Search results for: renewable energy forecasting

Authors: Qudratullah Muradi

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Geothermal energy is a renewable energy source that can be found in abundance on our planet. Only a small fraction of it is currently converted to electrical power, though in recent years installed geothermal capacity has increased considerably all over the world. In this paper, we assumed a model for designing of Enhanced Geothermal System, EGS. We used computer modeling group, CMG reservoir simulation software to create the typical Hot Dry Rock, HDR reservoir. In this research two wells, one injection of cold water and one production of hot water are included in the model. There are some hydraulic fractures created by the mentioned software. And cold water is injected in order to produce energy from the reservoir. The result of injecting cold water to the reservoir and extracting geothermal energy is defined by some graphs at the end of this research. The production of energy is quantified in a period of 10 years.

Keywords: geothermal energy, EGS, HDR, hydraulic fracturing

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Authors: J. P. Reges, L. C. S. Mazza, E. J. Braga, J. A. Bessa, A. R. Alexandria

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This paper proposes the development of control and automation of irrigation system located sunflower harvest in the Teaching Unit, Research and Extension (UEPE), the Apodi Plateau in Limoeiro do Norte. The sunflower extraction, which in turn serves to get the produced oil from its seeds, animal feed, and is widely used in human food. Its nutritional potential is quite high what makes of foods produced from vegetal, very rich and healthy. The focus of research is to make the autonomous irrigation system sunflower crop from programmable logic control energized with alternative energy sources, solar photovoltaics. The application of automated irrigation system becomes interesting when it provides convenience and implements new forms of managements of the implementation of irrigated cropping systems. The intended use of automated addition to irrigation quality and consequently brings enormous improvement for production of small samples. Addition to applying the necessary and sufficient features of water management in irrigation systems, the system (PLC + actuators + Renewable Energy) will enable to manage the quantitative water required for each crop, and at the same time, insert the use of sources alternative energy. The entry of the automated collection will bring a new format, and in previous years, used the process of irrigation water wastage base and being the whole manual irrigation process.

Keywords: automation, control, sunflower, irrigation, programming, renewable energy

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Authors: Benny Benyamin Nasution, Rahmat Widia Sembiring, Abdul Rahman Dalimunthe, Nursiah Mustari, Nisfan Bahri, Berta br Ginting, Riadil Akhir Lubis, Rita Tavip Megawati, Indri Dithisari

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The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer.

Keywords: hierarchical graph neuron, multidimensional hierarchical graph neuron, single layer hierarchical graph neuron, natural disaster forecasting, earthquake forecasting, earth coordinate system

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Authors: Janaina Camile Pasqual Lofhagen, David Savarese, Veronika Vazhnik

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The urgency of the climate crisis requires both innovation and practicality. The energy transition framework allows industry to deliver resilient cities, enhance adaptability to change, pursue energy objectives such as growth or efficiencies, and increase renewable energy. This paper investigates a real-world application perspective for the use of biogas in Brazil and the U.S.. It will examine interventions to provide a foundation of infrastructure, as well as the tangible benefits for policy-makers crafting law and providing incentives.

Keywords: resilience, vulnerability, risks, biogas, sustainability.

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Authors: Kostas Philippopoulos, Chris G. Tzanis, Despina Deligiorgi

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Climate change adaptation requires the exploitation of renewable energy sources such as wind. However, climate variability can affect the regional wind energy potential and consequently the available wind power production. The goal of the research project is to examine the impact of atmospheric circulation on wind energy production over Greece. In the context of synoptic climatology, the proposed novel methodology employs Self-Organizing Maps for grouping and classifying the atmospheric circulation and nationally-aggregated capacity factor time series for a 30-year period. The results indicate the critical effect of atmospheric circulation on the national aggregated wind energy production values and therefore address the issue of optimum distribution of wind farms for a specific region.

