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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.

Данные исследования выполнены благодаря исходной информации, представленной горнодобывающими предприятиями Криворожского железорудного бассейна, в частности “Евраз Сухая Балка” и “Центральный ГОК”.

This study aims to produce biochar and sugars from a macroalga Eucheuma denticulatum using dilute sulfuric acid hydrolysis along with microwave-assisted heating. The reactions were operated at sulfuric acid concentrations of 0.1 and 0.2M, reaction temperatures of 150-170°C and a heating time of 10min. Compared to the raw macroalga, biochar qualities were improved with increased carbon content and lower ash and moisture contents. The calorific value of the biochar could be intensified up to 45%, and 39% of energy yield was recovered. Apart from producing biochar, the highest total reducing sugars were 51.47g/L (74.84% yield) along with a low by-product 5-HMF of 0.20g/L, when the biomass was treated under the optimum conditions at 160°C with 0.1M H2SO4. Thus, this study demonstrated that macroalgae could be potentially used as biomass feedstock under microwave-assisted acid hydrolysis for the production of biofuel and value-added products.

The global drive towards sustainability has ushered in a new era of transportation, prominently featuring the rise of Battery Electric Vehicles (BEVs). The rapid rise of BEVs has been widely hailed as a crucial milestone in promoting sustainable transportation and combating climate change. The existing empirical evidence provides undeniable support for the essential role of BEVs to support net zero targets. However, like any disruptive technology, BEVs are not without their hidden effects. This paper seeks to explore and analyse these lesser-known repercussions (i.e., externalities) of BEV adoption. In doing so, it sheds light on the environmental, infrastructure, socio-economic and safety externalities of BEVs, aiming to foster a holistic understanding of their impact and facilitate informed decision-making. Furthermore, it highlights the critical role of public awareness and user education in maximising the benefits of BEVs and the importance of maintaining a balance of information in developing such campaigns. Empowering individuals, communities, and policymakers with accurate information, dispelling misconceptions, and fostering responsible BEV practices are essential for realising maximal environmental impacts of BEVs. The discussion emphasises the need for moving beyond climate-change targets that are merely based on tailpipe emissions, towards life-cycle-based approaches.

Abstract In Australia, the government spending on public buildings’ energy and water consumption is considerable; however the building energy and water retrofit market potential has been diminished by a number of barriers, especially financial. In contrast, in other advanced economies there are several reported financing strategies that have been shown to accelerate retrofit projects implementation. In this study, a coupled Bayesian Network – System Dynamics model was developed with the core aim to assess the likely influence of those novel financing options and procurement procedures on public building retrofit outcomes scenarios in the Australian context. A particular case-study focusing on Australian public hospitals was showcased as an example in this paper. Stakeholder engagement was utilised to estimate likely preferences and to conceptualise causal relationships of model parameters. The scenario modelling showed that a revolving loan fund supporting an energy performance contracting procurement procedure was preferred. Subsequently, the specific features of this preferred framework were optimised to yield the greatest number of viable retrofit projects over the long term. The results indicated that such a financing scheme would lead to substantial abatement of energy and water consumption, as well as carbon emissions. The strategic scenario analysis approach developed herein provides evidence-based support to policy-makers advocating novel financing and procurement models for addressing a government’s sustainability agenda in a financially responsible and net-positive manner.

Exhaust hoods are commonly used to capture all emissions from stationary combustion systems that are open to the environment, such as residential heaters or stoves. For experimental purposes, emissions are sampled at one, or more, discrete locations downstream in the exhaust duct. Point-wise measurements in the duct are often taken with the assumption that the emissions are homogeneously distributed across the duct cross-section, because the flow is turbulent and therefore believed to be thoroughly mixed. However, the length of such systems is rarely sufficient to ensure fully-developed flow, and the actual homogeneity is seldom assessed. In the present work the mixing within the duct is investigated by simulating the emissions distribution within various hood and duct configurations. The simulations include a straight duct with and without baffles and two different exhaust hood configurations, namely at the Stove Testing Lab at Lawrence Berkeley National Laboratory (LBNL) and at the University of Adelaide that meet standard requirements. The air flow in the ducts was simulated using Reynolds-averaged (RANS) turbulence modelling, with carbon monoxide (CO) as a representative combustion product, injected at three locations in the straight duct and two locations (centre and side) in the exhaust hoods. Simulations predict that, in isolation, neither a straight duct without baffles, nor a hood with a 90° elbow followed by a straight duct without baffles, provide sufficient mixing to achieve a near uniform distribution of CO at the sampling locations. However, simulations show that adequate mixing of dilution air and CO is achieved with baffles-induced flow detachment and recirculation, not from turbulent mixing in the straight section of the duct itself. The simulations also suggest that elbows, baffles, expansions or other geometrical features are needed to induce thorough mixing. For example, in the Stove Testing Lab at LBNL, flow disturbance is induced by an expansion into a larger diameter straight duct immediately downstream of the hood and the 90° elbow. Although these two systems demonstrate sufficient mixing of CO within the exhaust, the RANS simulations in this study suggest that other systems relying solely on mixing within a specified duct length (viz. 8–12 diameters) may not be sufficient. Refereed/Peer-reviewed