Abstract
An adaptive linear model predictive control strategy is introduced for the problem of demand side energy management, involving a photovoltaic device, a battery, and a heat pump. Moreover, the heating influence of solar radiation via the glass house effect is considered. Global sunlight radiation intensity and the outside temperature are updated by weather forecast data. The identification is carried out after adapting to a time frame witch sufficiently homogeneous weather. In this way, in spite of the linearity an increase in precision and cost reduction of up to 46% is achieved. It is validated for an open and closed loop version of the MPC problem using real data of the ambient temperature and the global radiation.
Highlights
The inclusion of continuously changing electricity market price variability in predictive modelling of efficient demand side energy management including heat pump operation has been studied under the assumption of the customer's ability to shift energy load [4]
There has not been any work including the photovoltaic devices, the heat-pump, as well as the solar global radiation altogether in the optimization. We show that this is possible within a purely linear, adaptive model predictive control (MPC) approach
1.1 Previous Work related to adaptive MPC
Summary
The inclusion of continuously changing electricity market price variability in predictive modelling of efficient demand side energy management including heat pump operation has been studied under the assumption of the customer's ability to shift energy load [4]. In energy management using weather forecasts, model predictive control (MPC) has previously been identified as superior over non-predictive methods [3, 4, 12]. There has not been any work including the photovoltaic devices, the heat-pump, as well as the solar global radiation altogether in the optimization. We show that this is possible within a purely linear, adaptive MPC approach
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