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Special Issue "Energy Economy, Sustainable Energy and Energy Saving"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 30 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Sang-Bing Tsai

University of Electronic Science and Technology of China Zhongshan Institute, China & Civil Aviation University of China, China & Foshan University, China
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Interests: Green Operation, Sustainability, Environmental Health, Sustainable Energy, Management Science
Guest Editor
Prof. Xiaohong Chen

Institute of Big Data and Internet Innovation, Hunan University of Commerce, School of Business, Central South University
E-Mail
Interests: resource-conserving; environment; energy saving; management science
Guest Editor
Prof. Jintao Xu

National School of Development (NSD), Peking University
Director, China Center for Energy and Development, Peking University
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Interests: energy research; energy-saving; carbon reduction
Guest Editor
Prof. Qinghua Zhu

Antai College of Economics & Management, Shanghai Jiao Tong University
E-Mail
Interests: energy research; energy-saving; carbon reduction; management science
Guest Editor
Prof. Baozhuang Niu

South China University of Technology
E-Mail
Interests: management science; sustainability; energy research; sustainable energy

Special Issue Information

Dear Colleagues,

Energy is the basic driving force of economic growth and development, but energy development has produced a large amount of gases that have led to theglobal warming problem. Therefore, energy, economy, and the environment (3E) are closely correlated to each other. Moreover, energy is an important factor affecting national security, economic development, and people’s lives. Thus, governments all around the world regard energy policy as the key governmental policy and formulate energy policies suitable for national development according to the different conditions in their geographical environment, natural resources (mineral resources), and economic development process, as well as the international situation and energy supply status.

The promotion of sustainable energy policy mainly starts with “clean energy” (energy supply) and “energy saving” (energy demand). In terms of energy supply, great efforts must be made to promote the modification of energy structure and actively develop carbon-free energy and renewable energy. In terms of energy demand, various departments must focus on doing a good job in energy saving and carbon reduction. The industrial department should adjust industrial structure to make industry develop towards the direction of high value added and low energy consumption, encourage enterprises to realize clean production, and support the green energy industry, so as to achieve energy saving and carbon reduction. The transportation departments should mainly start with reducing vehicles’ energy consumption. The construction department should mainly promote green buildings and improve the energy efficiency of lighting and other electrical equipment. In terms of the public, government should encourage and promote the universal energy saving and carbon reduction movement and low-carbon consumption.

This Special Issue provides a practical and comprehensive forum for exchanging novel research ideas or empirical practices which bridge the latest energy economy, sustainable energy, energy policy, and energy saving.

The SI encompasses theoretical, analytical, empirical research, comprehensive reviews of relevant research, conceptual frameworks, and case studies of effective applications in this area.       

We invite colleagues to contribute to this Special Issue. Potential topics include, but are not limited to:

  • Energy economy
  • Green energy
  • Sustainable energy
  • Energy research
  • Energy development
  • Energy Saving
  • Energy Management
  • Energy Policy
  • Carbon Reduction
  • Renewable Energy

Prof. Sang-Bing Tsai
Prof. Xiaohong Chen
Prof. Jintao Xu
Prof. Qinghua Zhu
Prof. Baozhuang Niu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy economy
  • green energy
  • sustainable energy
  • energy research
  • energy development
  • energy saving
  • energy management
  • energy policy
  • carbon reduction
  • renewable energy

Published Papers (14 papers)

