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Abstract This study explores the optimal balance between grid‐forming (GFM) and grid‐following (GFL) converter capacities within power stations to ensure stable operations. The investigation introduces a novel, generic modelling approach for analysing multiple converter systems in the wind and photovoltaic power plants. The method aims to elucidate the dynamic characteristics of the converters in power plants, particularly focusing on the continuity and existence of the equilibrium manifolds and their impact on system stability. Findings reveal a pronounced difference in the recovery capabilities of GFM and GFL following synchronization losses, highlighting an asymmetry in their abilities. Specifically, GFL converters exhibit more effectiveness in reinstating synchrony after synchronization losses caused by GFM. Conversely, GFM demonstrates a lesser capacity to recover from synchronization losses induced by GFL. Furthermore, analysis indicates that when the capacity ratio of GFL to the system's short‐circuit capacity significantly exceeds that of GFM (exceeding a 1:5 ratio), the system experiences an absence of a stable equilibrium point, thereby affecting the synchronization stability of GFM. These conclusions have been validated through joint controller hardware‐in‐the‐loop testing.

Abstract The increase in wind power‐based microgrids emphasizes the importance of addressing stability challenges during low‐voltage ride‐through (LVRT) events in weak AC grid‐connected doubly fed induction generator systems. Compliance with grid standards, notably LVRT capabilities, is critical as wind power plants integrate increasingly into power systems, raising concerns about generation loss and post‐fault oscillations in microgrids. Previously, researchers have utilized techniques like fuzzy logic, ant colony, and genetic algorithms for static synchronous compensator (STATCOM) tuning to enhance microgrid stability during fault scenarios. This study uses the grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and a novel hybrid GOA‐PSO. On the main grid, the power system is subject to both symmetrical and asymmetrical faults. The proposed novel technique aims to improve LVRT, minimize generation loss during faults, and reduce after‐fault oscillations by optimizing reactive power flow between the point of common coupling and the microgrid while adhering to the LVRT grid code. MATLAB/Simulink is utilized to evaluate the LVRT performance of a 16 MW DFIG‐based microgrid operating in grid‐connected mode. The performance of the GOA‐PSO‐tuned STATCOM is evaluated by comparing it with conventional, PSO, and GOA‐tuned STATCOM in three fault scenarios. The comparison shows that GOA‐PSO‐tuned STATCOM improves grid stability and reliability.

Abstract The transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Abstract Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%.

Abstract This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs.

Abstract The flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN’s power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results.

Abstract With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues. In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Abstract The efficiency of the power‐to‐hydrogen (P2H) alkaline electrolyzer (AE) is the primary factor constraint on its large‐scale application. Decreasing the specific energy consumption (SEC) of AE is positive to enhancing the efficiency of P2H system. However, the factors influencing the SEC of AE are yet to be fully revealed. In this paper, the influence of power quality and stack temperature on the SEC of AE is explored. Firstly, the models of double star rectifier and PWM current‐type rectifier are established. And the mathematical model on the SEC of rectifiers is proposed. The influence of power quality on the SEC is analyzed by comparing the current and voltage waveforms under four types of rectifiers. Secondly, the model of AE that considers the stack temperature is proposed. Unlike the existing simplified models, the proposed model can assess the SEC considering changes in stack temperature. Finally, the results show that the SEC with the 6‐pulse rectifier is 10.99% and 11.98% higher than that with the double‐star rectifier and PWM current‐type rectifier. The stack temperature is inversely related to the SEC of AE. This study provides a reference for reducing the SEC and enhancing the efficiency of the P2H system.

Abstract Wind energy being a free source of energy is becoming popular over the past decades and is being studied extensively. Integration of wind turbines is now being expanded to urban and offshore settings in contrast to the conventional wind farms in relatively open areas. The direct installation of wind turbines poses a potential risk, as it may result in financial losses in scenarios characterized by inadequate wind resource availability. Therefore, wind energy availability analysis in such urban environments is a necessity. This research paper presents an in‐depth investigation conducted to predict the exploitable wind energy at four distinct locations within Nottingham, United Kingdom. Subsequently, the most suitable location, Clifton Campus at Nottingham Trent University, is identified where a comprehensive comparative analysis of power generation from eleven different wind turbine models is performed. The findings derived from this analysis suggest that the QR6 wind turbine emerges as the optimal choice for subsequent experimental investigations to be conducted in partnership with Nottingham Trent University. Furthermore, this study explores the selection of an appropriate probability density function for assessing wind potential considering seven different distributions namely, Gamma, Weibull, Rayleigh, Log‐normal, Genextreme, Gumbel, and Normal. Ultimately, the Weibull probability distribution is selected, and various methodologies are employed to estimate its parameters, which are then ranked using statistical assessments.

