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Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.
Die fluktuierende Verfügbarkeit regenerativer Energiequellen stellt eine Herausforderung bei der Planung und Auslegung regenerativer Gebäudeenergiesysteme dar. Die in einem System benötigten Speicherkapazitäten hängen dabei sowohl von der eingesetzten Regelungsstrategie als auch von den temperaturabhängigen Wirkungsgraden der Anlagenkomponenten ab. Genauere Einblicke in das Betriebsverhalten eines Gesamtsystems können dynamische Simulationen liefern, die eine Analyse der Systemtemperaturen und von Teilenergiekennwerten ermöglichen.
Simulation based studies for operational energy system analysis play a significant role in evaluation of various new age technologies and concepts in the energy grid. Various modelling approaches already exist and in this original paper, four models representing these approaches are compared in two real-world hybrid energy system scenarios. The models, namely TransiEnt, µGRiDS, and OpSim (including pandaprosumer and mosaic) are classified into component-oriented or system-oriented approaches as deduced from the literature research. The methodology section describes their differences under standard conditions and the necessary parameterization for the purpose of creating a framework facilitating a closest possible comparison. A novel methodology for scenario generation is also explained. The results help to quantify primary differences in these approaches that are also identified in literature and qualify the influence of the accuracy of the models for application in a system-wide analysis. It is shown that a simplified model may be sufficient for the system-oriented approach especially when the objective is an optimization-based control or planning. However, from a field level operational point of view, the differences in the time series signify the importance of the component-oriented approaches.
Dort, wo Modelle der operativen Energiesystemanalyse untereinander Überschneidungen aufweisen, stellt sich zunächst die Frage, ob sie bei gleichgearteten Fragestellungen auch die gleichen Antworten liefern. Dies zu beantworten war erstes Ziel des hier beschriebenen Vorhabens. Das zweite Ziel war, im Falle von Differenzen zu ermitteln, worin diese begründet liegen. Es waren nicht nur die Modelle selbst, sondern auch das methodische Vorgehen zur Modellerstellung und Simulation in Betracht zu ziehen. Die darauf aufbauende Identifikation von individuellen Optimierungspotenzialen war das dritte Ziel. Da die operative Energiesystemanalyse noch ein recht junger Forschungsbereich ist, existiert darüber hinaus Klärungsbedarf, welches Modell sich für welche Untersuchungen besonders eignet und welches methodische Vorgehen sich empfiehlt. Die Beantwortung dieser Fragen stellte das vierte Ziel des Vorhabens dar.