Load modeling and scheduling optimization for energy sharing in prosumers network

In the smart home scenario, the deployment of smart meters, smart sensors and home energy management systems (HEMSs) is expected to enable the optimal and dynamic schedule of the domestic energy devices according to different objectives (e.g. self-consumption maximization, peak-period consumption avoidance and emission reduction), while keeping satisfied end-users comfort constraints. However, despite interesting results can be achieved at single-house level, only HEMSs interaction under new business models (e.g. P2P markets) are expected to unleash most of the untapped value. For these reasons, we aim to develop a HEMS model with the right compromise between accuracy and computational burden, suitable to investigate the HEMS performances both at single house level, as in this work, and at community level through a multi-agent simulation framework for future researches. In order to find realist optimal schedule from an end-user perspective, we incorporate detailed sub-models to optimize the trade-off between cost and comfort with a Mixed Integer Linear Problem (MILP) formulation. We consider wet appliances (dish washer, washing machine and tumble dryer), thermal appliances, an energy storage system (ESS) and PV modules. Reported results on the scheduling optimization show the potentiality of the proposed approach.