Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model

Carmen Gervet; Mohammad Atef

RAIRO - Operations Research - Recherche Opérationnelle (2013)

  • Volume: 47, Issue: 2, page 125-150
  • ISSN: 0399-0559

Abstract

top
Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno−economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short and longer term decisions with respect to park creation and placements. Existing approaches have essentially focused on past data to tackle variations of this problem. In this paper, we introduce a novel approach that considers both past and forecast data, and show the impact for accounting for both sets of data and constraints in a two−stage optimization model. We first show that integer linear programming is best suited to solve the past data model compared to Dynamic Programming and Constrained Local Search. We then introduce our two − stage model that accounts for forecast data as well, adding new constraints to the initial model. Our empirical results show that the two − stage model improves solution quality and overall costs, and can be solved effectively to optimality using Integer Linear Programming.

How to cite

top

Gervet, Carmen, and Atef, Mohammad. "Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model." RAIRO - Operations Research - Recherche Opérationnelle 47.2 (2013): 125-150. <http://eudml.org/doc/275043>.

@article{Gervet2013,
abstract = {Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno−economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short and longer term decisions with respect to park creation and placements. Existing approaches have essentially focused on past data to tackle variations of this problem. In this paper, we introduce a novel approach that considers both past and forecast data, and show the impact for accounting for both sets of data and constraints in a two−stage optimization model. We first show that integer linear programming is best suited to solve the past data model compared to Dynamic Programming and Constrained Local Search. We then introduce our two − stage model that accounts for forecast data as well, adding new constraints to the initial model. Our empirical results show that the two − stage model improves solution quality and overall costs, and can be solved effectively to optimality using Integer Linear Programming.},
author = {Gervet, Carmen, Atef, Mohammad},
journal = {RAIRO - Operations Research - Recherche Opérationnelle},
keywords = {optimization modeling; constraint-based reasoning; park placement problem; renewable energy economics},
language = {eng},
number = {2},
pages = {125-150},
publisher = {EDP-Sciences},
title = {Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model},
url = {http://eudml.org/doc/275043},
volume = {47},
year = {2013},
}

TY - JOUR
AU - Gervet, Carmen
AU - Atef, Mohammad
TI - Optimal Allocation of Renewable Energy Parks: A Two–stage Optimization Model
JO - RAIRO - Operations Research - Recherche Opérationnelle
PY - 2013
PB - EDP-Sciences
VL - 47
IS - 2
SP - 125
EP - 150
AB - Applied research into Renewable Energies raises complex challenges of a technological, economical or political nature. In this paper, we address the techno−economical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to build a decision support tool that will provide private and governmental investors into renewable energy systems, valuable insights to make informed short and longer term decisions with respect to park creation and placements. Existing approaches have essentially focused on past data to tackle variations of this problem. In this paper, we introduce a novel approach that considers both past and forecast data, and show the impact for accounting for both sets of data and constraints in a two−stage optimization model. We first show that integer linear programming is best suited to solve the past data model compared to Dynamic Programming and Constrained Local Search. We then introduce our two − stage model that accounts for forecast data as well, adding new constraints to the initial model. Our empirical results show that the two − stage model improves solution quality and overall costs, and can be solved effectively to optimality using Integer Linear Programming.
LA - eng
KW - optimization modeling; constraint-based reasoning; park placement problem; renewable energy economics
UR - http://eudml.org/doc/275043
ER -

