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.

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NCSU students present work on Assessing the Risk of Hurricane Damage to Marine Hydrokinetic Devices at the Ocean Renewable Energy Conference in Portland

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Victor de Faria and Neda Jamaleddin, Ph.D. students at NCSU, presented yesterday our work on Assessing the Risk of Hurricane Damage to Marine Hydrokinetic Devices at Ocean Renewable Energy Conference in Portland. This conferences was sponsored by the University Marine Energy Research Community (UMERC) and the The Marine Energy Technology Symposium (METS). This work shows the developments of the research project led by professor Mo Gabr from NCSU and myself and has been sponsored by the NC Reneable Ocean Energy Program (NCROEP). We used Bayesian analysis and mechanical model simulations in Ansys-Aqwa to create fragility curve estimates for ocean current devices. The picture below shows Victor and Neda meeting with George Bonner (NCROEP program director).

Neda Jamaleddin, George Bonner and Victor de Faria at the Ocean Renewable Energy Conference in Portland

First report of the Open Energy Outlook for the United States is available

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The first report of the Open Energy Outlook (OEO) has been published. I am very happy to see this project milestone been accomplished. Congratulations to the co-PIs Paulina Jaramillo, Joseph DeCarolis (former co-PI now at EIA), Jeremiah Johnson and the whole OEO core team. The OEO is an initiative of Carnegie Mellon University (CMU) and NC State University that aims to examine U.S. energy futures to inform energy and climate policy efforts by applying policy-focused academic modeling. One of the main goals of the OEO is to maximize transparency, and build a networked community. The project has been funded by the Alfred P. Sloan Foundation. The modeling effort is based on Tools for Energy Model Optimization and Analysis developed at NC State University in previous grants. The technical report can be accessed at the CMU website linked here.

Congratulations to Dr. Neha Patankar who joins Binghamton University as Tenure-track Assistant Professor

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One of the best and most bright students that I have worked with during my career was recently hired by the prestigious Binghamton University. Neha Patankar, who I had the privilege to co-mentor with professor Joseph DeCarolis at the Operations Research Graduate program at NC State University, was offered a position to be a tenure-track assistant professor of Systems Science and Industrial Engineering. After her Ph.D. at NC State, Neha work a postdoc for a few years at the Andlinger Center for Energy and the Environment at Princeton University working with professor Jesse Jenkings. An excerpt from Binghamton University webpage: “Neha Patankar’s expertise is in multi-attribute computational modeling and decision-making for energy transition, focusing on the rapidly evolving electricity sector. Her research supports energy policy decisions under deep techno-economic uncertainty, reveals system-wide technology and resource tradeoffs, and evaluates pathways for economy-wide decarbonization.” Dr. Patankar has a bright future ahead of her and Binghamton University and its students are lucky to have her as a faculty that will perform top-notch research and teaching related to decision-making and the energy transition. Congrats again Neha! (click here for Neha’s Linkedin post)

Neha Patankar, Tenure-track Assistant Professor at Binghamton University

Professor Joseph F. DeCarolis named the new EIA Administrator

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Amazing news!!! Professor Joseph F. DeCarolis, NCSU professor at the Civil, Construction and Environmental Engineering Department, is named by the Biden administration the new administrator of the Energy Information Administration (EIA). EIA could not be in better and more prepared hands. Congratulations again my friend and colleague that will help the country substantially towards a brilliant energy future!

Amazing talk from Al Gore on Frontiers Forum

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It was great to see Al Gore present today The Case for Climate Optimism in the Frontiers Forum. Thank you to the amazing speaker and to the Frontiers organizers (Kamila Markram and her team) to have provided a great event for approximately 5000 participants. People are experiencing critical times due to changes in climate and the future is in our hands to make the necessary changes. Towards a cleaner, renewable and sustainable world. Frontiers made available the talk on their youtube channel and it is linked below.

Let me tell you how research ideas connect things and people towards the achievement of goals and development of science

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Back in 2012, after my Ph.D. at The University of Texas at Austin, I joined research projects on Analytics applied to energy systems in Brazil. One of those was related to efficiency analysis of electricity Distribution companies (DISCOs) and methods such the data envelopment analysis. On that topic I started to work with an undergraduate student Giulia Medeiros at Universidade Federal de Itajubá in a collaboration with professors José Wanderley Marangon Lima and Luana Medeiros Marangon Lima. In November of the same year I went back to the US for the INFORMS Annual Meeting in Phoenix AZ where I had a talk about Stochastic Optimization and Energy. After the conference I had the opportunity to go back to Austin and discuss ideas with my former Ph.D. advisor David Morton. At that same week I was able to watch the weekly OR seminar (I remember so many of those as student), where professor Ahti Salo as a guest speaker was talking about a new method that he developed the Ratio-based efficiency analysis (REA). The talk caught my attention and at the end I was deeply interested in the method, I asked a few questions and mentioned the efficiency analysis of electricity DISCOs that we were working on (thinking we potentially could apply). Professor J. Eric Bickel, which was coordinating the seminar series that semester, replied at the time as well and said that this could be a nice way to rank the electric utilities, that helped to increase the brightness of the light bulb :) I came back to Brazil and started to explain the idea to my colleagues and Giulia Medeiros, who quickly accepted the research challenge and started to attempt making the new application of the REA a reality. After lots of time spending studying, training in scientific programming and algebraic modeling in Python and Pyomo, she was able to create efficiency analysis optimization models and extrapolate the analysis of DISCOs for the whole Brazilian power grid. Many things happened along the process, Giulia finished her degree in electrical engineering and her masters in the same field and started to pursue her Ph.D. Recently she achieved her first journal publication in the topic with others manuscripts in preparation. I need to say that I am extremely proud of her, for the hard work, knowledge developed and her courage to explore complex problems. I need to thank each people named here for the things that happened, for the opportunity to interact with them during this process and for sharing knowledge. We thank also CPFL Energia who funded this ANEEL R&D grant. Research is fascinating to me and I hope to develop this feeling and spark lights in the lives of my students as my professors did and still do. imgpaperdea

Methodology to Estimate Capacity Credit of Solar PV and Energy Storage

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It is great to see this paper related to capacity credit of solar PV and energy storage published in the journal Renewable Energy. Congrats everybody!

