Cyber-physical Energy Systems Research Articles

Real-time cyber/physical interplay in scheduling for peak load optimisation in Cyber–Physical Energy Systems

This paper is about reducing the power consumption of Cyber–Physical Energy Systems (CPESs) composed of many loads through the usage of a scheduling technique inspired by the real-time-computing domain; the electric-load coordination guarantees a more efficient operation of the entire system by avoiding unnecessary concurrent activation of loads and thus limiting the peak load. We represent the power loads themselves as “physical” components and the computing devices that coordinate them as “cyber” components and formally derive the relationship between the operations in cyber and physical domains as the interplay between the schedules enforced on each component: indeed, the schedule of the loads – generated by combining a two-dimensional bin-packing and an optimal multi-processor real-time scheduling algorithm – influences the timing of the processing tasks that are dedicated to the activation/deactivation of loads themselves. We also consider non-schedulable loads by introducing a policy to cope with the presence of such loads. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method. The usage of real-time scheduling in this context provides inherent resource optimisation by limiting the number of concurrent loads that are active at the same time, thus directly reducing the overall peak load; moreover, thanks to the limited computational complexity of the algorithms, it scales to large systems, overcoming the scalability issues of common optimisation methods. Numerical simulations and experiments confirm the good performance of the proposed peak load reduction method.

Communication network robust routing optimization in an integrated energy cyber–physical system based on a random denial-of-service attack

The integration of power grids and communication networks in smart grids enhances system safety and reliability but also exposes vulnerabilities to network attacks, such as Denial-of-Service (DoS) attacks targeting communication networks. A multi-index evaluation approach is proposed to optimize routing modes in integrated energy cyber-physical systems (IECPS) considering potential failures from attacks. Security and economic service evaluation indexes are incorporated to quantify the significance of information flow routing. An optimization model for electric, heat, and gas routing in worst-case scenarios is formulated and solved using a column and constraint generation algorithm. The optimized routing method effectively circumvents specified attack areas, reducing the correlation degree of communication links within the attack area. Comparison with single-service optimization methods demonstrates the superiority of the proposed approach in mitigating the impact of network attacks on IECPS. The study highlights the importance of considering security and economic factors in optimizing routing modes to enhance the resilience of integrated energy cyber-physical systems against network attacks, particularly DoS attacks on communication networks. The evaluation index approach presented in this study provides a comprehensive method for assessing the importance of communication links in IECPS and optimizing routing modes to improve system robustness and reliability in the face of network attacks.

A hardware-in-the-loop (HIL) testbed for cyber-physical energy systems in smart commercial buildings

In recent years, there has been a growing trend toward the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenarios before deploying them in the real world. This is where a Hardware-in-the-loop (HIL) testbed designed for studying sensor and control-related studies in smart buildings can be highly valuable. With the growing threat of cyber-attacks and physical faults targeting smart buildings, it is essential to ensure the security of building operations. A HIL testbed can emulate cyber-attack and physical fault scenarios, allowing researchers to develop and test threat detection and mitigation algorithms. This enables researchers to identify potential issues and optimize the algorithms in a safe and controlled environment before they are deployed in real-world settings, reducing the risk of failures that can negatively impact occupant comfort, safety, and energy efficiency. Therefore, this paper developed a HIL testbed designed for cyber-physical energy systems (e.g. buildings automation system (BAS)) in smart commercial buildings. The HIL testbed is comprised of a real-time building and Heating, Ventilation, and Air-Conditioning (HVAC) emulator using Modelica-based dynamic models, a set of BAS controllers, and a BAS computer server. The data generation capability of the HIL testbed is demonstrated by tracking normal and faulty operating data in the BAS, as well as monitoring detailed network traffic in the local BAS network. This study further demonstrates the HIL testbed’s capability by conducting case studies on real-time physical fault and cyber-attack experiments using a Department of Energy (DOE) prototype commercial building. It is anticipated that the fully functional HIL testbed will be utilized for a variety of sensor and control-related studies, including but not limited to testing, developing, validating of different HVAC control strategies, fault detection & diagnosis, energy monitoring and analysis, cyber security study, etc.

