Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ($\mu$-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications

Two-Sided Tacit Collusion: Another Step towards the Role of Demand-Side

In the context of agent-based simulation framework of collusion, this paper seeks for two-sided tacit collusion among supply-side and demand-side participants in a constrained network and impacts of this collusion on the market outcomes. Tacit collusion frequently occurs in electricity markets due to strategic behavior of market participants arose from daily repetition of energy auctions. To attain detailed analysis of tacit collusion, state-action-reward-state-action (SARSA) learning algorithm and the standard Boltzmann exploration strategy based on the Q-value are used to model market participants’ behavior. A model is presented that integrates exploration and exploitation into a single framework, with the purpose of tuning exploration in the algorithm. In order to appraise the feasibility of collusion, a theoretical study on a three-node power system with three scenarios is depicted considering three Gencos and two Discos which proves the formation of two-sided tacit collusion between Genco and Disco. Simulation results show different collusive strategies of participants and how parameters of the algorithm impact on simulation outcomes. It is also shown that congestion on transmission line has a significant influence on behavior of market participants