Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array

JOURNAL OF POWER SOURCES

卷:333

Article

Wu, Xiaohua¹  Hu, Xiaosong²  Moura, Scott³  Yin, Xiaofeng¹  Pickert, Volker⁴ 

[1] Xihua Univ, Sch Automobile & Transportat, Chengdu 610039, Peoples R China

[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China

[3] Univ Calif Berkeley, Energy Controls & Applicat Lab, Berkeley, CA 94720 USA

[4] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

关键词: Energy management;    Stochastic dynamic optimization;    Smart home;    Plug-in electric vehicle;    Batteries;    Photovoltaic array;   

DOI  :  10.1016/j.jpowsour.2016.09.157

来源: Elsevier

【 摘 要 】

Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the random-variable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

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