arXiv subjects

Tiago Andrade

Publications and source records attributed to Tiago Andrade.

8 recordsLinked to original sources

arXiv2026

Optimization of Closed-Loop Pumped-Storage Hydropower Siting

Accelerating variable renewable energy integration introduces substantial operational challenges to modern power grids. Frequent curtailment of surplus renewable generation highlights the pressing need for long-duration energy storage capable of shifting generation to high-demand periods while providing essential ancillary services and grid inertia. Socio-environmental constraints increasingly limit conventional hydropower development; however, closed-loop pumped-storage hydropower (PSH) presents a viable alternative because its off-river reservoirs avoid natural watercourses. This paper introduces HERA-S, a computational modeling framework developed by PSR (supported by EDF, CTG, Brookfield, and Rio Light) to streamline and standardize regional PSH site prospecting. HERA-S automates spatial screening, dam optimization, cost estimation, and socio-environmental impact scoring. Applied to a case study in Rio de Janeiro, Brazil, the framework employs an integer programming model to optimize dam-fill and excavation mass balances against inter-reservoir distance, significantly improving pre-feasibility planning agility.

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arXiv2025

Penalty-Free SDDP: Feasibility Cuts for Robust Multi-Stage Stochastic Optimization in Energy Planning

Multi-stage decision problems under uncertainty can be efficiently solved with the Stochastic Dual Dynamic Programming (SDDP) algorithm. However, traditional implementations require all stage problems to be feasible. Feasibility is usually enforced by adding slack variables and penalizing them in the objective function, a process that depends on case-specific calibration and often distorts the economic interpretation of results. This paper proposes the Penalty-Free SDDP, an extension that introduces a Future Feasibility Function alongside the traditional Future Cost Function. The new recursion handles infeasibilities automatically, distinguishing between temporary and truly infeasible cases, and propagates feasibility information across stages through dedicated feasibility cuts. The approach was validated in a large-scale deterministic case inspired by the Brazilian hydrothermal system, achieving equivalent feasibility to the benchmark solution while eliminating miscalibrated artificial penalties. Results confirm its robustness and practicality as a foundation for future stochastic, multi-stage applications.

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arXiv2025

On the tightness of linear relaxations of alternative mixed integer programming formulations for the generator maintenance scheduling problem

This paper presents a comprehensive theoretical analysis of six distinct Mixed-Integer Programming (MIP) formulations for preventive Generator Maintenance Scheduling (GMS), a critical problem for ensuring the reliability and efficiency of power systems. By comparing the tightness of their linear relaxations, we identify which formulations offer superior dual bound and, thus, better computational performance. Our analysis includes establishing relationships between the formulations through definitions, lemmas, and propositions, demonstrating that some formulations provide tighter relaxations that lead to more efficient optimization outcomes. These findings offer valuable insights for practitioners and researchers in selecting the most effective models to enhance the scheduling process of preventive generator maintenance.

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arXiv2023

QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization

We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of optimization problems in JuMP, Julia's mathematical programming package, for straightforward application in many physics and physics-inspired solution methods whose standard model form is equivalent to QUBO. These methods include quantum annealing, quantum gate-circuit optimization algorithms (Quantum Optimization Alternating Ansatz, Variational Quantum Eigensolver), other hardware-accelerated platforms, such as Coherent Ising Machines and Simulated Bifurcation Machines, and more traditional methods such as simulated annealing. In addition to working with reformulations, QUBO.jl allows its users to interface with the aforementioned hardware, sending QUBO models to these devices and retrieving results for subsequent analysis. QUBO.jl was written as a JuMP / MathOptInterface (MOI) layer that automatically maps between the input and output frames, thus providing a smooth modeling experience.

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arXiv2021

An Integrated Progressive Hedging and Benders Decomposition with Multiple Master Method to Solve the Brazilian Generation Expansion Problem

This paper exploits the decomposition structure of the large-scale hydrothermal generation expansion planning problem with an integrated modified Benders Decomposition and Progressive Hedging approach. We consider detailed and realistic data from the Brazilian power system to represent hourly chronological constraints based on typical days per month and year. Also, we represent the multistage stochastic nature of the optimal hydrothermal operational policy through co-optimized linear decision rules for individual reservoirs. Therefore, we ensure investment decisions compatible with a nonanticipative (implementable) operational policy. To solve the large-scale optimization problem, we propose an improved Benders Decomposition method with multiple instances of the master problem, each of which strengthened by primal cuts and new Benders cuts generated by each master's trial solution. Additionally, our new approach allows using Progressive Hedging penalization terms for accelerating the convergence of the method. We show that our method is 60\% faster than the benchmark. Finally, the consideration of a nonanticipative operational policy can save 7.64\% of the total cost (16.18\% of the investment costs) and significantly improve spot price profiles.

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arXiv2020

An integer programming model for the selection of pumped-hydro storage projects

Energy storage systems - in particular, Pumped Hydropower Storage (PHS) - will be increasingly important to support the transition of power systems toward zero emissions. The reason is that PHS can mitigate the variability and uncertainty of renewable energy production from solar and wind power to balance electricity demand with supply. In this paper, we propose an integer programming problem for PHS siting that uses a Digital Elevation Model (DEM) to meet an energy storage requirement. It assumes the existence of a reservoir, lake, or river, and decides where to build a reservoir that will constitute the PHS with the existing body of water. This model finds minimum-cost project candidates given parameters such as desired head, power, and operation time. The paper discusses different solution methods to assure reservoir closure and avoid its fragmentation. A heuristic explores the representations of the DEM, from more aggregate to more precise, to sequentially refine the solution based on the last selected site, which reduces computational effort. The formulation is general and the objective function includes both construction and equipment costs. Constraints are related to the energy storage target and reservoir closure based on the DEM. We illustrate the methodology by selecting multiple PHS projects next to the reservoir of the Sobradinho hydropower plant in Brazil. The result of this model can be seen as a bottom-up step that prepares PHS candidate projects to be considered by an integrated resource planning model in a top-down step, that would select from these shortlisted projects.

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arXiv2020

Application of Progressive Hedging to Var Expansion Planning Under Uncertainty

This paper describes the application of a Progressive Hedging (PH) algorithm to the least-cost var planning under uncertainty. The method PH is a scenario-based decomposition technique for solving stochastic programs, i.e., it decomposes a large scale stochastic problem into s deterministic subproblems and couples the decision from the s subproblems to form a solution for the original stochastic problem. The effectiveness and computational performance of the proposed methodology will be illustrated with var planning studies for the IEEE 24-bus system (5 operating scenarios), the 200-bus Bolivian system (1,152 operating scenarios) and the 1,600-bus Colombian system (180 scenarios).

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arXiv2020

A robust optimization and an area decomposition approach to large-scale network planning under uncertainty

This paper describes an area decomposition and a robust optimization strategy for the planning of large-scale transmission networks considering uncertainty in variable renewable energy (VRE) and hydropower generation. The strategy is illustrated with a study of Brazil's power grid expansion from 2027 to 2036. The country's network has over 10 thousand buses and 14 thousand circuits and covers an area equivalent to the USA. A clustering scheme based on bus marginal costs is used to decompose the grid into seven regions. Each regional planning is then solved by a heuristic and Benders decomposition methods to produce a robust least-cost plan for 3,000 dispatch scenarios.

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