@article{torres_lima_reboita_queiroz_lima_2024, title={Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System}, volume={16}, ISSN={["2073-4441"]}, url={https://www.mdpi.com/2073-4441/16/4/586}, DOI={10.3390/w16040586}, abstractNote={Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations.}, number={4}, journal={WATER}, author={Torres, Francisca Lanai Ribeiro and Lima, Luana Medeiros Marangon and Reboita, Michelle Simoes and Queiroz, Anderson Rodrigo and Lima, Jose Wanderley Marangon}, year={2024}, month={Feb} }
 @misc{aquila_scianni morais_faria_marangon lima_marangon lima_queiroz_2023, title={An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience}, volume={16}, ISSN={["1996-1073"]}, url={https://doi.org/10.3390/en16217444}, DOI={10.3390/en16217444}, abstractNote={The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience.}, number={21}, journal={ENERGIES}, author={Aquila, Giancarlo and Scianni Morais, Lucas Barros and Faria, Victor Augusto and Marangon Lima, Jose Wanderley and Marangon Lima, Luana Medeiros and Queiroz, Anderson Rodrigo}, year={2023}, month={Nov} }
 @article{faria_queiroz_decarolis_2023, title={Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies}, volume={270}, ISSN={["1873-6785"]}, url={http://dx.doi.org/10.1016/j.energy.2023.126946}, DOI={10.1016/j.energy.2023.126946}, abstractNote={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. For the scenario generation, a generative adversarial neural network is developed to generate synthetic energy scenarios considering resources distributed over large geographic regions. These scenarios are then fed to a stochastic model, which objective to determine the optimal location and number of turbines for each technology. In the stochastic model formulation, a representation of the limits in the portfolio Levelized Cost of Energy and the maximization of the five percent lower energy generation conditions, also known as Conditional Value at Risk, is presented. The framework proposed here is tested considering data from a portion of the U.S. East coast, where the generative model was successful in creating energy scenarios statistically consistent with the historical data for wind, wave, and ocean current resources at more than 500 sites. Furthermore, the Conditional Value at Risk portfolio optimization model was used to construct efficient frontiers for a combination of different technologies, showing the significance of resource diversification as a tool to improve system security.}, journal={ENERGY}, publisher={Elsevier BV}, author={Faria, Victor A. D. and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2023}, month={May} }
 @article{morais_aquila_faria_lima_lima_queiroz_2023, title={Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system}, volume={348}, ISSN={["1872-9118"]}, url={https://publons.com/wos-op/publon/53188330/}, DOI={10.1016/j.apenergy.2023.121439}, abstractNote={This paper 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. A real study case is presented for the Brazilian interconnected power system and the results generated are compared with the forecasts from the Brazilian Independent System Operator model. In general terms, results show that the bidirectional versions of long-short term memory and gated recurrent unit produce better and more reliable predictions than the other models. From the obtained results, the recurrent neural networks reach Nash-Sutcliffe values up to 0.98, and mean absolute percentile error values of 1.18%, superior than the results obtained by the Independent System Operator models (0.94 and 2.01% respectively). The better performance of the neural network models is confirmed under the Diebold-Mariano pairwise comparison test.}, journal={APPLIED ENERGY}, author={Morais, Lucas Barros Scianni and Aquila, Giancarlo and Faria, Victor Augusto Duraes and Lima, Luana Medeiros Marangon and Lima, Jose Wanderley Marangon and Queiroz, Anderson Rodrigo}, year={2023}, month={Oct} }
 @article{ford_queiroz_decarolis_sankarasubramanian_2022, title={Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation}, volume={8}, ISSN={["2352-4847"]}, url={http://dx.doi.org/10.1016/j.egyr.2022.06.017}, DOI={10.1016/j.egyr.2022.06.017}, abstractNote={Climate variability accounts for distinct seasonal differences in electricity demand and streamflow potential, which power systems rely on to assess available hydropower and to cool thermal power plants. Understanding the interactions between reservoir and power networks under varying climate conditions requires an integrated analysis of both systems. In this study, we develop Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that optimizes both systems with respect to reducing power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). Three optimization schemes of varying degrees of model integration are applied to Tennessee Valley Authority’s reservoir and electricity systems for the summer and winters from 2003 to 2015. We find that co-optimization of the systems results in more efficient water allocation decisions than separate optimization. Co-optimization solutions reduce reservoir spill and allocate water for hydropower only when and where it is beneficial to the power system as compared to stand-alone water system optimization. As the penetration of solar and wind power continues to increase, power systems will be more reliant on flexible reliable generating services such as reservoir systems and co-optimization of both systems will become more essential for efficient seasonal planning and operation.}, journal={ENERGY REPORTS}, publisher={Elsevier BV}, author={Ford, Lucas and Queiroz, Anderson and DeCarolis, Joseph and Sankarasubramanian, A.}, year={2022}, month={Nov}, pages={8061–8078} }
 @article{medeiros_marangon-lima_queiroz_marangon-lima_santos_barbosa_alvares_2022, title={Efficiency analysis for performance evaluation of electric distribution companies}, volume={134}, ISSN={["1879-3517"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85111867132&partnerID=MN8TOARS}, DOI={10.1016/j.ijepes.2021.107430}, abstractNote={In power systems, the electricity distribution sector requires proper regulation to guarantee power supply security, electricity tariffs modicity and universal service to customers. Generally, electric utilities operate differently and are distinguished in terms of costs, quality of supply, market and network size, and other aspects, that affect their efficiency. In this context, the Data Envelopment Analysis has been used in electricity distribution regulation to define efficiency scores and compare practices. The Data Envelopment Analysis application sometimes comes with weight restrictions and negative variables that modify the original methodology which affects the efficiency scores. The main goal of this paper is to evaluate weights restrictions influence on efficiencies results and to perform a sensitivity analysis of efficiency scores using additional benchmarking techniques. We apply the Cross-Efficiency Analysis and the Ratio-based Efficiency Analysis benchmarking methods, in order to provide relevant quantitative information to compute relative efficiency scores and perform peer evaluations among utilities even if they are outside of the efficient frontier. The Brazilian electricity distribution system is selected as study case. Brazil has strong diversity in terms of economic development, climate and geography, and the current procedure adopted by the regulator determine efficiency metrics for all distribution companies based on their operation cost. Results from our analysis show that the diversity of concession areas significantly influence the stability of efficiency scores. Moreover, considering the approach proposed here it is possible to identify an efficiency relationship among all the distribution companies and not only using the ones that are in the efficiency frontier.}, journal={INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS}, author={Medeiros, Giulia O. S. and Marangon-Lima, Luana M. and Queiroz, Anderson R. and Marangon-Lima, Jose W. and Santos, Lorena C. B. and Barbosa, Maria A. and Alvares, Jairo E.}, year={2022}, month={Jan} }
 @article{sioshansi_denholm_arteaga_awara_bhattacharjee_botterud_cole_cortes_queiroz_decarolis_et al._2022, title={Energy-Storage Modeling: State-of-the-Art and Future Research Directions}, volume={37}, ISSN={["1558-0679"]}, url={http://dx.doi.org/10.1109/tpwrs.2021.3104768}, DOI={10.1109/TPWRS.2021.3104768}, abstractNote={Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.}, number={2}, journal={IEEE TRANSACTIONS ON POWER SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sioshansi, Ramteen and Denholm, Paul and Arteaga, Juan and Awara, Sarah and Bhattacharjee, Shubhrajit and Botterud, Audun and Cole, Wesley and Cortes, Andres and Queiroz, Anderson de and DeCarolis, Joseph and et al.}, year={2022}, month={Mar}, pages={860–875} }
 @article{faria_queiroz_de carolis_2022, title={Optimizing offshore renewable portfolios under resource variability}, volume={326}, ISSN={["1872-9118"]}, url={http://dx.doi.org/10.1016/j.apenergy.2022.120012}, DOI={10.1016/j.apenergy.2022.120012}, abstractNote={The deployment of offshore wind, wave, and ocean current technologies can be coordinated to provide maximum economic benefit. 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. A model relaxation is also developed to improve the computational efficiency of the optimization process, allowing the inclusion of more than 5000 candidate generation sites. The model is tested using renewable resource estimates from the coast of North Carolina, along the eastern US coast. Different combinations of technology-specific offshore technologies are compared in terms of their levelized cost of electricity and energy variability. 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. Furthermore, this research shows that substantial cost reductions are still necessary to realize the deployment of these technologies in the region investigated.}, journal={APPLIED ENERGY}, publisher={Elsevier BV}, author={Faria, Victor A. D. and Queiroz, Anderson R. and De Carolis, Joseph F.}, year={2022}, month={Nov} }
 @article{patankar_eshraghi_queiroz_decarolis_2022, title={Using robust optimization to inform US deep decarbonization planning}, volume={42}, ISSN={["2211-4688"]}, url={http://dx.doi.org/10.1016/j.esr.2022.100892}, DOI={10.1016/j.esr.2022.100892}, abstractNote={US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. A robust strategy that explicitly considers future uncertainty has expected savings in total system cost of 12% and an 8% reduction in the standard deviation of expected costs relative to a strategy that ignores uncertainty. The robust technology deployment strategy also entails more diversified technology mixes across the energy sectors modeled.}, journal={ENERGY STRATEGY REVIEWS}, publisher={Elsevier BV}, author={Patankar, Neha and Eshraghi, Hadi and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2022}, month={Jul} }
 @article{faria_queiroz_lima_lima_silva_2021, title={An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems}, volume={7}, ISSN={["1735-2630"]}, url={https://doi.org/10.1007/s13762-021-03565-y}, DOI={10.1007/s13762-021-03565-y}, number={7}, journal={INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY}, publisher={Springer Science and Business Media LLC}, author={Faria, V. A. D. and Queiroz, A. R. and Lima, L. M. and Lima, J. W. M. and Silva, B. C.}, year={2021}, month={Jul} }
 @article{decarolis_jaramillo_johnson_mccollum_trutnevyte_daniels_ak?n-ol?um_bergerson_cho_choi_et al._2021, title={Erratum: Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts (Joule (2020) 4(12) (2523–2526), (S2542435120305109), (10.1016/j.joule.2020.11.002))}, volume={5}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85100695904&partnerID=MN8TOARS}, DOI={10.1016/j.joule.2021.01.004}, abstractNote={(Joule 4, 2523–2531; December 16, 2020) In the originally published version of this article, author Greg Schivley’s surname was spelled incorrectly. This error has been corrected in the online version. The authors regret this error and apologize for any confusion. Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling EffortsDeCarolis et al.JouleNovember 23, 2020In BriefDeCarolis et al. articulate the benefits of forming collaborative teams with a wide array of disciplinary and domain expertise to conduct analysis with macro-energy system models. Open-source models, tools, and datasets underpin such efforts by enabling transparency, accessibility, and replicability among team members and with the broader modeling community. Full-Text PDF Open Access}, number={2}, journal={Joule}, author={DeCarolis, J.F. and Jaramillo, P. and Johnson, J.X. and McCollum, D.L. and Trutnevyte, E. and Daniels, D.C. and Ak?n-Ol?um, G. and Bergerson, J. and Cho, S. and Choi, J.-H. and et al.}, year={2021}, pages={507} }
