An Energy Control Center for a Network of Distributed Generators

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 ?