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Mixed-integer NMPC for real-time supervisory energy management control in residential buildings
(2023)
In recent years, building energy supply and distribution systems have become more complex, with an increasing number of energy generators, stores, flows, and possible combinations of operating modes. This poses challenges for supervisory control, especially when balancing the conflicting goals of maximizing comfort while minimizing costs and emissions to contribute to global climate protection objectives. Mixed-integer nonlinear model predictive control is a promising approach for intelligent real-time control that is able to properly address the specific characteristics and restrictions of building energy systems. We present a strategy that utilizes a decomposition approach, combining partial outer convexification with the Switch-Cost Aware Rounding procedure to handle switching behavior and operating time constraints of building components in real-time. The efficacy is demonstrated through practical applications in a single-family home with a combined heat and power unit and in a multi-family apartment complex with 18 residential units. Simulation studies show high correspondence to globally optimal solutions with significant cost savings potential of around 19%.
In this paper a new rotor position observer for permanent magnet synchronous machines (PMSM) based on an Extended-Kalman-Filter (EKF) is presented. With this method, just one single EKF is sufficent to evaluate the position information from electromotive force (EMF) and anisotropy. Thus, the PMSM can be controlled for the entire speed range without a position sensor and without the need to switch or synchronize between different observers. The approach covers online estimation of permanent magnetic field and mechanical load. The resulting EKF-based rotor position estimator is embedded in the existing cascaded control concept of the PMSM without need of additional angle trackers or signal filters. The experimental validation for the position sensorless control shows optimized dynamic behaviour.
We present a methodology based on mixed-integer nonlinear model predictive control for a real-time building energy management system in application to a single-family house with a combined heat and power (CHP) unit. The developed strategy successfully deals with the switching behavior of the system components as well as minimum admissible operating time constraints by use of a special switch-cost-aware rounding procedure. The quality of the presented solution is evaluated in comparison to the globally optimal dynamic programming method and conventional rule-based control strategy. Based on a real-world scenario, we show that our approach is more than real-time capable while maintaining high correspondence with the globally optimal solution. We achieve an average optimality gap of 2.5% compared to 20% for a conventional control approach, and are faster and more scalable than a dynamic programming approach.
This paper proposes an extended Petri net formalism as a suitable language for composing optimal scheduling problems of industrial production processes with real and binary decision variables. The proposed approach is modular and scalable, as the overall process dynamics and constraints can be collected by parsing of all atomic elements of the net graph. To conclude, we demonstrate the use of this framework for modeling the moulding sand preparation process of a real foundry plant.
This paper presents a databased approach for improving the precision of the moulding sand compressibility in the moulding sand mixer of a foundry. In this approach, the deviation between the measured and the target compressibility is reduced by controlling the water addition. The complex dynamic behaviour of the process variables and their influence on the water addition is modelled with a long short-term memory (LSTM) network. Another LSTM network as control path simulates the impact of the water addition on the compressibility. Simulation and experimental results with the applied model for water prediction in a feedforward control yield relevant improvements of the moulding sand compressibility.