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Electrical power distribution systems are experiencing a deep penetration of renewable energy integration, which is reversing the power flow during some periods and modifying their conventional operation. In this context, the curtailment of renewable distributed generation is needed to consider the electrical limitations of transformers and lines, mainly voltage deviations and power overloads. Multiport converters with energy storage stand as a potential solution for enhancing power flow operation in the distribution system and minimising renewable generation curtailments. This paper presents an optimisation-based methodology to size multiport power converters and a techno-economic comparison to other conventional approaches. The optimisation includes energy storage components and AC power flow constraints. Moreover, specific days are considered to reduce computational requirements. This methodology is evaluated in a case study based on a medium voltage benchmark network from CIGRE to identify the best scenarios where multiport converters can be considered as a potential solution. Results conclude that the multiport converter provides clear advantages and is economically viable specially in scenarios with unbalanced distributed generation in the feeders, where their rated power is around 25 MVA, or if batteries are initially installed in the feeders., Peer Reviewed, Postprint (published version)

In response to the growing need for precise energy demand forecasting in distribution grids, this study examines the effectiveness of the Dynamic Mode Decomposition (DMD) algorithm for short, medium, and long-term demand forecasting in distribution grids, leveraging diverse time series energy consumption data. The DMD algorithm, a data-driven approach renowned for its capacity to discern frequencies and trends, will be compared against established forecasting techniques, including XGBoosting Regressor, Long Short-Term Memory, and PROPHET. The research is conducted through a comprehensive case study comprising three distinct phases: study on pattern capturing of DMD, comparison in Long term horizon with all models studied and robustness study of DMD. It recalls upon real aggregated hourly energy consumption data sourced from a Spanish distribution grid. The practical applications and benefits of DMD in time series forecasting are examined carefully in order to show its comparative advantages over alternative forecasting models. To ensure a rigorous assessment of the findings, the analysis will employ four key statistical metrics: Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results obtained with the case studies showed a 75% reduction on computational waste compared to other models and the results are closer to classical models, obtaining an improvement in the best case study of 44% comparing RMSE with Prophet. The insights gained can help future applications and research in the field., Postprint (published version)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Non-technical losses (NTL) pose multiple challenges across distribution grids. This paper introduces a comprehensive framework combining data-driven methods to detect and categorize fraud due to meter tampering or direct connections while identifying potential culprits. The hybrid methodology utilizes grid and consumer-related data to obtain NTL curves through an energy balance approach, yielding indicators such as magnitude, duration, and other features. A Random Forest classifier trained with real historical cases of NTL achieves a weighted F1 score of 0.859, effectively labelling fraud types. Additionally, an unsupervised detection model, integrating clustering and correlation methods, enables accurate identification of tampered meters. The paper introduces two adjustable parameters enabling utilities to fine-tune meter tampering detection strategies based on economic considerations. The results demonstrate that true positives can be increased at the expense of increasing false positives. Accurate fraud identification is achieved using the Fuzzy C-Means algorithm, with an F1 score of 0.9. The algorithm is tested on grids with distributed generation, with a decrease of 10% on the predictive performance when half of the users are prosumers, demonstrating the methodology’s promising performance in real-world scenarios., This work was supported in part by the Investigación, Desarrollo e Innovación (I+D+i) Project Analytic Tools for eLectricAl Systems (ATLAS) (Digitalization Using Novel Data Analytic Methods and Toolboxes For Secure, Renewable, and Flexible Grids) funded by MCIN/AEI/10.13039/501100011033 under Grant PID2021-128101OB-I00; and in part by the European Regional Development Fund (ERDF): A Way of Making Europe. The work of Marc Jené-Vinuesa was supported by the Generalitat de Catalunya under the Formació Investigador-Agència de Gestió d’Ajuts Universitaris i de Recerca (FI-AGAUR) Pre-Doctoral Grants and Scholarships (FI-2020). The work of Mònica Aragüés-peñalba was supported by the Catalan Institution for Research and Advanced Studies (ICREA) Academia Program., Postprint (published version)

Conventional grid-code specifications for dynamic ancillary services provision such as fast frequency and voltage regulation are typically defined by means of piece-wise linear step-response capability curves in the time domain. However, although the specification of such time-domain curves is straightforward, their practical implementation in a converter-based generation system is not immediate, and no customary methods have been developed yet. In this paper, we thus propose a systematic approach for the practical implementation of piece-wise linear time-domain curves to provide dynamic ancillary services by converter-based generation systems, while ensuring grid-code and device-level requirements to be reliably satisfied. Namely, we translate the piece-wise linear time-domain curves for active and reactive power provision in response to a frequency and voltage step change into a desired rational parametric transfer function in the frequency domain, which defines a dynamic response behavior to be realized by the converter. The obtained transfer function can be easily implemented e.g. via a proportional-integral (PI)-based matching control in the power loop of standard converter control architectures. We demonstrate the performance of our method in numerical grid-code compliance tests, and reveal its superiority over classical droop and virtual inertia schemes which may not satisfy the grid codes due to their structural limitations., This work was supported by the European Union’s Horizon 2020 and 2023 research and innovation programs (Grant Agreement Numbers 883985 and 101096197)., Postprint (published version)

In recent years, there has been an exploration of innovative electric grid concepts centered around power routers, devices capable of controlling power flows as desired. One of which is the Power Router Grid, a novel high-controllable network design fully based on power routers. Its ability to arbitrarily change power flows inside controllable lines raises the challenge of determining the optimal operation within such systems. This paper aims to push further the analysis of the operation and benefits of such networks by proposing a novel optimization model tailored for power router grids based on the most recent literature for the convexification of optimal power flows. Moreover, it demonstrated that radial-based models can be expanded for grids meshed through power routers. The model presented is also convex in the form of a second-order cone, ensuring a global optimum for diverse grid configurations. It has been implemented in Python using the PYOMO modeling language and applied to three case studies investigating the effects of power router operation modes on the grid’s optimal operation and associated costs. Results show that the definition of the power router grid design and the ports operation mode must be carefully decided when demand uncertainty of over 25% is taken into consideration. Moreover, controlling power flow near lines of higher impedance can lead up to a 21% increase in line losses., This publication is part of the project TED2021-131753B-I00, funded by MICIU/AEI/ 10.13039/501100011033 and by the European Union “NextGenerationEU*/PRTR”. The work of Andreas Sumper is supported by the ICREA Academia program. Eduard Bullich-Massagué is a lecturer of the Electrical Engineering Department of UPC of the Serra Húnter Programme., Postprint (published version)

Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas (“the Iberian exception”), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators’ data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy., Peer Reviewed, Postprint (published version)

