World Electricity Generation Research Articles

Predicting world electricity generation by sources using different machine learning algorithms

Predicting world electricity generation by sources using different machine learning algorithms

Predicting world electricity generation by sources using different machine learning algorithms

Predicting world electricity generation by sources using different machine learning algorithms

Methodology for renewable energy risk assessment applied to photovoltaic systems in Mexico

Due to the increase in demand for electricity, global warming and energy sustainability, there has been a significant increase in the use of renewable energy technologies. This can be seen in reports from the International Renewable Energy Agency (IRENA), which in 2018 reported a total world electricity generation capacity of 2,356,346 MW with technologies based on these energy sources. These data constitute a sustained annual growth over the last five years of between 8 and 9 % (IRENA, 2019, p. 2). Therefore, the use of these technologies is a strategy adopted worldwide to reduce both the dependence on fossil fuels and the environmental impact of electricity generation.

How to Improve an Offshore Wind Station

The ocean is approximately 71% of the Earth’s surface and has a lot of resources available. Nowadays, human beings are looking for renewable ways to obtain energy. Offshore power can be obtained in several different ways. Offshore wind power is the most used renewable offshore energy. Since 2017, offshore wind power has a competitive price in comparison with conventional sources. In the 2010s, offshore wind power grew at over 30% per year. Although it has remained less than one percent of the overall world electricity generation, offshore wind power becomes quite relevant on the northern European countries from 2020. However, there are other ways to obtain energy offshore such as using tides and the sun. These types of farms are expensive and difficult to install and, therefore, we propose a combination of several renewable energies in one farm. The main ambition of this work is to try to reduce the installation and maintenance costs of the two types of offshore renewable energies by creating a structure capable of supporting the two types of turbines. To accomplish it, a theoretical study will be made, a brief state-of-the-art will be presented, the chosen items and the environment chosen for installation will be referred to, a prototype will be simulated using a multiphysics software and, finally, the results and conclusions will be presented, based on a Portuguese case study. How piezoelectric materials can enter offshore farms to increase efficiency is also referred to. The project proved to be possible of producing approximately 12.5 GWh of energy annually, more or less enough to supply 10 thousand homes. However, the installation of the piezoelectric materials did not prove to be viable as it is an expensive technology and does not produce a large amount of energy.

Editorial

Since 1985, the year of the first issue of our magazine, the world's population has grown from less than 5 billion to almost 8 billion people. During the same period, electricity generation grew by more than 280%. The growth is continuing. From prospections, there will be more than 8.5 billion people, with 6 billion in urban conglomerations, in 2030. World electricity generation is expected to grow by 40% from 27,000 terawatthour (TWh) in 2021 to 37,000 in 2030, which should play a big role in helping 70 million people globally who do not have access to electricity.

Operational Parametric Based Reliability Estimation & Justification of Failure Criterion of Hydro Power Plant: A Case Study

Abstract: Generating more Power are complex at cheaper cost, also continuous energy supplied are important Hydro power generation is one of the most successful renewable energy resources for the production electrical energy without any environmental hazard and presently it providing more than 86% of all electricity generated by renewable sources worldwide and accounts for about 20% of world electricity. To increase the percentage of green energy in account of world electricity generation the analysis must be performed to get the information about the working conditions of each component in plants so that the required maintenance action should be taken. Maintenance and operation of a hydro power plant is very complicated and the process to calculate and analyzing its compatibility and reliability is very important. In this work introducing a Markov model to evaluate the reliability parameter of THPS-I Sirmour, Rewa. For this work the operational data regarding failure and maintenance time taken to repaired and analysis of all parts of generating unit of the power plant for period of 2010-2015 is considered. The availability and reliability of individual unit of power plant is evaluated by taking into account different reliability Parameters, namely failure rate (λ), repair rate (µ), MTTR, MTTF, MTBF through the collected data and tabulating the required information for the analysis. By this analysis work we can improve reliability of all the components of each unit of power plant. The sub-unit that is commonly failed during operation is like- penstock, butter fly valve, spiral case, turbine, generator, excitation system, speed governor etc. Reliability plays a key role in the cost-effectiveness of systems Keywords: Hydro power plant, Reliability evaluation, Reliability parameters, Markov analysis, Total schedule outage hrs and Total forced outage hrs.

