A collaborative operation decision model for distributed building clusters. (1st May 2015)

• Record Type: Journal Article
• Authors: Dai, Rui; Hu, Mengqi; Yang, Dong; Chen, Yang
• Is Part Of: Energy. Volume 84(2015)
• Page Start: 759
• Page End: 773
• Publication Date: 2015-05-01
• Subjects: Smart buildings -- Collaborative decision -- Multi-objective optimization -- Pareto optimality -- Combined cooling heating and power system; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.03.042
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In the context of smart grid, the building can freely connect with other buildings to form clusters which are termed as building clusters to share energy. However, less study is conducted to develop optimal operation strategy for building clusters and evaluate the performance of building clusters in terms of different measures under different operation modes. Therefore, this research proposes a collaborative decision model to study the energy exchange among building clusters where the buildings share a combined cooling, heating and power system, thermal storage, and battery, and each building aims to minimize its energy cost, carbon emission or primary energy consumption. A collaborative decision framework is proposed to obtain Pareto operation decisions for the building clusters. We compare the performance of the collaborative strategy with the non-cooperative strategy where no energy sharing among the buildings. It is demonstrated that the collaborative strategy can significantly reduce energy cost, carbon emission and primary energy consumption under both grid connected and disconnected operation modes. The collaborative strategy under dynamic pricing plan is more cost effective than the strategy under flat pricing plan, which indicates that the collaborative strategy can motive buildings to more efficiently utilize the shared energy under dynamic pricing plan. Highlights: A building clusters model is proposed to study energy sharing among buildings. A collaborative decision model is developed to obtain Pareto operation decisions. Three performance measures are proposed to evaluate the building clusters. Building clusters can reduce energy cost, energy consumption, and carbon emission. Building clusters can be more resilient to power disturbances.