This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

In market operations, distributed generators (DGs) and price-sensitive loads participate in a microgrid energy market implemented in JADE. Each DG and each price-sensitive load is represented by the respective agents which perform various functions such as scheduling, coordination and market clearing subject to system, DG and load constraints. Each agent is assigned to one of the several agent objectives which maximizes either DG or load surpluses or both. In simulated operation of a microgrid, hourly power reference signals and load control signals from JADE are passed to DG and load models developed in MATLAB/Simulink using MACSimJX. Simulated operation of DGs and loads are studied by performing simulations under different agent objectives. Results from simulation studies demonstrate the effectiveness of implementing multi-agent system (MAS) in the distributed management of microgrids.

This paper focuses on the implementation of a hybrid Firefly Algorithm-Particle Swarm Optimization (FAPSO) scheme for optimizing the parameters of an Interval Type-2 Fractional Order Fuzzy Proportional Integral Derivative (IT2FOFPID) based Power System Stabilizer (PSS) to minimize the low frequency oscillations in a power system. Here, the IT2FOFPID based PSS is designed by considering speed deviation and acceleration as input signals. In this paper, a single machine infinite bus (SMIB) system and the New England 10 machine 39-bus power system (NEPS) are used for testing and comparing the approaches. Stability studies are also performed using OPAL-RT's OP5600, a real-time digital simulator. The comparative studies demonstrate that the hybrid FAPSO optimized IT2FOFPID-PSS provides better damping and stability performance when compared with the PSSs based on the FA/PSO/hGABFO (hybrid genetic algorithm and bacterial foraging optimization) and hDEPS (hybrid differential evolution and pattern search) optimized IT2FOFPID approaches under various operating scenarios.

This paper studies false data injection attacks against automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions such as disconnecting customer loads or generators, leading to blackouts and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of false data injections, that minimizes the remaining time until the onset of remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of false data injections on power grids, and provides an analysis framework to guide the protection of sensor data links. Our analysis and algorithms are validated by experiments on a physical 16-bus power system testbed and extensive simulations based on a 37-bus power system model.

Currently, the distribution systems are moving towards decentralized operation due to the high penetration of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has been an emerging concept that promotes autonomous DER participation in energy markets while preserving their privacy concerns. In this work, a novel P2P energy trading enabled decentralized market framework is proposed for the optimal operation of distribution grids. Nodal agents and P2P agents are established as market participants, and market equilibrium is iteratively achieved via alternating direction method of multipliers based algorithms. The proposed market framework guarantees grid constraint satisfaction, market equilibrium, and global optimality for all market participants without violating their privacy concerns. The agent coordination and local optimization are designed such that fairness of the market clearing mechanism, prosumer autonomy, and prosumer anonymity is preserved without compromising the market efficiency. Further, costs/rewards of ancillary services associated with the P2P energy transactions are considered as trade-offs within the market mechanism, and those are accurately allocated to the respective trading pairs. The case studies illustrate the effectiveness and scalability of the proposed market framework.