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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.

Currently, renewable energy-based generators are considered worldwide to achieve net zero targets. However, the stochastic nature of renewable energy systems leads to regulation and control challenges for power system operators, especially in remote and regional grids with smaller footprints. A hybrid system (i.e., solar, wind, biomass, energy storage) could minimise this issue. Nevertheless, the hybrid system is not possible to develop in many islands due to the limited land area, geographical conditions, and others. Hydrogen as a carrier of clean energy can be used in locations where the installation of extensive or medium-scale renewable energy facilities is not permissible due to population density, geographical constraints, government policies, and regulatory issues. This paper presents a techno-economic assessment of designing a green hydrogen-based microgrid for a remote island in North-east Australia. This research work determines the optimal sizing of microgrid components using green hydrogen technology. Due to the abovementioned constraints, the green hydrogen production system and the microgrid proposed in this paper are located on two separate islands. The paper demonstrates three cost-effective scenarios for green hydrogen production, transportation, and electricity generation. This work has been done using Hybrid Optimisation Model for Multiple Energy Resources or HOMER Pro simulation platform. Simulation results show that the Levelized Cost of Energy using hydrogen technology can vary from AU$0.37/kWh to AU$1.08/kWh depending on the scenarios and the variation of key parameters. This offers the potential to provide lower-cost electricity to the remote community. Furthermore, the CO2 emission could be reduced by 17,607,77 kg/year if the renewable energy system meets 100% of the electricity demand. Additionally, the sensitivity analysis in this paper shows that the size of solar PV and wind used for green hydrogen production can further be reduced by 50%. The sensitivity analysis shows that the system could experience AU$0.03/kWh lower levelized cost if the undersea cable is used to transfer the generated electricity between islands instead of hydrogen transportation. However, it would require environmental approval and policy changes as the islands are located in the Great Barrier Reef.

This article proposes a supply chain-based green hydrogen microgrid modelling for a number of remote Australian communities. Green hydrogen can be used as an emissions-free fuel source for electricity generation in places where large-scale renewable energy production is impossible due to land availability, population, or government regulations. This research focuses on the Torres Strait Island communities in northern Australia, where the transition from diesel to renewable electricity generation is difficult due to very limited land availability on most islands. Due to geographical constraints, low population and smaller electrical load, the green hydrogen needs to be sourced from somewhere else. This research presents a green hydrogen supply chain model that leverages the land availability of one island to produce hydrogen to supply other island communities. In addition, this research presents a model of producing and transporting green hydrogen while supplying cheaper electricity to the communities at focus. The study has used a transitional scenario planning approach and the HOMER simulation platform to find the least-cost solution. Based on the results, a levelised cost of energy range of AU$0.42 and AU$0.44 was found. With the help of a green hydrogen supply chain, CO2 emissions at the selected sites could be cut by 90 %. This study can be used as a guide for small clustered communities that could not support or justify large-scale renewable generation facilities but need more opportunities to install renewable generation.

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.

This paper demonstrates a 2-D numerical model to represent two conceptual pyrocumulonimbus (pyroCb) thundercloud structures: i) tilted dipole and ii) tripole structure with enhanced lower positive charge layer, which are hypothesized to explain the occurrence of lightning flashes in pyroCb storms created from severe wildfire events. The presented model considers more realistic thundercloud charge structures to investigate the electrical states and determine surface charge density for identifying potential lightning strike areas on Earth. Simulation results on dipole structure-based pyroCb thunderclouds confirm that the wind-shear extension of its upper positive (UP) charge layer by 2–8 km reduces the electric field and indicates the initiation of negative surface charge density around the earth periphery underneath the anvil cloud. These corresponding lateral extensions have confined the probable striking zone of –CG and +CG lightning within 0–23.5 km and 23.5–30 km in the simulation domain. In contrast, pyroCb thundercloud possessing the tripole structure with enhanced lower positive charge develops a negative electric field at the cloud's bottom part to block the progression of downward negative leader and cause the surface charge density beneath the thundercloud to become negative, which would lead to the formation of +CG flashes. Later, a parametric study is conducted assuming a positive linear correlation between the charge density and aerosol concentration to examine the effect of high aerosol concentration on surface charge density in both pyroCb thunderclouds. The proposed model can be expanded into 3-D to simulate lightning leader movement, aiding wildfire risk management.

This paper assesses the techno-economic feasibility of a green hydrogen-based microgrid for a remote Australian island. Hydrogen can be used to provide clean energy in areas where large-scale renewable energy sources are not feasible owing to geography, government regulations, or regulatory difficulties. This study not only identifies the appropriate component size for a hydrogen-based microgrid but also provides an economic perspective of decarbonising Thursday Island in Torres Straits, Queensland, Australia. Due to geographical constraints, the green hydrogen production system needs to be distinct from the electrical network. This research shows how to produce green hydrogen, transport it, and generate power at a low cost. The study was performed utilising the HOMER simulation platform to find the least cost solution. The simulation results demonstrate an AU$0.01 reduction in Levelised Cost of Energy compared to the present electricity generation cost which is AU$0.56. The inclusion of a green hydrogen system will potentially minimise CO2 emissions by 99.6% while ensuring almost 100% renewable penetration. The results of this study will also serve as a guide for the placement of hydrogen-based microgrids in similar remote locations around the world where numerous remote energy systems are located close to each other.

The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively.

High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources’ participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory.

Abstract Energy storage systems are recognised as the potential solution to alleviate the impacts of reduced inertia and intermittency in power systems due to the integration of renewable energy sources. Several energy storage technologies are available in the market with diverse power and energy characteristics, operational limitations, and costs. Besides, frequency regulations in power systems have different requirements, for example, inertial response requires high power for a short period while primary frequency regulation requires steady power for a longer time. Thus, it is crucial to find out the optimum sizes and types of storage technologies for these services. In this paper, a methodology for sizing fast responsive energy storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is developed. The sizing of storage systems for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation is done separately. The sizing of storage for inertial response is done in two steps. A region reduction iterative algorithm is proposed to estimate the storage size for inertial response. The sizing of the storage system for primary frequency regulation is done analytically. The sizing methodology incorporates the frequency dynamics of storage, converters, and other associated controls that affect the frequency response. Moreover, an economic analysis is carried out to find the optimum combination of storage technologies for inertial response, primary frequency regulation, and both inertial response and primary frequency regulation services. The accuracy of the proposed sizing method has been compared with the metaheuristic algorithm based technique. The effectiveness of the proposed method is also compared with those in the literature. Simulation results show that the proposed method outperforms the existing methods in the literature. Finally, the non‐linear simulations revealed the validity of the optimal solutions.

With the increasing number of renewable generations, the prospects of long-distance bulk power transmission impels the expansion of point-to-point High Voltage Direct Current (HVDC) grid to an emerging Multi-terminal high-voltage Direct Current (MTDC) grid. The DC grid protection with faster selectivity enhances the operational continuity of the MTDC grid. Based on the reactor voltage gradient (RVG), this paper proposes a fast and reliable fault identification technique with precise discrimination of internal and external DC faults. Considering the voltage developed across the modular multilevel converter (MMC) reactor and DC terminal reactor, the RVG is formulated to characterise an internal and external DC fault. With a window of four RVG samples, the fault is detected and discriminated by the proposed main protection scheme amidst a period of five sampling intervals. Depending on the reactor current increment, a backup protection scheme is also proposed to enhance the protection reliability. The performance of the proposed scheme is validated in a four-terminal MTDC grid. The results under meaningful fault events show that the proposed scheme is capable to identify the DC fault within millisecond. Moreover, the evaluation of the protection sensitivity and robustness reveals that the proposed scheme is highly selective for a wide range of fault resistances and locations, higher sampling frequencies, and irrelevant transient events. Furthermore, the comparison results exhibit that the proposed RVG method improves the discrimination performance of the protection scheme and thereby, proves to be a better choice for future DC fault identification.

In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems.

Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs).

Large-scale renewable energy-based power plants are becoming attractive technically and economically for generation mix around the world. Nevertheless, network operation has significantly changed due to the rapid integration of renewable energy in supply side. The integration of more renewable resources, especially inverter-based generation, deteriorates power system resilience to disturbances and substantially affects stable operations. The dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs) due to the limited capabilities of inverter-based resources (IBRs). A heavily loaded and stressed renewable rich grid is susceptible to fault-induced delayed voltage recovery. Hence, it is crucial to examine the system response upon disturbances, to understand the voltage signature, to determine the optimal location and sizing of grid-connected IBRs. Moreover, the IBRs fault contribution mechanism investigation is essential in adopting additional grid support devices, control coordination, and the selection of appropriate corrective control schemes. This article utilizes a comprehensive assessment framework to assess power systems' dynamic voltage signature with large-scale PV under different realistic operating conditions. Several indices quantifying load bus voltage recovery have been used to explore the system' s steady-state, transient response, and voltage trajectories. The recovery indices help extricate the signature and influence of IBRs. The proposed framework's applicability is carried out on the New England IEEE - 39 bus test system using the DIgSILENT platform.

