Hydropower bidding in a multi-market setting

Springer Science and Business Media LLC - Tập 10 Số 3 - Trang 543-565 - 2019
Ellen Krohn Aasgård1, Stein–Erik Fleten1, Michal Kaut2, Kjetil Trovik Midthun2, Gerardo A. Pérez-Valdés2
1Norwegian University of Science and Technology Trondheim, Norway#TAB#
2Applied Economics, SINTEF Technology and Society, Trondheim, Norway

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Aasgård, E.K., Andersen, G.S., Fleten, S.-E., Haugstvedt, D.: Evaluating a stochastic-programming-based bidding model for a multireservoir system. IEEE Trans. Power Syst. 29(4), 1748–1757 (2014)

Aasgård, E.K., Naversen, C.Ø., Fodstad, M., Skjelbred, H.I.: Optimizing day-ahead bid curves in hydropower production. Energy Systems, pp. 1–19 (2017)

Anbazhagan, S., Kumarappan, N.: Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Syst. J. 7(4), 866–872 (2013)

Anderson, E.J., Philpott, A.B.: Using supply functions for offering generation into an electricity market. Oper Res 50(3), 477–489 (2002)

Andersen, J., Kaut, M., Tomasgard, A.: Stochastic model for short-term balancing of supply and consumption of electricity. In: Modelling and Optimisation of Renewable Energy Systems, pp. 37–66. School of Business and Social Sciences, Aarhus University, Aarhus (2015). ISBN 9788793195189, http://pure.au.dk/portal/en/publications-research/modelling-and-optimisation-of-renewable-energy-systems%2880b927a9-a945-4d22-9e8e-3210714bea9c%29.html

Bayraksan, G., Morton, D.P.: Assessing solution quality in stochastic programs. Math Program 108(2–3), 495–514 (2006)

Bayraksan, G., Morton, D.P., Partani, A.: Simulation-based optimality tests for stochastic programs. In: Infanger, G. (ed.) Stochastic Programming: The State of the Art In Honor of George B. Dantzig, International Series in Operations Research & Management Science, pp. 37–55. Springer, Berlin (2011). https://doi.org/10.1007/978-1-4419-1642-6

Berrada, A., Loudiyi, K., Zorkani, I.: Valuation of energy storage in energy and regulation markets. Energy 115(Part 1), 1109–1118 (2016). https://doi.org/10.1016/j.energy.2016.09.093

Boomsma, T.K., Juul, N., Fleten, S.-E.: Bidding in sequential electricity markets: the Nordic case. Eur. J. Oper. Res. 238(3), 797–809 (2014)

Brolin, M.O., Söder, L.: Modeling Swedish real-time balancing power prices using nonlinear time series models. In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 358–363. IEEE (2010)

Chan, K.F., Gray, P., Van Campen, B.: A new approach to characterizing and forecasting electricity price volatility. Int. J. Forecast. 24(4), 728–743 (2008)

Chopra, V., Ziemba, W.: The effects of errors in means, variances, and covariances on optimal portfolio choice. J. Portfolio Manag. 19(2), 6–11 (1993)

Conejo, A.J., Contreras, J., Espínola, R., Plazas, M.A.: Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 21(3), 435–462 (2005)

De Ladurantaye, D., Gendreau, M., Potvin, J.-Y.: Strategic bidding for price-taker hydroelectricity producers. IEEE Trans. Power Syst. 22(4), 2187–2203 (2007). https://doi.org/10.1109/TPWRS.2007.907457

De Ladurantaye, D., Gendreau, M., Potvin, J.-Y.: Optimizing profits from hydroelectricity production. Comput. Oper. Res. 36(2), 499–529 (2009). https://doi.org/10.1016/j.cor.2007.10.012

Dupačová, J., Consigli, G., Wallace, S.W.: Scenarios for multistage stochastic programs. Ann. Oper. Res. 100, 25–53 (2000)

Faria, E., Fleten, S.-E.: Day-ahead market bidding for a nordic hydropower producer: taking the elbas market into account. Comput. Manag. Sci. 8(1), 75–101 (2011)

Fleten, S.-E., Kristoffersen, T.K.: Stochastic programming for optimizing bidding strategies of a nordic hydropower producer. Eur. J. Oper. Res. 181(2), 916–928 (2007). https://doi.org/10.1016/j.ejor.2006.08.023

