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Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach.

The deployment of a microgrid with renewable energy sources (RESs) generally considers two aspects i.e., the need for the energy storage and the presence of many uncertainties due to the intermittent nature of several RESs and the load demand variability. The latter justifies the use of stochastic optimization for the scheduling purpose. Therefore, this paper aims to investigate the optimal stochastic scheduling and evaluate the expected performance of a microgrid in grid-connected mode with a hybrid energy storage system consisting of a battery and a supercapacitor. The proposed optimization approach is formulated as a stochastic mixed-integer linear programming (MILP) problem and applied to a modified ORNL-DECC microgrid test system. The system uncertainties are associated with wind power, solar power, and load demand, which are represented by Gaussian distributed scenarios with standard deviations of 10%, 5%, and 3%, respectively. To investigate the effect of supercapacitor addition on the scheduling stochastic performance, the power rating of supercapacitor is varied between 0 and 20 kW. The analysis is performed based on the results of scheduling using the deterministic and the stochastic optimization schemes. In the deterministic scheme, the addition of supercapacitor is reviewed based on the achieved total cost. In the stochastic optimization scheme, we use the expected value of perfect information (EVPI) to observe the stochastic performance in addition to the expected total cost. The results are beneficial when assessing the advantage and disadvantage of supercapacitor deployment in a microgrid scheduling.

A new multi-objective method called Multi-Objective Electric Charged Particles Optimization (MOECPO) is proposed to optimally place wind turbines and at the same time select their optimal heights in an extensive area of wind farm considering several scenarios. This method permits the designer/planner to get several designs in just one run where the maximum efficiency and maximum output power are the objectives. Furthermore, while dealing with an optimization problem, reducing the number of design variables helps to simplify the problem and consequently improves the final solutions. In this paper, a smart approach which is based on binary coding is proposed and implemented for design variables reduction by a high factor. Under the scope of this research work, four case studies are investigated using two different sites and considering the same or different hub heights. The obtained Pareto set of solutions is represented by 96, 95, 199 and 209 solutions, for CASE 1, CASE 2, CASE3 and CASE 4, respectively. This front is spread over a large region and offers several interesting solutions with different number of turbines in the considered wind farm. The results or findings from the proposed approach allow the designer or planner to select the best solution or layout considering many factors. In addition, it can be used to plan the future extensions.

Saudi Arabia consumed about 290 TWh of energy in 2019, with a per capita consumption of 9100 kWh. The majority of this energy is generated using nonrenewable energy sources, with the residential sector consuming more than half of it. This study investigates the application of a combined dispatch strategy to optimize hybrid energy systems for six remote locations in Saudi Arabia. The system is designed for approximately 127 households in each location, with an annual electrical load of 2.5 MWh, a yearly peak load of 754 kW, and a primary load of 7620 kWh/day. The technical and economic viability of the hybrid and fully renewable energy system designs are assessed using HOMER. For PV/Wind/Diesel system design, Abha and Hail were found to have the least Levelized cost of energy (LCOE) of 0.248 US$/kWh, while the annualized costs and the net present cost (NPC) were minimum for Hail. In addition, for fully renewable system (PV/Wind/Battery) design, the LCOE was in the range of 0.305–0.399 US$/kWh, around 30–50 % higher at different locations. The results presented in this study are the first of its kind in the KSA and can serve as a hybrid energy solution for other parts of the world with similar weather conditions.

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.