An Optimized EV Charging Model Considering TOU price

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An Optimized EV Charging Model Considering TOU price and SOC curve Authors: Y. Cao,

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 Ø Introduction Ø Optimal Model for EV Charging Ø Case Study Ø Conclusion

Outline Ø Introduction Ø Optimal Model for EV Charging Ø Case Study Ø Conclusion

Introduction (1) l EV charging loads increase in the near future – Negative impacts

Introduction (1) l 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 Mk. W. l Three ways for EV-friendly access the power grid – V 2 G – Energy management equipment – Electricity pricing (Customers respond to price) 3

Introduction (2) l Regulated electricity market (China) – Electricity remain unchanged once decided. –

Introduction (2) l 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. l 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 4

Problem Description l Formulate optimized charging scheme with a specific starting time and ending

Problem Description l 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 :

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

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

Constraints SOC v. s. maximum charging power

Algorithm (1) l The optimized model is discretized: T is divided into N periods,

Algorithm (1) l The optimized model is discretized: T is divided into N periods, each with length.

Algorithm (2) l A heuristic algorithm is proposed - i and j are ascending

Algorithm (2) l 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)

Algorithm (3)

Case Study - Setting Charging curve Distribution of start time TOU price Initial SOC

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 (1) Single EV Multiple EVs

Case Study – Results (2)

Case Study – Results (2)

Conclusions • Response to TOU can reduce EV charging cost and meet the demand

Conclusions • Response to TOU can reduce EV charging cost and meet the demand response requirements in regulated market. 15