Energy routing based optimal solution for residential peak power reduction with photovoltaic and electric vehicle penetration

ABSTRACT

A customized design for an efficient home energy management system for peak power limiting and energy routing-based demand response strategy is proposed in this paper. The proposed system encompasses acquisition of historic data of residential load, photovoltaic (PV) generation and electric vehicle (EV) details, followed by load and solar power generation data forecast using the regression tree model. Gaussian distribution is used to model EV-related information, viz. arrival and departure schedule, SoC of EV batteries, etc. The predicted profiles are used in the adopted optimization process to estimate future energy balances. The presented optimization framework is a sequential process using whale optimization algorithm and fuzzy logic to schedule the connection time of residential appliances and knapsack algorithm for energy routing from additional energy sources, like rooftop solar photovoltaic systems and electric vehicles. Finally, the methodology is tested for its robustness and flexibility considering the actual data of a residential community having a variety of consumers. Promising results obtained validate the effectiveness of the proposed work with an average 27% reduction in peak power drawn, 30.69% in peak-to-average ratio, and 6.5% in the consumer electricity bill in the community.

KEYWORDS:

• Demand response
• energy management system
• vehicle to home
• vehicle to grid
• energy routing
• residential energy management

Nomenclature

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Abbreviations

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Acknowledgments

This work has received funding from the Science and Engineering Research Board (SERB), a statutory body established through an Act of Parliament: SERB Act 2008, Government of India under the scheme, MATRICS, for the project titled, “Internet of Energy based Interactive Residential Load Management System.”

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Science and Engineering Research Board India [MTR/2019/000753 dated 04 February 2020].

Notes on contributors

Nethravathi Shivanaganna

Nethravathi Shivanaganna received a bachelor’s degree in Electrical and Electronics Engineering in 2005 and a master’s degree in Power Systems Engineering in 2008 from Visvesvaraya Technological University and is currently working toward a doctorate in Electrical and Electronics Engineering at the National Institute of Technology Tiruchirappalli. The proposed work is the research project of this author.

Venkatakirthiga Murali

Venkatakirthiga Murali (M’13–SM’19) received a B.E. degree in Electrical and Electronics from Bharathidasan University, Tiruchirappalli, India, in 2000, and M.Tech. degree in Power Systems and a Doctorate in Distributed Generation and Microgrids from the National Institute of Technology Tiruchirappalli (NITT), Tiruchirappalli, in 2004 and 2014, resp. She is currently working as an Associate Professor with the Department of Electrical and Electronics Engineering, NITT. She has a total teaching experience of 19 years and serves as a reviewer to many reputed international journals. Her research interests include power systems, HVDC systems, distribution systems, and electrical machines. She is also a Fellow Institution of Engineers, India. This author serves as the research supervisor for the first author and for the proposed project work.