Computation of Capacity Benefit Margin using Differential Evolution

Mustafa

Int Trans Electr Energ Syst

et al. 2019

Summary The ceaseless efforts by power system industries to promote sustainable and competitive electric power market structure in the deregulated environment have given rise to enormous research in the area of transfer capability of transmission networks. Due to high demand for electricity, transmission components are stressed to operate close to their operating limits, and this leads to a decrease in transmission efficiency. To address this issue, efficient evaluation of available transfer capability (ATC) is crucial for system planning, operation, and control. Several approaches have been proposed for ATC computation. Surprisingly, a comprehensive literature review on ATC computation is yet to be efficiently presented. Researchers have been able to come up with fast algorithms, but most of these algorithms are not accurate, and the presented accurate techniques are not fast enough for online applications. This paper presents a comprehensive review of the different approaches for ATC determination. It provides the concepts, methods, and the features of the ATC. For each technique, the state of the art of the several contributions made by researchers has been highlighted. This review reveals that there are issues regarding ATC calculation methods that need attention: the development of fast and accurate algorithm incorporating system dynamics and system uncertainties in ATC determination. Additionally, efforts on the incorporation of renewable energy generation in the ATC evaluation need to be intensified. This review will serve as one in all for researchers as well as a guide for the entrants in this field.

Section: Total Transfer Capability and Transfer Capability Marginsmentioning

confidence: 99%

Available transfer capability calculation methods: A comprehensive review

Mustafa

Int Trans Electr Energ Syst

et al. 2019

“…For more than a decade, various techniques have been proposed to evaluate CBM 7,9,12,17‐26 . The simple technique that is commonly used to calculate CBM among interconnected areas is established by using trial and error, by predefining 5% of the maximum transfer capability 18 or using an assumption of taking CBM as zero 19,20 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Differential evolution (DE) and Monte Carlo simulation (MCS) are used in Reference 22 for CBM computation of each area of the interconnected system. The objective function is to maximize the area generation capacity in conjunction with the external generation from other areas subject to maximum CBM limit from other areas and the specified LOLE value.…”

Section: Literature Reviewmentioning

confidence: 99%

Capacity benefit margin assessment in the presence of renewable energy

Int Trans Electr Energ Syst

Mustafa

Aman

et al. 2020

Summary Energy sustainability and reliability issues have created great concerns globally, instigating various utilities to create interconnections with one another for system security and techno‐economic benefits. Capacity benefit margin (CBM) is the amount of transmission capacity reserved between interconnected systems for emergency power exchange between utilities. It is usually estimated by evaluating the reliability of the generating units of interconnected systems to know the amount of external generation required during emergency supply shortage. Various techniques have been employed in literature to evaluate CBM. However, despite the global awareness on the continuous shift from conventional power generation sources to renewable energy sources, the assessment of CBM in the presence of renewable energy has not been addressed. Moreover, existing techniques for CBM calculation are based on iterative optimization and may not scale well to more than one deficient area. To solve these issues, this article incorporates wind power (WP) generation in CBM computation. The proposed technique is based on graph theory and can be used to calculate CBM in the presence of multi‐deficient areas and renewable energy sources, in particular, WP. Simulations using the IEEE 24‐bus reliability test system in MATLAB show that the proposed technique is significantly better in terms of scalability and accuracy as compared to existing techniques. The proposed technique can be employed by utilities for CBM estimation in the presence of renewable energy sources.

“…However, these improved schemes do not generally inherit SAs ability of escaping from local optima (Henderson et al, 2003). In evolutionary computation field, population-based meta-heuristics, such as differential evolution algorithm (Ali et al, 2009;Mashwani, 2014;Rajathy et al, 2010;Storn and Price, 1997;Zhang et al, 2014), particle swarm optimisation (PSO) algorithm Gao et al, 2012;Kennedy and Eberhart, 1995;Wang and Wang, 2014;Xia et al, 2014) ant colony optimisation (ACO) algorithm (Colorni et al, 1992a(Colorni et al, , 1992b, etc., are equipped with intelligent learning ability to guide their searching, so they have showed better efficiency.…”

Section: Introductionmentioning

confidence: 99%

Swarm simulated annealing algorithm with knowledge-based sampling for travelling salesman problem

Wang

Lin

Zhong

et al. 2016

IJISTA

Simulated annealing (SA) algorithm is a popular intelligent optimisation algorithm, but its efficiency is unsatisfactory. To improve its efficiency, this paper presents a swarm SA (SSA) algorithm by exploiting the learned knowledge from searching history. In SSA, a swarm of individuals run SA algorithm collaboratively. Inspired by ant colony optimisation (ACO) algorithm, SSA stores knowledge in construction graph and uses the solution component selection scheme of ACO algorithm to generate candidate solutions. Candidate list with bounded length is used to speed up SSA. The effect of knowledge-based sampling is verified on benchmark travelling salesman problems. Comparison studies show that SSA algorithm has promising performance in terms of convergence speed and solution accuracy.