A Critical Review of the Modeling and Optimization of Combined Heat and Power Dispatch

被引:21
作者
Kazda, Kody [1 ]
Li, Xiang [1 ]
机构
[1] Queens Univ, Dept Chem Engn, 19 Div St, Kingston, ON K7L 3N6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
cogeneration; combined heat and power; global optimization; metaheuristic optimization; economic dispatch; emission disptach; PARTICLE SWARM OPTIMIZATION; IMPROVED GENETIC ALGORITHM; CUCKOO SEARCH ALGORITHM; ARTIFICIAL BEE COLONY; SOLVING COMBINED HEAT; SCALE COMBINED HEAT; ECONOMIC-DISPATCH; MULTIOBJECTIVE OPTIMIZATION; GLOBAL OPTIMIZATION; PENALTY-FUNCTION;
D O I
10.3390/pr8040441
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical programming is a useful tool to support the operation of CHP systems, and has been the subject of substantial research attention since the early 1990s. This paper critically reviews the modeling and optimization work that has been done on the CHP economic dispatch problem, and the CHP economic and emission dispatch problem. A summary of the common models used for these problems is provided, along with comments on future modeling work that would beneficial to the field. The majority of optimization approaches studied for CHP system operation are metaheuristic algorithms. A discussion of the limitations and benefits of metaheuristic algorithms is given. Finally, a case study optimizing five classic CHP system test instances demonstrates the advantages of the using deterministic global search algorithms over metaheuristic search algorithms.
引用
收藏
页数:29
相关论文
共 120 条
[1]  
Adhvaryyu PK, 2014, 2014 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT ASIA), P338, DOI 10.1109/ISGT-Asia.2014.6873814
[2]   Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications [J].
Ahmadi, Mohammad Ali .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015
[3]   Strong mixed-integer programming formulations for trained neural networks [J].
Anderson, Ross ;
Huchette, Joey ;
Ma, Will ;
Tjandraatmadja, Christian ;
Vielma, Juan Pablo .
MATHEMATICAL PROGRAMMING, 2020, 183 (1-2) :3-39
[4]  
[Anonymous], 2018, General Algebraic Modeling System (GAMS)
[5]  
[Anonymous], 2018, TECHNICAL REPORT
[6]  
[Anonymous], 2014, World Energy Outlook 2014, DOI [DOI 10.1016/0301-4215(73)90024-4, 10.1787/9789264277854-en, DOI 10.1787/9789264277854-EN]
[7]  
[Anonymous], 2019, Power Generation Technologies, DOI [10.1016/B978-0-08-102631-1.00008-0, DOI 10.1016/B978-0-08-102631-1.00013-4, DOI 10.1016/B978-0-08-102631-1.00008-0]
[8]  
[Anonymous], 2005, Genetic algorithms. Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, DOI [DOI 10.1007/0-387-28356-04, 10.1007/0-387-28356-0{_}4]
[9]   GENETIC ALGORITHM SOLUTION TO THE ECONOMIC-DISPATCH PROBLEM [J].
BAKIRTZIS, A ;
PETRIDIS, V ;
KAZARLIS, S .
IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1994, 141 (04) :377-382
[10]   Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II [J].
Basu, M. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2013, 53 :135-141