Energy companies and governments have considered alternative solutions over traditional planning problem where electricity, natural gas, oil are treated as an independent problem. The optimization and integration of the energy sources and demands in a common framework is one of the current challenges. New methodologies and tools for system planning and operation that include multiple energy carriers with sufficient topological details are being developed. This paper proposes a method that integrates the natural gas and electricity systems in with the objective of study their expansion together. The natural gas network model includes the natural gas well and pipelines whereas the electrical systems the hydrothermal power generation and transmission systems. The paper describes the main equations associated to the energy transformation between natural gas and electrical energy which links the two structures. A mathematical model of this problem is formulated as a multistage m optimization problem where the objective function is to minimize the integrated gas-electricity investment and operation costs. A simplified example with the Brazilian system is used for testing the proposed methodology.

This paper integrates the natural gas and hydrothermal systems in terms of power and gas optimal short- term operations planning and unit commitment. In this paper, the natural gas network model including storages and pipelines are integrated with hydrothermal systems to optimize short-term operation of both systems simultaneously. The proposed model considers the constraints at the hydrothermal system, natural gas extraction, natural gas storage operation and pipeline. The proposed model results in a large-scale mixed-integer nonlinear programming problem where the objective function is to minimize the integrated gas-electricity system operation costs. To solve the integrated electricity-gas optimal short-term planning problem an approach based on Dual Decomposition, Lagrangian Relaxation and Dynamic Programming is employed. A didactic integrated hydrothermal and gas system case study is presented. The proposed model shows the importance of the integration of the two systems in terms of operation, planning, security and reliability.

The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience.