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Abstract Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

The increase of extremely hot days globally has led to more frequent wildfires and frequent power outages directly and indirectly caused by the wildfires. This has sought an urgency to investigate the power system security issues associated with wildfires and the implementation of risk mitigation strategies. This article provides a comprehensive review of power system security in relation to wildfires, exploring methods to enhance the system security. The article starts with the multi-scale aspect of power system security and resiliency associated with wildfires. Additionally, it investigates the direct and indirect impacts of wildfires on renewable energy generation and the overall security of the system. Furthermore, the emerging technologies to enhance the system security against wildfires are discussed. To conclude, this paper sheds light on the industry, academic research gaps, and future direction.

Capacitive deionization is used to purify water by applying an electric charge across two porous electrodes. These electrodes are positioned in a spacer channel that transports the desalinated water. Capacitive deionization is a more energy-efficient approach to water purification than traditional methods. The process is believed to create clean water and store energy in a capacitive electrical double layer near the charged surface. Unlike previous review papers in this area, which mostly focus on energy recovery in terms of the technological advancement, this paper aims to conduct a bibliometric review and analysis of capacitive deionization cells, specifically focused on energy recovery mechanisms for treating contaminated water. The manuscript also analyzes capacitive deionization energy efficiency and energy-efficient capacitive deionization technology. Various relevant databases are utilized to select articles under specific conditions. Our findings suggest that the utilization capacitive deionization’s energy recovery mechanism from industrial wastewater has valuable opportunities for further research and growth. The selection and review of relevant articles and their analysis in this paper can aid in the systematic development of the capacitive deionization module. This would offer viable suggestions for future research towards more efficient energy recovery and consumption, which will subsequently contribute to net zero transitioning.

Short-term voltage stability (STVS) of power grids could be jeopardized due to the nonlinear dynamic characteristics of loads such as induction motors (IMs) and the retirement of synchronous generators. Moreover, the appearance of inverter-based generators (IBGs) in the system would make the grids more susceptible to voltage instability. Hence, there is an indispensable need to identify adequate mitigation measures to deal with these enduring challenges. This paper proposes a driven-data trajectory approach to locate the dynamic VAr support (DVS) to maintain STVS in power grids with high penetration of IBGs. The proposed data-driven trajectory approach ranks the best locations for DVS by comparing grid responses of different possible DVS sites with respect to the desired reference response. The developed approach covers the full signature of grid dynamics in generation and load sides. For illustration, this approach is applied to the Reliability and Voltage Stability (RVS) test system designed for STVS analysis. Several scenarios are tested, including various IM penetrations and IBG integration, control and load compositions, to demonstrate the viability and robustness of the proposed approach. Moreover, the STVS performance of the system with the proposed algorithm is verified through the motor stalling scan. The comprehensive assessment shows that the system exhibits the best STVS performance with DVS placement using the proposed method.

This paper presents the development of a tool that aims to help stakeholders make informed decisions to invest in renewable energy and understand the impact of different tariffs on the economic viability of renewable energy investments. This includes evaluating the costs and benefits, and assessing the impacts of different tariff structures on the economic feasibility of those options. Furthermore, the tool can help in identifying the potential risks and challenges associated with renewable energy integration projects, such as market and network charges fluctuations. Therefore, this tool provides various evaluations to inform users about their energy consumption in relation to sport market energy prices, network tariffs, and retailer charges. It enables the assessment of a site’s economic operation over specific timeframes, calculates potential energy savings from on-site renewable sources, and determines economic indicators based on life-cycle cost analysis. The tool has been designed and validated with data from the Australian energy market, focusing on investment decisions for renewable energy projects in Victoria state. It adheres to the Australian Energy Market Regulations and incorporates feed-in tariff rates particular to the Victorian energy market and its regulatory framework.

Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spot fires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model for estimating the lightning of different types and its striking zone for the conceptual tripole thundercloud structure which is theorized to produce the lightning phenomenon in pyroCb storms. More emphasis is given to the lower positive charge layer, and the impacts of strong wind shear are also explored to thoroughly examine various electrical parameters including the longitudinal electric field, electric potential, and surface charge density. The simulation outcomes on pyroCb thunderclouds with a tripole structure confirm the presence of negative longitudinal electric field initiation at the cloud’s lower region. This initiation is accompanied by enhancing the lower positive charge region, resulting in an overall positive electric potential increase. Consequently, negative surface charge density appears underneath the pyroCb thundercloud which has the potential to induce positive (+CG) lightning flashes. With wind shear extension of upper charge layers in pyroCb, the lightning initiation potential becomes negative to reduce the absolute field value and would generate negative (−CG) lightning flashes. A subsequent parametric study is carried out considering a positive correlation between aerosol concentration and charge density to investigate the sensitivity of pyroCb electrification under the influence of high aerosol conditions. The suggested model would establish the basis for identifying the potential area impacted by lightning and could also be expanded to analyze the dangerous conditions that may arise in wind energy farms or power substations in times of severe pyroCb events.