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Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market

Published: 01 January 2013 Publication History

Abstract

The basis of an efficient functioning of a power grid is an accurate balancing of the electricity demand of all the consumers at any instant with supply. Nowadays, this task involves only the grid operator and retail electricity providers. One of the facets of the Smart Grid vision is that consumers may have a more active role in the problem of balancing demand with supply. With the deployment of intelligent information and communication technologies in domestic environments, homes are becoming smarter and able to play a more active role in the management of energy. We use the term Smart Consumer Load Balancing to refer to algorithms that are run by energy management systems of homes in order to optimise the electricity consumption, to minimise costs and/or meet supply constraints. In this work, we analyse different approaches to Smart Consumer Load Balancing based on (distributed) artificial intelligence. We also put forward a new model of Smart Consumer Load Balancing, where consumers actively participate in the balancing of demand with supply by forming groups that agree on a joint demand profile to be contracted in the market with the mediation of an aggregator. We specify the business model as well as the optimisation model for load balancing, showing the economic benefits for the consumers in a realistic scenario based on the Spanish electricity market.

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Cited By

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  • (2018)Utilizing Device-level Demand Forecasting for Flexibility MarketsProceedings of the Ninth International Conference on Future Energy Systems10.1145/3208903.3208922(108-118)Online publication date: 12-Jun-2018

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Published In

cover image Artificial Intelligence Review
Artificial Intelligence Review  Volume 39, Issue 1
January 2013
93 pages

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Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2013

Author Tags

  1. Coalitions
  2. Load balancing
  3. Optimisation
  4. Smart grid

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  • (2018)Utilizing Device-level Demand Forecasting for Flexibility MarketsProceedings of the Ninth International Conference on Future Energy Systems10.1145/3208903.3208922(108-118)Online publication date: 12-Jun-2018

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