Short-Term Day-Ahead Hydrothermal Scheduling with Energy Renewables Variable, Storage, Load Shedding using Artificial Intelligence Techniques for Demand Forecasting

Authors

  • Alfonso Vazquez Mendoza Graduate Program and Research in Electrical Engineering, Technological Institute of Morelia https://orcid.org/0009-0002-2123-7640
  • Héctor Francisco Ruiz Paredes Graduate Program and Research in Electrical Engineering, Technological Institute of Morelia https://orcid.org/0000-0002-0833-5935

Keywords:

Short-term hydrothermal scheduling, nonlinear scheduling, economic dispatch, energy storage systems, variable renewables

Abstract

Short-Term Hydrothermal Scheduling (STHS) is a very complex, multimodal, nonlinear optimization problem that has primarily been addressed by conventional and, more recently, metaheuristic optimization algorithms. The objective of conventional STHS is to optimize the hourly energy production of hydroelectric power plants and other generation sources over a specific period of time, allowing for the determination of the optimal economic operation of the Power Electrical System (PES).  The conventional STHS formulation is widely used in the planning, analysis and operation of PES. However, nowadays PES incorporate variable renewable generation such as wind and solar photovoltaic power, as well as Energy Storage Systems (ESS), transmission grid models and load shedding scenarios in case of possible operational contingencies. This paper presents a STHS formulated and simulated using nonlinear programming for a day ahead, using artificial neural networks (ANN) for demand forecasting. The integration of wind and solar photovoltaic generation, ESS and cascaded hydroelectric power plants is considered, along with the transmission grid and load shedding models, all within a single optimization problem. The objective is to minimize generation costs and optimize power usage, dispatching the units in the most efficient manner. The efficient assignment of thermal, hydro, solar, wind units and ESS allows for optimal use of available water without exceeding reservoir limits. The formulation is validated using the IEEE 30-node system, obtaining optimal solutions in all scenarios, without the need to relax system constraints for convergence.

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Author Biographies

Alfonso Vazquez Mendoza, Graduate Program and Research in Electrical Engineering, Technological Institute of Morelia

Alfonso Vázquez Mendoza received his B.Sc. in Electrical Engineering from Technological Institute of Morelia, Mexico. He obtained his M.Sc. degree in 2023 from the same institution and is currently a Ph.D. student there. He is working in the Western Regional Transmission Management, CFE – Transmission and is an IEEE Member. His areas of interest include power systems analysis, electricity markets, supervisory control, and automation of electrical power systems.

Héctor Francisco Ruiz Paredes, Graduate Program and Research in Electrical Engineering, Technological Institute of Morelia

Héctor F. Ruiz Paredes received his B.Sc. in Electrical Engineering from the Technological Institute of Morelia, Mexico. He obtained his M.Sc. degree in 1977 from the Technological Institute and Superior Studies of Monterrey and his Ph.D. in 1992 from the University of Manchester Institute      of    Science      and Technology, U.K. He is a professor and researcher at the Technological Institute of Morelia. Dr. Ruiz is an IEEE Life Senior Member and a member of the National System of Researchers in Mexico. His research interests include the analysis of power and distribution systems.

 

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Published

2025-06-26

How to Cite

Vazquez Mendoza, A., & Ruiz Paredes, H. F. (2025). Short-Term Day-Ahead Hydrothermal Scheduling with Energy Renewables Variable, Storage, Load Shedding using Artificial Intelligence Techniques for Demand Forecasting. IEEE Latin America Transactions, 23(8), 696–705. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9632

Issue

Vol. 23 No. 8 (2025): Special Issue on Technology Megatrends

Section

Electric Energy