| Summary: | This work reports a contribution, in a model predictive control multi-agent systems context, introducing a novel integrative methodology to manage energy networks from the demand-side point of view, in the strong presence of intermittent energy sources, including energy storage in households or car batteries. In particular, the article presents a control-based solution for indoor comfort, which, in addition, optimizes the usage of a limited shared energy resource. The control management is applied, in a distributed way, to a set of so-called thermal control areas (TCAs) and demand units, with the objective of minimizing the cost of energy while maintaining the indoor temperature within the comfort zone bounds, and simultaneously not exceeding a limited amount of shared renewable energy. The thermal control areas are, in general, thermodynamically connected, and are also coupled by energy interrelation constraints established in the particular optimization solution. Energy management is performed with a fixed sequential order established from a previously carried out auction, wherein the bids are made by each unit’s demands, acting as demand-side management agents, based on the daily energy price. The developed solution is explained by a basic algorithm that has been applied to different scenarios, and the results have been compared so as to illustrate the benefits and flexibility of the proposed approach, showing less energy consumption and a 37% cost saving.
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