 @article{patankar_fell_queiroz_curtis_decarolis_2021, title={Improving the representation of energy Efficiency in an energy system optimization model}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85119246754&partnerID=MN8TOARS}, journal={arXiv}, author={Patankar, N. and Fell, H.G. and Queiroz, A.R. and Curtis, J. and DeCarolis, J.F.}, year={2021} }
 @article{mukhopadhyay_sankarasubramanian_de queiroz_2021, title={Performance Comparison of Equivalent Reservoir and Multireservoir Models in Forecasting Hydropower Potential for Linking Water and Power Systems}, volume={147}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001343}, DOI={10.1061/(ASCE)WR.1943-5452.0001343}, abstractNote={To link water and power systems on a regional scale, equivalent reservoir models—an aggregated representation of a multireservoir system—are commonly used because conventional river-basin scale optimization models become computationally expensive with increasing dimensionality. Although equivalent reservoir models are widely applied in power system operation, analyses comparing the performance of equivalent reservoir models with multireservoir cascade models are limited. To this end, 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. Findings from the pilot basin are generalized by altering the storage-to-demand ratio (SDR) to understand the effect of different system characteristics on the equivalent reservoir representation under observed and predicted inflows of different skills. Equivalent reservoir models perform similarly to the cascade model for systems with large SDRs, but for systems with smaller SDRs, equivalent reservoir models perform poorly because spill and other losses from individual reservoirs cannot be effectively represented in the aggregated approach.}, number={4}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Mukhopadhyay, Sudarshana and Sankarasubramanian, A. and de Queiroz, Anderson Rodrigo}, year={2021}, month={Apr}, pages={04021005} }
 @article{henry_eshraghi_lugovoy_waite_decarolis_farnham_ruggles_peer_wu_queiroz_et al._2021, title={Promoting reproducibility and increased collaboration in electric sector capacity expansion models with community benchmarking and intercomparison efforts}, volume={304}, ISSN={["1872-9118"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85114748043&partnerID=MN8TOARS}, DOI={10.1016/j.apenergy.2021.117745}, abstractNote={Electric sector capacity expansion models are widely used by academic, government, and industry researchers for policy analysis and planning. Many models overlap in their capabilities, spatial and temporal resolutions, and research purposes, but yield diverse results due to both parametric and structural differences. Previous work has attempted to identify some differences among commonly used capacity expansion models but has been unable to disentangle parametric from structural uncertainty. Here, we present 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. We demonstrate how a systematic model comparison process allows us to pinpoint specific and important structural differences among our models, including specification of technologies as baseload or load following generation, battery state-of-charge at the beginning and end of a modeled period, application of battery roundtrip efficiency, treatment of discount rates, formulation of model end effects, and digit precision of input parameters. Our results show that such a process can be effective for improving consistency across models and building model confidence, substantiating specific modeling choices, reporting uncertainties, and identifying areas for further research and development. We also introduce an open-source test dataset that the modeling community can use for unit testing and build on for benchmarking exercises of more complex models. A community benchmarking effort can increase collaboration among energy modelers and provide transparency regarding the energy transition and energy challenges, for other stakeholders such as policymakers.}, journal={APPLIED ENERGY}, publisher={Elsevier BV}, author={Henry, Candise L. and Eshraghi, Hadi and Lugovoy, Oleg and Waite, Michael B. and DeCarolis, Joseph F. and Farnham, David J. and Ruggles, Tyler H. and Peer, Rebecca A. M. and Wu, Yuezi and Queiroz, Anderson and et al.}, year={2021}, month={Dec} }
 @article{esraghi_queiroz_sankarasubramanian_decarolis_2021, title={Quantification of climate-induced interannual variability in residential U.S. electricity demand}, volume={236}, ISSN={["1873-6785"]}, url={http://dx.doi.org/10.1016/j.energy.2021.121273}, DOI={10.1016/j.energy.2021.121273}, abstractNote={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. We use state-level monthly demographic, energy, and climate data from 2005 to 2017 in a linear regression model and find that interannual climate variability explains a significant share of seasonal household electricity demand variation: in 42 states, more than 70% and 50% of demand variability in summer and winter, respectively, is driven by climate. Our work suggests the need for new datasets to quantify unexplained variance in the winter and summer electricity demand. Findings from this study are critical to developing seasonal electricity demand forecasts, which can aid power system operation and management, particularly in a future with greater electrification of end-use demands.}, journal={Energy}, publisher={Elsevier BV}, author={Esraghi, H. and Queiroz, Ade and Sankarasubramanian, A. and DeCarolis, J.}, year={2021}, month={Dec}, pages={121273} }
 @article{cawthorne_rodrigo de queiroz_eshraghi_sankarasubramanian_decarolis_2021, title={The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US}, volume={3}, ISSN={["2624-9634"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85123099133&partnerID=MN8TOARS}, DOI={10.3389/frsc.2021.644789}, abstractNote={The reliable and affordable supply of energy through interconnected systems represent a critical infrastructure challenge. Seasonal and interannual variability in climate variables—primarily precipitation and temperature—can increase the vulnerability of such systems during climate extremes. The objective of this study is to understand and quantify the role of temperature variability on electricity consumption over representative areas of the Southern United States. We consider two states, Tennessee and Texas, which represent different climate regimes and have limited electricity trade with adjacent regions. Results from regression tests indicate that regional population growth explains most of the variability in electricity demand at decadal time scales, whereas temperature explains 44–67% of the electricity demand variability at seasonal time scales. Seasonal temperature forecasts from general circulation models are also used to develop season-ahead power demand forecasts. Results suggest that the use of climate forecasts can potentially help to project future residential electricity demand at the monthly time scale. Capsule Summary: Seasonal temperature forecasts from GCMs can potentially help in predicting season-ahead residential power demand forecasts for states in the Southern US.}, journal={Frontiers in Sustainable Cities}, author={Cawthorne, D. and Rodrigo de Queiroz, A. and Eshraghi, H. and Sankarasubramanian, A. and DeCarolis, J.F.}, year={2021}, month={Jun} }
 @article{sodano_decarolis_queiroz_johnson_2021, title={The symbiotic relationship of solar power and energy storage in providing capacity value}, volume={177}, ISSN={["1879-0682"]}, DOI={10.1016/j.renene.2021.05.122}, abstractNote={Ensuring power system reliability under high penetrations of variable renewable energy is a critical task for system operators. 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. Increasing penetrations of solar PV alter the net load profile on the grid, shifting the peak net load to hours with little or no solar generation and leading to diminishing capacity credits for each additional increment of solar. However, the presence of solar PV decreases the duration of daily peak demands, thereby allowing energy-limited storage capacity to dispatch electricity during peak demand hours. Thus, solar PV and storage exhibit a symbiotic relationship when used in tandem. We find that solar PV and storage used together make a more significant contribution to system reliability: as much as 40% more of the combined capacity can be counted on during peak demand hours compared to scenarios where the two technologies are deployed separately. Our test case demonstrates the important distinction between winter and summer peaking systems, leading to significantly different seasonal capacity values for solar PV. These findings are timely as utilities replace their aging peaking plants and are taking energy storage into consideration as part of a low carbon pathway.}, journal={RENEWABLE ENERGY}, author={Sodano, Daniel and DeCarolis, Joseph F. and Queiroz, Anderson Rodrigo and Johnson, Jeremiah X.}, year={2021}, month={Nov}, pages={823–832} }
 @article{medeiros_queiroz_lima_pereira_santos_czank junior_santos_eden_2021, title={Transmission towers spotting in power systems considering engineering and environmental aspects: A dynamic programming approach}, volume={31}, ISSN={["2050-7038"]}, url={https://doi.org/10.1002/2050-7038.13000}, DOI={10.1002/2050-7038.13000}, abstractNote={In recent years, electricity transmission systems' planning has become a subject of significant discussions worldwide due to increasing investments in renewable power and the need to optimize resources. Planning results directly affect the price of electricity for the final consumers; therefore, it is necessary to determine precise, robust, and relevant plans for the system expansion. Optimization techniques have been successfully employed in several problems associated with transmission line expansion planning, with emphasis on electricity interconnections, routing studies, and tower spotting, among others. The use of these techniques is intended to support planning processes with information that will assist the analyst in the pursuit of defined goals. The present work proposes a methodology based on dynamic programming 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. The methodology is tested in two different case studies including a real transmission line project with 39 km of extension. The results obtained show, approximately, 3.8% of cost savings obtained using the proposed approach when comparing with the real transmission line project. We also note that it is possible to verify a great similarity between the tower arrangements defined in the real project and the optimal decisions generated by the proposed approach, demonstrating its usefulness as a tool to support decision-making in early stages of investment planning and long-term auctions.}, number={9}, journal={INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS}, author={Medeiros, Giulia O. S. and Queiroz, Anderson Rodrigo and Lima, Rodolfo M. and Pereira, Camilo R. S. and Santos, Afonso H. M. and Czank Junior, Luiz and Santos, Renato A. and Eden, L. C. Junior}, year={2021}, month={Jul} }
 @article{aquila_queiroz_rotela junior_rocha_pamplona_balestrassi_2020, title={Contribution for bidding of wind-photovoltaic on grid farms based on NBI-EFA-SNR method}, volume={40}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85086744711&partnerID=MN8TOARS}, DOI={10.1016/j.seta.2020.100754}, abstractNote={Methods for supporting the bidding processes of hybrid wind-photovoltaic (W-PV) farms are scarce, especially when numerous goals are included in the optimization problem. Therefore, the primary objective of this study is to develop a novel model that can help bidding of W-PV 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. Using the proposed approach the optimal system configuration can be obtained in these cases with low computational costs. A non-linear multi-objective optimization (NL-MO) is proposed to optimize the area occupied by the W-PV farm, minimum feasibility price, electricity production expected, and standard-deviation of the electricity produced. The model has been elaborated from non-linear optimization using the normal-boundary intersection (NBI) method, exploratory factor analysis (EFA), and Taguchi signal-to-noise ratio (SNR). The optimal values for the response variables are an area of 132.92 km2, minimum price of 182.95 R$/MWh, annual electricity production of 72.17 GWh, with a standard deviation of 1.74 GWh and the ideal share is 41% wind power and 59% PV power.}, journal={Sustainable Energy Technologies and Assessments}, author={Aquila, G. and Queiroz, A.R. and Rotela Junior, P. and Rocha, L.C.S. and Pamplona, E.D.O. and Balestrassi, P.P.}, year={2020} }