Keywords: wind energy, atmospheric circulation, capacity factor, self-organizing maps

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Authors: Aqoul H. H. Alanezy, Ali M. Abdelsalam, Nouby M. Ghazaly

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In recent years, progress has been made in increasing the renewable energy share in the power sector particularly in the wind. The experimental study conducted in this paper aims to investigate the effects of number of blades and inflow wind speed on vibration signals of a vertical axis Savonius type wind turbine. The operation of the model of Savonius type wind turbine is conducted to compare two, three and four blades wind turbines to show vibration amplitudes related with wind speed. It is found that the increase of the number of blades leads to decrease of the vibration magnitude. Furthermore, inflow wind speed has reduced effect on the vibration level for higher number of blades.

Keywords: Savonius type wind turbine, number of blades, renewable energy, vibration signals

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Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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Authors: Emile Niringiyimana, LI Ji Qing, Giovanni Dushimimana, Virginie Umwere

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The development and utilization of renewable energy (RE) can not only effectively reduce carbon dioxide (CO2) emissions, but also became a solution to electricity shortage mitigation in rural areas. Hybrid RE systems are promising ways to provide consistent and continuous power for isolated areas. This work investigated the prospect and cost effectiveness of hybrid system complementarity between a 100kW solar PV system and a small-scale 200kW hydropower station in the South of Rwanda. In order to establish the optimal size of a RE system with adequate sizing of system components, electricity demand, solar radiation, hydrology, climate data are utilized as system input. The average daily solar radiation in Rukarara is 5.6 kWh/m2 and average wind speed is 3.5 m/s. The ideal integrated RE system, according to Homer optimization, consists of 91.21kW PV, 146kW hydropower, 12 x 24V li-ion batteries with a 20kW converter. The method of enhancing such hybrid systems control, sizing and choice of components is to reduce the Net present cost (NPC) of the system, unmet load, the cost of energy and reduction of CO2. The power consumption varies according to dominant source of energy in the system by controlling the energy compensation depending on the generation capacity of each power source. The initial investment of the RE system is $977,689.25, and its operation and maintenance expenses is $142,769.39 over a 25-year period. Although the investment is very high, the targeted profits in future are huge, taking into consideration of high investment in rural electrification structure implementations, tied with an increase of electricity cost and the 5 years payback period. The study outcomes suggest that the standalone hybrid PV-Hydropower system is feasible with zero pollution in Rukara community.

Keywords: HOMER optimization, hybrid power system, renewable energy, NPC and solar pv systems

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Authors: Khaled M. Khader, Mamdouh I. Elimy, Omayma A. Nada

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Sustainable development is the nominal goal of most countries at present. In general, fossil fuels are the development mainstay of most world countries. Regrettably, the fossil fuel consumption rate is very high, and the world is facing the problem of conventional fuels depletion soon. In addition, there are many problems of environmental pollution resulting from the emission of harmful gases and vapors during fuel burning. Thus, clean, renewable energy became the main concern of most countries for filling the gap between available energy resources and their growing needs. There are many renewable energy sources such as wind, solar and wave energy. Energy can be obtained from the motion of sea waves almost all the time. However, power generation from solar or wind energy is highly restricted to sunny periods or the availability of suitable wind speeds. Moreover, energy produced from sea wave motion is one of the cheapest types of clean energy. In addition, renewable energy usage of sea waves guarantees safe environmental conditions. Cheap electricity can be generated from wave energy using different systems such as oscillating bodies' system, pendulum gate system, ocean wave dragon system and oscillating water column device. In this paper, a multi-criteria model has been developed using Analytic Hierarchy Process (AHP) to support the decision of selecting the most effective system for generating power from sea waves. This paper provides a widespread overview of the different design alternatives for sea wave energy converter systems. The considered design alternatives have been evaluated using the developed AHP model. The multi-criteria assessment reveals that the off-shore Oscillating Water Column (OWC) system is the most appropriate system for generating power from sea waves. The OWC system consists of a suitable hollow chamber at the shore which is completely closed except at its base which has an open area for gathering moving sea waves. Sea wave's motion pushes the air up and down passing through a suitable well turbine for generating power. Improving the power generation capability of the OWC system is one of the main objectives of this research. After investigating the effect of some design modifications, it has been concluded that selecting the appropriate settings of some effective design parameters such as the number of layers of Wells turbine fans and the intermediate distance between the fans can result in significant improvements. Moreover, simple dynamic analysis of the Wells turbine is introduced. Furthermore, this paper strives for comparing the theoretical and experimental results of the built experimental prototype.

