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Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives.

The effects of exogenous trehalose (Tre) on salt tolerance of pharmaceutical plant Catharanthus roseus and the physiological mechanisms were both investigated in this study. The results showed that the supplement of Tre in saline condition (250 mM NaCl) largely alleviated the inhibitory effects of salinity on plant growth, namely biomass accumulation and total leaf area per plant. In this saline condition, the decreased level of relative water content (RWC) and photosynthetic rate were also greatly rescued by exogenous Tre. This improved performance of plants under high salinity induced by Tre could be partly ascribed to its ability to decrease accumulation of sodium, and increase potassium in leaves. The exogenous Tre led to high levels of fructose, glucose, sucrose and Tre inside the salt-stressed plants during whole the three-week treatment. The major free amino acids such as proline, arginine, threonine and glutamate were also largely elevated in the first two-week course of treatment with Tre in saline solution. It was proposed here that Tre might act as signal to make the salt-stressed plants actively increase internal compatible solutes, including soluble sugars and free amino acids, to control water loss, leaf gas exchange and ionic flow at the onset of salt stress. The application of Tre in saline condition also promoted the accumulation of alkaloids. The regulatory role of Tre in improving salt tolerance was optimal with an exogenous concentration of 10 mM Tre. Larger concentrations of Tre were supra-optimum and adversely affected plant growth.

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

To understand the effects of physicochemical factors on nitrite transformation by microalgae, a lipid-rich Chlorella with high nitrite tolerance was cultured with 8 mmol/l sodium nitrite as sole nitrogen source under different conditions. The results showed that nitrite transformation was mainly dependent on the metabolic activities of algal cells rather than oxidation of nitrite by dissolved oxygen. Light intensity, temperature, pH, NaHCO3 concentrations, and initial cell densities had significant effects on the rate of nitrite transformation. Single-factor experiments revealed that the optimum conditions for nitrite transformation were light intensity: 300 μmol/m(2); temperature: 30°C; pH: 7-8; NaHCO3 concentration: 2.0 g/l; and initial cell density: 0.15 g/l; and the highest nitrite transformation rate of 1.36 mmol/l/d was achieved. There was a positive correlation between nitrite transformation rate and the growth of Chlorella. The relationship between nitrite transformation rate (mg/l/d) and biomass productivity (g/l/d) could be described by the regression equation y = 61.3x (R(2) = 0.9665), meaning that 61.3 mg N element was assimilated by 1.0 g dry biomass on average, which indicated that the nitrite transformation is a process of consuming nitrite as nitrogen source by Chlorella. The results demonstrated that the Chlorella suspension was able to assimilate nitrite efficiently, which implied the feasibility of using flue gas for mass production of Chlorella without preliminary removal of NOX.

AbstractBioenergy projects can lead to direct and indirect land use change (LUC), which can substantially affect greenhouse gas balances with both beneficial and adverse outcomes for bioenergy's contribution to climate change mitigation. The causes behind LUC are multiple, complex, interlinked, and change over time. This makes quantification uncertain and sensitive to many factors that can develop in different directions—including land use productivity, trade patterns, prices and elasticities, and use of by‐products associated with biofuels production. Quantifications reported so far vary substantially and do not support the ranking of bioenergy options with regard to LUC and associated emissions. There are however several options for mitigating these emissions, which can be implemented despite the uncertainties. Long‐rotation forest management is associated with carbon emissions and sequestration that are not in temporal balance with each other and this leads to mitigation trade‐offs between biomass extraction for energy use and the alternative to leave the biomass in the forest. Bioenergy's contribution to climate change mitigation needs to reflect a balance between near‐term targets and the long‐term objective to hold the increase in global temperature below 2°C (Copenhagen Accord). Although emissions from LUC can be significant in some circumstances, the reality of such emissions is not sufficient reason to exclude bioenergy from the list of worthwhile technologies for climate change mitigation. Policy measures to minimize the negative impacts of LUC should be based on a holistic perspective recognizing the multiple drivers and effects of LUC.This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Short term load forecasting plays an increasingly important role in Smart Grid. Short term load forecasting is also an important part of enterprise power system management. Providing accurate load time series data for a certain period of time in the future can enable enterprises to ensure the smooth operation of production while making a reasonable power plan, reducing power consumption and basic electricity charges, thus reducing the production cost of enterprises. In addition, lower electricity consumption means lower carbon dioxide emissions, which has far-reaching implications for sustainable development strategies. This paper presents a short-term load forecasting method based on time series. The model divides the time series data into four parts: trend item, period item, holiday item and error item. In the experiment part, this paper provides a set of preprocessing method flow. Aiming at the problem that the sampling rate of the current smart grid data is not constant, a data smoothing algorithm is proposed.

Abstract The central role of occupants for achieving energy savings in residential buildings is increasingly recognised. Simulation programmes able to take into account occupant behaviour are considered to be powerful tools for bridging the gap between the predicted and the actual energy consumption for new buildings. Nevertheless, the majority of residential buildings that will constitute the housing stock in 2050 have already been built today, therefore occupant behaviour and building simulation tools need to be fully exploited for supporting the renovation of existing housing stock. The aim of this paper is to explore the role of occupant behaviour modelling in supporting decision-makers dealing with the design of renovation strategies for residential buildings. An Italian multi-family public housing building is assumed as case study to estimate the influence of three dimensions linked with occupant behaviour – management of the thermostat, management of the heating system, variation of building characteristics – on energy heating consumption. The results show that, while the occupant behaviour influences the heating loads up to 1/3 in case of high level of building retrofit, the less the building is renovated, the higher is the behavioural impact in absolute terms of energy reduction. Therefore, in order to be effective, renovation strategies are required to design appropriate informative instruments at an early stage to support behaviour changes towards responsible energy consumption.

With the aim to fully exploit the by-products obtained after the industrial extraction of starch from sweet potatoes, a cascading approach was developed to extract high-value molecules, such as proteins and pectins, and to valorize the solid fraction, rich in starch and fibrous components. This fraction was used to prepare new biocomposites designed for food packaging applications. The sweet potato residue was added to poly(3-hydroxybutyrate-co-3-hydroxyvalerate) in various amounts up to 40 wt % by melt mixing, without any previous treatment. The composites are semicrystalline materials, characterized by thermal stability up to 260 °C. For the composites containing up to 10 wt % of residue, the tensile strength remains over 30 MPa and the strain stays over 3.2%. A homogeneous dispersion of the sweet potato waste into the bio-polymeric matrix was achieved but, despite the presence of hydrogen bond interactions between the components, a poor interfacial adhesion was detected. Considering the significant percentage of sweet potato waste used, the biocomposites obtained show a low economic and environmental impact, resulting in an interesting bio-alternative to the materials commonly used in the packaging industry. Thus, according to the principles of a circular economy, the preparation of the biocomposites closes the loop of the complete valorization of sweet potato products and by-products.