Showing papers in "Energies in 2020"

Internet of Things (IoT) and the Energy Sector

Abstract: Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

Review on Life Cycle Assessment of Solar Photovoltaic Panels

Abstract: The photovoltaic (PV) sector has undergone both major expansion and evolution over the last decades, and currently, the technologies already marketed or still in the laboratory/research phase are numerous and very different. Likewise, in order to assess the energy and environmental impacts of these devices, life cycle assessment (LCA) studies related to these systems are always increasing. The objective of this paper is to summarize and update the current literature of LCA applied to different types of grid-connected PV, as well as to critically analyze the results related to energy and environmental impacts generated during the life cycle of PV technologies, from 1st generation (traditional silicon based) up to the third generation (innovative non-silicon based). Most of the results regarded energy indices like energy payback time, cumulative energy demand, and primary energy demand, while environmental indices were variable based on different scopes and impact assessment methods. Moreover, the review work allowed to highlight and compare key parameters (PV type and system, geographical location, efficiency), methodological insights (functional unit, system boundaries, etc.), and energy/environmental hotspots of 39 LCA studies relating to different PV systems, in order to underline the importance of these aspects, and to provide information and a basis of comparison for future analyses.

Sustainable Solutions for Green Financing and Investment in Renewable Energy Projects

Abstract: The lack of long-term financing, the low rate of return, the existence of various risks, and the lack of capacity of market players are major challenges for the development of green energy projects. This paper aimed to highlight the challenges of green financing and investment in renewable energy projects and to provide practical solutions for filling the green financing gap. Practical solutions include increasing the role of public financial institutions and non-banking financial institutions (pension funds and insurance companies) in long-term green investments, utilizing the spillover tax to increase the rate of return of green projects, developing green credit guarantee schemes to reduce the credit risk, establishing community-based trust funds, and addressing green investment risks via financial and policy de-risking. The paper also provides a practical example of the implementation of the proposed tools.

Ammonia as Effective Hydrogen Storage: A Review on Production, Storage and Utilization

Abstract: Ammonia is considered to be a potential medium for hydrogen storage, facilitating CO2-free energy systems in the future. Its high volumetric hydrogen density, low storage pressure and stability for long-term storage are among the beneficial characteristics of ammonia for hydrogen storage. Furthermore, ammonia is also considered safe due to its high auto ignition temperature, low condensation pressure and lower gas density than air. Ammonia can be produced from many different types of primary energy sources, including renewables, fossil fuels and surplus energy (especially surplus electricity from the grid). In the utilization site, the energy from ammonia can be harvested directly as fuel or initially decomposed to hydrogen for many options of hydrogen utilization. This review describes several potential technologies, in current conditions and in the future, for ammonia production, storage and utilization. Ammonia production includes the currently adopted Haber–Bosch, electrochemical and thermochemical cycle processes. Furthermore, in this study, the utilization of ammonia is focused mainly on the possible direct utilization of ammonia due to its higher total energy efficiency, covering the internal combustion engine, combustion for gas turbines and the direct ammonia fuel cell. Ammonia decomposition is also described, in order to give a glance at its progress and problems. Finally, challenges and recommendations are also given toward the further development of the utilization of ammonia for hydrogen storage.

Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature

Abstract: Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

Snapshot of Photovoltaics—February 2020

Abstract: Since the demonstration of the first modern silicon solar cells at Bell Labs in 1954, it took 58 years until the cumulative installed photovoltaic electricity generation capacity had reached 100 GW by the end of 2012. Then, it took another five years to reach an annual installation capacity of over 100 GW in 2017 and close to 120 GW in 2019. As a consequence, the total world-wide installed photovoltaic electricity generation capacity exceeded 635 GW at the end of 2019. Although it witnessed a 20% and 25% decrease in annual installations in 2018 and 2019, respectively, China was again the largest market with 30 GW of annual installations. The number of countries in the club with more than 1 GW annually has increased to 18 countries in 2019. The use of local battery storage systems in solar farms as well as decentralized photovoltaic electricity generation systems combined has again increased, due to the falling storage system costs.

