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The increasing variety of combinations of different building technology components offers a high potential for energy and cost savings in today's buildings. However, in most cases, this potential is not yet fully exploited due to the lack of intelligent supervisory control systems that are required to manage the complexity of the resulting overall systems. In this article, we present the implementation of a mixed-integer nonlinear model predictive control approach as a smart realtime building energy management system. The presented methodology is based on a forward-looking optimization of the overall energy costs. It takes into account energy demand forecasts and varying electricity market prices. We achieve real-time capability of the controller by applying a decomposition approach, which approximates the optimal solution of the underlying mixed-integer optimal control problem by convexification and rounding of the relaxed solution. The quality of the suboptimal solution is evaluated by comparison with the globally optimal solution obtained by the dynamic programming method. Based on a real-world scenario, we demonstrate that utilization of the real-time capable mixedinteger nonlinear model predictive control approach in a building control system leads to savings of 16% in the total operating costs and 13% in primary energy compared to the state-of-the-art control strategy without any loss of comfort for the residents.
With the use of an energy management system in an industrial company according to ISO 50001, a step-by-step increase in energy efficiency can be achieved. The realization of energy monitoring and load management functions requires programs on edge devices or PLCs to acquire the data, adapt the data type or scale the values of the energy information. In addition, the energy information must be mapped to communication interfaces (e.g. based on OPC UA) in order to convey this energy information to the energy management application. The development of these energy management programs is associated with a high engineering effort, because the field devices from the heterogeneous field level do not provide the energy information in standardized semantics. To mitigate this engineering effort, a universal energy data information model (UEIM) is developed and presented in this paper.
Requirements for an energy data information model for a communication-independent device description
(2021)
With the help of an energy management system according to ISO 50001, industrial companies obtain the opportunities to reduce energy consumption and to increase plant efficiencies. In such a system, the communication of energy data has an important function. With the help of so-called energy profiles (e.g. PROFIenergy), energy data can be communicated between the field level and the higher levels via proven communication protocols (e.g. PROFINET). Due to the fact that in most cases several industrial protocols are used in an automation system, the problem is how to transfer energy data from one protocol to another with as less effort as possible. An energy data information model could overcome this problem and describe energy data in a uniform and semantically unambiguous way. Requirements for a unified energy data information model are presented in this paper.
With regard to climate change, increasing energy efficiency is still a significant issue in the industry. In order to acquire energy data at the field level, so-called energy profiles can be used. They are advantageous as they are integrated into existing industrial ethernet standards (e.g. PROFINET). Commonly used energy profiles such as PROFIenergy and sercos Energy have been established in industrial use. However, as the Industrial Internet of Things (IIoT) continues to develop, the question arises whether the established energy profiles are sufficient to fullfil the requirements of the upcoming IIoT communication technologies. To answer this question the paper compares and discusses the common energy profiles with the current and future challenges of energy data communication. Furthermore, this analysis examines the need for further research in this field.