Biblio

Recently, cyber security in power systems has captured significant interest. This is because the world has seen a surge in cyber attacks on power systems. One of the prolific cyber attacks in modern power systems are False Data Injection Attacks (FDIA). In this paper, we analyzed the impact of FDIA on the operation cost of power systems. Also, we introduced a novel Advanced Persistent Threat (APT) based attack strategy that maximizes the operating costs when attacking specific nodes in the system. We model the attack strategy using an optimization problem and use metaheuristics algorithms to solve the optimization problem and execute the attack. We have found that our attacks can increase the power generation cost by up to 15.6%, 60.12%, and 74.02% on the IEEE 6-Bus systems, 30-Bus systems, and 118-Bus systems, respectively, as compared to normal operation.

The research proposal discloses a novel drone-based ad-hoc network that leverages acoustic information for power plant surveillance and utilizes a secure blockchain model for protecting the integrity of drone communication over the network. The paper presents a vision for the drone-based networks, wherein drones are employed for monitoring the complex power plant machinery. The drones record acoustic information generated by the power plants and detect anomalies or deviations in machine behavior based on collected acoustic data. The drones are linked to distributed network of computing devices in possession with the plant stakeholders, wherein each computing device maintains a chain of data blocks. The chain of data blocks represents one or more transactions associated with power plants, wherein transactions are related to high risk auditory data set accessed by the drones in an event of anomaly or machine failure. The computing devices add at least one data block to the chain of data blocks in response to valid transaction data, wherein the transaction data is validated by the computing devices owned by power plant personnel.

We present a privacy-preserving distributed reinforcement learning-based control scheme to address the problem of frequency control and economic dispatch in power generation systems. The proposed control approach requires neither a priori system model knowledge nor the mathematical formulation of the generation cost functions. Due to not requiring the generation cost models, the control scheme is capable of dealing with scenarios in which the cost functions are hard to formulate and/or non-convex. Furthermore, it is privacy-preserving, i.e. none of the units in the network needs to communicate its cost function and/or control policy to its neighbors. To realize this, we propose an actor-critic algorithm with function approximation in which the actor step is performed individually by each unit with no need to infer the policies of others. Moreover, in the critic step each generation unit shares its estimate of the local measurements and the estimate of its cost function with the neighbors, and via performing a consensus algorithm, a consensual estimate is achieved. The performance of our proposed control scheme, in terms of minimizing the overall cost while persistently fulfilling the demand and fast reaction and convergence of our distributed algorithm, is demonstrated on a benchmark case study.

It is important to address the physical and cyber security needs of critical infrastructures, including systems, facilities, and assets. Security requirements may include capabilities to prevent and protect against both physical and digital intrusion, hazards, threats, and incidents, and to expeditiously recover and reconstitute critical services. Energy security has serious repercussions for mission critical facilities. Mission critical facilities do not have the luxury of being able to shut down or run at a reduced capacity during outages, whether they last minutes, hours, or days. Disaster recovery plans are a necessity for mission critical facilities, involving the proper training of business continuity personnel to enact enterprise-level plans for business resiliency. Steps need to be taken to improve information security and mitigate the threat of cyber-attacks. The Smart Grid is the convergence of electric distribution systems and modern digital information technology.

Local flexibility markets are a concept to integrate distributed flexibilities such as power-to-gas, power-to-heat systems, electric vehicles, and battery storage systems into congestion management of distribution and transmission grids. However, the activation of the flexibility of storage systems changes their state-of-charge and thus reduces their available flexibility. Counter-trading or compensation of activated flexibility in later points of time lets storage operators regain flexibility. However, this compensation might create or amplify congestions when not supervised by system operators. Therefore, we propose the inclusion of compensation within the flexibility market clearing process by system operators. We further develop a linear formulation of flexibility market bids by storage systems that takes the need for compensation into account. For this, first, the operational planning formulation of a storage system is expanded by flexibility market participation. Subsequently, the linear formulation of bids is derived and demonstrated.

