This paper proposes an accurate harmonic identification strategy for microgrids and distributed power systems. The harmonic identification strategy is one of the complex tasks in microgrids due to the need of high computational burden in terms of memory and computational time. The complexity of the considered problem is resulted from solving the transcendental nonlinear equations that characterize harmonics especially in real time is considered as a highly challenged problem. The proposed identification strategy aims at detecting individual and total harmonic distortion levels that is generated from several harmonic sources. In the current paper, the Machine Learning Regression Analysis (MLRA) including location-specific data and the Artificial Neural Networks (ANNs) are proposed to identify the harmonic distortion. To enhance the identification and prediction performance, the standard IEEE 34-bus test feeder with verified harmonic sources power system is emulated for various scenarios using Electric Transient Analysis Program (ETAP). An extracting procedure for individual and total harmonic components are employed. The higher reduction level of error with approximately maximum median of 3.7127e- ¹⁵ % assures the accurate prediction of harmonic components. In addition, this work investigates the impact of several practical cases when the voltages of the renewable clean energy source arrays vary (e.g., solar cell, wind turbine, and EV), and a double-stage topology is needed to have the same amount of voltage at the input of the inverter before inversion.

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.

The optimal power flow (OPF) problem considers as an optimization problem, in which the utility strives to reduce its global costs while pleasing all of its constraints. Therefore, Interior Search Algorithm (ISA) is applied to treat this problem. Where, ISA is a specific type of artificial intelligence and a mathematical programming, not a meta- heuristic algorithm. The effectiveness of this method in solving the OPF problem is evaluated on two test power systems, the IEEE 30-bus and the IEEE 57-bus test systems. For the first example, the ISA-OPF algorithm finds an answer that agrees with published results. For the 57-bus system, the ISA-OPF demonstrates its ability to transact with larger systems. Thus, the ISA-OPF algorithm is shown to be a robust tool to treat this optimization compared with other methods. Moreover, the advantage of ISA is that it has only one parameter of control which makes the simplicity in the main algorithm.

Microgrids consist of small energy resources such as the solar power, wind power, and many distributed generation units. This article focuses on the application of a chaotic krill herd (CKH) algorithm for solving economic dispatch (ED) and combined economic emission dispatch (CEED) problems in a microgrid. The ED problem finds the power scheduling of renewable energy resources (RESs) units to minimize the entire production costs of generation while all system constraints are preserved into their acceptable limitations. Meanwhile, achieving this value from the fuel cost and fossil fuel fired power plant emission should be as little as possible. The proposed CKH is a bio-inspired optimization algorithm that is built in the Lagrangian and evolutionary behavior of krill people in nature. The Chaotic guide is a variation in qualities combined with KH. A singer map is integrated with the KH algorithm and tested on different scenarios for improving its global research and enhancing its performance. The elitism scheme is also served to save the best krill during amid the procedure when updating the krill. The results clearly show the superiority of the CKH algorithm to search for the best function values when compared with the standard KH and other well-known meta-heuristic search algorithms.