Abstract Strong uncertainties of distributed renewable generations, loads and real‐time market prices impose higher requirements on the online scheduling for microgrids. Traditional model‐driven methods exist several limitations due to low solving efficiency, difficulty in handling uncertainties, reliance on accurate prediction information, and inability to leverage accumulated historical data. This paper proposes a data‐driven improved imitation learning based approach for online microgrids optimization. First, a mixed integer linear programming model is established to derive offline optimal decisions within the given scenarios, which serve as expert demonstrations to help construct a sample database for imitation learning. Next, a direct imitation learning model based on eXtreme Gradient Boosting (XGBoost) is established to learn the mapping relationship between the system state and the scheduling decision, and the model training is refined by input clustering and output classification. At the input end, the operation scenarios are clustered to form multiple sub‐feature sets to achieve targeted training for different scenarios. At the output end, a binary variable is added to the label set to realize high‐precision decisions of the action of the energy storage system. Numerical case studies demonstrate the performance advantages of the proposed method.

Abstract Recently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Abstract Amid escalating geopolitical conflicts and extreme weather, the transformation to a renewable‐dominated power system faces increasing supply security risks. The current literature lacks comprehensive multidimensional assessment index systems to quantify these risks. This paper first identifies the key factors influencing supply security in renewable‐dominated power system. Subsequently, it constructs a supply security risk assessment model based on a comprehensive evaluation method combining the analytic hierarchy process method and the entropy weight method. The effectiveness of the approach is then evaluated through simulations of supply security risks across 11 provinces, including Inner Mongolia and Sichuan. Finally, the analysis of supply security risks in the Sichuan power system is conducted at different stages, focusing on changes in supply security risk levels and risk transmission mechanisms during Sichuan power crunch events in 2022. The results show that during Sichuan's 2022 high‐temperature power rationing event, the comprehensive supply security risk score of the Sichuan power system dropped from 88.7 to 76.91. This decline was mainly due to a significant reduction in hydropower output, which lowered scores of the source‐side risk and consequently increased the system's supply security risk.

Abstract The large scale deployment of modern wind turbines and the yearly increase of installed capacity have drawn attention to their operation and maintenance issues. The development of highly reliable and low‐maintenance wind turbines is an urgent demand in order to achieve the low‐carbon goals, and the arrival of fault diagnosis provides assurance for its satisfactory operation and maintenance. Numerous statistical studies have pointed out that generator failures are a main cause of wind turbine system downtime. The generator, as one of the core components, converts rotating mechanical energy into electrical energy. However, the generators can hardly operate reliably towards the end of the turbine life owing to the variable‐speed conditions and harsh electromagnetic environments. This article first provides a comprehensive and up‐to‐date review of the electrical and mechanical failures of various parts (stator, rotor, air gap and bearings) of the generator. Then the fault characteristics and diagnostic processes of generators are investigated, and the principles and processes of fault diagnosis are discussed. Finally, the application of four categories of model‐based, signal‐based, knowledge‐based and hybrid approaches to wind turbine generator fault diagnosis is summarized. The comprehensive review shows that the hybrid approach is now the leading and most accurate tool for real‐time fault diagnosis for wind turbine generators. A qualitative and quantitative assessment of algorithm performance using false alarm rates is proposed. The methodology can subsequently be applied to the wind industry.

Abstract In high voltage direct current (HVDC) grids, the reference values of power controllers are normally adjusted based on the DC‐link voltage as the DC voltage is regulated by the droop controllers. As a matter of fact, except for the DC slack bus, there is a trade‐off between power‐sharing and voltage regulation of all the remaining HVDC buses. The main issue with this control strategy is that in case of contingencies, significant voltage and power oscillations throughout HVDC grids can be expected. To address such issues, the integration of a battery energy storage system into HVDC grids through a multi‐port DC/DC power converter is investigated in this paper. The DC/DC converter used in this paper consists of three ports: (1) two ports are connected in cascade with the intended DC transmission line, formerly named cascaded power flow controller, and (2) the third port is for the integration of the battery energy storage system to form a multi‐functional device. Therefore, the proposed framework can potentially achieve small‐signal stability enhancement and regulate/adjust the power throughout the HVDC grids.

