Economic Assessment of Power Grid Development Using Artificial Bee Colony Algorithm

Abstract

The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.

LUMINA: Linguistic unified multimodal Indonesian natural audio-visual dataset

“…To avoid excessively long recording durations, the filtering process is repeated, and sentence combinations are selected while still considering the even distribution of syllable coverage using the Artificial Bee Colony algorithm as used in [ 12 , 13 ]. This is also used to reduce the frequency of the occurrence of the same syllables in the combinations of words used in dataset formation, as can be seen in Fig.…”

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

“…Metaheuristic algorithms (MAs) can be classified into four distinct categories, which include evolutionary algorithms (EAs), swarm intelligence (SI) methods, approaches based on natural phenomena, and algorithms inspired by human behavior [170]. Over a few decades, numerous MAs have been developed, among which genetic algorithm (GA) [129,[171][172][173][174][175], particle swarm optimization (PSO) [176][177][178][179][180][181], ant colony optimization (ACO) [182][183][184][185], and artificial bee colony (ABC) [186][187][188][189][190][191] and their variants have been extensively applied to solve the EDP. The main feature of these nature-inspired algorithms is their reliance on searching the space of potential solutions to find optimal or near-optimal outcomes.…”