LAUSR

Abstract This paper focuses on the jointly operational optimization of a network of buildings while considering energy exchange among them. Based on the operation model of each individual building, coordinated information and strategies of the energy exchange among all buildings are used to maximally reduce the energy cost of all the buildings while reducing the complexity of the network operation. The optimization problem of a network of multiple buildings is formulated as a stochastic mixed integer programming problem, and a Lagrangian relaxation-based decentralized algorithm is developed to solve this problem. Based on this method, a price mechanism for the energy exchange among buildings which can guarantee the fairness of all the buildings is developed by introducing Lagrangian multipliers as the coordinated signals for the decentralized optimization. The performance of the proposed method is discussed based on case studies of a network of four buildings in Singapore. Numerical results show that the method can not only achieve significant energy cost savings for all the buildings, but also provide huge ability to accommodate the uncertainties in the network. It is also found that the proposed price mechanism for the energy exchange provides incentives for the buildings to participate in the joint optimization.