Controlled islanding is an active and effective way of avoiding catastrophic wide area blackouts. It is usually considered as a constrained combinatorial optimization problem. However, the combinatorial explosion of the solution space that occurs for large power systems increases the complexity of solving it. This paper proposes a two-step controlled islanding algorithm that uses spectral clustering to find a suitable islanding solution for preventing the initiation of wide area blackouts by un-damped electromechanical oscillations. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. In the first step of the algorithm, the generator nodes are grouped using normalized spectral clustering, based on their dynamic models, to produce groups of coherent generators. In the second step of the algorithm, the islanding solution that provides the minimum power-flow disruption while satisfying the constraint of coherent generator groups is determined by grouping all nodes using constrained spectral clustering. Simulation results, obtained using the IEEE 9-, 39-, and 118-bus test systems, show that the proposed algorithm is computationally efficient when solving the controlled islanding problem, particularly in the case of a large power system.

The increasing development of large-scale offshore wind farms around the world has caused many new technical and economic challenges to emerge. The capital cost of the electrical network that supports a large offshore wind farm constitutes a significant proportion of the total cost of the wind farm. Thus, finding the optimal design of this electrical network is an important task, a task that is addressed in this paper. A cost model has been developed that includes a more realistic treatment of the cost of transformers, transformer substations, and cables. These improvements make this cost model more detailed than others that are currently in use. A novel solution algorithm is used. This algorithm is based on an improved genetic algorithm and includes a specific algorithm that considers different cable cross sections when designing the radial arrays. The proposed approach is tested with a large offshore wind farm; this testing has shown that the proposed algorithm produces valid optimal electrical network designs.

The inertia of a power system is a key factor in determining the initial frequency decline after a disturbance. Low system inertia can allow large frequency declines to occur that could lead to violation of frequency security limits, particularly in smaller power systems, or play a key role in allowing cascading outages to occur. Future developments in power systems will mean that the system inertia will become highly variable and take values that would traditionally have been considered very low. Presented is a method for the robust estimation of the generator inertia available in the system during a disturbance. This method has been validated using simulations of the IEEE 39-bus system in DIgSILENT ^TM PowerFactory ^® . Inertia estimates for a variety of disturbance types and noise conditions have been made, and found to have a median error of 1.53% with inter-quartile range of 6.6%.

The ongoing reduction of the total rotational inertia in modern power systems brings about faster frequency dynamics that must be limited to maintain a secure and economical operation. Electrical energy storage systems (EESSs) have become increasingly attractive to provide fast frequency response services due to their response times. However, proper management of their finite energy reserves is required to ensure timely and secure operation. This paper proposes a deep reinforcement learning (DRL) based controller to manage the state of charge (SOC) of a Multi-EESS (M-EESS), providing frequency response services to the power grid. The proposed DRL agent is trained using an actor-critic method called Deep Deterministic Policy Gradients (DDPG) that allows for continuous action and smoother SOC control of the M-EESS. Deep neural networks (DNNs) are used to represent the actor and critic policies. The proposed strategy comprises granting the agent a constant reward for each time step that the SOC is within a specific band of its target value combined with a substantial penalty if the SOC reaches its minimum or maximum allowable values. The proposed controller is compared to benchmark DRL methods and other control techniques, i.e., Fuzzy Logic and a traditional PID control. Simulation results show the effectiveness of the proposed approach.