Active Fault-Tolerant and Attack-Resilient Control for a Renewable Microgrid Against Power-Loss Faults and Data Integrity Attacks.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

Modelling the propagation of properties across services in cyber-physical energy systems

Modern power systems, referred to as cyber-physical energy systems (CPESs), are complex systems with strong interdependencies between power and information and communication technology (ICT) systems. CPESs also have dependencies between the essential grid services. For instance, coordinated voltage control depends on state estimation, which depends on measurement acquisition. Since the operation of CPESs is largely influenced by these grid services, assessing their performance is crucial for assessing the performance of a CPES. Most of these grid services are enabled by the ICT system, i.e., they rely to a high degree on ICT. Hence, properties such as availability, correctness and timeliness, which depend on the involved software, hardware and data of the ICT system, must be considered for assessing the performance of an ICT-enabled grid service. Disturbances and repairs in CPESs impact these properties, which can then propagate and affect the performance of a grid service as well as other dependent grid services. There is, therefore, a need to model the influence of the properties of software, hardware and data on ICT-enabled grid services for single services as well as across several services, resulting in a propagation of these parameters. Current literature lacks such a model, which can used not only to investigate but also to visualise the impact of these properties on the overall perfromance of a grid service as well as other dependent grid services. This paper proposes a meta model for assessing the performance of ICT-enabled grid services, which can be instantiated for different grid services considering their dependencies. A multi-dimensional operational state space, which serves as a visualisation of the performance of grid services in terms of their state trajectory, is also proposed in this paper. The contributions are then demonstrated by a case study with a state estimation service and the widely-used CIGRE medium voltage benchmark power grid augmented with an ICT system. Three scenarios with disturbances are presented to show the benefits of the contributions. Specifically, the performance of the state estimation service considering the disturbances is investigated using the meta model, and the change in performance is visualised as trajectories using the operational state space. These contributions enable new possibilities for planning and vulnerability analyses: property changes in parts of the ICT system can be simulated to investigate their consequences throughout the ICT-enabled grid services. A trajectory representing their performance can then be visualized in the state space based on which measures could be implemented to potentially improve the resilience of the service against the considered disturbances.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Self-Organization in Cyberphysical Energy Systems: Seven Practical Steps to Agent-Based and Digital Twin-Supported Voltage Control

Self-Organization in Cyberphysical Energy Systems: Seven Practical Steps to Agent-Based and Digital Twin-Supported Voltage Control

Handling the Constraints in Min-Max MPC

One of the major sources of conservativeness in min-max model predictive control (MPC) is the handling of constraints, where the ellipsoidal robust invariant set is utilized for the proposal of sufficient and conservative conditions for the satisfaction of the constraints. In this article, in order to reduce the conservativeness due to the constraint handling, we add additional relaxation variables to the physical constraints and propose a two-step approach through relaxing the constraints by the amount which is determined by the calculating of the maximal admissible set (MAS). The constraints are relaxed in an iterative manner to avoid the constraints violation, and the constraint relaxation variables are degrees of freedom for relaxing the constraints and improving the control performance. Moreover, we show that under certain circumstance, the physical constraints can be removed without the constraint violation. The proposed approach is shown to be recursively feasibility and its effectiveness is verified through an air conditioning control in a building energy system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Buildings are account for large percentage of worldwide energy consumptions. One of the most applicable method for optimization of building energy system subject to multiple constraints is the model predictive control (MPC). However, the industrial MPC is usually not recursively feasible, which implies that the optimization problem can become infeasible and the software will be terminated at some time. In order to apply the MPC synthesis approach (MPC with recursive feasibility guarantee), we have to overcome the conservativeness problem due to the handling of constraints. We propose a useful approach in this work by introducing relaxation variables which act as degrees of freedom for improving the control performance, while the physical constraints are still satisfied. The proposed approach is verified through an example of a 24m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> office room located in Cyber-Physical Energy System (CPES) lab in Western China Science and Technology Innovation Harbour in Xianyang, China. The numerical results show the performance improve of the proposed approach.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising

Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising

Assess: anomaly sensitive state estimation with streaming systems

Information and communication technology (ICT) is an increasing part of modern power systems, which are, therefore, recognised as cyber-physical energy system (CPESs). The increase of ICT affects the situational awareness in CPESs, which is traditionally solely based on information about the power system but not about the ICT system. However, CPESs are facing various challenges regarding the integrity, correctness, and availability of process data due to the interconnection with ICT. Examples are stealthy false data injection attack (FDIAs). This paper pursues a holistic approach to describe the quality of process data, which brings together aspects like integrity, correctness, and availability in multivariate trust values. The arising research question this paper deals with is, how multivariate trust in physical measurements in a CPES can be modelled, estimated, and integrated into situational awareness. A proposed framework implements a context-sensitive and multivariate trust model as well as a trust sensitive state estimation. While these two artefacts are already published, the focus of this paper is on the implementation of the framework and the fulfilment of the requirements for timeliness, interoperability, flexibility, and scalability. It is evaluated in three different scenarios with CIGRE and IEEE benchmark grids.

