Optimization methods applied to renewable and sustainable energy: A review

被引:1175
作者
Banos, R. [2 ]
Manzano-Agugliaro, F. [1 ]
Montoya, F. G. [1 ]
Gil, C. [2 ]
Alcayde, A. [1 ]
Gomez, J. [3 ]
机构
[1] Univ Almeria, Dept Rural Engn, Almeria 04120, Spain
[2] Univ Almeria, Dept Comp Architecture & Elect, Almeria 04120, Spain
[3] Univ Almeria, Dept Languages & Computat, Almeria 04120, Spain
关键词
Renewable energy systems; Optimization; Multi-criteria decision analysis; Design; Planning; Control; PARTICLE SWARM OPTIMIZATION; POWER-GENERATION SYSTEM; OPTIMAL-DESIGN; NEURAL-NETWORK; MULTIOBJECTIVE OPTIMIZATION; WIND TURBINES; DECISION-ANALYSIS; SIZING OPTIMIZATION; CONTROL STRATEGIES; HEATING SYSTEMS;
D O I
10.1016/j.rser.2010.12.008
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1753 / 1766
页数:14
相关论文
共 214 条
[1]   Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks [J].
Abdel-Aal, R. E. ;
Elhadidy, M. A. ;
Shaahid, S. M. .
RENEWABLE ENERGY, 2009, 34 (07) :1686-1699
[2]   A relaxation-based heuristic for the design of cost-effective energy conversion systems [J].
Ahadi-Oskui, T. ;
Alperin, H. ;
Nowak, I. ;
Cziesla, F. ;
Tsatsaronis, G. .
ENERGY, 2006, 31 (10-11) :1346-1357
[3]   Multi-objective planning of distributed energy resources: A review of the state-of-the-art [J].
Alarcon-Rodriguez, Arturo ;
Ault, Graham ;
Galloway, Stuart .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2010, 14 (05) :1353-1366
[4]  
Alba E, 2005, WILEY SER PARA DIST, P1, DOI 10.1002/0471739383
[5]   Methodology for optimization of distributed biomass resources evaluation, management and final energy use [J].
Alfonso, D. ;
Perpina, C. ;
Perez-Navarro, A. ;
Penalvo, E. ;
Vargas, C. ;
Cardenas, R. .
BIOMASS & BIOENERGY, 2009, 33 (08) :1070-1079
[6]   Long term electric load forecasting based on particle swarm optimization [J].
AlRashidi, M. R. ;
El-Naggar, K. M. .
APPLIED ENERGY, 2010, 87 (01) :320-326
[7]   GRASP and path relinking for project scheduling under partially renewable resources [J].
Alvarez-Valdes, R. ;
Crespo, E. ;
Tamarit, J. M. ;
Villa, F. .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2008, 189 (03) :1153-1170
[8]   Daily Hydrothermal Generation Scheduling by a new Modified Adaptive Particle Swarm Optimization technique [J].
Amjady, Nima ;
Soleymanpour, Hassan Rezai .
ELECTRIC POWER SYSTEMS RESEARCH, 2010, 80 (06) :723-732
[9]  
AMMAR MB, 2010, APPL ENERG, V87, P2340
[10]   Simulation and size optimization of a pumped-storage power plant for the recovery of wind-farms rejected energy [J].
Anagnostopoulos, J. S. ;
Papantonis, D. E. .
RENEWABLE ENERGY, 2008, 33 (07) :1685-1694