1. NATIONAL TECHNICAL UNIVERSITY OF ATHENS
CHEMICAL ENGINEERING SCHOOL
COMPUTATIONAL FLUID DYNAMICS UNIT
DEVELOPMENT OF AN EFFICIENT REAL-TIME OPTIMIZATION STRATEGY FOR A HYBRID POWER GENERATION SYSTEM CONSISTING OF PHOTOVOLTAIC ARRAYS AND FUEL CELLS (PV-FC)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos
ENERTECH 2007, October, Athens
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

2. Objective of the Study
Development of a Global Solar Irradiance Prediction Model on horizontal surface  Prediction of Photovoltaic Power Generation
Development of an optimal-decision strategy for a Renewable Energy System with Hydrogen Storage based on the Model Predictive Control rolling horizon concept
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

3. Part I: Global Solar Irradiance Model
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

4. Part I: Global Solar Irradiance Model
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

5. Part I: Global Solar Irradiance Model
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

6. Part I: Prediction of the PV Power Generation
Global Solar Irradiance on tilted surface
Location data, astronomical data, slope of the PV Array
Global Solar Irradiance on horizontal surface
Neural Network Model
Sun
Ambient Temperature
PVOut=f(GSIT,Tamb) V I
Photovoltaic Αrray
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

7. Proposed Hybrid Power Generation System Purchased Electricity
Part II: Hybrid Power Generation System
Proposed Hybrid Power Generation System
Consumer
Fuel Cells
Metal Hydride Tanks
Electrolyzer
Purchased Electricity
PV(kW)
PE(kW)
CS(kW)
SE(kW)
Elin(kW)
Elout(m3/h)
FCin(m3/h)
FCiout(kW)
INV
Sold Electricity
Photovoltaic
Array
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

8. Part II: Formulation of an LP optimization problem
Decision Variables
Energy consumed by the electrolyzer
Purchased/Sold Electricity from/to the grid
Amount of Hydrogen consumed by Fuel Cells Stack
Amount of Hydrogen stored in Metal Hydride Tanks
Objective Function
to minimize
Cost of electricity
Energy produced by the Photovoltaic Arrays
Consumption Profile
Data
Formulation and solution of
a Linear Programming Problem
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

9. Part II: Equations-Constraints
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

10. Part II: PV Power Generation-Load Profile (Deterministic)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

11. Part II: Specifications of installed hybrid systems
1st Scenario
2nd Scenario
PV array
5kWp
Electrolyzer
0.25Nm3/h
0.5Nm3/h
Fuel Cell Stack
1.2kW
Metal Hydride
Tanks
5m3
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

12. Part II: Decision strategy for two different scenarios of a RESHS: Electrolyzer 0.25Nm3/h-0.5Nm3/h
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

13. Part II: Purchased electricity as a function of the hydrogen storage capacity (Electrolyzer 0.25Nm3/h-0.5Nm3/h)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

14. Part II: PV Power Generation-Load Profile (Updated)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

15. Part II: Specifications of the installed hybrid system
1st Scenario
PV array
5kWp
Electrolyzer
0.25Nm3/h
Fuel Cell Stack
1.2kW
Metal Hydride
Tanks
5m3
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

16. Total Purchased Energy (kWh) Total Discarded Energy (kWh)
Part II: Deterministic and updated decision strategies for scenario 1 with true PVOut and CS profiles
Total Purchased Energy (kWh)
Total Discarded Energy (kWh)
Deterministic schedule using true PVOut and CS temporal profiles
Updated schedule using true PVOut and CS temporal profiles
9.9677
8.1868
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

17. Conclusions
Development of a prediction model for the GSI daily profile on horizontal surfaces based on the RBF neural network architecture
Forecasting GSI on the tilted surface of the PV array, using the following information: the GSI prediction on horizontal surfaces, astronomical and geographical data and the slope of the PV array
Estimating the electrical energy produced by the PV array, given the ambient temperature and the GSI on the tilted surface
Development of a model that realistically describes the performance and the constraints of the hybrid system
Formulation and solution of an optimization problem that minimizes the cost for purchasing electrical energy. The formulation takes into account the estimated photovoltaic power generation over a future prediction horizon and a profile of the energy demand over the same time horizon
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

18. Finally the proposed model..
..may prove to be a very useful tool
for optimal decision making in
hybrid power generation systems which
combine RES and hydrogen technologies
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

19. NATIONAL TECHNICAL UNIVERSITY OF ATHENS
CHEMICAL ENGINEERING SCHOOL
COMPUTATIONAL FLUID DYNAMICS UNIT
DEVELOPMENT OF AN EFFICIENT REAL-TIME OPTIMIZATION STRATEGY FOR A HYBRID POWER GENERATION SYSTEM CONSISTING OF PHOTOVOLTAIC ARRAYS AND FUEL CELLS (PV-FC)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos
ENERTECH 2007, October, Athens
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

20. APPENDIX

21. Prediction of Global Solar Irradiance by a Gaussian-type function
Part I: Global Solar Irradiance Model
Prediction of Global Solar Irradiance by a Gaussian-type function
Description
State
Clear 6
Few Clouds 5
Partly Cloudy 4
Cloudy 3
Heavy Clouds 2
Rainfall 1
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

22. P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

24. Decision strategies for November reference day

25. Decision strategies for November reference day

26. Decision strategies for March reference day

27. Formulation of the LP optimization problem
minfTx A*x<=b
x Aeq*x=beq
lb<=x<=ub
x = linprog(f, [], [],Aeq,beq)
7 variables*72 time periods
4*72 time periods (equalities)
1*72 time periods (inequatlities)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

28. Part II: PV Power Generation-Load Profile (Deterministic)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit

29. Part II: PV Power Generation-Load Profile (Deterministic)
P. L. Zervas, H. Sarimveis, J. Α. Palyvos, N. C. Markatos, ENERTECH 2007, October
NTUA, Chemical Engineering School, Computational Fluid Dynamics Unit