: Qifeng surname: Li fullname: Li, Qifeng organization: 2Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
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Copyright	The Institution of Engineering and Technology 2019 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	PV/wind/diesel/battery microgrids integer programming nonlinear programming PV inverter photovoltaic power systems convex programming stochastic MINNP model distributed power generation photovoltaic inverter state-space approximate dynamic programming algorithm power generation dispatch stochastic optimal dispatch stochastic MISOCP model diesel generators Markov processes diesel-electric power stations mixed-integer nonlinear nonconvex programming Markov decision process battery storage plants wind power plants stochastic mixed-integer second-order cone programming model concave programming hybrid power systems
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Snippet	In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state...
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SubjectTerms	battery storage plants concave programming convex programming diesel generators diesel‐electric power stations distributed power generation hybrid power systems integer programming Markov decision process Markov processes mixed‐integer nonlinear nonconvex programming nonlinear programming photovoltaic inverter photovoltaic power systems power generation dispatch PV inverter PV/wind/diesel/battery microgrids Research Article state‐space approximate dynamic programming algorithm stochastic MINNP model stochastic MISOCP model stochastic mixed‐integer second‐order cone programming model stochastic optimal dispatch wind power plants
Title	Stochastic optimal dispatch of PV/wind/diesel/battery microgrids using state-space approximate dynamic programming
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