Abstract In this study, the integration of grid‐forming (GFM) converters in power systems is discussed in terms of both the fundamental aspects of system stability and the technical possibilities of converter‐based resources. The paper provides a survey and comparison of various GFM control concepts with respect to their transient and stationary behavior. A method for black box characterization and conformity testing of GFM converters is presented and relevant results of lab and field tests with GFM converters are discussed. Finally, important system aspects are addressed. This includes an approach to consider the behavior of GFM converters in distribution grid models and investigations on the minimum share of GFM units required to ensure system stability. The findings of this paper provide valuable insights for the design and implementation of GFM converters in modern converter‐based power systems.

Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

Abstract Distributed cooperative control strategies for DC microgrids have been rapidly evolving in recent years. However, introducing a cyber layer to enhance robustness, scalability, and reliability also exposes the system to potential cyber‐attacks. The damage inflicted by such attacks on the system performance can be catastrophic, reaching a point where it may devastate the system normal operation. By using model reference adaptive control (MRAC), this article proposes a resilient approach that does not require an accurate model of the system and despite the uncertainties for detecting false data injections into the reference DC voltage and simultaneously mitigating their adverse effects on the system stability and performance. The proposed technique employs an observer to detect possible false data injections in an online manner. By emulating the behavior of an ideal reference model, the MRAC ensures adaptive adjustments of the control parameters over time to mitigate the negative effects of potential attacks effectively and despite non‐idealities such as measurement noise, parameter variations, and environmental changes in DC microgrids, the MRAC effectively manages false data injection attacks. Simulation studies are conducted using diverse scenarios involving a three‐node DC microgrid to show the effectiveness of the proposed method.

Abstract The harmonic problems caused by non‐linear factors of the grid connected inverter (GCI) system are more complicated, including both non‐characteristic harmonics emitted by the dead‐time and the changes in harmonic impedance characteristics. Harmonics interact with the changing impedance, and even cause harmonic amplification and resonance. The harmonic deterioration of the multiple GCIs system is serious. To analyse the mechanism and way of harmonic deterioration in grid‐connected system caused by nonlinear factors, the active impedance models of single inverter and multiple GCIs system including dead‐time effect and digital control delay are established first. In view of this, the influence mechanism of non‐linear factors on system stability is explored. The improved modal analysis method is used to traverse the network series parallel resonance caused by nonlinear factors. The results show that both the dead‐time effect and digital control delay reduce phase margin of the system, resulting in the resonant frequency shift and the resonant peak increase. When the harmonic excitation source interacts with the complex network under the influence of nonlinear factors, it will lead to further deterioration of harmonics. Finally, based on the MATLAB and RT‐LAB hardware‐in‐the‐loop simulation platforms, a multiple GCIs system model is built to verify the correctness of the established active impedance model and harmonic deterioration analysis.

Abstract A large scale of distributed photovoltaics is accessed through a low voltage distribution network in a single‐phase or two‐phase, which causes three‐phase unbalance and over‐voltage problems. These problems can be solved by using a voltage source converter to realize AC/DC interconnection and flexible power transfer between lines. How to utilize the characteristics of voltage source converters to optimize the power qualities, and fully promote distributed PV systems consumption in low voltage distribution network is an important research point. This paper proposes a power regulation model of VSC. The goal of this model is to adjust and optimize over‐voltage and three‐phase unbalance problems. By constructing a voltage‐power sensitivity calculation model for a three‐phase four‐wire system and integrating different control modes and adjustment capabilities of voltage source converters, a control strategy based on voltage source converters is proposed to address three‐phase unbalance and over‐voltage issues. Finally, the simulation results demonstrate the effectiveness of the proposed strategy.

Abstract In this research, Net‐Zero Energy Houses (ZEHs) is investigated that harness regionally produced electricity from photovoltaic (PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This article examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. Two potential scenarios are proposed: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, the Monte Carlo sample average approximation (SAA) is applied to obtain solvable deterministic convex problems. Last, to demonstrate the practical applicability of these models, the proposed scenarios are implemented in the Jono neighborhood in Kitakyushu, Japan.