With increasing improvements in different areas, Internet control has been making prominent impacts in almost all areas of technology that has resulted in reasonable advances in every discrete field and therefore the industries too are proceeding to the field of IoT (Internet of Things), in which the communication among heterogeneous equipments is via Internet broadly. So imparting these advances of technology in the Power Station Plant sectors i.e. the power plants will be remotely controlled additional to remote monitoring, with no corporal place as a factor for controlling or monitoring. But imparting this technology the security factor needs to be considered as a basic and such methods need to be put into practice that the communication in such networks or control systems is defended against any third party interventions while the data is being transferred from one device to the other device through the internet (Unrestricted Channel). The paper puts forward exercising RSA,DES and AES encrypting schemes for the purpose of data encryption at the Data Link Layer i.e. before it is transmitted to the other device through Internet and as a result of this the security constraints are maintained. The records put to use have been supplied by NTPC, Dadri, India plus simulation part was executed employing MATLAB.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

The new instrumentation and control (I&C) systems of the nuclear power plants (NPPs) improve the ability to operate the plant enhance the safety and performance of the NPP. However, they bring a new type of threat to the NPP's industry-cyber threat. The early fault diagnostic system (EDS) is one of the decision support systems that might be used online during the operation stage. The EDS aim is to prevent the incident/accident evolution by a timely troubleshooting process during any plant operational modes. It means that any significative deviation of plant parameters from normal values is pointed-out to plant operators well before reaching any undesired threshold potentially leading to a prohibited plant state, together with the cause that has generated the deviation. The paper lists the key benefits using the EDS to counter the cyber threat and proposes the framework for cybersecurity assessment using EDS during the operational stage.

Realizing the importance of the concept of “smart city” and its impact on the quality of life, many infrastructures, such as power plants, began their digital transformation process by leveraging modern computing and advanced communication technologies. Unfortunately, by increasing the number of connections, power plants become more and more vulnerable and also an attractive target for cyber-physical attacks. The analysis of interdependencies among system components reveals interdependent connections, and facilitates the identification of those among them that are in need of special protection. In this paper, we review the recent literature which utilizes graph-based models and network-based models to study these interdependencies. A comprehensive overview, based on the main features of the systems including communication direction, control parameters, research target, scalability, security and safety, is presented. We also assess the computational complexity associated with the approaches presented in the reviewed papers, and we use this metric to assess the scalability of the approaches.

As nuclear power plant Instrumentation and Control(I&C) systems have turned into digital systems, the possibility of cyber-attacks has increased. To protect the nuclear power plant from cyber-attacks, digital assets are classified and managed as critical digital assets which have safety, security and emergency preparedness functions. However, critical digital assets represent 70-80% of total digital assets, and applying and managing the same security control is inefficient. Therefore, this paper presents the criteria for identifying digital assets that are classified as vital digital assets that can directly affect the serious accidents of nuclear power plants.

The cyber-physical systems (CPSes) rely on computing and control techniques to achieve system safety and reliability. However, recent attacks show that these techniques are vulnerable once the cyber-attackers have bypassed air gaps. The attacks may cause service disruptions or even physical damages. This paper designs the built-in attack characterization scheme for one general type of cyber-attacks in CPS, which we call time delay attack, that delays the transmission of the system control commands. We use the recurrent neural networks in deep learning to estimate the delay values from the input trace. Specifically, to deal with the long time-sequence data, we design the deep learning model using stacked bidirectional long short-term memory (LSTM) units. The proposed approach is tested by using the data generated from a power plant control system. The results show that the LSTM-based deep learning approach can work well based on data traces from three sensor measurements, i.e., temperature, pressure, and power generation, in the power plant control system. Moreover, we show that the proposed approach outperforms the base approach based on k-nearest neighbors.

Loss of field (LOF) relay, with ANSI code 40, is one of the most important protection functions for synchronous generators in power plants. Although many LOF protection schemes have been presented in the literature during the last decades, a few numbers of them such as impedance and admittance based schemes are accepted by the industry. This paper explores and compares the performances of some industrial LOF protection schemes through simulation studies and from speed, reliability and security viewpoints. The simulation studies are carried out in the real-time-digital-simulator, where a realistic power generation unit is developed by employing the phase domain model of synchronous generator. Using such a realistic system, various types of LOF events can be simulated in accordance with IEEE Standard C37.102-2006, so that the performance of any method can be evaluated through careful LOF studies.

The aim of this paper is to explore the performance of two well-known wave energy converters (WECs) namely Floating Buoy Point Absorber (FBPA) and Oscillating Surge (OS) in onshore and offshore locations. To achieve clean energy targets by reducing greenhouse gas emissions, integration of renewable energy resources is continuously increasing all around the world. In addition to widespread renewable energy source such as wind and solar photovoltaic (PV), wave energy extracted from ocean is becoming more tangible day by day. In the literature, a number of WEC devices are reported. However, further investigations are still needed to better understand the behaviors of FBPA WEC and OS WEC under irregular wave conditions in onshore and offshore locations. Note that being surrounded by Bay of Bengal, Bangladesh has huge scope of utilizing wave power. To this end, FBPA WEC and OS WEC are simulated using the typical onshore and offshore wave height and wave period of the coastal area of Bangladesh. Afterwards, performances of the aforementioned two WECs are compared by analyzing their power output.

