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Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

Author

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  • Chris Ogwumike

    (School of Science and Engineering, Teesside University, Middlesbrough TS1 3BA, UK)

  • Michael Short

    (School of Science and Engineering, Teesside University, Middlesbrough TS1 3BA, UK)

  • Fathi Abugchem

    (School of Science and Engineering, Teesside University, Middlesbrough TS1 3BA, UK)

Abstract

Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.

Suggested Citation

  • Chris Ogwumike & Michael Short & Fathi Abugchem, 2015. "Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model," Energies, MDPI, vol. 9(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:9:y:2015:i:1:p:6-:d:61114
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    References listed on IDEAS

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    1. Myeong Jin Ko & Yong Shik Kim & Min Hee Chung & Hung Chan Jeon, 2015. "Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm," Energies, MDPI, vol. 8(4), pages 1-26, April.
    2. Zdenek Bradac & Vaclav Kaczmarczyk & Petr Fiedler, 2014. "Optimal Scheduling of Domestic Appliances via MILP," Energies, MDPI, vol. 8(1), pages 1-16, December.
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    Cited by:

    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Ghulam Hafeez & Nadeem Javaid & Sohail Iqbal & Farman Ali Khan, 2018. "Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units," Energies, MDPI, vol. 11(3), pages 1-27, March.
    3. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.

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