Keywords: renewable energy, oscillating water column, multi-criteria selection, Wells turbine

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Osamah S. Alanazi, Mohammad G. Kotbi, Mohammed O. AlFadil

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While renewable energy technology is developing in Saudi Arabia, and the ambitious 2030 vision encourages the shift towards more efficient and clean energy usage. The research on the application of geothermal resources in residential use for the Saudi Arabian context will contribute towards a more sustainable environment. This paper is a part of an ongoing master's thesis, which its main goal is to investigate the possibility of achieving a zero-carbon house in Riyadh by applying a ground-coupled system into a current sustainable house that uses a grid-tied solar system. The current house was built and designed by King Saud University for the 2018 middle east solar decathlon competition. However, it failed to reach zero-carbon operation due to the high cooling demand. This study will redesign and validate the house using Revit and Carriers Hourly Analysis 'HAP' software with the use of ordinary least square 'OLS' regression. After that, a ground source ventilation system will be designed using the 'GCV Tool' to reduce cooling loads. After the application of the ground source system, the new electrical loads will be compared with the current house. Finally, a simple economic analysis that includes the cost of applying a ground source system will be reported. The findings of this study will indicate the possibility and feasibility of reaching a zero-carbon house in Riyadh, Saudi Arabia, using a ground-coupled ventilation system. While cooling in the residential sector is the dominant energy consumer in the Gulf region, this work will certainly help in moving towards using renewable sources to meet those demands. This paper will be limited to highlight the literature review, the methodology of the research, and the expected outcome.

Keywords: renewable energy, zero-carbon houses, sustainable buildings, geothermal energy, solar PV, GCV Tool

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Authors: Rachita Lal

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Solar energy is a clean energy source. Because it is readily available in India and has many potential decentralized uses, urban local authorities may use it in various ways to manage the energy needs in the territory under their control. Apart from these and other services for which people pay a substantial number of money, urban local councils play a crucial role in administering essential services like water supply, street lighting, and health care. ULBs may contribute considerably to the transition to solar energy, both for their benefit and simultaneously for several additional direct and indirect advantages at multiple levels. The research primarily focuses on using clean energy management to reduce urban areas' reliance on traditional (electricity) energy. A technique for estimating the rooftop solar power potential using GIS (Geographical Information System) is described. Given that the combustion of fossil fuels produces 75% of India's power, meeting the country's energy needs through renewable energy sources is a step toward sustainable development and combating climate change. The study will further help in categorization, phasing, and understanding the demand and supply and thus calculating the cumulative benefits. The main objectives are to study the consumption of conventional energy in the study area and to identify the potential areas where solar photovoltaic intervention can be installed.

Keywords: solar energy, GIS, clean energy management, sustainable development

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Authors: Alex Maina, Mwenda Makathimo, Adwek George, Charles Opiyo

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This research entails the simulation of three possible power scenarios for Kenya from 2015 to 2035 using the Low Emissions Analysis Platform (LEAP). These scenarios represent the unfolding future electricity generation that will fully satisfy the demand while considering the following: energy security, power generation cost and impacts on the environment. These scenarios are Reference Scenario (RS), Nuclear Scenario (NS) and More Renewable Scenario (MRS). The findings obtained reveals that the most sustainable scenario while comparing the costs was found to be the coal scenario with a Net Present Value (NPV) of $30,052.67 million though it has the highest Green House Gases (GHGs) emissions. However, the More Renewable Scenario (MRS) had the least GHGs emissions but was found to be a most expensive scenario to implement with an NPV of $30,733.07 million.