Artificial Neural Networks for Water Inflow Forecasting - The power of Machine Learning to Real Life Problems

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Approximately 4 years ago, we embarked on a journey around the world of machine learning and artificial neural networks to seek ways where we could achieve improvements associated with the process of forecasting natural water inflows at hydroelectric power plants. Victor A. D. Faria, at that time still a graduate student in the electrical engineering course at the Federal University of Itajubá, and today a Ph.D. at North Carolina State University, accepted the research challenge with me, and professors José Wanderley Marangon Lima and Luana Medeiros Marangon Lima. Since then, a lot has happened and many fruits are being harvested with an unprecedented and important publication in the International Journal of Environmental Science and Technology that deals with water inflow forecasting using multi-layer Perceptrons neural networks for all hydroelectric plants that participate in centralized dispatch in the Brazilian power system. Results obtained in this study point to greater accuracy and precision of the neural network models developed in relation to those obtained by the models in use in the Brazilian electricity sector. This work comes at a very important time for the sector, where concerns associated with droughts, hydrothermal dispatch and energy prices are in the spotlight. In order to plan and make more robust decisions, it is necessary to improve the representation of future uncertainties (water inflows, electricity demand, wind and solar generation, climate/weather, etc). Congratulations Victor for your dedication and hard work and also to everyone else involved in the work for their important contributions. Article link: An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems

Hadi Eshraghi received his Ph.D. at NCSU and is ready to help with our Energy Future

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It has been a great ride and opportunity to work with Hadi Eshraghi during his Ph.D. at NCSU. Hadi is a bright and extremely capable professional that always brings knowledge and commitment to work to be pursued. He developed impactful research at the CCEE department at NCSU and published technical papers in top journals such as Energy and Enviromental Science and Technology (ES&T). I cannot be more proud and thankful for been part of this. Congrats Hadi!

Electricity planning strategies in a conflict-prone environment

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Very happy to see this published at Energy for Sustainable development from Elsevier. This is a fruit of collaboration with Neha Patankar (first author - congrats Neha! She is a Ph.D. candidate in OR at North Carolina State University - NCSU) and professors Joseph DeCarolis (NCSU), Morgan D. Bazilian Colorado School of Mines) and Deb Chattopadhyay (The World Bank). This piece explores electricity planning strategies in a conflict-prone environment. A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is proposed. Our analysis for South Sudan shows that solar photovoltaics can play a critical role in composing the future electric power system for the country. In addition to mitigating greenhouse gas emissions and increasing access to electricity, the analysis suggests that solar energy can be used to hedge against economic losses incurred by conflict. While this piece focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.

Energy Storage Options for North Carolina, Final Report available

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NCSU and NCCU research team drafted a proposal in late 2017 to the NC Policy Collaboratory that outlines our approach to this study. This effort was mandated through the NC General Assembly’s authorization language from HB 589 (2017) (see Part XII, Section 12). The project final report was submitted to the NC General Assembly on December 3 of 2018. In addition to the report, data and models used to perform the analysis are publicly available and can be found at the energy storage study website hosted at NCSU. imgpaper42

Novel energy management frameworks for energy storage devices and distributed solar generation

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I am glad to say that as result of an amazing collaboration work with Ph.D. candidate Faeza Hafiz and professors Iqbal Husain and Poria Fajri two new journal papers were published in the last few days. The first one on the technical journal Applied Energy from Elsevier proposes a novel energy management framework and energy storage sizing for a community composed of multiple houses and distributed solar generation. A multi-stage stochastic program model is designed to minimize community electricity purchase costs and to support decision-making by creating control policies for energy management. The second paper was published in the IEEE Electrification Magazine and talks about challenges and solutions for coordinated control at the residential level in modern power systems with the increasing deployment of solar energy, storage and plug-in electric vehicles. Congrats to my colleagues and especially to Faeza Hafiz (first author in both papers) that is heating up her engines for her Ph.D. defense (12/04/2018) at the FREEDM system center at North Carolina State University. imgpaper42

Climate change impacts in the revenues of hydropower plants in Brazil

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I am glad to see this piece published in the journal Renewable Energy from Elsevier. This is a joint piece with Victor Durães (his 2nd journal collaboration as undergrad) and professors Luana Medeiros Marangon Lima from Duke University and José Wanderley Marangon Lima from Universidade Federal de Itajubá. Here, we analyze potential impacts of climate change in the revenues of hydropower plants. One important input for designing and evaluating investment opportunities in #hydropower is the water inflows historical data. However, such information alone may not project well future power generation due to the influence of climate change in the water inflow patterns. This paper introduces spatio-temporal information of the future climate into the operational planning of the Brazilian power systems. Global climate models from IPCC are considered along with downscaled regional climate models. Results at the individual plant level show the importance of taking into account climate change information when performing hydro generation planning studies.

