Day-ahead forecasting of grid carbon intensity in support of heating, ventilation and air-conditioning plant demand response decision-making to reduce carbon emissions

Abstract

Electrical heating, ventilation and air-conditioning loads in buildings are suitable candidates for use in demand response activity. This paper demonstrates a method to support planned demand response actions intended explicitly to reduce carbon emissions. Demand response is conventionally adopted to aid the operation of electricity grids and can lead to greater efficiency; here it is planned to target times of day when electricity is generated with high carbon intensity. Operators of heating, ventilation and air-conditioning plant and occupants of conditioned spaces can plan when to arrange shutdown of plant once they can foresee the opportune time of day for carbon saving. It is shown that the carbon intensity of the mainland UK electricity grid varies markedly throughout the day, but that this tends to follow daily and weekly seasonal patterns. To enable planning of demand response, 24 h ahead forecast models of grid carbon intensity are developed that are not dependent on collecting multiple exogenous data sets. In forecasting half-hour periods of high carbon intensity either linear autoregressive or non-linear artificial neural network models can be used, but a daily seasonal autoregressive model is shown to provide a 20% improvement in carbon reduction. Practical application: The forecast method demonstrated in the paper would enable building operators to plan demand response activity to target times of high carbon intensity on the UK electricity grid. The method would be easy to implement as the only data required are publicly available.