Skip to main content
SpringerLink
Log in

Introduction to Programming in GAMS

  • Chapter
  • First Online:
Power System Optimization Modeling in GAMS

Abstract

The General Algebraic Modeling System (GAMS) is a modeling tool for mathematical programming and optimization purpose. This chapter provides the instruction on different programming elements in GAMS. It can be used in solving different types of optimization problems. Some basic optimization models used in power system literature are described in this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J.L.C. Meza, M.B. Yildirim, A.S.M. Masud, A model for the multiperiod multiobjective power generation expansion problem. IEEE Trans. Power Syst. 22(2), 871–878 (2007)

    Article  Google Scholar 

  2. S. Kannan, S. Baskar, J.D. McCalley, P. Murugan, Application of NSGA-II algorithm to generation expansion planning. IEEE Trans. Power Syst. 24(1), 454–461 (2009)

    Article  Google Scholar 

  3. R. Fang, D.J. Hill, A new strategy for transmission expansion in competitive electricity markets. IEEE Trans. Power Syst. 18(1), 374–380 (2003)

    Article  Google Scholar 

  4. R. Romero, A. Monticelli, A. Garcia, S. Haffner, Test systems and mathematical models for transmission network expansion planning. IEE Proc. Gener. Transm. Distrib. 149(1), 27–36 (2002)

    Article  Google Scholar 

  5. V. Miranda, J.V. Ranito, L.M. Proenca, Genetic algorithms in optimal multistage distribution network planning. IEEE Trans. Power Syst. 9(4), 1927–1933 (1994)

    Article  Google Scholar 

  6. S. Gerbex, R. Cherkaoui, A.J. Germond, Optimal location of multi-type facts devices in a power system by means of genetic algorithms. IEEE Trans. Power Syst. 16(3), 537–544 (2001)

    Article  Google Scholar 

  7. C. Wang, M.H. Nehrir, Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Trans. Power Syst. 19(4), 2068–2076 (2004)

    Article  Google Scholar 

  8. W. El-Khattam, K. Bhattacharya, Y. Hegazy, M.M.A. Salama, Optimal investment planning for distributed generation in a competitive electricity market. IEEE Trans. Power Syst. 19(3), 1674–1684 (2004)

    Article  Google Scholar 

  9. A. Keane, M. O’Malley, Optimal allocation of embedded generation on distribution networks. IEEE Trans. Power Syst. 20(3), 1640–1646 (2005)

    Article  Google Scholar 

  10. S. Sundhararajan, A. Pahwa, Optimal selection of capacitors for radial distribution systems using a genetic algorithm. IEEE Trans. Power Syst. 9(3), 1499–1507 (1994)

    Article  Google Scholar 

  11. B. Milosevic, M. Begovic, Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Trans. Power Syst. 18(1), 69–75 (2003)

    Article  Google Scholar 

  12. B. Gou, Generalized integer linear programming formulation for optimal PMU placement. IEEE Trans. Power Syst. 23(3), 1099–1104 (2008)

    Article  Google Scholar 

  13. Y.M. Atwa, E.F. El-Saadany, Optimal allocation of ESS in distribution systems with a high penetration of wind energy. IEEE Trans. Power Syst. 25(4), 1815–1822 (2010)

    Article  Google Scholar 

  14. H. Oh, Optimal planning to include storage devices in power systems. IEEE Trans. Power Syst. 26(3), 1118–1128 (2011)

    Article  Google Scholar 

  15. P. Maghouli, A. Soroudi, A. Keane, Robust computational framework for mid-term techno-economical assessment of energy storage. IET Gener. Transm. Distrib. 10(3), 822–831 (2016)

    Article  Google Scholar 

  16. A. Soroudi, M. Afrasiab, Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty. IET Renew. Power Gener. 6(2), 67–78 (2012)

    Article  Google Scholar 

  17. G. Blanco, F. Olsina, F. Garces, C. Rehtanz, Real option valuation of facts investments based on the least square monte carlo method. IEEE Trans. Power Syst. 26(3), 1389–1398 (2011)

    Article  Google Scholar 

  18. B. Chen, J. Wang, L. Wang, Y. He, Z. Wang, Robust optimization for transmission expansion planning: minimax cost vs. minimax regret. IEEE Trans. Power Syst. 29(6), 3069–3077 (2014)

