Abstract
Optimal control of Lévy jump-driven stochastic differential equations plays a central role in management of resource and environment. Problems involving large Lévy jumps are still challenging due to their mathematical and computational complexities. We focus on numerical control of a real-scale dam and reservoir system from the viewpoint of forward-backward stochastic differential equations (FBSDEs): a new mathematical tool in this research area. The problem itself is simple but unique, and involves key challenges common to stochastic systems driven by large Lévy jumps. We firstly present an exactly-solvable linear-quadratic problem and numerically analyze convergence of different numerical schemes. Then, a more realistic problem with a hard constraint of state variables and a more complex objective function is analyzed, demonstrating that the relatively simple schemes perform well.
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References
Jónsdóttir, G.M., Milano, F.: Stochastic modeling of tidal generation for transient stability analysis: a case study based on the all-island Irish transmission system. Electr. Power Syst. Res. 189, 106673 (2020). https://doi.org/10.1016/j.epsr.2020.106673
Wang, Y., Hu, J., Pan, H., Failler, P.: Ecosystem-based fisheries management in the pearl river delta: applying a computable general equilibrium model. Mar. Policy 112, 103784 (2020). https://doi.org/10.1016/j.marpol.2019.103784
Zavala-Yoe, R., Iqbal, H.M., Ramirez-Mendoza, R.A.: Understanding the evolution of pollutants via hierarchical complexity of space-time deterministic and stochastic dynamical systems. Sci. Total Environ. 710, 136245 (2020). https://doi.org/10.1016/j.scitotenv.2019.136245
Øksendal, B., Sulem, A.: Applied Stochastic Control of Jump Diffusions. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02781-0
Bertoni, F., Castelletti, A., Giuliani, M., Reed, P.M.: Discovering dependencies, trade-offs, and robustness in joint dam design and operation: an ex-post assessment of the Kariba dam. Earth’s Future 7(12), 1367–1390 (2019). https://doi.org/10.1029/2019EF001235
Yoshioka, H.: Stochastic control of dam discharges. Wiley StatsRef: Statistics Reference Online. 0.1002/9781118445112.stat08365. (In press)
Picarelli, A., Vargiolu, T.: Optimal management of pumped hydroelectric production with state constrained optimal control. J. Econ. Dyn. Contr. 126, 103940 (2020). https://doi.org/10.1016/j.jedc.2020.103940
Yoshioka, H., Yoshioka, Y.: Regime switching constrained viscosity solutions approach for controlling dam-reservoir systems. Comput. Math. Appl. 80(9), 2057–2072 (2020). https://doi.org/10.1016/j.camwa.2020.09.005
Lesmana, D.C., Wang, S.: An upwind finite difference method for a nonlinear black-scholes equation governing European option valuation under transaction costs. Appl. Math. Comput. 219(16), 8811–8828 (2013). https://doi.org/10.1016/j.amc.2012.12.077
Janga Reddy, M., Nagesh Kumar, D.: Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review. H2Open J. 3(1), 135–188 (2020). https://doi.org/10.2166/h2oj.2020.128
Yoshioka, H., Yoshioka, Y.: Tempered stable Ornstein–Uhlenbeck model for river discharge time series with its application to dissolved silicon load analysis. In: IOP Conference Series: Earth and Environmental Science, vol. 691, p. 012012 (2021). https://doi.org/10.1088/1755-1315/691/1/012012
Delong, Ł: Backward Stochastic Differential Equations with Jumps and Their Actuarial and Financial Applications. Springer, London (2020)
Wu, Z., Yu, Z.: Dynamic programming principle for one kind of stochastic recursive optimal control problem and Hamilton–Jacobi–Bellman equation. SIAM J. Contr. Optim. 47(5), 2616–2641 (2008). https://doi.org/10.1137/060671917
Chassagneux, J.F., Chotai, H., Muûls, M.: A Forward-Backward SDEs Approach to Pricing in Carbon Markets. Springer, Cham (2017)
Chau, K.W., Tang, J., Oosterlee, C.W.: An SGBM-XVA demonstrator: a scalable python tool for pricing XVA. J. Math. Ind. 10(1), 1–19 (2020). https://doi.org/10.1186/s13362-020-00073-5
Fujii, M., Takahashi, A., Takahashi, M.: Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs. Asia-Pacific Finan. Markets 26(3), 391–408 (2019). https://doi.org/10.1007/s10690-019-09271-7
Khedher, A., Vanmaele, M.: Discretisation of FBSDEs driven by càdlàg martingales. J. Math. Anal. Appl. 435(1), 508–531 (2016). https://doi.org/10.1016/j.jmaa.2015.10.022
Madan, D., Pistorius, M., Stadje, M.: Convergence of BSΔEs driven by random walks to BSDEs: The case of (in) finite activity jumps with general driver. Stoch. Process. Appl. 126(5), 1553–1584 (2016). https://doi.org/10.1016/j.spa.2015.11.013
Kawai, R., Masuda, H.: Exact discrete sampling of finite variation tempered stable Ornstein-Uhlenbeck processes. Monte Carlo Method Appl. 17(3), 279–300 (2011). https://doi.org/10.1515/mcma.2011.012|
Song, K., Wang, F., Yi, Q., Lu, S.: Landslide deformation behavior influenced by water level fluctuations of the three gorges reservoir (China). Eng. Geol. 247, 58–68 (2018). https://doi.org/10.1016/j.enggeo.2018.10.020
Abdelhady, H.U., Imam, Y.E., Shawwash, Z.: Ghanem, A: Parallelized Bi-level optimization model with continuous search domain for selection of run-of-river hydropower projects. Renew. Energ. 167, 116–131 (2021). https://doi.org/10.1016/j.renene.2020.11.055
Ji, S., Zhou, X.Y.: A maximum principle for stochastic optimal control with terminal state constraints, and its applications. Commun. Inform. Syst. 6(4), 321–338 (2006). https://projecteuclid.org/euclid.cis/1183729000
Hu, Y., Øksendal, B., Sulem, A.: Singular mean-field control games. Stoch. Analy. Appl. 35(5), 823–851 (2017). https://doi.org/10.1080/07362994.2017.1325745
Zhang, P., Yang, Z., Cai, L., Qiao, Y., Chen, X., Chang, J.: Effects of upstream and downstream dam operation on the spawning habitat suitability of Coreius guichenoti in the middle reach of the Jinsha River. Ecol. Eng. 120, 198–208 (2018). https://doi.org/10.1016/j.ecoleng.2018.06.002
Chau, K.W., Oosterlee, C.W.: Stochastic grid bundling method for backward stochastic differential equations. Int. J. Comput. Math. 96(11), 2272–2301 (2019). https://doi.org/10.1080/00207160.2019.1658868
Acknowledgements
Kurita Water and Environment Foundation 19B018 and 20K004 and a grant from MLIT Japan for surveys of the landlocked Ayu sweetfish and management of seaweeds in Lake Shinji support this research. The author thanks all the members of mathematical analysis study group in Shimane University for their valuable comments on this research.
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Yoshioka, H. (2021). Solutions and Challenges in Computing FBSDEs with Large Jumps for Dam and Reservoir System Operation. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_40
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