Anderson Rodrigo de Queiroz, Ph.D.

Anderson Rodrigo de Queiroz, Ph.D.

Associate Professor in the CCEE Department at NCSU. Adjunct Professor at NCCU. Interested in data-driven optimization, analytics, and sustainable systems.

Assessing the Risk of Hurricane Damage to Marine Hydrokinetic Devices

May 11, 2022

Conference proceedings talk, NC Renewable Ocean Energy Symposium, Wanchese, NC

In this talk we propose the use of data analytics and mechanical model simulations to construct fragility curve estimates for ocean current devices. We model fragility curves using Bayesian statistics and these will be incorporated into our NC power system capacity expansion model to assess impacts of hurricane conditions on this technology deployment.

Machine Learning for Electricity Price Forecasting in Brazil

August 04, 2020

Conference proceedings talk, IEEE Power Engineering Society General Meeting, Online, USA

In this talk we present the use of Machine Learning methods applied to electricity price forecasting in the Brazilian electric power system. A combination of artificial neural network models to generate water inflows at the hydropower plants reservoir are used in connection of optimization models used to define prices in the market.

Data Analytics to Improve Wind and Hydro Coordination under the Threat of Climate Change

October 30, 2019

Conference proceedings talk, UNC Biostatistcs 70th Anniversary, Durham - NC, USA

In this talk we present a analytical approach to investigate wind-hydro-thermal coordination in the Brazilian electric power system under the threat of climate chage. This talk was given in the session “Recent innovations for topics in Biostatistics and Data Science”

An Overview About the Brazilian Electricity Market: Optimization and Pricing

April 11, 2019

Presentation at the Nicholas School of the Environment, Duke University, Durham - NC, USA

In this talk we present an overview of the Brazilian Electric Power System. Coordination and optimization problems of power generation resources are discussed as well as the challenges associated with pricing formation in the electricity market.

Value Streams for Utility Scale Storage Projects for Providing Generation Adequacy Services

November 15, 2018

Conference, Center for Advanced Power Engineering Research (CAPER), Charleston - SC, USA

In this talk we present value streams for utility scale energy storage projects in the state of North Carolina. This is part of the NC Energy Storage Study which was mandated through the NC General Assembly’s authorization language from HB 589 (2017) (see Part XII, Section 12). The NC Policy Collaboratory selected NC State to conduct the study.

Data Analytics to Improve Wind and Hydro Coordination under the Threat of Climate Change

August 08, 2018

Conference proceedings talk, IEEE Power Engineering Society General Meeting, Portland - OR, USA

In this talk we present a analytical approach to investigate wind-hydro-thermal coordination in the Brazilian electric power system under the threat of climate chage. This talk was given in the session “Big Data Analytics” of the Power Engineering Economics Subcommittee.

The Value of Stochastic Programming for Energy Systems Planning

November 01, 2016

Conference proceedings talk, INFORMS Annual Conference, Nashville - TN, USA

Energy system models should reflect the reality that planners must make decisions prior to the realization of future uncertainties. Multi-stage stochastic programs, which embed uncertainty in the decision process, optimize over future possibilities to yield a near-term decision strategy. We use the expected value of perfect information and the value of the stochastic solution as metrics to quantify the value of such strategies for long-term capacity expansion of energy systems.

On the Solution Quality Assessment in Multi-stage Stochastic Optimization Under Different Model Representations

June 27, 2016

Conference proceedings talk, XIV International Conference on Stochastic Programming, Buzios - RJ, Brazil

In this talk we discuss the idea behind the classical hydro-thermal scheduling problem (HTSP) with different model formulations. We describe a Sampling-based Decomposition Algorithm (SBDA) and apply it to approximately solve multi-stage stochastic programs versions of the HTSP. In this case, it is important to assess the solution quality that can be obtained from the resulting policy applied to out-of-sample paths and scenario trees.

Stochastic Power Generation Scheduling Using TEMOA

June 27, 2016

Conference proceedings talk, INFORMS Optimization Conference, Princeton - NJ, USA

In this talk we discuss new model capabilities for Tools for Energy Model Optimization Analysis - TEMOA. These new capabilities allows the model to be simulated considering a short-term horizon representation in a power generation scheduling scheme. Moreover, we discuss the a stochastic optimization formulation for the model.

Analysis of Multiple Optimal Solutions in the Firm Energy Rights Computation (in Portuguese)

August 26, 2015

Conference proceedings talk, Brazilian Symposium of Operations Research, Porto de Galinhas - PE, Brazil

In this talk we investigate the definition of firm energy rights of hydro power plants in Brazil. The firm energy values influence directly in the remuneration of the Brazilian hydro plants, besides that, linear optimization models are widely used in the literature for studies of firm energy. In this work, the authors show that the linear optimization models used in the calculus of firm energy rights can present multiple optimal solutions, more specifically, that it is possible to solve the problem to optimality and find different individual firm energy values, this behavior is not desirable in the firm energy models since this parameter impacts directly in the remuneration of the hydro plants.