Keywords: energy security, Kenya, low emissions analysis platform, net-present value, greenhouse gases

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Authors: Funda Ates

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Primary energy sources, such as petroleum, coal and natural gas are principle responsible of world’s energy consumption. However, the rapid worldwide increase in the depletion of these energy sources is remarkable. In addition to this, they have damaging environmentally effect. Renewable energy sources are capable of providing a considerable fraction of World energy demand in this century. Biomass is one of the most abundant and utilized sources of renewable energy in the world. It can be converted into commercial fuels, suitable to substitute for fossil fuels. A high number of biomass types can be converted through thermochemical processes into solid, liquid or gaseous fuels. Pyrolysis is the thermal decomposition of biomass in the absence of air or oxygen. In this study, barley straw has been investigated as an alternative feedstock to obtain fuels and chemicals via pyrolysis in fixed-bed reactor. The influence of pyrolysis temperature in the range 450–750 °C as well as the catalyst effects on the products was investigated and the obtained results were compared. The results indicated that a maximum oil yield of 20.4% was obtained at a moderate temperature of 550 °C. Oil yield decreased by using catalyst. Pyrolysis oils were examined by using instrumental analysis and GC/MS. Analyses revealed that the pyrolysis oils were chemically very heterogeneous at all temperatures. It was determined that the most abundant compounds composing the bio-oil were phenolics. Catalyst decreased the reaction temperature. Most of the components obtained using a catalyst at moderate temperatures was close to those obtained at high temperatures without using a catalyst. Moreover, the use of a catalyst also decreased the amount of oxygenated compounds produced.

Keywords: Barley straw, pyrolysis, catalyst, phenolics

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Authors: Abdulla Rahil, Rupert Gammon, Neil Brown

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The rapid growth of renewable energy sources and their integration into the grid have been motivated by the depletion of fossil fuels and environmental issues. Unfortunately, the grid is unable to cope with the predicted growth of renewable energy which would lead to its instability. To solve this problem, energy storage devices could be used. Electrolytic hydrogen production from an electrolyser is considered a promising option since it is a clean energy source (zero emissions). Choosing flexible operation of an electrolyser (producing hydrogen during the off-peak electricity period and stopping at other times) could bring about many benefits like reducing the cost of hydrogen and helping to balance the electric systems. This paper investigates the price of hydrogen during flexible operation compared with continuous operation, while serving the customer (hydrogen filling station) without interruption. The optimization algorithm is applied to investigate the hydrogen station in both cases (flexible and continuous operation). Three different scenarios are tested to see whether the off-peak electricity price could enhance the reduction of the hydrogen cost. These scenarios are: Standard tariff (1 tier system) during the day (assumed 12 p/kWh) while still satisfying the demand for hydrogen; using off-peak electricity at a lower price (assumed 5 p/kWh) and shutting down the electrolyser at other times; using lower price electricity at off-peak times and high price electricity at other times. This study looks at Derna city, which is located on the coast of the Mediterranean Sea (32° 46′ 0 N, 22° 38′ 0 E) with a high potential for wind resource. Hourly wind speed data which were collected over 24½ years from 1990 to 2014 were in addition to data on hourly radiation and hourly electricity demand collected over a one-year period, together with the petrol station data.