Decarbonization of the Iron and Steel Industry with Direct Reduction of Iron Ore with Green Hydrogen

Abstract: Production of iron and steel releases seven percent of the global greenhouse gas (GHG) emissions. Incremental changes in present primary steel production technologies would not be sufficient to meet the emission reduction targets. Replacing coke, used in the blast furnaces as a reducing agent, with hydrogen produced from water electrolysis has the potential to reduce emissions from iron and steel production substantially. Mass and energy flow model based on an open-source software (Python) has been developed in this work to explore the feasibility of using hydrogen direct reduction of iron ore (HDRI) coupled with electric arc furnace (EAF) for carbon-free steel production. Modeling results show that HDRI-EAF technology could reduce specific emissions from steel production in the EU by more than 35 % , at present grid emission levels (295 kgCO2/MWh). The energy consumption for 1 ton of liquid steel (tls) production through the HDRI-EAF route was found to be 3.72 MWh, which is slightly more than the 3.48 MWh required for steel production through the blast furnace (BF) basic oxygen furnace route (BOF). Pellet making and steel finishing processes have not been considered. Sensitivity analysis revealed that electrolyzer efficiency is the most important factor affecting the system energy consumption, while the grid emission factor is strongly correlated with the overall system emissions.

A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

Abstract: The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

Global Indicators of Sustainable Development: Evaluation of the Influence of the Human Development Index on Consumption and Quality of Energy

Abstract: The article explores the impact of the quality and volume of energy consumption of the population on the human development index using a sample of a number of countries as an example. The hypothesis concerning the relationship between the amount of energy consumed, the human development index (HDI), and the environment (CO2 emissions into the atmosphere) has been verified. The study results show that the size and rating of the HDI are influenced by such factors as urbanization growth, gross domestic product (GDP), gross national income (GNI) per capita, the share of “clean” energy consumption by the population and business in total energy consumption, the level of socio-economic development, and R&D expenses. In the course of building the model, the recommendations by the United Nations (UN) and the Organization for Economic Co-operation and Development (OECD) were used. The results show that the volume of energy consumption not only affects the human development index in a particular country, but is also an important factor in determining the level of sustainable development. The results, obtained in the course of the study and described in the article, may be applicable in the practice of research related to the assessment of human development and sustainable development.

Characterization of Hybrid-nano/Paraffin Organic Phase Change Material for Thermal Energy Storage Applications in Solar Thermal Systems

Abstract: In this work, the experimental investigations were piloted to study the influence of hybrid nanoparticles containing SiO2 and CeO2 nanoparticles on thermo-physical characteristics of the paraffin-based phase change material (PCM). Initially, the hybrid nanoparticles were prepared by blending equal mass of SiO2 and CeO2 nanoparticles. The hybrid-nano/paraffin (HnP) samples were prepared by cautiously dispersing 0, 0.5, 1.0, and 2.0 percentage mass of hybrid nanoparticles inside the paraffin, respectively. The synthesized samples were examined under different instruments such as field emission scanning electron microscope (FESEM), Fourier transform infrared spectrometer (FTIR), differential scanning calorimetry (DSC), thermogravimetric analyzer (TGA), and thermal properties analyzer to ascertain the influence of hybrid nanoparticles on thermo-physical characteristics of the prepared samples. The obtained experimental results proved that the hybrid nanoparticles were uniformly diffused in the paraffin matrix without affecting the chemical arrangement of paraffin molecules. Prominently, the relative thermal stability and relative thermal conductivity of the paraffin were synergistically enriched up to 115.49% and 165.56%, respectively, when dispersing hybrid nanoparticles within paraffin. Furthermore, the hybrid nanoparticles appropriately amended the melting and crystallization point of the paraffin to reduce its supercooling, and the maximum reduction in supercooling was ascertained as 35.81%. The comprehensive studies indicated that the paraffin diffused with SiO2 and CeO2 hybrid nanoparticles at 1.0 mass percentage would yield a better outcome compared to the next higher mass fractions without much diminishing the latent heat of paraffin. Hence, it is recommended to utilize the hybrid-nano/paraffin with 1.0 mass fraction of the aforementioned hybrid nanoparticles for effectively augmenting the thermal energy capacity of low-temperature solar thermal systems.

Multi-Criteria Decision-Making (MCDM) for the Assessment of Renewable Energy Technologies in a Household: A Review

Abstract: Different power generation technologies have different advantages and disadvantages. However, if compared to traditional energy sources, renewable energy sources provide a possibility to solve the climate change and economic decarbonization issues that are so relevant today. Therefore, the analysis and evaluation of renewable energy technologies has been receiving increasing attention in the politics of different countries and the scientific literature. The household sector consumes almost one third of all energy produced, thus studies on the evaluation of renewable energy production technologies in households are very important. This article reviews the scientific literature that have used multiple-criteria decision-making (MCDM) methods as a key tool to evaluate renewable energy technologies in households. The findings of the conducted research are categorized according to the objectives pursued and the criteria on which the evaluation was based are discussed. The article also provides an overview and in-depth analysis of MCDM methods and distinguishes the main advantages and disadvantages of using them to evaluate technologies in households.

