This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.

Generation expansion planning (GEP) is a vital step in power system planning after load forecast. It is a highly constrained, dynamic, combinatorial, and discrete optimization problem. Mathematically, it is modeled as a mixed-integer nonlinear programming problem with high dimensionality and stochastic characteristics. The integration of renewable energy sources makes the GEP problem a complicated task and less reliable due to its intermittent nature. Meta-heuristic approaches are considered as potential solution methodologies to optimize the least cost GEP problem. This paper presents a novel GEP optimization framework to pursue the least cost GEP achieving a certain reliability level according to the forecasted demand for a planning horizon. The proposed GEP optimization framework is a correction matrix method with an indicator-based discrete water cycle algorithm (DWCA-CMMI). In DWCA-CMMI, a new parallel constraint handling approach, called a correction matrix method with indicators (CMMI), has been developed. DWCA-CMMI requires a smaller number of iterations and search agents to minimize the total GEP cost as compared to penalty factor-based metaheuristic approaches. Hence, CMMI enhances the convergence speed of the algorithm, avoids trapping in local optima, and improves both exploration and particularly exploitation. The proposed optimization framework is applied to reliability constrained and emission constrained GEP problems (test systems) from the literature. The proposed framework shows the promising results in terms of least cost and runtime as compared to results given by recent approaches presented in the literature. The applicability of the proposed approach has also been evaluated by applying to a real case study of Pakistan's power system to devise the feasible generation expansion plan.