This paper presents a modular modelling approach for long-term analysis and design of renewable-powered hydrogen generation and storage facilities, encompassing both power generation and hydrogen system components. The proposed model can be used to integrate different sizes of solar and wind energy resources, different battery energy storage systems, a backup power source (if required), and main hydrogen system modules in power demand calculations. As a part of the paper’s novelty, the proposed modelling approach is modular and case study-free, which allows for generalisation to a variety of case studies. The expandability of the modelling method is strengthened by presenting a unified modelling framework for all modules required in modelling the system. As the second main paper’s contribution, a comprehensive set of performance metrics is proposed to support a multi-objective optimisation framework for optimal sizing of system components. Although the metrics focus on different technical and economic aspects, environmental issues can be covered using some metrics, like the grid share of total energy requirements for the hydrogen system. Both proposed modelling and sizing methods enable renewable power plant designers to evaluate different configurations and make informed decisions based on weighted performance criteria. The proposed model and sizing problem are implemented in a combined Editor and Simulink environment in MATLAB for a case study as a real feasibility study in the UK to operate a renewable-supplied hydrogen system, including a 1 MW electrolyser. Simulation results for the representative case study validate the model’s behaviour and its reliability through various primary output profiles, e.g., power profiles, and secondary outputs, e.g., met hydrogen demand and levelised cost of hydrogen. The proposed modelling and optimisation methods can easily be expanded for case studies with more technical data or different load demands, e.g., combined hydrogen, heat, and power.

The misalignment performance evaluation in multiphase wireless power transfer (WPT) coils using conventional Finite Element Analysis (FEA) is typically computationally intensive. To address this problem, this paper proposes a novel analytical model for the rapid evaluation of multiphase WPT coil misalignment characteristics. The model is used to evaluate three-phase, five-phase, and six-phase coil configurations. The results indicate that the three-phase and five-phase coils are insensitive to rotational misalignment due to their generation of a stable rotating magnetic field. In contrast, the six-phase coil exhibits significantly higher sensitivity. This is attributed to the specific geometrical constraints of the six-phase topology, which necessitate a coil polarity arrangement distinct from the other configurations, thereby disrupting its tolerance to rotational misalignment. Compared to the traditional FEA approach, which calculates the coupling coefficients and mutual inductances for each coil individually, the proposed method employs an equivalent coil model to rapidly evaluate coil performance under specific conditions.

This article proposes a novel model-based Luenberger state observer for interturn short-circuit (ITSC) fault diagnostics. The residuals between the observed currents and the measured currents in the alpha- and beta-axes serve as fault indicator, which can be used to detect ITSC faults not only at an early stage with contact resistance but also at the fully short-circuited stage. These currents are observed by the Luenberger observer, which is designed under the assumption that the machine is operating in a healthy condition. In addition, the investigation results indicate that with a greater fault ratio, larger load current, and higher speed, detecting the ITSC fault becomes easier. Moreover, three sets of Luenberger observers, assuming the ITSC fault is in phases A, B, and C, have been designed to identify the faulted phase. A series of experiments have been carried out to validate the developed fault detection method.

In this paper, a novel pulse-width-modulation (PWM) strategy to operate in overmodulation range with low switching-to-fundamental frequency ratio is developed for permanent magnet synchronous machine drives based on a two-level voltage source inverter (VSI). The proposed strategy is able to operate at low switching-to-fundamental frequency ratio and presents an inherent overmodulation capability. The strategy applies a fixed switching sequence in a fundamental period, similar to six-step modulation, but including zero-vectors to regulate the voltage magnitude, and then, conventional control techniques can be employed. The duty cycles of the output voltage pulses and the switching angles are computed as linear functions of the modulation index. Two switching angles are used to reduce the number of commutations and avoid the problems associated to low switching-to-fundamental frequency ratios. A harmonic analysis of the generated voltage is also presented. The performance of the proposed strategy is tested and validated with simulations and experiments on a laboratory prototype.

Partial discharge (PD) has been shown to affect quality of insulation in power electronics modules. This paper provides a comprehensive characterisation of PD activity in gel-encapsulated direct bonded copper (DBC) power module substrates when subjected to fast unipolar square excitations. PD characteristics in low and high humidity environments are compared. PD inception voltage (PDIV) testing and time-resolved PD (TRPD) analysis is performed to investigate discharges in each condition. Two PD groups were identified, categorised by their amplitude, and prompting further investigation of the weaker group with a statistics-based approach. The strong group did not vary with humidity and is attributed to discharges between the top and bottom copper of the DBC. The weaker group varied significantly with humidity and is attributed to discharges within the lateral trench gap of the DBC top copper.

