Trade-off between computation time and solution quality for integrated generation and transmission expansion planning with N-1 security criterion
The generation and transmission (G&T) expansion planning of large-scale systems is usually carried out hierarchically due to the high complexity of the problem. However, this hierarchical plan may be more expensive than a fully integrated (co-optimized) G&T plan that, on the other hand, requires high computation time. Therefore, the trade-off between computation time and solution quality is of great importance, especially with the integration of renewable generation. This paper proposes and assesses alternative formulations of the integrated G&T planning problem, also considering the system operation simulation under the N-1 security criterion, seeking to balance solution optimality and computational effort. The assessments are illustrated for the Chilean electrical system. The main outcome is that one of the proposed methods, in which the future cost function is maintained fixed during the generation and transmission optimization and is recalculated only in the final simulation of the system operation, achieves results very close to the fully integrated generation, transmission, and operation optimization method. This method presents a cost reduction of 8 % compared to a hierarchical approach, which represents savings of around 700 million dollars, and 50 % less computation time compared to the fully integrated method. For the same proposed method, the preliminary calculation of an optimal solution without applying the N-1 security constraint as a starting point, followed by re-optimization with active N-1 security constraints, contributes to a 65 % reduction of the computation time without significantly impacting the quality of the solution.
Integrated Spatiotemporal Life Cycle Assessment Framework for Hydroelectric Power Generation in Brazil
This study proposes and empirically validates a spatiotemporal life cycle assessment (LCA) framework for hydroelectric power generation applied to the Sinop Hydroelectric Power Plant in Brazil. Unlike conventional LCA, which assumes spatial and temporal homogeneity, the framework incorporates annual temporal discretisation and geographically differentiated impacts across all phases of assessment. The methodology combines the Enhanced Structural Path Analysis (ESPA) method with temporal modeling and region-specific inventory data. The results indicate that environmental impacts peak in the fourth year of the ‘Construction and Assembly’ stage, primarily due to the intensive production of concrete and steel. A spatial analysis shows that these impacts extend beyond Brazil, with notable contributions from international supply chains. By identifying temporal and geographical hotspots, the framework offers a refined understanding of impact dynamics and drivers. Uncertainty analysis further demonstrates that temporal discretisation significantly affects impact attribution, with the ‘Construction and Assembly’ stage results varying by up to ±15%, depending on scheduling assumptions. Overall, the study advances the LCA methodology while offering robust empirical evidence to guide sustainable decision-making in Brazil’s power sector and to inform global debates on low-carbon energy transitions.
From fields to fuel: analyzing the global economic and emissions potential of agricultural pellets, informed by a case study
QARBoM.jl: a framework for Classical and Quantum-Assisted Training of Restricted Boltzmann Machines
This thesis presents QARBoM.jl, a platform for benchmarking quantumassisted against classical training of Restricted Boltzmann Machines (RBMs). Recent works have been testing the training of RBMs using quantum sampling techniques, such as Quantum Annealing, and comparing their results against classical methods. However, these projects are mainly limited to a specific dataset and only one RBM classical training procedure to compare. With that said, QARBoM.jl establishes an agnostic benchmarking framework where, with minor code adjustments, one can select over different training algorithms
(classical or quantum-assisted) and parameters to model integer or real-valued datasets, expediting the research endeavor on the applications of
Quantum Computing for RBMs.
Assessing power system reliability under temperature-dependent variables – A case study of the Brazilian Power System
This work focuses on explicitly representing key variables of the power sector under temperature-dependent conditions, with particular emphasis on electricity demand and generator availability. By jointly modeling these two critical dimensions, the thesis seeks to evaluate the reliability of the Brazilian National Interconnected System (SIN) under stress conditions, particularly in the context of growing exposure to climatic extremes.
The SIN, coordinated by the Independent System Operator (ONS), supplies nearly the entire country, except for the state of Roraima, which is still electrically isolated but expected to be interconnected by the end of 2025. The system is divided into four major subsystems (SE/CO, S, NE, N), each with distinct socioeconomic, climatic, and load characteristics. The relevance of demand forecasting and generator availability analysis lies not only in their role for short-term operations, but also in medium- and long-term planning, capacity expansion, and market mechanisms. Currently, the country is discussing the implementation of capacity auctions, which will seek to contract firm capacity, mostly from thermal plants, to meet demand at critical times. Incorporating temperature into these variables is particularly critical in Brazil, where heat waves and seasonal variability significantly affect both consumption patterns and thermal unit performance.
To address this challenge, a comprehensive and historically consistent database was compiled. For demand, semi-hourly records from the ONS API were compiled and complemented by the identification of critical daily peaks. For climate, hourly records of temperature from the National Institute of Meteorology (INMET) were collected, consolidated, and treated with statistical methods, seeking to solve the missing data set and apply a clustering process to select representative weather stations for each subsystem, ensuring both spatial and climatic diversity.
On the demand modeling side, two families of methods were applied. Econometric regression (OLS) and time-series models (ARIMAX and SARIMAX) were used to forecast monthly demand and critical daily profiles, incorporating economic indicators, temperature-based variables such as Heating and Cooling Degree Days, and seasonal harmonics. Variable selection was performed using backward elimination and LASSO regularization to enhance interpretability and robustness. Finally, Monthly dummies variables and an alternative with Dynamic Harmonic Regression was applied to represent the seasonality. A stochastic scenario generation procedure was implemented through a double bootstrap of historical climate data, preserving temporal
correlations across stations and allowing for the inclusion of linear warming trends, in order to provide future temperature scenarios and in this sense analyze the probabilistic distribution of the forecasted demand.
