Davi Michel Valladão

• D.Sc.
• Professor at Pontifical Catholic University of Rio de Janeiro

Hydroelectricity accounted for roughly 66% of the total generation in Brazil in 2023 and addressed most of the intermittency of wind and solar generation. Thus, one of the most important steps in the operation planning of this country is the forecast of the natural inflow energy (NIE) time series, an approximation of the energetic value of the wate...

Time series analysis by state-space models is widely used in forecasting and extracting unobservable components like level, slope, and seasonality, along with explanatory variables. However, their reliance on traditional Kalman filtering frequently hampers their effectiveness, primarily due to Gaussian assumptions and the absence of efficient subse...

Sequential Decision Making under Uncertainty (SDMU) is ubiquitous in many domains such as energy, finance, and supply chains. Some SDMU applications are naturally modeled as Multistage Stochastic Optimization Problems (MSPs), but the resulting optimizations are notoriously challenging from a computational standpoint. Under assumptions of convexity...

PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally robust optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written a...

O presente trabalho analisa os impactos de um mecanismo de liquidação dupla no mercado elétrico brasileiro, com uma liquidação com base em previsões do dia anterior e outra com base no despacho real -, a partir de uma configuração realista do sistema em 2030. De modo a representar a incerteza na geração renovável, são emulados erros de previsão, in...

Two-stage stochastic programming is a mathematical framework widely used in real-life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision makers make use of numerical methods to obtain a near-optimal solutio...

We propose a novel semi-parametric structural model to estimate the electricity forward curves based on elementary forward prices. The proposed model (i) explores the nonarbitrage relations between contracts with overlapping delivery periods, (ii) considers a parametric structure for price seasonality and exogenous variables, and (iii) uses non-par...

Black and Litterman proposed a portfolio selection model that blends investor’s views on asset returns with market equilibrium concepts to construct optimal portfolios. However, the model efficiency relies on the performance of investors’ views regarding tradable assets, which is challenging in practice. Venturing to improve Black-Litterman practic...

The number of new Covid-19 cases is still high in several countries, despite the vaccination of the population. A number of countries are experiencing new and worse waves. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental importance. We propose a simple statistical method for s...

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-...

We study the optimal corporate policy of a risk-averse shareholder under leverage-dependent borrowing costs and other financial frictions. The firm’s objective is to maximize the risk-adjusted shareholder value by co-optimizing investment, dividend, and debt policies considering endogenous (leverage-dependent) leveraging costs, tax shield, as well...

The method of continuous gas lift has been commonly used in the oil industry to enhance production. Existing optimization models consider an approximate performance curve anchored by production test data, often disregarding reservoir uncertainty. We propose a robust optimization model that jointly considers the most recent data and an uncertainty s...

The sustainable utilization of hydro energy highly relies on accurate estimates of the opportunity cost of the water. This value is calculated through long-term hydrothermal dispatch problems (LTHDP), and the recent literature has raised awareness about the consequences of modeling simplifications in these problems. The inaccurate representation of...

The sustainable utilization of hydro energy highly relies on accurate estimates of the opportunity cost of the water. This value is calculated through long-term hydrothermal dispatch problems (LTHDP), and the recent literature has raised awareness about the consequences of modeling simplifications in these problems. The inaccurate representation of...

We study decomposition methods for two-stage data-driven Wasserstein-based DROs with right-hand-sided uncertainty and rectangular support. We propose a novel finite reformulation that explores the rectangular uncertainty support to develop and test five new different decomposition schemes: Column-Constraint Generation, Single-cut and Multi-cut Bend...

Hydropower is one of the world’s primary renewable energy sources whose usage has profound economic, environmental, and social impacts. We focus on the dispatch of generating units and the storage policy of hydro resources. In this context, an accurate assessment of the water opportunity-cost is crucial for driving the sustainable use of this scarc...

HydroPowerModels.jl is a Julia package for solving multistage, steady-state, hydro-dominated, power network optimization problems with stochastic dual dynamic programming (SDDP). Our state-of-the-art open source tool is flexible enough for practitioners in the electrical sector to test new ideas in an efficient way. This tool was made possible by t...

Video: https://www.youtube.com/watch?v=xUpX-k0oZmo&feature=emb_title. Description: Planning the operation of Power Systems is an important task to guarantee low operational costs and reliability. In practice, model simplifications are used given problem complexity. The objective of this work is to propose a framework, comprised of a methodology and...

The Brazilian natural gas sector is currently characterized by low maturity and dynamism of the market. The stochastic behavior of the demand for natural gas added to its associated market price volatility motivates the usage of underground storage to provide supply flexibility and protection against price fluctuations. However, the existing litera...

The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers -- i...

Continuous gas lift is a popular method to enhance productivity in offshore oil platforms. We propose a steady-state two-stage stochastic programming model to maximize production, where the first-stage injection level determines the production potential, while recourse actions ensure capacity and platform constraints for each uncertainty realizatio...