Contracting wind-photovoltaic projects in power systems, a method to support decision-making in long-term energy auctions

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Happy to see this work focused in contracting wind-photovoltaic projects in power systems been published at Renewable & Sustainable Energy Reviews. This is a joint piece with Giancarlo Aquila (1st author, congrats Gian!), Edson Pamplona, LCS Rocha, PP Balestrassi, P Rotela and Marcelo Nunes Fonseca. Brazil has recently seen a significant raise in the development of wind-solar PV hybrid plants. This work presents a model to assist the government in long-term energy auctions seeking to maximize socioeconomic welfare in the power sector. Multiobjective programming is used to simultaneously handle two conflictive objective functions (maximize reduced emission density and minimize the levelized cost of electricity). The approach uses mixture design of experiments and the normal boundary intersection method to perform the optimization and construct Pareto frontiers for the evaluated projects. Additionally, a metric based on the ratio between entropy and the global percentage error is used to identify the optimal Pareto solution. imgpaper42

Contracting wind-photovoltaic projects in power systems, a method to support decision-making in long-term energy auctions

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What a great day at the IEEE PES GM in Portland OR. Early In the morning we were able to participate in the Panel Session about Big Data Analytics for flexible Electricity Networks, Markets and Prosumers, where I gave a talk about Data Analytics to Improve Wind and Hydro Coordination under the Threat of Climate Change. This was a joint work originated from the master’s research of Nayana Scherner. There were great other talks in this session chaired by prof Tao Hong, such as his in the topic about opportunities for Energy Analytics professionals. Later in the day other great topics were discussed in the conference such as the 100% penetration of renewables as well as future possibilities for electricity markets under potential new environments with zero marginal cost. imgpaper42

portfolio

Climate Change Impacts in the Assured Energy of the Brazilian Power Generation System

Funded by Brazilian Electricity Regulation Agency (ANEEL) Strategic R&D Project, 2011-2014

The goal of this project is to evaluate possible climate change effects in the Brazilian hydropower systems. We aim to evaluate results of the climate projections from the 4th report of the Intergovernamental Pannel on Climate Change (IPCC) and their association with future water inflows in all the major water basins in the country. An interaction among regional climate, water inflows modeling, and optimal dispatch is considered in this work assessing potential impacts for the Brazilian hydropower system.

Refinement of the FREEDM Center Cost-Benefit Model

Funded by NCSU Future Renewable Electric Energy Delivery and Management Systems Center (FREEDM), 2016-2017

The goal of this project is to perform a benefit comparison between competing technologies with respect to cost and performance of alternatives as well as simulations of FREEDM and alternatives in OpenDSS.

Diversifying Investments in NC Offshore Renewable Energy Technologies

Funded by NC Renewable Ocean Energy Program (NCROEP), 2017-2018

The goal of this project is to identify optimal resource portfolios that consider multiple offshore renewable energy sources. To achieve this goal, we will enhance the portfolio optimization framework we developed during the previous fiscal year and apply it to multiple offshore renewable sources off the North Carolina coast, including offshore wind, wave, and Gulf Stream current energy.

A Proposed Energy Storage Study for North Carolina

Funded by UNC Policy Collaboratory, 2017-2018

The objective of this project is to generate an analysis of the economics of energy storage devices in the state of North Carolina that provides clear policy guidance to the NC General Assembly, NC Utilities Commission, and the NC Energy Policy Council, informed by stakeholder engagement and the application of open and transparent modeling tools.

Collaborative Research: NSF-NSFC: Improving FEW system sustainability over the SEUS and NCP: A cross-regional synthesis considering uncertainties in climate and regional development

Funded by National Science Foundation (NSF), 2018-2023

The overarching research question of this proposal is to understand the potential effects of uncertain climate and FEW development scenarios (i.e., policy and demand changes) on the sustainability of regional Food Energy and Water (FEW) system. Given the FEW interdependency complexities, we consider as study regions the Southeast US and North China Plain, that are large enough to capture the cascading impacts across the three systems from both the US and China. In that way, local/regional climatic impacts or policy changes on any of the FEW systems could be used to understand the impacts on the regional FEW systems.

Optimizing Investments in Offshore Renewable Energy in the North Carolina Electric Sector

Funded by NC Renewable Ocean Energy Program (NCROEP), 2019-2020

The goal of this project is to conduct portfolio optimization analysis of renewable energy resources using hourly data and identify the most promising site locations and generating technologies for the North Carolina coast. We will integrate the optimal offshore energy portfolio into our open source capacity expansion model of the NC electric sector and evaluate the cost-effectiveness of offshore energy in the state. Such systems-level economic analysis of offshore energy in North Carolina provides critical information to third party developers, electric utilities, and electricity cooperatives.

Analyzing Investments in NC Offshore Renewable Energy Under Uncertain Resource Availability and Hurricane Damages

Funded by NC Renewable Ocean Energy Program (NCROEP), 2020-2021

The goal of this project is to quantify the risk of low probability, high impact events associated with low offshore energy production and the risk of hurricane damage. Such systems-level economic analysis of offshore energy in North Carolina has never been done and will provide critical information to third-party developers, electric utilities, and electricity cooperatives. The consideration of these low probability, high impact events can provide critical insights that affect the selection of optimal offshore energy portfolios in North Carolina.

Assessing the Risk of Hurricane Damage to Marine Hydrokinetic Devices

Funded by NC Renewable Ocean Energy Program (NCROEP), 2021-2022

The goal of this project is to rigorously analyze the vulnerability and potential consequences of hurricane damage to wave and ocean current devices located off the North Carolina coast. The analysis of extreme events in the context of marine renewable energy is of critical importance to the future deployment of these technologies in North Carolina.

A Data-Driven Risk-Based Enterprise for Operational Decision Support

Funded by Department of Homeland Security - Center of Excellence on Cross-Border Threat Screening and Supply Chain Defense, 2021-2023

This project seeks to mitigate the risks of transboundary pest and disease threats (TPDT’s) by developing data integration and forecasting methods that improve decisions about the presence of possible TPDT’s before they reach our ports of entry. This project will develop and use artificial intelligence algorithms that learn, adapt, and evolve to improve decisions to aimed at detecting suspect imports and, therefore, preventing the introduction of TPDT’s in the U.S.

Mooring System Analysis and Fragility Curve Estimation: The Economic Impact of Low Probability High Impact Events on Ocean Current Devices

Funded by NC Renewable Ocean Energy Program (NCROEP), 2022-2023

In this project, we aim to improve the modeling/analysis of the mooring systems for ocean current devices and propose a fragility curve design capable of representing the most critical conditions for this technology. In addition, an aligned goal is to integrate the fragility analyses into a capacity expansion model of the NC energy system to make an economic assessment of the impact of these mooring fragility during operating conditions.