The significant growth in demand for electricity has led to increasing congestion on distribution networks. The challenge is twofold: it is needed to expand and modernize our grid to meet this increased demand but also to implement smart grid technologies to improve the efficiency and reliability of electricity distribution. In order to mitigate these congestions, novel approaches by using flexibility sources such as battery energy storage can be used. This involves the use of battery storage systems to absorb excess energy at times of low demand and release it at peak times, effectively balancing the load and reducing the stress on the grid. In this paper, two optimal power flow formulations are discussed: the branch flow model (non-convex) and the relaxed bus injection model (convex). These formulations determine the optimal operation of the flexibility sources, i.e., battery energy storage, with the objective of minimizing power losses while avoiding congestions. Furthermore, a comparison of the performance of these two formulations is performed, analyzing the objective function results and the flexibility operation. For this purpose, a real Spanish distribution network with its corresponding load data for seven days has been used., Peer Reviewed, Postprint (published version)

In real applications, lithium-ion cells are usually used in individual interconnections, with the worst cell determining capacity, performance and service life. Due to different ageing rates of the assembled cells, cell-to-cell variation changes over lifetime. In literature, cell-to-cell variation is usually investigated near room temperature under one operating condition. Data regarding cell-to-cell variation above and below room temperature under different operating conditions are hard to find. To fill the gap in literature, this study considers cell-to-cell variation as a function of different charge/discharge currents at ambient temperatures between 0 °C and 40 °C on commercial NMC/graphite cells (Samsung SDI ICR18650-26J). The results demonstrate that cell-to-cell variation strongly depends on the selected operating conditions. In particular, operating conditions near the lithium plating boundary at the beginning of the ageing process have a particularly high risk of developing a pronounced variation. Fast charging or charging at lower temperatures should therefore be avoided. Operation at moderate temperatures and currents, on the other hand, only leads to a slight increase in cell-to-cell variation in the tests carried out. A 24-month storage during the testing period at 30% SOC and room temperature of the same cells shows even a slight reduction in cell-to-cell variation., Supported by the publication fund of the Technical University of Applied Sciences Würzburg-Schweinfurt ., Peer Reviewed, Postprint (published version)

This paper investigates the application of fractional proportional–resonant controllers within the voltage control loop of grid-forming inverters. The use of such controllers introduces an additional degree of freedom, enabling greater flexibility in manipulating frequency trajectories. This flexibility can be harnessed to improve tracking error and enhance disturbance rejection, particularly in applications requiring precise voltage regulation. The paper conducts a conceptual stability analysis of ideal fractional proportional–resonant controllers using the Nyquist criterion. A tuning procedure based on robustness criteria for the proposed controller is also addressed. This tuning strategy is used to compare different controllers under the same conditions. In addition, a sensitivity analysis is provided, comparing the performance of fractional proportional–resonant controllers with traditional proportional–resonant controllers equipped with harmonic compensation. The controller’s formulation and performance are validated through simulations and tested with a 20 kVA inverter under high non-linear loads. Compared to classical control approaches, the fractional tuning parameter enhances tracking performance, reduces phase delay, and improves disturbance rejection. These improvements are achieved with a controller designed to minimise computational demands in terms of memory usage and execution time., Postprint (published version)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The use of flexibility in the form of demand response, storage, or power flow control is a desirable feature for modern electricity distribution systems where uncertainty is a major concern. In this respect, we focus on large-scale irrigation systems characterised by seasonal high-power demand. Considering that most of the year there is a large volume of energy available in the form of stored water, demand flexibility, and the possibility of exchanging energy with the distribution grid are a viable way to provide storage-like network services. However, there is a lack of open source software available for the design and operation of these systems considering all of the physical effects involved. Thus, we introduce an open source Python-based tool to optimise the operation of irrigation systems, as well as size new equipment. Our library benefits from block formulation of the Pyomo package to generate large optimisation problems in a modular way. In this document, a detailed description of the developed package is given along with the presentation and analysis of a study case., We would like to thank professor Xavier Escaler from the Department of Fluid Mechanics at the UPC for his insights on hydraulics, EPRI Europe for their comments and Infraestructures de la Generalitat de Catalunya for the data provided., Postprint (author's final draft)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., This article proposes a control method for virtual synchronous generator-based grid-forming converters to improve the stability of power electronic-dominated power grids. Overcurrent protection and fault ride-through (FRT) are crucial for the control of converter-based generators, given the limitations of power electronics components. This article introduces a finite-set model predictive control (FS-MPC)-based FRT control technique that ensures system stability during symmetrical and asymmetrical fault types by incorporating overcurrent protection and contribution to the system voltage, while maintaining the voltage mode functionalities of the grid-forming converters. Compared to conventional control methods, the proposed MPC control technique allows for faster and improved dynamics, which makes control objectives more accessible. In addition, the approach coordinates voltage and current control and their limitations to ensure system stability during and after fault. The proposed method also adapts to different fault types and sizes without requiring fault type detection. Simulation results in MATLAB/Simulink illustrate the effectiveness of the proposed control method in limiting the current and ensuring high power quality by providing clean sinusoidal voltage and current waveforms. Hardware-in-the-loop real-time simulation is also presented to validate the controller performance under fault conditions., Postprint (author's final draft)

To ensure that future electrical distribution networks can effectively meet the dynamic demands of the evolving energy landscape due to the penetration of renewable energy sources, the energy-oriented industrial sector has the opportunity to enhance hosting grid capacity in response to short-term requests from system operators. This is achieved by increasing energy resources available to the grid through flexible capacity offers, aiming to prevent periods of system instability. This flexibility has traditionally been provided by large generators on the supply-side, but new opportunities for demand-side flexibility can be integrated into the industrial sector through incentive-based demand response programs. One option is to strategically allocate capacity for balancing service markets through load reduction curves. While large consumers may compete against generators in flexibility-markets, small and medium-sized industries remain largely unexplored. This paper builds on a flexible multi-single machine manufacturing problem and proposes a method for providing day-ahead demand-side flexibility offers combined with price-based real-time pricing demand response. The study is approached from the perspective of a non-intensive industry under an aggregator platform, enabling participation in balancing service auctions. The paper also examines the viability and applicability of flexibility offers within the constraints of low-capacity offers, to determine the optimal flexibility strategy., This work has been supported by the FLEX4FACT project, funded by the European Union under the Horizon Europe research and innovation programme, under the grant agreement number 101058657. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Eduard Bullich-Massagué is lecturer of the Serra Húnter programme., Postprint (published version)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Phase-locked loop (PLL) stands as a critical component for achieving synchronization in grid-following converters. However, conventional PLLs may grapple with stability issues or synchronization challenges under grid disturbances. Therefore, a robust PLL (RPLL) is proposed in this article, which is designed to robustly operate against grid disturbances in terms of frequency, voltage amplitude, and impedance variations. The operational mechanism of RPLL is analyzed, while also establishing small-signal models of both RPLL and the grid-following converter with RPLL, facilitating the analysis of small-signal stability. Finally, the effectiveness and robustness of RPLL are validated through dynamic simulations in several scenarios, as well as corresponding experimental results. © 2024 IEEE., This work was supported by the National Natural Science Foundation of China under Grant 62173148 and 52377186., Postprint (author's final draft)

