Annals of Operations Research

, Volume 245, Issue 1–2, pp 177–207 | Cite as

A participatory multi-criteria approach for power generation and transmission planning

Article

Abstract

The energy sector continues to undergo substantial structural changes. Currently, the expansion of renewable energy sources and the decentralisation of energy supply lead to new players entering the market who pursue different objectives and have different preferences. Thus, multiple and usually conflicting targets need to be considered. Moreover, recent public reactions towards infrastructure projects highlight the importance of considering public acceptance as a key dimension of decision making in the energy sector. As a result, decision processes grow more complex at all levels from political to strategic, tactical and operational decisions in companies. We therefore present an approach combining power systems analysis considering grid constraints and multi-criteria decision analysis. The approach focusses on multi-dimensional sensitivity analyses allowing for simultaneous variations of the different preference parameters determined within the decision analysis aimed at facilitating preference elicitation and consensus building in group decisions. The focus of the paper is the demonstration of the presented approach for a power generation and transmission planning case study in the context of the energy transition in Germany.

Keywords

Multi-criteria decision analysis (MCDA) Power systems analysis (PSA) Transformation of energy systems Participatory decision processes 

References

  1. Andor, M., Flinkerbusch, K., Janssen, M., Liebau, B., & Wobben, M. (2010). Negative strompreise und der vorrang erneuerbarer energien. Zeitschrift für Energiewirtschaft, 34(2), 91–99.CrossRefGoogle Scholar
  2. Bell, M. L., Hobbs, B. F., & Ellis, H. (2003). The use of multi-criteria decision-making methods in the integrated assessment of climate change: Implications for ia practitioners. Socio-Economic Planning Sciences, 37(4), 289–316.CrossRefGoogle Scholar
  3. Berry, C. A., Hobbs, B. F., Meroney, W. A., O’Neill, R. P., & Stewart, W. R, Jr. (1999). Understanding how market power can arise in network competition: A game theoretic approach. Utilities Policy, 8(3), 139–158.CrossRefGoogle Scholar
  4. Bertsch, V., & Geldermann, J. (2008). Preference elicitation and sensitivity analysis in multi-criteria group decision support for industrial risk and emergency management. International Journal of Emergency Management, 5(1/2), 7–24.CrossRefGoogle Scholar
  5. Bertsch, V., Treitz, M., Geldermann, J., & Rentz, O. (2007). Sensitivity analyses in multi-attribute decision support for off-site nuclear emergency and recovery management. International Journal of Energy Sector Management, 1(4), 342–365.CrossRefGoogle Scholar
  6. Butler, J., Jia, J., & Dyer, J. (1997). Simulation techniques for the sensitivity analysis of multi-criteria decision models. European Journal of Operational Research, 103, 531–546.CrossRefGoogle Scholar
  7. Dietrich, K., Leuthold, F., & Weigt, H. (2010). Will the market get it right? The placing of new power plants in Germany. Zeitschrift für Energiewirtschaft, 34, 255–265.CrossRefGoogle Scholar
  8. Durbach, I. N., & Stewart, T. J. (2012). Modeling uncertainty in multi-criteria decision analysis. European Journal of Operational Research, 223(1), 1–14.CrossRefGoogle Scholar
  9. Eßer-Frey, A. (2012). Analyzing the regional long-term development of the German power system using a nodal pricing approach. PhD Thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe.Google Scholar
  10. Edwards, W. (1977). How to use multiattribute utility measurement for social decision making. IEEE Transactions on Systems, Man, and Cybernetics, SMC–7, 326–340.CrossRefGoogle Scholar
  11. Enzensberger, N. (2003). Entwicklung und Anwendung eines Strom- und Zertifikatmarktmodells für den europäischen Energiesektor. Düsseldorf: VDI Verlag.Google Scholar
  12. Ferrero, R., Shahidehpour, S., & Ramesh, V. (1997). Transaction analysis in deregulated power systems using game theory. IEEE Transactions on Power Systems, 12(3), 1340–1347.CrossRefGoogle Scholar
  13. French, S. (1995). Uncertainty and imprecision: Modelling and analysis. Journal of Operational Research Society, 46, 70–79.CrossRefGoogle Scholar
  14. Geldermann, J., Bertsch, V., & Rentz, O. (2006). Multi-criteria decision support and uncertainty handling, propagation and visualisation for emergency and remediation management. In H.-D. Haasis, H. Kopfer, & J. Schönberger (Eds.), Operations research proceedings 2005 (pp. 755–760). Berlin: Springer.Google Scholar
