The modern power grid is becoming more susceptible to cyber-attacks due to an increase in digitalization, leading to a larger attack surface for malicious actors to attack. Such attacks on critical infrastructure could lead to partial power outages, minor societal disruption, or in the worst-case scenario, a rolling black-out in which the entire country has no access to electricity. Electrical utility companies can decrease the likelihood of a successful cyber-attack on the Cyber Physical Power System (CPPS) – consisting of the physical power grid, and vulnerable Information Technology (IT) and Operational Technology (OT)- by implementing cyber security interventions. Investing in these cyber security mechanisms is not cheap, which is why it is expected to have a certain return on investment. However, it is hard to quantify the effects of prospective cyber security investments. The main research question of this study is: “To what extent can cyber security measures decrease the risk of cyber attacks on CPPS substations?” This research question is answered by means of an implicitly mixed research approach that uses computer-assisted attack tree modelling and Monte Carlo simulation. The model is based on the publicly available technical system information of known suppliers of relevant substation components and other documentation acquired by means of multiple literature studies and document analyses. The change in likelihood and subsequent risk has been studied by extensively modelling the possible attack paths of a digital substation. This has been combined with financial analysis in the form of a societal cost-benefit analysis. As a result, potential cyber security investments can be evaluated on their merits in the form of risk reduction and their required costs as expressed in dollars. The contribution of the performed research to science is the elaboration of existing models to more accurately represent reality, and simultaneously provide the cyber security decision-making process with a tool that provides guiding Key Performance Indicators (KPIs). This study has shown that suggested measures from the quantified model are able to increase the TTCavg needed by malicious actors to reach their intended target, and therefore cause a decrease in likelihood and subsequent risk of the studied scenarios. An important finding of this study emphasizes the need for extensive attack path modelling. This finding was the fact that the application of some well-intended countermeasure (such as remote-attestation), might have no significant effect on the likelihood and risk of a certain scenario at all, but only changes the dominant attack path. While the constructed quantified model, as proposed in this study, is able to provide quantified insights into the effects of proposed cyber security investments, it is merely a simplified tool that should be expanded upon to generate more accurate insights. Besides the aforementioned there have been additional findings from this study. Such as a list of weaknesses in the current state of digital substation cyber security. This list has been created by an extensive document analysis of over 40 sources. Also, an overview of 23 different possible cyber security interventions has been compiled by a systemic literature review of over 16 sources. According to the quantified model, a reduction (between 21.8% and 93%) in the total risk of certain attack scenarios against digital substation by malicious actors can be achieved. The costs for these possible risk reductions range between $28 thousand for a honeypot deception system and $413 thousand for a combination of all the simulated countermeasures. These countermeasures could, in comparison to a base case with no protection, potentially reduce the total risk by an amount between $3.7 billion and $15.9 billion. According to the general societal cost-benefit analyses, the best Retun-on- Investment (ROI)/cost-effectiveness of investment is the investment in a honeypot (scenario 5) which has an ROI of 247,390, and the least cost-effective is the investment in remote attestation (scenario 4), which has an ROI of -2,066. Altogether, this study has shown that there is added value in using a simplified quantified model to aid in decision-making for digital substation cyber security investments aimed at risk reduction.

The proliferation of Distributed Energy Resources (DERs) is decentralizing the power system, with more and more capacity installed in the distribution grids. Concurrently, the energy sector is embracing the Internet of Things (IoT) paradigm, resulting in the emergence of the Internet of Energy. However, this transformation introduces new concerns regarding cyber security. As the number of interconnected devices increases, the possible attack surface for malicious actors expands. Recognizing this challenge, researchers are investigating the potential cyber security benefits of applying blockchain in power systems. Blockchain offers some secure-by-design features, such as the immutability of the stored data, that can be leveraged to improve the cyber security of smart grids.
In this work, a blockchain-based application for the monitoring and control of a feeder in the Low-Voltage (LV) distribution grid is designed and tested. A smart contract is created and deployed in a private Ethereum blockchain utilizing the Proof of Authority (PoA) consensus mechanism. The blockchain application enhances the cyber security of the LV distribution system in three ways. First, it detects cyber attacks targeting DERs by comparing the setpoints received by prosumers with smart meter measurements. Second, it prevents cyber attacks by enabling the exchange of measurements and setpoints on-chain and by preventing unreliable prosumers from participating in the voltage regulation market. Third, it mitigates the effects of cyber attacks on the steady-state voltage magnitudes by enforcing a novel voltage regulation mechanism, in which a new metric is proposed to quantify the power-to-voltage relationship while considering the location of the power exchange.