Investment Decision Analysis for Retrofit of Hydropower Plants Considering Climate Change Scenarios (in Portuguese)

August 26, 2015

Conference proceedings talk, Brazilian Symposium of Operations Research, Porto de Galinhas - PE, Brazil

This work presents an analysis of the alternative for motorization and retrofit of hydroelectric plants considering the effects of climate change in Brazil. Candidate hydroelectric plants to be motorized or pass through retrofit are identified and simulations of hydrothermal dispatch are performed considering the motorization of these plants. The work seeks to analyze the results obtained in the simulations and identify the best investment alternative for an investor.

Solution Quality and Jackknife Estimators in Large Scale Hydroelectric Scheduling

November 08, 2014

Conference proceedings talk, INFORMS Annual Conference, San Francisco - CA, USA

In stochastic programs the assessment of the solution quality is vital, especially when the size of the problem is so large that cannot be solved to optimality. We model a hydroelectric scheduling problem as a large-scale multi-stage stochastic linear program. We solve this problem via a sampling-based decomposition algorithm. We use Jackknife estimators for bias reduction when estimating the optimal value of the stochastic program.

Mathematical Decomposition and Solution Quality in Multi-stage Hydrothermal Scheduling

November 05, 2014

Conference proceedings talk, Workshop of Optimization Under Uncertainty: Energy, Transportation and Natural Resources - UC Davis, Davis - CA, USA

In this talk, we present the Hydrothermal scheduling problem as a Multi-stage Stochastic Program. Sampling-based decomposition algorithms and Solution Quality Evaluation in Multistage Stochastic Programs are discussed.

Effects of Wind Penetration in the Scheduling of a Hydro-Dominant Power System

July 20, 2014

Conference proceedings talk, IEEE Power Engineering Society General Meeting, National Harbor - MD, USA

This talk presents a computational model that is able to determine the optimal economic generation scheduling considering decisions in a system with hydro, thermal and wind power plants. The algorithm is based on the class of sampling-based decomposition algorithms used to solve large-scale multi-stage stochastic optimization problems. A case study composed by several simulation runs of the model is presented and the results about wind power effects in the scheduling of power generators are discussed. The model and the solution strategy based on Stochastic Dual Dynamic Programming is implemented in Python and Pyomo.

Energy Supply Risk Due to Selling Over the Physical Generation Capacity

October 14, 2012

Conference proceedings talk, INFORMS Annual Conference, Phoenix - AR, USA

In Brazil the electricity market agents have to present full physical generation coverage. In other words, only the generator’s assured energy can be negotiated. This work explores the possibility of financial leverage in terms of energy by allowing the negotiation of contracts that extrapolates the physical capacity. Short-term price simulations are used to value contracts and to study risk. The procedure gives more flexibility and liquidity to the market amplifying the business possibilities.

On a Sampling-based Decomposition Algorithm Applied to Hydrothermal Scheduling: Solution Quality and Bounds

November 16, 2011

Conference proceedings talk, INFORMS Annual Conference, Charlotte - NC, USA

In this talk, we present a sampling-based decomposition algorithm (SBDA) to be applied to solve the multi-stage stochastic optimization problem version of the hydro-scheduling problem. We also present a procedure to assess the solution quality with respect to the true problem in a multi-stage setting.

On a Sampling-based Decomposition Algorithm Under Aggregate Interstage Dependency Model

November 13, 2011

Conference proceedings talk, INFORMS Annual Conference, Charlotte - NC, USA

In this talk, we presented an extension of the cut-sharing procedure to deal with aggregate interstage dependency models in multi-stage stochastic programs. We perform a computational study of the stochastic hydro-thermal scheduling problem in order to analyze the computational efficiency of both formulations as the problem size scales large. We also present a parallel programming version of a Sampling-based decomposition algorithm used to solve these multi-stage stochastic programs.

Thermal Generation Investment Analysis Using Decision Tools

July 28, 2011

Conference proceedings talk, IEEE Power Engineering Society General Meeting, Detroit - MI, USA

In this talk we present an investment problem where a decision maker from a company has to decide on the best among four possible alternatives of power supply. This work combines decision analysis tools, as the influence diagram and decision tree, with investment analysis to help the decision maker to select the best supply alternative for the company.

Hydroelectric Scheduling: Inflow Forecasting and Parallel Decomposition

November 08, 2010

Conference proceedings talk, INFORMS Annual Conference, Austin - TX, USA

In this talk, we discuss the hydro-thermal scheduling problem as a mathematical program. The stochastic process that governs the water inflows at the reservoirs of the hydropower plants is presented. A parallel representation of a Sampling-based decomposition algorithm is presented and future research directions are discussed.

Determining the Optimal Transmission System Usage Contracts for a Distribution Company

July 29, 2010

Conference proceedings talk, IEEE Power Engineering Society General Meeting, Detroit - MI, USA

In this talk we propose a methodology to set the transmission system usage contracts at each connection point between electricity distribution networks and the transmission grid. The proposed method can help the decision makers to find the best way to define contracts based on a risk neutral approach. A numerical example for a Brazilian distribution company is discussed.