Low voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low-income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies., Engineering and Physical Sciences Research Council,Grant/Award Number: EP/T021829/1, Peer Reviewed, Postprint (published version)

This paper addresses the power capability domain calculation for the specific M2DC-based AC/DC/DC multi-port converter. Such a power capability domain will represent all the possible power flow scenarios (comprehensive of active and reactive power components) the converter can withstand without exceeding the physical limitations (due to the established ratings of installed components, e.g., semiconductors current and voltage). Concerning conventional two-port modular multilevel converters, the P-Q power capability curve is one of the most common tools employed to track the safe operating region of the converter. However, the increased number of ports and the topology changes on multi-port converters lead to evaluating and representing the power capability domain in a non-conventional way. This article evaluates the power capability of the specific M2DC-based AC/DC/DC asymmetrical monopole converter. Moreover, internal converter asymmetries are considered in the study to express analytical voltages and current behaviour as a function of terminals powers. Then, the power capability domain is calculated and represented for a use-case scenario in which the multi-port converter interconnects High Voltage DC and Medium Voltage DC networks to the AC transmission network. The study also includes power capability domain modifications under port contingencies scenarios, such as fault and port disconnection., Postprint (published version)

There are obstacles to the widespread use of small electric vehicles (EVs) in Rwanda, including concerns regarding the battery range and lifespan. Lithium-ion batteries (LIBs) play an important role in EVs. However, their performance declines over time because of several factors. To optimize battery management systems and extend the range of EVs in Rwanda, it is essential to understand the influence of the driving profiles on lithium-ion battery degradation. This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions under deep discharge (). By being aware of these variables, battery failure can be slowed and improved battery performance can be achieved to promote the transition to cleaner transportation in Rwanda for the productive use of energy. The analyzed parameters that affect battery performance are temperature, driving cycles, and state of charge. It was found that the higher the temperature, the higher was the rate of fading. On the other hand, the EVs that operate in the region with higher elevation (hilly region) combined with a flat surface where the riders use their physical forces to propel the E-bike and their batteries lose their capacity rapidly compared to those operating in regions where the energy from the lithium-ion battery assists for the entire mileage. By draining the battery to 10% and charging it to 90% of its initial capacity, the capacity fading decreased by 5%., This study was supported by the Global Challenges Research Fund (GCRF) through the Engineering and Physical Sciences Research Council UK under project RENGA (EP/R030235/1). It was also funded by the African Center of Excellence in Energy for Sustainable Development (ACE-ESD) under the University of Rwanda, College of Science and Technology., Peer Reviewed, Postprint (published version)

Industrial demand response (IDR) will play a crucial role in shaping future electricity systems, as it is a key element of a just energy transition and industrial development. The aim of this work is to provide an overview of the current status of IDR in a holistic perspective. First, the main benefits and potential of IDR are reviewed, together with the motivations and challenges for the industrial sector. Most recent advances in European markets and regulations with specific focus on IDR applications are explored. Then, the different resources which are currently available to help industries participate and implement IDR programmes are reviewed. In particular: 1) the (possible) tools for defining energy-aware scheduling and planning of the manufacturing systems are analysed; 2) The role of aggregators (i.e. intermediaries between industries and power markets) for facilitating explicit IDR is examined; 3) the importance of digitalisation to provide better IDR services from the manufacturing industry is highlighted, pointing out that digital twins, cyber-physical systems, Internet of Things sensors, robots, edge computing, artificial intelligence, and big data are promising technologies; and 4) most recent related research projects are reviewed. Finally, it is analysed and discussed how each of those resources can address the different challenges that are still preventing industries to apply IDR programmes., This work has been supported by the FLEX4FACT project, funded by the European Union under the Horizon Europe research and innovation programme, under the grant agreement number 101058657. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Eduard Bullich-Massagué belongs to the Serra Húnter programme., Postprint (published version)

In modern power systems, with a growing integration of inverter-based resources, small-signal stability is becoming increasingly important. However, current practice in operation and planning studies is based on solving optimization problems, mainly based on Optimal Power Flow calculation (OPF), considering only static stability constraints. The small-signal stability of such optimal solutions is typically addressed in separate, subsequent studies. Avoid costly preventive actions or post-oscillation corrective actions require a tool that can compute optimal and stable solutions within a unified framework. To address this need, this paper proposes a Small-signal Stability Constrained Optimal Power Flow (SSC-OPF). To alleviate the computational burden, leveraging Machine Learning to formulate the stability constraints is an emerging and effective solution. In this work regression functions are employed, quantifying the oscillation damping via a numerical stability indicator (the Damping Index (DI)). Through qualitative and quantitative analyses, Multivariate Adaptive Regression Splines emerges as a suitable regression technique, offering a continuous and derivable expression. This choice bypasses the need for integer variables and the formulation of challenging Mixed-Integer Linear Programming optimization, making SSC-OPF implementation more feasible with the use of the Interior-point algorithm. In this study, the SSC-OPF is implemented in two configurations. In one scenario, the optimization control variables include only the generators’ power dispatch. In the second scenario, the possibility of tuning converters control parameters is also considered. Both implementations are tested and compared on a 9-bus power system, with two objective functions exemplifying the use of the SSC-OPF for power system operation and planning. In all test cases, the regression-based constraint exhibits high accuracy in determining the DI, with an $R^{2}$ higher than 0.96. This accuracy, This work was supported by Fondo Europeo de Desarrollo Regional (FEDER)/Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación, through the project REFORMING under Grant PID2021-127788OA-I00. The work of Eduardo Prieto-Araujo and Marc Cheah-Mane was supported by the Serra Húnter Program. The work of Oriol Gomis-Bellmunt was supported by the Institución Catalana de Investigación y Estudios Avanzados (ICREA) Academia Program., Postprint (published version)

This paper focuses on the voltage control of grid-forming Voltage Source Converters (VSCs) connected to AC networks. The work aims to identify the best pairing between voltage control structures and usual ac filter topologies. The two filter topologies considered are the inductive (L) and the inductive–capacitive (LC) filter. The control structures studied are the Cascaded Control (CC, cascaded voltage and current loop) and the direct Voltage Control (VC, without current loop). To select the best control-filter pair, the following methodology is suggested. First, for each pair, a voltage control parameter sweeping methodology is developed to find suitable designs considering the following limits: minimum bandwidth, maximum converter voltage, point of connection voltage deviation and maximum current. Second, this action is repeated for different filter parameters and network Short-Circuit Ratios (SCRs). Finally, based of the obtained results, the most relevant control-filter pairs can be selected, prioritizing them according to the fulfilment of the defined limits. To conclude, time-domain simulations are carried out to validate pairing selection., This work was supported by the European Union’s Horizon 2020 Research and Innovation Program [Powering System Flexibility in the Future through Renewable Energy Sources (POSYTYF) Project] under Grant 883985. The work of Oriol Gomis-Bellmunt was supported by the Institució Catalana de Recerca i Estudis Avançats (ICREA) Academia Program. The work of Jaume Girona-Badia was supported by FI-SDUR grant from AGAUR., Postprint (published version)