 @article{oliveira_aquila_balestrassi_paiva_queiroz_oliveira pamplona_camatta_2020, title={Evaluating economic feasibility and maximization of social welfare of photovoltaic projects developed for the Brazilian northeastern coast: An attribute agreement analysis}, volume={123}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85079879120&partnerID=MN8TOARS}, DOI={10.1016/j.rser.2020.109786}, abstractNote={Recently, renewable energy projects, such as photovoltaic systems, have become interesting generation alternatives thanks to the incentive strategies developed by several countries. For the user of photovoltaic microgeneration, there is interest in the financial return of the investment, which is most often financed by public banks with a limited budget. Therefore, it is necessary to analyze variables related both to the point of view of the investor in microgeneration and to the public banks that subsidize them. However, defining the configuration of photovoltaic systems that guarantees the economic feasibility for those who invest without excessively burdening the public resource is a complex task and requires the analysis of different experts. To fill this gap, this paper proposes an innovative approach for evaluating photovoltaic projects based on Attribute Agreement Analysis. Experts on photovoltaic systems with different profiles and experience were asked about 16 scenarios, planned according to a factorial design with four factors: installed power capacity, PV cell type, debt ratio, and loan interest rate. The results demonstrated that the proposed approach fulfills the objective of simultaneously assessing the impact of investments in photovoltaic systems, considering the investors' and public banks’ viewpoints. In the case analyzed, although the evaluations are performed in a judicious way (Wwithin> 0.85), there is a low agreement between the experts (Woverall < 0.70). In addition, an expert bias was observed regarding loan interest for economic feasibility (W = 0.61), as well as a controversial perception of the maximization of social welfare (W = 0.2361). The Net Present Value profile, determined by the installed power capacity of the system, was used with these results to discuss the current Brazilian renewable energy financing policy. The results supported that experts tend to overestimate the impact of the financing interest rate on financial returns.}, journal={Renewable and Sustainable Energy Reviews}, author={Oliveira, L.G. and Aquila, G. and Balestrassi, P.P. and Paiva, A.P. and Queiroz, A.R. and Oliveira Pamplona, E. and Camatta, U.P.}, year={2020} }
 @article{decarolis_jaramillo_johnson_mccollum_trutnevyte_daniels_akin-olcum_bergerson_cho_choi_et al._2020, title={Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts}, volume={4}, ISSN={["2542-4351"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097654384&partnerID=MN8TOARS}, DOI={10.1016/j.joule.2020.11.002}, abstractNote={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. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. 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. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. Many nations have committed to mitigating climate change by designing and implementing policy solutions that enable deep decarbonization of their energy systems. Due to global reliance on fossil fuels, appropriate action requires fundamental and coordinated changes in the way societies generate and use energy. Policy makers face the monumental challenge of crafting effective energy and climate policy in the face of a highly uncertain future. The stakes are high because energy infrastructure often involves large, up-front investments in long-lived assets. Macro-energy system models, which are distinguished from other energy models by their energetic, temporal, and spatial scales,1Levi P.J. Kurland S.D. Carbajales-Dale M. Weyant J.P. Brandt A.R. Benson S.M. Macro-Energy Systems: Toward a New Discipline.Joule. 2019; 3: 2282-2286Abstract Full Text Full Text PDF Scopus (29) Google Scholar provide a systematic way to examine future decarbonization pathways, evaluate technology choices, test the effects and consequences of proposed policies, and explore decisions under future uncertainty. Analyses using these models yield critical insights that inform energy and climate policymaking around the world and underpin influential reports, including the World Energy Outlook by the International Energy Agency,2International Energy AgencyWorld Energy Outlook 2019.https://www.iea.org/reports/world-energy-outlook-2019Date: 2019Google Scholar the Annual Energy Outlook by the US Energy Information Administration,3US Energy Information AdministrationAnnual Energy Outlook 2020.https://www.eia.gov/outlooks/aeo/Date: 2020Google Scholar the Special Report on Global Warming of 1.5°C by the Intergovernmental Panel on Climate Change,4Hoegh-Guldberg, O., Jacob, D., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., Guiot, J., and Hijioka, Y. (2018). Impacts of 1.5 C global warming on natural and human systems. Global warming of 1.5°C. An IPCC Special Report. https://www.ipcc.ch/sr15/.Google Scholar and many others. It is an ongoing challenge for macro-energy system modeling teams to meet the universal and unprecedented policy needs associated with climate change mitigation. We envision a paradigm shift in the process of conducting model-based analysis from single-institution modeling teams to distributed, collaborative teams, allowing access to a much wider array of disciplinary and domain expertise to inform a given analysis. While some European efforts are already moving in this direction, the potential for collaborative, model-based analysis has yet to be realized. Energy system models vary considerably in their scope and complexity, and the choice of model should always be based on the research questions driving the analysis.5DeCarolis J. Daly H. Dodds P. Keppo I. Li F. McDowall W. Pye S. Strachan N. Trutnevyte E. Usher W. Winning M. Formalizing best practice for energy system optimization modelling.Appl. Energy. 2017; 194: 184-198Crossref Scopus (159) Google Scholar Here, we focus attention on employing macro-energy system models that cover the whole energy system and are used to inform policy at scales ranging from national to global. In this broadest macro-scale context, the boundaries of the modeled systems present numerous challenges for modeling deep decarbonization pathways. First, many supply- and demand-side technologies at varying stages of development could help decarbonize energy systems. Many of these technologies are novel (e.g., direct air capture and hydrogen-based steel production), have rapidly changing costs (e.g., solar photovoltaics, lithium-ion batteries, and electrolyzers), or have location-specific attributes (e.g., heat pumps and wind farms). These qualities make the projection of technology cost and performance characteristics over the multi-decade timescale of deep decarbonization very challenging. Second, the many decision makers across the energy system, each with their own objectives and preferences, make it difficult to model technology uptake, behavioral change, and public acceptance. Third, there is a need for modeling with high spatiotemporal resolution and multiple years of weather data in order to properly represent high penetrations of renewables with energy storage and other options for flexibility, since the modeled spatial variation in resource availability and temporal variation in supply and demand can have a significant impact on results. Fourth, policy-relevant insights should account for key underlying uncertainties affecting the modeled energy system. Neglecting any of these four challenges can lead to oversimplified model representations of the energy system with misleading conclusions; yet, including them increases model complexity, data requirements, and computational burden. Resolving this tension, given available resources, is difficult. Addressing the technical challenges of modeling decarbonization pathways requires considerable coordination of effort and broad domain expertise. When the effort is centralized at a single institution, institutional and governance structures can limit its effectiveness. Energy system modeling efforts housed within a single research group can suffer from a limited breadth of expertise. At the other extreme, some of the oldest and most established energy system models have been produced by government agencies and intergovernmental organizations that have the scale to draw on deep internal expertise across the energy system, but model-based analyses produced by these organizations can be subject to political considerations that limit the range of technologies or policies they will consider. In addition, commercial modeling efforts often rely on proprietary models and data that are not available to the broader expert community or interested stakeholders and therefore result in outcomes that cannot be easily reproduced and scientifically verified. To help address these shortcomings, distributed modeling teams can utilize existing open-source models, datasets, and tools to conduct collaborative, model-based analysis. Open-source efforts in the macro-energy space have proliferated over the last decade, and the resultant models, tools, and datasets serve as an important foundation for distributed modeling efforts because they enable transparency, accessibility, and replicability among team members and with the broader modeling community. Distributed efforts focused on model-based analysis allow for the flexible arrangement of teams to conduct different macro-energy modeling exercises, with each team configured to meet project-specific research objectives. The flexible arrangement of teams, in turn, means that specific modeling efforts can include participants with different disciplinary backgrounds and domain expertise who contribute to the diversity of ideas that can be explored in the analysis. The collective consideration of those ideas better reflects the system being modeled. For example, participants with a background in public policy, public administration, or economics can assist with the formulation, execution, and interpretation of more realistic policy scenarios, informed by debates and discussions in their respective communities. Modeling teams with collectively broad expertise across a range of issues and disciplines permit a more comprehensive analysis of the technical, social, economic, and policy features of deep decarbonization pathways, which are difficult to encode in models. In fact, all team members need not write code—the purposeful inclusion of non-modelers can lead to new insights and approaches associated with the model-based analysis.6Trutnevyte E. Hirt F.L. Bauer N. Cherp A. Hawkes A. Edelenbosch O.Y. Pedde S. van Vuuren D.P. Societal transformations in models for energy and climate policy: The ambitious next step.One Earth. 2019; 1: 423-433Abstract Full Text Full Text PDF Scopus (52) Google Scholar Diverse teams participating across the full project life cycle—from the formulation of key research questions, to the decision on how to represent a particular concept quantitatively, and then to the interpretation of model results as policy-relevant insights—can more effectively capture and assimilate novel ideas compared to conventional system modeling approaches that seek feedback at the end of the project or at discrete points during the project life cycle. These insights and ideas can range widely and may include the identification and proper use of a new dataset, a new model feature that captures a system dynamic critical to the issue under analysis, or the use of more efficient algorithms or methods that improve computational performance. Modeling teams that lack the appropriate depth and breadth are less able to effectively search, select, and incorporate new ideas from the broader macro-energy idea space into the analysis. Model parsimony should also be a design objective in order to avoid needless complexity,5DeCarolis J. Daly H. Dodds P. Keppo I. Li F. McDowall W. Pye S. Strachan N. Trutnevyte E. Usher W. Winning M. Formalizing best practice for energy system optimization modelling.Appl. Energy. 