Keywords: hydrogen filling station off-peak electricity, renewable energy, off-peak electricity, electrolytic hydrogen

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

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Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Sarah Alamolhoda, Kevin J. Smith, Xiaotao Bi, Naoko Ellis

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For millennials, biomass has been the most important source of fuel used to produce energy. Energy derived from biomass is renewable by re-growth of biomass. Various technologies are used to convert biomass to potential renewable products including combustion, gasification, pyrolysis and fermentation. Gasification is the incomplete combustion of biomass in a controlled environment that results in valuable products such as syngas, biooil and biochar. Syngas is a combustible gas consisting of hydrogen (H₂), carbon monoxide (CO), carbon dioxide (CO₂), and traces of methane (CH₄) and nitrogen (N₂). Cleaned syngas can be used as a turbine fuel to generate electricity, raw material for hydrogen and synthetic natural gas production, or as the anode gas of solid oxide fuel cells. In this work, syngas as a product of woody biomass gasification in British Columbia, Canada, was introduced to two consecutive fixed bed reactors to perform a catalytic water gas shift reaction followed by a catalytic methanation reaction. The water gas shift reaction is a well-established industrial process and used to increase the hydrogen content of the syngas before the methanation process. Catalysts were used in the process since both reactions are reversible exothermic, and thermodynamically preferred at lower temperatures while kinetically favored at elevated temperatures. The water gas shift reactor and the methanation reactor were packed with Cu-based catalyst and Ni-based catalyst, respectively. Simulated syngas with different percentages of CO, H₂, CH₄, and CO₂ were fed to the reactors to investigate the effect of operating conditions in the unit. The water gas shift reaction experiments were done in the temperature of 150 ˚C to 200 ˚C, and the pressure of 550 kPa to 830 kPa. Similarly, methanation experiments were run in the temperature of 300 ˚C to 400 ˚C, and the pressure of 2340 kPa to 3450 kPa. The Methanation reaction reached 98% of CO conversion at 340 ˚C and 3450 kPa, in which more than half of CO was converted to CH₄. Increasing the reaction temperature caused reduction in the CO conversion and increase in the CH₄ selectivity. The process was designed to be renewable and release low greenhouse gas emissions. Syngas is a clean burning fuel, however by going through water gas shift reaction, toxic CO was removed, and hydrogen as a green fuel was produced. Moreover, in the methanation process, the syngas energy was transformed to a fuel with higher energy density (per volume) leading to reduction in the amount of required fuel that flows through the equipment and improvement in the process efficiency. Natural gas is about 3.5 times more efficient (energy/ volume) than hydrogen and easier to store and transport. When modification of existing infrastructure is not practical, the partial conversion of renewable hydrogen to natural gas (with up to 15% hydrogen content), the efficiency would be preserved while greenhouse gas emission footprint is eliminated.

Keywords: renewable natural gas, methane, hydrogen, gasification, syngas, catalysis, fuel

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Authors: Martin Kittel, Alexander Roth

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The continuous integration of variable renewable energy sources (VRE) in the power sector is required for decarbonizing the European economy. Power sectors become increasingly exposed to weather variability, as the availability of VRE, i.e., mainly wind and solar photovoltaic, is not persistent. Extreme events, e.g., long-lasting periods of scarce VRE availability (‘VRE droughts’), challenge the reliability of supply. Properly accounting for the severity of VRE droughts is crucial for designing a resilient renewable European power sector. Energy system modeling is used to identify such a design. Our analysis reveals the sensitivity of the optimal design of the European power sector towards VRE droughts. We analyze how VRE droughts impact optimal power sector investments, especially in generation and flexibility capacity. We draw upon work that systematically identifies VRE drought patterns in Europe in terms of frequency, duration, and seasonality, as well as the cross-regional and cross-technological correlation of most extreme drought periods. Based on their analysis, the authors provide a selection of relevant historical weather years representing different grades of VRE drought severity. These weather years will serve as input for the capacity expansion model for the European power sector used in this analysis (DIETER). We additionally conduct robustness checks varying policy-relevant assumptions on capacity expansion limits, interconnections, and level of sector coupling. Preliminary results illustrate how an imprudent selection of weather years may cause underestimating the severity of VRE droughts, flawing modeling insights concerning the need for flexibility. Sub-optimal European power sector designs vulnerable to extreme weather can result. Using relevant weather years that appropriately represent extreme weather events, our analysis identifies a resilient design of the European power sector. Although the scope of this work is limited to the European power sector, we are confident that our insights apply to other regions of the world with similar weather patterns. Many energy system studies still rely on one or a limited number of sometimes arbitrarily chosen weather years. We argue that the deliberate selection of relevant weather years is imperative for robust modeling results.

