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## Deploying stochastic coordination of electric vehicles for V2G services with wind

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In this work, we evaluate the effect of charging and discharging plan on integration of electric vehicles (EVs) with wind penetrated distribution grid. A probabilistic model is established by considering the stochastic environment of wind and the accessibility of network constraints. Next, a methodology based on fuzzy approach is applied to model the load profile of EVs with the stochastic availability of EVs, that is, arrival and departure times. Based on this stochastic load profile of EVs, particle swarm optimization with evolution strategy (ESPSO) procedure is applied to incorporate EVs into the distribution grid. Peak load shaving is discussed with vehicle to grid (V2G), having considered the EV charging cost, degradation cost of EV battery, and frequency regulation incomes along with spinning reserve earnings in single objective function. The study is further extended with a varied range of EV penetration in the distribution network

Chapter Contents:

• Notation
• 13.1 Introduction
• 13.2 Test system
• 13.3 Probabilistic model of wind power
• 13.4 Stochastic load modeling of EV
• 13.4.1 Stochastic adaptive fuzzy model of EVs
• 13.4.2 Charging level and type of EVs
• 13.4.3 Initial SOC and EVs load profile
• 13.5 Financial and operational modeling
• 13.5.1 Real-time pricing policy
• 13.5.2 Degradation cost of EVs battery
• 13.5.3 Frequency regulation
• 13.5.4 Spinning reserves
• 13.6 Formulation of charging/discharging strategy
• 13.6.1 Charging/discharging energy of EVs
• 13.6.2 Optimizing strategy and function
• 13.6.3 ESPSO
• 13.7 Case studies and discussion
• 13.7.1 Comparison of load profiles of EVs on different modeling schemes
• 13.7.2 Impact of wind and EV penetration
• 13.8 Conclusion
• References

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