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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 Dilute sulphuric acid pretreatment followed by enzyme saccharification of Sorghum bicolor straw was undertaken to examine its potential as a feedstock in bioethanol production in Australia. Factorial design experiments evaluated the impact of pretreatment parameters on hemicellulose solubilisation and cellulose enzymatic hydrolysis. Sugar yields in prehydrolysate and saccharified liquors were found to increase with treatment severity; temperature was found to have the greatest impact. Degradation products were minimal; acetate and total phenolics peaked at 33 and 1.5 mg/g respectively. Conditions for maximum hemicellulose solubilisation (2% H 2 SO 4 for 60 min at 121 °C) differed to those associated with maximum glucose release from solid residue saccharifications (1% H 2 SO 4 /90 min /121 °C). Water extractive sugars accounted for over 20% total sugars recovered. Addition of β-glucosidase and xylanase to enzyme saccharification enhanced reaction rates and final sugar yields three-fold, whilst reducing cellulase dosage. Considering its abundance, high sugar potential and apparent ease of conversion, sorghum straw is an appropriate feedstock for the production of second generation fuels.

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.