Logo PTI Logo FedCSIS

Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Price-Shaped Optimal Water Reflow in Prosumer Energy Cascade Hydro Plants

,

DOI: http://dx.doi.org/10.15439/2023F6642

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1001–1005 ()

Full text

Abstract. In the face of the recent surge in energy prices, intensified use of free renewable sources of energy (RSE) gains much importance. Unfortunately, the operation of RSE highly depends on weather conditions, which perturb the balance between the industrial and home energy dissipation patterns. This disparity induces price fluctuations or even destabilizes the energy supply system, yet can be alleviated by the installation of energy depots. While electrochemical depots are hardly cost-effective, they may be supplemented or replaced by small hydro plants with the ponds located above the plant recognized as energy reservoirs. However, inappropriate use of the plant is likely to cause floods or droughts down the river. In this paper, following a rigorous mathematical argument, a cost-optimal controller of a cascade of hydro plants is designed and its properties are formally proved. It is shown to flatten the price pattern, by reducing the load fluctuation of the legacy supply system, as well as provide a concrete revenue for prosumers.

References

  1. S. Kolosok, Y., Bilan, T. Vasylieva, A. Wojciechowski, and M. Morawski, „A scoping review of renewable energy, sustainability and the environment.” Energies 14(15): 4490, 2021.
  2. J. Wirfs-Brock. “IE Questions: Why Is California Trying To Behead The Duck?,” [Online]. Available: https://insideenergy.org/2014/10/02/ie-questions-why-is-california-trying-to-behead-the-duck/, 2014, Accessed: 2023-05-12.
  3. PSE, “Market Energy Prices.” [Online]. Available: https://www.pse.pl/dane-systemowe/funkcjonowanie-rb/raporty-dobowe-z-funkcjonowania-rb/podstawowe-wskazniki-cenowe-i-kosztowe/rynkowa-cena-energii-elektrycznej-rce.
  4. D. Borkowski, D. Cholewa, and A. Korzeń, „A run-of-the-river Hydro-PV battery hybrid system as an energy supplier for local loads.” Energies, 14, 5160, 2021.
  5. A. Wijesinghe, and L. Lai, “Small hydro power plant analysis and development,” Proc. 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 25-30, 2011.
  6. K. Wyszkowski, Z. Piwowarek, and Z. Pałejko, „Małe elektrowne wodne w Polsce”, Technical Report, UN Global. In Polish, [Online]. Available: https://ungc.org.pl/wp-content/uploads/2022/03/Raport_Male_elektrownie_wodne_w_Polsce.pdf, 2022.
  7. G. Alvarez, “An optimization model for operations of large scale hydro power plants,” IEEE Latin America Transactions, 18(9), 1631–1638, 2020.
  8. C. Danielson, F. Borrellia, D. Oliver, D. Anderson, and T. Phillips, “Constrained flow control in storage networks: Capacity maximization and balancing,” Automatica, 49(9), 2612–2621, 2013.
  9. M. V. Basin, F. Guerra-Avellaneda, and Y. B. Shtessel, “Stock management problem: Adaptive fixed-time convergent continuous controller design,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4974–4983, 2020.
  10. P. Ignaciuk, “Dead-time compensation in continuous-review perishable inventory systems with multiple supply alternatives,” Journal of Process Control 22(5), 915–924, 2012.
  11. Y. Yu, Y. Wu, and Q. Sheng, “Optimal scheduling strategy of cascade hydropower plants under the joint market of day-ahead energy and frequency regulation.” IEEE Access, 2021.
  12. A. R. Shaw, et al. “Hydropower optimization using artificial neural network surrogate models of a high-fidelity hydrodynamics and water quality model,” Water Resources Research, 53, 9444–9461, 2017.
  13. P. Ignaciuk and Ł. Wieczorek, “Continuous genetic algorithms in the optimization of logistic networks: Applicability assessment and tuning,” Applied Sciences, 10(21), 7851, 2020.
  14. I. Ahmadianfar, A. Samadi-Koucheksaraee, and M. Asadzadeh, “Extract nonlinear operating rules of multi-reservoir systems using an efficient optimization method,” Sci. Rep. 12, 18880, 2022.
  15. J. Bernardes, Jr., at all. “Hydropower operation optimization using machine learning: A systematic review,” AI, 3(1), 78–99, 2022.
  16. Ł. Wieczorek and P. Ignaciuk, “Hydropower operation optimization using machine learning,” International Journal of Shipping and Transport Logistics, 15(1–2), 111–143, 2022.
  17. P. Ignaciuk, “DARE solutions for LQ optimal and suboptimal control of systems with multiple input-output delays,” Journal of The Franklin Institute 353(5), 974–991, 2016.