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Open AccessArticle A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems
Energies 2018, 11(10), 2777; https://doi.org/10.3390/en11102777
Received: 30 September 2018 / Revised: 9 October 2018 / Accepted: 10 October 2018 / Published: 16 October 2018
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Abstract
To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed
[...] Read more.
To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Competing Retailers’ Environmental Investment: An Analysis under Different Power Structures
Energies 2018, 11(10), 2719; https://doi.org/10.3390/en11102719
Received: 28 September 2018 / Revised: 8 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
PDF Full-text (938 KB)
Abstract
Sustainability issues in supply chains have received increasingly significant concern. Facing incentives such as environmental tax and consumer environmental awareness, firms and even retailers have started to make sustainability investments. To evaluate the retailer’s contribution to sustainability issues, we study a supply chain
[...] Read more.
Sustainability issues in supply chains have received increasingly significant concern. Facing incentives such as environmental tax and consumer environmental awareness, firms and even retailers have started to make sustainability investments. To evaluate the retailer’s contribution to sustainability issues, we study a supply chain with one manufacturer and two symmetric competing retailers who have the option to make sustainable investment in their upstream members directly in green technology or clean production. We investigate the optimal sustainable investment and operation decisions under three power structures: (1) firms have the same power (Nash game); (2) the manufacturer is more powerful (Manufacturer-lead Stackelberg game) and (3) the retailers are more powerful (Retailer-lead Stackelberg game). By analyzing the optimal decisions and the economic performances, we show that the retailers always have incentives to make sustainable investment in all power structures. However, the retailers’ power affects firms’ decisions, the economic and the environmental performances. When the investment cost is low, the emission reduction due to investment is the most significant with less powerful retailers. With relatively high investment cost, whether the retailers having more power make more sustainable investment depends on the unit tax saving and effect factor of emission reduction on the demand. From the environmental perspective, simultaneous games may conduct the most significant total emission reduction in most cases. We also consider an asymmetric case and compare it with the symmetric one. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
Open AccessArticle Power Plant Economic Analysis: Maximizing Lifecycle Profitability by Simulating Preliminary Design Solutions of Steam-Cycle Conditions
Energies 2018, 11(9), 2245; https://doi.org/10.3390/en11092245
Received: 21 July 2018 / Revised: 22 August 2018 / Accepted: 24 August 2018 / Published: 27 August 2018
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Abstract
Many existing financial models for power plants chose a design based on the maximum thermal efficiency excluding the operational (OPEX) and capital (CAPEX) cost variations of technical factors. These factors are often fixed because including them in financial assessments can be burdensome and
[...] Read more.
Many existing financial models for power plants chose a design based on the maximum thermal efficiency excluding the operational (OPEX) and capital (CAPEX) cost variations of technical factors. These factors are often fixed because including them in financial assessments can be burdensome and it is assumed that maximum efficiency equals maximum profit. However, this assumption may not always be right. Through 19,440 power plant steam-cycle design solutions and their associated OPEX and CAPEX, this study found the eighth most thermally-efficient solution to be $1.284 M more profitable than the traditional thermally-optimized design solution. As such, this paper presents a model incorporating technical factors through parametric estimation by minimizing the burden on decision makers. While this may reduce precision, it allows for quick cost assessments across differing design solutions. The data for model development was collected from a Korean-constructed, operational 600 MW coal-fired power plant in the Philippines. Using the Thermoflex software, nearly all design configurations’ heat rate outputs are simulated. Profitability is then optimized based on the resultant design configuration’s impact on revenue and CAPEX and OPEX costs. The simulation inputs included variables found to be most impactful on the steam generated power efficiency per existing literature. Lastly, the model includes an assessment of cost impacts among recent environmental regulations by incorporating carbon tax costs and a sensitivity analysis. The economic analysis model discussed in this paper is non-existent in current literature and will aid the power-plant project investment industry through their project feasibility analyses. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning
Energies 2018, 11(7), 1882; https://doi.org/10.3390/en11071882
Received: 24 May 2018 / Revised: 9 July 2018 / Accepted: 18 July 2018 / Published: 19 July 2018
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Abstract
Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the
[...] Read more.
Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle A Systems Analysis of the Development Status and Trends of Rural Household Energy in China
Energies 2018, 11(7), 1741; https://doi.org/10.3390/en11071741
Received: 4 June 2018 / Revised: 21 June 2018 / Accepted: 28 June 2018 / Published: 3 July 2018
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Abstract
This paper attempts to present a complete picture of the status quo and future trends of the development of rural household energy in China based on a literature review and a systems analysis. First, a comprehensive literature review was conducted from the perspectives
[...] Read more.
