Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.

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Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications

CO2 subcooling has resulted a method to upgrade the performance of CO2 refrigeration plants in the recent years, with overall improvements up to 12% with internal heat exchangers, 22% with economizers, 25.6% with thermoelectric systems and 30.3% with dedicated subcooling methods. This paper comprehensively reviews the recent studies that consider subcooling as a way to upgrade the performance of CO2 refrigeration cycles. The review is limited to CO2 refrigeration cycles with accumulation receiver for commercial purposes and does not consider air conditioning or MAC systems. It is organized as follows: first, the thermodynamic aspects of subcooling in CO2 refrigeration cycles are described and discussed; second, the main results and conclusions of the recent investigations are analysed inside two big groups: subcooling internal methods and subcooling external methods. Finally, the review synthesizes the current state of the art and points out the lines of research that deserve future developments.

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Subcooling methods for CO2 refrigeration cycles: A review

Commercial refrigeration comprises food freezing and conservation in retail stores and supermarkets, so, it is one of the most relevant energy consumption sectors, and its relevance is increasing. This paper reviews the most recent developments in commercial refrigeration available in literature and presents a good amount of results provided these systems, covering some advantages and disadvantages in systems and working fluids. Latest researches are focused on energy savings to reduce CO2 indirect emissions due to the burning of fossil fuels. They are focused on system modifications (as dedicated subcooling or the implementation of ejectors), trigeneration technologies (electrical, heating and cooling demand) and better evaporation conditions control. Motivated by latest GWP regulations that are intended to reduce high GWP HFC emissions; R404A and R507 are going to phase out. Besides hydrocarbons and HFO, CO2 appears as one of the most promising HFC replacements because its low contribution to global warming and high efficiencies when used in transcritical and low-stage of cascade systems.

Commercial refrigeration – An overview of current status

Extremum seeking control of COP optimization for air-source transcritical CO2 heat pump water heater system

Incorporating traffic information in power management optimization process for electrified and connected vehicles offers opportunities for improving fuel economy. Integrating the management of thermal load (such as those used for heating, ventilation, & air conditioning (HVAC) of the passenger compartment, and for the battery cooling) with the power management process can provide even greater benefits for connected and automated vehicles (CAVs). However, given the relatively slow dynamics associated with the thermal subsystems, the lack of reliable power and thermal loads prediction over an extended prediction horizon is the main challenge for efficient thermal management using model predictive control (MPC). This paper presents a hierarchical two-layer MPC scheme which exploits vehicle speed and traffic preview predictions over short and long prediction horizons to schedule optimal thermal trajectories for the cabin and battery cooling in hybrid electric vehicles (HEVs) via a novel intelligent online constraint handling (IOCH) approach. These trajectories are next incorporated into the vehicle-level controller to determine the proper power split between electric motor and internal combustion engine (ICE). We present the development and experimental validation of control-oriented models used for prediction of the vehicle thermal dynamics and loads over a long planning horizon. Compared to a more traditional single-layer MPC approach, the proposed two-layer MPC shows that depending on the driving cycle and traffic conditions, 2.2% to 5.3% reductions in HEV fuel consumption can be achieved for urban driving and congested city driving cycles, respectively, in CAV operation scenario. This fuel economy improvement is a direct result of taking proactive actions through real-time prediction and optimization to avoid conservative and inefficient thermal responses, while enforcing cabin and battery operating constraints.

Cabin and Battery Thermal Management of Connected and Automated HEVs for Improved Energy Efficiency Using Hierarchical Model Predictive Control

With vapor compression systems consuming a significant portion of the overall U.S. energy consumption each year, extensive efforts have been made toward the development of control strategies which aim to maximize system efficiency while providing the desired cooling. However, previous control strategies under-utilize a degree of freedom corresponding to the amount of refrigerant in the system, which is related to condenser subcooling and can significantly affect system efficiency. In this paper, an alternative system architecture, which utilizes a receiver and an additional electronic expansion valve, is used to provide independent control of condenser subcooling. Simulation and experimental results show there exists an optimal subcooling which maximizes system efficiency; however, this optimal subcooling changes with operating conditions. Therefore, extremum seeking control is implemented to find the optimal subcooling in an adaptive, model-free manner. Experimental results demonstrate a 9% increase in efficiency using the alternative architecture and extremum seeking control.

Optimal subcooling in vapor compression systems via extremum seeking control: Theory and experiments

This paper presents methods for using zonotopes and constrained zonotopes to improve the practicality of a wide variety of set-based operations commonly used in control theory. The proposed methods extend the use of constrained zonotopes to represent sets resulting from operations including halfspace intersections, convex hulls, robust positively invariant sets, and Pontryagin differences. Order reduction techniques are also presented that provide lower-complexity inner-approximations of zonotopes and constrained zonotopes. Numerical examples are used to demonstrate the efficacy and computational advantages of using zonotope-based set representations for dynamic system analysis and control.

Set operations and order reductions for constrained zonotopes.

Stability of decentralized model predictive control of graph-based power flow systems via passivity

Partially decentralized control of large-scale variable-refrigerant-flow systems in buildings

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Justin P. Koeln

Papers

Optimal subcooling in vapor compression systems via extremum seeking control: Theory and experiments

Set operations and order reductions for constrained zonotopes.

Stability of decentralized model predictive control of graph-based power flow systems via passivity

Partially decentralized control of large-scale variable-refrigerant-flow systems in buildings

Dynamical Graph Models of Aircraft Electrical, Thermal, and Turbomachinery Components