Grid Support Research Articles

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

Compactability of granular materials increasing the durability of operated railway track structure and subgrade

The progressive degradation of the railway track structure increases the impact of the rolling stock transferred to the track and subgrade, which requires ensuring appropriate track support through the selection of material, grain size and density of granular layers. The study presents research and analysis of increased loads transferred to the ballast. By using protective layers of the track substructure made of appropriate materials, proper conditions of cooperation between the track and subgrade are achieved, ensuring the elastic operation of the surface and minimizing its permanent deformation. Currently used methods and solutions for strengthening and stabilizing granular materials are described. Particular analysis was carried out to ensure proper support of the track grid by providing appropriate granular material and its compaction (including ballast obtained from recycled – unsorted stone).

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

Benefit to Harm (B2H) Ratio for Bulk Vehicle to Grid (V2G) Services Using Lifetime Degradation Digital Twin: Emulating Various Dominant Degradation Mechanism

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H). We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G.B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty.Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits.Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLICal). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking.We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAMNeg). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2].We show that, in cases with a higher contribution of calendar aging to degradation (LLICal/LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3].With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty.

Strategy-based optimization and life cycle environmental impact assessment of a stable photovoltaic power-to-hydrogen system

With the growing global emphasis on reducing greenhouse gas emissions and transitioning to a low-carbon economy, renewable hydrogen is expected to play a vital role in the chemical industry. However, there is a contradiction between the continuous and stable hydrogen demand of chemical process systems and the intermittent and fluctuating nature of renewable energy systems when coupling them together in the industrial chemical production. There are currently few studies addressing year-round stable hydrogen load systems while considering renewable energy intermittency and capacity optimization. Moreover, the comprehensive life cycle environmental impact study of such stable renewable-hydrogen production system is extremely limited. Therefore, this study focuses on a photovoltaic power-to-hydrogen system that can operate stably for 8,760 h per year, specifically targeting its application in the chemical industry. Two operating strategies of the hydrogen system were considered: fully off-grid and ultra-light grid-connected scenarios. The capacity configuration under each scenario was optimized using gird search algorithm, with the objective of minimizing the levelized cost of hydrogen. Based on the optimization results, the energy flow network, cost-effectiveness, and comprehensive life cycle environmental impacts of such system were analyzed. The results show that the ultra-light grid-connected hydrogen production system, due to the support of the power grid, has improved the reliability of the hydrogen production system and significantly reduced the capacity configuration of system components, thereby demonstrating better performance in terms of economics and overall lifecycle environmental impact compared to a completely off-grid hydrogen production system. Its global warming potential (GWP) and levelized cost of hydrogen under electricity sales scenario are 12% and 31% lower, respectively, compared to fully off-grid hydrogen production. Considering China’s future demand for large-scale renewable hydrogen, ultra-light grid-connected hydrogen production may become the primary transitional pathway in the near to medium term.

Accounting calendar and cyclic ageing factors in diagnostic and prognostic models of second-life EV batteries application in energy storage systems

The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary applications such as energy storage becomes critical to addressing environmental and resource management issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the importance of accounting for calendar and cyclic aging factors to optimize battery performance and longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC), and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and increased internal resistance. The combined effects of these aging processes necessitate the development of high-precision diagnostic and prognostic models to manage the performance and longevity of second-life batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used electric vehicles. This situation necessitates the prompt development of strategies for repurposing these batteries for energy storage applications. The complexities associated with final recycling processes make secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate immediate challenges related to battery waste and resource scarcity while supporting the transition to renewable energy sources. This paper highlights the need for an integral degradation index (DI) that combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure of battery health. Such an index is essential for optimizing battery management practices, including the scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The study also presents practical recommendations for implementing these models in various energy storage scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle charging stations. By adopting optimized battery management strategies, the potential for extending the lifespan of secondary batteries and reducing operational costs is significant. This approach supports sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular economy principles. Keywords: Lithium-Ion Battery, Electric Vehicle, Energy Storage, Battery Degradation, Calendar Ageing, Cyclic Ageing, Integral Degradation Index, Remaining Useful Life, State of Health.

