Abstract
The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively.
Highlights
Optimal Power Flow (OPF) is one of the significant tools used over decades to date in energy management systems for reliable operation and proper planning of modern power systems
In order to validate the feasibility and effectiveness of the proposed method, the algorithm was tested on an IEEE 30-bus system
The power system model consists of six generator buses at buses 1, 2, 3, 8, 11, and 13, four transformers in lines 6–9, 6–10, 4–12, and 28–27, and nine shunt compensations at buses 10, 12, 15, 17, 20, 21, 23, 24, and 29
Summary
Optimal Power Flow (OPF) is one of the significant tools used over decades to date in energy management systems for reliable operation and proper planning of modern power systems. This problem is a non-linear, non-convex, and multi-dimensional optimization problem with control variables such as voltage magnitude and real power generation as continuous variables, and transformer tap ratios and shunt capacitor as discrete variables [1,2,3,4]. Nmewthtoodn, amndetlhinodea,rt,hneonin-ltienreiaorr, qpuoaidntramtice,tahnodd,mainxeddlinteeagre, rnporno-glrinaemamr, inqguahdarvaetibce, eannsducmceixssefdulilnytuesgeedr ptorosgorlvame OmPinFgphroabvleembe[e6n–1s2u]c.cTehssefsuellmy eutsheoddstogisvoelvthe eOoPpFtimpraolbrleesmult[s6–b1u2t].faTilhaetselomcaeltmhoindismgaiv, ief the oinpitimalaplorienstuisltsnobtuatsfsauiml aetdloclcoaslemtointihme as,oilfutihoen.inInitiadl dpiotionnt ,isthneoqtuaaslsituymoefdsoclluotsieontso hthigehslyolduetigorna.dIens ads dthiteionnu, mthbeerquofalciotyntorof lsvoaluritaiobnlesshinigchrelyasdese.grFaudretshears, tthhee cnoummpblexritoyf ocfonthtreopl rvoabrlieambleis vinecrryeahsiegsh.
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