A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage

Rui Tang, Hangxin Li and Shengwei Wang

Applied Energy, 2019, vol. 242, issue C, 809-820

Abstract: The development of smart grids requires more active and effective participation of buildings in power balance. However, most of building demand management and demand response control strategies focus on single buildings only. For a group of buildings at cluster-level, which are often involved in an electricity charge account, such control strategies will not be effective. A game theory-based decentralized control strategy is therefore developed to address the demand management of cluster-level buildings. The indoor temperature set-point and the charging/discharging process of active cold storages in central air-conditioning systems are optimized simultaneously. Rather than optimizing the power demand of all buildings on a central optimization system, the proposed strategy optimizes the power demand of all buildings collectively in a decentralized manner. Using this strategy, buildings manage their own power demands locally only using the aggregated power demand of building cluster as the common reference for their demand controls. This distributed computing allows the optimization of large systems or complex optimization problems to be divided into a few simple optimization tasks, providing enhanced applicability and robustness in practical applications. Case studies are conducted and results show that the proposed game theory-based decentralized control strategy can increase the aggregated peak demand reduction and electricity cost saving more than two times compared with that when the demand management of building cluster is conducted in an uncoordinated manner. Meanwhile, the control performance of proposed decentralized strategy is close to that using a perfect demand management control strategy.

Keywords: Peak demand limiting; Demand side management; Energy flexibility; PCM storage; Distributed optimization; Game theory (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (17)

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DOI: 10.1016/j.apenergy.2019.03.152

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