Fleten, S.-E., Haugstvedt, D., Steinsbø, J.A., Belsnes, M.M., Fleischmann, F.: Bidding hydropower generation: integrating short- and long-term scheduling. In: Proceedings of 17th Power Systems Computations Conference PSCC 2011, pp. 352–358 (2011)

Garcia, R.C., Contreras, J., Van Akkeren, M., Garcia, J.B.C.: A garch forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 20(2), 867–874 (2005)

Gjelsvik, A., Mo, B., Haugstad, A.: Long- and medium-term operations planning and stochastic modelling in hydro-dominated power systems based on stochastic dual dynamic programming. In: Pardalos, P.M., Rebennack, S., Pereira, M.V.F., Iliadis, N.A. (eds.) Handbook of Power Systems I, pp. 33–55. Springer, Berlin (2010)

Guo, J.-J., Luh, P.B.: Improving market clearing price prediction by using a committee machine of neural networks. IEEE Trans. Power Syst. 19(4), 1867–1876 (2004)

Haldrup, N., Nielsen, M.Ø.: A regime switching long memory model for electricity prices. J. Econom. 135(1), 349–376 (2006)

Heitsch, H., Römisch, W., Strugarek, C.: Stability of multistage stochastic programs. SIAM J. Optim. 17(2), 511–525 (2006). https://doi.org/10.1137/050632865

Heitsch, H., Römisch, W.: Scenario tree modelling for multistage stochastic programs. Math. Program. 118(2), 371–406 (2009). https://doi.org/10.1007/s10107-007-0197-2

Heitsch, H., Römisch, W.: Scenario tree generation for multi-stage stochastic programs. In: Bertocchi, M., Consigli, G., Dempster, M.A.H. (eds.) Stochastic Optimization Methods in Finance and Energy, International Series in Operations Research and Management Science, vol. 163, chapter 14, pp. 313–341. Springer, Berlin (2011). https://doi.org/10.1007/978-1-4419-9586-5_14

Høyland, K., Kaut, M., Wallace, S.W.: A heuristic for moment-matching scenario generation. Comput. Optim. Appl. 24(2–3), 169–185 (2003)

Jónsson, T., Pinson, P., Nielsen, H.A., Madsen, H., Nielsen, T.S.: Forecasting electricity spot prices accounting for wind power predictions. IEEE Trans. Sustain. Energy 4(1), 210–218 (2013)

Kaut, M.: A copula-based heuristic for scenario generation. Comput. Manag. Sci. 11(4), 503–516 (2014). https://doi.org/10.1007/s10287-013-0184-4

Kaut, M.: Forecast-based scenario-tree generation method. Optimization Online, e-print ID 2017-03-5898 (2017). http://www.optimization-online.org/DB_HTML/2017/03/5898.html

Kaut, M., Wallace, S.W.: Evaluation of scenario generation methods for stochastic programming. Pac. J. Optim. 3, 257–271 (2007)

Kaut, M., Midthun, K.T., Werner, A.S., Tomasgard, A., Hellemo, L., Fodstad, M.: Multi-horizon stochastic programming. Comput. Manag. Sci. 11(1–2), 179–193 (2014). https://doi.org/10.1007/s10287-013-0182-6 . Special Issue: Computational Techniques in Management Science

Kiesel, R., Paraschiv, F.: Econometric analysis of 15-minute intraday electricity prices. Energy Econ. 64, 77–90 (2017)

King, A.J., Wallace, S.W.: Modeling with Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer, Berlin (2012). https://doi.org/10.1007/978-0-387-87817-1

Klæboe, G., Fosso, O.B.: Optimal bidding in sequential physical markets—a literature review and framework discussion. PowerTech (POWERTECH), 2013 IEEE Grenoble, pp. 1–6 (2013)

Klæboe, G., Eriksrud, A.L., Fleten, S.-E.: Benchmarking time series based forecasting models for electricity balancing market prices. Energy Syst. 6(1), 43–61 (2015)

Kosater, P., Mosler, K.: Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices. Appl. Energy 83(9), 943–958 (2006)

Kristiansen, T.: Forecasting Nord Pool day-ahead prices with an autoregressive model. Energy Policy 49, 328–332 (2012)

