1 / 23

Considering impacts of PEVs in planning optimal hybrid systems

This paper explores the integration of plug-in electric vehicles (PEVs) into optimal hybrid energy systems. It presents a stochastic-heuristic optimization framework to account for variable generation and load patterns, utilizing scenario-based Monte Carlo simulations and particle swarm optimization. The study emphasizes the significant influence of PEV load variations on net load dynamics, highlighting the importance of controlled PEV charging in system planning. Results reveal the benefits of incorporating PEVs in hybrid active systems, providing insights for decision-makers in energy planning and optimization.

peyton
Télécharger la présentation

Considering impacts of PEVs in planning optimal hybrid systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Considering impacts of PEVs in planning optimal hybrid systems K. N. Toosi University Hamed V. HAGHI M. A. GOLKAR S. M. HAKIMI valizadeh@ieee.org

  2. Main Topics • General Outline • Hybrid Active System with PEVs - Modeling • The Stochastic-Heuristic Algorithm • Results • Conclusion Haghi – Iran – RIF Session 4 – Paper 0664

  3. Studied Problem: Optimal Sizing - both the generation side and the load side are distributed Haghi – Iran – RIF Session 4 – Paper 0664

  4. General Outline • High penetration of stochastic energy flows spatially distributed throughout Microgrid • Variable generation (Wind, PV, etc) • Variable load demand (PEVs, etc) • Representation of PEV load variations • daily load shape • locational displacement Haghi – Iran – RIF Session 4 – Paper 0664

  5. General Outline • Strong dependence structure of load, generation and storage behavior over a year • Time dependence  Wind power autoregressive behavior impacts in planning storage (Markov chain fails for example) • Multivariate dependence  Correlation between load and generation Haghi – Iran – RIF Session 4 – Paper 0664

  6. General Outline – Scenario-based Optimization • Planning for net load  capture both spatial and temporal diversity of PEV • Stochastic simulation (Monte Carlo approach)  variability of PEV load on a multivariate modeling • Particle swarm optimization (PSO)  optimization subroutine Haghi – Iran – RIF Session 4 – Paper 0664

  7. Main Topics • General Outline • Hybrid Active System with PEVs - Modeling • The Stochastic-Heuristic Algorithm • Results • Conclusion Haghi – Iran – RIF Session 4 – Paper 0664

  8. Hybrid Active System - Optimization • Multi-objective optimization problem - weighted sum method Haghi – Iran – RIF Session 4 – Paper 0664

  9. Hybrid Active System with PEVs • By inserting impacts of PEVs on net load of system through a multivariate modeling • PEVs can cause a reversal of power flow through the distribution system • distribution network rely on a coincidence factor of loads for sizing all of the system’s components Haghi – Iran – RIF Session 4 – Paper 0664

  10. Hybrid Active System with PEVs • Probability of coincident operation of PEVs is much higher • PEV controlled charging • Actual demands are quite modest compared to normal electricity demands • Additional benefits as some kind of DSM • controlled charging, with 20% randomness Haghi – Iran – RIF Session 4 – Paper 0664

  11. PEVs Impact – Scenario-based Representation Haghi – Iran – RIF Session 4 – Paper 0664

  12. Modeled planning dataset • Net load with no PEV • Net load with 20% partially controlled PEV demand based on DSM indexes • wind speed Haghi – Iran – RIF Session 4 – Paper 0664

  13. Main Topics • General Outline • Hybrid Active System with PEVs - Modeling • The Stochastic-Heuristic Algorithm • Results • Conclusion Haghi – Iran – RIF Session 4 – Paper 0664

  14. Scenario-based Optimization Scenarios, all together, represent long-term behaviour of PEV load and wind Optimal set, considering uncertain variables space, to be analysed Haghi – Iran – RIF Session 4 – Paper 0664

  15. Main Topics • General Outline • Hybrid Active System with PEVs - Modeling • The Stochastic-Heuristic Algorithm • Results • Conclusion Haghi – Iran – RIF Session 4 – Paper 0664

  16. Results – Benefits of Adding Controlled PEV Distributions of differences when the results of scenarios without PEV are subtracted from the results of scenarios with 20% PEV penetration Haghi – Iran – RIF Session 4 – Paper 0664

  17. Differences – Optimal Sizes with and without PEVs WT FC EL HT Haghi – Iran – RIF Session 4 – Paper 0664

  18. Results – Optimal Sizes Correlation Haghi – Iran – RIF Session 4 – Paper 0664

  19. Simulated size sets for all 12,000 samples Haghi – Iran – RIF Session 4 – Paper 0664

  20. Main Topics • General Outline • Hybrid Active System with PEVs - Modeling • The Stochastic-Heuristic Algorithm • Results • Conclusion Haghi – Iran – RIF Session 4 – Paper 0664

  21. Conclusions • A PSO-embedded stochastic simulation • Realistic modeling of the wind power and load demand data • A set of optimal sizes are obtained as final outputs which is then analyzed to provide a measure for making the optimal decision Haghi – Iran – RIF Session 4 – Paper 0664

  22. Conclusions • A worthwhile optimal selection would be the mean values of all scenarios at the cost of reducing the reliability, but to an acceptable level most of the time • Sensitivity analysis of optimal sets • Other relationships could also be implied to help decision-maker Haghi – Iran – RIF Session 4 – Paper 0664

  23. Thank You! Contact: Hamed VALIZADEH HAGHI PhDc, P.Eng Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology, Tehran 16315-1355, Iran +98 (21) 2793 5698 valizadeh@ieee.org

More Related