An Optimized EV Charging Model
Considering TOU price and SOC curve
Authors: Y. Cao, S. Tang, C. Li, P. Zhang, Y. Tan, Z. Zhang
and J. Li
Presenter: Nan Cheng
2013.8.14
Outline
 Introduction
 Optimal Model for EV Charging
 Case Study
 Conclusion
Introduction (1)
 EV charging loads increase in the near future
– Negative impacts on stability
– Risk system operations and management
– 200 million EV in China in 2050 with charge load
330 MkW.
 Three ways for EV-friendly access the power grid
– V2G
– Energy management equipment
– Electricity pricing (Customers respond to price)
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Introduction (2)
 Regulated electricity market (China)
– Electricity remain unchanged once decided.
– Catalog price, stepwise power tariff & time-ofuse (TOU) price
– TOU price varies in different periods of a day.
 This paper:
– Proposes an optimized charging model to adjust
charging power and time based on TOU and SOC
– Reduce the cost of costumers
– Balance load demand
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Problem Description
 Formulate optimized charging scheme with a
specific starting time and ending time
– Consider TOU price
– Consider SOC curve to determine the charging
constraints
– Aim to minimize cost + peak clipping & valley
filling
Objective Function
–
–
–
–
: starting time of charging
: duration of charging
: unit price at time t
: charging power at time t
Constraints
–
–
–
: maximum power set by EV user
: maximum power EV charger can output
: maximum allowed charging power to
protect the battery based on the current state of
charge.
Constraints
SOC v.s. maximum charging power
Algorithm (1)
 The optimized model is discretized: T is divided
into N periods, each with length .
Algorithm (2)
 A heuristic algorithm is proposed
- i and j are ascending sorted sequences, i.e.,
- Energy q is optimal step for the algorithm, the
corresponding power step
.
Algorithm (3)
Case Study - Setting
Charging curve
Distribution of start time
TOU price
Initial SOC distribution:
Case Study – Results (1)
Single EV
Multiple EVs
Case Study – Results (2)
Conclusions
• Response to TOU can reduce EV charging cost and meet the
demand response requirements in regulated market.
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