Air Conditioning Load Optimization Dispatching Model Considering Users' Comfort Compensation

被引:2
|
作者
Zhang Y. [1 ]
Hao J. [1 ]
机构
[1] School of Electric Power, Guangdong Key Laboratory of Green Energy Technology, South China University of Technology, Guangzhou, 510640, Guangdong
基金
中国国家自然科学基金;
关键词
Air conditioning load; Demand response; Load aggregator; Renewable energy;
D O I
10.12141/j.issn.1000-565X.180496
中图分类号
学科分类号
摘要
Through the dispatching of demand side response resources, the adverse effects of distributed power output fluctuation on grid operation can be alleviated, and the consumption of renewable energy can be promoted. Firstly, based on the operation framework of the load aggregator, the cost and benefit of the load aggregator were analyzed. In the component of the load aggregator cost, the demand response compensation strategy based on users' comfort was proposed. Secondly, the temperature variation model of air conditioning load was established. Finally, the air conditioning load optimization dispatching model was proposed. The objective function of the model is the maximum total revenue of the air conditioning load aggregator, taking into account the air conditioning temperature, wind power fluctuation and aggregator revenue constraints. The simulation results show that the proposed model has good economic benefits and improves the smoothing effect on distributed wind power fluctuations. © 2019, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码:1 / 8
页数:7
相关论文
共 23 条
  • [11] Gao C., Zhang L., Yang X., Research on load aggregation of central air conditioning and its participation in the operation of power system, Proceedings of the CSEE, 37, 11, (2017)
  • [12] Song M., Gao C., Su W., Modeling and controlling of air-conditioning load for demand response applications, Automation of Electric Power Systems, 40, 14, pp. 158-167, (2016)
  • [13] Niu W., Li Y., Wang B., Demand response based virtual power plant modeling considering uncertainty, Proceedings of the CSEE, 34, 22, pp. 3630-3637, (2014)
  • [14] Zhou L., Zhang Y., Lin X., Et al., Optimal sizing of PV and BESS for a smart household considering different price mechanisms, IEEE Access, 6, pp. 41050-41059, (2018)
  • [15] Chiu T.C., Shih Y.Y., Pang A.C., Et al., Optimized day-ahead pricing with renewable energy demand-side management for smart grids, IEEE Internet of Things Journal, 4, 2, pp. 374-383, (2017)
  • [16] Ma L., Liu N., Zhang J., Et al., Optimal operation model of user group with photovoltaic in the mode of automatic demand response, Proceedings of the CSEE, 36, 13, (2016)
  • [17] Ma X., Guo X., Zhou C., Et al., Typical operation mode analysis for distributed photovoltaic generation system invested by power grid corporation, Southern Power System Technology, 12, 3, pp. 52-59, (2018)
  • [18] Soliman H.M., Leon-Garcia A., Game-Theoretic demand-side management with storage devices for the future smart grid, IEEE Transactions on Smart Grid, 5, 3, pp. 1475-1485, (2014)
  • [19] Vishal D., Krishan K.P., Pardeep S., Et al., Optimization of photovoltaic power system: a comparative study, Protection and Control of Modern Power Systems, 2, 2, pp. 29-39, (2017)
  • [20] Liu X., Gao B., Luo J., Et al., Non-co-operativegame based hierarchical dispatch model of residential loads, Automation of Electric Power Systems, 41, 14, pp. 54-60, (2017)