Day-night weather-based economic power dispatch

Palanichamy, C.; Babu, N.S.

doi:10.1109/tpwrs.2002.1007920

Cited by 31 publications

(9 citation statements)

References 13 publications

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“…It is applied on a 11-thermal units system [38] and 4 cascaded hydro units as described in Table 1. The values of electricity price are given in Table 2 [23].…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust optimization based self scheduling of hydro-thermal Genco in smart grids

Soroudi

2013

Energy

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Publication informationEnergy, 61 (2013): 262-271 Publisher ElsevierItem record/more information http://hdl.handle.net/10197/6154 Publisher's statement þÿ T h i s i s t h e a u t h o r s v e r s i o n o f a w o r k t h a t w a s a c c e p t e d f o r p u b l i c a t i o ni n E n e r g y . C h a n g e s resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy (VOL 61, ISSUE 2013, (2013 AbstractThis paper proposes a robust optimization model for optimal self scheduling of a hydro-thermal generating company. The proposed model is suitable for price taker Gencos which seeks the optimal schedule of its thermal and hydro generating units for a given operating horizon. The uncertainties of electricity prices are modeled using robust optimization approach to make it more practical. It considers various technical constraints like water balance and water traveling time between cascaded power stations and emission allowance. Finally, different case studies are analyzed to demonstrate the strength of the proposed methodology.Key words: Robust optimization, hydro-thermal scheduling, uncertainty modeling, smart grids. T E Total emission

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“…It is applied on a 11-thermal units system [38] and 4 cascaded hydro units as described in Table 1. The values of electricity price are given in Table 2 [23].…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust optimization based self scheduling of hydro-thermal Genco in smart grids

Soroudi

2013

Energy

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“…It is applied to a 11-units system [29] as described in Table I. The values of electricity prices for the upcoming day are given in Table III [23].…”

Section: Simulation Resultsmentioning

confidence: 99%

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Soroudi

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY This paper provides a self‐scheduling tool for price taker Gencos. This methodology is based on Robust Optimization (RO) to deal with the uncertainties of market price values in the day‐ahead electricity pool market. The Genco is assumed to be the entity who decides about the operating schedules of its thermal units and Compressed Air Energy Storage units. The benefits of Genco brought by smart grid technology and energy storage systems are investigated in this work. The applicability of the proposed method is analyzed through different scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

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“…In recent years, many researchers have focused on solving the EED problem associated with wind power [1][2][3][4][5][6][7]. Kumar and Suna in [1] developed a chemical reaction optimization algorithm based on the chemical molecular reaction to optimize total cost in wind-fossil-fuel-based power systems.…”

Section: Introductionmentioning

confidence: 99%

“…In [5], Tan et al utilized two-point estimation method combined with particle swarm optimization to solve the stochastic economic load dispatch problem in microgrid which integrates the renewable energy generation. A traditional low-carbon emission dispatch analysis combined with the impact of wind power was proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

Wen

Lan

et al. 2017

Energies

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Abstract:With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms.

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Day-night weather-based economic power dispatch

Cited by 31 publications

References 13 publications

Robust optimization based self scheduling of hydro-thermal Genco in smart grids

Robust optimization based self scheduling of hydro-thermal Genco in smart grids

Smart self-scheduling of Gencos with thermal and energy storage units under price uncertainty

Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

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