In this work, we propose a multi-objective Mixed Integer Linear Programming formulation for addressing the Collaborative Energy Management Problem with the aim of maximizing the net profit of both the Building Manager and all the apartments. The planning horizon is discretized into a finite set of periods, i.e., time intervals. In this way, both the residents’ tasks and the storage activity can be scheduled over the time horizon. The decision variables take into account both the energy resources shared among the apartments, i.e., the ones administrated by the Building Manager, and the local energy resources of each apartment. Together with the traditional scheduling constraints, we also impose both time windows and priority conditions. In particular, regarding the former, each task can be scheduled starting from a specific period. While, according to the latter, each task has a list of prior tasks, of the same resident, that have to be scheduled before it. The proposed formulation is evaluated by investigating the management of a block of four apartments. In one scenario, the apartments are considered as independent entities. In the other one, a collaborative management is performed. The performance comparison reveals that the collaborative management can improve the energy sale revenue up to 26%, providing additional profit to be shared among the apartment owners and the Building Manager.

Collaborative Energy Management in Micro-Grid environments through multi-objective optimization / Severini, Marco; Pisacane, Ornella; Fagiani, Marco; Squartini, Stefano. - 2018-July:(2018). (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 2018 tenutosi a Rio de Janeiro, Brazil nel July, 7-13, 2018) [10.1109/IJCNN.2018.8489640].

Collaborative Energy Management in Micro-Grid environments through multi-objective optimization

Marco Severini;Ornella Pisacane;Marco Fagiani;Stefano Squartini
2018-01-01

Abstract

In this work, we propose a multi-objective Mixed Integer Linear Programming formulation for addressing the Collaborative Energy Management Problem with the aim of maximizing the net profit of both the Building Manager and all the apartments. The planning horizon is discretized into a finite set of periods, i.e., time intervals. In this way, both the residents’ tasks and the storage activity can be scheduled over the time horizon. The decision variables take into account both the energy resources shared among the apartments, i.e., the ones administrated by the Building Manager, and the local energy resources of each apartment. Together with the traditional scheduling constraints, we also impose both time windows and priority conditions. In particular, regarding the former, each task can be scheduled starting from a specific period. While, according to the latter, each task has a list of prior tasks, of the same resident, that have to be scheduled before it. The proposed formulation is evaluated by investigating the management of a block of four apartments. In one scenario, the apartments are considered as independent entities. In the other one, a collaborative management is performed. The performance comparison reveals that the collaborative management can improve the energy sale revenue up to 26%, providing additional profit to be shared among the apartment owners and the Building Manager.
2018
Proceedings of the International Joint Conference on Neural Networks
978-150906014-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/259204
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