Abstract When wind power and energy storage operate in tandem, their operational state undergoes continuous shifts during dynamic processes. Determining the frequency modulation capability of the combined wind and energy storage system during frequency modulation participation is challenging, often leading to a decline in power generation efficiency. To address this, the current study introduces an optimal frequency response coordinated control strategy for hybrid wind‐storage power plants, anchored in state reconstruction. The frequency modulation capability is restructured based on the current state of charge (SOC) of the energy storage system. Subsequently, the equivalent active power reserve demand is evaluated for wind power, leading to the formulation of a wind power reserve active power level and its corresponding operational strategy. For swift active power support, an adaptive virtual inertia control is designed for the energy storage system, contingent on its current SOC. Concurrently, an adaptive virtual inertia control for wind power is developed, grounded in effective kinetic energy. The hybrid wind‐storage power plant engages in primary frequency regulation, tailored to the nature of frequency disturbances and prevailing state characteristics. Simulation analyses affirm that this proposed strategy, which takes into account the SOC of the hybrid wind‐storage power plant and the power grid's dynamics, offers robust active power frequency support during load disturbances.

Abstract This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super‐Twisting Algorithm (STA) and Grey Wolf Optimizer (GWO). The STA‐GWO‐MPPT method improves efficiency in dynamic conditions by using STA for control and GWO for parameter optimization, enhancing stability and robustness. Performance evaluation is conducted through MATLAB/Simulink simulations and experimental validation on a small‐scale test bench. Various quantitative metrics, including rise time, settling time, power production, efficiency, root mean square error (RMSE), and standard deviation (STD), are employed for assessment. Results indicate significantly faster convergence speeds for the proposed method compared to conventional MPPT techniques. Specifically, the rise time for the proposed method is 0.0129 seconds, outperforming Fuzzy Logic Control (FLC) (0.2638 seconds) and Grey Wolf Optimizer with Sliding Mode Control (GWO‐SMC) (0.0181 seconds). Additionally, the proposed method exhibits superior tracking efficiency, with an average efficiency of 99.33%, surpassing FLC (96.93%) and GWO‐SMC (99.19%). Moreover, it reduces power fluctuations, with an RMSE of 7.819% and STD of 6.547%, compared to FLC (RMSE: 13.471%, STD: 4.519%) and GWO‐SMC (RMSE: 8.507%, STD: 6.108%). Overall, this study contributes valuable insights into enhancing MPPT efficiency in solar PV systems, with implications for both research and practical applications.

Abstract Owing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side.

Abstract Considered as basic structures of next‐generation energy system, environment‐friendly and flexible microgrid (MG) systems are potential solutions to address integration issues of stochastic renewable energy sources. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a problem‐oriented review of energy management in MG systems. This paper first comprehensively reviews recent research studies on MG, particularly in multi‐microgrid (MMG). Then, this paper proposes a concept of energy utilization model for energy management, which includes a discussion of modern concepts including MG, MMG along with picogrid, nanogrid and virtual power plant. And a synthetic energy management framework including stability, touch, efficiency, evenness, and resilience dimensions is proposed and formulated based on energy utilization mode. Then energy management system is illustrated from the perspectives of system function, management architecture, operation logic and data analysis, and further, a systematic four‐layer hierarchical architecture of management systems for whole MG and MMG systems is proposed. Moreover, key technologies in energy management are summarized and reviewed from the aspects of control, communication, prediction, optimization, and evaluation. Last, eight main prospects on the future trend of energy management in MG and MMG are also presented.

Abstract The integration of large‐scale distributed power sources increases the voltage fluctuation in AC/DC hybrid distribution network (AD‐HDN). Power electronics devices such as photovoltaic (PV) inverters, soft open point (SOP), and voltage source converters (VSCs) can be utilized for voltage/var control (VVC) to alleviate the risk of voltage fluctuation and violation. This paper proposes a distributed optimal VVC method in power electronics dominated AD‐HDN. Firstly, the reactive power and voltage characteristics of PV inverters, SOP, and VSCs are analysed, and an optimal VVC optimization model for AD‐HDN to minimize node voltage deviation, PV curtailment, and network loss is proposed. Then, the second‐order cone (SOC) relaxation technique is used to re‐formulate the model into a convex optimization model. A distributed optimal VVC framework based on the alternating direction method of multipliers (ADMM) is constructed. Based on the residual balance principle and relaxation technique, an accelerated ADMM method is further proposed to solve the proposed model. Finally, case studies are conducted on the IEEE 33‐node and 85‐node systems to verify the superiority and effectiveness of the proposed method.

Abstract To improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG.

