Energy Demand Management Through Uncertain Data Forecasting: An Hybrid Approach

Although Smart Grids may represent the solution to the limits of nowadays Power Grid, the turnover may not occur in the next future yet due the complex nature of energy distribution. Thus, as a more short term effort, to improve the responsiveness of the energy demand to the power grid load, more and more energy providers apply dynamic pricing schemes for grid users. Believing that dynamic pricing policies may be an effective asset even at a micro-grid level, an hybrid energy management scheme is proposed in this contribution. While the nonlinear nature of a micro grid, involving the task allocation and the thermal constraint satisfaction, can be modeled as a mixed integer nonlinear programming problem, neural-network forecasting abilities can provide a sustainable support under realistic operating conditions. Based on the forecast of solar energy production and grid energy prices and outdoor temperature, the optimization of tasks allocation is aimed to lower both the user costs and the grid burden while accounting the thermal comfort of the user. Through computer simulations, whose degree of realism is enhanced by the adoption of forecast data, the shift of the grid load towards low energy price hours is confirmed.