Advanced Grid Connectivity Using Hybrid Solar, Wind, and Battery Technologies

Global warming has led to the proliferation of electric vehicles, which seem to be the best option for internal combustion engines. With so many electric cars on the road, it's not practical or economical to charge cars using traditional power lines. Using renewable energy when charging stations control the electric vehicle. The project describes the charging system (SWCM) based on solar energy (250W), 15 modules in series and 2 parallel in series, and wind power (5.78kW) to generate electricity to charge the battery of the EV (electric vehicle). The regenerative charging station consists of wind turbines and PV (solar photovoltaic) modules. Charging mechanisms based on wind energy reduce CO2 and CO2-related emissions by reducing the need for fossil fuels to generate electricity. EV (electric vehicle) charging stations containing solar, wind, grid and BESS (battery energy storage systems) are designed according to the current situation. Additional grid support is also assumed to provide uninterrupted power to the charging stations without placing additional load on the grid. To balance the requirements, the system is connected to the grid via a three-phase bidirectional DC-AC (alternating current) frequency converter. The results show that the regenerative power generator is suitable for charging electric vehicles and creating a Pollution-free environment. The results are demonstrated with a MATLAB simulation run.

A grid-forming approach utilizing DC bus dynamics for low inertia power systems with HVDC applications

While power electronic converters, such as voltage source converters (VSCs), are crucial for the operation of converter-dominated renewables and their integration with the electricity grid, their reliance on VSCs can pose a challenge. The limited inertia of these sources can lead to a deterioration of the rate of change of frequency, potentially causing power system stability issues. A grid-forming approach utilizing dc-link dynamics is one of the attractive alternatives to achieve grid synchronization and support grid frequency. Existing grid-forming control schemes, which assume a constant or virtually constant dc source, rely on a fixed physical dc-link capacitor. Nonetheless, the inertia support from such a capacitor is brief, owing to its limited energy storage capability. Consequently, enhancing inertia becomes imperative; otherwise, it may result in an increased rate of change of voltage on the dc side, potentially leading to issues with protection, undesirable interactions, and system instability. This paper proposes a new grid-forming control strategy that considers a virtual capacitor to achieve grid synchronization while simultaneously providing the network with inertia response services during power imbalances. Moreover, including a virtual resistor in the controller effectively attenuates power and dc voltage oscillations. Simulations using Simulink and small signal stability analysis are conducted to validate the efficacy of the proposed controller.

An Adaptive Virtual Inertial Control Strategy for DC Distribution Networks

The DC distribution network is a low-inertia system, which is very sensitive to load disturbance, system failure, and other factors. By adding virtual capacitance to the converter connected to the power supply, the virtual inertia of the DC power grid can be improved. Firstly, this paper proposes a strategy for adjusting virtual capacitance based on the voltage change rate to achieve adaptive control of virtual inertia, which enables the converter to quickly absorb or release energy during power fluctuations. Secondly, the adaptivity of the strategy is improved and the main control parameters in the proposed control method are qualitatively analyzed. Finally, the four-terminal photovoltaic storage DC distribution network system is constructed. Through Simulink, the adaptive virtual inertia control is incorporated on the battery side to simulate and validate the effectiveness of the strategy and the rationality of parameter analysis. The results show that this method can provide flexible and adjustable inertia support for DC grids and improve the voltage stability of DC grids.

Power Hardware-in-the-Loop Smart Inverter Testing with Distributed Energy Resource Management Systems

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter’s performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter’s dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid.

Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

Generalized control for distributed energy inverter in community microgrid system: Off-grid and grid-connected operation

With the increasing penetration of distributed energy resources (DER) in microgrids, DER power inverters have become a critical asset for providing power support to these microgrids. Meanwhile, the grid-forming (GFM) inverters, among these DER inverters, have gained significant attention in microgrid applications for their capability to enable the DERs to operate in different microgrid conditions and various operation modes. Moreover, with the implementation of these GFM inverters, smooth operation mode transition, GFM functions as well as black start functions can be obtained to improve the operation of the microgrid systems. In this article, a generalized control method for a single-phase GFM inverter is developed for community microgrid applications, facilitating smooth operation behavior in both operation modes with grid support functions and stable transition for different microgrid conditions. The control design procedure and function analysis of the proposed control method are explained in detail based on the community microgrid system. The effectiveness of the method in this paper is demonstrated on a 10 kW single-phase GFM inverter prototype with comparison to a model predictive method in recent literature.