Abstract Conventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi‐location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi‐location information convolution transformer adopts a new model combining multi‐location information convolution and multi‐scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi‐location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

Abstract The linear permanent magnet generator (LPMG)‐based direct drive wave energy conversion system (DDWECS) works under perpetual fluctuations of ocean waves. Short‐term energy storage, such as electrochemical energy storage, is usually adopted in a supplementary energy storage system (SESS) to buffer power fluctuations. Since the investigated DDWECS consists of various non‐linear components such as insulated gate bipolar transistors (IGBTs)‐based power electronic converters, the unremitting changes of system working conditions caused by ocean wave effects will degrade the dynamic performance with conventional linear controllers which are implemented via local linearization around a single working condition. This paper proposes the multiple feedback linearization based non‐linear coordinated control (MFLNCC) scheme for coordinating non‐linear plants in DDWECS with SESS such as the machine‐side converter (MSC), the grid‐side converter (GSC) and DC/DC converter in SESS. The enhanced dynamic performance of the proposed MFLNCC in DDWECS with SESS is validated under different scenarios.

Abstract This article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

Abstract The battery energy storage system (EES) deployed in power system can effectively counteract the power fluctuation of renewable energy source. In the planning and operation process of grid side EES, however, the incorporation of power flow constraints into the optimization problem will strongly affect the solving efficiency. Therefore, a bi‐level planning and operation co‐optimization model for EES is proposed based on a constraint boundarization technique. Firstly, a constraint boundarization method for considering power flow constraints is proposed to solve the efficiency problem of the optimization model. Secondly, in terms of the upper and lower bounds of storage's operation curve obtained by the constraint boundarization method, an operation risk evaluation index is defined to describe the potential risk of storage planning scheme. Thirdly, this paper establishes a bi‐level planning and operation co‐optimization model for EES, in which the planning problem with a goal of minimizing total cost is taken as an outer layer and the operation problem for pursuing renewable energy consumption and reducing the load curtailment is taken as an inner layer. Finally, case studies are carried out, and results demonstrate the feasibility and effectiveness of the proposed model and method.

Abstract The existing methods for determining the module arrangement in photovoltaic (PV) farms are considered insufficient as they are generally limited to the environment of flat ground without considering both physical and electrical factors. The orientations of PV modules may be very diverse when installed in places with complex topography, e.g. mountains and abandoned mine sites. Thus, the received irradiance by the modules is inconsistent directly results in current differences and hence leads to significant mismatch loss in the PV arrays. This paper proposes a solution to determine the most appropriate combination of tilts and orientations of PV modules as well as the arrangement of PV arrays. The complex topographies are fully considered to minimize the mismatch loss phenomenon, and hence the power generation degradation. The solution adopts a set of models, i.e. the irradiation model for the calculation of optimal module tilts and orientations, the shadow model for shadow effect analysis, and the mismatch model for mismatch condition analysis. A two‐layer multi‐objective optimization is implemented for the optimal arrangement. The proposed solution is assessed through the case study of a 30 MW PV farm. The result confirms the effectiveness of the proposed solution for the optimal spatial module placement.

Abstract As inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Abstract Bringing floating offshore wind turbines (FOWTs) to a real industrial maturity and reducing the levelized cost of floating wind energy are key to significantly increasing the penetration of renewables in the energy mix of Mediterranean countries, especially if in combination with suitable energy storage systems, such as those involving green hydrogen production. The present study analyses techno‐economic aspects of some of the technologies related to FOWTs and hydrogen production by means of offshore‐generated energy, aiming to evaluate the potential of a floating wind farm integrated with a power‐to‐gas energy storage system in a specific installation site near the Sardinian shores. In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. Proposed results obtained with the statistic and the time‐dependent approach show values ranging between 3.79 and 5.47€/kg, respectively. These outcomes are thought to provide an interesting comparison between different fidelity approaches and realistic reference values for the levelized cost of hydrogen by floating wind in the Mediterranean Sea.

Abstract The flexible interconnection device (FID) offers significant advantages for interconnecting different distribution networks flexibly. This paper focuses on the significant advantages offered by the FID for interconnecting different distribution networks flexibly. It specifically delves into FID‐based multi‐voltage and multi‐substation distribution networks, proposing a preferable scheme applicable to major load‐centred cities. Beginning with an analysis of constructed FID‐based flexible interconnected distribution network projects, key configurations and features are summarized. Subsequently, typical configurations, electrical parameters, facilities, relevant power functionalities, and application scenarios of multi‐voltage multi‐substation distribution networks are outlined. Building upon this foundation, a suitable interconnection scheme tailored for current urban use is explored to meet the specific needs of load‐centred cities, while incorporating recent advancements in high‐power‐density IGCT technology. An EMT model of a 10 kV/10 MW IGCT‐based four‐substation distribution network is developed in PSCAD/EMTDC. Through thorough analysis under different conditions, the operational performance and benefits are evaluated, providing insights into the efficiency and resilience of the proposed FID‐based interconnection. Lastly, challenges and prospects are discussed from various perspectives to advance the development of FID‐based flexible interconnection solutions. This study aims to contribute to the advancement and implementation of robust interconnection solutions to meet the evolving needs of major load‐centred cities.

