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

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

3 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)

4 Introduction (2) Regulated electricity market (China) – Electricity remain unchanged once decided. – Catalog price, stepwise power tariff & time-of- use (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

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 curveDistribution of start time TOU price Initial SOC distribution:

Case Study – Results (1) Single EVMultiple EVs

Case Study – Results (2)

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