Keywords: energy systems, numerical optimization, variable renewable energy sources, energy drought, flexibility

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Authors: T. Y. Liu, C. H. Lin, Y. M. Ferng

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Wind energy is one of the clean renewable energy. However, the low frequency (20-200HZ) noise generated from the wind turbine blades, which bothers the residents, becomes the major problem to be developed. It is useful for predicting the aerodynamic noise by flow field and pressure distribution analysis on the wind turbine blades. Therefore, the main objective of this study is to use different turbulence models to analyse the flow field and pressure distributions of the wing blades. Three-dimensional Computation Fluid Dynamics (CFD) simulation of the flow field was used to calculate the flow phenomena for the National Renewable Energy Laboratory (NREL) Phase VI horizontal axis wind turbine rotor. Two different flow cases with different wind speeds were investigated: 7m/s with 72rpm and 15m/s with 72rpm. Four kinds of RANS-based turbulence models, Standard k-ε, Realizable k-ε, SST k-ω, and v2f, were used to predict and analyse the results in the present work. The results show that the predictions on pressure distributions with SST k-ω and v2f turbulence models have good agreements with experimental data.

Keywords: horizontal axis wind turbine, turbulence model, noise, fluid dynamics

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Md. Aminul Islam, Adel Merabet, Rachid Beguenane, Hussein Ibrahim

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This paper presents a simulation and mathematical model of stand-alone solar-wind-diesel based hybrid energy system (HES). A power management system is designed for multiple energy resources in a stand-alone hybrid energy system. Both Solar photovoltaic and wind energy conversion system consists of maximum power point tracking (MPPT), voltage regulation, and basic power electronic interfaces. An additional diesel generator is included to support and improve the reliability of stand-alone system when renewable energy sources are not available. A power management strategy is introduced to distribute the generated power among resistive load banks. The frequency regulation is developed with conventional phase locked loop (PLL) system. The power management algorithm was applied in Matlab®/Simulink® to simulate the results.

Keywords: solar photovoltaic, wind energy, diesel engine, hybrid energy system, power management, frequency and voltage regulation

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Authors: Fuchun Li, Hong Xiao

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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Suk-Hyun Yu, Heeyong Kwon

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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: artificial intelligence, air quality prediction, neural networks, pattern recognition, PM-10

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Authors: Joseph Levodo, Andy Ford, ISSA Chaer

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Africa enjoys some of the best solar radiation levels in the world averaging between 4-6 kWh/m2/day for most of the year and the global economic and political conditions that tend to make African countries more dependent on their own energy resources have caused growing interest in wanting renewable energy based technologies. However to-date, implementation of Modern Energy Technologies in Africa is still very low especially the use of solar conversion technologies. It was initially speculated that the low uptake of solar technology in Africa was associated with the continent’s high poverty levels and limitations in technical capacity as well as awareness. Nonetheless, this is not an academic based speculation and the exact reasons for this low trend in technology adoption are unclear and require further investigation. This paper presents literature review and analysis relating to the techno-economic feasibility of solar photovoltaic power generation in Africa. The literature review would include the following four main categories: design methods, techno-economic feasibility of solar photovoltaic power generation, performance evaluations of various systems, Then it looks at the role of policy and potential future of technological development of photovoltaic (PV) by exploring the impact of alternative policy instruments and technology cost reductions on the financial viability of investing solar photovoltaic (PV) in Africa.