Abstract This paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method.

Abstract With the rapid development of energy storage technology, it is significant to evaluate the operating status of lithium‐ion batteries efficiently and accurately, so as to ensure their safe operation and reduce the probability of accidents. For the problems of long simulation time and low accuracy in existing models, this paper proposes a construction method of lithium‐ion batteries thermoelectric coupling model based on digital twin. First, the digital twin structure system of lithium‐ion batteries is proposed. Second, considering the coupling effects of the thermodynamic model and the equivalent circuit model, the thermoelectric coupling model is constructed based on the digital twin platform ANSYS TwinBuilder. The thermodynamic model is reduced in order and the simulation time is reduced to second level, which improves the simulation efficiency and meets the real time simulation requirements of the digital twin. Furthermore, considering that the operating parameters of lithium‐ion batteries are variable, the parameters of the equivalent circuit model are identified online based on the variable forgetting factor recursive least squares algorithm. It updates parameters of the model and improves the simulation accuracy. Finally, the efficiency and accuracy of the model are verified through simulation analysis.

Abstract There are significant differences in distributed generators (DGs) distribution and load characteristics between different voltage levels, which makes it difficult to match sources and loads. We focus on the problem of different consumption capacities at different voltage levels and the divergence of interests among investment entities, and propose a coordinated planning model for DGs and soft open points (SOPs) based on the Stackelberg game. Firstly, a model of a multi‐voltage level distribution networks (DNs) is constructed based on SOPs. Next, the source‐load matching degree is proposed as a measure of the degree of matching between sources and loads in the DN, and the source‐load consumption rate is selected as an indicator to evaluate the impact of the load on DG consumption. Following this, the interest demands of DG investors and distribution company (DisCo) in multi‐voltage levels DN are analyzed, a planning mode based on the Stackelberg game is proposed, and this is solved by combining the genetic algorithm with second‐order cone programming. Finally, the effectiveness of the planning model is tested and verified using an improved IEEE 28‐node system. The results show that the proposed model improves the DG consumption capacity of DNs with multiple voltage levels while protecting the interests of DG investors and DisCo.

Abstract To address the issues of node interaction power overrun and high carbon emissions that may arise during distributed optimization in multi‐energy parks (MEPs), this paper proposes a distributed low‐carbon and economic operation method for multi‐energy parks based on a cloud platform that considers network transmission capacity.The proposed method achieves maximun profit by designing a two layer collaborative architecture for distributed optimization operations. At the top level, cloud platform services are utilized to build a model for checking network transport capacity and carbon emission quotas, optimizing network node over‐limit inspection . The bottom layer constructs a distributed optimization model for multi‐energy complementation in each park, taking into account the information privacy and individual interests of each multi‐energy park. An improved alternating direction multiplier method (ADMM) is proposed to effectively solve the two‐layer framework. The case studies show that the distributed optimization method under cloud platform services proposed in this paper can achieve maximum revenue for integrated energy service provider while ensuring the safe operation of multi‐energy parks, and reasonably allocate the benefits of collaborative operation among various parks while promoting carbon emission reduction.

Abstract To promote the energy accommodation of both electrical and heating power while considering the source–load uncertainties, a peer‐to‐peer (P2P) energy trading model among prosumers in the integrated electric–thermal system is proposed here, considering the energy trading agent (ETA) with self‐build energy system. The solar heat‐pump hybrid thermal water system (SPTS) and the transferring loss of heating power is considered and modelled based on the principle of steady‐state thermal transfer. Since the variations of load and PV cannot be described by any single common distribution, the chance‐constraints programming based on the Gaussian mixture model (GMM) is proposed to handle the uncertainty of the net load. As the proposed power trading problem is a non‐convex problem with binary variables, an improved alternating direction method of multiplier (ADMM) based on the predictor‐corrector and two‐stage cycle iterations is proposed to enhance the convergence performance of standard ADMM. Finally, an example simulation verifies the effectiveness; this results show that the proposed model can decrease the total cost by 26.7% and enhance local energy balance by 61.8% compared to other cases.

