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Microalgae have tremendous potential for producing liquid renewable fuel. Many methods for converting microalgae to biofuel have been proposed; however, an economical and energetically feasible route for algal fuel production is yet to be found. This paper presents a review on the comparison of the most promising conversion pathways of microalgae to liquid fuel: hydrothermal liquefaction (HTL), wet extraction and non-destructive extraction. The comparison is based on important assessment parameters of product quality and yield, nutrient recovery, GHG emissions, energy and the cost associated with the production of fuel from microalgae, in order to better understand the pros and cons of each method. It was found that the HTL pathway produces more oil than the wet extraction pathway; however, higher concentrations of unwanted components are present in the HTL oil produced. Less nutrients (N and P) can be recovered in HTL compared to wet extraction. HTL consumes more fossil energy and generates higher GHG emissions than wet extraction, while the production cost of fuel from HTL pathway is lower than wet extraction pathway. There is considerable uncertainty in the comparison of the energy consumption and economics of the HTL pathway and the wet extraction pathway due to different scenarios analysed in the assessment studies. To be able to appropriately compare methodologies, the conversion methods should be analysed from growth to upgradation of oil utilising sufficiently similar assumptions and scenarios. Based on the data in available literature, wet oil extraction is the more appropriate system for biofuel production than HTL. However, the potential of alternative extraction/conversion technologies, such as, non-destructive extraction, need to be further assessed.

The vehicle will generate an amount of current while the electric vehicle just starting to regeneratively brake. In order to avoid the impact of high current on the traction battery, a novel electrohydraulic hybrid electric vehicle has been proposed. The main power source is supplied by the electric drive system, and the hydraulic system performs the auxiliary drive system that fully exerts the advantages of the electric drive system and the hydraulic drive system. A proper regenerative braking control strategy is presented, and the control parameters are determined by the fuzzy optimization algorithm. The simulation analysis built the model through the united simulation of AMESim and MATLAB/Simulink. The results illustrated that the optimized control strategy can reduce battery consumption by 1.22% under NEDC-operating conditions.

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.

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.

Abstract In the current study, a new solar-driven triple cycle is proposed to allow power generation during low insolation periods. This triple cycle integrates the solar gas-turbine top cycle, the steam Rankine cycle, and the Kalina bottom cycle. During the top cycle of the proposed system, compressed air was heated to 1000 °C or higher in the solar tower receiver. The heated compressed air was then used to drive the gas turbine to generate electricity. A Rankine cycle with a back-pressure steam turbine was utilized to recover waste heat from the gas turbine, thereby generating electricity through the steam turbine. The bottom cycle is the Kalina cycle, which comprises another back-pressure turbine and utilizes ammonia–water mixture as working fluid. After driving the steam Rankine cycle, the flue gas from the gas turbine sequentially heats the ammonia–water mixture to produce power. A new operational strategy was presented to generate electricity during low insolation period without the backup of fossil fuel. In middle insolation periods, the air is heated by the solar field and then directly drives the steam Rankine cycle, bypassing the gas turbine. In low insolation periods, the heated air directly drive the Kalina cycle, bypassing the Brayton cycle and the steam Rankine cycle. The off-design performance was investigated and the irreversibility was disclosed with the aid of the energy-utilization diagram method. Thus, the proposed system can utilize low insolation to generate electricity. This study provides a possibility to improve the solar–electric efficiency.

With the integration of a high penetration of wind power into distribution system, the ability to cope with the voltage fluctuation issue arising from the intensified uncertainty should be improved. Energy storage system (ESS) with effective control strategies keeps a key role in smoothing the voltage fluctuation caused by the uncertainty of wind turbine generators (WTGs). Considering the constraints of state of charge (SOC), charge-discharge efficiency and continuous operation of ESS, this paper proposes a novel approach to determine the minimum capacity of ESS to satisfy the voltage fluctuation limit based on unbalanced three-phase interval power flow. An unbalanced forward-backward sweep interval power flow considering the constraints of ESS and the voltage fluctuation limit is proposed. The interval model of uncertain WTG output is established on basis of wind speed fluctuation curve. The interval model of the practical ESS output is obtained on basis of the present SOC, nominal capacity and comprehensive efficiency. The output voltages of power flow can directly demonstrate the voltage fluctuation rate due to their interval forms. Therefore, a feedback mechanism is established between the output voltage fluctuation rate and the input ESS practical power, which is used to determine the minimum capacity of ESS to satisfy the fluctuation limit. A modified IEEE 33-bus system with high penetration of WTGs is implemented to test the proposed method. Results demonstrate the validity and effectiveness of the proposed approach.