  15. Geldermann, J., & Rentz, O. (2004). Environmental decisions and electronic democracy. Journal of Multi-criteria Analysis, 12(2–3), 77–92.Google Scholar
  16. Goletsis, Y., Psarras, J., & Samouilidis, J.-E. (2003). Project ranking in the armenian energy sector using a multicriteria method for groups. Annals of Operations Research, 120(1–4), 135–157.CrossRefGoogle Scholar
  17. Hauff, J., Heider, C., Arms, H., Gerber, J., & Schilling, M. (2011). Gesellschaftliche Akzeptanz als Sule der energiepolitischen Zielsetzung. Energiewirtschaftliche Tagesfragen, 61(10), 85–87.Google Scholar
  18. Hemmati, R., Hooshmand, R.-A., & Khodabakhshian, A. (2013a). Comprehensive review of generation and transmission expansion planning. Generation, Transmission & Distribution, IET, 7(9), 955–964.Google Scholar
  19. Hemmati, R., Hooshmand, R.-A., & Khodabakhshian, A. (2013b). State-of-the-art of transmission expansion planning: Comprehensive review. Renewable and Sustainable Energy Reviews, 23, 312–319.CrossRefGoogle Scholar
  20. Hobbs, B., & Meier, P. (1994). Multicriteria methods for resource planning: An experimental comparison. IEEE Transactions on Power Systems, 9(4), 1811–1817.CrossRefGoogle Scholar
  21. Hobbs, B. F. (2001). Linear complementarity models of nash-cournot competition in bilateral and poolco power markets. IEEE Transactions on Power Systems, 16(2), 194–202.CrossRefGoogle Scholar
  22. Hobbs, B. F., & Horn, G. T. (1997). Building public confidence in energy planning: A multimethod mcdm approach to demand-side planning at bc gas. Energy Policy, 25(3), 357–375.CrossRefGoogle Scholar
  23. Hobbs, B. F., Metzler, C. B., & Pang, J.-S. (2000). Strategic gaming analysis for electric power systems: An mpec approach. IEEE Transactions on Power Systems, 15(2), 638–645.CrossRefGoogle Scholar
  24. IEA. (2012). World energy outlook 2012. International Energy Agency (IEA).Google Scholar
  25. Kahraman, C., & Kaya, İ. (2010). A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications, 37(9), 6270–6281.CrossRefGoogle Scholar
  26. Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley.Google Scholar
  27. Kirkwood, C. W. (1997). Strategic decision making—Multiobjective decision analysis with spreadsheets. Belmont: Duxbury Press.Google Scholar
  28. Kowalski, K., Stagl, S., Madlener, R., & Omann, I. (2009). Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis. European Journal of Operational Research, 197(3), 1063–1074.CrossRefGoogle Scholar
  29. Lahdelma, R., Hokkanen, J., & Salminen, P. (1998). Smaa-stochastic multiobjective acceptability analysis. European Journal of Operational Research, 106(1), 137–143.CrossRefGoogle Scholar
  30. Lahdelma, R., & Salminen, P. (2001). Smaa-2: Stochastik multicriteria acceptability analysis for group decision making. Operations Research, 49(3), 444–454.CrossRefGoogle Scholar
  31. Lahdelma, R., & Salminen, P. (2012). The shape of the utility or value function in stochastic multicriteria acceptability analysis. OR Spectrum, 34(4), 785–802.CrossRefGoogle Scholar
  32. Latorre, G., Cruz, R. D., Areiza, J. M., & Villegas, A. (2003). Classification of publications and models on transmission expansion planning. IEEE Transactions on Power Systems, 18(2), 938–946.CrossRefGoogle Scholar
  33. Løken, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584–1595.CrossRefGoogle Scholar
  34. Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined mcda-ip approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.CrossRefGoogle Scholar
  35. Monteiro, C., Miranda, V., Ramirez-Rosado, I. J., Zorzano-Santamaria, P. J., Garcia-Garrido, E., & Fernández-Jiménez, L. A. (2005). Compromise seeking for power line path selection based on economic and environmental corridors. IEEE Transactions on Power Systems, 20(3), 1422–1430.CrossRefGoogle Scholar
  36. Morgan, M. G., & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. New York: Cambridge University Press.CrossRefGoogle Scholar
  37. Möst, D. (2006). Zur Wettbewerbsfähigkeit der Wasserkraft in liberalisierten Elektrizitätsmärkten - Eine modellgestützte Analyse dargestellt am Beispiel des schweizerischen Energieversorgungssystems. Frankfurt a.M.: Peter Lang.Google Scholar
  38. Munoz, F. D., Sauma, E. E., & Hobbs, B. F. (2013). Approximations in power transmission planning: Implications for the cost and performance of renewable portfolio standards. Journal of Regulatory Economics, 43(3), 305–338.CrossRefGoogle Scholar
  39. Mustajoki, J., Hämäläinen, R. P., & Salo, A (2005). Decision support by interval SMART/SWING—Incorporating imprecision in the SMART and SWING methods. Decision Sciences, 36(2), 317–339.Google Scholar
