Skip to main content

Advertisement

Log in

Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper, we consider a long-term unit commitment problem with thermal and renewable energy sources, where system operating costs have to be minimized. The problem is enhanced by adding pumped storages, where water is stored in reservoirs, being turbinated or pumped up if it is beneficial in terms of reducing the operating costs. We present a tight mixed-integer linear programming model with a redefinition of decision variables and a reformulation of constraints, e.g., for the spinning reserve. The model serves as a basis for a new decomposition method, where fix-and-optimize schemes are used. In particular, a time-oriented, a unit-oriented, and a generic fix-and-optimize procedure are presented. A computational performance analysis shows that the mixed-integer linear model is efficient in supporting the solution process for small- and medium-scale instances. Furthermore, the fix-and-optimize procedures are able to tackle even large-scale instances. Particularly, problem instances with real-world energy demands, power plant-specific characteristics, and a one-year planning horizon with hourly time steps are solved to near-optimality in reasonable time.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.wiwi.tu-clausthal.de/results_ucp_fo.pdf.

References

  • 50 Hertz Transmission (2015). Renewable feed-in of market operator 50 Hertz Transmission GmbH in Germany, available at http://www.50hertz.com.

  • Afkousi-Paqaleh, M., Rashidinejad, M., & Pourakbari-Kasmaei, M. (2010). An implementation of harmony search algorithm to unit commitment problem. Electrical Engineering, 92, 215–225.

    Article  Google Scholar 

  • Álvarez López, J., Ceciliano-Meza, J. L., & Guillén Moya, I. (2015). The challenges of the unit commitment problem for real-life small-scale power systems. International Journal of Electrical Power & Energy Systems, 71, 112–122.

    Article  Google Scholar 

  • Álvarez López, J., Ceciliano-Meza, J. L., Guillén Moya, I., & Nieva Gómez, R. (2012). A MIQCP formulation to solve the unit commitment problem for large-scale power systems. International Journal of Electrical Power & Energy Systems, 36(1), 68–75.

    Article  Google Scholar 

  • Amprion (2015). Renewable feed-in of market operator Amprion GmbH in Germany, available at http://www.amprion.net.

  • Bai, Y., Zhong, H., Xia, Q., Xin, Y., & Kang, C. (2014). Inducing-objective-function-based method for long-term SCUC with energy constraints. Electrical Power and Energy Systems, 63, 971–978.

    Article  Google Scholar 

  • Barnett, D., & Bjoronsgaard, K. (2000). Electric power generation: A nontechnical guide. Oklahoma, Penn-Well: Tulsa.

    Google Scholar 

  • Bendotti, P., Fouilhoux, P., & Rottner, C. (2017). On the complexity of the unit commitment problem. Optimization online, http://www.optimization-online.org/DB_HTML/2017/06/6061.html (pp. 1–9).

  • Borghetti, A., Frangioni, A., Lacalandra, F., & Nucci, C. (2003). Lagrangian heuristics based on disaggregated bundle methods for hydrothermal unit commitment. IEEE Transactions on Power Systems, 18(1), 313–323.

    Article  Google Scholar 

  • Cardozo, C., Capely, L., & Dessante, P. (2017). Frequency constrained unit commitment. Energy Systems, 8(1), 31–56.

    Article  Google Scholar 

  • Cardozo Arteaga, C. (2016). Optimisation of power system security with high share of variable renewables: Consideration of the primary reserve deployment dynamics on a Frequency Constrained Unit Commitment model. Theses: Université Paris-Saclay.

  • Carrión, M., & Arroyo, J. M. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21, 1371–1378.

    Article  Google Scholar 

  • Cohen, A., & Sherkat, V. (1987). Optimization-based methods for operations scheduling. Proceedings of the IEEE, 75, 1574–1591.

    Article  Google Scholar 

  • Dai, H., Zhang, N., & Su, W. (2015). A literature review of stochastic programming and unit commitment. Journal of Power and Energy Engineering, 3, 206–214.

    Article  Google Scholar 

  • Delarue, E., Cattrysse, D., & D’Haeseleer, W. (2013). Enhanced priority list unit commitment method for power systems with a high share of renewables. Electric Power Systems Research, 105, 115–123.

    Article  Google Scholar 

  • Dentcheva, D., Gollmer, R., Möller, A., Römisch, W., & Schultz, R. (1997). Solving the unit commitment problem in power generation by Primal and Dual methods (pp. 332–339). Wiesbaden: Springer.

    Google Scholar 

  • Dorneles, Á . P., De Araújo, O. C . B., & Buriol, L . S. (2014). A fix-and-optimize heuristic for the high school timetabling problem. Computers and Operations Research, 52(PART A), 29–38.

