Energy Management Systems for effective gap reduction between actual and predicted power in smart homes and buildings

In order to optimize energy efficiency and to achieve cost savings in smart buildings and grid-connected smart homes that include renewable generators and electrical storage systems, Energy Management Systems (EMSs) are today the most up to date solution. Besides achieving these two goals, a suitable design of the EMS can provide a quite deterministic management of power flows, reducing the gap between actual and predicted power due to forecasting errors. On the basis of a previously proposed EMS that allows reducing both the end-user's electricity bill and the generation/demand uncertainty impact, this paper proposes a detailed analysis of several factors affecting the EMS's performance. Variations of algorithm strategy parameters, market constraints and size of hardware components have been investigated and the results have been evaluated in terms of reduction of power gap and cash flow. Simulation results obtained in a six-day period for a grid connected smart home with a 3 kWp photovoltaic generator and a battery storage system are presented and some guidelines for proper EMS design have been proposed.