A robust optimization method for planning regional-scale electric power systems and managing carbon dioxide

Chen, C.; Li, Y.P.; Huang, Guohe; Li, Y.F.

doi:10.1016/j.ijepes.2012.02.007

Cited by 30 publications

(10 citation statements)

References 42 publications

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“…This implies that the renewable energy recourses would be encouraged over the planning horizon. The optimized electricity generation targets could help the decision makers (14,15,16), (17,18,19)] [ (15,16,17), (18,19,20)] [ (16,17,18), (19,20,21)] Surplus cost [(11, 12, 13), (14,15,16)] [ (12,13,14), (15,16,17)] [ (13,14,15), (16,17,18)] Gas-fired Regular cost [ (19,20,21), (23,24,25)] [ (20,21,22), (24,25,26)] [ (21,22,23), (25,…”

Section: Optimized Electricity Generationmentioning

confidence: 99%

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Risk analysis for Shanghai's electric power system under multiple uncertainties

Piao

Huang

et al. 2015

Energy

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Section: Optimized Electricity Generationmentioning

confidence: 99%

“…Fixed cost [ (26,27,28), (31,32,33)] [ (27,28,29), (32,33,34)] [ (28,29,30), ( [ (13,14,15), (16,17,18)] [ (15,16,17), (18,19,20)] [ (17,18,19), (20,21,22) Fig. 3 shows the excess electricity generation under different a levels (r ¼ 1).…”

Section: Optimized Electricity Generationmentioning

confidence: 99%

Risk analysis for Shanghai's electric power system under multiple uncertainties

Piao

Huang

et al. 2015

Energy

Self Cite

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“…It's a new approach to optimization problems affected by uncertainty specially in case of lack of full information on the nature of uncertainty [29]. The successful application of this method in power systems have been reported in recently published papers like: energy hub management [30], unit Commitment With Wind Power and Pumped Storage hydro [31], optimal adjustment of power system stabilizers [32], integration of plug-in hybrid electric vehicles (PHEVs) into the electric grid [33] and planning regional-scale electric power systems and managing carbon dioxide [34]. The concept of robust optimization is described as follows: consider a function like z = f (x, y)…”

Section: Proposed Robust Optimization Approachmentioning

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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“…However, in order to ensure to achieve feasible solutions in extreme cases, robust optimization is often at the expense of economy to exchange for promotion of robustness, as a result, the optimization results are often too conservative and sometimes cannot be found in extreme cases [10], [11]. Ben-Tal et al [12] proposed a more flexible and adjustable robust optimization method.…”

Section: Introductionmentioning

confidence: 99%

Flexible Robust Optimization Dispatch for Hybrid Wind/Photovoltaic/Hydro/Thermal Power System

Peng

Xie

Pan

et al. 2015

IEEE Trans. Smart Grid

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With sustained growth of intermittent power supplies connected to grid, the randomness and volatility of intermittent power supplies will bring new challenges to power system optimization dispatch. For dealing with the uncertainty of large-scale intermittent power supply, this paper introduces robust optimization theory into optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power systems. Meanwhile in order to make an ideal compromise between reliability and economy of system dispatch, this paper also introduces concept of uncertainty to cover shortcoming of conventional robust optimization which is conservative. A flexible robust optimization with adjustable uncertainty budget dispatch model is built for hybrid power system to achieve coordination between reliability and economy, and then the uncertainty budget decision methods are studied to reduce blindness of uncertainty budget decisions. Moreover, a new compound differential evolution (CDE) algorithm is designed in this paper. The diversity of individuals and convergence speed are taken into account in process of differential evolution (DE) calculation by introducing a series of operations into DE algorithm, such as individual ranking, population dividing, CDE, and population restructuring. Finally, a sample application is carried out to verify the validity and practicability of the model and the method.Index Terms-Adjustable uncertainty budget, compound differential evolution (CDE) algorithm, intermittent power supplies, optimization dispatch, robust optimization. NOMENCLATURE Sets XDecision variable set. U Bounded uncertainty set. R # t Set of intermittent power supplies of extreme output. he is currently a Professor. His current research interests include operations, planning, and economics of power systems, optimization, stochastic programming, and electricity markets.

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A robust optimization method for planning regional-scale electric power systems and managing carbon dioxide

Cited by 30 publications

References 42 publications

Risk analysis for Shanghai's electric power system under multiple uncertainties

Risk analysis for Shanghai's electric power system under multiple uncertainties

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

Flexible Robust Optimization Dispatch for Hybrid Wind/Photovoltaic/Hydro/Thermal Power System

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