Long-Term Electricity Supply and Demand Forecast (2018–2040): A LEAP Model Application towards a Sustainable Power Generation System in Ecuador

Rivera-González, Luis; Bolonio, David; Mazadiego, Luis Felipe; Valencia-Chapi, Robert

doi:10.3390/su11195316

Cited by 42 publications

(18 citation statements)

References 27 publications

(22 reference statements)

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“…3 CIP optimized for group A mixtures. 4 CIP just above the critical pressure for group B mixtures. The performances of the pure s-CO 2 and sCO 2 -mixtures' recompression Brayton cycle have been analyzed.…”

Section: Assumptions and Methodsmentioning

confidence: 97%

“…The ever-increasing necessity to reduce the environmental impact of industrial and urban energy conversion processes has led engineers to consider the use of sCO 2 -mixtures as working fluid for thermodynamic power and refrigeration cycles [2,3], emphasizing the mitigation of air pollution and the emission of greenhouse gases, and reducing energy production costs [4]. The novelty of this work over the state of the art is new mixtures with mixing ratio and new fluids, in the recompression Brayton cycle.…”

Section: Introductionmentioning

confidence: 99%

“…CIT's range for group B mixtures 3. CIP optimized for group A mixtures 4. CIP just above the critical pressure for group B mixtures.…”

mentioning

confidence: 99%

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Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants

2019

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This work quantifies the impact of using sCO2-mixtures (s-CO2/He, s-CO2/Kr, s-CO2/H2S, s-CO2/CH4, s-CO2/C2H6, s-CO2/C3H8, s-CO2/C4H8, s-CO2/C4H10, s-CO2/C5H10, s-CO2/C5H12 and s-CO2/C6H6) as the working fluid in the supercritical CO2 recompression Brayton cycle coupled with line-focusing solar power plants (with parabolic trough collectors (PTC) or linear Fresnel (LF)). Design parameters assessed are the solar plant performance at the design point, heat exchange dimensions, solar field aperture area, and cost variations in relation with admixtures mole fraction. The adopted methodology for the plant performance calculation is setting a constant heat recuperator total conductance (UAtotal). The main conclusion of this work is that the power cycle thermodynamic efficiency improves by about 3–4%, on a scale comparable to increasing the turbine inlet temperature when the cycle utilizes the mentioned sCO2-mixtures as the working fluid. On one hand, the substances He, Kr, CH4, and C2H6 reduce the critical temperature to approximately 273.15 K; in this scenario, the thermal efficiency is improved from 49% to 53% with pure s-CO2. This solution is very suitable for concentrated solar power plants coupled to s-CO2 Brayton power cycles (CSP-sCO2) with night sky cooling. On the other hand, when adopting an air-cooled heat exchanger (dry-cooling) as the ultimate heat sink, the critical temperatures studied at compressor inlet are from 318.15 K to 333.15 K, for this scenario other substances (C3H8, C4H8, C4H10, C5H10, C5H12 and C6H6) were analyzed. Thermodynamic results confirmed that the Brayton cycle efficiency also increased by about 3–4%. Since the ambient temperature variation plays an important role in solar power plants with dry-cooling systems, a CIT sensitivity analysis was also conducted, which constitutes the first approach to defining the optimum working fluid mixture for a given operating condition.

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Section: Assumptions and Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants

2019

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“…In this way, the hierarchical tree has been designed into its different branches with the available data for the base year [55].…”

Section: Road Transport In the Leap Modelmentioning

confidence: 99%

Long-Term Forecast of Energy and Fuels Demand Towards a Sustainable Road Transport Sector in Ecuador (2016–2035): A LEAP Model Application

et al. 2020

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The total energy demand in the transport sector represented 48.80% of the total consumption in Ecuador throughout 2016, where 89.87% corresponded to the road transport sector. Therefore, it is crucial to analyze the future behavior of this sector and assess the economic and environmental measures towards sustainable development. Consequently, this study analyzed: (1) the total energy demand for each vehicle class and fuel type; (2) the GHG (greenhouse gas) emissions and air pollutants NO x and PM 10 ; and (3) the cost attributed to the fuel demand, between 2016 and 2035. For this, four alternative demand scenarios were designed: BAU: Business As Usual; EOM: Energy Optimization and Mitigation; AF: Alternative Fuels; and SM: Sustainable Mobility using Long-range Energy Alternatives Planning system. After analysis, the EOM, AF, and SM scenarios have advantages relative to BAU, where SM particularly stands out. The results show that SM compared to BAU, contributes with a 12.14% (141,226 kBOE) decrease of the total energy demand, and the economic savings for this fuel demand is of 14.22% (26,720 MUSD). Moreover, global NO x and PM 10 emissions decreased by 14.91% and 13.78%, respectively. Additionally, accumulated GHG emissions decreased by 13.49% due to the improvement of the fuel quality for the vehicles that mainly consume liquefied petroleum gas, natural gas, and electricity.Sustainability 2020, 12, 472 2 of 26 consumption levels related to liquid fuels, they have a faster growth forecast of approximately three times between 2015 and 2040. For example, the consumption of natural gas for passenger and cargo transport would increase by almost 500% in the same period [5].Between 2000 and 2012, in several Latin American countries, the energy demand in the road transport sector has been continuously increasing with high intensity every year; in Paraguay, it grew by 11%, in Panama and Bolivia by 5%, in Argentina and Costa Rica by 4% [6]. This trend has remained relatively constant, generating a significant increase in the vehicle fleet, with a 3.5% average each year, which is mainly composed of cars [7].In 2012, in the countries of this same region, the transport sector had a total energy consumption share of 27% in the case of Nicaragua to 55% in the case of Ecuador. Inside this sector, road transport demanded around 85% of the total energy consumption, excluding Panama, where air transport continues to be very significant with 30% of the total energy consumption in the transport sector due to its geographical condition [8].The high levels of liquid petroleum fuels consumption in road transport cause great concern at the global level but mainly in emerging market countries such as India, China, the United States of America, and large regions such as the European Union, Latin America, Southeast Asian, Eastern Europe, the Middle East, and Africa, which are the world's most energy demanded.This concern is primarily related to the following reasons: (1) the localized polluting gases emissions within cities that reduce the ...

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“…It is a way to increase the accuracy by predicting the volatility of power demand in every time zone [12]. Since it is needed at each process, including data collection, preprocessing, feature extraction, and so on, power demand forecasting systems consume a lot of time and efforts [13,14].…”

Section: Introductionmentioning

confidence: 99%

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020

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The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

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Long-Term Electricity Supply and Demand Forecast (2018–2040): A LEAP Model Application towards a Sustainable Power Generation System in Ecuador

Cited by 42 publications

References 27 publications

Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants

Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants

Long-Term Forecast of Energy and Fuels Demand Towards a Sustainable Road Transport Sector in Ecuador (2016–2035): A LEAP Model Application

Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

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