References

top
  1. [1] Model documentation electricity capacity planning submodule of the electricity market module. Technical report, Nuclear and Electricity Analysis Branch, Energy Supply and Conversion Division, Office of Integrated Analysis and Forecasting, Energy Information Administration (1994). 
  2. [2] A.N. Arnette, A Spatial Decision Support System for the Development of Multi–Source Renewable Energy Systems. PhD thesis, Virginia Polytechnic Institute and State University (2010). 
  3. [3] Y. Ben-Haim and I. Elishakoff, Convex Models of Uncertainty in Applied Mechanics. Elsevier (1990). Zbl0703.73100MR1210955
  4. [4] A. Ben-Tal and A. Nemirovski, Robust convex optimization. Math. Operat. Res.23 (1998) 1–38. Zbl0977.90052MR1662410
  5. [5] P. Brisset, H. El Sakkout, T. Fruewirth, C. Gervet, W. Harwey and M. Meier, Le Provost T. Novello, S., J. Schimpf, K. Shen and M. Wallace, Eclipse constraint library manual. Technical report (2012). 
  6. [6] S.R. Bull, Renewable energy today and tomorrow. Proceed. IEEE89 (2001) 1216–1226. 
  7. [7] S.R. Bull, The integration of renewables, in the First international conference on the integration of renewable energy sources and distributed energy resources, Brussels, 1-3 December 2004. 
  8. [8] S. Dreyfus, Richard bellman on the birth of dynamic programming. Operat. Res. (2002) 48–51. Zbl1163.01305
  9. [9] Energy efficiency ad Renewable energy. www.eere.energy.gov/wind. 
  10. [10] M.N. El-Kordy, M.A. Badr, K.A. Abed and S.M.A. Ibrahim, Economical evaluation of electricity generation considering externalities. Renewable Energy25 (2002) 317–328. 
  11. [11] Saher El-Neklawy. Extracting solar radiation data from egyptian solar atlas. Technical report, GUC (2011). 
  12. [12] D. Ghosh, N. Chakravarti, SS Fatima, M.Wooldridge, and NR Jennings. A competitive local search heuristic for the subset sum problem. Comput. Operat.s Res. 26 (1999) 271–280,. Zbl0947.90053MR1656827
  13. [13] M. Haklay and P. Weber Openstreetmap: User-generated street maps. Pervasive Computing, IEEE 7 (2008) 12–18. 
  14. [14] N.G. Mortensen, J.C. Hansen, J. Badger et al. Wind Atlas For Egypt. NREA (2005). 
  15. [15] G. Heal. The economics of renewable energy. National Bureau Economic Res. (2009). Zbl21.0740.03
  16. [16] M. Juenger, T.M. Liebling, D. Naddef, G. Nemhauser, W.R. Pulleyblank, W.R. Reinelt, G. Rinaldi and G. Wolsey,50 Years of Integer Programming 1958-2008. Springer (2010). Zbl1181.90003MR2640549
  17. [17] H. Kellerer, U. Pferschy and D. Pisinger,Knapsack Problems. Springer, Berlin (2004). Zbl1103.90003MR2161720
  18. [18] R. King, H. Rughooputh and K. Deb, Evolutionary multi–objective environmental/economic dispatch: Stochastic versus deterministic approaches. In Evolutionary Multi–Criterion Optimization. Springer (2005) 677–691. Zbl1109.68583
  19. [19] J.M. Loiter and Norberg-Bohm V. Technology policy ad renewable energy:public roles in the development of new energy technologies. Energy Policy 27 (1999) 85–97. 
  20. [20] S. Martello and P. Toth, Knapsack problems: algorithms and computer implementations (1990). Zbl0708.68002MR1086874
  21. [21] T. Nakata, K. Kubo and A. Lamont. Design for renewable energy systems with application to rural areas in japan. Energy Policy33 (2005) 209–219. 
  22. [22] New and Renewable Energy Authority. Egyptian Solar Radiation Atlas. NREA (1998). 
  23. [23] S.D. Pohekar and Ramachandran M. Application of multi–criteria decision making to sustainable energy planning - A Review. Renewable and Sustainable Energy Rev.8 (2004) 365–381. 
  24. [24] A. Saad, C. Gervet and S. Abdennadher, Constraint reasoning eith uncertain data using cdf–intervals, in Proceedings of CP-AIOR’10. Springer Verlag (2010). Zbl1285.68163
  25. [25] A. Schriver. Theory of Linear and Integer Programming. John Wiley and Sons Ltd (1996). Zbl0665.90063
  26. [26] SciPy – Scientific Tools for Python. http://www.scipy.org/. 
  27. [27] M.A. Mosalam Shaltout, Solar energy variability over horizontal and inclined passive systems in egypt, in In proceedings of PLEA’88, Energy and Buildings for temperate Climates, A Mediterranean Regional Approach. Pergamon Press (1988). 
  28. [28] K. Subramanyan, U.M. Diwekar and A. Goyal, Multi–objective optimization for hybrid fuel cells power system under uncertainty. J. Power Sources132 (2004) 99–112. 
  29. [29] E. Trends, Energy and resources. The environmental information portal (2003). 
  30. [30] P. Van Hentenryck and L. Michel, Constraint-Based Local Search. The MIT Press (2005). Zbl1160.68556
  31. [31] L. Wang and C. Singh, Environmental/economic power dispatch using a fuzzified multi–objective particle swarm optimization algorithm. Electric Power Systems Res.77 (2007) 1654–1664. 
  32. [32] N. Yorke-Smith and C. Gervet, Certainty closure: Reliable constraint reasoning with incomplete and erroneous data. ACM Transact. Computat. Logic 10 (2009). MR2537733

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.