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Using a multivariate DOE method for congestion study under impacts of PEVs

Using a multivariate DOE method for congestion study under impacts of PEVs. K. N. Toosi University. Hamed V. HAGHI M. A. GOLKAR valizadeh@ieee.org. Main Topics. General Outline Design of Experiment (DOE) Technique Generalized linear model (GLM) Multivariate DOE by frank Copula

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Using a multivariate DOE method for congestion study under impacts of PEVs

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  1. Using a multivariate DOE method for congestion study under impacts of PEVs K. N. Toosi University Hamed V. HAGHI M. A. GOLKAR valizadeh@ieee.org

  2. Main Topics • General Outline • Design of Experiment (DOE) Technique • Generalized linear model (GLM) • Multivariate DOE by frank Copula • Congestion study • Conclusion Haghi – Iran – RIF Session 5 – Paper 0718

  3. General Outline • Undertaking a partial development in the planning stage is further encouraged in ADN • Proliferation of plug-in electric vehicles (PEVs) • congestion may appear if a network development decision is not taken at the right time • Assuming overestimated network developments may be economically unsuccessful Haghi – Iran – RIF Session 5 – Paper 0718

  4. General Outline • Evaluation of potential impacts of PEVs • Probabilistic projections of both spatial and temporal diversity • Monte Carlo simulation • Simulations are composed of probabilistic assignment of PEVs to the distribution base case Haghi – Iran – RIF Session 5 – Paper 0718

  5. General Outline • Each PEV is randomly assigned a location, type, and daily charge profiles based on the provided pdf for each characteristic • Multiple probabilistic scenarios are generated from the system and pdf • There are millions of possible configurations when the chosen factors vary Haghi – Iran – RIF Session 5 – Paper 0718

  6. General Outline • Design of experiment (DOE) method • To create an optimal DOE of fewer configurations chosen between the millions of possible configurations • Multivariate distribution underlying a pre-chosen model Haghi – Iran – RIF Session 5 – Paper 0718

  7. General Outline • Proposed DOE method for impacts of PEVs • bivariate DOE for two of the correlated variables in the randomization process • PEVs location • Base typical load profiles • Using a Frank Copula function to create multivaraite distributional dependency Haghi – Iran – RIF Session 5 – Paper 0718

  8. General Outline 1. Modeling uncertainties (database creation) 2. Applying multivariate DOE 3. Power flow calculations on the reduced scenarios 4. Statistical analysis of the results Haghi – Iran – RIF Session 5 – Paper 0718

  9. Main Topics • General Outline • Design of Experiment (DOE) Technique • Generalized linear model (GLM) • Multivariate DOE by frank Copula • Congestion study • Conclusion Haghi – Iran – RIF Session 5 – Paper 0718

  10. A very general model of a system Haghi – Iran – RIF Session 5 – Paper 0718

  11. A very general model of PEV behavior • Controllable variables • Modern tariff structures • charging start time • Uncontrollable variables • battery’s state of charge • charging start time • location Haghi – Iran – RIF Session 5 – Paper 0718

  12. A very general model of PEV behavior • designing a most informative reduced set of scenarios, all variables are better to be treated as controllable variables as well in order to have their part in the final outcome • These optimally-chosen runs are more than enough to fit the model Haghi – Iran – RIF Session 5 – Paper 0718

  13. Design of Experiment (DOE) Technique • A technique to obtain and organize the maximum amount of conclusive information from minimum empirical work • Efficiency • getting more information from fewer experiments/data • Focusing • collecting only the information that is really needed Haghi – Iran – RIF Session 5 – Paper 0718

  14. Design of Experiment (DOE) Technique • The critical part is to decide which variables to change, the intervals for this variation, and the pattern of the experimental points • limited resource here is the computational time required for calculating load flow for all scenarios Haghi – Iran – RIF Session 5 – Paper 0718

  15. DOE of PEVs • A probabilistic model should be fitted the system response • Here, the generalized linear model (GLM) is used Haghi – Iran – RIF Session 5 – Paper 0718

  16. Main Topics • General Outline • Design of Experiment (DOE) Technique • Generalized linear model (GLM) • Multivariate DOE by frank Copula • Congestion study • Conclusion Haghi – Iran – RIF Session 5 – Paper 0718

  17. Generalized linear model (GLM) • A generalization of linear regression • Avoids approximations such as CLT • Magnitude of variance of each measurement is a function of its expected value • A change/shift in the expected value of the total power demand of PEV chargers (maybe due to a shift in timing) correlates with a change in its variance Haghi – Iran – RIF Session 5 – Paper 0718

  18. Generalized linear model (GLM) • GLM consists of three elements • A probability distribution from the exponential family • A linear predictor η = Xβ. • A link function g such that E(Y)= μ = g-1(η) Haghi – Iran – RIF Session 5 – Paper 0718

  19. Main Topics • General Outline • Design of Experiment (DOE) Technique • Generalized linear model (GLM) • Multivariate DOE by frank Copula • Congestion study • Conclusion Haghi – Iran – RIF Session 5 – Paper 0718

  20. Multivariate DOE by frank Copula • Copulas provide a way to create distributions that model correlated multivariate data Haghi – Iran – RIF Session 5 – Paper 0718

  21. Main Topics • General Outline • Design of Experiment (DOE) Technique • Generalized linear model (GLM) • Multivariate DOE by frank Copula • Congestion study • Conclusion Haghi – Iran – RIF Session 5 – Paper 0718

  22. Congestion study • 33-bus distribution system test case • The 200 configurations/ scenarios • final outcome is about knowing which lines will be simultaneously congested under impacts of PEVs Haghi – Iran – RIF Session 5 – Paper 0718

  23. Scenario simulations for five practically correlated feeders Haghi – Iran – RIF Session 5 – Paper 0718

  24. Rank Correlation Coefficients Together with Confidence Measures (P-values) for five practically correlated feeders Haghi – Iran – RIF Session 5 – Paper 0718

  25. Conclusions • Correlation analysis applicable to a database of currents in the lines • Forecast which congestions are correlated • Illustrate where congestions will appear in the future • Planner could implement a line reinforcement which removes correlated congestions • A technique to take into account the impacts of PEVs in other types of studies Haghi – Iran – RIF Session 5 – Paper 0718

  26. 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

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