    Article  Google Scholar 

  • Droste-Franke, B., Carrier, M., Kaiser, M., Schreurs, M., Weber, C., & Ziesemer, T. (2015). Improving energy decisions: Towards better scientific policy advice for a safe and secure future energy system. Cham: Springer.

    Book  Google Scholar 

  • ENTSO-E (2015). ENTSO-E central information transparency platform, available at https://www.entsoe.eu.

  • Farhat, I. A., & El-Hawary, M. E. (2009). Interior point methods application in optimum operational scheduling of electric power systems. IET Generation, Transmission & Distribution, 11, 1020–1029.

    Article  Google Scholar 

  • Frangioni, A., Gentile, C., & Lacalandra, F. (2008). Solving unit commitment problems with general ramp constraints. International Journal of Electrical Power and Energy Systems, 30(5), 316–326.

    Article  Google Scholar 

  • Frangioni, A., Gentile, C., & Lacalandra, F. (2011). Sequential Lagrangian-MILP approaches for unit commitment problems. International Journal of Electrical Power and Energy Systems, 33(3), 585–593.

    Article  Google Scholar 

  • Fu, Y., Shahidehpour, M., & Li, Z. (2005). Long-term security-constrained unit commitment: Hybrid dantzig-wolfe decomposition and subgradient approach. IEEE Transactions on Power Systems, 20, 2093–2106.

    Article  Google Scholar 

  • Gollmer, R., Möller, A., Nowak, M. P., Römisch, W., & Schultz, R. (1999). Primal and dual methods for unit commitment in a hydro-thermal power system. In 13th power system computation conference (Trondheim 1999) (vol. 2, pp. 724–730).

  • Gollmer, R., Nowak, M. P., Römisch, W., & Schultz, R. (2000). Unit commitment in power generation—a basic model and some extensions. Annals of Operations Research, 96, 167–189.

    Article  Google Scholar 

  • Hashimoto, H., Hirano, H., Hirose, K., Isaji, K., & Takahashi, J. (2014). Long-term generation scheduling method with constrained numbers of unit commitment and fuel constraints on thermal units. Electrical Engineering in Japan, 187, 18–28.

    Article  Google Scholar 

  • Helber, S., & Sahling, F. (2010). A fix-and-optimize approach for the multi-level capacitated lot sizing problem. International Journal of Production Economics, 123, 247–256.

    Article  Google Scholar 

  • IBM (2015). Continuous CPLEX MIP optimizer performance improvement since 2000, available at http://ibm.com/software/commerce/optimization/cplex-performance.

  • Jebaraja, S., & Iniyan, S. (2006). A review of energy models. Renewable and Sustainable Energy Reviews, 10, 281–311.

    Article  Google Scholar 

  • Kallrath, J., Pardalos, P. M., Rebennack, S., & Scheidt, M. (2005). Optimization in the energy industry. Berlin: Springer.

    Google Scholar 

  • Kazarlis, S., Bakirtzis, J. M., & Petridis, V. (1996). A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems, 11, 83–92.

    Article  Google Scholar 

  • Kjeldsen, N. H., & Chiarandini, M. (2012). Heuristic solutions to the long-term unit commitment problem with cogeneration plants. Computers and Operations Research, 39, 269–282.

    Article  Google Scholar 

  • Lemaréchal, C., Sagastizábal, C., Pellegrino, F., & Renaud, A. (1996). Bundle methods applied to the unit-commitment problem. Boston, MA: Springer.

    Book  Google Scholar 

  • Li, C., Svoboda, A. J., Tseng, C. L., Johnson, R. B., & Hsu, E. (1997). Hydro unit commitment in hydro-thermal optimization. IEEE Transactions on Power Systems, 12, 764–769.

    Article  Google Scholar 

  • Li, X., Li, T., Wei, J., Wang, G., & Yeh, W. W.-G. (2014). Hydro unit commitment via mixed integer linear programming: A case study of the three gorges project, China. IEEE Transactions on Power Systems, 29, 1232–1241.

    Article  Google Scholar 

  • Liao, G.-C. (2006). Application of an immune algorithm to the short-term unit commitment problem in power system operation. IEE Proceedings Generation, Transmission and Distribution, 153, 309–320.

    Article  Google Scholar 

  • Luh, P. B., Zhang, D., & Tomastik, R. N. (1998). An algorithm for solving the dual problem of hydrothermal scheduling. IEEE Transactions on Power Systems, 13(2), 593–600.

