Evolving robust policies for community energy system management
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Cardoso:2019:GECCO,
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author = "Rui P. Cardoso and Emma Hart and Jeremy V. Pitt",
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title = "Evolving robust policies for community energy system
management",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "1120--1128",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321763",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Multi-agent
system, Community energy system management",
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size = "9 pages",
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abstract = "Community energy systems (CESs) are shared energy
systems in which multiple communities generate and
consume energy from renewable resources. At regular
time intervals, each participating community decides
whether to self-supply, store, trade, or sell their
energy to others in the scheme or back to the grid
according to a predefined policy which all participants
abide by. The objective of the policy is to maximise
average satisfaction across the entire CES while
minimising the number of unsatisfied participants. We
propose a multi-class, multi-tree genetic programming
approach to evolve a set of specialist policies that
are applicable to specific conditions, relating to
abundance of energy, asymmetry of generation, and
system volatility. Results show that the evolved
policies significantly outperform a default hand
crafted policy. Additionally, we evolve a generalist
policy and compare its performance to specialist ones,
finding that the best generalist policy can equal the
performance of specialists in many scenarios. We claim
that our approach can be generalised to any multi-agent
system solving a common-pool resource allocation
problem that requires the design of a suitable
operating policy.",
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notes = "Also known as \cite{3321763} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Rui P Cardoso
Emma Hart
Jeremy V Pitt
Citations