The results demonstrate that explicitly incorporating temperature improves predictive accuracy of demand by reducing RMSE by over 38% on average in the monthly forecast demand process. Also, superior results are obtained when ARIMA models are applied, compared with traditional OLS and with the SARIMA models. In the hourly forecast assessment, the impacts by considering the temperature are clear, with some months presenting differences above 10% for the same hour under different temperature conditions.
Finally, the reliability of the system is assessed under the generated forecast demand for 2028, considering a temperature-dependent outage probability for the thermal generators. For this purpose, taking as a starting point the database made available from the official forecasts from the Brazilian System Operator, the capacity requirement for the thermal fleet is estimated, and subsequently stress tested considering the temperature scenarios. The methodology applies a cut-based approach to the Brazilian interconnected system, accounting for hydroelectric reservoirs with explicit regulation constraints, stochastic renewable generation scenarios built from ERA5 reanalysis data through the TSL platform, and a gamma distribution to represent the aggregated expected forced outage of thermal capacity per subsystem. In total, one hundred inflow and renewable scenarios are combined with one hundred peak demand scenarios, focusing on the critical hours of supply between 5 p.m. and 2 a.m., generating a broad set of stress conditions.
The results confirm that the most critical deficits occur at sunset (5–7 p.m.), when solar falls while demand remains high. Deficits exceed 10 GW and surpass 13 GW in extreme scenarios, while even median cases reveal structural deficits above 5 GW. When thermal failures are modeled as temperature dependent, the capacity requirement rises by about 500 MW, highlighting the systemic risks of ignoring climate dependency. Economically, this additional capacity equates to R$ 412 million per year under current firm capacity benchmarks, rising to nearly R$ 2 billion if contracted under emergency auctions, as occurred in 2021. These findings stress the need to explicitly integrate climate dependencies in planning and underscore the importance of adding flexible capacity capable of supporting the system during sunset peaks.
EFEITO DO DESPACHO POR OFERTA EM SISTEMAS PREDOMINANTEMENTE HIDRELÉTRICOS: PODER DE MERCADO E SEGURANÇA DE SUPRIMENTO NO CONTEXTO COLOMBIANO
O mercado de eletricidade da Colômbia, caracterizado por sua alta dependência de geração hidrelétrica e por uma estrutura liberalizada, é vulnerável à variabilidade hidrológica e a eventos climáticos como El Niño e La Niña. Diferentemente de seus vizinhos na América do Sul, a Colômbia opera um mercado atacadista baseado em ofertas, no qual os preços de eletricidade são determinados a partir das ofertas dos agentes de geração. Essas ofertas incluem quantidades, representando a disponibilidade de geração do agente, e preços, que geralmente representam os custos variáveis de produção das usinas (ou custos de oportunidade para hidrelétricas) somados a um componente de prêmio de risco. No entanto, um dos principais desafios nesses mercados é separar o que de fato corresponde a parcela de custo variável da usina e a margem do agente relacionada a sua percepção
de risco no mercado. Neste contexto, este estudo investiga os potenciais markups (margens) dos agentes no mercado atacadista de eletricidade colombiano por meio de uma análise contrafactual que simula um despacho baseado em custos de geração para um período de dez anos (Jan/2014-
Mar/2024). Ao comparar os preços simulados do despacho por custo com os preços reais de mercado, foi identificado um markup médio que pode ser explicado por diferentes variáveis refletindo as condições do mercado em diferentes momentos. Modelos de regressão com variáveis explicativas de
mercado foram estimados para analisar o comportamento desses markups ao longo do período analisado. Uma primeira conclusão que se pôde extrair desta análise é que os markups dos agentes aumentam em situações de escassez – aplicando um multiplicador mais elevado sobre os preços também elevados associados a momentos de El Niño. Uma segunda conclusão relevante diz respeito a um fato relevante ocorrido durante o evento de El Niño de 2023-24, quando houve um anúncio pelo governo de uma possível regulação de preço teto. Os resultados indicam que, em linha com o esperado pela teoria, a ameaça regulatória reduziu as ofertas de preço dos agentes, destacando o impacto das expectativas dos agentes na formação dos preços de eletricidade. Este estudo contribui para a compreensão da dinâmica de formação de preços spot, do comportamento dos agentes no mercado e
das implicações regulatórias no setor elétrico colombiano, oferecendo insights relevantes para formuladores de políticas em mercados similares, dependentes de geração hidrelétrica e baseados em ofertas.
METODOLOGIA PROBABILÍSTICA E LOCACIONAL PARA A VALORAÇÃO DOS BENEFÍCIOS DA GERAÇÃO DISTRIBUÍDA
Apresenta-se, nesta tese, uma metodologia para analisar potenciais benefícios e impactos sistêmicos da geração distribuída (GD) na rede de distribuição para comparar com os incentivos fornecidos para esses recursos. A metodologia proposta permite capturar os benefícios locacionais e temporais da GD na rede de distribuição e na interface com os sistemas de transmissão. O efeito temporal indica que os benefícios e custos variam ao longo do tempo, dependendo, por exemplo, do portfólio de geração, perfil de demanda e granularidade temporal considerada na simulação. Já o efeito locacional indica que os benefícios da presença de GD podem se relacionar à localização destes na rede de distribuição e na fronteira da transmissão, próximos aos centros de consumo. A metodologia consiste na otimização da expansão da distribuição para avaliar o trade-off entre comprar a energia do sistema de transmissão e utilizar a energia da GD. Ao final, o benefício da geração distribuída é dado pela diferença do custo de expansão e operativo dos sistemas considerando o caso com a GD e o caso sem a GD. Essa abordagem é aplicada em um estudo de caso com geração solar e armazenamento distribuído, com um modelo detalhado da rede do Sistema Interligado Nacional (SIN) brasileiro e o uso de dados da Base de Dados Georreferenciada (BDGD) da distribuidora.