    Article  Google Scholar 

  • Martinéz, M. G. Diniz, A. L., & Sagastizábal, C. (2008). A comparative study of two forward dynamic programming techniques for solving local thermal unit commitment problems. In 16th PSCC conference, Glasgow, Scotland (pp. 1–8).

  • Martinez Diaz, D. J. (2008). Production cost models with regard to liberalised electricity markets. Ph.D. thesis, Karlsruhe Institute of Technology.

  • Maubach, K.-D. (1994). Mittelfristige Energieeinsatzoptimierung in Versorgungssystemen mit Kraft-Wärme-Kopplung. Ph.D. thesis, Bergische Universität-Gesamthochschule Wuppertal.

  • Morales-España, G., Latorre, J. M., & Ramos, A. (2013). Tight and compact MILP formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 28, 4897–4908.

    Article  Google Scholar 

  • Möst, D., & Keles, D. (2010). A survey of stochastic modelling approaches for liberalised electricity markets. European Journal of Operational Research, 207, 543–556.

    Article  Google Scholar 

  • Nicolosi, M. (2012). The economics of renewable electricity market integration. An empirical and model-based analysis of regulatory frameworks and their impacts on the power market. Ph.D. thesis, University of Cologne.

  • Niknam, T., Khodaei, A., & Fallahi, F. (2009). A new decomposition approach for the thermal unit commitment problem. Applied Energy, 86(9), 1667–1674.

    Article  Google Scholar 

  • Ongsakul, W., & Petcharaks, N. (2004). Unit commitment by enhanced adaptive lagrangian relaxation. IEEE Transactions on Power Systems, 19, 620–628.

    Article  Google Scholar 

  • Ordoudis, C., Pinson, P., Zugno, M., & Morales, J. M. (2015). Stochastic unit commitment via progressive hedging—extensive analysis of solution methods. IEEE Eindhoven, PowerTech, 2015 (pp. 1–6).

  • Ostrowski, J., Anjos, M. F., & Vannelli, A. (2012). Tight mixed integer linear programming formulations for the unit commitment problem. IEEE Transactions on Power Systems, 27, 39–46.

    Article  Google Scholar 

  • Padhy, N. P. (2004). Unit commitment–a bibliographical survey. IEEE Transactions on Power Systems, 19, 1196–1205.

    Article  Google Scholar 

  • Pang, C . K., Sheble, G . B., & Albuyeh, F. (1981). Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitments. IEEE Transactions on Power Apparatus and Systems, PAS–100(3), 1212–1218.

    Article  Google Scholar 

  • Raglend, I. J., & Padhy, N. P. (2006). Solutions to practical unit commitment problems with operational, power flow and environmental constraints. In IEEE power engineering society general meeting, Montreal, Canada (pp. 1–8).

  • Rajan, D., & Takriti, S. (2005). Minimum up/down polytopes of the unit commitment problem with start-up costs. IBM Research Report, RC23628, 1–14.

    Google Scholar 

  • Redondo, N. J., & Conejo, A. J. (1999). Short-term hydro-thermal coordination by Lagrangian relaxation: Solution of the dual problem. IEEE Transactions on Power Systems, 14(1), 89–95.

    Article  Google Scholar 

  • Rieck, J., Ehrenberg, C., & Zimmermann, J. (2014). Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery. European Journal of Operational Research, 236, 863–878.

    Article  Google Scholar 

  • Rockafellar, R. T., & Wets, R. J.-B. (1991). Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research, 16, 119–147.

    Article  Google Scholar 

  • Saravanan, B., Das, S., Sikri, S., & Kothari, D. P. (2013a). A solution to the unit commitment problem–a review. Frontiers in Energy, 7, 223–236.

    Article  Google Scholar 

  • Saravanan, B., Mishra, S., & Nag, D. (2014). A solution to stochastic unit commitment problem for a wind-thermal system coordination. Frontiers in Energy, 8, 192–200.

    Article  Google Scholar 

  • Saravanan, B., Sikri, S., Swarup, K. S., & Kothari, D. P. (2013b). Unit commitment using dynamic programming—an exhaustive working of both classical and stochastic approach. Frontiers in Energy, 7, 333–341.

    Article  Google Scholar 

  • Saravanan, B. & Vasudevan, E. R. (2014). Emission constrained unit commitment problem solution using invasive weed optimization algorithm, International conference on advances in electrical engineering, Vellore, India (pp. 1–6).

  • Senjyu, T., Miyagim, T., Saber, A. Y., Urasaki, N., & Funabashi, T. (2006). Emerging solution of large-scale unit commitment problem by stochastic priority list. Electric Power Systems Research, 76, 283–292.