    Google Scholar 

  19. V. Miranda, M.A.C.C. Matos, Distribution system planning with fuzzy models and techniques, in 10th International Conference on Electricity Distribution, 1989 (CIRED 1989), vol. 6, Brighton (1989), pp. 472–476

    Google Scholar 

  20. S. Civanlar, J.J. Grainger, H. Yin, S.S.H. Lee, Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Delivery 3(3), 1217–1223 (1988)

    Article  Google Scholar 

  21. M. Carrion, J.M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans. Power Syst. 21(3), 1371–1378 (2006)

    Article  Google Scholar 

  22. J.M. Arroyo, A.J. Conejo, Optimal response of a thermal unit to an electricity spot market. IEEE Trans. Power Syst. 15(3), 1098–1104 (2000)

    Article  Google Scholar 

  23. D.W. Ross, S. Kim, Dynamic economic dispatch of generation. IEEE Trans. Power Apparatus Syst. PAS-99(6), 2060–2068 (1980)

    Article  Google Scholar 

  24. Z.-L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. 18(3), 1187–1195 (2003)

    Article  Google Scholar 

  25. J. Endrenyi, S. Aboresheid, R.N. Allan, G.J. Anders, S. Asgarpoor, R. Billinton, N. Chowdhury, E.N. Dialynas, M. Fipper, R.H. Fletcher, C. Grigg, J. McCalley, S. Meliopoulos, T.C. Mielnik, P. Nitu, N. Rau, N.D. Reppen, L. Salvaderi, A. Schneider, C. Singh, The present status of maintenance strategies and the impact of maintenance on reliability. IEEE Trans. Power Syst. 16(4), 638–646 (2001)

    Article  Google Scholar 

  26. A.G. Bakirtzis, P.N. Biskas, A decentralized solution to the DC-OPF of interconnected power systems. IEEE Trans. Power Syst. 18(3), 1007–1013 (2003)

    Article  Google Scholar 

  27. R.D. Zimmerman, C.E. Murillo-Sanchez, R.J. Thomas, Matpower: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011)

    Article  Google Scholar 

  28. Y.M. Atwa, E.F. El-Saadany, M.M.A. Salama, R. Seethapathy, Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)

    Article  Google Scholar 

  29. K.R.C. Mamandur, R.D. Chenoweth, Optimal control of reactive power flow for improvements in voltage profiles and for real power loss minimization. IEEE Trans. Power Apparatus Syst. PAS-100(7), 3185–3194 (1981)

    Article  Google Scholar 

  30. Y. Bai, H. Zhong, Q. Xia, C. Kang, A two-level approach to ac optimal transmission switching with an accelerating technique. IEEE Trans. Power Syst. 32(2), 1616–1625 (2017)

    Google Scholar 

  31. N. Rotering, M. Ilic, Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets. IEEE Trans. Power Syst. 26(3), 1021–1029 (2011)

    Article  Google Scholar 

  32. F. Wen, A.K. David, Optimal bidding strategies and modeling of imperfect information among competitive generators. IEEE Trans. Power Syst. 16(1), 15–21 (2001)

    Article  Google Scholar 

  33. A. Baillo, M. Ventosa, M. Rivier, A. Ramos, Optimal offering strategies for generation companies operating in electricity spot markets. IEEE Trans. Power Syst. 19(2), 745–753 (2004)

    Article  Google Scholar 

  34. H.B. Gooi, D.P. Mendes, K.R.W. Bell, D.S. Kirschen, Optimal scheduling of spinning reserve. IEEE Trans. Power Syst. 14(4), 1485–1492 (1999)

    Article  Google Scholar 

  35. K.A. Papadogiannis, N.D. Hatziargyriou, Optimal allocation of primary reserve services in energy markets. IEEE Trans. Power Syst. 19(1), 652–659 (2004)

    Article  Google Scholar 

  36. A. Soroudi, P. Siano, A. Keane, Optimal DR and ESS scheduling for distribution losses payments minimization under electricity price uncertainty. IEEE Trans. Smart Grid 7(1), 261–272 (2016)

    Article  Google Scholar 

  37. S. Dutta, S.P. Singh, Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE Trans. Power Syst. 23(4), 1560–1569 (2008)

    Article  Google Scholar 

  38. C. Murphy, A. Soroudi, A. Keane, Information gap decision theory-based congestion and voltage management in the presence of uncertain wind power. IEEE Trans. Sust. Energy 7(2), 841–849 (2016)