Interconnected Microgrid (IMG) networks have been suggested as the best to build electrical networks in remote villages far from the main electricity grid by interconnecting the nearby distributed energy resources (DERs) through power electronic converters. Interconnecting different DERs results in voltage deviation with unequal power-sharing, while voltage performance is a significant challenge. The control strategies for these converters are essential in the operational stability of any IMG network under study. In this paper, we propose an improved droop control method aiming to manage the power flow among the IMGs by maintaining the constant desired voltages in the network with minimum voltage deviation, resulting in the minimization of power losses. We found that the minimum voltage deviation at the load side (converter-3) was between 0.58 and 0.56 V, while the voltage deviation for both converter-1 and converter-2 remained below 0.5 V. This leads to efficient voltage regulation, resulting in the stability of an IMG network. To verify the feasibility of this method, MATLAB/SIMULINK has been used., The authors of this paper would like to acknowledge the sufficient support from the EPSRC under the Project EP/R030235/1 “RENGA: Resilient Electricity Networks for a Productive Grid Architecture” from the Imperial College of London, UK. Furthermore, the authors acknowledge important support from the African Center of Excellence Energy for Sustainable Development located at the University of Rwanda, College of Science and Technology (UR-CST)., Peer Reviewed, Postprint (published version)

Energy communities are emerging across Europe, and each country is currently in the process of forming regulations for their integration into the electricity grid. The efficacy of energy communities depends upon various factors, including member demographics, technological aspects, load profiles, solar irradiation, and spot prices within the community’s geographical location. Notably, existing studies on energy communities predominantly focus on residential load profiles, with limited exploration into their impact on the distribution grid. This article aims to contribute to the existing literature by investigating the benefits of energy communities and their grid impact under diverse member configurations. Our approach involves the development of an optimisation model incorporating battery energy storage and shiftable loads, aimed at minimising the operational costs of energy communities over a one-year period. Case studies in Norway and Spain, with different load configurations: residential, commercial, and mixed load, are undertaken, utilising real hourly measurements to identify operational variations influenced by geographical location and seasonal fluctuations in load and photovoltaic (PV) generation. Additionally, we quantify the costs, CO2 emissions, and self-consumption rates for energy communities. Furthermore, we assess the distribution grid impact in terms of import and export dynamics. The results underscore the substantial influence of load configurations on member benefits and distribution grid impacts, attributable to the inherent correlation between load and PV generation. In the context of energy community benefits, commercial loads demonstrate the best outcomes in Norway, whereas residential loads exhibit superior results in Spain. Conversely, concerning distribution grid impact, commercial loads prove most advantageous in Norway, while mixed loads yield the best results in Spain. Overall, our findings indicate that Spanish energy communities consi, The authors would like to thank Harald T. Walnum, Synne K. Lien, Karen B. Lindberg and Igor Sartori for access to and discussions regarding the PROFet tool. We also thank Ida Fuchs for discussions regarding PV modelling and Marte F. Dynge for proofreading. This work was supported by the project consortium of the research project FINE (Flexible Integration of Local Energy Communities into the Norwegian Electricity Distribution System), financed by the Research Council of Norway [project number 308833]., Peer Reviewed, Postprint (published version)

We present a novel grid-forming control design approach for dynamic virtual power plants (DVPP). We consider a group of heterogeneous grid-forming distributed energy resources (DER) which collectively provide desired dynamic ancillary services, such as fast frequency and voltage control. To achieve that, we study the nontrivial aggregation of grid-forming DERs to establish the DVPP, and employ an adaptive divide-and-conquer strategy that disaggregates the desired control specifications of the aggregate DVPP via adaptive dynamic participation factors to obtain local desired behaviors of each DER. We then design local controllers at the DER level to realize these local desired behaviors. In the process, physical and engineered limits of each DER are taken into account. We extend the proposed approach to make it also compatible with grid-following DER controls, thereby establishing the concept of so-called hybrid DVPPs. Furthermore, we generalize the DVPP design to spatially dispersed DER locations in power grids with different voltage levels and R/X ratios. Finally, the DVPP control performance is verified via numerical case studies in the IEEE nine-bus transmission grid with an interconnected medium voltage distribution grid., Peer Reviewed, Postprint (published version)

This article proposes a PLL-less grid-forming (GFor) control for a PV central power plant topology designed to maintain the DC voltage and improve the AC voltage stability. The PV single-stage or central topology stands as the most widely adopted power plant structure. However, no GFor control capable of regulating the DC bus has yet been proposed for this layout. This work presents a PLL-less GFor control that can effectively control the DC voltage for a single-stage PV inverter, whether it operates at the Maximum Power Point (MPP) or with reserve. Moreover, this control can incorporate Maximum Power Point Tracking (MPPT) controls typically employed in grid-following structures. The primary advantage of this GFor control is the voltage support and stability it provides and the capability of working with a low Short Circuit Ratio (SCR). The performance and viability of the proposed control was analysed with several simulations of various faults (symmetrical and asymmetrical), frequency (large phase jump and frequency excursion with high ROCOF) and resource events (irradiation changes and DC voltage reference tracking analysis). The results demonstrate that the proposed GFor control presented not only meets the grid-code requirements but also exhibits the behaviour expected during the different analysed events., This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme [Powering System Flexibility in the Future through Renewable Energy Sources (POSYTYF) Project] under Grant 883985. This work also has been supported by the European Union’s Next Generation PRTR program (FAIR project), with reference TED2021-129796B-C22. The work of Oriol Gomis-Bellmunt was supported by the Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain Academia Program. Eduardo Prieto-Araujo is a member of the Serra Húnter Programme. The work of Jaume Girona Badia was supported by FI-SDUR grant from AGAUR, Spain ., Postprint (published version)