    Article  Google Scholar 

  • Senjyu, T., Shimabukuro, K., Uezato, K., & Funabashi, T. (2003). A fast technique for unit commitment problem by extended priority list. IEEE Transactions on Power Systems, 18, 882–888.

    Article  Google Scholar 

  • Sheble, G. B., & Fahd, G. N. (1994). Unit commitment literature synopsis. IEEE Transactions on Power Systems, 9, 128–135.

    Article  Google Scholar 

  • Sifuentes, W. S., & Vargas, A. (2007). Hydrothermal scheduling using benders decomposition: Accelerating techniques. IEEE Transactions on Power Systems, 22, 1351–1359.

    Article  Google Scholar 

  • Sioshansi, R., O’Neill, R., & Oren, S. S. (2008). Economic consequences of alternative solution methods for centralized unit commitment in day-ahead electricity markets. IEEE Transactions on Power Systems, 23(2), 344–352.

    Article  Google Scholar 

  • Soliman, S. A., & Mantawy, A. H. (2012). Modern optimization techniques with applications in electric power systems. New York: Springer.

    Book  Google Scholar 

  • Streiffert, D., Philbrick, R., & Ott, A. (2005). A mixed integer programming solution for market clearing and reliability analysis. In IEEE Power Engineering Society General Meeting, 2005, 2724–2731.

    Google Scholar 

  • Sun, N., Ellersdorfer, I., & Swider, D. J. (2014). Model-based long-term electricity generation system planning under uncertainty. In International conference on electric utility deregulation and restructuring and power technologies, Nanjuing, China (pp. 1298–1304).

  • Tahanan, M., van Ackooij, W., Frangioni, A., & Lacalandra, F. (2015). Large-scale unit commitment under uncertainty. 4OR, 13, 115–171.

    Article  Google Scholar 

  • Taktak, R., & D’Ambrosio, C. (2017). An overview on mathematical programming approaches for the deterministic unit commitment problem in hydro valleys. Energy Systems, 8, 57–79.

    Article  Google Scholar 

  • TenneT TSO (2015). Renewable feed-in of market operator TenneT TSO GmbH in Germany, available at http://www.tennettso.de.

  • TransnetBW (2015). Renewable feed-in of market operator TransnetBW GmbH in Germany, available at http://transnetbw.de.

  • Trivedi, A., Sharma, D., & Srinivasan, D. (2012). Multi-objectivization of short-term unit commitment under uncertainty using evolutionary algorithm. IEEE congress on evolutionary computation, Brisbane, Australia (pp. 271–278).

  • Tseng, C.-L. (1996). On Power System Generation Unit Commitment Problems. Ph.D. thesis, University of California.

  • van Ackooij, W., & Malick, J. (2016). Decomposition algorithm for large-scale two-stage unit-commitment. Annals of Operations Research, 238, 587–613.

    Article  Google Scholar 

  • Viana, A., Pinho de Sousa, J., & Matos, M. A. (2008). Fast solutions for UC problems by a new metaheuristic approach. Electric Power Systems Research, 78, 1385–1395.

    Article  Google Scholar 

  • Viana, A. M. (2004). Metaheuristics for the Unit Commitment Problem: The Constraint Oriented Neighbourhoods Search Strategy. Ph.D. thesis, University of Porto.

  • Viana, A. M., & Pedroso, J. P. (2013). A new MILP-based approach for unit commitment in power production planning. Electrical Power and Energy Systems, 44, 997–1005.

    Article  Google Scholar 

  • Weber, C. (2005). Uncertainty in the electric power industry: Methods and models for decision support. New York: Springer.

    Google Scholar 

  • Wood, A . J., Wollenberg, B . F., & Sheble, G . B. (2013). Power generation, operation and control (3rd ed.). New York: Wiley.

    Google Scholar 

  • Zhai, D., Snyder, W., Waight, J., Farah, J., Gonzalez, A., & Vallejo, P. (2001). Fuel constrained unit commitment with fuel mixing and allocation. In IEEE power engineering society international conference on power industry computer applications, Sydney, Australia (pp. 11–16).

  • Zheng, H., Jian, J., Yang, L., & Quan, R. (2016). A deterministic method for the unit commitment problem in power systems. Computers and Operations Research, 66, 241–247.

    Article  Google Scholar 

  • Zugno, M., Morales, J. M., & Madsen, H. (2014). Robust management of combined heat and power systems via linear decision rules. In IEEE international energy conference, Dubrovnik, Croatia (pp. 479–486).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexander Franz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Franz, A., Rieck, J. & Zimmermann, J. Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages. Ann Oper Res 274, 241–265 (2019). https://doi.org/10.1007/s10479-018-2900-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2900-5

Keywords

JEL Classification

Navigation