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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.

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.

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.

Digitalization has enabled the potential for artificial intelligence techniques to lead the power system to a sustainable transition by extracting the data generated by widely deployed edge devices, including advanced sensing and metering. Due to the increasing concerns about data privacy, federated learning has attracted much attention and is emerging as an innovative application for machine learning solutions in the power and energy sector. This paper presents a holistic analysis of federated learning applications in the energy sector, ranging from applications in generation, microgrids, and distribution systems to the energy market and cyber security. The following federated learning-based services for energy sectors are analyzed: non-intrusive load monitoring, fault detection, energy theft detection, demand forecasting, generation forecasting, energy management systems, voltage control, anomaly detection, and energy trading. The identification and classification of the data-driven methods are conducted in collaboration with federated learning implemented in these services. Furthermore, the interrelation is mapped between the categories of machine learning, data-driven techniques, the application domain, and application services. Finally, the future opportunities and challenges of applying federated learning in the energy sector will be discussed.

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.

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.

Recent studies have developed a power electronic device known as the power router, able to fully control the power flows over the connected power lines. The present paper aims to extend the application of power routers to an electrical grid and explore its characteristics and limitations. It defines the power router grid concept, including the conditions for interconnection and architecture; it proposes a mathematical modelling methodology, analyses its operational requirements, and explores the degree of flexibility the proposed grid architecture provides. Finally, two case studies are presented to show the application of the power router grid in a practical example.

Planning public electric vehicle (EV) charging infrastructure has gradually become a key factor in the electrification of mobility and decarbonization of the transport sector. In order to achieve a high level of electrification in mobility, in recent years, different studies have been presented, proposing novel practices and methodologies for the planning and operation of electric vehicles charging infrastructure. In this paper, the authors present an up-to-date analysis of the existing literature in this research field, organized by considering the perspectives and objectives of the principal actors/operators of the EV public charging infrastructure value chain. Among these actors, the electric vehicle, the charging operators and service providers, and the power system infrastructure (transmission and distribution system) are analyzed in depth. By classifying the reviewed literature based on this manifold viewpoints approach, this paper aims to facilitate researchers and technology developers in exploring the state-of-the-art methodologies for each actor’s perspective, and identify conflicting interests and synergies in charging infrastructure operation and planning.

This article proposes a novel methodology to evaluate the visual impact of high-voltage lines in urban areas based on photographic images. The use of photographs allows for calculating the overall aesthetic impact while eliminating the subjective factors of the observer. To apply the proposed methodology based on photographs, the impact of the position and angle where the photograph was taken was analyzed, and a sensibility analysis was carried out. Moreover, it was applied to an application case, and a comparison with results from a previous study of a visual impact was performed. The methodology shows good performance and a better resolution of the indicator.

This review provides the state of the art of energy management systems (EMS) and organizational structures of prosumers. Integration of renewable energy sources (RES) into the household brings new challenges in optimal operation, power quality, participation in the electricity market and power system stability. A common solution to these challenges is to develop an EMS with different prosumer organizational structures. EMS development is a multidisciplinary process that needs to involve several aspects of observation. This paper provides an overview of the prosumer organizational and control structures, types and elements, prediction methods of input parameters, optimization frameworks, optimization methods, objective functions, constraints and the market environment. Special attention is given to the optimization framework and prediction of input parameters, which represents room for improvement, that mitigate the impact of uncertainties associated with RES-based generation, consumption and market prices on optimal operation.

A modular battery-based energy storage system is composed by several battery packs distributed among different modules or parts of a power conversion system (PCS). The design of such PCS can be diverse attending to different criteria such as reliability, efficiency, fault tolerance, compactness and flexibility. The present paper proposes a quantitative and qualitative comparison among the most widely proposed PCSs for modular battery-based energy storage systems in literature. The obtained results confirm the high performance of those PCSs based on the parallel connection of different modules to a single point of common coupling, also identifying those based on modular multilevel cascaded converters as promising concepts according to the assumptions of the present paper.

Decision making to improve energy sustainability requires scientifically information based on sustainability. Since there are different sustainability targets and specific decision problems in this regard, hence to achieve these targets or solve these problems, relevant sustainability indicators are needed. In this study, is investigated and developed a variety of applicable indicators to enhance sustainable energy development index. Indeed, the main aim of this study is to present new effective indicators related to sustainable development goals, investigating the most important challenges related to energy sustainability. Additionally, this work is following to find the main gaps which are as obstacles in achieving energy sustainability for 12 different countries. To do this, firstly the required data from international energy agency (IEA) and world bank gathered and then is analyzed. Keywords: Energy sustainability, Indicators, Sub-indicators, Normalization

Electrification of mobility is paving the way in decreasing emissions from the transport sector; nevertheless, to achieve a more sustainable and inclusive transport system, effective and long-term planning of electric vehicles charging infrastructure will be crucial. Developing an infrastructure that supports the substitution of the internal combustion engine and societal needs is no easy feat; different modes of transport and networks require specific analyses to match the requirements of the users and the capabilities of the power grid. In order to outline best practices and guidelines for a cost-effective and holistic charging infrastructure planning process, the authors have evaluated all the aspects and factors along the charging infrastructure planning cycle, analysing different methodological approaches from scientific literature over the last few years. The review starts with target identification (including transport networks, modes of transport, charging technologies implemented, and candidate sites), second, the data acquisition process (detailing data types sources and data processing), and finally, modelling, allocation, and sizing methodologies. The investigation results in a decision support tool to plan high-power charging infrastructure for electric vehicles, taking into account the interests of all the stakeholders involved in the infrastructure investment and the mobility value chain (distributed system operators, final users, and service providers).

