Stochastic hydro-thermal scheduling optimization: An overview

被引:64
作者
de Queiroz, Anderson Rodrigo [1 ]
机构
[1] N Carolina State Univ, 2501 Stinson Dr, Raleigh, NC 27607 USA
基金
美国国家科学基金会;
关键词
Hydro-thermal scheduling; Multi-stage stochastic optimization; Renewable generation; Electrical power systems; Sampling-based decomposition algorithms; LINEAR-PROGRAMS; WIND POWER; HYDROELECTRIC GENERATION; SUSTAINABLE ENERGY; OPTIMAL OPERATION; STORAGE; SYSTEMS; DECOMPOSITION; INTEGRATION; COORDINATION;
D O I
10.1016/j.rser.2016.04.065
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents an overview about the hydro-thermal scheduling problem. In an electrical power system power generators have to be scheduled over a time horizon in order to supply system demand. The scheduling problem consists in dispatching the available generators to meet the system electric load while minimizing the operational costs related to fuel and possible load curtailments. In a system with a large share of hydro generation, different from a thermal dominant power system, the uncertainty of water inflows play an important role in the decision-making process. In this setting the scheduling of generators has to be determined considering different future possibilities for water availability. Also, in the existence of a cascade system, the availability of water to produce electricity in hydro plants is influenced by decisions taken in upstream reservoirs. These issues complicate the hydro-thermal scheduling problem that often in the literature is modeled as a multi-stage stochastic program. In this paper we aim to give an overview about the main ideas behind this problem. We present model formulations, a solution technique, and point out to new developments related to this research. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:382 / 395
页数:14
相关论文
共 86 条
[1]   Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES) [J].
Abbaspour, M. ;
Satkin, M. ;
Mohammadi-Ivatloo, B. ;
Lotfi, F. Hoseinzadeh ;
Noorollahi, Y. .
RENEWABLE ENERGY, 2013, 51 :53-59
[2]  
Aihara R, 2012, J INT COUNCIL ELECT, V2
[3]  
ALM Marcato, 2001, THESIS
[4]  
[Anonymous], 2012, Dynamic Programming and Optimal Control
[5]  
[Anonymous], 2002, 14 PSCC
[6]   COMPOSITE REPRESENTATION OF A MULTIRESERVOIR HYDROELECTRIC POWER SYSTEM [J].
ARVANITI.NV ;
ROSING, J .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1970, PA89 (02) :319-&
[7]   Sustainable energy systems: Role of optimization modeling techniques in power generation and supply-A review [J].
Bazmi, Aqeel Ahmed ;
Zahedi, Gholamreza .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2011, 15 (08) :3480-3500
[8]  
Bellman R. E., 1957, Dynamic programming. Princeton landmarks in mathematics
[9]   Partitioning procedures for solving mixed-variables programming problems [J].
Benders, J. F. .
COMPUTATIONAL MANAGEMENT SCIENCE, 2005, 2 (01) :3-19
[10]  
Birge JR, 2011, SPRINGER SER OPER RE, P3, DOI 10.1007/978-1-4614-0237-4