A Review on Thermoelectric Generators: Progress and Applications

Abstract: A thermoelectric effect is a physical phenomenon consisting of the direct conversion of heat into electrical energy (Seebeck effect) or inversely from electrical current into heat (Peltier effect) without moving mechanical parts. The low efficiency of thermoelectric devices has limited their applications to certain areas, such as refrigeration, heat recovery, power generation and renewable energy. However, for specific applications like space probes, laboratory equipment and medical applications, where cost and efficiency are not as important as availability, reliability and predictability, thermoelectricity offers noteworthy potential. The challenge of making thermoelectricity a future leader in waste heat recovery and renewable energy is intensified by the integration of nanotechnology. In this review, state-of-the-art thermoelectric generators, applications and recent progress are reported. Fundamental knowledge of the thermoelectric effect, basic laws, and parameters affecting the efficiency of conventional and new thermoelectric materials are discussed. The applications of thermoelectricity are grouped into three main domains. The first group deals with the use of heat emitted from a radioisotope to supply electricity to various devices. In this group, space exploration was the only application for which thermoelectricity was successful. In the second group, a natural heat source could prove useful for producing electricity, but as thermoelectricity is still at an initial phase because of low conversion efficiency, applications are still at laboratory level. The third group is progressing at a high speed, mainly because the investigations are funded by governments and/or car manufacturers, with the final aim of reducing vehicle fuel consumption and ultimately mitigating the effect of greenhouse gas emissions.

Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy

Abstract: At the moment of writing, in Italy, there is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its outbreak is leading to severe global socioeconomic disruptions impacting on all economic sectors from tourism, industry and the tertiary sector, up to the operational and opening of public offices, the closure of schools and the organization of families. Measures adopted by the Italian government to deal with the COVID-19 emergency have had direct effects both on people’s daily lives and on the activity of most industrial and commercial production companies. These changes have been unequivocally reflected also on the Italian electricity system, which has shown unprecedented behavior in terms of both energy consumption and volume—and subsequently, in the observed share of renewable and conventional production technologies. The goal of this study is to show the impact on the power industry of all the restrictions and lockdown of the activities in Italy and to discuss the effects of COVID-19 outbreak on the bulk power system and the entire electricity sector. In particular, the consequences on load profiles, electricity consumption and market prices in Italy, including the environmental aspects, are examined.

Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review

Abstract: To combat global climate change moving towards sustainable, mobility is one of the most holistic approaches. Hence, decarbonization of the transport sector by employing electric vehicles (EVs) is currently an environmentally benign and efficient solution. The EV includes the hybrid EV (HEV), the plug-in hybrid EV (PHEV), and the battery EV (BEV). A storage system, a charging station, and power electronics are the essential components of EVs. The EV charging station is primarily powered from the grid which can be replaced by a solar photovoltaic system. Wide uptake of EVs is possible by improving the technologies, and also with support from the government. However, greenhouse gas emission (GHG) saving potential of the EV is debatable when the required power to charge the EV comes from traditional fossil fuel sources.

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Abstract: Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting

Abstract: Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As the volume and variety of energy data provided by building automation systems, smart meters, and other sources are continuously increasing, long short-term memory (LSTM) deep learning models have become an attractive approach for energy load forecasting. These models are characterized by their capabilities of learning long-term dependencies in collected electric data, which lead to accurate prediction results that outperform several alternative statistical and machine learning approaches. Unfortunately, applying LSTM models may not produce acceptable forecasting results, not only because of the noisy electric data but also due to the naive selection of its hyperparameter values. Therefore, an optimal configuration of an LSTM model is necessary to describe the electric consumption patterns and discover the time-series dynamics in the energy domain. Finding such an optimal configuration is, on the one hand, a combinatorial problem where selection is done from a very large space of choices; on the other hand, it is a learning problem where the hyperparameters should reflect the energy consumption domain knowledge, such as the influential time lags, seasonality, periodicity, and other temporal attributes. To handle this problem, we use in this paper metaheuristic-search-based algorithms, known by their ability to alleviate search complexity as well as their capacity to learn from the domain where they are applied, to find optimal or near-optimal values for the set of tunable LSTM hyperparameters in the electrical energy consumption domain. We tailor both a genetic algorithm (GA) and particle swarm optimization (PSO) to learn hyperparameters for load forecasting in the context of energy consumption of big data. The statistical analysis of the obtained result shows that the multi-sequence deep learning model tuned by the metaheuristic search algorithms provides more accurate results than the benchmark machine learning models and the LSTM model whose inputs and hyperparameters were established through limited experience and a discounted number of experimentations.

