Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support

被引:327
作者
Bai, Linquan [1 ]
Wang, Jianhui [2 ,3 ]
Wang, Chengshan [4 ]
Chen, Chen [5 ]
Li, Fangxing [1 ]
机构
[1] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
[2] Southern Methodist Univ, Dept Elect Engn, Dallas, TX 75275 USA
[3] Argonne Natl Lab, Energy Syst Div, Lemont, IL 60439 USA
[4] Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin 300072, Peoples R China
[5] Argonne Natl Lab, Lemont, IL 60439 USA
基金
美国国家科学基金会;
关键词
Congestion management; day-ahead market; distribution locational marginal pricing (DLMP); market clearing; smart distribution system; voltage support; loss price; DISTRIBUTION-SYSTEMS; PHOTOVOLTAIC GENERATORS; NETWORK RECONFIGURATION; DISTRIBUTION CIRCUITS; LOSS REDUCTION; MARKETS; POWER;
D O I
10.1109/TPWRS.2017.2767632
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method.
引用
收藏
页码:4061 / 4073
页数:13
相关论文
共 34 条
[1]
The Evolution of the Market Designing a Market for High Levels of Variable Generation [J].
Ahlstrom, Mark ;
Ela, Erik ;
Riesz, Jenny ;
O'Sullivan, Jonathan ;
Hobbs, Benjamin F. ;
O'Malley, Mark ;
Milligan, Michael ;
Sotkiewicz, Paul ;
Caldwell, Jim .
IEEE POWER & ENERGY MAGAZINE, 2015, 13 (06) :60-66
[2]
[Anonymous], PNNL24044
[3]
[Anonymous], 2014, 154720032014 IEEE, DOI DOI 10.1109/IEEESTD.2014.6818982
[4]
Distribution Markets [J].
Bahramirad, Shay ;
Khodaei, Amin ;
Masiello, Ralph .
IEEE POWER & ENERGY MAGAZINE, 2016, 14 :102-106
[5]
Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation [J].
Baker, Kyri ;
Hug, Gabriela ;
Li, Xin .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2017, 8 (01) :331-340
[6]
NETWORK RECONFIGURATION IN DISTRIBUTION-SYSTEMS FOR LOSS REDUCTION AND LOAD BALANCING [J].
BARAN, ME ;
WU, FF .
IEEE TRANSACTIONS ON POWER DELIVERY, 1989, 4 (02) :1401-1407
[7]
California State, 2014, DISTR RES PLAN
[8]
Co-Optimization of Power and Reserves in Dynamic T&D Power Markets With Nondispatchable Renewable Generation and Distributed Energy Resources [J].
Caramanis, Michael ;
Ntakou, Elli ;
Hogan, William W. ;
Chakrabortty, Aranya ;
Schoene, Jens .
PROCEEDINGS OF THE IEEE, 2016, 104 (04) :807-836
[9]
Chao H, 2004, 2004 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, P557
[10]
Interval radial power flow using extended DistFlow formulation and Krawczyk iteration method with sparse approximate inverse preconditioner [J].
Ding, Tao ;
Li, Fangxing ;
Li, Xue ;
Sun, Hongbin ;
Bo, Rui .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2015, 9 (14) :1998-2006