Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming

This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochas...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 9; H. 2; S. 644 - 654
Hauptverfasser: Liu, Zhaoxi, Wu, Qiuwei, Oren, Shmuel S., Huang, Shaojun, Li, Ruoyang, Cheng, Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2018
Schlagworte:
Chance constrained programming
congestion management
distribution locational marginal pricing (DLMP)
distribution system operator (DSO)
electric vehicle (EV)
Electric vehicles
Optimization
Planning
Pricing
Programming
Sensitivity
Stochastic processes
ISSN:1949-3053, 1949-3061
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Zusammenfassung:This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through MIP. With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2559579