Abstract The crack fault of gear is usually accompanied by temperature rise. Therefore, to master the temperature characteristics of the cracked gear of the wind turbine gearbox, a temperature rise calculation model of wind turbine gearbox gear considering crack fault and tooth number difference is proposed. Firstly, the time‐varying meshing stiffness model of the cracked gear considering the tooth number difference is established based on the potential energy method. Secondly, the calculation method of the meshing surface normal load of the cracked gear is deduced. Thirdly, the temperature rise calculation model of the meshing surface of the cracked gear is constructed based on Blok flash temperature theory. Finally, the data of the high‐speed gear of a wind turbine gearbox in northern China is selected for simulation verification. By comparing with the finite element method, the effectiveness of the proposed method is verified. The simulation results reveal the gear temperature characteristics of the wind turbine gearbox with different crack ratios. The research can provide some theoretical support for the accurate fault diagnosis and maintenance of wind turbine gearbox, and can also be applied to the fault diagnosis of gear cracks in other mechanical structures with a large transmission ratio.

Abstract Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP.

Abstract As distributed generators (DG) and power electronic devices become more integrated, distribution networks have evolved from being passive to active, and power flow has shifted from unidirectional to bidirectional. This transformation has negatively impacted power quality, leading to weakened fault transient attributes and increased harmonic complexities. Consequently, the efficacy of traditional relay protection has diminished, elevating the risk of misoperation or misjudgment and compromising the safety of distribution networks. In response to these challenges, a fault section location method for active distribution network based on characteristic wave coupling is proposed to expand the fault difference. This method explores the principles of characteristic wave coupling, discusses characteristic wave parameter selection theory, examines the start‐up control strategy for characteristic wave coupling, and establishes a protection action criterion by comparing the energy difference of characteristic waves based on the fault identification principle. Subsequently, by utilizing multiple inverter interfaced distributed generators to actively couple characteristic waves into the distribution network during faults, achieves rapid identification and location of fault sections. Finally, the effectiveness of the method is substantiated through simulation results in MATLAB/Simulink and experimental outcomes obtained from a low‐voltage active distribution network experimental platform based on dSPACE1103.

Abstract Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks.

Abstract The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result.

Abstract Background In the context of urban energy transition, photovoltaic (PV) systems play an important role in electricity generation. However, PV technology has some environmental drawbacks that also need to be acknowledged and managed. Life cycle assessment (LCA) is widely used to assess the environmental impacts of systems, but LCA is very complex to perform. Therefore, this research work presents a proof of concept for a parameterized LCA tool for grid-tied photovoltaic systems in urban areas that allows non-experts in LCA to obtain LCA results reliably and quickly. Results The resulting methodology is an integration of three preexisting tools: PVGIS, Brightway and Ecoinvent, plus a Breakeven point analysis. The first step of the approach consists of identifying the main parameters of photovoltaic systems: geographical, technological, and temporal. Once the non-expert practitioner sets the influential parameters, the tool assesses the greenhouse gas emissions over the life cycle of the PV panels per unit of supplied electricity, allocates the emissions per component, and calculates the point at which the avoided emissions compensate for those produced by the power system. The algorithm strives to find the optimal PV system configuration to reduce the environmental impact, providing decision-making support for promoters and policymakers in the context of the urban energy transition. Two case studies are presented to illustrate the proposed method’s applicability and benefits. Conclusions The production of PV panels was confirmed as the main source of emissions in this kind of installation. The reasons are analyzed, allowing for improved design. Furthermore, the estimated break-even point where savings of conventional electricity offset emissions shows the influence of the parameters on the system’s environmental performance.

Abstract Background Demand response is an important option for accommodating growing shares of renewable electricity, and therefore, crucial for the success of the energy transition in Germany and elsewhere. In conjunction with smart meters, real-time (or ‘dynamic’) electricity tariffs can facilitate the flexibilization of power consumption and reduce energy bills. Whilst such tariffs are already quite common in several EU member states, Germany lags behind in this respect. The country makes for an interesting case study because of the sheer volume of additional flexibility that its energy transition necessitates. Main text This paper discusses how German policymakers can make real-time tariffs more attractive for households and thus entice them to better adapt their consumption to current market conditions. Following an analysis of the current impediments to the adoption of such tariffs, we discuss four policy options: (1) a more ambitious legal definition of real-time tariffs that can promote market transparency and leverage potential savings for consumers, (2) a shift in energy taxation that encourages the uptake of renewable power and increases price spreads, (3) a new model of dynamic network charges which combines grid-serving and market-serving incentives, and (4) a subsidy for users of real-time tariffs that helps internalise the benefits they provide to all electricity consumers. Given the similar regulatory framework, our suggestions should generally also apply to other countries in Europe and beyond. Conclusions Overall, we argue that there is considerable scope for policymakers to better exploit market forces to ensure security of electricity supply at lower social cost. Our call for stricter regulation in order to allow the markets to better guide consumer behaviour may seem like a paradox—but it is one well worth embracing.

