Cyber-physical risk modeling with imperfect cyber-attackers

peer reviewedWe model the risk posed by a malicious cyber-attacker seeking to induce grid insecurity by means of a load redistribution attack, while explicitly acknowledging that such an actor would plausibly base its decision strategy on imperfect information. More specifically, we introduce a novel formulation for the cyber-attacker’s decision-making problem and analyze the distribution of decisions taken with randomly inaccurate data on the grid branch admittances or capacities, and the distribution of their respective impact. Our findings indicate that inaccurate admittance values most often lead to suboptimal cyber-attacks that still compromise the grid security, while inaccurate capacity values result in notably less effective attacks. We also find common attacked cyber-assets and common affected physicalassets between all (random) imperfect cyber-attacks, which could be exploited in a preventive and/or corrective sense for effective cyber-physical risk management.CYPRESS project (https://cypress-project.be/

Recent Developments in Machine Learning for Energy Systems Reliability Management

peer reviewedThis paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, {\it etc.} can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems

Machine learning of real-time power systems reliability management response

peer reviewedIn this paper we study how supervised machine learning could be applied to build simplified models of real-time (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark. Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature “importances” computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties

Probabilistic Reliability Management Approach and Criteria for Power System Short-term Operational Planning

peer reviewedThis paper develops a probabilistic decision making framework for reliability management in the short-term operational planning context. We build upon our recent work, which proposed a probabilistic reliability management approach and criterion (RMAC) for the latest decision making opportunity of real-time system operation. Here, we transpose the RMAC to the preceding problem instance of short-term operational planning, wherein i) risk is aggravated by the uncertainty on power injections and weather conditions, and, ii) the problem scope concerns choosing `strategic' actions (e.g., starting additional generating units, granting outage requests for maintenance, etc.) to facilitate decision making during the forthcoming real-time system operation. To anticipate on the latter, we formalize the notion of a real-time `proxy' as a simplified model of the real-time decision making context, adequately accurate for the purpose of operational planning decision making. Stating a first proposal for such a proxy, we mathematically formulate the RMAC for short-term operational planning as a multi-stage stochastic decision making problem and demonstrate its main features by case studies on a modified version of the single area IEEE RTS-96 system

Probabilistic reliability management approach and criteria for power system real-time operation

peer reviewedThis paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of corrective (i.e., post-contingency) control and, iii) the socio-economic impact of service interruptions to end-users. Stressing the spatiotemporal variability of these factors, we argue for reliability criteria assuring a high enough probability of avoiding service interruptions of severe socio-economic impact by dynamically identifying events of nonnegligible implied risk. We formalise the corresponding decision making problem as a chance-constrained two-stage stochastic programming problem, and study its main features on the single area IEEE RTS-96 system. We also discuss how to leverage this proposal for the construction of a globally coherent reliability management framework for long-term system development, midterm asset management, and short-term operation planning

Whither probabilistic security management for real-time operation of power systems ?

peer reviewedThis paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between preventive and corrective control, while taking into account i) the prior probabilities of contingencies, ii) the possible failure modes of corrective control actions, iii) the socio-economic consequences of service interruptions. This work is a first step towards the construction of a globally coherent decision making framework for security management from long-term system expansion, via mid-term asset management, towards short-term operation planning and real-time operation

Using Machine Learning to Enable Probabilistic Reliability Assessment in Operation Planning

peer reviewedIn the context of operation planning, probabilistic reliability assessment essentially boils down to predicting, efficiently and with sufficient accuracy, various economic and reliability indicators reflecting the expected performance of the system over a certain look-ahead horizon, so as to guide the operation planner in his decision-making. In order to speed-up the crude Monte Carlo approach, which would entail a very large number of heavy computations, we propose in this paper an approach combining Monte Carlo simulation, machine learning and variance reduction techniques such as control variates. We provide an extensive case study testing this approach on the three-area IEEE-RTS96 benchmark, in the context of day-ahead operation planning while using a security constrained optimal power flow model to simulate real-time operation according to the N-1 criterion. From this case study, we can conclude that the proposed approach allows to reduce the number of heavy computations by about an order of magnitude, without sacrificing accuracy

An iterative AC-SCOPF approach managing the contingency and corrective control failure uncertainties with a probabilistic guarantee

This paper studies an extended formulation of the Security Constrained Optimal Power Flow (SCOPF) problem, which explicitly takes into account the probabilities of contingency events and of potential failures in the operation of post-contingency corrective controls. To manage such threats, we express the requirement that the probability of maintaining all system operational limits, under any circumnstance, should remain acceptably high by means of a chance-constraint. Further, representing power flow as per the full AC model, we propose a heuristic solution approach leveraging state-of-the art methodologies and tools originally developed to tackle the standard, robust-constrained SCOPF statement. We exemplify the properties of our proposal by presenting its application on the three area version of the IEEE-RTS96 benchmark, stressing the interpretability of both the chance-constrained reliability management strategy and of the heuristic algorithm proposed to determine it. This work serves to showcase that the first step on the transition towards probabilistic reliability management can be achieved by suitably adapting presently available operational practices and tools