An optimization method for new energy utilization using thermostatically controlled appliances

被引:0
作者
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Nankai District, Tianjin [1 ]
300072, China
不详 [2 ]
300010, China
机构
[1] Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Nankai District, Tianjin
[2] State Grid Tianjin Electric Corporation, Hebei District, Tianjin
来源
Dianwang Jishu | / 12卷 / 3457-3462期
关键词
Demand response; Direct load control; Micro-grid; Thermostatically controlled appliance;
D O I
10.13335/j.1000-3673.pst.2015.12.019
中图分类号
学科分类号
摘要
For micro-grid containingwind and solar powers, maximized usage of those clean powers is a significant control target of micro-grid energy management. Uncertainties brought by wind and solar powers make it difficult to use wind and solar powers without waste. Demand response (DR) is investigated for optimal use of wind and solar powers. A new DR algorithm called weighting coefficient queuing (WCQ) algorithmis developed based on modified coloredpower algorithm and state-queuing (SQ) model to control thermostatically controlled appliance (TCA) loads. It allows customers to choose different colors to represent response level of TCAs. WCQ algorithm works well to solvefairness problem that some specific customers' devices are controlled more times than others under SQ model. WCQ algorithm also provides good customer comfort. A micro-grid with 1000 air conditioners (ACs) is used as test example. Simulation result verifies effectiveness of the proposed control method. © 2015, Power System Technology Press. All right reserved.
引用
收藏
页码:3457 / 3462
页数:5
相关论文
共 15 条
  • [1] White Paper on Integration of Distributed Energy Resources, (2002)
  • [2] Lu N., An evaluation of the HVAC load potential for providing load balancing service, IEEE Transactions on Smart Grid, 3, 3, pp. 1263-1270, (2012)
  • [3] Yang X., Chen J., Zhu L., Et al., Optimization allocation of energy storage for microgrid based on economic dispatch, Power System Protection and Control, 41, 1, pp. 53-60, (2013)
  • [4] Wang C., Yu B., Xiao J., Et al., An energy storage system capacity optimization method for microgrid tie-line power flow stabilization, Automation of Electric Power Systems, 37, 3, pp. 12-17, (2013)
  • [5] Pacific Northwest National Laboratory, Wide Area Energy Storage and Management System Phase II Final Report-Flywheel Field Tests, (2010)
  • [6] Lin J., Sun Y.Z., Cheng L., Et al., Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model, Applied Energy, 96, pp. 21-32, (2012)
  • [7] Lawrence Berkeley National Laboratory, Loads Providing Ancillary Services: Review of International Experience, LBNL-62701, (2007)
  • [8] Lu N., Chassin D.P., A state queueing model of thermostatically controlled appliances, IEEE Transactions on Power Systems, 19, 3, pp. 1666-1673, (2004)
  • [9] Katipamula S., Lu N., Evaluation of residential HVAC control strategies for demand response programs, Winter Meeting of the American-Society-of-Heating-Refrigerating-and-Air-Conditioning-Engineers (ASHRAE), 112, pp. 535-546, (2006)
  • [10] Parkinson S., Wang D., Crawford C., Et al., Comfort-constrained distributed heat pump management, Energy Procedia, 12, pp. 849-855, (2012)