Assessing the Cyber Threat Landscape for Virtual Power Plants

Virtual Power Plants (VPPs) aggregate and coordinate Distributed Energy Resources (DER) as a single entity aiding in decarbonization of the energy generation mix. The infrastructure of VPPs relies heavily on rigorous and accurate exchange of information between the DER and the VPP, as well as other grid entities. This exposes them to possible cyber threats that impede their functions and can have negative impacts on the stability and reliability of the grid. In this paper, the threat landscape is evaluated against threats that affect VPPs. A heuristic method of assessing the impact and likelihood of attacks is constructed based on a) proposed methods in literature, b) standardization bodies, and c) in relation to a VPPs security profile. Our findings indicate that False Data Injection attacks are posing the greatest risk, competing with disruption of their functions due to Denial of Service

Vulnerability and resilience of cyber-physical power systems: results from an empirical-based study

Power systems are undergoing a profound transformation towards cyber-physical systems. Disruptive changes due to energy system transition and the complexity of the interconnected systems expose the power system to new, unknown and unpredictable risks. To identify the critical points, a vulnerability assessment was conducted, involving experts from power as well as information and communication technologies (ICT) sectors. Weaknesses were identified e.g.,the lack of policy enforcement worsened by the unreadiness of involved actors. The complex dynamics of ICT makes it infeasible to keep a complete inventory of potential stressors to define appropriate preparation and prevention mechanisms. Therefore, we suggest applying a resilience management approach to increase the resilience of the system. It aims at a better ride through failures rather than building higher walls. We conclude that building resilience in cyber-physical power systems is feasible and helps in preparing for the unexpected

Aging and Rejuvenation Models of Load Changing Attacks in Micro-Grids

Recent cyber-attacks in critical infrastructures have highlighted the importance of investigating how to improve Smart-Grids (SG) resiliency. In the future, it is envisioned that grid connected micro-grids would have the ability of operating in 'islanded mode’ in the event of a grid-level failure. In this work, we propose a method for unfolding aging and rejuvenation models into their sequential counterparts to enable the computation of transient state probabilities in the proposed models. We have applied our methodology to one specific security attack scenario and four large campus micro-grids case studies. We have shown how to convert the software aging and rejuvenation, with cycles, to its unfolded counterpart. We then used the unfolded counterpart to support the survivability computation. We were able to analytically evaluate the transient failure probability and the associated Instantaneous Expected Energy Not Supplied metric, for each of the four case studies, from one specific attack. We envision several practical applications of the proposed methodology. First, because the micro-grid model is solved analytically, the approach can be used to support micro-grid engineering optimizations accounting for security intrusions. Second, micro-grid engineers could use the approach to detect security attacks by monitoring for unexpected deviations of the Energy Not Supplied metric

Network and System Management using IEC 62351-7 in IEC 61850 Substations: Design and Implementation

Substations are a prime target for threat agents aiming to disrupt the power grid’s operation. With the advent of the smart grid, the power infrastructure is increasingly being coupled with an Information and Communication Technologies (ICT) infrastructure needed to manage it, exposing it to potential cyberattacks. In order to secure the smart grid, the IEC 62351 specifies how to provide cybersecurity to such an environment. Among its specifications, IEC 62351-7 states to use Network and System Management (NSM) to monitor and manage the operation of power systems. In this research, we aim to design, implement, and study NSM in a digital substation as per the specifications of IEC 62351-7. The substation is one that conforms to the IEC 61850 standard, which defines how to design a substation leveraging ICT. Our contributions are as follows. We contribute to the design and implementation of NSM in a smart grid security co-simulation testbed. We design a methodology to elaborate cyberattacks targeting IEC 61850 substations specifically. We elaborate detection algorithms that leverage the NSM Data Objects (NSM DOs) of IEC 62351- 7 to detect the attacks designed using our method. We validate these experimentally using our testbed. From this work, we can provide an initial assessment of NSM within the context of digital substations

Electric Vehicle - Smart Grid Integration: Load Modeling, Scheduling, and Cyber Security

The modern world has witnessed the surge of electric vehicles (EVs) driven by government policy worldwide to reduce transportation’s dependence on fossil fuels. According to (Slowik, 2019), the global EV market has grown sharply with the annual light-duty EV sales surpassing 2 million in 2018, which is about a 70% increase from 2017. The increase in EV population implies the rise in energy demand, and that introduces new challenges to the electricity sector. EV charging load demand in high penetration scenarios, which is foreseen, may lead to stability and quality issues in power grids. Generation capacity and the electricity infrastructure upgrade may be required to address those issues; however, it increases generation costs significantly. The most common EV chargers installed today deliver around 7 kW of power, which is over four times that of an average household power consumption in the US. EV charging load often shows two peaks in a day, one in the morning when people plug in the EV at the workplace and the other in the evening when people get home from work. Without proper energy management for EV charging, the vast power demand due to a large number of plugged-in EVs can stress the electric grid, degrade the electric power quality, and impact the wholesale electricity market. Although an EV battery may store energy up to 80 kWh, which requires more than 10 hours to charge at 7kW from empty, we found that most EVs need only 12 kWh per charge or 1.7 hours at 7 kW to meet daily commute requirement while they stay in the parking garage for a more extended period. This implies that EVs can have considerable time-flexibility for charging, and it is not necessary to start chargingright after plugging in, which is likely to result in the charging power add-up. A proper EV charging schedule can well allocate the charging load to prevent power peaks. Therefore, EV charging scheduling can play a significant role in mitigating the adverse effects of vast EV charging demand without upgrading the power grid capacity.To optimize the EV charging schedule while satisfies EVs’ charging demand, each EV’s stay duration and energy need are essential parameters for the optimization. Those parameters are based on predictions to minimize human intervention. Nonetheless, the uncertainty of EV user behavior poses a challenge to the prediction accuracy. Therefore, this dissertation demonstrates an ensemble machine learning-based method to model and predict the EV loads accurately, thereby improving the performance of EV charging scheduling.On the other hand, this smart EV-grid integration, which requires massive communication, including collecting, transmitting, and distributing real-time data within the network, makes it more susceptible to cyber-physical threats. Potential breaches could not only affect grid operation but also reduce consumers’ willingness to adopting EVs over conventional fuel-powered vehicles. This dissertation also presents the vulnerability analysis and risk assessment for a smart EV charging system to develop the countermeasures to secure the network. Also, while it is inevitable that the security has flaws, this dissertation provides a novel anomaly detection approach based on the invariant correlations of different measurements within the EV charging network