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• Whole-year nonlinear mixed-integer MPC simulation study for a thermal energy system. • General approximate MINLP solution method and measurement-based forecast correction. • Performance comparison of MPC to an elaborate conventional control strategy. • Energy consumption of the system is significantly reduced using MPC. • MPC autonomously identifies previously unconsidered, beneficial operation modes. This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real-time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes. [ABSTRACT FROM AUTHOR]

• A MPC for an air-conditioning system with dedicated outdoor air system is developed. • The MPC employs a linear building model to optimise energy use and indoor comfort. • The system is demonstrated experimentally in a lecture theatre for real-time control. • The MPC system achieves 20% energy saving while greatly improves indoor environment. Using separate cooling coils for sensible and latent loads provide extra control flexibility to optimise the energy efficiency and comfort in air-conditioning and mechanical ventilation (ACMV) systems. A popular implementation of such technology is dedicated outdoor air system (DOAS)-assisted separate sensible and latent cooling (SSLC) systems. However, a sophisticated control technique is needed to coordinate the control of multiple cooling coils in such systems. This paper presents a novel model predictive control (MPC) developed for a DOAS-assisted SSLC system. The MPC adopts a linear state-space model that captures building thermodynamics, thermal comfort and ACMV for building response prediction and optimization. Subsequently, a multi-objective cost function is employed to optimize energy use and thermal comfort while fulfilling constraints of predicted mean vote (PMV) (-0.5, 0.5) and relative humidity (0%, 65%) in buildings. The performance of the MPC for controlling a conventional single-coil air-handling unit (AHU) system and a DOAS-assisted SSLC system is experimentally investigated and compared to a conventional feedback-control-based building management system (BMS). The MPC system achieved 18% and 20% electricity savings for the single-coil AHU and DOAS-assisted SSLC, respectively, as compared to the BMS controlled single-coil AHU. Furthermore, indoor thermal comfort is significantly improved, compared to the BMS. DOAS-assisted SSLC is shown to be advantageous compared to single-coil AHU to achieve better indoor environment in terms of thermal comfort and humidity, when both systems are controlled by MPC. [ABSTRACT FROM AUTHOR]

• Participation of an aggregator of prosumers in the electricity markets. • Real-time provision of multiple electricity market products. • Real-time control of heterogeneous flexible resources. • Hierarchical model predictive control. The foreseen participation of aggregators of prosumers in the electricity markets will require the development of computational tools to support them in the definition and delivery of market products. This paper proposes a new hierarchical model predictive control (MPC) to support an aggregator in the delivery of multiple market products through the real-time control of heterogeneous flexible resources. The hierarchical MPC covers the participation of an aggregator in both energy and secondary reserve markets. The results show that the aggregator is capable of delivering several combinations of energy and secondary reserve without compromising the comfort and preferences of its clients. [ABSTRACT FROM AUTHOR]

• State space models are proposed for all-electric ships. • Novel predictive energy management and maneuvering control approaches are proposed. • Using the approaches, optimal engine loading is guaranteed. • The fuel efficiency increases by 2–15% depending on the operating profile. • Trajectory tracking performance is improved. Over the last few years, autonomous shipping has been under extensive investigation by the scientific community where the main focus has been on ship maneuvering control and not on the optimal use of energy sources. In this paper, the purpose is to bridge the gap between maneuvering control, energy management, and the control of the Power and Propulsion System (PPS) to improve fuel efficiency and the performance of the vessel. Maneuvering control, energy management, and the control of the PPS are in the literature typically studied independently from one another, while they are closely connected. A generic control methodology based on receding horizon control techniques is proposed for the ship maneuvering control as well as energy management. In the context of this research, Direct Current (DC) all-electric architectures are considered for the PPS where the relationship between the produced power by energy sources and vessel propellers is established by a DC microgrid. The objective of the proposed approach is to ensure the ship mission objectives by guaranteeing efficient power availability, decreasing the trajectory tracking error, and increasing the fuel efficiency. In this regard, for the ship motion control, a Model Predictive Control (MPC) algorithm is proposed which is based on Input–Output Feedback Linearization (IOFL). Through this algorithm, the required power for the ship mission is predicted and then, transferred to the proposed Predictive Energy Management (PEM) algorithm which decides on the optimal split between different on-board energy sources during the mission. As a result, the fuel efficiency and the power system stability can be increased. Several simulations are carried out for the evaluation of the proposed approach. The results suggest that by adopting the proposed approach, the trajectory tracking error decreases and the Specific Fuel Consumption (SFC) efficiency is significantly improved. [ABSTRACT FROM AUTHOR]