Evolutionary Oppositional Moth Flame Optimization for Renewable and Sustainable Wind Energy Based Economic Dispatch

Optim Control Appl Methods

2021

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Renewable energy‐based hydro‐thermal scheduling is a new assignment in solar‐wind‐hydro power structures including thermal plants with non‐convex fuel costs, a time delay of the multi‐reservoir cascaded hydro unit, generating units for wind power, and photo‐voltaic plant of the solar system. Renewable energy resources are used in immense quantity as they are naturally accessible and charge‐free. In this regard, this article presents a single‐objective economic replica of short‐term hydro‐thermal scheduling (HTS) problems having renewable solar and wind units. To speed up the convergence swiftness, of OBL is incorporated with the fundamental grasshopper optimization algorithm (GOA) method which is actively associated with the social communication of the grasshopper in the environment. Furthermore, HTS and hydro thermal scheduling incorporating solar and wind energy are considered for the benchmark test systems. Results presented by a few recent techniques (like fuzzy based evolutionary programming, teaching learning‐based optimization, etc.) have been compared with those obtained by the oppositional GOA (OGOA) to set up its effectiveness. Simulation results of OGOA technique clearly show that the renewable solar and wind units can significantly reduce the fuel cost of the power systems.

C_{t h} = \sum_{i = 1}^{N_{t r}} (a_{i} P_{i}^{* 2} + b_{t} P_{i}^{*} + c_{i} + |d_{i} \times \sin \{e_{i} \times (P_{i}^{\min} - P_{i}^{*})\}|)

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Section: Objective Functions and Mathematical Modelmentioning

confidence: 99%

Renewable energy incorporating short‐term optimal operation using oppositional grasshopper optimization

Hazra¹,

Optim Control Appl Methods

2021

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“…Owing to the stochastic characteristics of wind and solar assets, renewable available power is difficult to predict. 7 For this reason, the probability distribution function (PDF) is taken into account for the solar irradiance and wind speed profiles. Owing to doubtful mechanism such as wind speed, solar radiation intensity, and changes of power load, it brings complexities to short-term operation of wind-solar-hydro-based hybrid system.…”

Section: Introductionmentioning

confidence: 99%

“…The hydro‐thermal‐solar‐wind (HTSW) united system is a multistage, multiobjective, and multiconstraint multifaceted optimization subject to satisfy the load demands while minimizing the contaminant emission and entire production cost. Owing to the stochastic characteristics of wind and solar assets, renewable available power is difficult to predict 7 . For this reason, the probability distribution function (PDF) is taken into account for the solar irradiance and wind speed profiles.…”

Section: Introductionmentioning

confidence: 99%

Solar‐wind‐hydro‐thermal scheduling using moth flame optimization

Hazra¹,

Optim Control Appl Methods

2021

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Ever increasing concern of environmental safeguard makes renewable energy sources (RES) useful for emission reduction as well as for production cost minimization. In this article, the multiobjective economic emission dispatch (EED) model with security constraints incorporating photovoltaic, nonconvex thermal, and wind units is introduced for hydro‐thermal‐solar‐wind power scheduling arrangement. However, the significant reduction of emission is the foremost perspective for environmental sustainability and penetration of RESs into the electrical grid is being encouraged tremendously. To diminish the power generation expenditure and pollution generated by fossil fuels, renewable solar PV and wind power‐oriented hydro‐thermal scheduling have significant worth. Existing algorithms do not perform satisfactorily for unpredicted solar and wind‐based nonlinear hydro‐thermal‐wind‐solar scheduling problems and it may give local optimal solutions instead of global optimal solution. To overcome the shortcomings of the existing algorithms, an effective, and an intelligent robust algorithm, named moth flame optimization (MFO) has been proposed for solving the said nonlinear optimization problem. This article describes a scientific review on the application of the proposed method to obtain the scheduling of optimal generation for hydrothermal systems by incorporating RESs like solar PV and wind plant. Optimal solutions gained by the employment of different optimization methods for a variety of test instances are demonstrated and the projected methods are compared in terms of attained optimal solutions and convergence speed. The proposed MFO algorithm is competent for potential/hopeful outcomes, and it reduces the electrical power generation cost and emission significantly. The simulation outcomes reveal the usefulness and feasibility of the proposed method.

“…Hazra et al 42 implemented quasi‐oppositional chemical reaction optimization (QOCRO) for hybrid energy system. Furthermore, Hazra et al, 43 in their recent endeavor, proposed opposition‐based learning (OBL) technique for renewable wind incorporating load dispatch problem. Nourianfar et al 44 explained multi‐objective economic emission dispatch problems using fast non‐dominated sorting TVAC‐PSO.…”

Section: Introductionmentioning

confidence: 99%

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

Hazra

Int Trans Electr Energ Syst

2020

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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.