Optimal Planning of a Smart Microgrid Including Demand Response and Intermittent Renewable Energy Resources

被引:99
作者
Hakimi, S. M. [1 ]
Moghaddas-Tafreshi, S. M. [2 ]
机构
[1] KN Toosi Univ, Tehran 163151355, Iran
[2] Guilan Univ, Fac Engn, Rasht 163151355, Iran
关键词
Active controller; heating/cooling system; renewable energy; smart microgrid; COMFORT; MANAGEMENT; SYSTEM;
D O I
10.1109/TSG.2014.2320962
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
Heating/cooling systems have played an important role in building energy and comfort management. There have been intensive discussions about the integration of heating/cooling systems into the smart grid infrastructure over the past decade, yet controlling the operation of heating/cooling systems in a smart grid with high penetration of renewable resources has not been addressed clearly. This study has investigated the suitability of a novel active controller applied to heating/cooling systems in the context of smart grid with high penetration of renewable energies. The proposed controller operates by responding to a combination of internal set points and external signals (e.g. the availability of renewable energy resources and welfare of customers) from a single local controller. The heating/cooling systems management minimizes the overall cost of the simulated smart microgrid, the size of smart microgrid units, and the imported energy from the distribution grid through an optimization process. It also at the same time maximizes the reliability of the smart microgrid. Demonstrated results confirm the capability of the proposed heating/cooling system controller on the planning of a smart microgrid.
引用
收藏
页码:2889 / 2900
页数:12
相关论文
共 47 条
  • [1] [Anonymous], PNNL17167 US DEP EN
  • [2] [Anonymous], 1984, RELIABILITY EVALUATI, DOI DOI 10.1007/978-1-4899-1860-4
  • [3] Constrained fuzzy logic approximation for indoor comfort and energy optimization
    Ari, S
    Cosden, IA
    Khalifa, HE
    Dannenhoffer, JF
    Wilcoxen, P
    Isik, C
    [J]. NAFIPS 2005 - 2005 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, 2005, : 500 - 504
  • [4] ASHRAE, 2010, ANSI/ASHRAE Standard 55-2020
  • [5] Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control
    Aswani, Anil
    Master, Neal
    Taneja, Jay
    Culler, David
    Tomlin, Claire
    [J]. PROCEEDINGS OF THE IEEE, 2012, 100 (01) : 240 - 253
  • [6] Demand Dispatch
    Brooks, Alec
    Lu, Ed
    Reicher, Dan
    Spirakis, Charles
    Weihl, Bill
    [J]. IEEE POWER & ENERGY MAGAZINE, 2010, 8 (03): : 20 - 29
  • [7] The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller
    Calvino, F
    La Gennusa, M
    Rizzo, G
    Scaccianoce, G
    [J]. ENERGY AND BUILDINGS, 2004, 36 (02) : 97 - 102
  • [8] Carlisle A, 2000, IC-AI'2000: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 1-III, P429
  • [9] Chassin D. P., 2004, MODELING POWER SYSTE
  • [10] Fuzzy adaptive networks in thermal comfort
    Chen, K
    Jiao, Y
    Lee, ES
    [J]. APPLIED MATHEMATICS LETTERS, 2006, 19 (05) : 420 - 426