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

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.