The microgrid (MG) system is a controlled and supervised power system consisting of renewable energy (RE)-based distributed generation (DG) units, loads, and energy storage. The MG can be operated autonomously or while connected to the grid. Higher intermittencies and uncertainties can be observed in MGs compared to the conventional power system, which is the possible source of small-signal stability in MG systems. It can be seen as disturbances around the stable operating point, which potentially lead to the small-signal instability problem within MGs. Small-signal instability issues also emerge due to the lack of damping torque in the MG. The integration of power electronic devices and complex control algorithms within MGs introduces novel challenges in terms of small-signal stability and possible resonances. The occurrence of interaction in a low- or no-inertia system might worsen the stability margin, leading to undamped oscillatory instability. The interaction within the MG is characterized by various frequency ranges, from low-frequency subsynchronous oscillation to high-frequency ranges around the harmonic frequencies. This study presents an overview of the dynamic model, possible sources of small-signal instability problems, and resonance phenomena in MGs. The developed models of MG, including structure, converter-based power generation, and load and control algorithms, are briefly summarized to provide the context of MG system dynamics. A comprehensive critical review of the previous research, including small-signal stability and resonance phenomenon for MGs, is also provided. Finally, key future research areas are recommended.

Modern power systems consist of power electronics devices, which are used in renewable energy (RE) conversion. However, these devices, associated controllers, and uncertainty in RE output could bring new challenges to power system stability, especially oscillatory stability. Hence, the integration of battery energy storage systems (BESSs) is being developed to minimise the uncertainty and variability in renewables. Furthermore, to tackle the complex dynamics and inertia‐less characteristics of wind and PV plants additional controllers such as power oscillation damping (POD) control and virtual inertia scheme are sought. However, the primary challenges associated with the wide‐area oscillation damping controller are signal transmission delay, loss of communication signal, data drops, and others. This paper proposes a bat algorithm (BA) based resilient wide‐area multi‐mode controller (MMC) for enhancing oscillatory stability margin with high penetration of renewable power generations (RPGs) and BESSs. The Java 500 kV Indonesian grid is used to evaluate the performance of the resilient wide‐area MMC. From the results, it is found that the proposed controller effectively damp the critical mode of oscillation in the system even under communication failure as well as certain damping controller failures.

The integration of power-electronics-based power plants is developing significantly due to the proliferation of renewable energy sources. Although this type of power plant could positively affect society in terms of clean and sustainable energy, it also brings adverse effects, especially with the stability of the power system. The lack of inertia and different dynamic characteristics are the main issues associated with power-electronics-based power plants that could affect the oscillatory behaviour of the power system. Hence, it is important to design a comprehensive damping controller to damp oscillations due to the integration of a power-electronics-based power plant. This paper proposes a damping method for enhancing the oscillatory stability performance of power systems with high penetration of renewable energy systems. A resilient wide-area multimodal controller is proposed and used in conjunction with a battery energy storage system (BESS) to enhance the damping of critical modes. The proposed control also addresses resiliency issues associated with control signals and controllers. The optimal tuning of the control parameters for this proposed controller is challenging. Hence, the firefly algorithm was considered to be the optimisation method to design the wide-area multimodal controllers for BESS, wind, and photovoltaic (PV) systems. The performance of the proposed approach was assessed using a modified version of the Java Indonesian power system under various operating conditions. Both eigenvalue analysis and time-domain simulations are considered in the analysis. A comparison with other well-known metaheuristic methods was also carried out to show the proposed method’s efficacy. Obtained results confirmed the superior performance of the proposed approach in enhancing the small-signal stability of renewable-rich power systems. They also revealed that the proposed multimodal controller could enhance the penetration of renewable energy sources in the Javan power system by up to 50%.

In many future power systems, the power generation may be predominantly supplied from converter interfaced sources (i.e. wind, solar, HVDC interconnection). In the context of the UK, the government is aiming for a share of wind capacity in the total generation mix as high as 29 GW by 2020. For a small power system like Great Britain (GB) with no synchronous connection to continental Europe, frequency stability is identified as a major challenge. Therefore, additional primary response and inertia (or fast frequency containment response) are anticipated as being required to sustain the frequency stability of the GB system. Here, techno-economic assessment is carried out to explore the overall benefits of various frequency control strategies. Frequency regulation by VSC-HVDC system is realised as the best strategy regarding overall system benefits.

Dynamic voltage instability (DVI) issues are the primary concern in low-voltage distribution network (DN) due to growing integration of low-inertia compressor motor loads such as air-conditioner and refrigerator. The concern of DVI is likely to increase owing to high penetration of rooftop type single-phase photovoltaic (PV) units in DN. On the other hand, DNs are inherently unbalanced as a result of load and line characteristics along with unbalanced PV penetration. This paper examines the impact of imbalance on the dynamic voltage stability (DVS) in DN and provides solutions to mitigate any adverse effects. Dynamic models of the single-phase PV units are developed and utilised in the paper. The degree of unbalanced is defined first, and then its impact on the DVS is investigated. From the investigation, it is observed that degree of instability is increased with the increment of imbalance. The paper has also proposed a mitigation strategy i.e. reactive power injection by PV inverter. Case studies are conducted on modified IEEE 4 bus system which represents a low-voltage DN. Results reveal that reactive power injection by PV inverter can improve the DVS by mitigating the impact of unbalance.