An efficient hybrid heuristic for the transmission expansion planning under uncertainty
We address the stochastic transmission expansion planning (STEP) problem under uncertainty in renewable generation capacity and demand. STEP’s objective is to minimize total transmission investment and generation costs. To tackle the computational challenges posed by large-scale systems, we propose a heuristic strategy that combines the progressive hedging (PH) algorithm for scenario-wise decomposition with an integrated approach for solving the resulting subproblems. The latter combines a destroy-and-repair operator, a beam search procedure, and a mixed-integer programming solver. The proposed framework is evaluated on large-scale systems from the literature with up to 10,000 nodes, adapted to stochastic scenarios using parameters from the California test system (CATS). Compared with a non-trivial baseline algorithm that includes the same integrated approach, the proposed PH-based method consistently improved solution quality for the six systems considered (including CATS), achieving an average cost improvement of 5.28% within a 2-hour time limit.
Integrated investment and operational planning for sugarcane-based biofuels and bioelectricity under market uncertainty
Sugarcane biomass is a strategic resource for the energy transition, particularly in Brazil, where it underpins electricity and ethanol production. Investment planning is challenged by diverse production pathways, price volatility, and feedstock variability. This work develops a two-stage stochastic optimization model integrating investment and operational decisions for sugarcane facilities. The model aims to support robust planning for diversified biomass plants, aiding the sector’s decarbonization. The first stage defines capacity expansion under economies of scale through a power-law cost function. The second stage defines operational decisions under price and feedstock uncertainty, modeled via scenarios and Conditional Value-at-Risk. From an investor’s perspective, the objective is to minimize risk-adjusted net costs. In addition to its methodological contributions, this work also provides an open-source implementation of the proposed capacity expansion planning framework, referred to as OptBio. A Brazilian case study shows risk-neutral strategies favor sugar/ethanol but are vulnerable, whereas risk-averse strategies promote diversification. Sensitivity analyses indicate biomethane and hydrogen may become viable with favorable prices, while biochar boost productivity and profitability.
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.
