Narottam Jangir

Energy management of Renewable Energy Sources in a microgrid using Cuckoo Search Algorithm

In this paper, the Renewable Energy Resources (RES) are used in a specific small area which is known as microgrid. Microgrid consists of microsources, battery storage and loads. In this paper the research is focused on the islanded mode microgrid. In the microgrid, the energy management system having a problem of Economic Load Dispatch (ELD) and it is optimized by meta-heuristic techniques. The newly introduced Cuckoo Search Algorithm (CSA) is implemented for the solution of ELD problem in the MATLAB environment. The minimization of total cost is obtained for four different scenarios like all sources included, all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of CSA with the Reduced Gradient Method (RGM) for the ELD problem solution. The results are calculated for different power demand of 24 hours. The results obtained with CSA gives better cost reduction in less iterations as compared to RGM which shows the effectiveness of the suggested algorithm.

Voltage stability enhancement and voltage deviation minimization using BAT optimization algorithm

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic BAT Optimization Algorithm (BOA). BOA is inspired by the echolocation capability of micro-bats. BOA has a fast convergence rate. In order to solve the optimal power flow problem, standard IEEE-30 bus test system is used. BOA is implemented for the solution of proposed problem. The problems considered in the OPF problem are Fuel Cost Reduction, Voltage Deviation Minimization, and Voltage Stability Improvement. The results obtained by BOA is compared with other techniques such as Flower Pollination Algorithm (FPA) and Particle Swarm Optimizer (PSO). Results shows that BOA gives better optimization values as compared with FPA and PSO that confirms the effectiveness of the suggested algorithm.

Moth-Flame optimization Algorithm for solving real challenging constrained engineering optimization problems

In this paper, a novel nature-inspired optimization algorithm based on the navigation strategy of Moths in universe called the Moth-Flame optimization (MFO) Algorithm, is applied for constrained optimization and engineering design problems. A comparative analysis of MFO algorithm expresses the optimum functional value in term of accuracy and standard deviation over rest of well-known constraint optimization algorithms. Five constrained benchmark function of engineering problems have been calculated and gained solutions were compared with other recognized algorithms. The gained solution expresses that MFO algorithm provides better results in various design problems compared to other optimization algorithms.

Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel metaheuristic optimization algorithm Moth-Flame Optimizer (MFO). The MFO is inspired by the navigation strategy of moths in universe. MFO has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. MFO is applied to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, voltage profile improvement, voltage stability enhancement, active power loss minimization and reactive power loss minimization. The results obtained by MFO are compared with other techniques such as Flower Pollination Algorithm (FPA) and particle swarm optimizer (PSO). Results show that MFO gives better optimization values as compared with FPA and PSO which verifies the effectiveness of the suggested algorithm.

Adaptive Krill Herd Algorithm for Global Numerical Optimization

A recent bio-inspired optimization algorithm, that is, based on the Lagrangian and evolutionary behavior of krill individuals in nature is called the Krill Herd (KH) Algorithm. Randomization has a key role in both exploration and exploitation of a problem using KH algorithm. A new randomization technique termed adaptive technique is integrated with Krill Herd algorithm and tested on several global numerical functions. The KH uses Lagrangian movement which includes induced movement, random diffusion, and foraging motion, and therefore, it covers a vast area in the exploration phase. And then adding the powerful adaptive randomization technique potent the adaptive KH (AKH) algorithm to attain global optimal solution with faster convergence as well as less parameter dependency. The proposed AKH outperforms the standard KH in terms of both statistical results and best solution.

Voltage stability enhancement and voltage deviation minimization using multi-verse optimizer algorithm

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimization technique Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. MVO has a fast convergence rate. In order to solve the optimal power flow problem, standard IEEE-30 bus test system is used. MVO algorithm is implemented for the solution of proposed problem. The problems considered in the OPF problem are Fuel Cost Reduction, Voltage Deviation Minimization, and Voltage Stability Improvement. The results obtained by MVO is compared with other techniques such as Flower Pollination Algorithm (FPA) and Particle Swarm Optimizer (PSO). Results show that Multi-Verse Optimizer gives better optimization values as compared with FPA and PSO that confirms the effectiveness of the suggested algorithm.

An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem

Abstract The main ambition of utility is to provide continuous reliable supply to customers, satisfying power balance, transmission loss while generators are allowed to be operated within rated limits. Meanwhile, achieving this from fossil fuel fired power plant emission value and fuel cost should be as less as possible. An allowable deviation in fuel cost and feasible tolerance in fuel cost has been additively called as multi objective combined economic emission dispatch (MOCEED) problem. MOCEED problem is applied to newly proposed non dominated sorting genetic algorithm-III (NSGA-III). NSGA-III method is really powerful to handle problems with non-linear characteristics as well as having many objectives. The proposed NSGA-III is firstly applied to unconstraint/constraints multi-objective test functions then applied to solve MOCEED problem with 6-generation unit, IEEE 118 bus 14 generating unit system with a smooth quadratic fuel/emission objective functions and 10-unit with non-smooth/valve point loading effect test system. Statistical results of MOCEED problem obtained by NSGA-III is compared with other well-known techniques proposed in recent literature, validates the effectiveness of proposed approach.

A Novel Hybrid PSO–WOA Algorithm for Global Numerical Functions Optimization

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid particle swarm optimization (PSO)–whale optimizer (WOA) is exercised on some unconstraint benchmark test functions. Hybrid PSO–WOA is a combination of PSO used for exploitation phase and WOA for exploration phase in uncertain environment. Analysis of competitive results obtained from PSO–WOA validates its effectiveness compared to standard PSO and WOA algorithm.

A Novel Hybrid Approach Particle Swarm Optimizer with Moth-Flame Optimizer Algorithm

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new method hybrid PSO (Particle Swarm Optimization)—MFO (Moth-Flame Optimizer) is exercised on some unconstraint benchmark test functions and overcurrent relay coordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-MFO is combination of PSO used for exploitation phase and MFO for exploration phase in uncertain environment. Position and Velocity of particle is updated according to Moth and flame position in each iteration. Analysis of competitive results obtained from PSO-MFO validates its effectiveness compare to standard PSO and MFO algorithm.

An emission constraint environment dispatch problem solution with microgrid using Whale Optimization Algorithm

In this work, microgrid is modern small scale power system of the centralized electricity for a small community such as villages and commercial area. Microgrid consists of microsources like distribution generator, solar and wind units, etc., and different loads. In the microgrid, the energy management system (EMS) having a problem of Combined Economic Emission Dispatch (CEED) and it is optimized by metaheuristic techniques. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The Whale Optimization Algorithm (WOA) is applied for the solution of CEED problem in the MATLAB environment. The minimization of total cost and total emission are obtained for all sources included. The result shows the comparison of WOA with the Gradient Method (GM), Ant Colony Optimization (ACO) and Particle Swarm Optimizer (PSO) technique for the two different cases which are Economic Load Dispatch (ELD) without emission and with emission. The results are calculated for different power demand of 24 hours. The results obtained with WOA gives better cost reduction in less iterations as compared to GM, ACO and PSO which shows the effectiveness of the given algorithm. The key objective of this work is to solve the CEED problem to obtained optimal system cost.