Weather forecasts for microgrid energy management: Review, discussion and recommendations

被引:111
作者
Aguera-Perez, Agustin [1 ]
Carlos Palomares-Salas, Jose [1 ]
Jose Gonzalez de la Rosa, Juan [1 ,2 ]
Florencias-Oliveros, Olivia [1 ]
机构
[1] Univ Cadiz, Area Elect, EPSA, Res Grp MIDI TIC Computat Instrumentat & Ind Elec, Ave Ramon Puyol S-N, E-11202 Cadiz, Spain
[2] Res Unit PAIDI TIC 168, Cadiz, Spain
关键词
Microgrid; Weather forecast; Wind energy; Solar energy; PREDICTIVE CONTROL STRATEGY; DEMAND RESPONSE; OPTIMAL OPERATION; UNIT COMMITMENT; COST REDUCTION; MODEL; POWER; WIND; SOLAR; SYSTEM;
D O I
10.1016/j.apenergy.2018.06.087
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Meteorological conditions determine the renewable energy generation and, to a lesser extent, the load of microgrids. Weather forecasts are thus necessary to establish optimal plans according to the operational objectives and priorities of each microgid. Weather forecast errors are also responsible for deviations from these plans, thereby being an important source of uncertainty in the scheduling process. Despite this, weather information plays a secondary role in most of microgrid studies. This paper provides a general overview of the use of meteorological data in microgrids, focusing on the implementation of weather forecasts in microgrid energy management systems. Data sources, methodologies, uncertainty approaches and results from a selection of papers with complete information about the forecast context are analysed in detail. Additionally, similarities and differences regarding other energy forecast applications apart from microgrids are discussed. Finally, on the basis of the above, a list of recommendations for future implementations of weather forecasts in microgrid energy management systems is presented.
引用
收藏
页码:265 / 278
页数:14
相关论文
共 106 条
[1]  
Adinolfi F, 2015, IEEE POW ENER SOC GE
[2]  
Agüera-Pérez A, 2014, J WIND ENG IND AEROD, V127, P51, DOI [10.1016/jjweia.2014.02.006, 10.1016/j.jweia.2014.02.006]
[3]   An optimization procedure for Microgrid day-ahead operation in the presence of CHP facilities [J].
Aluisio, B. ;
Dicorato, M. ;
Forte, G. ;
Trovato, M. .
SUSTAINABLE ENERGY GRIDS & NETWORKS, 2017, 11 :34-45
[4]  
[Anonymous], 2013, 3 INT WORKSH INT SOL
[5]  
[Anonymous], 12 INT WORKSH LARG I
[6]  
[Anonymous], 2006, THESIS
[7]  
[Anonymous], D31B TU DENM
[8]   Review of photovoltaic power forecasting [J].
Antonanzas, J. ;
Osorio, N. ;
Escobar, R. ;
Urraca, R. ;
Martinez-de-Pison, F. J. ;
Antonanzas-Torres, F. .
SOLAR ENERGY, 2016, 136 :78-111
[9]   Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting [J].
Arcos-Aviles, Diego ;
Pascual, Julio ;
Guinjoan, Francesc ;
Marroyo, Luis ;
Sanchis, Pablo ;
Marietta, Martin P. .
APPLIED ENERGY, 2017, 205 :69-84
[10]   Energy management system for enhanced resiliency of microgrids during islanded operation [J].
Balasubramaniam, Karthikeyan ;
Saraf, Parimal ;
Hadidi, Ramtin ;
Makram, Elham B. .
ELECTRIC POWER SYSTEMS RESEARCH, 2016, 137 :133-141