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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. Published by AIP Publishing. https://doi.

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In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

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.

In the emerging age of the Internet of Things (IoT), energy-efficient and reliable connection among sensor nodes gain prime importance. Wireless engineers encounter a trade-off between sensors energy requirement and their reliable full connectivity. Consequently, the need to find the optimal solution draws the attention of many researchers. In this paper, the Electrostatic Discharge Algorithm (ESDA) is proposed, implemented, and applied to minimize energy needs of a sensor node while ensuring the fully-connectedness of each node. The obtained results show that the proposed method achieves better results than those found in the literature using the particle swarm optimization method in terms of energy savings and reliable connectivity.