Offshore wind power generation equipped with conventional grid following controls challenges the power system stability by reducing the inertia of the grid and weakening the AC offshore grid. To overcome this vulnerability a promising solution could be equipping the wind turbines with grid-forming (GFM) control schemes. Many methodologies have been presented in the literature for GFM inverters. In spite of this, their applications for WTGs have not been thoroughly explored. This paper presents the study of fault handling capability of wind turbines with several grid-forming control strategies. In this context, four different control schemes i.e. Visynch, P/f droop, Q/f droop and conventional grid following control are considered. The performance of these control strategies is assessed and compared during a three-phase symmetrical fault on the point of common coupling of an offshore wind farm. A case study which consists of two wind turbines connected to the main converter of an offshore substation equipped with grid-forming control is also considered. The results indicate that, during faults, each of the four control strategies (Visynch, P/f droop, Q/f droop, and Gfol) effectively limits the current and restores normal operation. A significant advantage of Q/F droop control is its reduced sensitivity to variations in system and fault parameters, adjustable frequency response, and independence from existing Phase-Locked Loops (PLLs). On the other hand, the P/f droop control provides greater voltage support during faults due to its voltage controllers. However, post-fault oscillations result from the stiff voltage controller. Visynch control, on the other hand, shows a lower margin of stability. The advantages and weaknesses of each control scheme is revealed using the simulation results., 10.13039/100014132-European Union’s Horizon 2020 Research and Innovation Programme [Powering System Flexibility in the Future through Renewable Energy Sources (POSYTYF) Project] (Grant Number: 883985) 10.13039/501100003741-Institució Catalana de Recerca i Estudis Avançats (ICREA) Academia Program. Eduardo Prieto-Araujo is a member of the Serra Húnter programme 10.13039/501100003030-Ajuts de Suport a Departaments i Unitats de Recerca Universitaris per a la contractació de personal investigador predoctoral en formació a Catalunya (FI-SDUR) grant from Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (Grant Number: 2021 FISDU 00156), Postprint (published version)

Steel fiber-reinforced concrete (SFRC) has proven to be a suitable structural material for constructing elevated flat slabs, and several existing office and residential buildings, in which SFRC was used for these components, highlighted the positive outcomes from both technical and sustainability perspectives. However, research on the influence of fiber orientation on the flexural performance of SFRC slabs is relatively scarce despite the fact that a number of studies revealed that the use of constitutive models derived from three-point bending tests on notched beams leads to overestimations of the bearing capacity of statically indeterminate slabs. In this context, this study aims at presenting an extensive experimental program that consisted of testing three SFRC slabs (3.0¿×¿3.0¿×¿0.1¿m3) subjected to point load under a statically indeterminate test configuration. The testing procedure was followed by the extraction of 88 cores in order to analyze both fiber distribution and orientation by means of a non-destructive inductive method. The results indicate that lower orientation numbers were observed in cylinders drilled from the slabs in comparison with those observed in the 150¿×¿150¿×¿600¿mm3 notched reference beams tested to characterize the pre- and post-cracking flexural performance of the SFRC produced. Based on these results, a straightforward design-oriented approach to compute an orientation factor was proposed and considered to develop the constitutive law used to simulate the structural response of SFRC slabs by means of nonlinear finite element model. The numerical simulation results showed a good agreement with the experimental response, this evidencing that the orientation factor may permit to account for the differences caused by geometric and statistical scale-induced factors between both the beams used for the SFRC flexural properties characterization and slabs produced with the same SFRC., This study was financially supported by the Spanish Ministry of Science and Innovation under the scope of project CREEF (PID2019-108978RB-C32/AEI/10.13039/501100011033)., Postprint (published version)

The objective of microgrid operation is to supply the energy demanded by the loads at minimum cost. To achieve this goal, new tools are being proposed in the literature, such as the use of optimal schedulers in the field of multi-vector management systems. An optimal scheduler provides the hourly schedule of the flexible loads that exist in a microgrid to maximize the use of local renewable resources. This work aims to investigate the application in the context of five optimization algorithms in terms of energy and computation costs and to demonstrate how optimal schedulers can contribute significantly to reducing energy operating costs in new and real microgrid scenarios. The analysis of the algorithms is carried out through an experimental process on the existing installations at Port of Borg (Norway), which contains photovoltaic production and different types of flexible assets, such as cranes, electric vehicle charging stations, and electrical storage. Real data gathered at the port's premises is used to assess the energy cost reduction when the optimal scheduler is part of the energy management system, and the computations are performed in real time to apply the proposed schedule to the pilot. The results show how the use of optimal schedulers can reduce operation costs up to 17.2%, augmenting local energy production utilization, and that using two OS algorithms in cascade can also reduce the computation time., Peer Reviewed, Postprint (published version)

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%., Peer Reviewed, Postprint (published version)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Grid-forming (GFM) control has been considered a promising solution for accommodating large-scale power electronics converters into modern power grids thanks to its grid-friendly dynamics, in particular, voltage source behavior on the AC side. The voltage source behavior of GFM converters can provide voltage support for the power grid, and therefore enhance the power grid (voltage) strength. However, grid-following (GFL) converters can also perform constant AC voltage magnitude control by properly regulating their reactive current, which may also behave like a voltage source. Currently, it still remains unclear what are the essential differences between the voltage source behaviors of GFL and GFM converters, and which type of voltage source behavior can enhance the power grid strength. In this paper, we will demonstrate that only GFM converters can provide effective voltage source behavior and enhance the power grid strength in terms of small signal dynamics. Based on our analysis, we further study the problem of how to configure GFM converters in the grid and how many GFM converters we will need. We investigate how the capacity ratio between GFM and GFL converters affects the equivalent power grid strength and thus the small signal stability of the system. We give guidelines on how to choose this ratio to achieve a desired stability margin. We validate our analysis using high-fidelity simulations., This work was jointly supported by the National Key Research and Development Program (2023YFB2406600) and the National Natural Science Foundation of China (No. U2166204 and No. 51922094). (Corresponding author: Linbin Huang.) H. Xin, C. Liu, and Y. Wang are with the College of Electrical Engineering, Zhejiang University, Hangzhou, China. (Email: xinhh@zju.edu.cn) X. Chen is with the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. E. Prieto-Araujo is with the Electrical Engineering Department, Technical University of Catalonia, 08034 Barcelona, Spain. L. Huang is with the Department of Information Technology and Electrical Engineering at ETH Z¨ urich, Switzerland. (Email: linhuang@ethz.ch), Postprint (author's final draft)

The integration of new types of consumers and prosumers into distribution networks presents significant challenges for network planners, especially given the uncertainties of future expansions. This paper introduces a method for distribution network planning that integrates a Long-Short Term Memory (LSTM) model with a confidence interval threshold for long-term scenario predictions. The proposed forecasting model considers datasets collected from monitoring systems which include aggregated demand and self-consumption from photovoltaic sources. The use of the LSTM model enables a more accurate prediction of expansions, as it captures nonlinear patterns in time series data while taking into account the inherent characteristics of non-stationary time series data. The proposed method was tested on an existing radial medium voltage distribution network in Spain, taking into account the operational thermal limits for lines and substation time series measurements. The proposed planning approach also considers asset costs, active planning solutions, and time-series load flow analysis. The results obtained indicate that the use of time-based forecasts from aggregated generation and demand is a more realistic solution for flexible planning solutions when they are compared to traditional strategies., Peer Reviewed, Postprint (author's final draft)

