Demand response potential of model predictive control of space heating based on price and carbon dioxide intensity signals

This paper reports on a simulation-based study that investigated the demand response potential of a model predictive controller (MPC) for space heating defined to minimize a weighted sum of electricity costs and CO2 emissions. The performance of the MPC was compared to a traditional controller and the results showed that an MPC with no weight on CO2 emissions reduced the total electricity costs, shifted consumption from high to low load periods and reduced consumption in the hour with the yearly maximum grid load; but it could also cause an increase in CO2 emissions. Contrary, the MPC with no weight on electricity costs reduced CO2 emissions; but it only reduced total costs marginally, it could cause a shift of consumption from low to high load periods and it increased consumption in the hour with the yearly maximum grid load. Finally, if the MPC used a weighted sum of electricity costs and CO2 emissions a range of intermediate results were obtained. The weighting factor can thus be used either to balance the performance of the MPC with respect to all performance indicators or to maximise it with respect to one indicator of particular interest.