Anita Martinčević

Model predictive control for energy-saving and comfortable temperature control in buildings

Model predictive control has been recognized as one of the essential solutions to achieve considerable energy savings in buildings. However, its performance on a building zone level can be inferior to a well-tuned conventional controller, especially in situations with constant energy prices and conservative comfort constraints. Optimization problem in the background has to be chosen to guarantee recursive feasibility and considerable energy savings without compromising the users comfort at the same time. This paper gives a novel formulation of the model predictive temperature control problem in buildings and its fair comparison with conventional controllers with the same level of flexibility allowed in zone temperature control. All controllers are tested for a system with seasonal heating and cooling, which is the most common case in real applications. It is shown that the introduced formulation leads to the model predictive controller that significantly outperforms conventional controllers both in energy consumption and users comfort.

Comfort control in buildings with adherence to the required thermal energy input in zones

This paper focusses on implementation of the energy management in building zones with fan coil units. The implementation is based on direct control of the required thermal energy inputs. We develop it as a necessary add-on to model predictive control algorithms that use thermal models of a building and compute the optimal energy inputs in different building zones for minimization of the overall energy cost required to keep the thermal comfort. The presented approach is substantially different to the generally accepted temperature control via local reactive control that only uses local measurements in deciding the control actions for zones thermal actuators. We demonstrate that closed loop performance, spent energy and the users comfort, is significantly improved over conventional control approaches. The model of a fan coil unit is identified and validated based on real data gathered from the living lab of University of Zagreb Faculty of Electrical Engineering and Computing. This model is then used for the subsequent control development and validation of the overall control approach via simulations.

Multi-level optimal control of a microgrid-supplied cooling system in a building

Recent studies highlighted the model predictive control as a promising platform for complex systems management and energy efficiency improvement in a large number of applications, particularly prominent in building climate and smart grid control. Involvement of microgrids offered great possibilities of power consumption peak shaving, improved grid stability and decentralisation, by introducing buildings as active market participants. To this aim, the paper is focused on the development of a microgrid optimal control that acts as an intermediary method for integration of a building to the smart grid with dynamic pricing of electricity. A multilevel optimal control is applied on a building cooling system and energy flow optimization of a DC microgrid that consists of a photovoltaic array, batteries stack and fuel cells stack with electrolyser, all connected to the utility grid via a bidirectional power converter. Performance of the proposed approach is verified through 4-month simulations of a microgrid integrated with a building cooling system, in actual meteorological and electricity price data scenario. Microgrid energy storages and the proposed control method fully exploit possibilities of dynamic pricing and greatly reduce cooling system operation costs while ensuring the high level of user comfort.

Nonlinear hierarchical building zone and microgrid control based on sensitivity analysis

Model predictive control has proven to be a promising platform for complex systems management and energy efficiency improvement in a large number of applications, particulary prominent in building climate or smart grids control. Interoperation of those systems often turns out to be of a nonlinear nature. The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on nonlinear model predictive control. The modularity of coordination implies technology separation with interaction through consumption profiles and equivalent prices, where nonlinearity occurs in electricity-heat energy conversion. A method based on sensitivity analysis is exploited and put to parametric formulation to tackle the problem of high computational complexity. The nonlinearity is addressed by choosing the convergence of the local optimum towards the microgrid global optimum in the direction of the lowest cost function values. Iterative approach between zone and microgrid level nonlinear problem finally results in the cost-optimal zone level operation. Results show the ability of the proposed approach to cope with system nonlinearities and illustrate how introduction of a central chiller unit characteristic rises the overall cost benefit of the system.

Estimation of disturbance heat flux in buildings

This paper investigates estimation of disturbance heat flux in a zone using autoregressive integrated moving average model with exogenous inputs (ARIMAX). The model is derived from simple continuous zone model. Data used for estimation is obtained from building simulation in IDA ICE. The procedure exhibits fast detection of the disturbance heat flux which opens several possible applications in energy-efficient buildings control, like possibility of instantaneous on-off reaction on the zone actuators or better building climate prediction for predictive control.