Energy Harvesting towards Self-Powered IoT Devices

Abstract: The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system’s life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article.

Overview on Grid-Forming Inverter Control Methods

Abstract: In this paper, different control approaches for grid-forming inverters are discussed and compared with the grid-forming properties of synchronous machines. Grid-forming inverters are able to operate AC grids with or without rotating machines. In the past, they have been successfully deployed in inverter dominated island grids or in uninterruptable power supply (UPS) systems. It is expected that with increasing shares of inverter-based electrical power generation, grid-forming inverters will also become relevant for interconnected power systems. In contrast to conventional current-controlled inverters, grid-forming inverters do not immediately follow the grid voltage. They form voltage phasors that have an inertial behavior. In consequence, they can inherently deliver momentary reserve and increase power grid resilience.

Potential Liquid-Organic Hydrogen Carrier (LOHC) Systems: A Review on Recent Progress

Abstract: The depletion of fossil fuels and rising global warming challenges encourage to find safe and viable energy storage and delivery technologies. Hydrogen is a clean, efficient energy carrier in various mobile fuel-cell applications and owned no adverse effects on the environment and human health. However, hydrogen storage is considered a bottleneck problem for the progress of the hydrogen economy. Liquid-organic hydrogen carriers (LOHCs) are organic substances in liquid or semi-solid states that store hydrogen by catalytic hydrogenation and dehydrogenation processes over multiple cycles and may support a future hydrogen economy. Remarkably, hydrogen storage in LOHC systems has attracted dramatically more attention than conventional storage systems, such as high-pressure compression, liquefaction, and absorption/adsorption techniques. Potential LOHC media must provide fully reversible hydrogen storage via catalytic processes, thermal stability, low melting points, favorable hydrogenation thermodynamics and kinetics, large-scale availability, and compatibility with current fuel energy infrastructure to practically employ these molecules in various applications. In this review, we present various considerable aspects for the development of ideal LOHC systems. We highlight the recent progress of LOHC candidates and their catalytic approach, as well as briefly discuss the theoretical insights for understanding the reaction mechanism.

Investigation of Inorganic Phase Change Material for a Semi-Transparent Photovoltaic (STPV) Module

Abstract: The semi-transparent photovoltaic (STPV) module is an emerging technology to harness the solar energy in the building. Nowadays, buildings are turning from energy consumers to energy producers due to the integration of the STPV module on the building envelopes and facades. In this research, the STPV module was integrated on the rooftop window of the experimental room at Kovilpatti (9°10′0″ N, 77°52′0″ E), Tamil Nadu, India. The performance of the STPV modules varies with respect to the geographical location, incident solar radiation, and surface temperature of the module. The surface temperature of the STPV module was regulated by the introduction of the mixture of graphene oxide and sodium sulphate decahydrate (Na2SO4·10H2O). The various concentration of the graphene oxide was mixed together with the Na2SO4·10H2O to enhance the thermal conductivity. The thermal conductivity of the mixture 0.3 concentration was found to be optimum from the analysis. The instantaneous peak temperature of the semi-transparent photovoltaic phase change material (STPV-PCM) module was reduced to 9 °C during summer compared to the reference STPV. At the same time, the energy conversion efficiency was increased by up to 9.4% compared to the conventional STPV module. Due to the incorporation of the graphene oxide and Na2SO4·10H2O, the daily output power production of the STPV module was improved by 12.16%.

