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Optimizing the Electricity Consumption with a High Degree of Flexibility Using a Dynamic Tariff and Stackelberg Game

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  • Simona-Vasilica Oprea

    (The Bucharest University of Economic Studies)

  • Adela Bâra

    (The Bucharest University of Economic Studies)

  • George Adrian Ifrim

    (Dunărea de Jos University)

Abstract

Recent advancements in the sensor industry, smart metering systems and communication technology have led to interesting electricity consumption optimization opportunities that contribute to both peak reduction and bill savings and better integration of flexible appliances (including e-mobility). In combination with advanced tariffs, there has been a promising demand side management strategy devised from different perspectives: consumers interested in cost minimization, retailers and grid operators interested in peak minimization or hybrid solutions which reduce the costs to the extent of a certain peak level. In this paper, we propose an optimization algorithm that is significantly enhanced by a Stackelberg-type dynamic nonzero-sum game in which the consumers optimize and send their 24-h consumption schedules to the electricity retailer and receive the hourly tariff rates until their savings and the Flattening Index are maximized. Thus, it has been demonstrated that the one-iteration optimization is not as rewarding as the proposed game-optimization algorithm and that the results are heavily influenced by the degree of flexibility of the appliances. The algorithm is tested and validated using a large real input dataset, recorded at 15-min interval for a period of one year from a small residential community that consists of 11 modern houses with more than 300 appliances and high flexibility in terms of shifting, and the results highlight the consumers’ gain, FI and peak to average ratio indicators.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & George Adrian Ifrim, 2021. "Optimizing the Electricity Consumption with a High Degree of Flexibility Using a Dynamic Tariff and Stackelberg Game," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 151-182, July.
    • Handle: RePEc:spr:joptap:v:190:y:2021:i:1:d:10.1007_s10957-021-01876-1
    DOI: 10.1007/s10957-021-01876-1
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    References listed on IDEAS

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    1. Fernandez, Edstan & Hossain, M.J. & Nizami, M.S.H., 2018. "Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources," Applied Energy, Elsevier, vol. 232(C), pages 245-257.
    2. Yang, Liu & Dong, Ciwei & Wan, C.L. Johnny & Ng, Chi To, 2013. "Electricity time-of-use tariff with consumer behavior consideration," International Journal of Production Economics, Elsevier, vol. 146(2), pages 402-410.
    3. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    4. Aqib Jamil & Turki Ali Alghamdi & Zahoor Ali Khan & Sakeena Javaid & Abdul Haseeb & Zahid Wadud & Nadeem Javaid, 2019. "An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
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    Cited by:

    1. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
    2. Oprea, Simona-Vasilica & Bâra, Adela & Ciurea, Cristian-Eugen, 2022. "A novel cost-revenue allocation computation for the competitiveness of balancing responsible parties, including RES. Insights from the electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 881-894.

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