FINANCIAL STABILITY OF ELECTRICITY COMPANIES IN THE CONTEXT OF THE MACROECONOMIC INSTABILITY AND THE COVID-19 PANDEMIC

The electricity sector is an important part of any country's economy as it holds a cross-sectoral importance and produces a socially significant product for residents and industries. Economically, the sector is less vulnerable during world crises, receiving many variations of the state support. Both world electricity consumption and electricity generation have grown steadily over 2007-2019, with China, USA, India, Russia, Japan, Canada, South Korea, Germany, Brazil and France being world market leaders. This article analyzes the current state and the main trends of the development of the electricity industry as a whole and the financial stability of its companies. The United States and Russia, with similar functioning market models, were chosen to assess. The analysis of the financial stability of PJSC Inter RAO and Exelon Corp, two electricity giants in Russia and in the United States, has shown that they demonstrate stable results: Exelon Corp is more profitable while PJSC Inter RAO is less dependent on financing from creditors. Overall, electricity companies and the industry as a whole should not suffer much from the COVID-19 pandemic: many financial support measures have been developed in both countries, helping the sector to recover to 2019 levels by 2021.Keywords: energy sector, electricity industry, economic and financial crisis, coronavirus pandemic (COVID-19), low-carbon economy, financial stability.JEL Classifications: G30, L94, Q43, Q48DOI: https://doi.org/10.32479/ijeep.11575

The Role of National Energy Policies and Life Cycle Emissions of PV Systems in Reducing Global Net Emissions of Greenhouse Gases

The energy sector and electricity generation in particular, is responsible for a great share of the global greenhouse gas (GHG) emissions. World electricity generation is still largely based on the burning of fossil fuels. However, Brazil has already a very low electricity carbon intensity due to the country’s large hydropower capacity. In countries with low grid carbon intensities such as Brazil, the investment in photovoltaic solar systems (PVSS) even if it is cost-effective, might become challenging as any new generation competes essentially against other renewable generation and the carbon offset is not a key driver for investment anymore. This study builds further upon that case to examine if national renewable energy incentives could actually lead to an increase of global net carbon emissions from the installation of PVSS in countries with a low grid carbon intensity. The study presents a life cycle analysis (LCA) of ten photovoltaic systems representative of the different operational conditions in regions across Brazil. It was found that the average energy payback time of the studied PV plants is between 3 and 5 years of operation. This result shows the feasibility and viability of such investments in the Brazilian context. When the LCA was integrated into the analysis though, the results showed that the “local” direct emissions avoidance from two out of ten studied PV plants would not manage to offset their “global” life cycle emissions due to the 2020 projected Brazilian grid emission factor which is already low. It is important to recognize that public policies of unrestricted, unconditional stimulus to photovoltaic systems investment might not help towards reducing global net emissions when the PV systems are installed at countries with a low carbon emission electric matrix. That is also something to consider for other countries as the carbon intensity of their grids will start reducing at levels similar to Brazil’s. It is likely that in the near future, the real net carbon offset achieved by PV systems at the global level will be largely defined by the manufacture procedures and the production’s carbon intensity at the country of origin of the PV panels.

Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

The existence of barriers and proposed recommendations for the development of renewable energy in Indian perspective

Day by day, the exponential growth in the electricity demand is being seen, while the resources of fossil fuel are decreasing. The limited resources of fossil fuel force the world to use renewable energy. The renewable resources are characterized by a source of clean/green energy, omnipresent, freely available. Even though the renewables have strong potential to meet the future electricity demand; it only contributes approximately 24% to the world electricity generation. India has a strong potential of the possibility of roughly 1001 GW of power generations from renewable energy, but nearly 69 GW of power have been installed presently. This scenario reveals that there is a need for development in renewable technologies. In this direction, India has set the target of 175 GW of power generations from the renewable resources by the year 2022. The addition and expansion of the renewable energies experience some barriers in the present situation. The present article concentrates on the identification and addressing of barriers toward the renewable developments and recommendations proposed to overcome these barriers. The current energy scenario of India and estimated potential and future targets of the renewable energies have also been presented.

Municipal solid waste as an alternative energy source

One of the main objectives in the field of waste management today is the implementation of waste-to-energy concept, because it decreases the amount of municipal solid waste landfilled and economizes the traditional hydrocarbon fuels. One of the main objectives of this study was to assess the global energetic potential of municipal solid waste and its potential contribution in energy balance. This evaluation was based on the analysis of main municipal solid waste properties such as component and chemical compositions, and heating value. The comparison of municipal solid waste properties and energetic potential with those for traditional solid fuels was also discussed. For comparison, the biomass samples and coals collected from Russian coal basins were considered. Study of municipal solid waste properties showed that municipal solid waste represents a well-flammable fuel with high carbon content (up to 50–60% for dry ash-free basis), and relatively high O/C atomic ratio. Total world energetic potential of municipal solid waste was estimated as 20 billion GJ that is 12.6% from energetic potential of coal mined. It was shown that by municipal solid waste incineration it is possible to produce 800 billion kWh of electrical energy that is about 3.5% of total world electricity generation. It was shown that in 2035 energetic potential of municipal solid waste may share up to 21.7% from energetic potential of coal. Executed estimations confirmed the urgency of municipal solid waste waste-to-energy implementation.

