A Planner-Trader Decomposition for Multimarket Hydro Scheduling

References

• Aasgård E, Andersen G, Fleten S, Haugstvedt D (2014) Evaluating a stochastic-programming-based bidding model for a multireservoir system. IEEE Trans. Power Systems 29(4):1748–1757.Crossref, Google Scholar
• Aasgård E, Fleten S, Kaut M, Midthun K, Perez-Valdes G (2019) Hydropower bidding in a multi-market setting. Energy Systems 10:543–565.Crossref, Google Scholar
• Abgottspon H, Andersson G (2012) Approach of integrating ancillary services into a medium-term hydro optimization. Proc. 12th Sympos. of Specialists in Electric Oper. and Expansion Planning (Zurich), 1–10.Google Scholar
• Abgottspon H, Njálsson K, Bucher MA, Andersson G 2014. Risk-averse medium-term hydro optimization considering provision of spinning reserves. Proc. Internat. Conf. on Probabilistic Methods Applied to Power Systems (IEEE, Piscataway, NJ), 1–6.Google Scholar
• Andrieu L, Henrion R, Römisch W (2010) A model for dynamic chance constraints in hydro power reservoir management. Eur. J. Oper. Res. 207(2):579–589.Crossref, Google Scholar
• Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
• Bodur M, Luedtke JR (2022) Two-stage linear decision rules for multi-stage stochastic programming. Math. Programming 191(1):347–380.Crossref, Google Scholar
• Carpentier P-L, Gendreau M, Bastin F (2013) Long-term management of a hydroelectric multireservoir system under uncertainty using the progressive hedging algorithm. Water Resources Res. 49(5):2812–2827.Crossref, Google Scholar
• Chazarra M, Pérez-Díaz JI, García-González J (2018a) Optimal joint energy and secondary regulation reserve hourly scheduling of variable speed pumped storage hydropower plants. IEEE Trans. Power Systems 33(1):103–115.Crossref, Google Scholar
• Chazarra M, Pérez-Díaz JI, García-González J (2014) Optimal operation of variable speed pumped storage hydropower plants participating in secondary regulation reserve markets. Proc. 11th Internat. Conf. on the Eur. Energy Market (IEEE, Piscataway, NJ), 1–5.Google Scholar
• Chazarra M, García-González J, Pérez-Díaz J, Arteseros M (2016) Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Res. 130:67–77.Crossref, Google Scholar
• Chazarra M, Pérez-Díaz JI, García-González J, Praus R (2018b) Economic viability of pumped-storage power plants participating in the secondary regulation service. Appl. Energy 216:224–233.Crossref, Google Scholar
• de Matos V, Philpott A, Finardi E (2015) Improving the performance of stochastic dual dynamic programming. J. Comput. Appl. Math. 290:196–208.Crossref, Google Scholar
• Delage E, Iancu D (2015) Robust multistage decision making. Oper. Res. Revolution 20–46.Google Scholar
• DOE (2020) DOE OE global energy storage database. Accessed March 1, 2021, https://www.sandia.gov/ess-ssl/global-energy-storage-database/.Google Scholar
• Dyer M, Stougie L (2006) Computational complexity of stochastic programming problems. Math. Programming 106(3):423–432.Crossref, Google Scholar
• European Commission (2020) European Commission METIS technical note T4: Overview of European electricity markets. Accessed March 1, 2021, https://ec.europa.eu/energy/sites/default/files/documents/overview_of_european_electricity_markets.pdf.Google Scholar
• Fernández-Muñoz D, Pérez-Díaz JI, Guisández I, Chazarra M, Fernández-Espina Á (2020) Fast frequency control ancillary services: An international review. Renewable Sustainable Energy Rev. 120:109662.Crossref, Google Scholar
• Fodstad M, Aarlott M, Midthun K (2018) Value-creation potential from multi-market trading for a hydropower producer. Energies 11(1):1–15.Google Scholar
• Gauvin C, Delage E, Gendreau M (2017) Decision rule approximations for the risk averse reservoir management problem. Eur. J. Oper. Res. 261(1):317–336.Crossref, Google Scholar
• Gauvin C, Delage E, Gendreau M (2018) A stochastic program with time series and affine decision rules for the reservoir management problem. Eur. J. Oper. Res. 267(2):716–732.Crossref, Google Scholar
