摘要 :
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...
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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.
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摘要 :
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 unit...
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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.
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摘要 :
Ubiquitous and ecologically friendly renewable wind energy are promising options to execute the energy requirement as well as to reducing emission. Conventional thermal power economic transmit (ET) problem including wind generator...
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Ubiquitous and ecologically friendly renewable wind energy are promising options to execute the energy requirement as well as to reducing emission. Conventional thermal power economic transmit (ET) problem including wind generator model deals with minimizing the generation cost and pollutant emission by fulfilling variety of constraints. The stochastic scenery of wind speed and the discrepancy charges of overestimation and underestimation wind cost, which is essentially a random variable, are taken into account by introducing Weibull probability density function (W-pdf). In order to generate optimal generation scheduling under renewable energy environment, moth flame optimization (MFO) algorithm is proposed, and it is tested on three different benchmark load systems. It is observed that the newly developed enhanced MFO method is proficient, and it can provide lower generation cost and smaller pollutant emission for real-world problems.
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摘要 :
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-integra...
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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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摘要 :
The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA), which depends on the social dealings of the gra...
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The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA), which depends on the social dealings of the grasshopper in nature, to solve renewable energy-based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition-based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability, the Weibull distribution is used. A standard system containing two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed that OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.
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