This paper proposes a methodology for evaluating investment decisions in waterway terminals, with the purpose of reducing logistics’ costs. Based on a Brazilian terminal, in order to reduce excessive costs of demurrage, a project is evaluated through the Real Options Theory. The case study involves statistical data collection and interviews.

Oil refineries are complex projects subject to uncertainties. Given that Brazil is an oil product importer, this work provides an investment analysis of a refinery considering managerial flexibilities. Crack spread and exchange rate are modeled as stochastic processes and the deferral and shutdown options are evaluated.

Dynamic portfolio optimization has a vast literature exploring different simplifications by virtue of computational tractability of the problem. Previous works provide solution methods considering unrealistic assumptions, such as no transactional costs, small number of assets, specific choices of utility functions and oversimplified price dynamics....

Goal: The objective of this article is twofold: (i) analyze the investment in a new refinery in Brazil and identify the optimal moment to invest; and (ii) model the crack spread adjusted to the Brazilian market. Design / Methodology / Approach: The main uncertainties given by the crack spread and the foreign exchange rate were modeled as a continu...

In defined contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk–return profiles. In this article, we challenge this approach and show that such funds can exhibit erratic risk–return profiles that deviate significantly fr...

A feasible policy is a decision rule that delivers implementable actions for every state of a system. Stochastic dual dynamic programming (SDDP) is a powerful decomposition method largely used to solve multistage stochastic problems in power system applications. The current SDDP implementations rely on a one-step-ahead anticipative process called h...

L Simpósio Brasileiro de Pesquisa Operacional. Campinas : GALOÁ. 2018. Disponível em: <https://proceedings.science/sbpo/papers/selecao-de-carteira-de-contratos-futuros-via-otimizacao-robusta-direcionado-por-dados>. RESUMO Modelos de otimização robusta para seleção de ativos são amplamente apresentados na literatura financeira. No entanto, eles ger...

We propose an investment strategy based on the Black-Litterman model with conditional information. We present how observed price-earnings ratio and past returns can be used to determine 1-step ahead returns, considering investors with different risk profiles. The provided approach updates the conditional probability distribution of asset returns an...

In de�ned contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk-return profiles. In this article, we challenge this approach and show that such funds can exhibit erratic risk-return profiles that deviate significantly fro...

Open private pension schemes are subject to risk-based regulation. In this context, asset and liability management (ALM) frameworks for pension plan operators are increasingly based on multistage stochastic programming (MSP). The significant advances in MSP modelling notwithstanding, previous works ignore risk-based regulatory constraints such as t...

One of the most used methods for long-term hydrothermal operation planning is the Stochastic Dual Dynamic Programming (SDDP). Using this method, the immediate and future water opportunity cost can be balanced and an economicdispatch policy defined for multiple reservoirs under inflow uncertainty. In this framework, equipment outages and reserve del...

The current state-of-the-art method used for medium- and long-term planning studies of hydrothermal power system operation is the stochastic dual dynamic programming (SDDP) algorithm. The computational savings provided by this method notwithstanding, it still relies on major system simplifications to achieve acceptable performances in practical app...

The literature of portfolio optimization is extensive and covers several important aspects of the asset allocation problem. However, previous works consider simplified linear borrowing cost functions that leads to suboptimal allocations. This paper aims at efficiently solving the leveraged portfolio selection problem with a thorough borrowing cost...

In the hydrothermal energy operation planning of Brazil and other hydro-dependent countries, Stochastic Dual Dynamic Programming (SDDP) computes a risk-averse optimal policy that often considers river-inflow autoregressive models. In practical applications, these models induce an undesirable variability of primal (thermal generation) and dual (marg...

Robust portfolio optimization models widely presented in the financial literature usually assume that asset returns lie in a parametric uncertainty set with a controlled level of conservatism expressed in terms of the variability of the uncertain parameters. In practice however, it is not clear how investors should choose the conservatism parameter...

Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programming model that optimizes bond issuance by minimizing the mean funding cost while keeping leverage under control and insolvency risk at...

This paper aims at resolving a major obstacle to practical usage of time-consistent risk-averse decision models. The recursive objective function, generally used to ensure time consistency, is complex and has no clear/direct interpretation. Practitioners rather choose a simpler and more intuitive formulation, even though it may lead to a time incon...

The economical performance of an oil eld operation is uncertain and highly influenced by strategic and operational decisions variables such as well placement, scheduling and control. Based on numerically intensive reservoir simulators, the evaluation of an extensive list of possible decisions across all possible realizations becomes computationally...

This paper proposes an Asset Liability Management (ALM) multistage stochastic programming model and a new method for measuring and controlling the equilibrium risk of a pension fund in the Brazilian context. According to the Society of Actuaries, ALM can be defined as an ongoing financial management process of formulating, implementing, monitoring,...

This paper proposes an Asset and Liability Management (ALM) for pen- sion funds via multistage stochastic programming and an equilibrium risk measuring method. The ALM of a pension fund consists in finding the optimal investment policy given the stochastic nature of the asset returns and the liability cash flows. Since it refers to a dynamic portfo...