  40. Nolden, C., Schönfelder, M., Eßer-Frey, A., Bertsch, V., & Fichtner, W. (2013). Network constraints in techno-economic energy system models: Towards more accurate modeling of power flows in long-term energy system models. Energy Sytems, 4(3), 267–287.CrossRefGoogle Scholar
  41. Oikonomou, V., Flamos, A., Gargiulo, M., Giannakidis, G., Kanudia, A., Spijker, E., et al. (2011). Linking least-cost energy system costs models with MCA: An assessment of the EU renewable energy targets and supporting policies. Energy Policy, 39, 2786–2799.CrossRefGoogle Scholar
  42. Østergaard, P. A. (2009). Reviewing optimisation criteria for energy systems analyses of renewable energy integration. Energy, 34(9), 1236–1245.CrossRefGoogle Scholar
  43. Papadopoulos, A., & Karagiannidis, A. (2008). Application of the multi-criteria analysis method electre iii for the optimisation of decentralised energy systems. Omega, 36(5), 766–776.CrossRefGoogle Scholar
  44. Pohekar, S., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planninga review. Renewable and Sustainable Energy Reviews, 8(4), 365–381.CrossRefGoogle Scholar
  45. Ren, H., Zhou, W., Nakagami, K., Gao, W., & Wu, Q. (2010). Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects. Applied Energy, 87(12), 3642–3651.CrossRefGoogle Scholar
  46. Ribeiro, F., Ferreira, P., & Araújo, M. (2011). The inclusion of social aspects in power planning. Renewable and Sustainable Energy Reviews, 15(9), 4361–4369.CrossRefGoogle Scholar
  47. Ribeiro, F., Ferreira, P., & Araujo, M. (2013). Evaluating future scenarios for the power generation sector using a multi-criteria decision analysis (MCDA) tool: The Portuguese case. Energy, 52, 126–136.CrossRefGoogle Scholar
  48. Rios, I., & French, S. (1991). A framework for sensitivity analysis in discrete multi-objective decision making. European Journal of Operational Research, 54, 176–190.CrossRefGoogle Scholar
  49. Rosen, J. (2007). The future role of renewable energy sources in European electricity supply: A model-based analysis for the EU-15. Karlsruhe: Universitätverlag Karlsruhe.Google Scholar
  50. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  51. Salo, A., & Hämäläinen, R. P. (1995). Preference programming through approximate ratio comparisons. European Journal of Operational Research, 82, 458–475.CrossRefGoogle Scholar
  52. Sauma, E. E., & Oren, S. S. (2006). Proactive planning and valuation of transmission investments in restructured electricity markets. Journal of Regulatory Economics, 30(3), 261–290.CrossRefGoogle Scholar
  53. Schweppe, F., Caraminis, M., Tabor, R., & Bohn, R. (1987). Spot pricing of electricity. New York: Kluwer.Google Scholar
  54. Slednev, V., Bertsch, V., Nolden, C., & Fichtner, W. (2014). Multi-criteria decision support for power grid expansion planning. In S. Langton, A. Morton, M.J. Geiger & J. Siebert (Eds.), Decision analysis and multiple criteria decision making (pp. 133–159). Aachen: Shaker.Google Scholar
  55. Tervonen, T. (2014). Jsmaa: Open source software for smaa computations. International Journal of Systems Science, 45(1), 69–81.CrossRefGoogle Scholar
  56. Tervonen, T., & Figueira, J. R. (2008). A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis, 15(1–2), 1–14.CrossRefGoogle Scholar
  57. Thomé, F. S., Binato, S., Pereira, M. V., Campodónico, N., Fampa, M. H., & Costa, L Cd, Jr. (2013). Decomposition approach for generation and transmission expansion planning with implicit multipliers evaluation. Pesquisa Operacional, 33(3), 343–359.CrossRefGoogle Scholar
  58. Tor, O. B., Guven, A. N., & Shahidehpour, M. (2008). Congestion-driven transmission planning considering the impact of generator expansion. IEEE Transactions on Power Systems, 23(2), 781–789.CrossRefGoogle Scholar
  59. Tseng, C.-L., Oren, S. S., Cheng, C. S., Li, C.-A., Svoboda, A. J., & Johnson, R. B. (1999). A transmission-constrained unit commitment method in power system scheduling. Decision Support Systems, 24(3), 297–310.CrossRefGoogle Scholar
  60. Unsihuay-Vila, C., Marangon-Lima, J., Zambroni de Souza, A., & Perez-Arriaga, I. (2011). Multistage expansion planning of generation and interconnections with sustainable energy development criteria: A multiobjective model. International Journal of Electrical Power & Energy Systems, 33(2), 258–270.CrossRefGoogle Scholar
  61. Ventosa, M., Baıllo, A., Ramos, A., & Rivier, M. (2005). Electricity market modeling trends. Energy Policy, 33(7), 897–913.Google Scholar
  62. Wang, J.-J., Jing, Y.-Y., Zhang, C.-F., & Zhao, J.-H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263–2278.CrossRefGoogle Scholar
  63. Winterfeldt, D. V., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Chair of Energy EconomicsKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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