Tactical and operational management of wind energy systems with storage using a probabilistic forecast of the energy resource

被引:28
作者
Azcarate, Cristina [1 ]
Mallor, Fermin [1 ]
Mateo, Pedro [2 ]
机构
[1] Univ Publ Navarra, Pamplona 31006, Spain
[2] Univ Zaragoza, E-50009 Zaragoza, Spain
关键词
Renewable energy; Energy storage; Management policy; Stochastic optimization; Tactical and operational policies; TECHNOLOGIES;
D O I
10.1016/j.renene.2016.10.064
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The storage of energy facilitates the management of renewable energy systems by reducing the mismatch between the supplied energy and the forecasted production due to forecasting errors. The storage increases the reliability of the renewable energy system and enables participation in the electricity market by committing to the sale of electricity for the following day. Nevertheless, the inclusion of the energy storage capacity requires the development of new management policies. In this paper, we propose a management strategy for a renewable energy system with storage capacity that integrates tactical and operational decisions in a single mathematical model that makes use of an updated probabilistic wind speed forecast. Management policies are obtained by solving a sequence of rolling-horizon stochastic optimization problems whose formulation is inspired by the Stochastic Approximation Average technique. The management policies are illustrated by their application to wind-farms using hydrogen as the energy storage medium. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:445 / 456
页数:12
相关论文
共 29 条
[11]   Wind power impacts and electricity storage - A time scale perspective [J].
Hedegaard, K. ;
Meibom, P. .
RENEWABLE ENERGY, 2012, 37 (01) :318-324
[12]  
Kim S., 2015, INT SERIES OPERATION, V216
[13]   Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts [J].
Kou, Peng ;
Gao, Feng ;
Guan, Xiaohong .
RENEWABLE ENERGY, 2015, 80 :286-300
[14]   Ensemble forecasting [J].
Leutbecher, M. ;
Palmer, T. N. .
JOURNAL OF COMPUTATIONAL PHYSICS, 2008, 227 (07) :3515-3539
[15]   Advanced Materials for Energy Storage [J].
Liu, Chang ;
Li, Feng ;
Ma, Lai-Peng ;
Cheng, Hui-Ming .
ADVANCED MATERIALS, 2010, 22 (08) :E28-+
[16]   The role of compressed air energy storage (CAES) in future sustainable energy systems [J].
Lund, Henrik ;
Salgi, Georges .
ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (05) :1172-1179
[17]   Overview of current development in electrical energy storage technologies and the application potential in power system operation [J].
Luo, Xing ;
Wang, Jihong ;
Dooner, Mark ;
Clarke, Jonathan .
APPLIED ENERGY, 2015, 137 :511-536
[18]   Operational management of renewable energy systems with storage using an optimisation-based simulation methodology [J].
Mallor, F. ;
Azcarate, C. ;
Blanco, R. ;
Mateo, P. .
JOURNAL OF SIMULATION, 2015, 9 (04) :263-278
[19]   Including Risk in Management Models for the Simulation of Energy Production Systems [J].
Mallor, Fermin ;
Azcarate, Cristina ;
Blanco, Rosa .
CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, 2009, :1821-1826
[20]   Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland [J].
Meibom, Peter ;
Barth, Ruediger ;
Hasche, Bernhard ;
Brand, Heike ;
Weber, Christoph ;
O'Malley, Mark .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2011, 26 (03) :1367-1379