Fused Portfolio, Site, and Device Sizing Optimization for Harnessing North Carolina’s Coastal Renewable Energy Resources

Funded by NC Renewable Ocean Energy Program (NCROEP), 2022-2023

The objective of this project is to create an integrated framework that fuses (i) site selection, (ii) number of each type of device at each site, and (iii) device sizing sub-problems into a single tool, simultaneously accounting for the resource models, technical performance models, and cost models. The proposed work will lie at the intersection of two stated interests of the NCROEP, namely (i) resource assessment (via synthetic resource modeling) and (ii) device efficiency improvement (via optimization). In developing the aforementioned integrated tool, our team will build upon independent resource modeling, portfolio optimization, and device performance analysis to create a nested integrated optimization framework.

publications

Sharing Cuts under Aggregated Forecasts when Decomposing Multi-stage Stochastic Programs

de Queiroz, A.R., Morton, D.P., (2013) Sharing Cuts under Aggregated Forecasts when Decomposing Multi-stage Stochastic Programs, Operations Research Letters, 41(3): 311-316

We extend previous methods for sharing cuts in Sampling-based decomposition algorithms (SBDAs), establishing new results under a novel interaction between a class of interstage dependency models, and how they appear in the stochastic program.

Least-cost path Analysis and Multi-Criteria Assessment for Routing Electricity Transmission Lines

Mendes de Lima, Osis, R., de Queiroz, A.R., Moreira Santos, A.H., (2016) Least-cost path Analysis and Multi-Criteria Assessment for Routing Electricity Transmission Lines, IET Generation, Transmission & Distribution, 10(16): 4222-4230

This paper aims at verifying the applicability of a GIS methodology for TL routing using analytic hierarchy process (AHP) for weighting criteria and Dijkstra shortest path algorithm.

Wind power generation: An impact analysis of incentive strategies for cleaner energy provision in Brazil

Aquila, G., Rocha, L.C.S., Rotela Jr, P., Pamplona, E.O., de Queiroz, A.R., de Paiva, A.P., (2016) Wind power generation: An impact analysis of incentive strategies for cleaner energy provision in Brazil, Journal of Cleaner Production, 137: 1100-1108

This paper aims to analyze the impact of incentive strategies on the financial risk of wind power generation projects in Brazil in different market environments.

Stochastic Hydro-thermal Scheduling Optimization: An Overview

de Queiroz, A.R., (2016) Stochastic Hydro-thermal Scheduling Optimization: An Overview, Renewable and Sustainable Energy Reviews, 62: 382-395

This paper presents an overview about the hydro-thermal scheduling problem. In an electrical power system power generators have to be scheduled over a time horizon in order to supply system demand.

Climate Change Impacts in the Energy Supply of the Brazilian Hydro-dominant Power System

de Queiroz, A.R., Lima, L.M.M., Lima, J.W.M., Silva, B.C., Scianni, L.A., (2016) Climate Change Impacts in the Energy Supply of the Brazilian Hydro-dominant Power System, Renewable Energy, 99: 379-389

We present a new framework to evaluate impacts of climate change in hydropower. An interaction among regional climate, water inflows modeling, and optimal dispatch is showed for the Brazilian hydropower system.

An Overview of Incentive Policies for the Expansion of Renewable Energy Generation in Electricity Power Systems and the Brazilian Experience

Aquila, G., Pamplona, E.O., de Queiroz, A.R., Rotela Jr, P., Fonseca, M.N., (2017) An Overview of Incentive Policies for the Expansion of Renewable Energy Generation in Electricity Power Systems and the Brazilian Experience, Renewable and Sustainable Energy Reviews, 70: 1090-1098

In this study, long-term policies that have been applied in several countries, such as feed-in tariffs, shares with commercialization of certificates, auctions, and net metering, are investigated and discussed. The main advantages and disadvantages of these incentive strategies are emphasized, focusing on applications.

Wind Power Feasibility Analysis under Uncertainty in the Brazilian Electricity Market

Aquila, G., Rotela Jr, P., Pamplona, E.O., de Queiroz, A.R., (2017) Wind Power Feasibility Analysis under Uncertainty in the Brazilian Electricity Market, Energy Economics, 65: 127-136

This paper proposes a framework for investment analysis capable of encompassing different uncertainties and possibilities for wind power generators in a regulated market, characterized by auctions. To reach the proposed objective we employ a simulation technique which allows to model cash flows considering uncertainties in variables related to project financial premises, electricity generation and producer exposure to the short-term market.

The Economics of Electricity Generation from Gulf Stream Currents

Li, B., de Queiroz, A.R., DeCarolis, J.F., Bane, J., He, R., Keeler, A.G., Neary, V.S., (2017) The Economics of Electricity Generation from Gulf Stream Currents, Energy, 134: 649-658

The economics of Gulf Stream energy off the North Carolina coast are assessed. A portfolio optimization model is developed to identify optimal generation sites. The optimal portfolio reduces the variance in monthly electricity generation tenfold.

Multi-objective Optimization Applied for Designing Hybrid Power Generation Systems in Isolated Networks

Fonseca, M.N., Pamplona, E.O., de Queiroz, A.R., et al., (2018) Multi-objective Optimization Applied for Designing Hybrid Power Generation Systems in Isolated Networks, Solar Energy, 161: 207-219

This proposes an approach to select the optimal configuration of hybrid power generation systems for isolated regions by means of combining the techniques of Mixing Design of Experiments, Normal Boundary Intersection and analysis of super efficiency using Data Envelopment Analysis. The proposed approach is applied to a set of four isolated regions in the Amazonas state located in the northern region of Brazil.

Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system

Aquila, G., Peruchi, R.S., Rotela Junior, P., Rocha, L.C.S., de Queiroz, A.R., Pamplona, E.O., Balestrassi, P.P. (2018) Analysis of the wind average speed in different Brazilian states using the nested GR&R measurement system, Measurement, 115: 217-222

This study aims to evaluate the behavior of wind average speed at the four major wind energy-producing states. The main contribution of this research is to use the Nested Gage Repeatability & Reproducibility (NGR&R) study, generally applied on manufacturing quality management.

