Optimal bidding strategy for wind farms considering local demand response resources

被引:5
作者
Zhao, Shengnan [1 ]
Wang, Beibei [1 ]
Yang, Xuechun [2 ]
Yang, Shengchun [3 ]
机构
[1] Southeast Univ, Sch Elect Engn, Nanjing 210096, Jiangsu, Peoples R China
[2] Changzhou Power Supply Co, Changzhou 213000, Peoples R China
[3] China Elect Power Res Inst Nanjing, Nanjing 210003, Peoples R China
基金
中国国家自然科学基金;
关键词
pricing; power markets; wind power plants; optimisation; wind power; demand side management; power generation economics; wind farms; local demand response resources; power systems; different time scales; different auxiliary services; indicators; wind power resources; wind power curtailment; RENEWABLE GENERATION; MANAGEMENT; ELECTRICITY; MARKETS;
D O I
10.1049/iet-rpg.2018.5579
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The negative impacts imposed by wind power on power systems can be decomposed into different time scales. Demand response (DR) can relieve these impacts by providing different auxiliary services. However, based on the current integration requirement, wind farms cannot earn more even if they try to provide power with less negative impacts. In this regard, three indicators are proposed for three different time scales to evaluate these efforts of wind power resources. The system operator sets the purchasing price of wind power by using these indicators. It aims to encourage wind farms to improve their output curves by purchasing the DR supporting services products. In the next step, a bi-level optimisation model is developed to assist the wind farms in their bidding and DR purchasing strategies. The case studies show that the proposed model can reduce wind power curtailment and improve the power's features. Moreover, by using the indicators as the integration requirement, the revenue of wind farms and the economic benefit of system dispatch are improved.
引用
收藏
页码:1565 / 1575
页数:11
相关论文
共 36 条
  • [21] Demand-Side Contribution to Primary Frequency Control With Wind Farm Auxiliary Control
    Molina-Garcia, Angel
    Munoz-Benavente, Irene
    Hansen, Anca D.
    Gomez-Lazaro, Emilio
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2014, 29 (05) : 2391 - 2399
  • [22] Nur M., 2019, SMART GRIDS THEIR CO, P197
  • [23] Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem
    Pei, Wei
    Chen, Yanning
    Sheng, Kun
    Deng, Wei
    Du, Yan
    Qi, Zhiping
    Kong, Li
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 49 : 148 - 168
  • [24] Perlstein B., 2012, POTENTIAL ROLE DEMAN
  • [25] Porter K., VARIABLE GENERATION
  • [26] Demand response for sustainable energy systems: A review, application and implementation strategy
    Shariatzadeh, Farshid
    Mandal, Paras
    Srivastava, Anurag K.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 45 : 343 - 350
  • [27] [石涛 Shi Tao], 2016, [电网技术, Power System Technology], V40, P477
  • [28] Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option
    Tabar, Vahid Sohrabi
    Jirdehi, Mehdi Ahmadi
    Hemmati, Reza
    [J]. ENERGY, 2017, 118 : 827 - 839
  • [29] Look-Ahead Economic Dispatch With Adjustable Confidence Interval Based on a Truncated Versatile Distribution Model for Wind Power
    Tang, Chenghui
    Xu, Jian
    Sun, Yuanzhang
    Liu, Ji
    Li, Xiong
    Ke, Deping
    Yang, Jun
    Peng, Xiaotao
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (02) : 1755 - 1767
  • [30] Risk Measure Based Robust Bidding Strategy for Arbitrage Using a Wind Farm and Energy Storage
    Thatte, Anupam A.
    Xie, Le
    Viassolo, Daniel E.
    Singh, Sunita
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (04) : 2191 - 2199