Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty

Rao, B. Srinivasa; Vaisakh, K.

doi:10.1016/j.ijepes.2013.04.024

Cited by 30 publications

(5 citation statements)

References 34 publications

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“…The algorithms that were used for solving the CEED problem are the following: Self-Adaptive Firefly Algorithm [11], Grey Wolf Optimizer [12], Spiral Optimization Algorithm (SOA) [13], Multi-Objective Bacterial Foraging Algorithm [14], Particle Swarm Optimization (PSO) [15]- [17], hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) [18], [19], Galaxy-Based Search Algorithm [20], Gravitational Search Algorithm [21], [22], Hybrid Multi-Objective Optimization Algorithm [23], θ-Particle Swarm Optimization [24], Opposition-Based Gravitational Search Algorithm [25], Opozition-Based Harmony Search Algorithm [26], Parallel Particle Swarm Optimization Algorithm [27], Tribe-Modified Differential Evolution Algorithm [28], Honey-Bees Mating Optimization Algorithm [29], Clonal Selection Algorithm [30], Artificial Bee Colony Algorithm [31], Flower Pollination Algorithm [32], Biogeography-Based Optimization Algorithm [33], Multi-Objective Hybrid Evolutionary Algorithm [34], DE [35], Multi-Objective Differential Evolution Algorithm [36].…”

Section: Introductionmentioning

confidence: 99%

Moth Swarm Algorithm for Solving Combined Economic and Emission Dispatch Problem

Jevtić¹,

Jovanović²,

Radosavljević³

et al. 2017

ElAEE

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In this paper, the Moth Swarm Algorithm (MSA) is applied to the Combined Economic and Emission Dispatch (CEED) problem in thermal power plants. The analysis of behavior and the evaluation of performances of the algorithm are carried out on the standard test systems with 3 and 6 generators. The results of the MSA application to these test systems are compared with the results published in recent literature. The present paper shows that the proposed MSA gives an accurate and effective solution of the CEED problem. Index Terms-Combined economic and emission dispatch; moth swarm algorithm; power generation dispatch; power engineering computing.

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Section: Introductionmentioning

confidence: 99%

Moth Swarm Algorithm for Solving Combined Economic and Emission Dispatch Problem

Jevtić¹,

Jovanović²,

Radosavljević³

et al. 2017

ElAEE

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show abstract

“…Hence, in this article, total fuel cost has been minimized with quadratic fuel characteristics, piecewise fuel characteristics and valve‐point loading effects of generators, real power loss, minimization of bus voltage deviation from preset value, total emission and voltage stability enhancement, are taken in the consideration as the objective functions of OPF problem. In recent past, the new problems in solving OPF can be resolved by new stochastic algorithms such as genetic algorithm (GA); 9‐12 evolutionary programming, 13‐15 particle swarm optimization (PSO), 16‐18 differential evolution (DE), 19‐22 frog leaping, 23 harmony search, 24 gravitational search, 25 clonal search, 26 artificial bee colony, 27 teaching–learning, 28 evolutionary algorithm 29 and bacteria foraging 30 …”

Section: Introductionmentioning

confidence: 99%

“…In Reference 25, gravitational search algorithm (GSA) was recommended to determine the optimal solution for OPF problem in a modern power system. In Reference 26, a new MO optimization approach based on adaptive clonal selection algorithm (ACSA) was established to solve complex environmental/economic dispatch (EED) problem of thermal alternators in generation system. In the aforesaid paper, an adaptive clonal selection approach was integrated with non‐dominated based sorting technique and crowding distance concept was used to solve and achieve Pareto‐optimal set.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimal power flow using grasshopper optimization algorithm

Mandal,

Roy

2023

Optim Control Appl Methods

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SummaryThis paper introduces grasshopper optimization algorithm to efficiently prove its superiority in the optimal power flow problem. To demonstrate the efficiency of the proposed algorithm, it is implemented on the standard IEEE 30‐bus, IEEE 57‐bus, and IEEE 118‐bus test systems with different objectives that reveal presentation indices of the power system. Twelve different cases, in single and multi‐objective optimization space, are considered on different curves of fuel cost, environmental pollution emission, voltage profile, and active power loss. The simulation results obtained from grasshopper optimization algorithm techniques are compared to other new evolutionary optimization methods surfaced in the current state‐of‐the‐art literature. It is revealed that the proposed approach secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence speed. The results obtained in this work demonstrate that the grasshopper optimization algorithm method can successfully be applied to solve the non‐linear problems connected to power systems.

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“…For the past ten years, many optimization techniques have been introduced by researchers and electrical engineers for optimal reactive power control such as genetic algorithm [4]- [13], particle swarm optimization [14]- [18] and linear programming [19], [20], simulated annealing approach [21], self-directing evolutionary operation [12]- [15] and artificial bee colony (ABC) [22]- [26]. One of the meta-heuristic optimization techniques that have caught the attention of electrical engineers to solve power system problems is artificial immune system (AIS).…”

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Control Using Compensating Capacitor Based on Artificial Immune System

Razali

Roslan²,

Mansor³

et al. 2019

IJATCSE

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This paper presents the application of compensating capacitors for optimal power flow in transmission system. The compensating capacitors are placed at suitable locations in the transmission system and the size of reactive power to be injected by the capacitors to the system is obtained using artificial immune system (AIS) optimization technique. AIS will find the best value of reactive power of the compensating capacitors so that the total system loss is minimum. It is found that applying the AIS optimization technique is prospective approach for finding optimal reactive power output of the installed compensating capacitors in controlling reactive power of transmission system.

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Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty

Cited by 30 publications

References 34 publications

Moth Swarm Algorithm for Solving Combined Economic and Emission Dispatch Problem

Moth Swarm Algorithm for Solving Combined Economic and Emission Dispatch Problem

Multi‐objective optimal power flow using grasshopper optimization algorithm

Optimal Reactive Power Control Using Compensating Capacitor Based on Artificial Immune System

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