Techno-economic analysis of the wind park Bogdanci in the Republic of Macedonia

Wind energy as a renewable energy source with the highest growth rate in the last two decades, is considered as a very important source of electricity for the future. The forecasts for development of wind energy are highly optimistic and state that this type of energy will be really important in the future. Following recent data and analysis from certain studies, the share of wind energy in the global world electricity generation in 2020 is estimated to reach 12%. In the same context, and in order to reduce high dependence on fossil fuels, Macedonian government took some steps to introduce and enforce utilization of renewable energy sources. First wind park with capacity of 36.8 MW, installed and operated by the state-owned electricity producing company (JSC Macedonian Power Plants - ELEM), started with operation in April 2014, on a location near Bogdanci. The techno-economic analysis regarding its performance in terms of investment costs, operation and maintenance costs, electricity production and return on investment period, considering valid feed-in tariffs for wind energy production in the country is also presented in the article.

Database engines: Evolution of greenness

AbstractInformation technology consumes up to 10% of the world's electricity generation, contributing to CO2 emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an energy‐efficient (green) database engine could reduce energy consumption and CO2 emissions. The goal of this study is to understand the factors driving databases' energy consumption and execution time throughout their evolution. We conducted an empirical case study of energy consumption by 2 MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the relationships of 4 software metrics to energy consumption and execution time to determine which metrics reflect the greenness and performance of a database. Our analysis shows that database engines' energy consumption and execution time increase as databases evolve. Moreover, the lines of code (LOC) metric is correlated moderately to strongly with energy consumption and execution time in 88% of cases. Our findings provide insights to practitioners and researchers. Database administrators may use them to select a fast, green release of the MySQL database engine. MySQL developers may use LOC to assess products' greenness and performance. Researchers may use our findings to further develop new hypotheses or build models predicting greenness and performance of databases.

Application of Energy Efficiency Techniques While Using ICT Equipment

Information and Communication Technology (ICT) devices and services are becoming more and more widespread in all aspects of human life. Computers and home office equipment represent one of the fastest growing categories of home energy use. While ICT equipment has become more energy efficient in recent years, computers, peripherals, and data centers still use significant amounts of energy, especially if they are left on constantly. The information and technology ecosystem now represents around 10% of the world's electricity generation and it represents the largest area of potential energy savings, as well. Most of these devices now have power management features that allow them to enter standby or "sleep" mode during periods of inactivity but only a fraction of users, know about the potential energy savings features. The aim of this paper is to offer practical and actionable tips for saving energy with computers and home office equipment. I review and test the main opportunities, explain them in plain language, and provide practical tips for leading an initiative to increase ICT energy efficiency in order to cut energy consumption per unit in half – or more. This paper presents the survey conducted about ICT equipment energy use in four offices and in five residential buildings in Korça region, Albania. The users were advised to use some basic energy efficiency tips, and energy data were collected before and after the new practices. Experimental results show that the methods used for increasing efficiency were very effective and the electricity consumption was reduced to about 10% - 45% in offices and about 5% - 15% in residential buildings.

Is the 2040 Projection on Net Electricity Generation by Energy Information Administration Too Conservative? In Comparison to Alternate Projection Using Experience Curve

The Energy Information Administration (EIA) of the U.S. Department of Energy has projected net electricity generation through 2040 for ten selected countries and the world. These ten countries including China and the U.S. are projected to produce 70% of the world electricity generation. Some of the past projections by EIA have been rated too conservative. Using a simple experience curve, we produce alternative projections for comparison. Our projections are significantly higher for eight out of the ten countries. Utilities operating in these eight countries may need to reexamine their plan for future net electricity generation.

Arm Effect on Performance of Vertical Axis Current Turbine Using RANS Simulation

Climate changing, electrical demands, rising diesel fuel prices, as well as fossil fuel-based energy is limited and in fact is depleting ,are subjects to use of renewable technologies. Among the different renewable energy technologies, hydro power generation (large and small scale) is the prime choice in terms of contribution to the world's electricity generation by using water current turbines.CFD simulation using two equations turbulence model was carried out to compare performance of conventional savonius turnine and new consept of vertical axis turbine. This study was coducted to reach suitable desigh of vertical axis turbines for capturing more kineticenergy of current via of others esspecially,low current speed.

