The rapid deployment of electric vehicle charging stations (EVCS) within distribution networks requires intelligent, adaptive control to maintain the grid's resilience and reliability. In this work, we propose PHANTOM, a physics-aware adversarial network through training and optimization of multi-agent reinforcement learning model. PHANTOM integrates a physics-informed neural network (PINN) enabled by federated learning (FL) that functions as a digital twin of EVCS-integrated systems, ensuring physically consistent modeling of operational dynamics and constraints. Building on this digital twin, we construct a multi-agent RL environment that uses deep Q-networks (DQN) and soft actor-critic (SAC) methods to develop adversarial false data injection (FDI) strategies that can bypass conventional detection mechanisms. To examine the broader grid-level consequences, a transmission-distribution (T&D) dual simulation platform is developed, allowing us to capture cascading interactions between EVCS disturbances at the distribution level and the operations of the bulk transmission system. Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T&D boundaries. These findings highlight the critical need for physics-aware cybersecurity to ensure the resilience of large-scale vehicle-grid integration.

This paper presents a model-free solution to distributed transmit beamforming using mobile agents. Each agent is equipped with an antenna and the agents represent the individual elements in an antenna array. The agents are tasked to coordinate their relative location, phase offsets, and amplitude to construct a desired beam-pattern. As a prospective solution, we propose a model-free optimization algorithm based on real-time feedback that does not require a model that maps the control parameters (relative location, phase offsets, and amplitude) to a radiation pattern. We evaluate the performance of proposed approach for different motion constraints. Numerical results are presented to validate the theory.

In this paper, we present an overview of the applications of graph theory in power system modeling, dynamics, coherency, and control. First, we study synchronization of generator dynamics using both nonlinear and small-signal representations of classical structure-preserving models of power systems in light of their network structure and the weights associated with the nodes and edges of the network graph. We overview important necessary and sufficient conditions for both phase and frequency synchronization. We highlight the role of graph structure in coherency properties, and introduce the idea of generator and bus aggregation whereby dynamic equivalent models of large power grids can be developed while retaining the concept of a “bus” in the network graph of the equivalent model. We also discuss several new results on graph sparsification for designing distributed controllers for power flow oscillation damping.

Multi-agent networked linear dynamic systems have attracted the attention of researchers in power systems, intelligent transportation, and industrial automation. The agents might cooperatively optimize a global performance objective, resulting in social optimization, or try to satisfy their own selfish objectives using a noncooperative differential game. However, in these solutions, large volumes of data must be sent from system states to possibly distant control inputs, thus resulting in high cost of the underlying communication network. To enable economically viable communication, a game-theoretic framework is proposed under the communication cost, or sparsity, constraint, given by the number of communicating state/control input pairs. As this constraint tightens, the system transitions from dense to sparse communication, providing the tradeoff between dynamic system performance and information exchange. Moreover, using the proposed sparsity-constrained distributed social optimization and noncooperative game algorithms, we develop a method to allocate the costs of the communication infrastructure fairly and according to the agents' diverse needs for feedback and cooperation. Numerical results illustrate utilization of the proposed algorithms to enable and ensure economic fairness of wide-area control among power companies.

In this tutorial we present the state-of-the-art as well as new methods for simulating various planning, operation, stability, reliability, and economic models of electric power systems. The discussion is driven by both first-principle models and empirical models. First-principle models result from the fundamental physics and engineering principles that govern the behavior of various components of a grid. Empirical models, on the other hand, are models that result from statistics and data analysis. We overview a wide spectrum of applications starting from planning models with a time-scale of simulation in years to real-time models where the time-scale can be in the order of milliseconds. We present a list of simulation software popularly used by the power engineering research community across the world. The increasingly important roles of power electronics, communication and computing, model aggregation, hybrid simulation, faster-than-real-time simulation, and co-simulation in emulating the daily operation of a grid are enumerated. The importance of research testbeds for testing, verification and validation of complex grid models at various temporal and spatial scales is also highlighted. The overall goal is to provide a vision on how simulations and their supporting implementation methods can help us in understanding the evolving behavior of tomorrow's power networks as a truly intelligent cyber-physical system.

Marginal-cost-based dynamic pricing of electricity services, including real power, reactive power, and reserves, may provide unprecedented efficiencies and system synergies that are pivotal to the sustainability of massive renewable generation integration. Extension of wholesale high-voltage power markets to allow distribution network connected prosumers to participate, albeit desirable, has stalled on high transaction costs and the lack of a tractable market clearing framework. This paper presents a distributed, massively parallel architecture that enables tractable transmission and distribution locational marginal price (T&DLMP) discovery along with optimal scheduling of centralized generation, decentralized conventional and flexible loads, and distributed energy resources (DERs). DERs include distributed generation; electric vehicle (EV) battery charging and storage; heating, ventilating, and air conditioning (HVAC) and combined heat & power (CHP) microgenerators; computing; volt/var control devices; grid-friendly appliances; smart transformers; and more. The proposed iterative distributed architecture can discover T&DLMPs while capturing the full complexity of each participating DER's intertemporal preferences and physical system dynamics.

In this paper we address the problem of implementing wide-area oscillation monitoring algorithms for large power system networks using distributed processing of Synchrophasor measurements. We consider two computational approaches, namely decentralized least squares (DLS) and its recursive implementation (RLS). Both algorithms are executed using multiple phasor data concentrators (PDC), deployed as virtual computing machines communicating over a fiber-optic communication network. Results are demonstrated using the US-Wide ExoGENI communication network connected to a PMU test bed at NC State University, and analyze the end-to-end computational and communication delays for both algorithms.