Multiport power converters integrate three or more energy devices into a single (potentially highly controllable and efficient) hub. These characteristics suggest that multiport power converters may be valuable for the decarbonisation of distribution networks, where the increase of converter-interfaced devices has degraded system reliability and efficiency. This review analyses the suitability of a wide range of multiport power converter solutions for four example distribution network applications (where previous studies have focussed on a limited range of topologies or applications) and the research areas that can progress their maturity. A review of grid codes and standards overviews the base capability that multiport power converters are likely to require, some of which are carried forward as requirements for a novel comparison tool. The comparison tool is developed to qualify and score reviewed topologies in terms of a range of features that are weighted for the applications. Isolated and partially-isolated topologies perform well due to their flexibility to be configured for the specifications and their operational capabilities (including modularity and voltage decoupling). Further research should focus on the complex control interactions between ports and scaling of these topologies for medium voltages. In contrast, many direct current non-isolated topologies do not qualify due to their low flexibility to be configured for the applications. This suggests that future research could focus on the development of a more flexible non-isolated multiport power converter configuration to take advantage of the high efficiency and low footprint that these topologies might otherwise offer for low voltage applications., This project was funded by the Innovate UK Horizon Europe Guarantee application number 10039599. This work was also funded by the European Union's Horizon Europe research and innovation programme in the framework of the project “iPLUG” with grant agreement 101069770. ., Peer Reviewed, Postprint (published version)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, Learning-based state estimators can represent a cost-effective opportunity for distribution system operators to perform grid monitoring and control in low-voltage grids where the measuring infrastructure is minimal, if not absent. This study lays the foundation for designing and evaluating neural network-based state estimators for low-voltage radial distribution grids. A simulation-based methodology is proposed for generating synthetic training data-sets relying only on minimal grid data. Additionally, a novel framework for performance analysis of low voltage learning-based state estimators is considered, which relies on a bi-dimensional evaluation of the absolute error and the parallel observation of relative metrics. The applicability and potential of these estimators have been tested and validated through various low-voltage radial case studies, showing promising results especially for large distribution grids. Finally, a propagation error study has been conducted to observe how these estimators handle errors in input measurements., This work was supported in part by MCIN/AEI/10.13039/501100011033 under Project MERIDIAN TED2021-131753B-I00, and in part by the European Union ‘‘NextGenerationEU/PRTR.’’ The work of Andreas Sumper was supported by the Catalan Institution for Research and Advanced Studies (ICREA) Academia Program., Postprint (published version)

In remote areas of Sub-Saharan Africa, as well as in Rwanda, communities face the problem of finding an affordable and suitable way of transport for their daily life. Currently, the short-range transportation of people and goods rely on manual traction (e.g., bicycles, handmade wooden bicycle, etc.) and on small combustion engines. However, this region has a large renewable energy source (solar) which can help to mitigate this problem. Electric bicycles may be used to tackle the problem, although their use is still not largely widespread despite its potential. In addition, the local society does not have an easy way to accurately estimate the amount of energy consumed for a certain itinerary to be sure of how the electric vehicles perform. Thus, characteristic analysis of consumed energy is very important to analyze the performance of electric vehicle storage systems, model charging infrastructure, size vehicles, planning for itineraries, etc. This paper presents a methodology to estimate the power consumed by electric bikes for specific itineraries. In order to accomplish so, the WebPlotDigitizer tool and Google Maps were used to produce driving profile patterns. The results are compared against experimental data, showing the accuracy of the methodology presented. The simulated results show that the consumed power value differs by only 1.68% from the experimental recorded value; the difference can be explained by traffic congestion, the density of traffic, and the intersection that occurred during the experiment. The presented method of driving energy requirement estimation using WebPlotDigitizer is attractive, affordable, and easy to use., This study supported by; University of Rwanda (UR_CST) African Center for Excellence Energy for Sustainable Development, Mechanical and Energy Engineering Department., Peer Reviewed, Postprint (published version)

The increase in renewable energy sources and new technologies such as electric vehicles and storage can generate uncertainties in distribution grid operations, increasing the likelihood of congestions in power lines. Distribution system operators (DSOs) face several challenges while operating their grids in such conditions. These congestions deteriorate the electrical equipment in the long term, reducing its life span. This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. The framework is tested on a modified IEEE-33 bus system and CINELDI MV Reference system with hourly synthetic data. The results showed that the framework is able to closely predict the congestions on the lines. Computational capabilities are reported and discussed. The study indicates that the framework is a suitable tool for day-to-day congestion predictions in smart distribution grids yielding low error in expected values., The authors would like to thank Rubi Rana for discussion regarding the CINELDI Reference System. This work was supported by the project consortium of the research project FINE (Flexible Integration of Local Energy Communities into the Norwegian Electricity Distribution System), financed by the Research Council of Norway [project number 308833]. This work has also been supported by the BD4OPEM H2020 project, which has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 872525. Mònica Aragüés is Associate Professor and Eduard Bullich-Massagué is a lecturer of the Serra Húnter programme., Peer Reviewed, Postprint (published version)

The popular single-input single-output control structures and classic design methods (e.g., root locus analysis) for the power control of grid-forming converters have limitations in applying to different line characteristics and providing favorable performance. This paper studies the grid-forming converter power loops from the perspective of multi-input multi-output systems. First, the error dynamics associated with power control loops (error-based state-space model) are derived while taking into account the natural dynamical coupling terms of the power converter models. Thereafter, the controllability Gramian of the grid-forming converter power loops is studied. Last, a full-state feedback control design using only the local measurements is applied. By this way, the eigenvalues of the system can be arbitrarily placed in the timescale of power loops based on predefined time-domain specifications. A step-by-step construction and design procedure of the power control of grid-forming converters is also given. The analysis and proposed method are verified by experimental results and system-level simulation comparisons in Matlab/Simulink., Peer Reviewed, Postprint (published version)

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces., DA has been funded by the Collaboration in University Departments AGAUR Grant (COLAB 2020). AG and EF have been partially funded by the MINECO-FEDER-UE-MTM Grant RTI2018-093860-B-C21, the Generalitat de Catalunya-AGAUR projects 2021SGR01039 (AG) and 2021SGR00376 (EF), the Grants PID-2021-122954NB-I00 and PID2022-137708NB-I00 funded by MCIN/ AEI/ 10.13039/ 501100011033 and by ERDF ‘A way of making Europe’ (AG) and the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R &D (CEX2020-001084-M) (AG). We are grateful to the Institut d’Organització i Control (IOC-UPC) for the access to its high-performance computing facilities to perform all the computations of this work., Postprint (published version)