Demand-Side Management Via Photovoltaic Generation with Storage in The Context of the Brazilian Tariff Model

Rodrigues, L.F., do Carmo, B.K.S., Gomes, A.A.L., de Queiroz, A.R., Lima, J.W.M., Ribeiro, P.F., (2018) Demand-Side Management Via Photovoltaic Generation with Storage in The Context of the Brazilian Tariff Model, Hipótese, v. 4(2): 172-189 (in portuguese)

Com o avanço da geração de energia elétrica produzida junto ao consumo de carga, aliado com o cenário atual de tarifação de energia no Brasil, este trabalho apresenta o gerenciamento pelo lado da demanda (DSM) em uma microrrede, realizando estudos de comparação entre três modelos distintos aplicados no contexto da tarifa branca e no contexto da tarifa convencional. Observou-se uma economia considerável quando utilizada uma microrrede com DSM, sem provocar uma mudança de hábito do consumidor.

Cooperative game theory and last addition method in the allocation of firm energy rights

Faria, V.A.D., de Queiroz, A.R., Lima, L.M.M., Lima, J.W.M., (2018) Cooperative game theory and last addition method in the allocation of firm energy rights, Applied Energy, 226: 905-915

This work proposes a formulation to compute the firm energy rights of hydro plants using cooperative game theory and the last addition allocation method. The main goal is to integrate the interests of hydro agents with the needs of the regulatory agencies, searching in the core of the game for solutions that give the right incentives to the optimal system development.

Proposed Method for Contracting of Wind-Photovoltaic Connected Projects to the Brazilian Electric System using Multi-objective Programming

Aquila, G., Rocha, L.C.S., Pamplona, E.O., de Queiroz, A.R., Fonseca, M.N., (2018) Proposed Method for Contracting of Wind-Photovoltaic Connected Projects to the Brazilian Electric System using Multi-objective Programming, Renewable and Sustainable Energy Reviews, 97: 377-389

This paper presents a model to assist the government in contracting projects that maximize the socioeconomic well-being of the Brazilian electricity sector. Multiobjective programming is used to simultaneously handle two objective functions—maximally reducing emission density and minimizing the levelized cost of electricity (LCOE) with the aid of the mixture arrangement technique. In this respect, the optimization method called normal boundary intersection (NBI) is applied to solve the multiobjective problem and construct the Pareto frontier.

Methodology for Electricity Transmission Lines Pole Spotting Based on Dynamic Programming

Medeiros, G.O.S., Lima, R.M., de Queiroz, A.R., et al., (2018) Methodology for Electricity Transmission Lines Pole Spotting Based on Dynamic Programming, Brazilian Energy Magazine, 24(3): 51-64 (in portuguese)

O presente trabalho propõe uma metodologia de locação otimizada das estruturas de transmissão por meio da modelagem de dados característicos do local de estudo, utilizando programação dinâmica, de maneira que o empreendimento proposto apresente o menor custo. Esse trabalho apresenta também um estudo de caso que compara os resultados obtidos pela metodologia proposta com a configuração do projeto real associado a LT Machadinho-Campos Novos.

US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy

Eshraghi, H., de Queiroz, A.R., DeCarolis, J.F., (2018) US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy, Environmental Science & Technology, 52(17): 9595–9604

In this analysis, we utilize Tools for Energy Model Optimization and Analysis (Temoa), an open source, publicly available energy system optimization model (ESOM), to examine a large set of baseline US energy futures through 2040. Our objective is to rigorously explore the future decision landscape and quantify greenhouse gas (GHG) emissions in a future where energy system changes are driven by market forces rather than top-down federal policy.

Solar Generation, Storage, and Electric Vehicles in Power Grids: Challenges and Solutions with Coordinated Control at the Residential Level

Hafiz, F., de Queiroz, A.R., Husain, (2018) Solar Generation, Storage, and Electric Vehicles in Power Grids: Challenges and Solutions with Coordinated Control at the Residential Level, IEEE Electrification Magazine, 6(4): 83-90

Solar energy is an abundant renewable energy source that is available all around the world every day. Wind energy is another important renewable resource available in large amounts every day. These two renewable energy sources are attracting significant investment as countries seek technology cost reductions to aid sustainability.

Hydro Power Revenues Under the Threat of Climate Change in Brazil

de Queiroz, A.R., de Faria, V.A.D., Lima, L.M.M., Lima, J.W.M., (2019) Hydro Power Revenues Under the Threat of Climate Change in Brazil, Renewable Energy, 133: 873-882

We present a new framework to evaluate climate change impacts in hydropower plants. An interaction among regional climate, water inflows modeling, and optimization dispatch models is showed. This paper can help to support decision-making regarding investments in new hydro plants. This multidisciplinary research provides guidance for future planning of the generation system.

Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach

Hafiz, F., de Queiroz, A.R., Fajri, P., Husain, I., (2019) Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach, Applied Energy, 236: 42-54

The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization.

Building Conflict Uncertainty into Electricity Planning: A South Sudan Case Study

Patankar, N., de Queiroz, A.R., DeCarolis, J.F., Bazilian, M., Chattopadhyay, D., (2019) Building Conflict Uncertainty into Electricity Planning: A South Sudan Case Study, Energy for Sustainable Development, 49: 53-64

This paper explores electricity planning strategies in South Sudan under future conflict uncertainty. A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is presented.

Utilizing Demand Response for Distribution Service Restoration to Achieve Grid Resiliency against Natural Disasters

Hafiz, F., Chen, C., Chen, B., de Queiroz, A.R., Husain, I., (2019) Utilizing Demand Response for Distribution Service Restoration to Achieve Grid Resiliency against Natural Disasters, IET Generation, Transmission & Distribution, 13(14): 2942-2950

This paper demonstrates that effective distribution service restoration (DSR) can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a novel framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR.

