An ever-increasing demand of electricity and the advent of clean renewable energy along with the limitations of conventional sources of energy are the prime driving force for the planners to incorporate distributed generation in electric power systems at the distribution side. The main objective of the Optimal Integration of Distributed Generation (OIDG) in distribution systems is to optimize the operation and planning of such systems. In the last two decades, significant research efforts have been devoted to formulating and solving the OIDG problem in distribution systems. The main aim of this work is to present a review describing the majority of high quality research work pertaining to OIDG in distribution systems.

The localization of the nodes in wireless sensor networks is essential in establishing effective communication among different devices connected, within the Internet of Things. This paper proposes a novel method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilizes the signal strength received at the target node to identify its location in the localized grid system. The Most Valuable Player Algorithm is used to solve the localization problem. Initially, the algorithm is implemented on four test cases with a varying number of sensor nodes to display its robustness under different network occupancies. Afterward, the study is extended to incorporate actual readings from both indoor and outdoor environments. The results display higher accuracy in the localization of unknown sensor nodes than previously reported.

This paper investigates the performance of a chaotic salp swarm optimization (CSSO) algorithm for solving optimal power flow (OPF) problems. The proposed CSSO-based method is applied on five different types of objective functions (OFs) which include generation costs minimization, environmental pollution/emission reduction, minimizing the transmission active power losses, enhancing the voltage profile, and upgrading system stability. Single and multi-objective frameworks are considered to attain various operational, economic, environmental and technical benefits. Initially, single OFs are used to formulate the optimization problem, and at later stage, simultaneous multiple objectives are optimized subject to a set of equality and inequality constraints. To prove the viability of the proposed CSSO-based OPF, standard IEEE 30-bus and 57-bus test systems via 16 studied cases are investigated. In addition, the subsequent cropped results are compared with other competing recent optimization methods in the literature. It can be reported that the cropped best fuel costs when they are optimized using the CSSO are 798.93 $/h and 41,666.66 $/h for the IEEE 30-bus and 57-bus test cases, respectively. The numerical results and performance tests along with comprehensive comparisons clearly indicate the superiority of the CSSO in achieving the given objectives.