Do Tense Geopolitical Factors Drive Crude Oil Prices

Abstract: Geopolitical factors are considered a crucial factor that makes a difference in crude oil prices. Over the last three decades, many political events occurred frequently, causing short-term fluctuations in crude oil prices. This paper aims to examine the dynamic correlation and causal link between geopolitical factors and crude oil prices based on data from June 1987 to February 2020. By using a time-varying copula approach, it is shown that the correlation between geopolitical factors and crude oil prices is strong during periods of political tensions. The GPA (geopolitical acts) index, as the real factor, drives the rise in prices of crude oil. Moreover, the dynamic correlation between geopolitical factors and crude oil prices shows strong volatility over time during periods of political tensions. We also found unidirectional causality running from geopolitical factors to crude oil prices by using the Granger causality test.

A review of the methanol economy: The fuel cell route

Abstract: This review presents methanol as a potential renewable alternative to fossil fuels in the fight against climate change. It explores the renewable ways of obtaining methanol and its use in efficient energy systems for a net zero-emission carbon cycle, with a special focus on fuel cells. It investigates the different parts of the carbon cycle from a methanol and fuel cell perspective. In recent years, the potential for a methanol economy has been shown and there has been significant technological advancement of its renewable production and utilization. Even though its full adoption will require further development, it can be produced from renewable electricity and biomass or CO2 capture and can be used in several industrial sectors, which make it an excellent liquid electrofuel for the transition to a sustainable economy. By converting CO2 into liquid fuels, the harmful effects of CO2 emissions from existing industries that still rely on fossil fuels are reduced. The methanol can then be used both in the energy sector and the chemical industry, and become an all-around substitute for petroleum. The scope of this review is to put together the different aspects of methanol as an energy carrier of the future, with particular focus on its renewable production and its use in high-temperature polymer electrolyte fuel cells (HT-PEMFCs) via methanol steam reforming.

Multi-faceted analysis of systematic risk-based wind energy investment decisions in E7 economies using modified hybrid modeling with IT2 fuzzy sets

Abstract: This study aimed to analyze the systematic risks of wind energy investments. Within this framework, E7 countries are included in the scope of the examination. A large literature review was carried out and 12 different systematic risk factors that could exist in wind energy investments were identified. The analysis process of the study consisted of two different stages. First, the specified risk criteria were weighted with the help of the interval type 2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Second, E7 countries were ranked according to the risk management effectiveness in wind energy investments. In this process, the IT2 fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach was taken into consideration. The findings show that volatility in exchange rates and interest rates were the most important risks in wind energy investments. In addition, it was determined that China and Indonesia were the most successful countries in managing risks in wind energy investments. In contrast, India, Russia, and Turkey were determined to be the least successful. Additionally, the IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method was applied as a robustness check of the extended VIKOR method. It was concluded that the ranking results of the IT2 fuzzy TOPSIS method were similar to the results of the IT2 fuzzy VIKOR. It can be understood that the proposed ranking method was consistent with the comparative analysis results. From this point of view, it was observed that countries should take measures regarding their exchange rate and interest rate risks in order to increase the efficiency in wind energy investments. In this context, companies should first ensure that they do not have a foreign exchange short position in their balance sheets by conducting an effective financial analysis. In addition, it is important to use financial derivatives to minimize the exchange rate and interest rate risks. Using these results, it will be possible to manage this risk by taking the reverse position for the existing foreign currency and interest risk. In this way, it will be possible to increase the efficiency of wind energy investments, which will contribute to the social and economic development of each respective country.

A Review on Time Series Aggregation Methods for Energy System Models

Abstract: Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

Smart Electrochromic Windows to Enhance Building Energy Efficiency and Visual Comfort

Abstract: Electrochromic systems for smart windows make it possible to enhance energy efficiency in the construction sector, in both residential and tertiary buildings. The dynamic modulation of the spectral properties of a glazing, within the visible and infrared ranges of wavelengths, allows one to adapt the thermal and optical behavior of a glazing to the everchanging conditions of the environment in which the building is located. This allows appropriate control of the penetration of solar radiation within the building. The consequent advantages are manifold and are still being explored in the scientific literature. On the one hand, the reduction in energy consumption for summer air conditioning (and artificial lighting, too) becomes significant, especially in "cooling dominated" climates, reaching high percentages of saving, compared to common transparent windows; on the other hand, the continuous adaptation of the optical properties of the glass to the changing external conditions makes it possible to set suitable management strategies for the smart window, in order to offer optimal conditions to take advantage of daylight within the confined space. This review aims at a critical review of the relevant literature concerning the benefits obtainable in terms of energy consumption and visual comfort, starting from a survey of the main architectures of the devices available today.

