1 / 28

An Energy Control Center for a Network of Distributed Generators

An Energy Control Center for a Network of Distributed Generators. By: Etienne Dupuis Supervisor: Dr. J.H Taylor. Topics. Power systems and Distributed Generators. A control center for distributed generators. Renewable energy and forecasting. Control center algorithms.

Faraday
Download Presentation

An Energy Control Center for a Network of Distributed Generators

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. An Energy Control Center for a Network of Distributed Generators By: Etienne Dupuis Supervisor: Dr. J.H Taylor

  2. Topics • Power systems and Distributed Generators. • A control center for distributed generators. • Renewable energy and forecasting. • Control center algorithms. • A new Unit Commitment Algorithm.

  3. Power Systems • Large units and high voltage transmission lines. • Fifteen units in New Brunswick. • Installed capacity of 3948 MW. • 6665 km of transmission lines. Source: www.nbpower.com

  4. Distributed Generators • Ratings from tens of kW to a few MW. • Both renewable and non-renewable technologies are available. • Some units can be situated close to the customer. • The NIMBY factor and deregulation favor distributed generation.

  5. Renewable Wind Turbine, 30kW Dorchester, NB Small Hydro, 25kW York Mills, NB Non-Renewable Fuel Cell, Saint-John’s, NL Micro-gas turbine, Fredericton, NB ASPRI’s Distributed Generators

  6. This Project • Aggregate the controls of distributed generators. • Provides a basis to include distributed generators in the economic dispatch and ancillary service dispatch. Integrating distributed generators

  7. Optimization of DG generation • Forecasting / Bidding • Generation Scheduling • Economic Dispatch • Unit Commitment • Ancillary services • Hydro-Thermal Scheduling

  8. Forecasting • Forecasting wind speed improves the scheduling of our power system. • Forecasting the hydraulic head of hydro units improves the hydro-thermal schedule. • Classical time series forecasting, neural networks and meteorological methods will be investigated in this project.

  9. Time Series Analysis • The correlation between measurements is used to estimate an ARMA model to fit the data. • Extensions are available for handling ‘seasonal series’. Lag 1 and lag 2 correlations for MA models

  10. Neural Networks • Feed-forward neural networks are composed of an input, hidden and output layer. • The inputs are weighted, summed and passed trough a non-linear function before being used as input to the next layer. • The weights of the network are adjusted so that the output of the network approximates that of the system.

  11. Meteorology • Forecasts from environment Canada are a start. • Numerical weather prediction provide increased lead times. • Ensemble forecasts are an interesting way to estimate confidence in the forecast. Source: weatheroffice.ec.gc.ca

  12. Economic Dispatch • Cost of thermal generators are expressed as quadratic functions of their power output. • The optimization is constrained by the physical limits of the generators and the need to meet the power demand. Cost Contours for 2 generators

  13. Unit Commitment • Another optimization problem. • Which thermal units to assign to meet demand and minimize cost. • Unlike the Economic Dispatch problem, Unit Commitment is hard! • To find the optimal solution for N generators, we could have to perform an economic dispatch 2N times.

  14. Lagrangian Relaxation • A commonly used solution to the Unit Commitment problem. • The solution is iterative and determines which unit to commit based on the profitability for a given marginal cost of power. • This method does not always yield an optimal solution.

  15. Another problem with Lagrangian Relaxation • Identical units are an irritant because they get committed by the algorithm at the same time. • If distributed generation becomes widely used, identical units are bound to turn up.

  16. Solving the UC by sintering • Use algebra to obtain the quadratic parameters of the optimal path. • We obtain the optimal commitments for the two units over their full feasible range.

  17. Algorithm output • We end up with 5 quadratic curves which represent the lowest cost as a function of power for these units. • The good news is we can keep going!

  18. Cost Curve Sintering for 20 units • The sintering method returned the same commitment vector as CE 100% of the time. • Sintering ran in 1.437 seconds, it took 220 minutes to run CE at P=5 resolution. • Sintering yields more info.

  19. Error Analysis • The difference between the costs returned by the two methods is introduced by the tolerance of the CE method. • The circles in the stem plot are x10 accuracy.

  20. Computation time for sintering • Sintering took 69 minutes • Predicted time for CE, 6.4*1050 … YEARS!

  21. Solving the UC for multiple hours • Start-up costs make solving the UC more difficult. • Sintering is a good match to Dynamic Programming, because it provides a list of good unit combinations.

  22. Including transition costs • The movie on the right shows the effect of progressively adding faster states to the sintered commitments. • DP is run over 100 time instances, with a sinusoidal forcing function.

  23. Sintered curves for bidding • Profit=P*($ / W)-Cost. • The degree of uncertainty of the wind forecast could be used alongside the profit curve to make a bid into the power pool.

  24. Hydro-thermal scheduling • Power from hydro plants is a function of flow, hydraulic head and turbine efficiency. • Constraints on the drawdown and storage can be significant factors. • Plants can be coupled hydraulically in series or parallel. • Solutions are specific to the hydro system under study.

  25. Hydrological Forecasting • Real time hydrometric data is available from Water Survey of Canada. • Water levels are a function of precipitation, soil saturation, vegetation and other factors. Source: Environment Canada

  26. References • Power Generation – Operation & Control, Allen J. Wood, Bruce F. Wollenberg. • Time Series Analysis – Forecasting and Control third edition, George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel. • Artificial Neural Networks, Forecasting Time Series, V. Rao Vemuri, Robert D. Rogers. • Wind Power Prediction using Ensembles, Riso institute. • Unit Commitment – A bibliographical Survey, Narayana Prasad Padhy.

  27. Significance • Power system scheduling at the distributed generator level enables this technology. • Generator aggregation could lead to a new solution to the unit commitment problem. • Renewable generators make the specific goals of ASPRI coherent with those of utilities incorporating wind power to their system.

  28. Questions ?

More Related