Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Hazra, Sunanda; Pal, Tapas; Roy, Provas Kumar

doi:10.4018/ijsir.2019010103

Cited by 24 publications

(9 citation statements)

References 49 publications

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“…The reliability of the system depends upon the spinning reserve and load demand satisfaction. For all Update the position of grasshopper using equation (18) and (19) No Fitness calculation of each grasshopper using equation 3Best grasshopper based on fitness value…”

Section: Binary Values Go (Bgoa)mentioning

confidence: 99%

“…Recently Seyedali Mirjalili proposed multi solutions based metaheuristic approach named as grasshopper optimization algorithm (GOA) which mimics swarming behavior of grasshopper in nature to solve the optimization problems. This is used to solve the multiple real value and binary optimization problems such as optimal reconfiguration for partial shaded photo voltaic array [16], frequency control of the load for interconnected multi area micro grid power system by tuning the gains of Fuzzy proportional integral derivative (Fuzzy PID) controller through GOA [17], economic load dispatch (ED) with renewable energy (wind mill) integration [18], optimal selection of conductor for radial distribution system [19], optimal control of voltage and frequency for an islanded micro grid [20], feature selection problem and short term load forecasting specific to the region [21] etc. Inspired by the successful applications of GOA and BGOA (binary grasshopper optimization algorithm) to research and industrial problems, this paper proposes BGOA to solve the combinatorial generation selection problem with a better solution quality as compared to the traditional GOA.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Shahid¹,

Malik²,

Said³

2021

Turk J Elec Eng & Comp Sci

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The unit commitment problem in power system is a highly nonlinear, non-convex, multi-constrained, complex, highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing and de-committing represents a discrete problem that requires binary/discrete optimization techniques to tackle with unit commitment optimization problem. The key functions of the unit commitment optimization problem involve deciding which units to commit and then to decide their optimum power (economic dispatch). This paper confers a binary grasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints. The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the natural swarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, the real/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an Sshaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4,

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Section: Binary Values Go (Bgoa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Shahid¹,

Malik²,

Said³

2021

Turk J Elec Eng & Comp Sci

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“…Barik et al [97] proposed a new way of thinking on the premise of GOA to coordinate the generation and load demand of micro-grid, aiming at coping with the unpredictability and reliance on the nature of renewable energy. Hazra et al [98] put forward a comprehensive approach, which proved the advantages of GOA in solving the availability of wind power in realizing the hybrid power system economic operation compared with another algorithm. Jumani et al [99] improved the GOA algorithm on existing MG controllers.…”

Section: Introductionmentioning

confidence: 99%

Spiral Motion Mode Embedded Grasshopper Optimization Algorithm: Design and Analysis

Gui

Heidari

et al. 2021

IEEE Access

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A new enhanced grasshopper optimization algorithm (GOA) has been developed and successfully applied to feature selection. GOA, as a heuristic algorithm, is proposed by simulating the living habits of grasshoppers in nature. Although GOA has an excellent global optimization capability, it still faces the disadvantage of low efficiency of searching optimization due to its ease of falling into the local optimum. Hence, based on the original GOA, this study integrates new ideas to reduce the defects to obtain a better global optimization ability. Because of the continuous optimization problem, the features of pursuing the best possible individual of spiral motion have been considered. The spiral motion is integrated into the GOA exploitation search stage, which further expands the diversification and intensification trends' capacities and effectively balances the exploration and exploitation procedures. Intuitively speaking, GOA with spiral search method can find better solutions in the exploration movement process, which is more efficient than the original search method. In the experimental comparison, to verify the proposed SGOA's ability in dealing with global unconstrained and constrained optimization problems, we compared it with other 30 IEEE 2017 benchmark tasks in meta-heuristic algorithms. Then, it is adopted to optimize engineering design and feature selection problems. We can know that the proposed SGOA has a good optimization ability in practical application from the experimental results. Spiral motion mode can significantly improve the original GOA's exploitation and exploration ability, and the proposed SGOA is of great assistance in practical fields. More info about this paper can be found on the web services https://aliasgharheidari.com.

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“…The principle of short‐term hydro system scheduling is to discover the optimum hourly hydro units generation 5 by the limited water supply in a schedule horizon to optimize the entirety advantage of hydro‐generated energy while fulfilling different constraints. For the wind resources stochastic nature, wind generation output is difficult to predict 6,7 . Weibull distribution 8 is used to validate statistics distribution of wind power.…”

Section: Introductionmentioning

confidence: 99%

Optimal dispatch using moth‐flame optimization for hydro‐thermal‐wind scheduling problem

Hazra

Roy

2020

Int Trans Electr Energ Syst

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A proficient algorithm, based on the moth‐flame optimization (MFO), is founded for solving economic and emission dispatch for hydro‐thermal‐wind (HTW) scheduling problem. The renewable wind power associated with hydropower‐integrated thermal power plant is a non‐linear, non‐convex optimization problem due to water discharge rate, hydraulic continuity constraint, reservoir storage limits, variable wind speed, scheduling time linkage, water transport delay, power balance constraints, as well as operation limits of renewable wind units. A renewable wind power‐oriented multi‐objective hydro‐thermal scheduling has significant value to trim down the power generation cost and emission. The usefulness of the projected method is verified on two case studies. In the most recent literature, the statistical outcomes of the applied MFO algorithm are compared with others evolutionary optimization. It is also observed that the proposed MFO method is skillful for developing hopeful outcomes and for significantly reducing the computation time.

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Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Cited by 24 publications

References 49 publications

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Spiral Motion Mode Embedded Grasshopper Optimization Algorithm: Design and Analysis

Optimal dispatch using moth‐flame optimization for hydro‐thermal‐wind scheduling problem

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