Rollout strategies for real-time multi-energy scheduling in microgrid with storage system

被引:36
作者
Lan, Yu [1 ]
Guan, Xiaohong [1 ,2 ]
Wu, Jiang [1 ]
机构
[1] Xi An Jiao Tong Univ, Minist Educ, Key Lab Intelligent Networks & Network Secur, Xian 710049, Shaanxi, Peoples R China
[2] Tsinghua Univ, Dept Automat, Ctr Intelligent & Networked Syst, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
wind power plants; power generation scheduling; distributed power generation; cogeneration; battery storage plants; load management; Markov processes; greedy algorithms; rollout strategies; real-time multienergy scheduling problem; microgrid; storage system; rising climatic concerns; power system utilisation; gas emission minimisation; renewable energy; multienergy systems; wind power scheduling; combined heating-and-power generation scheduling; battery scheduling; power grid scheduling; electricity load; heat loads; wind power generation; rolling horizon Markov decision process; greedy algorithm; ENERGY-STORAGE; COMBINED HEAT; MANAGEMENT; DISPATCH;
D O I
10.1049/iet-gtd.2015.0426
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The rising climatic concerns force the conventional power system to minimise the gas emissions. Microgrid integrating with renewable energy plays a key role in utilising power systems in a more environment friendly way. Compared with single renewable energy system, the microgrid with multi-energy systems is more attractive in practice. The schedule problem is considered to schedule wind power, combined heating and power generation (CHP), battery, and power grid in order to satisfy the electricity load and heat loads in the microgrid with the minimal cost in real time. Owing to the stochastic nature of wind power generation, it is a challenge to solve the multi-energy scheduling problem when using the obtained real-time information. In this study, the joint scheduling of the microgrid with multi-energy systems is formulated as a rolling horizon Markov decision process (MDP). Moreover, then the rollout algorithm is applied to solve the problem in order to deal with the large state and decision space of MDP, which is caused by the increase of the number of CHPs. The feasible base policy needed in the rollout algorithm is constructed using a greedy algorithm. Numerical results demonstrate that the algorithm can achieve the requirement of real-time scheduling effectively.
引用
收藏
页码:688 / 696
页数:9
相关论文
共 25 条
[1]   Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems) [J].
Aghaei, Jamshid ;
Alizadeh, Mohammad-Iman .
ENERGY, 2013, 55 :1044-1054
[2]   A risk-reward framework for the competitive analysis of financial games [J].
al-Binali, S .
ALGORITHMICA, 1999, 25 (01) :99-115
[3]  
[Anonymous], 2007, DYNAMIC PROGRAMMING
[4]  
Barto A., 1998, Reinforcement Learning: an Introduction
[5]  
BenTal A, 2009, PRINC SER APPL MATH, P1
[6]   Dynamic programming and suboptimal control: A survey from ADP to MPC [J].
Bertsekas, DP .
EUROPEAN JOURNAL OF CONTROL, 2005, 11 (4-5) :310-334
[7]   Rollout algorithms for stochastic scheduling problems [J].
Bertsekas, DP ;
Castañon, DA .
JOURNAL OF HEURISTICS, 1999, 5 (01) :89-108
[8]  
Cao Xi- Ren, 2007, STOCHASTIC LEARNING
[9]   Sizing of Energy Storage for Microgrids [J].
Chen, S. X. ;
Gooi, H. B. ;
Wang, M. Q. .
IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (01) :142-151
[10]   Energy-Efficient Buildings Facilitated by Microgrid [J].
Guan, Xiaohong ;
Xu, Zhanbo ;
Jia, Qing-Shan .
IEEE TRANSACTIONS ON SMART GRID, 2010, 1 (03) :243-252