Abstract Steam generating heat pump (SGHP) is a key technology for industrial decarbonization. For comprehensive evaluation of the feasibility and reliability of SGHP in different industrial sector, the work develops a thorough evaluation model for assessing the performance of SGHP by considering waste heat recovery, CO2 trading value, and pollutant emission cost, in addition to the conventional evaluation criteria. This work presents a thorough comparison of the thermodynamic performance and sustainability of various types of SGHPs across different industrial sector. Additionally, the conflicting relationships between the coefficient of performance (COP) and exergy efficiency are balanced through the application of the technique for order preference by similarity to ideal solution (TOPSIS). The results show that all the indexes of SGHP connected to an open heat pump (SGHPO) with different application scenarios are higher than those of SGHP connected to a flash tank (SGHPF). At the most unfavorable operating condition of the system, the COP minimum value is 1.31 and the exergy efficiency minimum value is 20.42%. These results indicate that replacing the coal-fired boiler with SGHP is feasible and the work could provide theoretical guidance for optimal design and equipment manufacture.

Abstract As a new type of composite two-dimensional material formed by the combination of Covalent Organic Frameworks (COFs) and two- dimensional (2D) MXenes, COF/MXene heterostructures (COF@MXene) inherit the stable porous two-dimensional structure of COFs and the excellent electrochemical performance and catalytic activity of MXenes, thus attracting widespread attention. Additionally, COF@MXene possesses various elemental affinity sites, efficient ion channels, and the ability to append various functional groups, which endow them with tremendous potential in electrochemical energy storage, energy conversion, and catalysis. Currently, there is a lack of extensive literature discussing the utilization of COF@MXene. The quest for enhanced physicochemical attributes through tailored modifications and composite strategies for COF@MXene is still a noteworthy hurdle. Furthermore, discovering novel application contexts that can harness the exceptional capabilities of these materials presents a formidable task. This review initiates with an exploration of the primary methodologies for synthesizing COF and MXene composites. Subsequently, it outlines the diverse applications of COF and MXene in energy storage, energy conversion, and environmental conservation. Lastly, it discusses the primary obstacles and future trajectories within these domains.

Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.

The economic viability of future large-scale airborne wind energy systems critically hinges on the achievable power output in a given wind environment and the system costs. This work presents a fast model for estimating the net power output of fixed-wing ground-generation airborne wind energy systems in the conceptual design phase. In this quasi-steady approach, the kite is represented as a point mass and operated in circular flight manoeuvres while reeling out the tether. This phase is subdivided into several segments. Each segment is assigned a single flight state resulting from an equilibrium of the forces acting on the kite. The model accounts for the effects of flight pattern elevation, gravity, vertical wind shear, hardware limitations, and drivetrain losses. The simulated system is defined by the kite, tether, and drivetrain properties, such as the kite wing area, aspect ratio, aerodynamic properties, tether dimensions and material properties, generator rating, maximum allowable drum speed, etc. For defined system and environmental conditions, the cycle power is maximised by optimising the operational parameters for each phase segment. The operational parameters include cycle properties such as the stroke length (reeling distance over the cycle), the flight pattern average elevation angle, and the pattern cone angle, as well as segment properties such as the turning radius of the circular manoeuvre, the wing lift coefficient, and the reeling speed. To analyse the scaling behaviour, we present a kite mass estimation model based on the wing area, aspect ratio, and maximum tether force. The computed results are compared with 6-degree-of-freedom simulation results of a system with a rated power of 150 kW. The results show the interdependencies between key environmental, system design, and operational parameters. Gravity penalises performance more at low wind speeds than at high wind speeds, and excluding gravity does not yield optimistic performance since it assists in the reel-in phase by reducing the required power. Thin tethers perform better at lower wind speeds but limit power extraction at higher wind speeds and vice versa for thick tethers. Upscaling results in a diminishing gain in performance with an increase in kite wing area. The proposed model is suitable for integration with cost models and is aimed at sensitivity and scaling studies to support design and innovation trade-offs in the conceptual design of systems.