Market Impact of Wind-Energy Storage Alliance Strategic Bidding Under Uncertainty

被引:5
作者
Li, Peiyue [1 ]
Wang, Zhijie [1 ]
Jin, Jiahui [1 ]
机构
[1] Shanghai Dianji Univ, Sch Elect Engn, Shanghai 201306, Peoples R China
基金
中国国家自然科学基金;
关键词
Energy storage; Wind energy; Regulation; Uncertainty; Wind turbines; Frequency control; Optimization; Wind-energy storage alliance; regulation participation ratio; bi-level optimization problem; strategic bidding; RESERVE MARKETS; MODEL; PV;
D O I
10.1109/ACCESS.2021.3130185
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The output of wind turbine is volatile and difficult to predict. Energy storage can help wind turbine offset the deviation between forecast and actual output. Based on the concept of sharing economy, there will be more alliance for wind turbines and energy storage in the electricity market. However, an open question is how the wind-energy storage alliance's participation affects market clearing and the profits of market participants. Therefore, a stochastic bi-level optimization model is proposed to describe the bidding behavior of wind-energy storage alliances in energy and frequency regulation markets. At the same time, a new quantitative index of bidding behavior is defined-regulation participation ratio. Considering the uncertainty of wind turbine output, the profits of wind-energy storage alliance are maximized in the upper level. The lower level minimizes the power purchase cost of distribution system operator (DSO) for the joint market clearing. The bi-level model is transformed into a mixed integer linear programming (MILP) model by Karush-Kuhn-Tucker (KKT) conditions, strong duality theory and large M method. Regulation participation ratio is set to different values in the case analysis, so as to analyze the influence of the alliance's bidding behavior on market. Moreover, the economic impact of alliance on wind turbine and energy storage is compared.
引用
收藏
页码:156537 / 156547
页数:11
相关论文
共 31 条
[1]   Participation of Pumped Hydro Storage in Energy and Performance-Based Regulation Markets [J].
Alharbi, Hisham ;
Bhattacharya, Kankar .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2020, 35 (06) :4307-4323
[2]   Development of Supercapacitor Technology and Its Potential Impact on New Power Converter Techniques for Renewable Energy [J].
Ariyarathna, Thilanga ;
Kularatna, Nihal ;
Gunawardane, Kosala ;
Jayananda, Dilini ;
Steyn-Ross, David Alistair .
IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2021, 2 (03) :267-276
[3]   Battery Storage Participation in Reactive and Proactive Distribution-Level Flexibility Markets [J].
Badanjak, Domagoj ;
Pandzic, Hrvoje .
IEEE ACCESS, 2021, 9 :122322-122334
[4]   Readiness of Small Energy Markets and Electric Power Grids to Global Health Crises: Lessons From the COVID-19 Pandemic [J].
Carmon, David ;
Navon, Aviad ;
Machlev, Ram ;
Belikov, Juri ;
Levron, Yoash .
IEEE ACCESS, 2020, 8 :127234-127243
[5]   Day-Ahead and Intra-Day Planning of Integrated BESS-PV Systems Providing Frequency Regulation [J].
Conte, Francesco ;
Massucco, Stefano ;
Schiapparelli, Giacomo-Piero ;
Silvestro, Federico .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2020, 11 (03) :1797-1806
[6]   Optimal PV and Battery Investment of Market-Participating Industry Facilities [J].
Covic, N. ;
Braeuer, F. ;
McKenna, R. ;
Pandzic, H. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2021, 36 (04) :3441-3452
[7]   Bidding Strategies in Energy and Reserve Markets for an Aggregator of Multiple EV Fast Charging Stations With Battery Storage [J].
Duan, Xiaoyu ;
Hu, Zechun ;
Song, Yonghua .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (01) :471-482
[8]   On the Efficiency of Sharing Economy Networks [J].
Georgiadis, Leonidas ;
Iosifidis, George ;
Tassiulas, Leandros .
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2020, 7 (03) :1094-1110
[9]   A risk-constrained decision support tool for EV aggregators participating in energy and frequency regulation markets [J].
Habibifar, Reza ;
Lekvan, Amir Aris ;
Ehsan, Mehdi .
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 185
[10]   Deep-Reinforcement-Learning-Based Capacity Scheduling for PV-Battery Storage System [J].
Huang, Bin ;
Wang, Jianhui .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (03) :2272-2283