• Gjelsvik A, Mo B, Haugstad A (2010) Long- and medium-term operations planning and stochastic modelling in hydro-dominated power systems based on stochastic dual dynamic programming. Pardalos P, Rebennack S, Pereira M, Iliadis N, eds. Handbook of Power Systems I (Springer, Berlin), 33–55.Crossref, Google Scholar
• Hanasusanto G, Kuhn D, Wiesemann W (2016) A comment on “Computational complexity of stochastic programming problems”. Math. Programming 159:557–569.Crossref, Google Scholar
• Helseth A, Fodstad M, Mo B (2016) Optimal medium-term hydropower scheduling considering energy and reserve capacity markets. IEEE Trans. Sustainable Energy 7(3):934–942.Crossref, Google Scholar
• Helseth A, Mo B, Fodstad M, Hjelmeland M (2015) Co-optimizing sales of energy and capacity in a hydropower scheduling model. Proc. IEEE Eindhoven PowerTech, 1–6.Google Scholar
• Helseth A, Fodstad M, Askeland M, Mo B, Bjarte Nilsen O, Pérez-Díaz JI, Chazarra M, et al. (2017) Assessing hydropower operational profitability considering energy and reserve markets. IET Renewable Power Generation 11(13):1640–1647.Crossref, Google Scholar
• IEA (2020) Renewables 2020 fuel report. Accessed March 1, 2021, https://www.iea.org/reports/renewables-2020/hydropower-bioenergy-csp-and-geothermal#hydropower.Google Scholar
• IEA (2022) Renewables data explorer. Accessed April 6, 2023, https://www.iea.org/data-and-statistics/data-tools/renewables-data-explorer.Google Scholar
• Klæboe G, Braathen J, Eriksrud A, Fleten S-E (2022) Day-ahead market bidding taking the balancing power market into account. TOP 30:683–703.Google Scholar
• Löhndorf N, Wozabal D, Minner S (2013) Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Oper. Res. 61(4):810–823.Link, Google Scholar
• Lorca A, Sun XA, Litvinov E, Zheng T (2016) Multistage adaptive robust optimization for the unit commitment problem. Oper. Res. 64(1):32–51.Link, Google Scholar
• Mayer J (2014) Electricity Production and Spot-Prices in Germany 2014 (Fraunhofer Institute for Solar Energy Systems).Google Scholar
• Morris C, Pehnt M (2015) Energy Transition: The German Energiewende (Heinrich Böll Foundation, Berlin).Google Scholar
• Pan L, Housh M, Liu P, Cai X, Chen X (2015) Robust stochastic optimization for reservoir operation. Water Resources Res. 51(1):409–429.Crossref, Google Scholar
• Pereira M, Pinto L (1991) Multi-stage stochastic optimization applied to energy planning. Math. Programming 52:359–375.Crossref, Google Scholar
• Philpott A, de Matos V (2012) Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion. Eur. J. Oper. Res. 218(2):470–483.Crossref, Google Scholar
• Philpott A, de Matos V, Kapelevich L (2018) Distributionally robust SDDP. Comput. Management Sci. 15:431–454.Crossref, Google Scholar
• Pritchard G, Philpott A, Neame P (2005) Hydroelectric reservoir optimization in a pool market. Math. Programming 103(3):445–461.Crossref, Google Scholar
• Schillinger M, Weigt H, Barry M, Schumann R (2017) Hydropower operation in a changing environment: A Swiss case study. Proc. 14th Internat. Conf. on the Eur. Energy Market (IEEE, Piscataway, NJ), 1–6.Google Scholar
• Shapiro A, Dentcheva D, Ruszczynski A (2009) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
• Shapiro A, Tekaya W, Soares M, da Costa J (2013) Worst-case-expectation approach to optimization under uncertainty. Oper. Res. 61(6):1435–1449.Link, Google Scholar
• Vayanos P, Kuhn D, Rustem B (2012) A constraint sampling approach for multi-stage robust optimization. Automatica J. IFAC 48(3):459–471.Crossref, Google Scholar
• Wirth H (2016) Recent Facts About Photovoltaics in Germany (Fraunhofer Institute for Solar Energy Systems, Freiburg, Germany).Google Scholar
• Yanikoğlu I, Gorissen BL, den Hertog D (2019) A survey of adjustable robust optimization. Eur. J. Oper. Res. 277(3):799–813.Crossref, Google Scholar