Transmission network expansion planning using a modified artificial bee colony algorithm

Abstract

Summary Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed‐integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificial bee colony (ABC) algorithm is one of the newest swarm intelligence‐based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real‐life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real‐world situation.

Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

“…based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm are corroborated in Garver's 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system -WDN 220 KV (Unified Egyptian Transmission Network (UETN)). At first proposed algorithm is reviewed in Garver's 6-bus test system [65]. It has six buses and lines, three generators and five loads [65] at buses.…”

“…At first proposed algorithm is reviewed in Garver's 6-bus test system [65]. It has six buses and lines, three generators and five loads [65] at buses. Table 1 shows the loss appraisal and Table 2 shows the voltage aberration evaluation.…”

“…Tizhoosh [64] did the work in Opposition-based learning. Verma et al [65] done in Modified Artificial Bee. Romero et al [66] worked in AC Model.…”

Security constrained AC transmission network expansion planning

“…As metaheuristic algorithm is a population dependant algorithm, a good variance in the population is needed for reaching the optimal solution. Hence, a value of the tuning parameter which exhibits a large variance of the population pool compared to the other settings is a good estimate for the optimal value of the respective parameter [34]. A few trials of the algorithm with lesser iterations per trial is enough for obtaining a good estimate as is shown in Tables 5 and 6.…”

“…In this research work, the ACTNEP and RPP problems are solved using MABC algorithm [34], which is a general optimization algorithm developed from the artificial bee colony (ABC) algorithm [35], [36]. MABC is developed by incorporating the concept of universal gravitation [37] and global attraction [5] in the original ABC algorithm.…”