    Article  Google Scholar 

  39. B. Hayes, I. Hernando-Gil, A. Collin, G. Harrison, S. Djoki, Optimal power flow for maximizing network benefits from demand-side management. IEEE Trans. Power Syst. 29(4), 1739–1747 (2014)

    Article  Google Scholar 

  40. A.J. Conejo, J.M. Morales, L. Baringo, Real-time demand response model. IEEE Trans. Smart Grid 1(3), 236–242 (2010)

    Article  Google Scholar 

  41. A. Rabiee, A. Soroudi, A. Keane, Information gap decision theory based OPF with HVDC connected wind farms. IEEE Trans. Power Syst. 30(6), 3396–3406 (2015)

    Article  Google Scholar 

  42. A. Soroudi, A. Rabiee, A. Keane, Information gap decision theory approach to deal with wind power uncertainty in unit commitment. Electr. Power Syst. Res. 145, 137–148 (2017)

    Article  Google Scholar 

  43. A.J. Conejo, M. Carrión, J.M. Morales, Decision Making Under Uncertainty in Electricity Markets, vol. 1 (Springer, New York, 2010)

    Book  MATH  Google Scholar 

  44. E. Zio, The Monte Carlo Simulation Method for System Reliability and Risk Analysis (Springer, London, 2013)

    Book  Google Scholar 

  45. A. Soroudi, Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment. IEEE Trans. Power Syst. 27(3), 1283–1293 (2012)

    Article  Google Scholar 

  46. S. Granville, Optimal reactive dispatch through interior point methods. IEEE Trans. Power Syst. 9(1), 136–146 (1994)

    Article  Google Scholar 

  47. T. Ding, R. Bo, F. Li, H. Sun, A bi-level branch and bound method for economic dispatch with disjoint prohibited zones considering network losses. IEEE Trans. Power Syst. 30(6), 2841–2855 (2015)

    Article  Google Scholar 

  48. S. Binato, M.V.F. Pereira, S. Granville, A new benders decomposition approach to solve power transmission network design problems. IEEE Trans. Power Syst. 16(2), 235–240 (2001)

    Article  Google Scholar 

  49. D.K. Molzahn, J.T. Holzer, B.C. Lesieutre, C.L. DeMarco, Implementation of a large-scale optimal power flow solver based on semidefinite programming. IEEE Trans. Power Syst. 28(4), 3987–3998 (2013)

    Article  Google Scholar 

  50. R.A. Jabr, Exploiting sparsity in SDP relaxations of the OPF problem. IEEE Trans. Power Syst. 27(2), 1138–1139 (2012)

    Article  Google Scholar 

  51. T. Wu, M. Rothleder, Z. Alaywan, A.D. Papalexopoulos, Pricing energy and ancillary services in integrated market systems by an optimal power flow. IEEE Trans. Power Syst. 19(1), 339–347 (2004)

    Article  Google Scholar 

  52. J.M. Arroyo, F.D. Galiana, On the solution of the bilevel programming formulation of the terrorist threat problem. IEEE Trans. Power Syst. 20(2), 789–797 (2005)

    Article  Google Scholar 

  53. D.I. Sun, B. Ashley, B. Brewer, A. Hughes, W.F. Tinney, Optimal power flow by Newton approach. IEEE Trans. Power Apparatus Syst. PAS-103(10), 2864–2880 (1984)

    Article  Google Scholar 

  54. F. Milano, Continuous Newton’s method for power flow analysis. IEEE Trans. Power Syst. 24(1), 50–57 (2009)

    Article  MathSciNet  Google Scholar 

  55. E.C. Finardi, E.L. da Silva, Solving the hydro unit commitment problem via dual decomposition and sequential quadratic programming. IEEE Trans. Power Syst. 21(2), 835–844 (2006)

    Article  Google Scholar 

  56. I.P. Abril, J.A.G. Quintero, Var compensation by sequential quadratic programming. IEEE Trans. Power Syst. 18(1), 36–41 (2003)

    Article  Google Scholar 

  57. B. Enacheanu, B. Raison, R. Caire, O. Devaux, W. Bienia, N. HadjSaid, Radial network reconfiguration using genetic algorithm based on the matroid theory. IEEE Trans. Power Syst. 23(1), 186–195 (2008)

    Article  Google Scholar 

  58. P. Maghouli, S.H. Hosseini, M.O. Buygi, M. Shahidehpour, A scenario-based multi-objective model for multi-stage transmission expansion planning. IEEE Trans. Power Syst. 26(1), 470–478 (2011)

