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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.

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.

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.

Abstract In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution at high charging rates. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at the cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at the module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, the thermal-electrochemical partial differential and algebraic equations for a Li-ion cell are solved via a spatial finite difference method. Simulation results show that the temperature differentials over the cell thickness at the cell level are not insignificant, particularly at high charging rates.

Lithium-ion batteries are fast becoming the battery of choice in applications such as electric/hybrid electric vehicles (EV/HEV) and renewable energy systems. This increasing usage demands an improved reliability of the battery systems, which in turn heavily relies on the control and optimization algorithms. Of particular importance is ensuring that each lithium-ion cell within a battery pack remains strictly within an acceptable charge range to avoid untimely degradation of the battery pack. Unfortunately, current battery models make the design of charge equalization circuitry difficult due to their limitations. The aim of this paper is to develop a component-wise control-oriented physics-based battery pack model to facilitate implementation of advanced model-based control and optimization algorithms. In the first stage some existing results are used to obtain a simplified electrochemical ODE model of an individual lithium-ion cell. Then, the cell model is used as the building block of the complete battery pack model. Different charge/discharge scenarios are presented to illustrate the potential of the modeling approach in facilitating the implementation of advanced control and optimization algorithms in improved power equalization and hence prolonging the battery pack lifetime.

Abstract Nanofluids have been introduced for the enhancement in the heat transfer phenomena in the last few years. In this paper a corrugated bottom triangular solar collector has been studied introducing water based nanofluids inside the enclosure. The corrugated bottom is kept at a constant high temperature whereas the side walls of the triangular enclosure are kept at a low temperature. Three types of nanoparticles are taken into consideration: Cu, Al2O3, and TiO2. The effect of solid volume fraction (ϕ) of the nanoparticle of nanofluid has been studied numerically by Galerkin weighted residual method of finite element for a wide range of Grashof number (Gr) 104–106. Calculations are carried out for ϕ = 0, 0.05, 0.08, and 0.1 and dimensionless time, τ = 0.1, 0.5, and 1. For the specified conditions streamlines and isotherm contours are obtained and detailed results of the interaction between different parameters are studied using overall Nusselt number. It has been found that both Grashof number and solid volume fraction have significant influence on streamlines and isotherms in the enclosure. It is also found that heat transfer increased by 24.28% from the heated surface as volume fraction ϕ increases from 0% to 10% at Gr = 106 and τ = 1 for copper water nanofluid.

In this paper, a comparison of power system frequency response is conducted for a simple modelled power system with primary frequency control being provided either by synchronous generators or by inverter-based Battery Energy Storage (BES) systems. Mathematical models of conventional governor and turbine are developed, representing conventional synchronous generator frequency control, and are used to illustrate system frequency response for a range of typical conventional generating units. A mathematical model of a power-electronics interfaced Li-ion BES system is developed and used to represent a non-synchronous inverter-based generator with primary frequency control capabilities. MATLAB/Simulink is used to build a model of a small power system, and simulations are carried out with at a range of typical conventional synchronous generating units with a nonsynchronous generating unit in the power system. The simulation results demonstrate that the BES can be used for primary frequency control providing a faster and better response. BES is also capable of almost eliminating frequency overshoot and reducing 70% of settling time while providing primary frequency control in power system with various types of conventional synchronous generating units and a nonsynchronous generating unit.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

This paper presents the design of an extended Kalman filter (EKF) as a state-of-charge (SoC) estimator for a lithium–titanate (LTO) battery cell. The design is based on a Thevenin model of the equivalent battery electrical circuit with two parallel resistive-capacitive (RC) elements for the emulation of electrolyte polarization effects. The effectiveness of the proposed SoC estimator is verified through computer simulations on the comprehensive nonlinear battery simulation model featuring battery parameter vs. SoC variations, which is subject to highly dynamic loading regimes.