Li, G., Shi, J., Qu, X.: Modeling methods for genco bidding strategy optimization in the liberalized electricity spot market—a state-of-the-art review. Energy 36(8), 4686–4700 (2011). https://doi.org/10.1016/j.energy.2011.06.015

Lindqvist, J.: Operation of a hydrothermal electric system: a multistage decision processr. AIEE Trans. Power Appar. Syst. 81, 1–7 (1962)

Löhndorf, N., Wozabal, D., Minner, S.: Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Oper. Res. 61(4), 810–823 (2013)

Lu, N., Chow, J.H., Desrochers, A.A.: Pumped-storage hydro-turbine bidding strategies in a competitive electricity market. IEEE Trans. Power Syst. 19(2), 834–841 (2004)

Olsson, M.: On optimal hydropower bidding in systems with wind power: modeling the impact of wind power on power markets. PhD thesis, KTH, Stockholm (2009)

Olsson, M., Söder, L.: Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Trans. Power Syst. 23(2), 443–450 (2008)

Paraschiv, F., Fleten, S.-E., Schürle, M.: A spot-forward model for electricity prices with regime shifts. Energy Econ. 47, 142–153 (2015). https://doi.org/10.1016/j.eneco.2014.11.003 . ISSN 0140-9883

Pereira, M.V., Pinto, L.M.: Multi-stage stochastic optimization applied to energy planning. Math. Program. 52(1–3), 359–375 (1991)

Pflug, G.C.: Version-independence and nested distributions in multistage stochastic optimization. SIAM J. Optim. 20(3), 1406–1420 (2010). https://doi.org/10.1137/080718401 . ISSN 1095-7189

Pflug, G.C., Pichler, A.: Dynamic generation of scenario trees. Comput. Optim. Appl. 62(3), 641–668 (2015). https://doi.org/10.1007/s10589-015-9758-0 . ISSN 1573-2894

Pflug, G.C., Pichler, A.: From empirical observations to tree models for stochastic optimization: convergence properties. SIAM J. Optim. 26(3), 1715–1740 (2016). https://doi.org/10.1137/15M1043376

Philpott, A., Guan, Z., Khazaei, J., Zakeri, G.: Production inefficiency of electricity markets with hydro generation. Utilities Policy 18(4), 174–185 (2010). ISSN 0957-1787. https://doi.org/10.1016/j.jup.2010.09.001 . URL http://www.sciencedirect.com/science/article/pii/S0957178710000585 . Designing Electricity Auctions

Pritchard, G., Philpott, A.B., Neame, P.J.: Hydroelectric reservoir optimization in a pool market. Math. Program. 103(3), 445–461 (2005)

Séguin, S., Fleten, S.-E., Côté, P., Pichler, A., Audet, C.: Stochastic short-term hydropower planning with inflow scenario trees. Eur. J. Oper. Res. 259(3), 1156–1168 (2017)

Stage, S., Larsson, Y.: Incremental cost of water power. Power apparatus and systems, part III. Trans. Am. Inst. Electr. Eng. 80(3), 361–364 (1961)

Steeger, G., Barroso, L.A., Rebennack, S.: Optimal bidding strategies for hydro-electric producers: a literature survey. IEEE Trans. Power Syst. 29(4), 1758–1766 (2014)

Triki, C., Beraldi, P., Gross, G.: Optimal capacity allocation in multi-auction electricity markets under uncertainty. Comput. Oper. Res. 32(2), 201–217 (2005). https://doi.org/10.1016/S0305-0548(03)00211-9

Vespucci, M.T., Bertocchi, M., Tomasgard, A., Innorta, M.: Integration of Wind Power Production in a Conventional Power Production System: Stochastic Models and Performance Measures, pp. 129–152. Springer, Berlin (2013). ISBN 978-3-642-41080-2. https://doi.org/10.1007/978-3-642-41080-2_5

Wang, P., Zareipour, H., Rosehart, W.D.: Descriptive models for reserve and regulation prices in competitive electricity markets. IEEE Trans. Smart Grid 5(1), 471–479 (2014)

Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30, 1030–1081 (2014)

Wolfgang, O., Haugstad, A., Mo, B., Gjelsvik, A., Wangensteen, I., Doorman, G.: Hydro reservoir handling in Norway before and after deregulation. Energy 34(10), 1642–1651 (2009)

Yakowitz, S.: Dynamic programming applications in water resources. Water Resour. Res. 18(4), 673–696 (1982)