This paper attempts to present a complete picture of the status quo and future trends of the development of rural household energy in China based on a literature review and a systems analysis. First, a comprehensive literature review was conducted from the perspectives of energy consumption, carbon emissions and pollutants, energy resources, energy technologies, and energy policies. The review revealed the complexities and dynamics of the current system of rural household energy in China, and several key issues were identified for further attention. These issues are further explored by a systems analysis based on the “Integrated strategy of Sustainable development objectives, Decision-making systems, Operation systems, and Physical systems” (I-SDOP) concept. Following this method, a complete picture of the market status and policy targets is presented, by mapping an energy Sankey diagram and by reviewing the behavior of the main players. The results indicate that synergy of various energies, technologies, and players, and a combination of flexible engineering schemes and policy designs, with adequate consideration of temporal variation and regional disparity, are key strategies to promote the sustainable development of rural household energy, especially the distributed utilization of renewable energy, in China. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Residential Electricity Consumption and Economic Growth in Algeria
Energies 2018, 11(7), 1656; https://doi.org/10.3390/en11071656
Received: 11 April 2018 / Revised: 8 June 2018 / Accepted: 21 June 2018 / Published: 26 June 2018
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Abstract
Within the framework of the COP21 (Conference of the Parties) agreement, Algeria submitted its Intended Nationally Determined Contribution pledging to reduce carbon emissions by at least 7% by 2030. However, it will be a difficult task to reach this target as total final
[...] Read more.
Within the framework of the COP21 (Conference of the Parties) agreement, Algeria submitted its Intended Nationally Determined Contribution pledging to reduce carbon emissions by at least 7% by 2030. However, it will be a difficult task to reach this target as total final energy consumption has increased 32% from 2010 to 2014, with the major energy increases being related to electricity use in the residential sector. In this context, the relationship between residential electricity consumption and income is analyzed for Algeria in the period 1970–2013, by estimating a residential electricity consumption per capita demand function which depends on GDP per capita, its squared and cubed terms, the electricity prices, and the goods and services imports. An extended Autoregressive Distributed Lag model (ARDL) was adopted to consider the different growth patterns registered in the evolution of GDP. The estimate results show that the relationships between electricity use and GDP (in per capita terms) present an inverted N-shape, with the second turning point having been reached. Therefore, promoting growth in Algeria could be convenient to reduce the electricity consumption, as a higher income level may allow the use of more efficient appliances. Additionally, renewable energies may be adequate to increase the electricity production in order to cover the increasing residential demand. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam
Energies 2018, 11(6), 1504; https://doi.org/10.3390/en11061504
Received: 21 May 2018 / Revised: 5 June 2018 / Accepted: 6 June 2018 / Published: 8 June 2018
Cited by 3 | PDF Full-text (2408 KB) | HTML Full-text | XML Full-text
Abstract
The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying
[...] Read more.
The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of the factors. The TOPSIS approach is then applied to rank the locations in the final step. The results show that Binh Thuan is the optimal location to build a solar power plant because it has the highest ranking score in the final phase of this study. The contribution of this study is the proposal of a MCDM model for solar plant location selection in Viet Nam under fuzzy environment conditions. This paper also is part of the evolution of a new approach that is flexible and practical for decision makers. Furthermore, this research provides useful guidelines for solar power plant location selection in many countries as well as a guideline for location selection of other industries. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization
Energies 2018, 11(6), 1426; https://doi.org/10.3390/en11061426
Received: 30 March 2018 / Revised: 29 May 2018 / Accepted: 30 May 2018 / Published: 2 June 2018
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Abstract
In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The
[...] Read more.
In this paper, a demand side management (DSM) scheme is used to make energy utilization more efficient. The DSM scheme encourages the consumer to change energy utilization patterns which benefit the utility. In return, the consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of consumer comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm called hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. A regional based time of use (ToU) tariff is used to make the system effective for different types of regions. Moreover, this enables the consumers to act according to the regional environment. The coordination can play a very important role in cost reduction as well as in consumer comfort maximization. The coordination is incorporated among the electrical devices by using cooperative game theory (GT) and dynamic programming (DP). Extensive simulations are performed to show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data
Energies 2018, 11(6), 1372; https://doi.org/10.3390/en11061372
Received: 1 May 2018 / Revised: 21 May 2018 / Accepted: 25 May 2018 / Published: 28 May 2018
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Abstract
Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world’s largest oil importer, China is highly dependent
[...] Read more.
Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world’s largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China’s various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China’s seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China’s financial industry. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessFeature PaperArticle Modelling the Dynamic Impacts of High Speed Rail Operation on Regional Public Transport—From the Perspective of Energy Economy
Energies 2018, 11(5), 1151; https://doi.org/10.3390/en11051151
Received: 31 March 2018 / Revised: 25 April 2018 / Accepted: 25 April 2018 / Published: 4 May 2018
Cited by 4 | PDF Full-text (1160 KB) | HTML Full-text | XML Full-text
Abstract
While the introduction of a high speed rail (HSR) provides passengers with another more environmentally friendly, convenient, and time-saving transport option, it also disrupts the existing passenger transport market. This study adopts time series analysis to model the dynamic competition in a regional
