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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.

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.

This study highlights how Chinese economic development detrimentally impacted water quality in recent decades and how this has been improved by enormous investment in environmental remediation funded by the Chinese government. To our knowledge, this study is the first to describe the variability of surface water quality in inland waters in China, the affecting drivers behind the changes, and how the government-financed conservation actions have impacted water quality. Water quality was found to be poorest in the North and the Northeast China Plain where there is greater coverage of developed land (cities + cropland), a higher gross domestic product (GDP), and higher population density. There are significant positive relationships between the concentration of the annual mean chemical oxygen demand (COD) and the percentage of developed land use (cities + cropland), GDP, and population density in the individual watersheds (p < 0.001). During the past decade, following Chinese government-financed investments in environmental restoration and reforestation, the water quality of Chinese inland waters has improved markedly, which is particularly evident from the significant and exponentially decreasing GDP-normalized COD and ammonium (NH4+-N) concentrations. It is evident that the increasing GDP in China over the past decade did not occur at the continued expense of its inland water ecosystems. This offers hope for the future, also for other industrializing countries, that with appropriate environmental investments a high GDP can be reached and maintained, while simultaneously preserving inland aquatic ecosystems, particularly through management of sewage discharge.

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.

In this chapter, turbulent flow of water-based optimization (TFWO) inspired based on whirlpools created in turbulent flow of water is used to solve the maximum power point tracking (MPPT) of photovoltaic systems in partial shading conditions. The TFWO is used to determine the optimal duty cycle of the DC/DC converter with the objective of maximizing the extracted power of the photovoltaic system. The capability of proposed method is evaluated in different patterns of partial shading to achieve global optimal power. The simulation results showed that TFWO is able to track the global maximum power point (GMPP), successfully in PSC and also fast climate changing. The TFWO has a better tracking capability with faster tracking speed and accuracy than particle swarm optimization (PSO) in obtaining the GMPP. Moreover, the results indicate that the use of buck–boost converter led to faster and more accurate access to the global optimal point than the system equipped with boost converter. The results showed that photovoltaic system with boost converter cannot obtain global maximum power in climate changing condition and limited the efficiency of the MPPT algorithm, while the photovoltaic system with buck–boost converter could be tracked GMPP due to its wider operating area.

Abstract There are instances in remote areas where heat is being wasted, e.g., in internal combustion, engines, etc. Some of this heat can be recovered to produce distilled water in solar stills. The solar still replaces the cooling tower, ponds, or radiators normally used to control the engine temperature. The diesel cooling water in such a system remains separate from the saline water in the solar still. The advantages of using such a system compared with a conventional solar still are: 1. (a) water costs are very much reduced 2. (b) the area occupied is much less, i.e., about 1 5 th 3. (c) production has much less seasonal variation 4. (d) the efficiency of the solar still is improved due to the higher operating temperatures. From experiments conducted at Highett using a Mk VI solar still fitted with a simple heat exchanger and a separate electrically-heated source of hot water to simulate the waste heat, design data are not available for application to working systems. The information required to match a solar still to a diesel's cooling requirement is: 1. (a) engine efficiency 2. (b) hourly fuel consumption 3. (c) hourly solar radiation 4. (d) hourly ambient temperatures. A by-product of this work has been the production of a “solar water heater” which costs less than that of the cheapest conventional system. This “solar” hot water system uses a heat exchanger similar to what is used to transfer the waste heat to the saline water. It is envisaged to have hot water productions approximately the same as the distilled water productions. The influence of hot water production on the output of the waste heat solar still is discussed.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.