2017; 194: 184-198Crossref Scopus (159) Google Scholar and thus, distributed modeling teams must judiciously filter new ideas for incorporation into the analysis. Furthermore, the expanding scope enabled by distributed teams must be balanced with limited time, funding, and computational resources. The European Union is already pioneering a distributed and collaborative approach under the €80 billion Horizon 2020 research and innovation program. Projects such as SET-NAV (https://www.set-nav.eu/), openENTRANCE (https://openentrance.eu/), SENTINEL (https://sentinel.energy/), Spine (http://www.spine-model.org/), and EMP-E (http://www.energymodellingplatform.eu/) involve large teams variously working to integrate different models into larger frameworks, solicit input from a wide array of stakeholders, and perform model-based analysis that informs European energy and climate policy. The European Union is uniquely positioned to lead such efforts, given its ambitious energy-climate policy portfolio, well-funded scientific research programs, and ambitions for pan-national integration. While many other nations and regions—including the US—cannot easily replicate the top-down European approach without a significant change in policy priorities, we nonetheless assert that it is possible for researchers to organize similar efforts from the bottom up by leveraging existing resources within the scientific community. While distributed efforts focused on model-based analysis present unique logistical challenges, they also provide the flexibility to organize teams that capture diverse domain expertise and disciplinary approaches. All of the necessary elements exist to coordinate distributed model-based analysis: open-source energy models, well-established software development tools, a wide range of collaborative communication tools, and an increasing number of publicly available datasets on which to build. First, the open energy modeling initiative (“openmod”), an active and vibrant community of energy modelers committed to open-source practices, has cataloged a large array of open-source models7Openmod InitiativeOpen Models.https://wiki.openmod-initiative.org/wiki/Open_ModelsDate: 2020Google Scholar and helped to promulgate best practice standards for model developers that include licensing, documentation, reproducibility, and user support.8DeCarolis J.F. Hunter K. Sreepathi S. The case for repeatable analysis with energy economy optimization models.Energy Econ. 2012; 34: 1845-1853Crossref Scopus (82) Google Scholar, 9Pfenninger S. Hirth L. Schlecht I. Schmid E. Wiese F. Brown T. Davis C. Gidden M. Heinrichs H. Heuberger C. Hilpert S. Opening the black box of energy modelling: Strategies and lessons learned.Energy Strategy Reviews. 2018; 19: 63-71Crossref Scopus (129) Google Scholar, 10Pfenninger S. DeCarolis J. Hirth L. Quoilin S. Staffell I. The importance of open data and software: Is energy research lagging behind?.Energy Policy. 2017; 101: 211-215Crossref Scopus (192) Google Scholar, 11Morrison R. Energy system modeling: Public transparency, scientific reproducibility, and open development.Energy Strategy Reviews. 2018; 20: 49-63Crossref Scopus (52) Google Scholar Second, many energy modelers are using modern software development tools, which enable distributed control of code and data, with changes archived in publicly accessible web repositories. Third, a variety of communication options, including traditional email, cloud-based collaboration platforms, and videoconferencing software, make it possible for distributed teams to collaborate on highly technical issues in near-real time and at low cost. These modes of communication have indeed become an increasingly familiar part of our lives given how the coronavirus disease (COVID-19) pandemic has disrupted normal meeting patterns. In addition, social media represents a particularly effective way to crowdsource new ideas and approaches from the broader stakeholder community. Fourth, the volume of available data to populate energy models has grown over time and can be used to better parameterize models. The challenge, however, is that modelers are not aware of all relevant datasets, particularly those curated outside of the energy modeling community, nor do they always understand the underlying assumptions and limitations. Diversity in expertise among the modeling team can help ensure the proper identification and use of such datasets. In the long run, by using open-source tools and drawing on the expertise of non-modelers who are typically disconnected from the modeling process, distributed modeling teams may counteract the “incumbency advantage” of “long-lived and dominant” energy models12Strachan N. Fais B. Daly H. Reinventing the energy modelling–policy interface.Nat. Energy. 2016; 1: 1-3Crossref Google Scholar by helping redefine the way energy models operate. We view this approach as a critical element in the reinvention of the modeling-policy interface.12Strachan N. Fais B. Daly H. Reinventing the energy modelling–policy interface.Nat. Energy. 2016; 1: 1-3Crossref Google Scholar As with any new approach, there will be attendant challenges. Macro-energy modeling efforts face the same funding and coordination challenges confronted by other large scientific endeavors. Funding challenges are more logistically difficult with teams spanning multiple institutions. There is no single solution: financial arrangements will necessarily be a product of the funding agency, team composition, and objectives of the analysis. While there may be circumstances where funding can be equitably distributed among all participants, there might be other times when one or two lead organization(s) take the bulk of the responsibility, with smaller support grants and in-kind contributions from other members of the distributed team. Furthermore, funding need not always be a requirement for participation: limited but strategic input from a broad constellation of team members delivered at the right time in the process can have a large, positive impact on the direction of the project. While the Stanford Energy Modeling Forum (https://emf.stanford.edu/) is focused on inter-model comparison, its long-term success demonstrates that participants are willing to contribute their time, often without financial compensation, in return for the opportunity to collaborate with others and produce new scholarly research. Another challenge is the incentive structure within academia. It takes significant upfront effort to establish a common language and align project goals among team members from different academic disciplines. In addition, receiving credit for work completed is an important aspect of scholarly work. Credit often takes the form of co-authorship on reports and journal articles, and it is important to track the contributions of team members to ensure their efforts are recognized in an appropriate way, commensurate with their own institutional and disciplinary incentive structures. Furthermore, academic institutions should formally recognize the effort required to develop the open-source models, tools, and datasets that underpin the model-based analysis. The CRediT taxonomy, used by this publisher (https://www.cell.com/pb/assets/raw/shared/guidelines/CRediT-taxonomy.pdf), provides an excellent way to track the various contributions to distributed macro-energy modeling efforts. New modeling efforts that leverage these emerging opportunities can fulfill a unique niche within the global energy modeling community. We have begun to see the benefits of such an approach in our own effort to develop an Open Energy Outlook for the US (https://openenergyoutlook.org/). In addition to using an open-source modeling platform to perform the analysis (https://temoacloud.com/), we have established an interdisciplinary and inter-sectoral team of experts who are working collaboratively on the project with a unified vision. Our international team involves a number of experts drawn from academia, non-profits, and government labs and includes both experienced macro-energy system modelers and domain experts. Funding is distributed across two institutions that have primary responsibility for the deliverables, while participants from the remaining 20+ institutions make in-kind contributions of their time to the effort. Our project has a fraction of the funding associated with the large European efforts referenced above, and thus relies heavily on our collective interest in the project objectives and the opportunity to collaboratively produce scholarly work. Because participants are already working in related areas, they are able to leverage ongoing research activities and resources for this project. Our current team is meant to be a starting point for this long-term effort. Just as open-source tools foster collaborative development, democratization of the team building process can ensure a greater diversity of perspectives and make the effort more adaptable to new challenges. To this end, we are currently working on a formal and open nomination process for team membership. In addition, we are building a broader network of contributors to the project, and have sought input through a variety of online outlets, including social media, virtual workshops, and mailing lists. While still in the early stages, the project has already benefited from the diverse perspectives of the participants. For example, the electricity experts have pushed for a novel approach to increase the model’s temporal resolution while maintaining computational tractability and also identified opportunities to leverage existing open-source tools (https://github.com/gschivley/PowerGenome) and datasets (https://github.com/catalyst-cooperative/pudl). Likewise, the building experts are pushing the project to consider building thermodynamics more explicitly in order to better represent building thermal performance. The value here is bidirectional: systems modelers gain more familiarity with tools and data within particular sectors, while domain experts gain a better understanding of how their expertise can influence long-term energy scenarios. If done well, such an approach allows us to rethink and redefine common modeling approaches, potentially leading to innovative methods that result in new insights that are rigorously grounded by careful consideration of how the energy system—and all its myriad connections and feedbacks—is modeled. We would like to thank the Alfred P. Sloan Foundation for supporting this work. We also thank the two anonymous reviewers whose detailed and insightful feedback significantly strengthened the manuscript. Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling EffortsDeCarolis et al.JouleFebruary 17, 2021In Brief(Joule 4, 2523–2531; December 16, 2020) Full-Text PDF Open Access}, number={12}, journal={JOULE}, publisher={Elsevier BV}, author={DeCarolis, Joseph F. and Jaramillo, Paulina and Johnson, Jeremiah X. and McCollum, David L. and Trutnevyte, Evelina and Daniels, David C. and Akin-Olcum, Gokce and Bergerson, Joule and Cho, Soolyeon and Choi, Joon-Ho and et al.}, year={2020}, month={Dec}, pages={2523–2526} }
 @article{aquila_queiroz_lima_balestrassi_marangon lima_pamplona_2020, title={Modelling and design of wind-solar hybrid generation projects in long-term energy auctions: A multi-objective optimisation approach}, volume={14}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85094890582&partnerID=MN8TOARS}, DOI={10.1049/iet-rpg.2020.0185}, abstractNote={: 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 L p method, as well as an efficiency metric designed to achieve diversification and to identify the Pareto dominant optimal portfolio. The proposed method is simple and flexible for practical applications. Moreover, its associated goals of choosing the Pareto dominant optimal solutions are aligned with the goals of the electricity regulators responsible to manage the hiring process for a new generation. To validate the method, wind-solar photovoltaic generation configurations in three Brazilian cities are analysed and the results are compared with other methods previously proposed in the literature. The results show that the proposed method has more intuitive criteria for the investor and regulator , without reducing the quality of the information provided to decision making.}, number={14}, journal={IET Renewable Power Generation}, author={Aquila, G. and Queiroz, A.R. and Lima, L.M.M. and Balestrassi, P.P. and Marangon Lima, J.W. and Pamplona, E.O.}, year={2020}, pages={2612–2619} }
 @article{li_thomas_queiroz_decarolis_2020, title={Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study}, volume={54}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078394675&partnerID=MN8TOARS}, DOI={10.1021/acs.est.9b04184}, abstractNote={Rigorous model-based analysis can help inform state-level energy and climate policy. In this study, we utilize an open-source energy system optimization model and publicly available datasets to examine future electricity generation, CO2 emissions, and CO2 abatement costs for the North Carolina electric power sector through 2050. Model scenarios include uncertainty in future fuel prices, a hypothetical CO2 cap, and an extended renewable portfolio standard. Across the modeled scenarios, solar photovoltaics represent the most cost-effective low-carbon technology, while trade-offs among carbon constrained scenarios largely involve natural gas and renewables. We also develop a new method to calculate break-even costs, which indicate the capital costs at which different technologies become cost-effective within the model. Significant variation in break-even costs are observed across different technologies and scenarios. We illustrate how break-even costs can be used to inform the development of an extended renewable portfolio standard in North Carolina. Utilizing the break-even costs to calibrate a tax credit for onshore wind, we find that the resultant wind deployment displaces other renewables, and thus has a negligible effect on CO2 emissions. Such insights can provide crucial guidance to policymakers weighing different policy options. This study provides an analytical framework to conduct similar analyses in other states using an open source model and freely available datasets.}, number={2}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Li, Binghui and Thomas, Jeffrey and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2020}, month={Jan}, pages={665–676} }