This paper deals with the modeling and control of the NEREIDA wave generation power plant installed in Mutriku, Spain. This kind of Oscillating Water Column (OWC) plants usually employ a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The stalling behavior of the Wells turbine limits the generated power. In this context, a sliding mode rotational speed control is proposed to help avoiding this phenomenon. This will regulate the speed by means of the Rotor Side Converter (RSC) of the Back-to-Back converter governing the generator. The results of the comparative study show that the proposed control provides a higher generated power compared to the uncontrolled case.

The NEREIDA wave generation power plant installed in Mutriku, Spain is a multiple Oscillating Water Column (OWC) plant. The power takeoff consists of a Wells turbine coupled to a Doubly Fed Induction Generator (DFIG). The stalling behavior present in the Wells turbine limits the generated power. This paper presents the modeling and a Harmony Search Algorithm-based airflow control of the OWC. The Harmony Search Algorithm (HSA) is proposed to help overcome the limitations of a traditionally tuned PID. An investigation between HSA-tuned controller and the traditionally tuned controller has been performed. Results of the controlled and uncontrolled plant prove the effectiveness of the airflow control and the superiority of the HSA-tuned controller.

While there has been considerable research on making power grid Supervisory Control and Data Acquisition (SCADA) systems resilient to attacks, the problem of transitioning these technologies into deployed SCADA systems remains largely unaddressed. We describe our experience and lessons learned in deploying an intrusion-tolerant SCADA system in two realistic environments: a red team experiment in 2017 and a power plant test deployment in 2018. These experiences resulted in technical lessons related to developing an intrusion-tolerant system with a real deployable application, preparing a system for deployment in a hostile environment, and supporting protocol assumptions in that hostile environment. We also discuss some meta-lessons regarding the cultural aspects of transitioning academic research into practice in the power industry.

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.

Critical infrastructures have suffered from different kind of cyber attacks over the years. Many of these attacks are performed using malwares by exploiting the vulnerabilities of these resources. Smart power grid is one of the major victim which suffered from these attacks and its SCADA system are frequently targeted. In this paper we describe our proposed framework to analyze smart power grid, while its SCADA system is under attack by malware. Malware propagation and its effects on SCADA system is the focal point of our analysis. OMNeT++ simulator and openDSS is used for developing and analyzing the simulated smart power grid environment.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

In this study, it is proposed to carry out an efficient formulation in order to figure out the stochastic security-constrained generation capacity expansion planning (SC-GCEP) problem. The main idea is related to directly compute the line outage distribution factors (LODF) which could be applied to model the N - m post-contingency analysis. In addition, the post-contingency power flows are modeled based on the LODF and the partial transmission distribution factors (PTDF). The post-contingency constraints have been reformulated using linear distribution factors (PTDF and LODF) so that both the pre- and post-contingency constraints are modeled simultaneously in the SC-GCEP problem using these factors. In the stochastic formulation, the load uncertainty is incorporated employing a two-stage multi-period framework, and a K - means clustering technique is implemented to decrease the number of load scenarios. The main advantage of this methodology is the feasibility to quickly compute the post-contingency factors especially with multiple-line outages (N - m). This concept would improve the security-constraint analysis modeling quickly the outage of m transmission lines in the stochastic SC-GCEP problem. It is carried out several experiments using two electrical power systems in order to validate the performance of the proposed formulation.

This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.

The Polish Power System is becoming increasingly more dependent on Information and Communication Technologies which results in its exposure to cyberattacks, including the evolved and highly sophisticated threats such as Advanced Persistent Threats or Distributed Denial of Service attacks. The most exposed components are SCADA systems in substations and Distributed Control Systems in power plants. When addressing this situation the usual cyber security technologies are prerequisite, but not sufficient. With the rapidly evolving cyber threat landscape the use of partnerships and information sharing has become critical. However due to several anonymity concerns the relevant stakeholders may become reluctant to exchange sensitive information about security incidents. In the paper a multi-agent architecture is presented for the Polish Power System which addresses the anonymity concerns.

Smart grids utilize computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. As such, they are among the most critical and complex cyber-physical systems. The success of smart grids in achieving their stated goals is yet to be rigorously proven. In this paper, our focus is on improvements (or lack thereof) in reliability. We discuss vulnerabilities in the smart grid and their potential impact on its reliability, both generally and for the specific example of the IEEE-14 bus system. We conclude the paper by presenting a preliminary Markov imbedded systems model for reliability of smart grids and describe how it can be evolved to capture the vulnerabilities discussed.