The efficacy of the blockchain application is tested in a co-simulation environment together with a modeled LV distribution network, simulated in DigSILENT PowerFactory. The distribution network model is first used to assess the impact of cyber attacks manipulating the setpoints of Battery Energy Storage Systems (BESSs), which have been identified as the most critical DERs. The simulation results demonstrate that the considered cyber attacks can force the disconnection of inverters by causing violations of the acceptable steady-state voltage magnitudes. One of the scenarios demonstrates that a cyber attack targeting half of the BESSs in a feeder can lead to the collapse of the voltage, causing a local outage. Finally, the results of the co-simulation of the blockchain-based monitoring and control system, achieved by the Open Platform Communications Unified Architecture (OPC UA) communication protocol and by a series of clients managing the data streams, demonstrate its efficacy in detecting cyber attacks and mitigating their impact on the voltage magnitude across the feeder, thus reducing the number of disconnected DERs.

A large contribution to the total share of electricity generated in European power grid will come from renewable sources of energy in the near future due to European initiative to become carbon neutral. RES are connected to the grid with PE devices, which if not modelled correctly provide lower level of system stability and security of supply. The existing power grid with mainly synchronous generators will not be suited to operate in the future, when synchronous machines will become less important sources of energy. An increase in dynamic behaviour of the system could cause significant challenges for the existing system, which calls for new monitoring and control strategies. The aim of the project is twofold; firstly, to enhance system stability by deploying WAMS in power system with a massive share of RES to total electricity generation. Secondly, it focuses on developing and proposing a tool which will improve consultancy in future power grids. A tool developed is Next Generation Grid Operations (NextGen GridOps) Knowledge Framework which is conceptually designed by DNV. The effectiveness of WAMS applications on system stability improvements and damping enhancements will be evaluated by studying rotor angle stability as well as effect of WAMS on damping of electromechanical oscillations. The response to a disturbance of the remaining synchronous generators will be studied to evaluate the effect of different types of control schemes (grid-following, grid forming control) on the rotor angle stability. Rotor angle stability will be examined to show whether different control structures and WAMS will show enhancements of the overall stability through time domain simulations. WAMS structure in this project consists of PMUs as well as WADCs. While PMUs are sensors deployed in the system to provide synchrophasor measurement, WADCs are damping controllers deployed with an intent of enhancing damping in the system. This project build upon findings from Horizon 2020 MIGRATE Project. The effectiveness of grid-following control and grid-forming control on stabilizing the grid with massive penetration of PE devices are conducted to set the base case for evaluating the effect of WAMS. Modelling software used in the project is DIgSILENT PowerFactory 2021 SP1 and the PowerFactory Thesis Licence was provided by DIgSILENT GmbH for research and educational purposes. Based on the off-line numerical simulations conducted in IEEE 39 Bus New England test system it was found that WAMS functionality with corresponding PMUs and WADCs can decrease oscillations in the system. It has been verified that grid-following control enables 60% of RES penetration and grid-forming control enables penetration of RES above 80%. There have been two grid-forming controllers used in the system, where DVC is able to receive the stabilizing signal while VSM grid-forming controller has a supporting role. WAMS and corresponding WADCs deployed on DVCs are able to enhance damping of the low-frequency modes with frequencies below 2.0 Hz, which is supported by time domain simulations and by conducting Prony analysis. Eigenvalue analysis results for some cases with WAMS deployed show no additional enhancements, which is a consequence of newly introduced controllers and interactions among them and existing controllers. The practical implications of this Master's Thesis study have been modelled in the NextGen GridOps Framework with an intent to make a step towards real world implementation of the findings developed during the project. Framework modelling focused on implementing client maturity classification of WAMS deployments, contributing towards development of WAMS roadmaps and further deployment of WAMS solutions for grid operators as part of the DNV Next Generation Grid Operations advisory services. Work done and information implemented will be valuable for DNV while solving the complex process of future grid operations and at the same time bringing newly developed knowledge into practice. The framework development part of the project ties in with scientific contribution by allowing newly developed information to be further explored. Effectiveness of WAMS and WADCs on improving selective damping and consequently enhancing system stability has been identified in this project through use of DVCs. It has been proven that VSM control has a better stabilizing effect and would allow even further enhancement of damping critical oscillation modes. Higher damping in the system increases the stability and consequently higher security of energy supply.