UK Government legislation has outlined the transition from Internal Combustion Engines (ICE) to Electric Vehicles (EVs) with the most recent Ten-Point Plan detailing the ending of sales of ICE vehicles by 2030. With EVs already gaining popularity, this transition is already underway. However, past large socio-techno transitions often leave rural communities behind (e.g. Internet and mobile connectivity). Therefore, a key part of engaging with rural communities is to provide a smoother transition for these areas to EV usage. This paper examines the nuances, particularly of the EV transition in rural areas identified through a survey distributed to households within the Peak District, a large rural area of the UK. Households were invited to complete an online survey about their travel patterns, current vehicle usage, awareness, and acceptance levels of EVs, electricity tariffs and charging, and access to public transport. This paper presents the findings from this survey.

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans.

This paper analyses the power requirements of electric vehicle charging infrastructure in UK workplaces. The requirements of employees charging private electric vehicles (EVs) during working hours, and of commercial fleet vehicles being charged outside of working hours arc assessed. Theoretical calculations are used to predict the usage requirements, then real-world data is used to confirm these predictions. It is shown that more than half of workplace EV charging events in the UK could be met with a 2.3kW mains/utility outlet, and more than 80% of charging events could be met with a 7kW charger. The potential benefits of using a lower power charger compared with a higher power charger are discussed.

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

This paper presents long-term modelling and second-by-second simulation of an autonomous microgrid (MG), including only renewable energy sources (RESs) and a hybrid energy storage system (HESS) as energy provider, and an electric vehicle (EV) Charge Station as a group load. The model uses forecast data for wind speed and solar radiation to provide wind turbine (WT) and photovoltaic (PV) generated powers, and statistical data for vehicles within a defined car park to model the EV demand. It is flexible and can support varying several planning parameters, e.g. varying sizes of WT and PV generation as well as various capacities of energy storage systems (ESSs). Therefore, in order to examine the impact of variations in RESs and ESS sizes, as well as the impact of EV demand uncertainties on the performance and efficiency of the MG, e.g. EV unmet energy, several sensitivity analyses are provided. Based on sensitivity analysis results, one can find reasonable ranges of MG module sizes, and make a decision for sizing of the overall system. For the case study represented here, results show that at least one WT is required, increasing PV panels is more effective to meet the midday EV load in at the target location, and a lower level of Li-ion ESS capacity is sufficient storage for the charging/discharging of the EVs.

This paper proposes a new phase-shifted-carrier (PSC)PWM-based control approach for Hybrid modular multilevel converters (HyMMC) operating in both buck and boost modes. When the conventional PSCPWM for Half Bridge (HB)MMC and Full Bridge (FB)MMC is (respectively) applied directly to HB and FB cells in HyMMC, some mismatched pulses occur in the output voltage, which cause lower effective switching frequency and reduced efficiency. The effective solution (based on PSCPWM) to remove these pulses is already demonstrated in the literature for the buck operating mode of HyMMC. However, the solution proposed in this work is effective for both buck and boost modes of HyMMC, and the effective switching frequency is equal to the HBMMC. In this approach, only one leg of FB cells is operated in PWM mode, while the other leg is switched at fundamental frequency in the boost mode, and no switching occurs in the buck mode. This creates further opportunity to improving efficiency and cost of FB cells in HyMMC by optimally selecting devices in the fundamental frequency switching leg. Simulation and experimental results validate the proposed approach.

Prior to the acquisition of an electric vehicle, pre-evaluation of vehicle energy use is desirable to assess whether the intrinsic vehicle electrical storage capability is satisfactory. However, inconsistency in general vehicle modelling may provide unreliable predictions concerning energy usage. To increase the prediction reliability, the use of route-specific driving cycle data is essential.This paper presents a case study of a novel method of extracting vehicle telemetry data from archived dashcam videos without the need to deploy conventional telemetry techniques. Utilising dashcam videos as input, and employing image processing and recognition technology, textual en-route driving data embedded in the video can be extracted. This data can then, in-turn, be used to model the performance of the vehicle, or an electric equivalent in terms of energy use and emissions. Results from preliminary testing with real-life dashcam videos, demonstrate negligible errors with regards to energy requirements and pollutants emitted from an EV operating on the modelled routes. Consequently, the proposed solution opens up the possibility to gather a significant amount of new data in order to better assess the transport sector’s energy requirements. This is especially important for situations where conventional telemetry is difficult to obtain. In addition, results from vehicle fleet modelling may inform policy decisions with regard to the impact of introducing low emission zones.

This article proposes a novel analytical fault model for permanent magnet machines with interturn short-circuits that considers the influence of various factors on the fault current. These factors include mutual inductances between the faulted turns and the remaining healthy windings (faulted and healthy phases), load current, and pulsewidth modulation (PWM) harmonics introduced by the drives. These three factors have been largely neglected by the published methods in literatures but could have significant influence on fault current depending on operating conditions. The investigation in this article shows that, without using the proposed fault model, the conventional fault model that only considers the back-electromotive force in the short-circuited loop and the self-impedance of the short-circuited turns, would underestimate the fundamental fault current by more than 45%. In addition, the proposed model establishes the relationship between the PWM ripple current in the short-circuited turns and that in the faulted phase. By doing so, the fault current including both the fundamental and PWM ripple components can be accurately predicted. The accuracy of the proposed method has been fully validated by a series of experiments.

