A Convex Optimization Algorithm for Electricity Pricing of Charging Stations

The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is tr...

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Veröffentlicht in:Algorithms Jg. 12; H. 10; S. 208
Hauptverfasser: Zhang, Jing, Zhan, Xiangpeng, Li, Taoyong, Jiang, Linru, Yang, Jun, Zhang, Yuanxing, Diao, Xiaohong, Han, Sining
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2019
Schlagworte:
Algorithms
Approximation
Branch and bound methods
Charging
charging station
Computational geometry
Convex analysis
convex optimization
Convexity
Electric power
Electric vehicles
Electricity
Electricity distribution
Electricity pricing
Heuristic methods
Integer programming
Leadership
Linear programming
Marginal pricing
Mixed integer
multi-objective programming
Nonlinear programming
Optimization
Optimization algorithms
Planning
polyhedral approximation
Prices
scaling method
Stations
Variables
ISSN:1999-4893, 1999-4893
Online-Zugang:Volltext
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Zusammenfassung:The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush–Kuhn–Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.
Bibliographie:ObjectType-Article-1
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ISSN:1999-4893
1999-4893
DOI:10.3390/a12100208