The TSO of a power system is mainly responsible for ensuring the stability of the grid. Through continuous monitoring and control of the power system, the TSO maintains stability through emergency actions. Until now, the conventional Static State Estimation has been the Energy Management System (EMS) tool for estimating and monitoring the grid state - bus voltages, currents, and powers. However, energy transition, which involves the decommissioning of conventional generation and their replacement by renewables, leads to a more dynamic grid. In such a case, the information provided by the static state estimator is insufficient. This has, hence, led to the development of the Dynamic State Estimator (DSE), to provide insight into the dynamic properties of a power system, such as rotor angle and rotor speed. The DSE typically uses a Wide-Area Monitoring (WAMS) architecture consisting of Phasor Measurement Units (PMU), to dynamically estimate the internal states of the generators under observation. Hence, the DSE provides improved situational awareness to the TSO. However, the existing literature do not elaborate on how their proposed DSE can be implemented in an online fashion to estimate the dynamic states in near real-time. Such an online implementation is of utmost importance as it showcases how a TSO can deploy the DSE in a real world scenario. Hence, this thesis proposes an online DSE algorithm that performs batch-wise estimation of dynamic states in a near real-time setting. By collecting measurements in batches and introducing the pre-processing steps necessary for these PMU measurements, the algorithm forms the premise for the real-world application of DSE. This algorithm is validated using a cyber-physical testbed comprising a Real Time Digital Simulator and a Synchrophasor Application Development Framework. Additionally, its performance is evaluated and a sensitivity study is conducted to find deterministic relationships between the input error introduced and the estimation error. Finally, future improvements are proposed to make the implementation more suitable to real-world application.

Power grids rely on Operational Technology (OT) networks, for real-time monitoring and control. These traditionally segregated systems are now being integrated with general-purpose Information and Communication Technologies (ICTs). The coupling of the physical power system and its communications infrastructure forms a complex, interdependent structure referred to as a Cyber-Physical System (CPS). As cyber attacks on critical infrastructures become more frequent, power systems are especially vulnerable, as their OT systems were not designed with cyber security considerations. Hence, identifying and quantifying the risk of cyber attacks on power grids is of utmost importance.
In this dissertation, a method for quantitative risk assessment is proposed. The impact of cyber attacks is examined on a holistic model of a cyber-physical power system and their likelihood is assessed through attack graphs. Firstly, the physical power system is modelled to analyze the impact of cyber attacks on power system operation. The dynamic model of the IEEE 39-bus is used to validate the proposed risk assessment method. Various protection schemes are implemented and coordinated to analyze how cyber attacks can lead to cascading failures and a blackout. The communication networks of digital substations are modelled and integrated with the power system model. They emulate the communication network traffic between the control center and digital substations. The physical and cyber system models are integrated via co-simulation.
Secondly, attack graphs for digital substations are designed and used for cyber attack analysis. The attack graph model is based on the topology of a digital substation, specified by industry and academia. A novel method is proposed for defining the probability distributions of the time-to-compromise for each attack step, which is used in the attack simulations to extract the global time-to-compromise of the targeted asset.
Furthermore, an impact assessment method is proposed, which correlates the impact on both layers of the cyber-physical system. Key performance indicators for the power system operation as well as the operation of its communication system are defined and implemented. The overall risk of a specific cyber attack scenario is assessed based on the impact indices, likelihood of the cyber attack to commence, and a proposed metric regarding power system restoration. The proposed methods are validated by examining various cyber attack scenarios on the developed cyber-physical system model. The examined scenarios are based on real-world cyber attacks. Additionally, a study regarding the effect of different attack sequences is conducted. The impact is assessed on both layers of the cyber-physical power system by running dynamic simulations.
On overall, the CPS simulation results show the effectiveness of the proposed methods to assess risks and identify the most critical systems per cyber attack scenario. The proposed methods correlate the vulnerability assessment of the modelled security infrastructure with the corresponding impact on the cyber-physical system. The risk assessment is validated by a comprehensive analysis of selected study cases, examining the cascading failure chains of the power system. These studies show the importance of examining various attack scenarios in order to identify the weak points and bottlenecks in the integrated cyber-physical power system.