 @article{li_thomas_queiroz_decarolis_2020, title={Open source energy system modeling using break-even costs to inform state-level policy: A North Carolina case study}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85093377128&partnerID=MN8TOARS}, journal={arXiv}, author={Li, B. and Thomas, J. and Queiroz, A.R. and DeCarolis, J.F.}, year={2020} }
 @article{hafiz_awal_queiroz_husain_2020, title={Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications}, volume={56}, ISSN={["1939-9367"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85084192067&partnerID=MN8TOARS}, DOI={10.1109/TIA.2020.2968534}, abstractNote={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). Existing offline energy management approaches for day-ahead scheduling of BES suffer from energy loss in real time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming algorithm in the receding horizon to update the optimal set point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods.}, number={3}, journal={IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS}, author={Hafiz, Faeza and Awal, M. A. and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2020}, pages={2216–2226} }
 @article{aquila_queiroz_balestrassi_rotella junior_rocha_pamplona_nakamura_2020, title={Wind energy investments facing uncertainties in the Brazilian electricity spot market: A real options approach}, volume={42}, ISSN={["2213-1396"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85093981217&partnerID=MN8TOARS}, DOI={10.1016/j.seta.2020.100876}, abstractNote={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. This novel approach considers uncertainties representation concerning electricity sales revenue in the spot market, and the uncertainty represented by the settlements of energy trading differences. Our results show that when considering these uncertainties, the abandonment option adds 30.3% to the value of the project, and the chance of not abandoning it until the end of the useful life is equal to 70.9%.}, journal={SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS}, author={Aquila, G. and Queiroz, A. R. and Balestrassi, P. P. and Rotella Junior, P. and Rocha, L. C. S. and Pamplona, E. O. and Nakamura, W. T.}, year={2020}, month={Dec} }
 @article{patankar_queiroz_decarolis_bazilian_chattopadhyay_2019, title={Building conflict uncertainty into electricity planning: A South Sudan case study}, volume={49}, ISSN={["0973-0826"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060864438&partnerID=MN8TOARS}, DOI={10.1016/j.esd.2019.01.003}, abstractNote={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. Strategies that hedge against future conflict have the greatest economic value in moderate conflict-related damage scenarios by avoiding expensive near-term investments in infrastructure that may be subsequently damaged. Model results show that solar photovoltaics can play a critical role in South Sudan's future electric power system. In addition to mitigating greenhouse gas emissions and increasing access to electricity, this analysis suggests that solar can be used to hedge against economic losses incurred by conflict. While this analysis focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.}, journal={ENERGY FOR SUSTAINABLE DEVELOPMENT}, author={Patankar, Neha and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F. and Bazilian, Morgan D. and Chattopadhyay, Debabrata}, year={2019}, month={Apr}, pages={53–64} }
 @article{hafiz_queiroz_husain_2019, title={Coordinated Control of PEV and PV-Based Storages in Residential Systems Under Generation and Load Uncertainties}, volume={55}, ISSN={["1939-9367"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85075497159&partnerID=MN8TOARS}, DOI={10.1109/TIA.2019.2929711}, abstractNote={Energy storage deployment in residential and commercial applications is an attractive proposition for ensuring proper utilization of solar photovoltaic (PV) power generation. Energy storage can be controlled and coordinated with PV generation to satisfy electricity demand and minimize electricity purchases from the grid. For optimal energy management, PV generation and load demand uncertainties need to be considered when designing a control method for the PV-based storage system. Another resource available at the residential level is the plug-in electric vehicle (PEV) which also has bi-directional power flow capability. The charging and discharging routines of the PEV can be controlled to help reduce the energy drawn from the power grid during peak hours. 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. Simulation analysis shows the advantage of the coordinated control compared to other control strategies.}, number={6}, journal={IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS}, author={Hafiz, Faeza and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2019}, pages={5524–5532} }
 @inproceedings{santos_medeiros_lima_queiroz_alvares_gomes_barbosa_marangon lima_2019, title={Efficiency analysis for performance evaluation of electricity distribution companies}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85079664013&partnerID=MN8TOARS}, booktitle={ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems}, author={Santos, L.C.B. and Medeiros, G.O.S. and Lima, L.M.M. and Queiroz, A.R. and Alvares, J.E. and Gomes, R. and Barbosa, M.A. and Marangon Lima, J.W.}, year={2019}, pages={1569–1581} }
 @article{queiroz_faria_lima_lima_2019, title={Hydropower revenues under the threat of climate change in Brazil}, volume={133}, ISSN={["0960-1481"]}, url={http://dx.doi.org/10.1016/j.renene.2018.10.050}, DOI={10.1016/j.renene.2018.10.050}, abstractNote={This work analyzes the 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. Unfortunately, the use of such information alone may not project well the 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 hydropower system. Global climate models from IPCC are considered along with downscaled regional climate models. Our results at the individual hydro plant level show the importance of taking into account climate change information when performing hydro generation planning studies.}, journal={RENEWABLE ENERGY}, publisher={Elsevier BV}, author={Queiroz, Anderson Rodrigo and Faria, Victor A. D. and Lima, Luana M. M. and Lima, Jose W. M.}, year={2019}, month={Apr}, pages={873–882} }
 @article{moreira santos_lima_silva pereira_osis_santos medeiros_queiroz_flauzino_paschoal correa cardoso_czank junior_santos_et al._2019, title={Optimizing routing and tower spotting of electricity transmission lines: An integration of geographical data and engineering aspects into decision-making}, volume={176}, ISSN={["1873-2046"]}, url={https://doi.org/10.1016/j.epsr.2019.105953}, DOI={10.1016/j.epsr.2019.105953}, abstractNote={In many parts of the world, electric sectors are already experiencing considerable rising in generation from renewable energy sources. Large amounts of new generation are expected in the near-term future, which will require additional transmission investments to properly integrate these resources into the existing electric power system. The transmission expansion planning has an important role in this environment in order to guarantee the security of the supply with the required levels of quality and price. Therefore, the implementation of new transmission lines (TL) must be fast and accurate in order to avoid delays to connect new power sources and potential supply and reliability problems. In this sense, Geographical Information Systems (GIS) can be a powerful tool that provides decision support techniques, which enables a transparent, sustainable, faster planning process for TLs in power systems. This paper presents a novel approach for the design of overhead TLs, considering geographical, engineering and cost aspects into the decision-making process. For this, routing and tower spotting optimization approaches are integrated into the proposed methodology, which is divided into three main steps: (i) Route Guideline Definition based on a raster-based least-cost path approach; (ii) Vertex Siting based on graph theory and the Dijkstra shortest path algorithm, applied in order to find the optimal vertex set along the route guideline; (iii) Tower Spotting based on Dynamic Programming, which is applied in order to find the optimal distribution of towers along the topographical profile of the route obtained in the previous step. 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. We show a case study using the proposed methodology for a real project of a 525 kV TL that interconnects Machadinho and Campos Novos (located in the Santa Catarina state in Brazil). The outcomes show that the proposed approach is capable of representing the technical and geographical constraints of a TL design, providing results with lower costs when compared to the original TL design.}, journal={ELECTRIC POWER SYSTEMS RESEARCH}, author={Moreira Santos, Afonso Henriques and Lima, Rodolfo Mendes and Silva Pereira, Camilo Raimundo and Osis, Reinis and Santos Medeiros, Giulia Oliveira and Queiroz, Anderson Rodrigo and Flauzino, Barbara Karoline and Paschoal Correa Cardoso, Arthur Rohr and Czank Junior, Luiz and Santos, Renato Antonio and et al.}, year={2019}, month={Nov} }
 @inproceedings{hafiz_awal_de queiroz_husain_2019, title={Real-time Stochastic Optimization of Energy Storage Management using Rolling Horizon Forecasts for Residential PV Applications}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85076767755&partnerID=MN8TOARS}, DOI={10.1109/IAS.2019.8912315}, abstractNote={In this paper, an energy management method for a residential PV-storage hybrid system composed of a solar photovoltaic (PV) generation and a battery energy storage (BES) is formulated as an offline optimization model concurrent with a real-time rule-based controller. Existing offline energy management approaches for day ahead scheduling of BES generally suffers from energy loss in real-time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome such limitations, we propose an integrated framework where optimization is performed in receding horizon utilizing the forecasted load and solar generation profiles from long short term memory (LSTM) in rolling horizon to reduce daily electricity purchase costs. The optimization model is formulated as a multi-stage stochastic program where we use the stochastic dual dynamic programming (SDDP) algorithm in the receding horizon to update the optimal set-point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update instants, we propose a rule-based controller beneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a realtime controller-hardware-in-the-Ioop (CHIL) test platform in an Opal-RT simulator. The proposed real-time method reduces the net electricity purchase cost relative to other existing energy management methods.}, booktitle={2019 IEEE Industry Applications Society Annual Meeting, IAS 2019}, author={Hafiz, F. and Awal, M.A. and De Queiroz, A.R. and Husain, I.}, year={2019} }
 @article{queiroz_mulcahy_sankarasubramanian_deane_mahinthakumar_lu_decarolis_2019, title={Repurposing an Energy System Optimization Model for Seasonal Power Generation Planning}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095310995&partnerID=MN8TOARS}, journal={arXiv}, author={Queiroz, A.R. and Mulcahy, D. and Sankarasubramanian, A. and Deane, J.P. and Mahinthakumar, G. and Lu, N. and DeCarolis, J.F.}, year={2019} }
 @article{de queiroz_mulcahy_sankarasubramanian_deane_mahinthakumar_lu_decarolis_2019, title={Repurposing an energy system optimization model for seasonal power generation planning}, volume={181}, ISSN={0360-5442}, url={http://dx.doi.org/10.1016/j.energy.2019.05.126}, DOI={10.1016/j.energy.2019.05.126}, abstractNote={Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test 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. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty.}, journal={Energy}, publisher={Elsevier BV}, author={de Queiroz, A.R. and Mulcahy, D. and Sankarasubramanian, A. and Deane, J.P. and Mahinthakumar, G. and Lu, N. and DeCarolis, J.F.}, year={2019}, month={Aug}, pages={1321–1330} }