Energy management control strategies addressing the rSOC degradation phenomena in a polygeneration microgrid

Economically competitive long-term energy provision and performance stability are becoming increasingly strategic for the upcoming energy market. Despite hydrogen-based technologies representing a viable way for the aforementioned targets, such systems are affected by performance reduction over time. This paper is aimed at presenting energy management mitigation policies for a reversible solid oxide-based (rSOC) polygeneration microgrid. Particularly, interest has been paid towards the rSOC degradation phenomena and its mitigation by applying strategies allowing meeting target performance and satisfying economics. In a multilevel framework, the rSOC degradation mitigation has been pursued proposing two central-level strategies, namely Grid support (GS) and Limited Grid support (LGS), which act between the power split targets coming from the supervisory level and working set points actuated by the low-level controllers. The former aims at compensating for the energy gap caused by degradation relying on external contributions: power grid network and boilers. Alike, the goal of the latter is to keep the rSOC turned on as much as possible, therefore, complying with the energy management goals given by supervisory level. The state of charge of the hydrogen storage tank has been chosen as a metric to best show the results obtained implementing the mitigation strategies, as it directly displays the rSOC active management and boundaries applied on the storage systems. Considering a 3 years scenario, the LGS strategy in comparison to the GS one has resulted to be more effective concerning: costs, microgrid self-sufficiency, reliability and overall robustness. Quantitatively, for the LGS strategy, considering degradation degrees in the range 0.25–1 %/kh, relative additional integration costs with respect to the case unaffected by degradation are seen to be in the range 30–105 %.

Life cycle assessment and life cycle costing approach for building decarbonization by design choices: A case study

AbstractDecarbonizing the urban environment has two significant challenges: Increasing electricity demand due to the electrification of space heating and increased renewables share in the electricity supply. The European Commission defines a new grid support mechanism for peak‐shaving products in renewed Electricity Market Design. This aims to enable a new market tool to stabilize electric supply and demand. This study examines a grid‐integrated thermal storage device's technical feasibility and economic performance to meet net zero building (nZEB) definitions. Alternative scenarios considering current national nZEB targets, present energy market options, and regulations are compared using the life cycle cost and the global warming potentials over the building lifetime. The results show that better building performance is possible even with a low investment increase of 6.7% compared to minimum building standards based on regulations. This enables older building cases to perform similarly with new building targets for Turkish National nZEB. Building electrification using heat pumps and thermal storage systems gives similar economic performances in the long term when a dynamic electricity tariff is available. Adapting the life cycle approach to decision‐making in construction contracts can lead to a 50% decrease in building emissions while staying within the same construction budget.

A novel method for fully distributed sealed bid power trading in a network of islanded microgrids using consortium blockchains and a framework for its implementation

Microgrids help in the better utilization of distributed energy resources. However, due to the intermittent nature of such resources, the net energy availability within microgrids varies with time. This adversely affects the power balance within microgrids, especially if energy storage systems or grid support are unavailable. In such cases, the microgrids in a locality can form a network to trade power among themselves. The most transparent and fair method is power trading using a double auction with sealed bidding. A novel method for sealed bid power trading using consortium blockchains that is entirely decentralized is introduced, ensuring better operational and data security. Here, all bidding data are shared twice. During bidding time, bids are shared by participating microgrids using one-way encryption and are added to the blockchain by all participants. After the bid closure, the respective microgrids share the same data unencrypted. By encrypting and comparing the bid data with the encrypted data already added to the blockchains, other participants can verify whether the microgrids have kept their bid data unmodified post-bid-closure. If the data has been kept unmodified, all participants add the bid data as verified data to their blockchain. Successful bids are then identified using a smart contract, and power transfer is effected accordingly. All power and monetary transactions are recorded distributively in blockchains. A decentralised framework for the implementation of the method is also envisaged. Further, a novel approach is proposed for distributively authenticating the blocks shared by participating microgrids using public and shared keys. The method and the framework are then successfully tested for decentralization, transparency, resilience, public verifiability, and transaction enforcement using simulations in a benchmark test system for networked microgrids.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression

Abstract. We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that incorporates several functionalities enhancing its performance and applicability for both industrial and academic studies. The code has been re-designed with a focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. This new version includes several major improvements: magnetisation vector inversion, inversion of multi-component magnetic data, wavelet compression, improved handling of topography with support for non-uniform grids, a new and efficient parallelisation scheme, a flexible parameter file, and optimised input–output operations. Extensive testing has been conducted on a large synthetic dataset and field data from a prospective area of the Eastern Goldfields (Western Australia) to explore new functionalities with a focus on inversion for magnetisation vectors and magnetic susceptibility, respectively. Results demonstrate the effectiveness of Tomofast-x 2.0 in real-world studies in terms of both the recovery of subsurface features and performances on shared and distributed memory machines. Overall, with its updated features, improved capabilities, and performances, the new version of Tomofast-x provides a free open-source, validated advanced and versatile tool for constrained gravity and magnetic inversion.