Optimizing Routing and Tower Spotting of Electricity Transmission Lines: An Integration of Geographical Data and Engineering Aspects into Decision-Making

Santos, A.H.M., de Lima, R.M., Pereira, C.R.S., Osis, R., Medeiros, G.O.S., de Queiroz, A.R., Flauzino, B.K., et al., (2019) Optimizing Routing and Tower Spotting of Electricity Transmission Lines: An Integration of Geographical Data and Engineering Aspects into Decision-Making, Electric Power Systems Research, 176: 105953

This paper presents a novel approach for the design of overhead Transmission Lines (TL), considering geographical, engineering and cost aspects into the decision-making process. The proposed methodology is focused on preliminary planning and decision-making for TL auctions, where the objective is to find design alternatives with the least cost.

Coordinated Control of PEV and PV-based Storage System under Generation and Load Uncertainty

Hafiz, F., de Queiroz, A.R., Husain, I., (2019) Coordinated Control of PEV and PV-based Storage System under Generation and Load Uncertainty, IEEE Transactions on Industry Applications, 55(6): 5524-5532

In this paper, a method of coordinated optimal control between PV-based storage and PEV storage is proposed considering the stochastic nature of solar PV generation and load demand. The stochastic dual dynamic programming algorithm is employed to optimize the charge/discharge profiles of PV-based storage and PEV storage to minimize the daily household electricity purchase cost from the grid.

Repurposing an Energy System Optimization Model for Seasonal Power Generation Planning

de Queiroz, A.R., Mulcahy, D., DeCarolis, J.F., Sankarasubramanian, A., Mahinthakumar, K., Lu, N., (2019) Repurposing an Energy System Optimization Model for Seasonal Power Generation Planning, Energy, 181: 1321-1330

This paper develops and tests a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model.

Real-time Stochastic Optimization of Energy Storage Management using Deep Learning based Forecasts for Residential PV Applications

Hafiz, F., Awal, M.A., de Queiroz, A.R., Husain, I., (2020) Real-time Stochastic Optimization of Energy Storage Management using Deep Learning based Forecasts for Residential PV Applications, IEEE Transactions on Industry Applications, 56(3): 2216 – 2226

A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). We propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller.

Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

Li, B., Thomas, J., de Queiroz, A.R., DeCarolis, J.F., (2020) Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study, Environmental Science & Technology, 54(2): 665-676

In this study, we utilize an open-source energy system optimization model and publicly available data sets to examine future electricity generation, CO2 emissions, and CO2 abatement costs for the North Carolina electric power sector through 2050. We illustrate how break-even costs can be used to inform the development of an extended renewable portfolio standard in the state.

Modeling and Design of Wind-Solar Hybrid Generation Projects in Long-term Energy Auctions: A Multi-objective Optimization Approach

Aquila, G., de Queiroz, A. R., Lima, L.M.M., Balestrassi, P.P., Lima, J.W.M., Pamplona, E.O., (2020) Modeling and Design of Wind-Solar Hybrid Generation Projects in Long-term Energy Auctions: A Multi-objective Optimization Approach, IET Renewable Power Generation, 14(14): 2612 – 2619

This study proposes an approach to help the bidding processes of hiring wind-photovoltaic farms in long-term energy auctions. The proposed approach aims to define an optimal solution to configure wind-photovoltaic farms based on mixture design of experiments and the Lp method, as well as an efficiency metric designed to achieve diversification and to identify the Pareto dominant optimal portfolio.

Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis

de Oliveira, L. G., Aquila, G., Balestrassi, P. P., de Paiva, A. P., de Queiroz, A. R., de Oliveira Pamplona, E., & Camatta, U. P. (2020). Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis. Renewable and Sustainable Energy Reviews, 123: 109786

This paper proposes an innovative approach for evaluating solar photovoltaic projects based on Attribute Agreement Analysis.

Contribution for bidding of wind-photovoltaic on grid farms based on NBI-EFA-SNR method

Aquila, G., de Queiroz, A. R., Junior, P. R., Rocha, L. C. S., de Oliveira Pamplona, E., & Balestrassi, P. P. (2020). Contribution for bidding of wind-photovoltaic on grid farms based on NBI-EFA-SNR method. Sustainable Energy Technologies and Assessments, 40, 100754

This study develops a novel model that can help bidding of Wind-photovoltaic farms considering a range of objectives that maximize the environmental and welfare benefits. This new approach contributes to energy planning for any type of hybrid farm through multi-objective programming, even in cases where the optimization of several correlated outputs is desired.

Performance Comparison of Equivalent Reservoir and Multi-Reservoir Models in forecasting hydropower potential for Linking Water and Power Systems

Mukhopadhyay, S., Arumugam, S., de Queiroz, A.R., (2020) Performance Comparison of Equivalent Reservoir and Multi-Reservoir Models in forecasting hydropower potential for Linking Water and Power Systems, Journal of Water Resources Planning and Management, 147(4): 04021005

This study systematically compares two equivalent reservoir models, an aggregated water balance and an energy balance representation, with a multireservoir cascade representation for a system of three reservoirs in series in Savannah, South Carolina, in terms of the total end-of-period release, hydropower and storage based on simulation, simulation optimization, and analytically over a 30-year period.

Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach

Aquila, G., de Queiroz, A. R., Balestrassi, P. P., Junior, P. R., Rocha, L. C. S., Pamplona, E. O., & Nakamura, W. T. (2020). Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach. Sustainable Energy Technologies and Assessments, 42, 100876

This study proposes a Real Options approach to investigate the economic feasibility of a wind power plant investment with the option of abandoning along the project life cycle in Brazil.