Reviewing Usage, Potentials, and Limitations of Renewable Energy Sources

Abstract: The world’s ever-increasing population, combined with economic and technological growth and a new, modern way of life, has led to high energy demand and consumption. Fossil fuels have been the main energy source for many years, but their use has many negative impacts on the environment. This has made the transition to renewable energy sources necessary in order to address climate change and meet the 1.5 °C goal. This paper is a review of the different types of renewables, their potentials and limitations, and their connection to climate change, economic growth, and human health. It also examines consumers’ willingness to pay for renewables in different countries, based on the existing literature. IEA (International Energy Agency) data are analyzed, concerning renewables’ current use, the evolution of their usage, and forecasts about their future usage. Finally, policies and strategies are recommended in order to address climate change and fully integrate renewables as a sustainable energy source.

The Linkage between Economic Growth, Renewable Energy, Tourism, CO2 Emissions, and International Trade: The Evidence for the European Union

Abstract: This paper evaluates the link between economic growth, renewable energy, tourism arrivals, trade openness, and carbon dioxide emissions in the European Union (EU-28). As an econometric strategy, the research uses panel data. In the first step, we apply the unit root test, and the results demonstrated that the variables used in this study are integrated I (1) in the first difference. In the second step, we apply the Pedroni cointegration test, and Kao Residual cointegration test, and we observe that the variables are cointegrated in the long run. The panel fully modified least squares (FMOLS), panel dynamic least squares (DOLS), and generalized moments system (GMM-System) estimator are considered in this research. The econometric results proved that trade openness and renewable energy decreased climate change and environmental degradation. The empirical study also found a positive effect of economic growth on carbon dioxide emissions. Moreover, tourism arrivals are negatively correlated with carbon dioxide emissions, showing sustainability practices of the tourism sector on the environment. Furthermore, carbon dioxide emissions in the long run present a positive impact, indicating that climate change increases. In this study, we also consider the recent methodology of Dumitrescu–Hurlin to observe the causality and the relationship between renewable energy, trade openness, economic growth, tourism arrivals, and carbon dioxide emissions.

The Impacts of Air Pollution on Health and Economy in Southeast Asia

Abstract: The accessibility of cheap fossil fuels, due to large government subsidies, promotes the accelerated gross domestic product (GDP) per capita growth in Southeast Asia. However, the ambient air pollution from fossil fuel combustion has a latent cost, which is the public health issues such as respiratory diseases, lung cancer, labor loss, and economic burden in the long-run. In Southeast Asia, lung cancer is the leading and second leading cause of cancer-related death in men, and women, respectively. This nexus study employs the panel vector error correction model (VECM) and panel generalized method of moments (GMM) using data from ten Southeast Asian countries from the period (2000–2016) to explore the possible association between emissions, lung cancer, and the economy. The results confirm that CO2 and PM2.5 are major risk factors for lung cancer in the region. Additionally, the increasing use of renewable energy and higher healthcare expenditure per capita tend to reduce the lung cancer prevalence. Governments specially in low oil price era, have to transfer subsidies from fossil fuels to renewable energy to create a healthy environment. Furthermore, cost creation for fossil fuel consumption through carbon taxation, especially in the power generation sector, is important to induce private sector investment in green energy projects.

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

Abstract: Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems

Abstract: There is a continuous and fast increase in electric vehicles (EVs) adoption in many countries due to the reduction of EVs prices, governments’ incentives and subsidies on EVs, the need for energy independence, and environmental issues. It is expected that EVs will dominate the private cars market in the coming years. These EVs charge their batteries from the power grid and may cause severe effects if not managed properly. On the other hand, they can provide many benefits to the power grid and get revenues for EV owners if managed properly. The main contribution of the article is to provide a review of potential negative impacts of EVs charging on electric power systems mainly due to uncontrolled charging and how through controlled charging and discharging those impacts can be reduced and become even positive impacts. The impacts of uncontrolled EVs charging on the increase of peak demand, voltage deviation from the acceptable limits, phase unbalance due to the single-phase chargers, harmonics distortion, overloading of the power system equipment, and increase of power losses are presented. Furthermore, a review of the positive impacts of controlled EVs charging and discharging, and the electrical services that it can provide like frequency regulation, voltage regulation and reactive power compensation, congestion management, and improving power quality are presented. Moreover, a few promising research topics that need more investigation in future research are briefly discussed. Furthermore, the concepts and general background of EVs, EVs market, EV charging technology, the charging methods are presented.