 @article{eshraghi_queiroz_decarolis_2019, title={US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85094022064&partnerID=MN8TOARS}, journal={arXiv}, author={Eshraghi, H. and Queiroz, A.R.D. and DeCarolis, J.F.}, year={2019} }
 @article{hafiz_chen_chen_queiroz_husain_2019, title={Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters}, volume={13}, ISSN={["1751-8695"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85069476402&partnerID=MN8TOARS}, DOI={10.1049/iet-gtd.2018.6866}, abstractNote={The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a 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. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.}, number={14}, journal={IET GENERATION TRANSMISSION & DISTRIBUTION}, publisher={Institution of Engineering and Technology (IET)}, author={Hafiz, Faeza and Chen, Bo and Chen, Chen and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2019}, month={Jul}, pages={2942–2950} }
 @inproceedings{faria_de queiroz_lima_lima_2018, title={Analysis of multiple solutions in the calculus of firm energy}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050202742&partnerID=MN8TOARS}, DOI={10.1109/SBSE.2018.8395694}, abstractNote={This paper uses a linear optimization model to investigate the calculus 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. Using data from the Brazilian Electrical System the authors could determine the intensity of these multiple optimal solutions in an example, the results show that the phenomenon is relevant, influencing significantly in the firm energy values.}, booktitle={SBSE 2018 - 7th Brazilian Electrical Systems Symposium}, author={Faria, V.A.D. and De Queiroz, A.R. and Lima, L.M.M. and Lima, J.W.M.}, year={2018}, pages={1–6} }
 @article{aquila_peruchi_rotela junior_souza rocha_queiroz_pamplona_balestrassi_2018, title={Analysis of the wind average speed in different Brazilian states using the nested GR & R measurement system}, volume={115}, ISSN={["1873-412X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85032342123&partnerID=MN8TOARS}, DOI={10.1016/j.measurement.2017.10.048}, abstractNote={Brazil presents remarkable potential for wind power generation. 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 NGR&R study (Nested Gage Repeatability & Reproducibility), generally applied on manufacturing quality management. Wind average speeds were collected for each month in four states, between the years of 2012 and 2015. Seasonality impact, measurements recurrence over the years and difference between states on wind average speed were assessed in this research. Time series, boxplot and control charts have been used to investigate not only wind average speed between months and states, but also range variation for each state by month. Study results show that the impact of these three factors is statistically significant and that the different location of these states presents the most relevant impact to wind mean speed variation in the country.}, journal={MEASUREMENT}, author={Aquila, Giancarlo and Peruchi, Rogerio Santana and Rotela Junior, Paulo and Souza Rocha, Luiz Celio and Queiroz, Anderson Rodrigo and Pamplona, Edson de Oliveira and Balestrassi, Pedro Paulo}, year={2018}, month={Feb}, pages={217–222} }
 @article{faria_queiroz_lima_lima_2018, title={Cooperative game theory and last addition method in the allocation of firm energy rights}, volume={226}, ISSN={["1872-9118"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85048638261&partnerID=MN8TOARS}, DOI={10.1016/j.apenergy.2018.06.065}, abstractNote={The firm energy rights of a hydro plant is a parameter used in some electricity markets to define the maximum amount of energy that a power plant can trade through contracts. In a centralized dispatch scheme, the coordinated operation of the hydro plants generates a synergetic gain in the system firm energy, in this setting, a question that often arises is how to fairly allocate this energy among each hydro plant. 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. In order to make simulations of real instances possible, it is proposed a reformulation of the traditional mixed integer linear programming model that computes the core constraints, which induces a significant speed-up of the algorithm solution time. It is shown an application of the proposed methodology to a real instance representing the Brazilian electric power system.}, journal={APPLIED ENERGY}, author={Faria, Victor. A. D. and Queiroz, Anderson Rodrigo and Lima, Luana M. M. and Lima, Jose W. M.}, year={2018}, month={Sep}, pages={905–915} }
 @inproceedings{hafiz_de queiroz_husain_2018, title={Coordinated control of PEV and PV-based storage system under generation and load uncertainties}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85057766366&partnerID=MN8TOARS}, DOI={10.1109/IAS.2018.8544636}, abstractNote={Energy storage is an attractive choice for deployment in residential and commercial applications aiming to ensure proper utilization of solar photovoltaic (PV) power generation. Energy storage can be controlled and coordinated with PV generation to satisfy electricity demand and minimize electricity purchases from the grid. However, PV generation and load profile depend on the real-time weather condition and the usage by the owners. Thus, PV generation and demand uncertainties need to be considered when designing a control scheme for the PV-based storage system. Another resource at the residential level is theplug-in electric vehicle (PEV) which has a bi-directional capability and can reduce the electric power draw from the grid during peak hours. Therefore, the charging and discharging routines of the PEV can be controlled to achieve optimal economic benefits. 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 (SDDP) algorithm is employed to optimize the charge/discharge profiles of PV-based energy storage and PEV storage to minimize the overall cost of the daily household electricity purchase from the grid. Simulation analysis is performed in order to show the advantage of coordinated control compared to the other control strategies.}, booktitle={2018 IEEE Industry Applications Society Annual Meeting, IAS 2018}, author={Hafiz, F. and De Queiroz, A.R. and Husain, I.}, year={2018} }
 @inproceedings{sun_thomas_singh_li_baran_lubkeman_decarolis_queiroz_white_watts_et al._2018, title={Cost-benefit assessment challenges for a smart distribution system: A case study}, volume={2018-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85046337981&partnerID=MN8TOARS}, DOI={10.1109/PESGM.2017.8274167}, abstractNote={The FREEDM system is a technology for a smarter and resilient distribution system that facilitates a higher level of distributed energy resource (DER) integration by offering effective voltage regulation, reactive power compensation and real time monitoring and control. This paper provides a framework for conducting a cost-benefit analysis for such a smart distribution system. The method first identifies the benefits, and then quantifies and monetizes them. OpenDSS time-series based power flow simulation is used to quantify the benefits accurately. The costs associated with the new components of the system are estimated based on prototype units. A cost-benefit analysis is adopted to identify the scenarios where employing such a system by a utility becomes economically attractive.}, booktitle={IEEE Power and Energy Society General Meeting}, author={Sun, L. S. and Thomas, J. and Singh, S. and Li, D. X. and Baran, M. and Lubkeman, David and DeCarolis, J. and Queiroz, A. R. and White, L. and Watts, S. and et al.}, year={2018}, pages={1–5} }
 @article{fonseca_pamplona_queiroz_mello valerio_aquila_silva_2018, title={Multi-objective optimization applied for designing hybrid power generation systems in isolated networks}, volume={161}, ISSN={["0038-092X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85039797533&partnerID=MN8TOARS}, DOI={10.1016/j.solener.2017.12.046}, abstractNote={The use of hybrid power generation systems is an attractive alternative to conventional fossil fuel generation since they may assist in mitigating the emission of gases that are harmful to the atmosphere when using clean and renewable sources of energy. However, finding the ideal configuration for the installation of a hybrid system composed of solar photovoltaic (PV)-diesel generation is a complex task. In this sense, the objective of this study is to develop 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 northern region of Brazil, more specifically in the state of Amazonas. The results show that for each region a different configuration is selected but with large shares of diesel generation at first. On the other hand, all these cases represent points in the Pareto frontier that are the most inefficient due to the high volume of CO2 emissions. From the application of the proposed approach, significant CO2 emission reductions are obtained by selecting the optimal configurations represented as the most efficient points in the Pareto frontier. Our results show that due to conflicting characteristics of the selected objectives, the installation of such hybrid power generation systems produces an increase in LCOE, mainly related to the high costs of the batteries, although less accentuated than the reductions in emissions.}, journal={SOLAR ENERGY}, author={Fonseca, Marcelo Nunes and Pamplona, Edson de Oliveira and Queiroz, Anderson Rodrigo and Mello Valerio, Victor Eduardo and Aquila, Giancarlo and Silva, Saulo Ribeiro}, year={2018}, month={Feb}, pages={207–219} }
 @article{aquila_rocha_oliveira pamplona_queiroz_junior_balestrassi_fonseca_2018, title={Proposed method for contracting of wind-photovoltaic projects connected to the Brazilian electric system using multiobjective programming}, volume={97}, url={https://doi.org/10.1016/j.rser.2018.08.054}, DOI={10.1016/j.rser.2018.08.054}, abstractNote={Owing to the wind and photovoltaic (PV) potential in Brazil, the country has recently seen increased exploration into the construction of wind-PV hybrid plants. However, as specific criteria for contracting this type of project have not yet been developed, this paper presents a model to assist the government in contracting projects that maximize the socioeconomic well-being of the Brazilian electricity sector. For this, 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. Additionally, a metric based on the ratio between entropy and the global percentage error (GPE) is used to identify the optimal Pareto solution. The model was applied to determine optimal configurations for wind-PV powerplants in twelve Brazilian cities, and the results obtained reveal the capacity of the model to indicate the optimum configuration according to the wind and PV potential of each city.}, journal={Renewable and Sustainable Energy Reviews}, author={Aquila, Giancarlo and Rocha, Luiz Célio Souza and Oliveira Pamplona, Edson and Queiroz, Anderson Rodrigo and Junior, Paulo Rotela and Balestrassi, Pedro Paulo and Fonseca, Marcelo Nunes}, year={2018}, month={Dec}, pages={377–389} }
 @article{hafiz_queiroz_husain_2018, title={Solar Generation Storage, and Electric Vehicles in Power Grids}, volume={6}, ISSN={["2325-5897"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85057761435&partnerID=MN8TOARS}, DOI={10.1109/MELE.2018.2871319}, abstractNote={Solar energy is an abundant renewable energy source that is available all around the world every day. Each hour, the solar rays that reach our Earth (if properly converted to electricity and other forms of energy) represent more than the total energy consumption of the entire human race over the course of one year. 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.}, number={4}, journal={IEEE ELECTRIFICATION MAGAZINE}, author={Hafiz, Faeza and Queiroz, Anderson Rodrigo and Husain, Iqbal}, year={2018}, month={Dec}, pages={83–90} }
 @article{eshraghi_queiroz_decarolis_2018, title={US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy}, volume={52}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85052916806&partnerID=MN8TOARS}, DOI={10.1021/acs.est.8b01586}, abstractNote={The planned US withdrawal from the Paris Agreement as well as uncertainty about federal climate policy has raised questions about the country's future emissions trajectory. Our model-based analysis accounts for uncertainty in fuel prices and energy technology capital costs and suggests that market forces are likely to keep US energy-related greenhouse gas emissions relatively flat or produce modest reductions: in the absence of new federal policy, 2040 greenhouse gas emissions range from +10% to -23% of the baseline estimate. Natural gas versus coal utilization in the electric sector represents a key trade-off, particularly under conservative assumptions about future technology innovation. The lowest emissions scenarios are produced when the cost of natural gas and electric vehicles declines, while coal and oil prices increase relative to the baseline.}, number={17}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Eshraghi, Hadi and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2018}, month={Sep}, pages={9595–9604} }
