• Energy Research
• 11. Sustainability close
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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 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.

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.

A sustainable and secure food supply within a low-carbon and resilient infrastructure is encapsulated in several of The United Nations’ 17 sustainable development goals. The integration of urban agriculture in buildings can offer improved efficiencies; in recognition of this, the first south European example of a fully integrated rooftop greenhouse (iRTG) was designed and incorporated into the ICTA-ICP building by the Autonomous University of Barcelona. This design seeks to interchange heat, CO2 and rainwater between the building and its rooftop greenhouse. Average air temperatures for 2015 in the iRTG were 16.5 °C (winter) and 25.79 °C (summer), making the iRTG an ideal growing environment. Using detailed thermophysical fabric properties, 2015 site-specific weather data, exact control strategies and dynamic soil temperatures, the iRTG was modelled in EnergyPlus to assess the performance of an equivalent ‘freestanding’ greenhouse. The validated result shows that the thermal interchange between the iRTG and the ICTA-ICP building has considerable moderating effects on the iRTG’s indoor climate; since average hourly temperatures in an equivalent freestanding greenhouse would have been 4.1 °C colder in winter and 4.4 °C warmer in summer under the 2015 climatic conditions. The simulation results demonstrate that the iRTG case study recycled 43.78 MWh of thermal energy (or 341.93 kWh/m2/yr) from the main building in 2015. Assuming 100% energy conversion efficiency, compared to freestanding greenhouses heated with oil, gas or biomass systems, the iRTG delivered an equivalent carbon savings of 113.8, 82.4 or 5.5 kg CO2(eq)/m2/yr, respectively, and economic savings of 19.63, 15.88 or 17.33 €/m2/yr, respectively. Under similar climatic conditions, this symbiosis between buildings and urban agriculture makes an iRTG an efficient resource-management model and supports the promotion of a new typology or concept of buildings with a nexus or symbiosis between energy efficiency and food production.

This paper examines the sources of changes in energy use of the Brazilian economy of industries and households from 1970 to 1996, using structural decomposition analysis based on the logarithmic mean divisia index technique. Energy use can be decomposed into eight factors that explain changes in overall energy use over the entire time period, and within five sub-periods. The growth of energy use between 1970 and 1996 was mainly influenced by changes in affluence, population and intersectoral dependencies, while changes in direct energy intensity and per capita residential energy use had a retarding impact on energy use. The novel contributions of the paper are the alignment of a previously disparate data set, the use of supply-use tables for SDA, and the application of such an SDA to a developing country. Both contributions involve solving a range of methodological issues pertaining to handling of large data sets.

Abstract Plug-in hybrid electric buses (PHEBs) have the potential to satisfy both the fuel efficiency and the driving-mileage under complex urban traffic conditions. However, the optimal charge and discharge management is still a pivotal challenge of energy management for the inherent uncertainty in driving conditions. The common reference state-of-charge (SOC) profile based methods are limited by the adaptiveness which restricts the economic performance of on-line energy management systems. Promisingly, reinforcement learning based energy management strategies exhibited the significant self-learning ability. However, for PHEBs, the sparse rewards by the long-term SOC shortage make the strategies easily trick into the local optimal solution. The work presented in this paper concentrates on combining battery power reduction in the form of conditional entropy into reinforcement learning based energy management strategy. The proposed method named adaptive policy optimization (APO) introduces a novel advantage function to evaluate energy-saving performance considering long-term SOC dynamic, and a Bayesian neural network based SOC shortage probability estimator is utilized to optimize the energy management strategy parameterized by a deep neural network. Several experiments in a standard driving cycle demonstrate the optimality, self-learning ability and convergence of the APO. Moreover, the adaptability and robust performance get validated over the real bus trajectories data. With the comprehensive experiments in this paper, the proposed model exhibits enhanced fuel economy and more suitable SOC planning in comparison with the existing energy management strategies. The results indicate that APO respectively outperforms the compared online strategies by 9.8 % and 2.6 % and reaches 98 % energy-saving rate of the offline global optimum.

Comprehensive global decarbonization will require that transportation services cease to rely on fossil fuels. Here we develop a generic life-cycle cost model to address two closely related questions central to the emergence of sustainable transportation: (i) the utilization rates (hours of operation) that rank-order alternative drivetrains in terms of their cost, and (ii) the cost-efficient share of clean energy drivetrains in a vehicle fleet of competing drivetrains. Calibrating our model framework in the context of urban transit buses, we examine how the comparison between diesel and battery-electric buses varies with the specifics of the duty cycle (route). We find that even for less favorable duty cycles, battery-electric buses will entail lower life-cycle costs once utilization rates exceed 20% of the annual hours. Yet, the current economics of that particular application still calls for a one-third share of diesel drivetrains in a cost-efficient fleet.