The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Development and Application of a Digital Twin for Chiller Plant Performance Assessment

As the complexity of industrial equipment continues to increase, the management of the individual machines and integrated operations becomes difficult without computer tools. The availability of streaming data from manufacturing floors, plant operations, and deployed fleets can be overwhelming to analyze, although it provides opportunities to improve performance. The use of dedicated monitoring systems in the plant and field to troubleshoot machinery can be integrated within a product lifecycle management (PLM) architecture to offer greater features. PLM offers virtual processes and software tools for the design, analysis, monitoring, and support of engineering systems and products. Within this paradigm, a digital twin can estimate system behavior based on the assembled physical models and the operating data for preventive maintenance efforts. PLM software can store computer-aided-design, computer-aided-engineering, advanced manufacturing, and data in cloud form for remote access. Integrating physical and performance data into a single database provides flexibility and adaptability while allowing distant commanding and health monitoring of dynamic systems. The recent attention on global warming, and the minimization of energy consumption can be partially addressed by examining those economic sectors that use large quantities of electric power. Across the United States, heating, ventilation, and air conditioning (HVAC) systems use a collective $14 Billion of resources to control the temperature of commercial and residential spaces. A typical commercial HVAC system consists of a chiller plant, water pumps for fluid circulation, multiple heat exchangers, and iii forced air blowers. In this research project, a digital twin is created for a single compressor chilled water-based HVAC system using a multi-disciplinary CAE software package. The system level models are assembled to describe a 1400 ton chiller located in the East-side chiller plant on the Clemson University (Clemson, SC) campus. The dynamic models that estimate the fluid pressures, temperatures, and flow rates, as well as the electrical and mechanical power consumption, are validated against the operating data streamed through the OptiCX System. To demonstrate the capabilities of this digital twin tool in a preventive maintenance mode, various degradations are virtually investigated in the chiller plant\u27s components. The mechanical pump efficiency, electric pump motor friction, pipe blockage, air flow rate sensor, and the expansion valve opening were degraded by 3% to 5%, which impacted component behavior and system performance. The analysis of these predicted plant signals helped to establish preventive maintenance thresholds on these components, which should promote improved plant reliability. A digital twin provides additional flexibility than stand-alone monitoring technologies due to the capability of simulating customized scenarios for analyzing failure-prone conditions and overall equipment effectiveness (OEE). The PLM-based digital twin offers a design and prognostic platform for HVAC systems