We consider a Thévenin equivalent circuit capturing the dynamics of a power grid as seen from the point of common coupling with a power electronic converter and propose a solution to the problem of online identification of the corresponding circuit parameters. For this purpose, we first derive a linear regression model in the conventional abc coordinates that, unfortunately, suffers from severe lack of identifiability properties. In spite of this fact, we design a bounded observer-based composite identifier that, requiring only local measurements and knowledge of the grid frequency, ensures global boundedness of the parameters estimation error and convergence to a practically small residual set. An extension that guarantees exponential convergence of these errors to zero, in the case that the grid is dominantly inductive, is further provided. The performance of the proposed identifier, which subsumes a conventional gradient descent algorithm, is illustrated via detailed computer simulations., The work of Oriol Gomis-Bellmunt was supported by the Institució Catalana de Recerca i Estudis Avançats (ICREA), Spain Academia Program. Ministerio de Ciencia e Innovación (Proyecto Equired) (PID2021-124292OB-I00)., Postprint (published version)

Este documento analiza: (1) el marco normativo de las instalaciones interiores, sus elementos y los criterios de diseño, (2) las distintas modalidades de fraude, enganches directos, enganches con contrato y puntualmente la manipulación de contadores y (3) los impactos del fraude, clasificados según su naturaleza y el actor afectado del sector eléctrico ., Preprint

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In hybrid AC/DC grids, Interconnecting Power Converters (IPCs) can assume different control roles, mainly among grid-following and grid-forming. The various possibilities of roles assignment are in this study indicated as Converter Control Roles Configurations (CCRCs). Their number can become large, as it grows both with the number of considered control roles and the number of IPCs in the system. This adds complexity to the studies aimed at assessing the stability of hybrid AC/DC grids operation, as all the possible CCRCs have to be considered. This paper presents a methodology for selecting a subset of the CCRCs that allows the operation of the system with enhanced small-signal stability performance. Data mining techniques are employed for clustering the CCRCs according to their stability performance behavior in the system operable space and revealing explicitly the discovered pattern through a knowledge extraction process. To exemplify the proposed methodology, a test system made of three AC sub-grids and two DC sub-grids is used and a small-signal stability performance indicator measuring the voltage deviation in the DC grids is considered., This work was supported by FEDER / Ministerio de Ciencia e Innovación- Agencia Estatal de Investigación, under the project REFORMING (PID2021-127788OA-I00). The work of O. Gomis was supported by the ICREA Academia program. The work of E. Prieto and M. Cheah was supported by Serra Húnter Program., Preprint

The energy industry is experiencing significant changes in terms of sustainability and competition, primarily driven by the introduction of renewable energy targets and emission limits. Demand response is a potential solution to reduce the critical peak; however, its implementation in industries can be challenging due to their production requirements. Technology enablers such as digital twin technology can enhance energy flexibility and optimize manufacturing and service processes. In this study, we aim to develop a framework that can help the manufacturing industry to optimise industrial demand response services and achieve a seamless interaction of different layers such as the physical, data infrastructure, digital twin, management, and aggregator. A systematic literature review and workshops were conducted to identify key technologies, decision areas and methods to enable both manufacturing and energy flexibility to reach demand response. Based on the results, an energy-flexible framework for manufacturing industries was developed., Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The renewable energy resources integrating HVDC transmission systems, consisting of grid-following and grid-forming converters, have complex physical and control dynamic interactions. Although the impedance analysis method is advisable to study these interactions, impedance modeling is still challenging when taking various converter control schemes into account. This article proposes a modular three-port admittance modeling framework for the hybrid AC/DC power systems. This approach separates the modeling process into three levels: controller, converter, and system levels. The current and voltage feedback loops are subdivided into multiple submodules at the controller level, and the submodule connection method is proposed to obtain the small-signal model of the unified main controller, which thus can eliminate the repetitive work caused by the diversity of control schemes. Due to the modular modeling advantage, the method is also versatile and scalable, as well as using unlimited and expandable interconnecting. In addition, the modeling separates the entire system into three components, and the stability criterion of the hybrid AC/DC power systems is derived by regrouping their transfer function matrix models. Then, the proposed modeling method is applied to assess the stability of hybrid power plants integrating HVDC systems with grid-following and grid-forming sources. The conclusions can be used to plan GFM converter capacity in power System plants, the installation location of HVDC transmission systems, and other scenarios. Simulations are carried out to validate the correctness of the impedance modeling method and stability criterion. Finally, the effect of the different capacity ratios of grid-forming sources to renewable energy power plants on system stability is discussed., This work was supported in part by the GuangDong Basic and Applied Basic Research Foundation (Offshore Wind Power Joint Fund) under Grant 2022A1515240030, and in part by the Shenzhen Science and Technology Innovation Commission through the Shenzhen Innovation and Entrepreneurship Project (Key Technologies R & D Program) under Grant JSGG20210802152540008., Postprint (published version)

Renewable energy resources have increasingly been integrated into electric power grids. Moreover, new challenges constantly arise, showing the need for modernization in monitoring, control, and protection. Transmission and distribution systems have explored the use of phasor measurement units (PMUs) in an attempt to meet these requirements. The decreasing cost of hardware components of PMUs, similar to what occurred with digital relays, is an important factor in terms of their worldwide use in different applications. Low-cost variations of PMUs focused on distribution systems, such as micro- and D-PMUs, are growing in number. Thus, this survey article presents significant and recent research conducted in the field of power systems using PMUs. Firstly, it briefly describes the characteristics of PMUs concerning hardware and phasor estimation algorithms. Afterward, recent studies related to transmission and distribution system protection based on PMUs are presented, such as fault detection and location, stability, and wide-area protection. Finally, the article also provided important guidelines, which certainly can be useful for future research in the years to come., Peer Reviewed, Postprint (author's final draft)

Phasor models have been widely used in short-circuit simulations, in which system operators verify the behaviour of the grid over thousands of different contingencies. However, it is still unclear if phasor models can still be used in fault studies in modern or future power grids dominated by renewable generation with power electronics and converters. Therefore, this paper analyses the suitability of phasor models to simulate short-circuit transients in grids with grid-following (GFL) and grid-forming (GFM) voltage-source converters (VSC). Phasor models of GFL and GFM were developed and tested in two test systems, one with 50% of converters in the generation mix and another powered 100% by converters. Asymmetrical and symmetrical faults were applied at different points of the systems and key variables were used to compare the phasor models against EMT models during the transients. The results showed that, despite neglecting transients in the grid, phasor models could be used in a preliminary stage of short-circuit studies as it was capable of tracking the steady-state value of almost all variables analysed. In this case, detailed EMT simulations, although necessary, would be used only at more advanced stages of the studies., Postprint (author's final draft)

