报告详情
Planning EV Charging Stations and Feeder Reinforcement When Future EV Demand Is Uncertain: A Comparison of Deterministic, Stochastic and Robust Plans
编号：45
访问权限：仅限参会人
更新：2026-11-10 16:30:25 浏览：9次
Online
报告开始：2026年11月21日 14:00（Asia/Kolkata）
报告时间：15min
所在会场：[S9] Smart Systems, Robotics and Intelligent Infrastructure/6G and Future Wireless Communication Systems [S9-2] Smart Systems, Robotics and Intelligent Infrastructure/6G and Future Wireless Communication Systems
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摘要
Electric vehicle (EV) sales are growing quickly, but no one can say how many EVs a given area will have in five years. Charging stations and feeder upgrades, on the other hand, are built once and stay in service for a long time. This paper asks a simple question: how much does a planner lose by sizing stations and feeder upgrades for one demand forecast, and how much does it cost to protect against a wrong forecast? Station siting, the number of charging spots and feeder reinforcement are modelled together as a mixed integer linear program (MILP) on the IEEE 33 bus feeder. Reinforcement adds parallel circuits, which raise the line rating and also lower its impedance, and this effect is linearized exactly. The same model is solved in three ways: deterministic (one forecast), two stage stochastic (five weighted scenarios) and scenario-based min max robust (worst scenario). Each finished plan is then tested on 500 Monte Carlo demand draws that were not used for planning. The value of the stochastic solution is $79,982. In the test, the stochastic plan costs 5.3% more to build than the deterministic plan and has the lowest mean cost. The robust plan costs 27.2% more to build, serves 95.8% of the demand on average and cuts the 95th percentile cost from $6.14 M to $5.20 M. A sensitivity study with ten cases gives the same ranking every time.
关键词
Electric vehicle,EV Charging Stations,Demand Uncertainity,optimal sizing and siting,deterministic plan,stochastic plan
报告人
DIKSHA JOSHI
STUDENT PANJAB UNIVERSITY稿件作者
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重要日期
-
会议日期
11月20日
2026
至11月22日
2026
-
10月31日 2026
初稿截稿日期
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11月07日 2026
提前注册日期
-
11月22日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Pondicherry University
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICon INDIA 2026
- ic******@usssociety.org
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