Distributed generations (DGs) have significant benefits in the electric power industry, such as a reduction in CO2 and NOX emissions in electricity generation, improvement of voltage profile in distribution feeders, amending voltage stability in heavy load levels, enhancement of reliability and power quality, as well as securing the power market. Despite the numerous advantages of DG technologies, weak capability in dispatching and management of DGs is a major challenge for distribution system operators. Hence, during recent years, several studies about various aspects of control, operation, placement, and sizing of DGs have been conducted. This paper presents a novel application of multiobjective particle swarm optimization with the aim of determining the optimal DGs places, sizes, and their generated power contract price. In the proposed multiobjective optimization, not only are the operational aspects, such as improving voltage profile and stability, power-loss reduction, and reliability enhancement taken into account, but also an economic analysis is performed based on the distribution company's and DG owner's viewpoints. The simulation study is performed on the IEEE 33-bus distribution test system and the consequent discussions prove the effectiveness of the proposed approach.

Integrating today's power systems with communication infrastructure makes them vulnerable to cyber-attacks, which can disrupt their normal operation undetectably. Automatic generation control (AGC) is one of the vulnerable controllers in power grids since it highly depends on communication systems. This paper first shows that false data injection attacks (FDIAs) against an AGC system can be carried out stealthily with destructive outcomes. Then, it proposes an anomaly-based attack detection and identification method for protecting the AGC system against cyber vulnerabilities. To detect attacks, the proposed method estimates the load frequency control (LFC) system's states using an unknown input observer (UIO), and calculates the UIO's residual function. Discrepancy between residual functions and a predefined threshold signifies an FDIA. Different identification UIOs are then used to determine the attack type, i.e., which system parameter(s) is (are) targeted by the attack. The effectiveness of the proposed method is corroborated using simulation results for a three-area power system.