Leveraging open-source tools for collaborative macro-energy system modeling efforts

DeCarolis, J. F., Jaramillo, P., Johnson, J. X., McCollum, D. L., Trutnevyte, E., Daniels, D. C., Akın-Olçum, G., Bergerson, J., Joon-Choi, S., Craig, M.T., de Queiroz, A.R., et al. (2020). Leveraging open-source tools for collaborative macro-energy system modeling efforts. Joule, 4(12), 2523-2526

The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts.

Quantification of Climate-Induced Interannual Variability in Residential US Electricity Demand

Eshraghi, H., de Queiroz, A.R., Sankarasubramanian, A., DeCarolis, J.F., (2021) Quantification of Climate-Induced Interannual Variability in Residential US Electricity Demand, Energy, 236: 121273

We assess the sensitivity of residential electricity demand in 48 U S. states to seasonal climate variations and structural changes pertaining to state-level household electricity demand. The main objective is to quantify the effects of seasonal climate variability on residential electricity demand variability during the winter and summer seasons.

The Symbiotic Relationship of Solar Power and Energy Storage in Providing Capacity Value

Sodano, D., DeCarolis, J., de Queiroz, A.R., Johnson, J.X., (2021) The Symbiotic Relationship of Solar Power and Energy Storage in Providing Capacity Value, Renewable Energy, 177: 823-832

In this study, we use a loss of load probability model to estimate the capacity credit of solar photovoltaics and energy storage under increasing penetrations of both technologies, in isolation and in tandem, to offer new understanding on their potential synergistic effects.

The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US

Cawthorne, D., de Queiroz, A.R., Eshraghi, H., Sankarasubramanian, A., and DeCarolis, J.F., (2021) The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US”, Frontiers in Sustainable Cities 3:644789

This paper shows that seasonal temperature forecasts from GCMs can potentially help in predicting season-ahead residential power demand forecasts for states in the Southern United States.

Transmission Towers Spotting in Power Systems Considering Engineering and Environmental Aspects: A Dynamic Programming Approach

Medeiros, G.O.S., de Queiroz, A.R., Lima, R.M., Pereira, C.R.S., Santos, A.H.M., Czank Jr, L., dos Santos, R.A., Carvalho Jr, E.L., (2021) Transmission Towers Spotting in Power Systems Considering Engineering and Environmental Aspects: A Dynamic Programming Approach, International Transactions on Electrical Energy Systems, 31(9): e13000

This paper proposes a dynamic programming model that seeks to obtain the optimal spotting of transmission towers considering environmental (type of land use, slope, and geotechnical class of the terrain) and engineering characteristics (minimum distance between the electric conductor and ground and tensions supported of each tower type) associated with the problem.

Efficiency analysis for performance evaluation of electric distribution companies

Medeiros, G.O.S., Lima, L.M.M., de Queiroz, A.R., Lima, J.W.M., dos Santos, L.C.B., Barbosa, M.A., Alvares, J.E., (2021) Efficiency analysis for performance evaluation of electric distribution companies, International Journal of Electrical Power and Energy Systems, 134:107430

This paper evaluates weights restrictions influence on efficiencies results and to perform a sensitivity analysis of efficiency scores of Electricity Distribution Companies using different benchmarking techniques.

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Sioshansi, R., Denholm, P., Arteaga, J., Awara, S., Bhattacharjee, S., Botterud, A., Cole, W., Cortes, A., de Queiroz, A.R., DeCarolis, J.F., Ding, Z., Diorio, N., Dvorkin, Y., Helman, U., Johnson, J., Konstantelos, I., Mai, T., Pandzic, H., Sodano, D., Stephen, G., Svoboda, A., Zareipour, H., and Zhang, Z. (2021). Energy-Storage Modeling: State-of-the-Art and Future Research Directions, IEEE Transactions on Power Systems, 37(2): 860-875

This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models.

An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems

de Faria, V. A. D., de Queiroz, A. R., Lima, L. M., Lima, J. W. M., & da Silva, B. C. (2022). An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems. Int. Journal of Environmental Science and Technology, 19(7), 5819-5838.

This work analyzes the use of artificial neural networks in short-term streamflow forecasting for large interconnected hydropower systems. We present an algorithm to define the neural network inputs and apply it to create models for 55 major hydro plants in the Paraná Basin, which contribute to more than 30% of the total power generated in Brazil.

Promoting reproducibility and increased collaboration in electric sector capacity expansion models with community benchmarking and intercomparison efforts

Henry, C. L., Eshraghi, H., Lugovoy, O., Waite, M. B., DeCarolis, J. F., Farnham, D. J., Peer, R.A.M., Wu, Y., de Queiroz, A.R., Potashnikov, V., Modi, V., and Caldeira, K. (2021). Promoting reproducibility and increased collaboration in electric sector capacity expansion models with community benchmarking and intercomparison efforts, Applied Energy, 304, 117745

This paper presents a model benchmarking effort using highly simplified scenarios applied to four open-source models of the U.S. electric sector. We eliminate all parametric uncertainty through using a common dataset and leave only structural differences.

Improving the Representation of Energy Efficiency in an Energy System Optimization Model

Patankar, N., Fell, H., DeCarolis, J., de Queiroz, A.R., Curtis, J., (2022) Improving the Representation of Energy Efficiency in an Energy System Optimization Model, Applied Energy, Vol 306(B): 118083

In this paper, the structure of an existing Energy System Optimization Model is adapted to include energy efficiency in way that is consistent with microeconomic theory. Therefore, the effectiveness of energy-efficient technologies in meeting energy service demands, and their potential to substitute electricity usage by conventional technologies is considered.

Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation

Ford, L., de Queiroz, A., DeCarolis, J., & Sankarasubramanian, A. (2022). Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation. Energy Reports, 8, 8061-8078

We present the Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that seeks to optimizes power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). COREGS is then applied to Tennessee Valley Authority’s system in the United States.

Using robust optimization to inform US deep decarbonization planning

Patankar, N., Eshraghi, H., de Queiroz, A. R., & DeCarolis, J. F. (2022). Using robust optimization to inform US deep decarbonization planning. Energy Strategy Reviews, 42, 100892

This work focuses on extending and applying robust optimization methods to TEMOA, an open source Energy System Optimization Model, to derive insights about low carbon pathways in the United States.