    Article  Google Scholar 

  59. T. Amraee, A.M Ranjbar, R. Feuillet. Immune-based selection of pilot nodes for secondary voltage control. Eur. Trans. Electr. Power 20(7), 938–951 (2010)

    Google Scholar 

  60. T. Satoh, K. Nara, Maintenance scheduling by using simulated annealing method [for power plants]. IEEE Trans. Power Syst. 6(2), 850–857 (1991)

    Article  Google Scholar 

  61. J.G. Vlachogiannis, K.Y. Lee. Quantum-inspired evolutionary algorithm for real and reactive power dispatch. IEEE Trans. Power Syst. 23(4), 1627–1636 (2008)

    Article  Google Scholar 

  62. C. Dai, W. Chen, Y. Zhu, X. Zhang, Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans. Power Syst. 24(3), 1218–1231 (2009)

    Google Scholar 

  63. T. Amraee, Coordination of directional overcurrent relays using seeker algorithm. IEEE Trans. Power Delivery 27(3), 1415–1422 (2012)

    Article  Google Scholar 

  64. D.N. Vo, P. Schegner, W. Ongsakul, Cuckoo search algorithm for non-convex economic dispatch. IET Gener. Transm. Distrib. 7(6), 645–654 (2013)

    Article  Google Scholar 

  65. A.A. El-fergany, A.Y. Abdelaziz, Capacitor allocations in radial distribution networks using cuckoo search algorithm. IET Gener. Transm. Distrib. 8(2), 223–232 (2014)

    Article  Google Scholar 

  66. M. Barati, M.M. Farsangi, Solving unit commitment problem by a binary shuffled frog leaping algorithm. IET Gener. Transm. Distrib. 8(6), 1050–1060 (2014)

    Article  Google Scholar 

  67. R. Roche, L. Idoumghar, B. Blunier, A. Miraoui, Imperialist competitive algorithm for dynamic optimization of economic dispatch in power systems, in International Conference on Artificial Evolution (Evolution Artificielle) (Springer, Berlin, 2011), pp. 217–228

    Google Scholar 

  68. W.-M. Lin, F.-S. Cheng, M.-T. Tsay, An improved tabu search for economic dispatch with multiple minima. IEEE Trans. Power Syst. 17(1), 108–112 (2002)

    Article  Google Scholar 

  69. C.F. Chang, Reconfiguration and capacitor placement for loss reduction of distribution systems by ant colony search algorithm. IEEE Trans. Power Syst. 23(4), 1747–1755 (2008)

    Article  Google Scholar 

  70. R.E. Rosenthal, A GAMS tutorial. Technical note (1992)

    Google Scholar 

  71. A. Brooke, D. Kendrick, A. Meeraus, R. Raman, R.E. Rosenthal, Gams. A Users Guide (GAMS Development Corporation, Washington, DC, 2005)

    Google Scholar 

  72. A. Geletu, Gams-Modeling and Solving Optimization Problems (TU-Ilmenau, Faculty of Mathematics and Natural Sciences, Department of Operation Research & Stochastrics, Ilmenau, 2008)

    Google Scholar 

  73. M.C. Ferris, Matlab and GAMS: interfacing optimization and visualization software. Mathematical Programming Technical Report, 98:19 (1998)

    Google Scholar 

  74. L. Wong et al., Linking Matlab and Gams: A Supplement (University of Victoria, Department of Economics, Victoria, BC, 2009)

    Google Scholar 

  75. M.C. Ferris, R. Jain, S. Dirkse, Gdxmrw: Interfacing GAMS and matlab (2011). http://www.gams.com/dd/docs/tools/gdxmrw.pdf

    Google Scholar 

  76. M.R. Bussieck, M.C. Ferris, A. Meeraus, Grid-enabled optimization with GAMS. INFORMS J. Comput. 21(3), 349–362 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  77. S.S. Nielson, A. Consiglio, Practical Financial Optimization: A Library of GAMS Models (Wiley, New York, 2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University College of Dublin, Belfield, Dublin, Ireland

    Alireza Soroudi

Authors
  1. Alireza Soroudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Soroudi, A. (2017). Introduction to Programming in GAMS. In: Power System Optimization Modeling in GAMS. Springer, Cham. https://doi.org/10.1007/978-3-319-62350-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62350-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62349-8

  • Online ISBN: 978-3-319-62350-4

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation