Addressing energy forecast errors: an empirical investigation of the capacity distribution impact in a variable storage

Abstract

Today energy load forecasting is used by retailers to predict their future energy needs and address them in cost-effective ways. However, with the prevalence of the smart grid, accurate forecasting is becoming increasingly important, as the stakeholder base expands and differentiates by transforming to active prosumers who make their own forecasting and take advantage of the new smart grid capabilities for addressing excess or shortage of energy. For instance, they can benefit from participation in demand-response programs, or more futuristic such as local energy marketplaces. To take advantage of such opportunities, controllable energy signature in order to gain predictability is of key importance. We present here an empirical approach towards understanding how the predictability of a stakeholder can be improved through the availability of a variable storage. The guiding question is to investigate the relevance of capacity sizing for absorption of the intra-day forecast errors. The increase of variable storage in existing facilities such as that offered by the presence of electric vehicles in a building’s charging stations, poses new potentially cost-effective ways that facility managers can consider in the effort to maintain control of their infrastructure’s energy signature. Here we show, in a step-by-step approach, how intra-day energy forecast errors of a building, impact the overall capacity variation required to absorb them, and how proper storage shaping can assist. Although the approach is empirical, the same steps may be applied in other similar cases.