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

The increasingly urgent need to decarbonize transport is leading to a much greater uptake of electric vehicles (EVs) in countries across the world. Also, the installation and use of urban light rail systems (trams) is seen as a way of breaking the reliance of commuters on the internal combustion engine, and therefore car ownership. Due to the simplicity of design, most conventional tram systems use unidirectional substations to draw power/energy from the utility supply. Due to their very nature, the substations are not able to return excess regenerated energy from the trams back into utility supply, with this energy often being dissipated in dump resistors onboard the trams to prevent over-voltages on the tram system. This paper explores the possibility of using EV's as temporary trackside energy storage systems on urban light rail systems through the use of bi-directional connection interfaces (chargers), which allow use of the vehicle battery in typical V2X scenarios. The paper uses the city of Sheffield (UK) Supertram network as an example network on which the effect of EV energy storage could be studied. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The Electricity Supply Emergency Code (ESEC) outlines the process for electricity rationing via a range of scenarios (Levels 1 to 18), should a critical supply incident affect a specific region, or the whole of the UK. Given recent global events, the threat of its implementation is currently attracting large mainstream media attention and genuine concern. With the uptake of Electric Vehicles (EVs) increasing, motorists are ever more reliant on a resilient electrical grid in order to charge their vehicles. This paper is the first of its kind to consider the impact on the ability to charge EVs should the ESEC specifically be invoked. This paper also focuses on rural areas, in particular a small rural village in the UK, Bradbourne, located in the Peak District. For which a novel EV Charging Model has been designed to incorporate the patterns of various levels of disconnections as laid out in the ESEC. Additionally, two behavioural approaches to charging within a planned power outage have been modelled. Real concerns arise in the eventuality of the higher level scenarios being implemented, with almost 30% of modelled EVs unable to complete their planned journeys. Although peaks of grid demand are reduced, results in fact show very little energy reductions overall depending on the charging behaviour, thus rendering the efforts of the ESEC mute.

Fleet electric vehicles offer excellent potential for vehicle-to-grid operation, as a result of their predictable use patterns. This paper investigates the potential for linking up urban fleet electric vehicles with wasted energy from urban light rail networks. Vehicle-to-grid charging could be deployed to reduce energy wastage from regenerative braking, while serving as temporary energy storage on the light rail system to reduce energy requirements during acceleration and providing an energy supply for fleets of electric vehicles based in urban areas. This paper uses GPS data from real light rail journeys to estimate regenerative braking energy availability for Edinburgh's light rail network. Findings indicate that electric vehicle charging linked to Edinburgh's light rail network would be beneficial for both parties.

With the increase in popularity of Electric Vehicles (EV’s) and their market share only predicted to increase, more and more homes will be (retro-)fitted with home charging points, and rural areas are not exempt. The large increase in energy and power demand due to EV charging events are a cause for concern for grid operators, more so given poorer grid infrastructure in rural areas. One potential solution is Demand Side Management (DSM). This paper presents three DSM scenarios, each with varying benefits to key stakeholders. Results demonstrate that significant gains can be achieved without requiring large infrastructure upgrades.

This article introduces a prescriptive control approach that improves the performance of grid-tied inverters. The current mainstream of contributions is predominantly based on combinations of canonical feedback actions, i.e., proportional ( ${P}$ ), integral ( ${I}$ ), derivative ( ${D}$ ), and resonant ( ${R}$ ). However, this classical view cannot synthesize suitable “intermediate” controller structures—which are not necessarily PIDR interconnections. In contrast, the proposed approach prescribes desired dynamics as initial step, and then synthesizes the exact feedback that enforces such prescription. It is also shown that prescriptive control is downward compatible with the popular PR control. The latter reaches only a narrow set of dynamical responses, since it is neglects crucial signal components, native to the proposed setting—which can improve the inverter performance in terms of achieving short settling times, small overshoots, robustness, and wide stability margins. Simulations and experiments are reported using a single-phase grid-tied inverter with an LCL filter.

This article presents a voltage controlled current source gate driver for an IGBT and compares its performance to a conventional voltage gate driver. Both gate drivers are designed using discrete components and evaluated using a double pulse test bench. The voltage gate driver is tested under different gate resistor conditions, while the proposed current source gate driver is tested under different gate current levels. The results show that the proposed gate driver reduces delay time and energy losses. Additionally, the proposed gate driver is tested under different gate current profiles during the turn-on and turn-off stages, and the results demonstrate that the dIc/dt and dVce/dt can be controlled.