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Abstract: State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. The periodic systems’ states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, bounded and subject to periodically time-dependent bounds. The objective is to design MPC laws that robustly enforce constraint satisfaction in a manner that is least-restrictive, i.e., have the largest possible domain. The proposed design method is demonstrated on a building climate control example. The proposed method is directly applicable to time-invariant MPC. [Copyright &y& Elsevier]

Abstract: This paper investigates the potential of using occupancy information to realize a more energy efficient building climate control. The study focuses on Swiss office buildings equipped with Integrated Room Automation (IRA), i.e. the integrated control of Heating, Ventilation, Air Conditioning (HVAC) as well as lighting and blind positioning of a building zone or room. To evaluate the energy savings potential, different types of occupancy information are used in a Model Predictive Control (MPC) framework, which is well-suited for this study due to its ability to readily include occupancy information in the control. An MPC controller, which controls the building based on a standard fixed occupancy schedule, is used as a benchmark. The energy use of this benchmark is compared with three other control strategies: first, the same MPC controller which uses the same schedule for control as the benchmark, but turns off the lighting in case of (an instantaneous measurement of) vacancy; second, the same MPC controller which uses the same schedule as the benchmark for control, but turns off lighting and ventilation in case of (an instantaneous measurement of) vacancy; and third, the same MPC controller as the benchmark but using a perfect prediction about the upcoming occupancy. This comparison is carried out for different buildings, HVAC systems, seasons and occupancy patterns in order to determine their influence on the energy savings potential. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice. [Copyright &y& Elsevier]

Abstract: Low energy buildings have attracted lots of attention in recent years. Most of the research is focused on the building construction or alternative energy sources. In contrary, this paper presents a general methodology of minimizing energy consumption using current energy sources and minimal retrofitting, but instead making use of advanced control techniques. We focus on the analysis of energy savings that can be achieved in a building heating system by applying model predictive control (MPC) and using weather predictions. The basic formulation of MPC is described with emphasis on the building control application and tested in a two months experiment performed on a real building in Prague, Czech Republic. [Copyright &y& Elsevier]

Higher penetration of renewable energy sources and market liberalization increase both the need for transmission capacity and the uncertainty in power system operation. New methods for power system operational planning are needed to allow for efficient use of the grid, while maintaining security against disturbances. In this paper, we propose a risk model for risks related to outages, accounting for available remedial measures and the impact of cascading events. The new risk model is used to formulate risk-based constraints for the post-contingency line flows, which are included in an optimal power flow (OPF) formulation. Forecast uncertainty is accounted for by formulating the relevant constraints as a joint chance constraint, and the problem is solved using a sampling-based technique. In a case study of the IEEE 30 bus system, we demonstrate how the proposed risk-based, probabilistic OPF allows us to control the risk level, even in presence of uncertainty. We investigate the trade-off between generation cost and risk level in the system, and show how accounting for uncertainty leads to a more expensive, but more secure dispatch. [ABSTRACT FROM AUTHOR]

Abstract: Climate changes, diminishing world supplies of non-renewable fuels, as well as economic aspects are probably the most significant driving factors of the current effort to save energy. As buildings account for about 40% of global final energy use, efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in an energy and cost effective manner with minimum retrofitting requirements. In such a predictive control approach, dynamic building models are of crucial importance for a good control performance. An algorithm which has not been used in building modeling yet, namely a combination of minimization of multi-step ahead prediction errors and partial least squares will be investigated. Subsequently, two case studies are presented: the first is an artificial model of a building constructed in Trnsys environment, while the second is a real-life case study. The proposed identification algorithm is then validated and tested. [Copyright &y& Elsevier]

Abstract: Recent results show that a predictive building automation can be used to operate buildings in an energy and cost effective manner with only a small retrofitting requirements. In this approach, the dynamic models are of crucial importance. As industrial experience has shown, modeling is the most time-demanding and costly part of the automation process. Many papers devoted to this topic actually deal with modeling of building subsystems. Although some papers identify a building as a complex system, the provided models are usually simple two-zones models, or extremely detailed models resulting from the use of building simulation software packages. These are, however, not suitable for predictive control. The objective of this paper is to share the years-long experience of the authors in building modeling intended for predictive control of the building''s climate. We provide an overview of identification methods for buildings and analyze their applicability for subsequent predictive control. Moreover, we propose a new methodology to obtain a model suitable for the use in a predictive control framework combining the building energy performance simulation tools and statistical identification. The procedure is based on the so-called co-simulation that has appeared recently as a feature of various building simulation software packages. [Copyright &y& Elsevier]