[...] Read more.
While the introduction of a high speed rail (HSR) provides passengers with another more environmentally friendly, convenient, and time-saving transport option, it also disrupts the existing passenger transport market. This study adopts time series analysis to model the dynamic competition in a regional passenger transport market when an HSR is introduced. The analyses include examining the long-run equilibrium and causal relationships, and the short-run causality and dynamic relationships between transport modes. In addition, based on the model we conduct impulse response tests and variance decomposition tests to further interpret the interactions between two transport modes. An empirical study is carried out, and the findings indicate that the HSR has a negative impact on conventional rail and air transport in the long-run. In the short-run dynamics, the air passenger transport volume could be regarded as a good predictor of HSR passenger volume. In turn, the HSR passenger volume could be used to predict conventional rail transport volume. The operations of HSR and conventional rail are complementary in the short term. From the short-run market viewpoint, the HSR and conventional rail meet different kinds of passenger demand. Therefore, a previous increased passenger volume for the HSR implies an overall increasing demand for regional transport. Consequently, the past increased HSR passenger volume could be used to predict the growth of conventional rail transport. Through the impulse response test, we can further track the responses of the three transport modes to the shocks from themselves and each other. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Economics of Renewable Energy Integration and Energy Storage via Low Load Diesel Application
Energies 2018, 11(5), 1080; https://doi.org/10.3390/en11051080
Received: 20 March 2018 / Revised: 13 April 2018 / Accepted: 24 April 2018 / Published: 27 April 2018
Cited by 1 | PDF Full-text (2667 KB) | HTML Full-text | XML Full-text
Abstract
One-quarter of the world’s population lives without access to electricity. Unfortunately, the generation technology most commonly employed to advance rural electrification, diesel generation, carries considerable commercial and ecological risks. One approach used to address both the cost and pollution of diesel generation is
[...] Read more.
One-quarter of the world’s population lives without access to electricity. Unfortunately, the generation technology most commonly employed to advance rural electrification, diesel generation, carries considerable commercial and ecological risks. One approach used to address both the cost and pollution of diesel generation is renewable energy (RE) integration. However, to successfully integrate RE, both the stochastic nature of the RE resource and the operating characteristics of diesel generation require careful consideration. Typically, diesel generation is configured to run heavily loaded, achieving peak efficiencies within 70–80% of rated capacity. Diesel generation is also commonly sized to peak demand. These characteristics serve to constrain the possible RE penetration. While energy storage can relieve the constraint, this adds cost and complexity to the system. This paper identifies an alternative approach, redefining the low load capability of diesel generation. Low load diesel (LLD) allows a diesel engine to operate across its full capacity in support of improved RE utilization. LLD uses existing diesel assets, resulting in a reduced-cost, low-complexity substitute. This paper presents an economic analysis of LLD, with results compared to conventional energy storage applications. The results identify a novel pathway for consumers to transition from low to medium levels of RE penetration, without additional cost or system complexity. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle Economic Performance and Emission Reduction of Supply Chains in Different Power Structures: Perspective of Sustainable Investment
Energies 2018, 11(4), 983; https://doi.org/10.3390/en11040983
Received: 28 March 2018 / Revised: 13 April 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
Cited by 1 | PDF Full-text (561 KB) | HTML Full-text | XML Full-text
Abstract
Environmental issues have increasingly received attention in both industry and academia. Many firms have started to make sustainable investments, such as adopting the pollution-abatement technologies, to reduce carbon emissions. To investigate the impacts of the sustainable investment on firms’ profit and emission reduction,
[...] Read more.
Environmental issues have increasingly received attention in both industry and academia. Many firms have started to make sustainable investments, such as adopting the pollution-abatement technologies, to reduce carbon emissions. To investigate the impacts of the sustainable investment on firms’ profit and emission reduction, we consider supply chains with uncertain demand in different power structures. Specifically, we examine the sustainable investment problem in three supply chain power structures, i.e., manufacturer Stackelberg (MS) power structure, vertical Nash (VN) power structure and retailer Stackelberg (RS) power structure. We first derive the optimal decisions for both the retailer and manufacturer in each power structure. Then, by comparing the results in the three power structures, we find that the manufacturer gets benefits from making the sustainable investment, especially in unequal power structures. When the average market size is large (small) enough, both of the supply chain members obtain more profits in the MS (RS) power structure. From an environmental perspective, we find that the emission reduction is more significant in sequential games (i.e., MS and RS power structures) than that in a simultaneous game (i.e., VN power structure). In addition, we conduct some numerical studies and discuss more managerial insights in the paper. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Open AccessArticle A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model
Energies 2018, 11(4), 819; https://doi.org/10.3390/en11040819
Received: 23 March 2018 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 2 April 2018
PDF Full-text (11552 KB) | HTML Full-text | XML Full-text
Abstract
The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar
[...] Read more.
The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet) is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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Other