Quantifying the resilience of ICT-enabled grid services in cyber-physical energy system

Information and Communication Technology (ICT) is vital for the operation of modern power systems, giving rise to Cyber-Physical Energy Systems (CPESs). ICT enables the grid services (GSs) needed for monitoring and controlling the physical parameters of the power system, especially for remedying the impact of disturbances. But the ICT integration makes the overall system more complex, leading to new and unforeseen disturbances. This motivates the need for a resilient system design capable of absorbing and recovering from such disturbances. The current state of the art lacks a comprehensive resilience assessment of ICT-enabled GSs in CPESs. To address this, a novel method and metrics to assess the resilience of GSs in CPESs are presented in this paper. An operational state model of a GS, with three states, i.e., normal, limited and failed, is used to capture its performance, which is essential for quantifying its resilience. Sequential Monte Carlo simulations are performed with the model to capture the behaviour of ICT components to compute the operational state trajectory of the GSs. Metrics are then derived to quantify the resilience and its constituting phases. The method is demonstrated using two ICT system designs for the CIGRE MV benchmark grid, considering the state estimation as an exemplary GS. The simulation results show that the proposed method can capture the differences between ICT system designs with regard to resilience metrics. The contribution can, therefore, be used to analyse, compare and potentially improve the resilience of ICT system designs for CPES.

Robust inter-reliant resilience of cyber-physical smart grids

Nowadays, information and cyber technologies have been integrated into traditional energy systems to enhance their energy performance. While this integration can improve the cost-benefit of smart grids, it may not fully capture the benefits in extreme conditions or during cascading outages.Electrical and cyber-physical energy systems have become more interconnected due to integration, which has also resulted in electrical energy systems (EESs) being more vulnerable to extreme natural disasters. The other system can be infiltrated by an outage in one system, leading to a cascading outage process. To tackle these challenges, a robust model of cyber-physical energy systems constrained by resilience considerations has been proposed to model the interdependences of EESs and cyber-physical systems (CPS) (i.e., EES & CPSs) under extreme natural disasters. The objective function of the proposed model is to improve cost-benefit and energy performance under extreme natural disasters. In this paper, it is explained how the energy efficiency for electrical energy systems can be improved under extreme natural disasters by the proposed operation processes for bulk integrated EESs & CPS. However, the highly nonlinear integrated EESs & CPS model is linearized to moderate the model complexity. Finally, the effectiveness of the proposed integrated EESs & CPS model is evaluated by investigating the modified IEEE-30 bus energy system.

Use cases also exist for attackers—how to foster the concept of misuse cases

Due to the new digital control structures of cyber-physical energy systems (CPES), where the control interventions no longer take place physically on site but are triggered, released, executed and acknowledged remotely by automated control systems, there is not only the risk of incorrect actions by plants or operators, but also of possible attacks or misuses. In this contribution, we propose an integrated security-by-design approach (on a conceptual level) for testing the interoperability of various heterogeneous systems (e.g., TSO-DSO communications) by combining multiple, but yet separated, state-of-the-art approaches. With the objective of eliminating or minimizing the impact of cyber incidents, best practices from various sectors have been adapted and integrated with well-established methods and standards from the energy sector, such as the IEC 62559‑2 use case template.

CPES-QSM: A Quantitative Method Toward the Secure Operation of Cyber–Physical Energy Systems

Power systems are evolving into cyber-physical energy systems (CPES) mainly due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The C-ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. Simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies.

Critical Data Visualization to Enhance Protection Schemes for State Estimation

Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive data flow between grid elements makes smart grids more vulnerable to cyber-attacks. Power system state estimation (SE)—an essential function of energy management systems—is one of these data integrity attacks’ targets. Integrity validation routines can fail when insufficient redundancy levels are reached, and spurious data occur. These levels are associated with critical data, i.e., those whose unavailability makes the grid unobservable. Data redundancy is a metric that gives a precarious indication that SE can run. Alternatively, it is more appropriate to quantify this function strength concerning its results’ reliability, which can be achieved by criticality analysis (CA). This paper proposes a novel approach to visualize the results of an extensive CA through representative graphs; they facilitate understanding the usefulness of CA. Critical sets of measurements are generalized, and several metrics are proposed to reveal measuring system vulnerabilities, assisting the design of protection schemes to resist cyber-attacks. Simulations attained on the IEEE-30bus system evince significant improvements in the interpretation/use of CA.

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&amp;D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.