Sustainable Residential Micro-Cogeneration System Based on a Fuel Cell Using Dynamic Programming-Based Economic Day-Ahead Scheduling

被引:55
作者
Sun, Li [1 ,2 ]
Jin, Yuhui [1 ]
Shen, Jiong [1 ]
You, Fengqi [2 ]
机构
[1] Southeast Univ, Sch Energy & Environm, Key Lab Energy Thermal Conyers & Control, Nanjing 210096, Peoples R China
[2] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA
关键词
fuel cell; sustainable cogeneration; dynamic programming; thermal energy storage; efficiency optimization;
D O I
10.1021/acssuschemeng.0c08725
中图分类号
O6 [化学];
学科分类号
070301 [无机化学];
摘要
Fuel cells are widely deemed as a sustainable cogeneration technology that promises great decarbonization potential. However, economic operations of such sustainable energy systems are challenging because of the coupling between the heat and power generation, as well as the imbalance between the production and consumption. To this end, this paper proposes an economic day-ahead scheduling strategy for a sustainable cogeneration system, consisting of fuel cell, battery, thermal energy storage (TES), and heat pump. Based on the component models, a dynamic programming framework is formulated, in which the optimization objective is minimizing the accumulative fuel consumption and the constraints are derived from safety requirements. Optimization based on a typical 24-h load profile results in an hourly heat and power flow pattern to exhibit a highly coordinated and complementary operation scenario within the whole planning horizon. The benefits of energy storage are fully exploited and the energy-saving potential of each component is explored. It is revealed that the incorporation of TES into the sustainable energy systems brings about 3.5% overall efficiency improvement and healthier battery operations. The effects of TES capacity on the efficiency of the sustainable energy systems are also discussed.
引用
收藏
页码:3258 / 3266
页数:9
相关论文
共 55 条
[1]
A review of materials, heat transfer and phase change problem formulation for latent heat thermal energy storage systems (LHTESS) [J].
Agyenim, Francis ;
Hewitt, Neil ;
Eames, Philip ;
Smyth, Mervyn .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2010, 14 (02) :615-628
[3]
Bejan A., 2016, Advanced Engineering Thermodynamics, V1st ed.
[4]
Bertsekas D. P., 2011, Dynamic Programming and Optimal Control, V2
[5]
Dynamic modeling and validation of a micro-combined heat and power system with integrated thermal energy storage [J].
Bird, Trevor J. ;
Jain, Neera .
APPLIED ENERGY, 2020, 271
[6]
Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade [J].
Cao, Xiaodong ;
Dai, Xilei ;
Liu, Junjie .
ENERGY AND BUILDINGS, 2016, 128 :198-213
[7]
Air-water heat pump modelling for residential heating and domestic hot water in Chile [J].
Correa, Fabian ;
Cuevas, Cristian .
APPLIED THERMAL ENGINEERING, 2018, 143 :594-606
[8]
Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery [J].
Deng, Zhongwei ;
Yang, Lin ;
Cai, Yishan ;
Deng, Hao ;
Sun, Liu .
ENERGY, 2016, 112 :469-480
[9]
Melting behaviors of PCM in porous metal foam characterized by fractal geometry [J].
Deng, Zilong ;
Liu, Xiangdong ;
Zhang, Chengbin ;
Huang, Yongping ;
Chen, Yongping .
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2017, 113 :1031-1042
[10]
Implementation of Dynamic Programming for n-Dimensional Optimal Control Problems With Final State Constraints [J].
Elbert, Philipp ;
Ebbesen, Soren ;
Guzzella, Lino .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (03) :924-931