Optimization of global and local pollution control in electricity production from coal burning

World electricity generation is expected to strongly increase over the next 25 years, mainly due to the demand of the emerging economies. In this context, coal is likely to remain as one of the most important energy sources. Several pollution control devices have been proposed so far as Best Available Techniques (BATs) to retrofit coal combustion plants in order to minimize their environmental impact. In this work we have developed a systematic method based on multi-objective optimization for the optimal design of pollution control devices in coal-fired plants that considers simultaneously economic and environmental criteria. Our formulation includes nonlinear models for predicting the cost and performance of the abatement technologies. The design task is formulated in mathematical terms as a bi-criteria mixed-integer nonlinear programming (MINLP) problem, the solution of which is defined by a set of Pareto points that represent the optimal trade-off between the two objective functions. The applicability of this method is explored through a case study based on a pulverized coal power plant of 500 MW (gross), burning low-sulfur bituminous coal. Numerical results show that it is possible to reduce the impact of these facilities at a marginal increase in cost by adjusting the operating conditions and capacities of the control pollution devices.

The South African coal mining industry: A need for a more efficient and collaborative supply chain

It is estimated that about two-thirds of global coal is used for power generation and that, in the next 20 years, over 70% of the demand for coal will come from China and India. Coal accounts for approximately 41% of the world's electricity generation. Demand for thermal coal is influenced by factors that include availability, prices of competing products such as oil, gas and nuclear power, and the demand for electricity. The aim of this article is to provide an exposition of supply chain dynamics within the South African coal mining industry and to argue for a more efficient and collaborative supply chain. The authors attempt to investigate at local and global level, the current trends pertaining to the level of reserves, production and consumption of coal. The article further demonstrates the shortcomings of current logistics in meeting the demand for coal in both domestic and export markets. The article draws from secondary data sourced from academic papers, government and agency documents in the exposition of the coal mining supply chain. The paper concludes by recommending the need for a scientific study on supply chain constraints facing the coal mining industry in South Africa.

Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

The presence of PV modules made with new technologies and materials is increasing in PV market, in special Thin Film Solar Modules (TFSM). They are ready to make a substantial contribution to the world's electricity generation. Although Si wafer-based cells account for the most of increase, technologies of thin film have been those of the major growth in last three years. During 2007 they grew 133%. On the other hand, manufacturers provide ratings for PV modules for conditions referred to as Standard Test Conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors characterisation in standard test conditions of PV modules is a controversial issue. Therefore, to carry out a correct photovoltaic engineering, a suitable characterisation of PV module electrical behaviour is necessary. The IDEA Research Group from Jaén University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN was able to generate V– I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method is going to be applied for electrical characterisation of PV CIS modules. Finally, a comparative study with other methods, of electrical characterisation, is done.

Thin film solar modules: the low cost, high throughput and versatile alternative to Si wafers

AbstractThin film solar cells (TFSC) have passed adolescence and are ready to make a substantial contribution to the world's electricity generation. They can have advantages over c‐Si solar modules in ease of large area, lower cost manufacturing and in several types of applications. Factors which limit TFSC module performance relative to champion cell performance are discussed along with the importance of increased throughput and yield. The consensus of several studies is that all TFSC can achieve costs below 1 $/W if manufactured at sufficiently large scale >100 MW using parallel lines of cloned equipment with high material utilization and spray‐on encapsulants. There is significant new commercial interest in TFSC from small investors and large corporations, validating the thin film approach. Unique characteristics are discussed which give TFSC an advantage over c‐Si in two specific markets: small rural solar home systems and building integrated photovoltaic installations. TFSC have outperformed c‐Si in annual energy production (kWhrs/kW), have demonstrated outdoor durability comparable to c‐Si and are being used in MW scale installations worldwide. The merits of the thin film approach cannot be judged on the basis of efficiency alone but must also account for module performance and potential for low cost. TFSC advocates should promote their unique virtues compared to c‐Si: lower cost, higher kWhr/kW output, higher battery charging current, attractive visual appearance, flexible substrates, long‐term stability comparable to c‐Si, and multiple pathways for deposition with room for innovation and evolutionary improvement. There is a huge market for TFSC even at today's efficiency if costs can be reduced. A brief window of opportunity exists for TFSC over the next few years due the Si shortage. The demonstrated capabilities and advantages of TFSC must be proclaimed more persistently to funding decision‐makers and customers without minimizing the remaining challenges. Copyright © 2006 John Wiley & Sons, Ltd.