Keywords: Africa Solar Potential, policy, photovoltaic, technologies

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Authors: Aravindhan Nepolean, Chidamabaranathan Bibin, Rajesh K., Gopinath S., Ashok Kumar R., Arun Kumar S., Sadasivan N.

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Wind turbine blades are an important parameter for surging renewable energy production. Optimizing blade profiles and developing new materials for wind turbine blades take a lot of time and effort. Even though many standards for wind turbine blades have been developed for large-scale applications, they are not more effective in small-scale applications. We used acrylonitrile-butadiene-styrene to make small-scale wind turbine blades in this study (ABS). We chose the material because it is inexpensive and easy to machine into the desired form. They also have outstanding chemical, stress, and creep resistance. The blade measures 332 mm in length and has a 664 mm rotor diameter. A modal study of blades is carried out, as well as a comparison with current e-glass fiber. They were able to balance the output with less vibration, according to the findings. Q blade software is used to simulate rotating output. The modal analysis testing and prototype validation of wind turbine blades were used for experimental validation.

Keywords: acrylonitrile-butadiene-styrene, e-glass fiber, modal, renewable energy, q-blade

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Authors: Berker Bayazit, Gulgun Kayakutlu

Abstract:

The World Energy Outlook shows that energy markets will substantially change within a few forthcoming decades. First, determined action plans according to COP21 and aim of CO₂ emission reduction have already impact on policies of countries. Secondly, swiftly changed technological developments in the field of renewable energy will be influential upon medium and long-term energy generation and consumption behaviors of countries. Furthermore, share of electricity on global energy consumption is to be expected as high as 40 percent in 2040. Electrical vehicles, heat pumps, new electronical devices and digital improvements will be outstanding technologies and innovations will be the testimony of the market modifications. In order to meet highly increasing electricity demand caused by technologies, countries have to make new investments in the field of electricity production, transmission and distribution. Specifically, electricity generation mix becomes vital for both prevention of CO₂ emission and reduction of power prices. Majority of the research and development investments are made in the field of electricity generation. Hence, the prime source diversity and source planning of electricity generation are crucial for improving the wealth of citizen life. Approaches considering the CO₂ emission and total cost of generation, are necessary but not sufficient to evaluate and construct the product mix. On the other hand, employment and positive contribution to macroeconomic values are important factors that have to be taken into consideration. This study aims to constitute new investments in renewable energies (solar, wind, geothermal, biogas and hydropower) between 2018-2023 under 4 different goals. Therefore, a multi-objective programming model is proposed to optimize the goals of minimizing the CO₂ emission, investment amount and electricity sales price while maximizing the total employment and positive contribution to current deficit. In order to avoid the user preference among the goals, Dinkelbach’s algorithm and Guzel’s approach have been combined. The achievements are discussed with comparison to the current policies. Our study shows that new policies like huge capacity allotment might be discussible although obligation for local production is positive. The improvements in grid infrastructure and re-design support for the biogas and geothermal can be recommended.

Keywords: energy generation policies, multi-objective linear programming, portfolio planning, renewable energy

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Authors: Meena Agrawal, Chaitanya P. Agrawal

Abstract:

