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Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. The focus of this study is to investigate the effects of adding communication throughout a population of TCLs on the resilience of the system. A Metric for resilience is calculated on varying populations of TCLs and verified with agent based modeling simulations. At the core of this study is an added thermostat criterion created from the combination of a proportional gain and the average compressor operating state of neighboring TCLs. Differing connection architectures are also analyzed. Resilience of the systems under different connection topologies, are calculated by analyzing algebraic connectivity at varying population sizes. The resilience analysis was verified through simulation. Results of the analysis show the effect of on delay schemes and connection architecture on stability limit of each system. Good concurrence was found between predicted and observed resilience for smaller dead-band sizes. Simulations showed varying results on the effect of a simulated attack based on location of the attack within the population.

 

As part of Industrial Control Systems (ICS), the control logic controls the physical processes of critical infrastructures such as power plants and water and gas distribution. The Programmable Logic Controller (PLC) commonly manages these processes through actuators based on information received from sensor readings. Therefore, boundary checking is essential in ICS because sensor readings and actuator values must be within the safe range to ensure safe and secure ICS operation. In this paper, we propose an ontology-based approach to provide the knowledge required to verify the boundaries of ICS components with respect to their safety and security specifications. For the proof of concept, the formal model of the Programmable Logic Controller (PLC) is created in UPPAAL and validated in UPPAAL-API. Then, the proposed boundary verification algorithm is used to import the required information from the safety/security ontology 

Safety and security are the two most important properties of industrial control systems (ICS), and their integration is necessary to ensure that safety goals do not undermine security goals and vice versa. Sometimes, safety and security co-engineering leads to conflicting requirements or violations capable of impacting the normal behavior of the system. Identification, analysis, and resolution of conflicts arising from safety and security co-engineering is a major challenge, an under-researched area in safety-critical systems(ICS). This paper presents an STPA-SafeSec-CDCL approach that addresses the challenge. Our proposed methodology combines the STPA-SafeSec approach for safety and security analysis and the Conflict-Driven Clause Learning (CDCL) approach for the identification, analysis, and resolution of conflicts where conflicting constraints are encoded in satisfiability (SAT) problems. We apply our framework to the Tennessee Eastman Plant process model, a chemical process model developed specifically for the study of industrial control processes, to demonstrate how to use the proposed method. Our methodology goes beyond the requirement analysis phase and can be applied to the early stages of system design and development to increase system reliability, robustness, and resilience. 

Any safety issues or cyber attacks on an Industrial Control Systems (ICS) may have catastrophic consequences on human lives and the environment. Hence, it is imperative to have resilient tools and mechanisms to protect ICS. To verify the safety and security of the control logic, complete and consistent specifications should be defined to guide the testing process. Second, it is vital to ensure that those requirements are met by the program control algorithm. In this paper, we proposed an approach to formally define the system specifications, safety, and security requirements to build an ontology that is used further to verify the control logic of the PLC software. The use of ontology allowed us to reason about semantic concepts, check the consistency of concepts, and extract specifications by inference. For the proof of concept, we studied part of an industrial chemical process to implement the proposed approach. The experimental results in this work showed that the proposed approach detects inconsistencies in the formally defined requirements and is capable of verifying the correctness and completeness of the control logic. The tools and algorithms designed and developed as part of this work will help technicians and engineers create safer and more secure control logic for ICS processes. 

Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulation scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in tool assessment of network’s resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities. 

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics do not provide adequate energy security. As a result, researchers have focused their efforts resilience metrics to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose an operational resilience metric for modern power distribution systems. The metric is based on the aggregation of system assets adaptive capacity in real and reactive power. This metric gives information to the magnitude and duration of a disturbance the system can withstand. We demonstrate resilience metric in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages. 

Motivated by decreased cost and climate change concerns, the penetration of solar photovoltaic (PV) energy generation and battery energy storage has been continually increasing. The variability in solar PV power generation has led to many new challenges for utilities and researchers. One challenge is the quantification of the resilience contribution to the grid from its assets and is the topic of this paper. In this work, we propose a framework for evaluating the resilience contribution of solar generation and battery storage assets on the grid. The metric provides a quantifiable adaptive capacity measure in terms of real and reactive power and includes uncertainty for solar PV assets. A case study using very short-term and short-term solar generation forecast demonstrates the framework and provides useful insight to the resilience solar and battery storage assets can contribute to the grid.