• Energy Research
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Abstract In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed.

Abstract The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Abstract Small Mediterranean islands are remote, off-grid communities characterized by carbon intensive electricity systems coupled with high energy consuming desalination technologies to produce potable water. The aim of this study is to propose a novel dynamic, multi-objective optimization approach for improving the sustainability of small islands through the introduction of renewable energy sources. The main contributions of our approach include: (i) dynamic modelling of desalination plant operations, (ii) joint optimization of system design and operations, (iii) multi-objective optimization to explore trade-offs between potentially conflicting objectives. We test our approach on the real case study of the Italian Ustica island by means of a comparative analysis with a traditional non-dynamic, least cost optimization approach. Numerical results show the effectiveness of our approach in identifying optimal system configurations, which outperform the traditional design with respect to different sustainability indicators, limiting the structural interventions, the investment costs and the environmental impacts. In particular, the optimal dynamic solutions able to satisfy the whole water demand allow high levels of penetration of renewable energy sources (up to more than 40%) to be reached, reducing the net present cost by about 2–3 M€ and the CO2 emissions by more than 200 tons/y.

Fossil fuel based power generation is and will still be the back bone of our world economy, albeit such form of power generation significantly contributes to global CO2 emissions. Solar energy is a clean, environmental friendly energy source for power generation, however solar photovoltaic electricity generation is not practical for large commercial scales due to its cost and high-tech nature. Solar thermal is another way to use solar energy to generate power. Many attempts to establish solar (solo) thermal power stations have been practiced all over the world. Although there are some advantages in solo solar thermal power systems, the efficiencies and costs of these systems are not so attractive. Alternately by modifying, if possible, the existing coal-fired power stations to generate green sustainable power, a much more efficient means of power generation can be reached. This paper presents the concept of solar aided power generation in conventional coal-fired power stations, i.e., integrating solar (thermal) energy into conventional fossil fuelled power generation cycles (termed as solar aided thermal power). The solar aided power generation (SAPG) concept has technically been derived to use the strong points of the two technologies (traditional regenerative Rankine cycle with relatively higher efficiency and solar heating at relatively low temperature range). The SAPG does not only contribute to increase the efficiencies of the conventional power station and reduce its emission of the greenhouse gases, but also provides a better way to use solar heat to generate the power. This paper presents the advantages of the SAPG at conceptual level.

Abstract Understanding the drivers of past and present energy consumption trends is important for a range of stakeholders, including governments, businesses and international development organizations, in order to prepare for impacts on global supply chains caused by changes in future energy price or availability shocks. In this paper we use environmentally-extended input–output tables to: (a) quantify the long-term drivers that have led to diversified energy footprint profiles of 186 countries around the world from 1990 to 2010; (b) identify which countries and sectors recorded an increase or decrease in energy footprints during this time period; (c) highlight the effect of international outsourcing of energy-intensive production processes by decomposing the structural and spatial change in energy footprints; and (d) discuss the implications for national economic policy for the identified drivers. To this end, we use a detailed Multi-Regional Input–Output database and three prevalent structural decomposition analysis methods. To reduce biases in the results due to time lapse and currency variations, we convert input–output tables to common US$ and 1990-constant prices. This study provides a broad overview of the magnitude and distribution of the drivers for energy footprints across countries. The results of this study demonstrate that for almost all countries affluence and population growth are driving energy footprints worldwide, which is in part counteracted by the retarding effect of industrial energy intensity. In particular, this study demonstrates that with increasing per-capita GDP, the total energy footprint of a country is increasingly concentrated on imports or consumption.

Abstract The emergence of smart grid technologies and applications has meant there is increasing interest in utilising smart meters. Smart meter penetration has significantly increased over the last decade and they are becoming more widespread globally. Companies such as Google, Nest, Intel, General Electric and Amazon are amongst those companies which have been developing end use applications such as home and battery energy management systems which leverage smart meter data. In addition, utilities and networks are becoming more aware of the potential benefits of using household smart meter data in demand side management strategies such as energy efficiency and demand response. Motivated by this fact, the amount of research in this area has grown considerably in recent years. This paper reviews the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimization. The study covers various applications such as Home and Battery Energy Management Systems and demand response strategies enabled by the analysis of smart meter data. From a comprehensive review of the literature, it was observed that there are remarkable discrepancies between the studies, which make in-depth comparison and analysis challenging. Data analysis and reporting guidelines are suggested for studies which use smart meter data. These guidelines could provide a consistent and common framework which could enhance future research.

Abstract The present energy system faces at least two challenges. For one thing, the power system’s stability is challenged by the increasing penetration of variable renewable energies, especially wind power, due to its fluctuation and intermittency. For the other, the transport sector is facing enormous difficulty to decarbonize. This paper proposes a new energy system that integrates the hydrogen production and distribution system to the combined cooling, heating and power (CCHP) system with significant wind power to solve these two challenges simultaneously. The new energy system can meet the energy needs of the building. At the same time, the wind power utilization rate reaches 92.6%, and the typical daily hydrogen production capacity in winter, transition season and summer is 500 kg, 500 kg and 266 kg, respectively. The system’s energy efficiency is 72%, and the energy of the system is utilized efficiently. By comparison, the new system can reduce costs and carbon dioxide emissions, save primary energy, and effectively improve energy efficiency.

Fossil fuels are an integral part of the US energy portfolio, playing a prominent role for current and future domestic energy security. A sustainable, low-carbon future will require CO2 to be captured from major coal and natural gas power plants. However, fossil fuel electricity generation CO2 emissions are typically highly variable throughout each day with daily generation profiles varying greatly between plants. We demonstrate that understanding this variability is absolutely critical for setting a suitable carbon price as well as identifying if and how much CO2 a power plant will capture. For example, we show that a CO2 emissions price (or tax) of anywhere between $85/tCO2 and $135/tCO2 will be required to incentivize a gas power plant to manage all its capturable CO2; this range is solely due to differences in CO2 emissions profile. Further, we show that the setting a carbon price is very sensitive to system-wide costs including the CO2 value for enhanced oil recovery and, in particular, the costs for CO2 transport and storage. We also find that, even though coal-fired plants are more CO2-intensive and thus incur greater CO2 management costs, coal plants require a significantly lower carbon price ($15/tCO2 lower) in order to encourage CO2 capture. We conclude that integrating fossil fuel power, particularly natural gas, into a large-scale CO2 capture and storage system is a complex problem that will require detailed research and modeling.