A two-layer multi-energy management system for microgrids with solar, wind, and geothermal renewable energy

被引:3
作者
Xu, Da [1 ,2 ,3 ,4 ]
Zhong, Feili [1 ,2 ,3 ,4 ]
Bai, Ziyi [5 ]
机构
[1] China Univ Geosci, Sch Automat, Wuhan, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat Co, Wuhan, Peoples R China
[3] Minist Educ, Engn Res Ctr Intelligent Technol Geoexplorat, Wuhan, Peoples R China
[4] Intelligent Elect Power Grid Key Lab Sichuan Prov, Chengdu, Sichuan, Peoples R China
[5] Univ Macau, Fac Sci & Technol, Dept Elect & Comp Engn, Zhuhai, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
energy management; renewable energy; energy storage; multi-energy systems; microgrid; CONTROL SCHEME; BIOGAS; PORTFOLIO;
D O I
10.3389/fenrg.2022.1030662
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The inherent intermittency of high-penetrated renewable energy poses economic and reliable issues of microgrid energy management. This study proposes a two-layer predictive energy management system (PEMS) for high renewable multi-energy microgrid (MEM). In this MEM, geothermal, solar, and wind energy is converted and conditioned for electricity, thermal, and gas supplies, in which multi-energy complementarities are fully exploited based on electrolytic thermos-electrochemical effects. The proposed microgrid multi energy management is a complicated and cumbersome problem because of their increasingly tight energy couplings and uncertainties of renewable energy sources (RESs). This intractable problem is thus processed by means of a two layer PEMS with different time scales, where the system operating costs are minimized in the upper layer and the renewable fluctuations are coped with in the lower layer. Simulation studies on a high-renewable MEM are provided to indicate its effectiveness and superiority over a single time scale scheme. Simulations results show that the operating cost can be reduced by 22.2% with high RESs accommodation.
引用
收藏
页数:12
相关论文
共 50 条
[21]   Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs [J].
Liu, Lei ;
Xu, Da ;
Lam, Chi-Seng .
ENERGY CONVERSION AND MANAGEMENT, 2023, 289
[22]   Modelling Aspects of Flexible Multi-Energy Microgrids [J].
Holjevac, Ninoslav ;
Capuder, Tomislav ;
Kuzle, Igor ;
Zhang, Ning ;
Kang, Chongquing .
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), 2018,
[23]   Technical and economic analysis of multi-energy complementary systems for net-zero energy consumption combining wind, solar, hydrogen, geothermal, and storage energy [J].
Li, Manfeng ;
Zhu, Kaiyang ;
Lu, Yiji ;
Zhao, Qingling ;
Yin, Kui .
ENERGY CONVERSION AND MANAGEMENT, 2023, 295
[24]   Short-Term Perspectives for Hybrid Wind/Solar/Geothermal Renewable Energy [J].
Boubaker, K. ;
Colantoni, A. ;
Allegrini, E. ;
Longo, L. ;
Di Giacinto, S. ;
Biondi, P. .
INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2013, 3 (02) :436-440
[25]   Renewable Energy Management with a Multi-Agent System [J].
Ben Abdessalem, Wahiba ;
Karaa, Sami ;
Ashour, Amira S. .
INTERNATIONAL JOURNAL OF ENERGY OPTIMIZATION AND ENGINEERING, 2015, 4 (03) :49-59
[26]   Development of renewable energy multi-energy complementary hydrogen energy system (A Case Study in China): A review [J].
Li, Zheng ;
Zhang, Wenda ;
Zhang, Rui ;
Sun, Hexu .
ENERGY EXPLORATION & EXPLOITATION, 2020, 38 (06) :2099-2127
[27]   Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach [J].
Wenhui Zhou ;
Jie Wu ;
Weifeng Zhong ;
Haochuan Zhang ;
Lei Shu ;
Rong Yu .
Mobile Networks and Applications, 2019, 24 :950-961
[28]   Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach [J].
Zhou, Wenhui ;
Wu, Jie ;
Zhong, Weifeng ;
Zhang, Haochuan ;
Shu, Lei ;
Yu, Rong .
MOBILE NETWORKS & APPLICATIONS, 2019, 24 (03) :950-961
[29]   Equilibrium Analysis of Multi-Energy Markets with Microgrids Bidding [J].
Wang, Xian ;
Zhang, Ying ;
Zhang, Shaohua .
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2020, 15 (07) :1020-1031
[30]   Research on the Collaborative Optimization of Multi-Energy Flow Microgrids [J].
Wu, Fan ;
Huang, Shangyuan ;
Li, Rui ;
Guo, Qinglai ;
Sun, Hongbin ;
Pan, Zhaoguang .
PROCEEDINGS OF RENEWABLE ENERGY INTEGRATION WITH MINI/MICROGRID (REM2016), 2016, 103 :345-350