Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

Economic model predictive control for optimal operation of combined heat and power systems

© 2019. ElsevierThe use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Hence, to determine the optimal operation of these systems in the changing energy market, the time-varying price profiles for both electricity as well as the required resources and the energy-market constraints should be considered into the design of the control strategies. To solve these issues and maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller, which will be designed according to the Economic Model Predictive Control (EMPC) approach. The proposed controller is designed considering a non-constant time step to get a high sampling frequency for the near instants and a lower resolution for the far instants. Besides, a soft constraint to met the market constraints for the sale of electric power is proposed. The proposed controller is developed based on a real CHP plant installed in the ETA research factory in Darmstadt, Germany. Simulation results show that lower computational time can be achieved if a non-constant step time is implemented while the market constraints are satisfied.Peer ReviewedPostprint (author's final draft

Prädiktive Energieflussregelung von Versorgungssystemen

Vor dem Hintergrund der Energiewende bieten sich insbesondere auch mit Digitalisierungstechnologien für Produktions- und Versorgungssysteme erhebliche Potenziale, durch einen effizienten sowie flexiblen Energieeinsatz die energiebezogenen Kosten an den Produktionsstandorten zu reduzieren. Durch eine thermohydraulische Vernetzung von Maschinen, technischer Gebäudeausstattung und dem Gebäude selbst wird in der ETA-Modellfabrik am Campus der TU Darmstadt demonstriert, wie u. a. Abwärme aus dem Produktionsprozess effizient zurückgewonnen und als Nutzenergie zur Klimatisierung und Prozesswärmeversorgung bereitgestellt werden kann. Dieser Ansatz einer gesamtenergetischen Betrachtung industrieller Produktionsbetriebe erhöht erheblich das Einsparungspotenzial, führt jedoch unweigerlich zu einer gesteigerten Komplexität der interagierenden (Teil-)Systeme. Durch das in diesem Beitrag beschriebene Konzept einer vorausschauenden (prädiktiven) Energieflussregelung, können auch unter dynamisch veränderlichen Randbedingungen, z. B. in volatilen Energiemärkten, die Erzeuger- und Speichersysteme im vielschichtigen Verbund optimal betrieben und Energiekosten reduziert werden

Data Collection for Energy Monitoring Purposes and Energy Control of Production Machines

Data collection is one major prerequisite for energy efficient production facilities, enabling further analyses by assessing KPIs, condition or status monitoring applications and direct feedback or evaluation of efficiency measures. This paper presents a standardized approach to collect energy relevant data of production machines and their components on the PLCs. Energy data can both be directly monitored on a connected HMI or made available for standard interfaces and saved on a generic data server for monitoring and further computation using Matlab DLLs. Besides monitoring aspects, an approach is presented for empowering machine PLCs with energy control functions, allowing them to switch energy modes (e.g. standby, production-ready, operational) to reduce energy costs and CO2 emissions. An implementation of the energy monitoring and control approach on a milling machine is presented in detail, followed by an overview of its use for energetic optimization

A Power Disaggregation Approach for fine-grained Machine Energy Monitoring by System Identification

Energy monitoring is one major prerequisite for energy efficiency measures. Energy and power data throughout different levels of production allow benchmarking and condition monitoring applications based on insightful energy performance indicators. However, fine-grained measurement concepts for energy and power require high investments with uncertain benefits. This paper presents a low-cost approach to monitor the component-by-component energy consumption with a minimum of sensor technology that can be applied to a variety of production machines. Aggregated energy data combined with components’ control signals are the basis for the determination of components’ energy consumptions using two system identification algorithms. While one method is realized in an offline-mode after data collection, the second approach utilizes real-time data based on a recursive least squares algorithm. Eventually, the feasibility of the theoretical system identification concepts is shown in a laboratory environment

Finite Control Set Model Predictive Current Control for Grid-Connected Voltage-Source Converters With LCL Filters: A Study Based on Different State Feedbacks

Abstract—This paper presents different state feedback approaches of finite control set model predictive control (FCS-MPC) applied to a grid-connected voltage-source converter (VSC) with an LCL filter. Besides converter-side current feedback, two multivariable control approaches and line-side current control are introduced and compared based on theoretical and experimental evaluation. As the LCL filter introduces an additional resonance frequency to the system, the use of different active damping (AD) methods in combination with FCS-MPC is discussed. Furthermore, practical control implementation issues are discussed. The presented methods reveal the great potential, high dynamic performance, and flexibility of FCS-MPC, enabling multivariable control as well as both reduced switching losses and low harmonic current distortion at the same time. Eventually, the feasibility of the theoretical control concepts is shown in a laboratory environment

A Metaheuristic for Energy Adaptive Production Scheduling with Multiple Energy Carriers and its Implementation in a Real Production System 2

Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system

Datengestützte Verfahren für einen energieeffizienten und flexiblen Betrieb von Produktions- und Versorgungstechnik (Teil 4): Reihe: Künstliche Intelligenz, Maschinendaten, Algorithmen, Effizienz, Geschäftsmodelle

Im Rahmen einer vierteiligen Artikelserie beschreiben die Experten des PTW Darmstadt das Potenzial, das die Analyse von Maschinendaten birgt. Wie die Integration innovativer Datenanalyse gelingen kann, wird anhand von Anwendungsfeldern aus Forschung und Praxis aufgezeigt