 @inproceedings{medeiros_lima_lima_de queiroz_2018, title={Weight limits in the DEA benchmarking model for Brazilian electricity distribution companies}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050233786&partnerID=MN8TOARS}, DOI={10.1109/SBSE.2018.8395921}, abstractNote={The periodic tariff review process is applied to tariff control and regulation of electrical energy distribution system. In Brazil, the method used to obtain the efficient operational cost (OPEX) is a benchmarking technique based in linear programming called Data Envelopment Analysis (DEA). This paper presents an analysis of the DEA applied to the Brazilian distribution companies focusing on the weight restrictions incorporated by the regulatory agency. An alternative technique, the Ratio-based Efficiency Analysis (REA) is also applied to the analysis to provide additional information regarding the performance of DEA coupled with weight restrictions for the Brazilian case.}, booktitle={SBSE 2018 - 7th Brazilian Electrical Systems Symposium}, author={Medeiros, G.O.S. and Lima, J.W.M. and Lima, L.M.M. and De Queiroz, A.R.}, year={2018}, pages={1–6} }
 @misc{aquila_pamplona_queiroz_rotela junior_fonseca_2017, title={An overview of incentive policies for the expansion of renewable energy generation in electricity power systems and the Brazilian experience}, volume={70}, ISSN={["1364-0321"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85007613506&partnerID=MN8TOARS}, DOI={10.1016/j.rser.2016.12.013}, abstractNote={Energy production from renewable sources is already a reality in many countries, and with that, different strategies for incentivizing investments in renewable energy generation have been proposed and used over the years. 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 overviewed and discussed. The main advantages and disadvantages of these incentive strategies are emphasized, focusing on applications. Some of these strategies that have already been applied in Brazil are analyzed in greater depth, emphasizing the potentialities and fragilities of these mechanisms observed within the country. Even though it is a country that stands out in relation to other Latin American countries regarding electricity generation from non-hydro renewable sources, Brazil still faces barriers that prevent a utilization compatible with its potential. Moreover, the trend for renewable sources, such as wind and solar power, is to represent an energy capacity reserve to cover hydrological risks and also to contribute to a distributed generation spread through electricity distribution networks.}, journal={RENEWABLE & SUSTAINABLE ENERGY REVIEWS}, author={Aquila, Giancarlo and Pamplona, Edson de Oliveira and Queiroz, Anderson Rodrigo and Rotela Junior, Paulo and Fonseca, Marcelo Nunes}, year={2017}, month={Apr}, pages={1090–1098} }
 @inproceedings{hafiz_de queiroz_husain_2017, title={Multi-stage stochastic optimization for a PV-storage hybrid unit in a household}, volume={2017-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044105670&partnerID=MN8TOARS}, DOI={10.1109/IAS.2017.8101704}, abstractNote={In the face of increasing global energy supply challenges, renewable energy sources provide a cleaner and environmentally friendly energy alternative. To address the intermittency in PV power generation, battery storage can be used to store energy during lower demand periods. This requires the charging and discharging routine of the storage system to be controlled to achieve optimal economic benefits. In this paper, coordinated control between PV component and an accompanying storage unit is presented considering the stochastic nature of PV generation. The stochastic dual dynamic programming (SDDP) algorithm is employed to optimize the charge/discharge profiles with the goal to minimize the overall cost of satisfying the daily household load demand. The PV-storage hybrid unit can jointly contribute in reducing the consumer costs as shown through simulation analysis.}, booktitle={2017 IEEE Industry Applications Society Annual Meeting, IAS 2017}, author={Hafiz, F. and De Queiroz, A.R. and Husain, I.}, year={2017}, pages={1–7} }
 @article{li_queiroz_decarolis_bane_he_keeler_neary_2017, title={The economics of electricity generation from Gulf Stream currents}, volume={134}, ISSN={["1873-6785"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020893529&partnerID=MN8TOARS}, DOI={10.1016/j.energy.2017.06.048}, abstractNote={Hydrokinetic turbines harnessing energy from ocean currents represent a potential low carbon electricity source. This study provides a detailed techno-economic assessment of ocean turbines operating in the Gulf Stream off the North Carolina coast. Using hindcast data from a high-resolution ocean circulation model in conjunction with the US Department of Energy's reference model 4 (RM4) for ocean turbines, we examine resource quality and apply portfolio optimization to identify the best candidate sites for ocean turbine deployment. We find that the lowest average levelized cost of electricity (LCOE) from a single site can reach 400 $/MWh. By optimally selecting geographically dispersed sites and taking advantage of economies of scale, the variations in total energy output can be reduced by an order of magnitude while keeping the LCOE below 300 $/MWh. Power take-off and transmission infrastructure are the largest cost drivers, and variation in resource quality can have a significant influence on the project LCOE. While this study focuses on a limited spatial domain, it provides a framework to assess the techno-economic feasibility of ocean current energy in other western boundary currents.}, journal={ENERGY}, publisher={Elsevier BV}, author={Li, Binghui and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F. and Bane, John and He, Ruoying and Keeler, Andrew G. and Neary, Vincent S.}, year={2017}, month={Sep}, pages={649–658} }
 @article{aquila_rotela junior_pamplona_queiroz_2017, title={Wind power feasibility analysis under uncertainty in the Brazilian electricity market}, volume={65}, ISSN={["1873-6181"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019185087&partnerID=MN8TOARS}, DOI={10.1016/j.eneco.2017.04.027}, abstractNote={Investors must be able to plan and analyze their investments in order to optimize decisions and turn them into profits associated with a particular project. Since electricity producers in the Brazilian electric power system are exposed to a short-term market, the goal of this paper is to propose a framework for investment analysis capable of encompassing different uncertainties and possibilities for wind power generators in a regulated market, characterized by auctions. In order to reach the proposed objective we employ a simulation technique which allows modeling cash flows considering uncertainties in variables related to project financial premises, electricity generation and producer exposure to the short-term market. For such goal, this study presents a new approach for investment analysis that allows the identification of the main uncertainty parameters and risks associated to this class of projects in the Brazilian electricity market. We also employ the Value at Risk technique to perform a risk management analysis in such context.}, journal={ENERGY ECONOMICS}, author={Aquila, Giancarlo and Rotela Junior, Paulo and Pamplona, Edson de Oliveira and Queiroz, Anderson Rodrigo}, year={2017}, month={Jun}, pages={127–136} }
 @article{queiroz_marangon lima_marangon lima_silva_scianni_2016, title={Climate change impacts in the energy supply of the Brazilian hydro-dominant power system}, volume={99}, ISSN={["1879-0682"]}, url={https://doi.org/10.1016/j.renene.2016.07.022}, DOI={10.1016/j.renene.2016.07.022}, abstractNote={Over the past few years, there has been a growing global consensus related to the importance of renewable energy to minimize the emission of greenhouse gases. The solution is an increase in the number of renewable power plants but unfortunately, this leads to a high dependence on climate variables which are already affected by climate change. Brazil is one of the largest producers of electricity by renewables through its hydro-dominant power generation system. However, hydro-generation depends on water inflows that are directly affected by climate change that consequently affect the electricity production. Therefore, these changes need to be considered in the operation and planning of a hydro-dominant power system. In this paper, we present the effects of different climate scenarios in the water inflows produced by the regional Eta climate model. Normally, studies use an optimization model to make decisions in case of a hydro-thermal scheduling problem and use the assured energy to evaluate the hydro-production. In this analysis, water inflows used in the optimization process consider different trends according to its associated climate scenario. Our paper shows that climate change may drastically impact the system assured energy and consequently, the system's capability to supply load.}, journal={RENEWABLE ENERGY}, publisher={Elsevier BV}, author={Queiroz, Anderson Rodrigo and Marangon Lima, Luana M. and Marangon Lima, Jose W. and Silva, Benedito C. and Scianni, Luciana A.}, year={2016}, month={Dec}, pages={379–389} }
 @article{lima_osis_queiroz_moreira santos_2016, title={Least-cost path analysis and multi-criteria assessment for routing electricity transmission lines}, volume={10}, ISSN={["1751-8695"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85001033144&partnerID=MN8TOARS}, DOI={10.1049/iet-gtd.2016.1119}, abstractNote={The classical approach for transmission line (TL) routing based on paper maps, aerial photographs and field visits can generate inconsistent results, besides being a time consuming and intensive labour activity. The application of methodologies based on geographic information system (GIS) combined with multi-criteria assessment (MCA) methods can generate time and cost savings on the planning step. However, this methodology still must be better assessed for its applicability and improvements can be made. Therefore, this study aims at verifying the applicability of a GIS methodology for TL routing using analytic hierarchy process (AHP) for weighting criteria. In addition, the effectiveness of AHP method is evaluated comparing the previously attained results with a route modelled using monetary values to weight the criteria. To achieve the objectives, the methodology is applied for an area in the northern region of Brazil (state of Para) where a 230 kV TL is already implemented: the TL Vila do Conde-Castanhal. As a result, routes with lower length and lower total cost than the implemented TL were obtained, which suggest the potential benefits of applying the proposed methodology compared with traditional route planning, which does not use quantitative MCA and more advanced GIS tools.}, number={16}, journal={IET GENERATION TRANSMISSION & DISTRIBUTION}, author={Lima, Rodolfo Mendes and Osis, Reinis and Queiroz, Anderson Rodrigo and Moreira Santos, Afonso Henriques}, year={2016}, month={Dec}, pages={4222–4230} }
 @misc{queiroz_2016, title={Stochastic hydro-thermal scheduling optimization: An overview}, volume={62}, ISSN={["1879-0690"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84973354146&partnerID=MN8TOARS}, DOI={10.1016/j.rser.2016.04.065}, abstractNote={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. The scheduling problem consists in dispatching the available generators to meet the system electric load while minimizing the operational costs related to fuel and possible load curtailments. In a system with a large share of hydro generation, different from a thermal dominant power system, the uncertainty of water inflows play an important role in the decision-making process. In this setting the scheduling of generators has to be determined considering different future possibilities for water availability. Also, in the existence of a cascade system, the availability of water to produce electricity in hydro plants is influenced by decisions taken in upstream reservoirs. These issues complicate the hydro-thermal scheduling problem that often in the literature is modeled as a multi-stage stochastic program. In this paper we aim to give an overview about the main ideas behind this problem. We present model formulations, a solution technique, and point out to new developments related to this research.}, journal={RENEWABLE & SUSTAINABLE ENERGY REVIEWS}, publisher={Elsevier BV}, author={Queiroz, Anderson Rodrigo}, year={2016}, month={Sep}, pages={382–395} }
