Virtual Budget: Integration of electricity load and price anticipation for load morphing in price-directed energy utilization

A methodology is presented for price-directed demand response steered by market signals and anticipation models. Electricity prices vary dynamically and with substantial volatility in a way that ceteris paribus (ceteris paribus is a Latin phrase meaning “with all other conditions remaining constant”) prices rise as the distance between available power and peak demand is reduced. Price can thus be a factor in overall load pattern morphing, that is, automated decision-making by energy management systems controlling devices consuming electricity. Load and price forecasting are key technologies for market participants to make optimal decisions regarding their demand response. To this end, this paper develops the Virtual Budget approach that couples price and load forecasting, and allows automated morphing of a user's electricity consumption. In particular, the Virtual Budget leverages price forecasts to modify the initial forecasted load demand via smart scheduling algorithms. The performance of the Virtual Budget is evaluated on a set of real historical data, and the reported results demonstrate that its use may lead to savings in electricity consumption costs.