The power systems are transforming and becoming smarter with innovations in technologies to enable embark simultaneously upon the sustainable energy needs, rising environmental concerns, economic benefits and quality requirements. The advantages provided by inter-connection of renewable energy resources are becoming more viable and dependable with the smart controlling technologies. The limitation of most renewable resources have their diversity and intermittency causing problems in power quality, grid stability, reliability, security etc. is being cured by these efforts. A necessitate of optimal energy management by intelligent Micro-Grids at the distribution end of the power system has been accredited to accommodate sustainable renewable Distributed Energy Resources on large scale across the power grid. All over the world Smart Grids are emerging now as foremost concern infrastructure upgrade programs. The hardware setup includes NI cRIO 9022, Compact Reconfigurable Input Output microcontroller board connected to the PC on a LAN router with three hardware modules. The Real-Time Embedded Controller is reconfigurable controller device consisting of an embedded real-time processor controller for communication and processing, a reconfigurable chassis housing the user-programmable FPGA, Eight hot-swappable I/O modules, and graphical LabVIEW system design software. It has been employed for signal analysis, controls and acquisition and logging of the renewable sources with the LabVIEW Real-Time applications. The employed cRIO chassis controls the timing for the module and handles communication with the PC over the USB, Ethernet, or 802.11 Wi-Fi buses. It combines modular I/O, real-time processing, and NI LabVIEW programmable. In the presented setup, the Analog Input Module NI 9205 five channels have been used for input analog voltage signals from renewable energy sources and NI 9227 four channels have been used for input analog current signals of the renewable sources. For switching actions based on the programming logic developed in software, a module having Electromechanical Relays (single-pole single throw) with 4-Channels, electrically isolated and LED indicating the state of that channel have been used for isolating the renewable Sources on fault occurrence, which is decided by the logic in the program. The module for Ethernet based Data Acquisition Interface ENET 9163 Ethernet Carrier, which is connected on the LAN Router for data acquisition from a remote source over Ethernet also has the module NI 9229 installed. The LabVIEW platform has been employed for efficient data acquisition, monitoring and control. Control logic utilized in program for operation of the hardware switching Related to Fault Relays has been portrayed as a flowchart. A communication system has been successfully developed amongst the sources and loads connected on different computers using Hypertext transfer protocol, HTTP or Ethernet Local Stacked area Network TCP/IP protocol. There are two main I/O interfacing clients controlling the operation of the switching control of the renewable energy sources over internet or intranet. The paper presents experimental results of the briefed setup for intelligent control of the micro-grid for renewable energy sources, besides the control of Micro-Grid with data acquisition and control hardware based on a microcontroller with visual program developed in LabVIEW.

Keywords: data acquisition and control, LabVIEW, microcontroller cRIO, Smart Micro-Grid

Procedia PDF Downloads 333

Authors: Adeyinka Adesanya, Isaac Bamgboye

Abstract:

There is a growing need to develop the processes to produce renewable fuels and chemicals due to the economic, political, and environmental concerns associated with fossil fuels. Lignocellulosic biomass is an excellent renewable feedstock because it is both abundant and inexpensive. This project aims at producing bioethanol from lignocellulosic plants (Sorghum Arundinacieum and Heteropogon Contortus) by biochemical means, computing the energy audit of the process and determining the fuel properties of the produced ethanol. Acid pretreatment (0.5% H2SO4 solution) and enzymatic hydrolysis (using malted barley as enzyme source) were employed. The ethanol yield of wild sorghum was found to be 20% while that of spear grass was 15%. The fuel properties of the bioethanol from wild sorghum are 1.227 centipoise for viscosity, 1.10 g/cm3 for density, 0.90 for specific gravity, 78 °C for boiling point and the cloud point was found to be below -30 °C. That of spear grass was 1.206 centipoise for viscosity, 0.93 g/cm3 for density 1.08 specific gravity, 78 °C for boiling point and the cloud point was also found to be below -30 °C. The energy audit shows that about 64 % of the total energy was used up during pretreatment, while product recovery which was done manually demanded about 31 % of the total energy. Enzymatic hydrolysis, fermentation, and distillation total energy input were 1.95 %, 1.49 % and 1.04 % respectively, the alcoholometric strength of bioethanol from wild sorghum was found to be 47 % and the alcoholometric strength of bioethanol from spear grass was 72 %. Also, the energy efficiency of the bioethanol production for both grasses was 3.85 %.

Keywords: lignocellulosic biomass, wild sorghum, spear grass, biochemical conversion

Procedia PDF Downloads 236

Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

Abstract:

This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

Procedia PDF Downloads 434