Abstract
Genetic optimisation is used to minimise operational costs across a system of electrical loads and generators controlled by local intelligent agents and connected to the electricity grid at market rates. Experimental results in a simulated environment show that coordinated market-sensitive behaviours are achieved. A large network of 500 loads and generators, each characterised by different randomly selected parameters, was optimised using a two-stage genetic algorithm to achieve scalability.
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Guo, Y., Li, J., James, G. (2005). Evolutionary Optimisation of Distributed Energy Resources. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_145
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DOI: https://doi.org/10.1007/11589990_145
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
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