Impact of Social Welfare Methods on Multi-objective Resource Allocation in Energy Systems

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Abstract

Multi-agent resource allocation refers to the distribution of resources among agents. Resource allocation can be particularly challenging if the agents have conflicting objectives over multiple interdependent issues. In such cases, multi-objective optimization methods can be used to find an optimal allocation of resources, that maximizes social welfare. Social welfare refers to the welfare of the entire society of agents and therefore considered as a suitable metric for assessing the overall system performance in multi-agent resource allocation. In this paper we study and discuss different notions of social welfare and investigate their impact on the optimization outcome specifically for the problems comprising multiple conflicting objectives with interdependent issues. To this end, we implement and apply different notions of social welfare to a real-world, complex problem, where a resource domain is responsible for making allocation of energy resources to multiple energy intensive co nsumers (Commercial Greenhouse Growers). The problem is modeled as a multi-objective optimization context. Our results show how different social welfare methods affect the optimization outcome and result in different socially optimal resource allocations, depending on the behavior we expect from the system.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Smart Cities and Green ICT Systems
EditorsCornel Klein, Brian Donnellan, Markus Helfert
Volume1: SMARTGREENS
PublisherSCITEPRESS Digital Library
Publication date2019
Pages179-186
ISBN (Electronic)978-989-758-373-5
DOIs
Publication statusPublished - 2019
Event8th International Conference on Smart Cities and Green ICT Systems - Heraklion, Greece
Duration: 3. May 20195. May 2019
Conference number: 8

Conference

Conference8th International Conference on Smart Cities and Green ICT Systems
Number8
CountryGreece
CityHeraklion
Period03/05/201905/05/2019

Keywords

  • Energy Systems
  • Evolutionary Computation
  • Multi-objective Optimization
  • Social Welfare

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