Jump to: Research

Open AccessCase Report Characterization of a Thermoelectric Generator (TEG) System for Waste Heat Recovery
Energies 2018, 11(6), 1555; https://doi.org/10.3390/en11061555
Received: 14 May 2018 / Revised: 1 June 2018 / Accepted: 1 June 2018 / Published: 14 June 2018
Cited by 2 | PDF Full-text (3302 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the development and characterization of a thermoelectric generator (TEG) system for waste heat recovery to low temperature in industrial processes. The relevance of this mode of electric energy harvest is that it is clean energy and it depends only on
[...] Read more.
This paper presents the development and characterization of a thermoelectric generator (TEG) system for waste heat recovery to low temperature in industrial processes. The relevance of this mode of electric energy harvest is that it is clean energy and it depends only on the capture of losses. These residual energies from industrial processes are, in principle, released into the environment without being exploited. With the proposed device, the waste energy will not be released into the environment and will be used for electrical generation, which is useful for heat production. The characterization of TEGs that are used a data-acquisition system have measured data for the voltage, current, and temperature, in real-time, for temperatures down to 200 °C without signal degradation. As a result, the measured data has revealed an open circuit voltage of VOC = 0.4306 × ΔT, internal resistance of R0 = 9.41 Ω, with tolerance ΔRint = ±0.77 Ω, where Rint = 9.41 ± 0.77 Ω. The measurements were made on the condition that the maximum output was obtained at a temperature gradient of ΔT = 80 °C, resulting in a maximum power gain of Pout ≈ 29 W. Full article
(This article belongs to the Special Issue Energy Economy, Sustainable Energy and Energy Saving)
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