 @article{aquila_souza rocha_rotela junior_pamplona_queiroz_paiva_2016, title={Wind power generation: An impact analysis of incentive strategies for cleaner energy provision in Brazil}, volume={137}, ISSN={["1879-1786"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84990942807&partnerID=MN8TOARS}, DOI={10.1016/j.jclepro.2016.07.207}, abstractNote={Brazil has adopted various strategies to encourage alternative renewable energy sources in pursuit of cleaner and sustainable energy production. To this end, strategies should support the reduction of the financial risk for potential investors in the renewable energy market. Therefore, this study aims to analyze the impact of incentive strategies on the financial risk of wind power generation projects in Brazil in different marketing environments. From a quantitative approach, using Monte Carlo Simulation in three scenarios, we evaluate the impact of funding from the National Development Bank and participation in the Clean Development Mechanism in the financial returns of the investor in a regulated contracting environment and free contracting environment, measured by the Net Present Value. We conduct a statistical analysis to find out if there were statistically significant differences in investor risk in each scenario. The main results of the study are as follows: the wind speed, the selling price of energy, and disbursement for the investment have the most significant impact on the financial return; the project viability probability is greater than 85% in all scenarios, regardless of the marketing environment; the regulated market is less risky for the producer than the free market, since there is a statistically significant difference in Net Present Value variances for all scenarios; in the regulated contracting environment, funding is critical to reducing risk; and carbon credit trading is not a suitable policy for providing financial security to renewable energy producers. Thus, we conclude that in Brazil the contracting of projects from auctions in the regulated contracting environment, with the support of the National Development Bank, has been important for neutralizing the producer's financial risks.}, journal={JOURNAL OF CLEANER PRODUCTION}, author={Aquila, Giancarlo and Souza Rocha, Luiz Celio and Rotela Junior, Paulo and Pamplona, Edson de Oliveira and Queiroz, Anderson Rodrigo and Paiva, Anderson Paulo}, year={2016}, month={Nov}, pages={1100–1108} }
 @inproceedings{silva_de queiroz_lima_lima_2014, title={Effects of wind penetration in the scheduling of a hydro-dominant power system}, volume={2014-October}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930995311&partnerID=MN8TOARS}, DOI={10.1109/PESGM.2014.6939121}, abstractNote={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 is presented. 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.}, number={October}, booktitle={IEEE Power and Energy Society General Meeting}, author={Silva, S.R. and De Queiroz, A.R. and Lima, L.M.M. and Lima, J.W.M.}, year={2014} }
 @article{de queiroz_morton_2013, title={Sharing cuts under aggregated forecasts when decomposing multi-stage stochastic programs}, volume={41}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84875926459&partnerID=MN8TOARS}, DOI={10.1016/j.orl.2013.03.003}, abstractNote={Sampling-based decomposition algorithms (SBDAs) solve multi-stage stochastic programs. SBDAs can approximately solve problem instances with many time stages when the stochastic program exhibits interstage dependence in its right-hand side parameters by appropriately sharing cuts. We extend previous methods for sharing cuts in SBDAs, establishing new results under a novel interaction between a class of interstage dependency models, and how they appear in the stochastic program.}, number={3}, journal={Operations Research Letters}, author={De Queiroz, A.R. and Morton, D.P.}, year={2013}, pages={311–316} }
 @inproceedings{scianni_queiroz_lima_lima_2013, title={The influence of climate change on hydro generation in Brazil}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84890889216&partnerID=MN8TOARS}, DOI={10.1109/PTC.2013.6652402}, abstractNote={More than 85% of the total electricity generation in Brazil comes from Hydro Plants. The hydroelectricity is a good example of renewable resources which, in this case, depends greatly on the precipitation. An optimization program is used in order to determine the dispatches of the plants and to minimize the operational costs. Given a set of hydro plants it is possible to calculate the power system assured energy or the amount of energy available to supply the load at a deficit risk of 5%. However, climate change can cause great variation on the assured energy. The problem drastically aggravates for the new available hydro potentials located in the Amazon region which probably will be affected by the global warming. This paper provides the initial results of a research project sponsored by generation companies and the regulatory agency in Brazil.}, booktitle={2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013}, author={Scianni, L.A. and Queiroz, A.R. and Lima, L.M.M. and Lima, J.W.M.}, year={2013} }
 @inproceedings{lima_queiroz_lima_2011, title={From voltage level to locational pricing of distribution network: The Brazilian experience}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-82855164073&partnerID=MN8TOARS}, DOI={10.1109/PES.2011.6039653}, abstractNote={The distribution systems in Brazil are currently priced based on voltage level and time usage. The tariff structure does not take into account the bus or region where the consumption takes place. Only transmission which is represented by lines and substations equal and above 230 kV has been using locational pricing since the end of 90's. The wheeling method is derived from the investment cost related price (ICRP) proposed in UK. So each bus at transmission grid has a nodal price to encompass transmission fixed costs. This locational approach does not continue through the distribution system which has a method that came from the 70's decade that still follows the idea of radial system, i.e., the costs increase as the voltage decreases. Although the peak and off-peak differentiation is considered, the effectiveness of it in terms of distribution operation and planning optimization is questioned. Today, the Brazilian regulators are working in improving the tariff structure including the locational signal and a better time-of-use approach taking into account the characteristics of each Brazilian region. Along with this improvement, the regulators are also studying how to add the new small generators that are spreading around the distribution grid. This paper tries to show how the current price is developed and the future aspects and solutions that the Brazilian regulators are facing in order to promote efficiency and sustainability of distribution energy systems.}, booktitle={IEEE Power and Energy Society General Meeting}, author={Lima, L.M.M. and Queiroz, A.R. and Lima, J.W.M.}, year={2011} }
 @inproceedings{queiroz_lima_lima_2011, title={Thermal generation investment analysis using decision tools}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-82855164203&partnerID=MN8TOARS}, DOI={10.1109/PES.2011.6039473}, abstractNote={In this paper 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. Two of these alternatives are related to investment in a thermal generator to produce electricity for the company own use. In this framework there are many uncertainties that have strong influence on the net present values of each alternative. Some of these uncertainties are identified and modeled for the purposes of this work. It is important to mention that electricity is an important input to the production process and represents considerable costs for the company. In order to avoid unnecessary expenses a solid analysis is necessary. 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.}, booktitle={IEEE Power and Energy Society General Meeting}, author={Queiroz, A.R. and Lima, L.M.M. and Lima, J.W.M.}, year={2011} }
 @inproceedings{de queiroz_lima_morton_lima_2010, title={Determining the optimal transmission system usage contracts for a distribution company}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-78649536365&partnerID=MN8TOARS}, DOI={10.1109/PES.2010.5589373}, abstractNote={Improvements in transmission and distribution networks can be noticed in most countries that had their system architecture changed by the deregulation process. In this new environment one of the biggest challenges is the transmission and distribution open access. In Brazil, the National Electricity Regulatory Agency has established that the monthly amount of transmission system usage contracted by a distribution company (DISCO) should be informed per connection point (one value for each point) between transmission and distribution network. The usage of the transmission assets are represented by the power flowing from the transmission system to the DISCO network. This implies in monthly charges at each border transformer (which represents a connection point) that the DISCO must pay to honor the contracts. If the DISCO exceeds the contract values by certain percentage, monetary penalties are incurred. The penalty costs can lead the DISCO to a more conservatory behavior at the time that the usage contracts are settled. On the other hand if the DISCO expects a low trend of its demand it has the possibility to contract less and save money. Determining the optimum amount to contract is a stochastic optimization problem because of future load uncertainties. This paper provides model formulations for the Transmission System Usage Problem with a real case study.}, booktitle={IEEE PES General Meeting, PES 2010}, author={De Queiroz, A.R. and Lima, L.M.M. and Morton, D.P. and Lima, J.W.M.}, year={2010} }
 @inproceedings{guardia_queiroz_lima_2010, title={Estimation of electricity elasticity for demand rates and load curve in Brazil}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-78649573760&partnerID=MN8TOARS}, DOI={10.1109/PES.2010.5588101}, abstractNote={In this paper a methodology is proposed to calculate the elasticity between demand profile and electricity price. This elasticity is essential in the tariff design process because it is the first way to represent the customer response about the price signal. In this calculation it is necessary to identify, by using a clustering analysis algorithm, typical load profiles among the consumers. The elasticity is then obtained by comparing the load profiles with tariff variation between two consecutive years. The focus is on the relative variation (relation between peak and off peak periods) of tariff and load among the daily 24 hours. This approach captures the customer willingness to change the load profile due to tariff signals. Some real examples are shown using the network of a distribution company in Brazil. The results obtained show that the elasticity does exist and it is different for each customer in many activity sectors.}, booktitle={IEEE PES General Meeting, PES 2010}, author={Guardia, E.C. and Queiroz, A.R. and Lima, J.W.M.}, year={2010} }
 @inproceedings{oliveira_queiroz_lima_ieee_balestrassi_2008, title={The influence of operational marginal cost simulation methods on electricity contract portfolio strategies in Brazil}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944097160&partnerID=MN8TOARS}, booktitle={16th Power Systems Computation Conference, PSCC 2008}, author={Oliveira, F.A. and Queiroz, A.R. and Lima, J.W.M. and Ieee, S.M. and Balestrassi, P.P.}, year={2008} }
 @inproceedings{queiroz_oliveira_marangon lima_balestrassi_2007, title={Simulating electricity spot prices in Brazil using neural network and design of experiments}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-50849140143&partnerID=MN8TOARS}, DOI={10.1109/PCT.2007.4538630}, abstractNote={The electricity price has been one of the most important variables since the introduction of deregulation on the electricity sector. On this way, efficient forecasting methods of spot prices have become crucial to maximize the agent benefits. In Brazil the electricity price is based on the marginal cost provided by an optimization software (NEWAVE). Forecasting the operational marginal cost (OMC) and its volatility has been one big problem in the Brazilian market because of the computational time taken by this software. This work presents a fast and efficient model to simulate the OMC using DOE (design of experiments) and ANN (artificial neural networks) techniques. The paper proved that the combined techniques provided a promising result and may be applied to risk management and investment analysis.}, booktitle={2007 IEEE Lausanne POWERTECH, Proceedings}, author={Queiroz, A.R. and Oliveira, F.A. and Marangon Lima, J.W. and Balestrassi, P.P.}, year={2007}, pages={2029–2034} }