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Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Author

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  • Anders Clausen

    (Center for Energy Informatics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark)

  • Aisha Umair

    (Center for Energy Informatics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark)

  • Yves Demazeau

    (Laboratoire d’Informatique de Grenoble, CNRS, Batiment IMAG—700 avenue Centrale, Domaine Universitaire—CS 40700, F-38058 Grenoble cx 9, France)

  • Bo Nørregaard Jørgensen

    (Center for Energy Informatics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark)

Abstract

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

Suggested Citation

  • Anders Clausen & Aisha Umair & Yves Demazeau & Bo Nørregaard Jørgensen, 2020. "Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization," Energies, MDPI, vol. 13(11), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2961-:d:369297
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    References listed on IDEAS

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    1. Heiskanen, Pirja & Ehtamo, Harri & Hamalainen, Raimo P., 2001. "Constraint proposal method for computing Pareto solutions in multi-party negotiations," European Journal of Operational Research, Elsevier, vol. 133(1), pages 44-61, August.
    2. Katsuhide Fujita & Takayuki Ito & Mark Klein, 2012. "A Secure and Fair Protocol that Addresses Weaknesses of the Nash Bargaining Solution in Nonlinear Negotiation," Group Decision and Negotiation, Springer, vol. 21(1), pages 29-47, January.
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    Cited by:

    1. Pedro Faria & Zita Vale, 2023. "Demand Response in Smart Grids," Energies, MDPI, vol. 16(2), pages 1-3, January.

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