The rapid and exponential changes in our world require the education of engineers who can develop solutions to future and long-term challenges such as climate change. Exploration and innovation methodologies such as Futures Thinking and Moonshot Thinking have the potential to equip engineering students with useful tools and skills to build sustainable futures. To this end, the InnoEnergy MSc Energy for Smart Cities programme at BarcelonaTech (UPC) has developed a challenge-based learning (CBL) course that applies moonshot thinking to tackle major energy problems. This paper presents the methodology refined over three years of implementing the CBL course with second-year Masters's students in Energy Engineering. The course begins by constructing a narrative working future using exploratory tools from the Futures Thinking methodology. Breakthrough technologies are introduced, and their disruptive potential is analysed. Students then define a long-term sustainability and energy problem and use various ideation methodologies to develop a solution. Using technologies such as 3D printing, artificial intelligence and open-source electronic prototyping platforms such as Arduino, they build a minimum viable product (MVP) and develop a business model. Finally, using an agile approach, students must design future iterations and analyse the potential exploitation of their solution. This subject equips students with the necessary skills to address complex energy and sustainability challenges, and the course has proven effective in preparing students to apply their knowledge in practical, real-world settings., Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.2 - Incorporar mesures relatives al canvi climàtic en les polítiques, les estratègies i els plans nacionals, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l’educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l’adaptació a aquest, la reducció dels efectes i l’alerta primerenca, Postprint (published version)

This paper addresses the limitations of virtual inertia provision from grid-forming interconnected renewable energy sources considering AC grid and resources dynamics. The instantaneous power that the grid-forming can provide is limited by the DC link power availability, which depends on the resource dynamic power capacity and the energy stored in the DC link. Here, an analysis of the maximum virtual inertia a renewable source can provide is presented. From this analysis, a criterion to select the maximum virtual inertia able to be provided from the converter taking into account the time response of the renewable source, the DC link stored energy and the AC network inertia, is derived., This work was supported by the European Union’s Horizon 2020 Research and Innovation Program [Powering System Flexibility in the Future through Renewable Energy Sources (POSYTYF) Project] under Grant 883985. The work of Oriol Gomis-Bellmunt was supported by the Institució Catalana de Recerca i Estudis Avançats (ICREA) AcademiaProgram. Eduardo Prieto Araujo is aSerra Hunter Lecturer. The work of Jaume Girona Badia was supported by FI-SDUR grant., Postprint (published version)

This paper presents a new approach for protecting microgrids based on multi-agent systems and using the angular variation of currents. The multi-agent system was structured using a congregation organization with three agents: section agent, circuit breaker agent, and switch agent. In this proposal, section agents are located at the microgrid lines, obtaining the difference in current angles between the buses. Then, if a threshold is reached, it sends a trip signal to the circuit breaker agent. This agent is responsible for receiving the trip signals and determining the correct line to be isolated. Finally, the switch agent is responsible for monitoring the microgrid operating scenario and updating the adequate thresholds of the agents. The proposed method was assessed firstly using simulations in PSCAD. Next, the multi-agent strategy was embedded into a lowcost microcontroller unit and tested through hardware-in-theloop (HIL) experiments using the Real-Time Digital Simulator (RTDS). The experimental and simulation results indicated the feasibility of the proposed method., This work was support in part by the São Paulo Research Foudation (FAPESP) [Grant Number 2021/04872-9], The Brazilian National Council for Scientific and Technological Development (CNPq) [Grant Number 134179/2020-0], and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) [Finance Code 001]., Postprint (author's final draft)

The GENIUS project aims at investigating the use of innovative digital tools to ease the ecological transition of industrial facilities. One of these technologies is a battery pack based on 2nd life batteries from electric trucks. This publication present the advances about the development and integration of this 2nd life battery pack in the pilot area of GENIUS project, as well as its interaction with the overall technological solution developed in the project. The testing, modeling and repurposing of batteries are key aspects on batteries’ circularity and the deployment of other innovations such as hybrid storage solutions., Postprint (published version)

Energy costs can represent a large portion of the total production costs, and therefore, any changes in electricity tariffs can have a significant impact on profitability. This paper analyses how different types of electricity tariffs can affect the scheduling of a case study model, in particular, how time-of-use tariffs and realtime-pricing tariffs affect the single-machine scheduling problem of a production process with the introduction of the energy vector in the optimization cost. The influence of tariffs is examined, and their impact on optimal production scheduling is evaluated from an approach in demand response price-based programs, for ensuring cost-effectiveness while looking at the carbon footprint of the industrial process. The results indicate that the cost improvement of one tariff over the other is not consistent across all time periods. Meanwhile, the carbon footprint is reduced with a real-time-pricing tariff, since the real-time-pricing mechanism and the generation mix of fossil fuel technologies are positively correlated., This work has been supported by the FLEX4FACT project, funded by the European Union under the Horizon Europe research and innovation programme, under the grant agreement number 101058657. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Eduard Bullich-Massagué is lecturer of the Serra Húnter programme., Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables, Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables::12.2 - Per a 2030, assolir la gestió sostenible i l’ús eficient dels recursos naturals, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energètica, Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The control role of power converters conforming an HVDC link is generally static, i.e. changing the control role of the power converters is not considered in the power system operation. In this paper a lab-scaled demonstration on the control role reconfiguration while an AC/DC/AC link is in operation is presented., Part of this work was carried out in the context of a UNITE.H2020 project pilot activity, investigating models for Research Infrastructure sharing between partner HEIs. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017408 and the project AGISTIN (ID:101096197) on the call HORIZON-CL5-2022-D3-01 funded by the European Union., Postprint (author's final draft)

Large electrical transmission networks are susceptible to undergo very large blackouts due to cascading failures, with a very large associated economical cost. In this work we propose segmenting large power grids using controllable lines, such as high-voltage direct-current lines, to reduce the risk of blackouts. The method consists in modifying the power flowing through the lines interconnecting different zones during cascading failures in order to minimize the load shed. As a result, the segmented grids have a substantially lower risk of blackouts than the original network, with reductions up to 60% in some cases. The control method is shown to be specially efficient in reducing blackouts affecting more than one zone., Peer Reviewed, Postprint (published version)

This paper discusses that the existing grid codes on the fault ride-through (FRT) opera-tion have certain complications to be implemented in the grid-forming (GFM) converters.In particular, they do not provide a clear guidelines on the suitable prioritization of thepositive- and negative-sequence currents needed for a desired voltage profile during FRToperation. To address this challenge, two FRT controls which are based on (i) voltagebalancing priority, and (ii) voltage magnitude priority are investigated for GFM convert-ers. The former gives priority to the elimination of negative-sequence voltage, while theremaining converter’s capacity is used to increase the positive-sequence voltage. On thecontrary, the latter prioritizes the increase of positive-sequence voltage, while it tries toreduce negative-sequence voltage by using the remaining converter’s capacity. The dynamicperformances of these two FRT controls are thoroughly discussed, and the simulationresults show that depending on the desired voltage profile during fault, one of them canbe implemented., Peer Reviewed, Postprint (published version)