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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. [ABSTRACT FROM AUTHOR]

Power generation planning (PGP) is a vigorous stage in power system planning subsequently forecasting the load demand. The nature of the PGP problem is highly constrained, dynamic, discrete, nonlinear, stochastic, and mixed-integer. The amalgamation of the renewable energy sources in strategic PGP generates the complexity in terms of feeble reliability because of variability. The meta-heuristic algorithms are referred to as efficient solution techniques for the optimization of challenging and minimum-cost PGP. In this paper, a novel PGP optimizer has been proposed to plan the power generation plants reflecting the minimum cost and attaining an explicit reliability evaluation for specific planning prospect. The formulated and proposed optimizer is called teaching–learning-based optimization employing correction matrix method-with-indicators (TLBO-CMMI). In TLBO-CMMI, a new radix-5-based mapping procedure has been developed for the representation of population search agents. An innovative constraint handling procedure known as correction matrix method-with-indicators and incremental cost-based strategy to compute variable operation and maintenance cost, have been embedded from our previous research work. The optimizer has been employed to perform the PGP for different test scenarios in the literature including reliability and emission constraints. The optimizer gives promising results considering minimum cost and computational time when compared with results by current methods in the literature. The suitability of the optimizer has also been assessed by applying to an actual scenario of the Pakistan power system to create a viable power generation plan. [ABSTRACT FROM AUTHOR]