Optimizing Offshore Renewable Portfolios Under Resource Variability

de Faria, V.A.D., de Queiroz, A.R., DeCarolis, J.F., (2022) Optimizing Offshore Renewable Portfolios Under Resource Variability, Applied Energy, 325-120012

In this paper, we develop a model formulation based on Mean-Variance portfolio theory to identify the optimal site locations for a given number of wind, wave, and ocean current turbines subject to constraints on their energy collection system and the maximum number of turbines per site location. The optimal portfolio results are then included in a capacity expansion model to derive economic targets that make the offshore portfolios cost-competitive with other generating technologies. Results of this work indicate that the integration of different offshore technologies can help to decrease the energy variability associated with marine energy resources.

Scenario Generation and Risk-averse Stochastic Portfolio Optimization Applied to Offshore Renewable Energy Technologies

de Faria, V.A.D., de Queiroz, A.R., DeCarolis, J.F., (2023) Scenario Generation and Risk-averse Stochastic Portfolio Optimization Applied to Offshore Renewable Energy Technologies, Energy, 325-120012.

This work proposes an analytical decision-making framework considering scenario generation using artificial neural networks and risk-averse stochastic programming to define renewable offshore portfolios of wind, wave, and ocean current technologies.

Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system

Morais, L. B. S., Aquila, G., de Faria, V. A. D., Lima, L. M. M., Lima, J. W. M., & de Queiroz, A. R. (2023). Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system. Applied Energy, 348, 121439.

This work focuses on the development of shallow and deep neural networks in the form of multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short-term load forecasting problem. Different model architectures are tested, and global climate model information is used as input to generate more accurate forecasts.

talks

Determining the Optimal Transmission System Usage Contracts for a Distribution Company

Published:

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.

Hydroelectric Scheduling: Inflow Forecasting and Parallel Decomposition

Published:

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.

Thermal Generation Investment Analysis Using Decision Tools

Published:

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.

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

Published:

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.

Energy Supply Risk Due to Selling Over the Physical Generation Capacity

Published:

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.

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

Published:

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.

Solution Quality and Jackknife Estimators in Large Scale Hydroelectric Scheduling

Published:

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.

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

Published:

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.

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

Published:

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.

Stochastic Power Generation Scheduling Using TEMOA

Published:

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.

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

Published:

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.

The Value of Stochastic Programming for Energy Systems Planning

Published:

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.

Machine Learning for Electricity Price Forecasting in Brazil

Published:

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.

Assessing the Risk of Hurricane Damage to Marine Hydrokinetic Devices

Published:

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.

teaching

Civil Engineering Systems - CE 399

Civil, Construction & Environmental Engineering - Undergraduate, NC State University, 2017

A broad perspective, systematic approach to civil planning, analysis, evaluation and design for large scale projects in construction, structures, transportation, water resources and other civil engineering areas.

Decision Sciences - DSC 3300

Business Administration - Undergraduate, School of Business at NCCU, 2018

This course provides an introduction to the use of mathematical concepts and models in managerial decision making. Mathematical modeling, linear programming, network programming, applied probabilistic concepts and decision theory.

Managerial Statistics - DSC 5200

Graduate Course - M.B.A., School of Business at NCCU, 2021

This course introduces basic concepts in probability and statistics with applications to managerial decision making. The topics covered include: basic data analysis, random variables and probability distributions, hypothesis testing, analysis of variance, linear and multivariable regression analysis.

Quantitative Methods for Business - DSC 2000

Business Administration - Undergraduate, School of Business at NCCU, 2022

This course introduces certain concepts in business, economics and finance, along with their mathematical formulation and solution, using linear and quadratic equations, systems of equations, functions, exponential and logarithmic functions, and differentiation.

Operations Management - DSC 3750

Business Administration - Undergraduate, School of Business at NCCU, 2022

Operations Management is the process of planning, organizing and controlling resources in order to produce goods and services to meet the goals of an organization. This course provides an introduction to the management of operating systems, and techniques & methods employed to plan and control goods and service oriented organizations.

Productions Systems and Management - DSC 5530

Graduate Course - M.B.A., School of Business at NCCU, 2022

This course provides a background in Productions and Systems Management. Techniques and methods employed to plan and control business organizations, service, forecasting, production scheduling, strategic decision making, quality control methods and measurement are presented and used to solve practical problems.

Special Topics in Data Analytics (Energy & Society) - STQM 4090

Data Analytics - Information Technology and Business, School of Business at NCCU, 2022

This iteration of the Special Topics course provides an overview of the role that energy plays in many aspects of society. It will give students a sense for observing the role of energy in the modern world, as well as provide a practical understanding of places where there may be career opportunities within the energy sector.

Applied Time Series Analysis - STQM 4020

Data Analytics - Information Technology and Business , School of Business at NCCU, 2022

This course focuses on time series analysis, modeling and forecasting, with emphasis on practical applications in business and other areas. Applied Time Series Analysis are performed using the software R for most of the computational exercises. Upon completion of the course, the students will be able to carry out basic Time Series analysis and fit a model to data.

Modeling and Optimization for Analytics - STQM 4010

Data Analytics - Information Technology and Business, School of Business at NCCU, 2022

This course provides a broad perspective of optimization models and methods. Linear Programming, Network Optimization and Integer Programming are presented in the context of planning, operations, marketing, management and other areas. Students will learn how create optimization models and solve them using Python, Pyomo & Gurobi.

Data Analytics in R - STQM 4000

Data Analytics - Information Technology and Business, School of Business at NCCU, 2022

The goal of this course is to present the foundations of Data Analytics (data I/O, data wrangling, visualization, databases and exploratory data analysis) and help students to develop the necessary skills to perform successful analysis of real life problems. R is used as the statistical programming language in the course along with several techincal libraries.