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Full electrification of the transport sector is a necessity to combat climate change and a pressing societal issue: climate agreements require a fuel shift of all the modes of transport, but while uptake of passenger electric vehicles is increasing, long haul trucks rely almost completely on fossil fuels. Providing highways with proper charging infrastructure for future electric mobility demand is a problem that is not fully investigated in literature: in fact, previous work has not addressed grid planning and infrastructure design for both passenger vehicles and trucks on highways. In this work, the authors develop a methodology to design the electrical infrastructure that supplies static and dynamic charging for both modes of transport. An algorithm is developed that selects substations for the partial electrification of a highway and, finally, the design of the electrical infrastructure to be implemented is produced and described, assessing conductors and substations sizing, in order to respect voltage regulations. The system topology of a real highway (E18 in Norway) and its traffic demand is analyzed, together with medium-voltage substations present in the area.

The integration of advanced measuring technologies in distribution systems allows distribution system operators to have better observability of dynamic and transient events. In this work, the applications of distribution grid measurement technologies are explored in detail. The main contributions of this review are: (a) a comparison of eight advanced measurement devices for distribution networks, based on their technical characteristics, including reporting periods, measuring data, precision, and sample rate; (b) a review of the most recent applications of micro-Phasor Measurement Units, Smart Meters, and Power Quality Monitoring devices used in distribution systems, considering different novel methods applied for data analysis; and (c) an input-output table that relates measured quantities from micro-Phasor Measurement Units and Smart Meters needed for each specific application found in this extensive review. This paper aims to serve as an important guide for researches and engineers studying smart grids.

Integration of renewable energy sources in the electrical power system is key for enabling the decarbonization of that system [...]

Electric Vehicles (EVs) have seen significant growth in sales recently and it is not clear how power systems will support the charging of a great number of vehicles. This paper proposes a methodology which allows the aggregated EV charging demand to be determined. The methodology applied to obtain the model is based on an agent-based approach to calculate the EV charging demand in a certain area. This model simulates each EV driver to consider its EV model characteristics, mobility needs, and charging processes required to reach its destination. This methodology also permits to consider social and economic variables. Furthermore, the model is stochastic, in order to consider the random pattern of some variables. The model is applied to Barcelona’s (Spain) mobility pattern and uses the 37-node IEEE test feeder adapted to common distribution grid characteristics from Barcelona. The corresponding grid impact is analyzed in terms of voltage drop and four charging strategies are compared. The case study indicates that the variability in scenarios without control is relevant, but not in scenarios with control. Moreover, the voltages do not reach the minimum voltage allowed, but the MV/LV substations could exceed their capacities. Finally, it is determined that all EVs can charge during the valley without any negative effect on the distribution grid. In conclusion, it is determined that the methodology presented allows the EV charging demand to be calculated, considering different variables, to obtain better accuracy in the results.

This article investigates the combined low voltage (LV) and medium voltage (MV) residential grid impact for slow and fast electric vehicle (EV) charging, for an increasing local penetration rate and for different residential slow charging strategies. A realistic case study for a Flemish urban distribution grid is used, for which three residential slow charging strategies are modeled: uncoordinated charging, residential off-peak charging, and EV-based peak shaving. For each slow charging strategy, the EV hosting capacity is determined, with and without the possibility of fast charging, while keeping the grid within its operating limits. The results show that the distribution grid impact is much less sensitive to the presence of fast charging compared to the slow charging strategy. EV-based peak shaving results in the lowest grid impact, allowing for the highest EV hosting capacity. Residential off-peak charging has the highest grid impact, due the load synchronization effect that occurs, resulting in the lowest EV hosting capacity. Therefore, the EV users should be incentivized to charge their EVs in a more grid-friendly manner when the local EV penetration rate becomes significant, as this increases the EV hosting capacity much more than the presence of fast charging decreases it.

Cities are at the center of the transition to a decarbonized economy. The high consumption of electricity in these urban areas causes them to be the main focus of greenhouse gas emissions. However, they have a high margin of capacity to increase energy efficiency and local energy generation. Along these lines, the smart urban management model has been proposed as a solution to the unsustainability of cities. Due to the global trend of population concentration in urban areas, cities tend to be representative of the population, energy consumption, and energy sustainability of their countries. Based on this hypothesis, this paper studied the relationship between the smart city model and the concept of energy sustainability. First, the research analyzed the relationship between urban population growth and energy sustainability; and then the self-consumption capacity of photovoltaic electricity of the main cities of the countries classified in the energy sustainability indicator (Energy Trilemma Index 2017) of the World Energy Council was analyzed. According to the results, the scope of action for self-consumption of photovoltaic electricity is broad and cities have the capacity to contribute significantly to the energy sustainability of their countries. Following the approach of other authors, the development of energy sustainability objectives and the installation of smart systems in distribution grids must be aligned with national objectives.

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

This paper describes a grid impact analysis of charging electric vehicles (EV) using charging curves with detailed battery modelling. A probabilistic method using Monte Carlo was applied to a typical Spanish distribution grid, also using mobility patterns of Barcelona. To carry out this analysis, firstly, an IEEE test system was adapted to a typical distribution grid configuration; secondly, the EV and its battery types were modeled taking into account the current vehicle market and the battery characteristics; and, finally, the recharge control strategies were taken into account. Once these main features were established, a statistical probabilistic model for the household electrical demand and for the EV charging parameters was determined. Finally, with these probabilistic models, the Monte Carlo analysis was performed within the established scenario in order to study the lines’ and the transformers’ loading levels. The results show that an accurate model for the battery gives a more precise estimation about the impact on the grid. Additionally, mobility patterns have been proved to be some of the most important key aspects for these type of studies.

This paper presents a novel architecture for rural distribution grids. This architecture is designed to modernize traditional rural networks into new Smart Grid ones. The architecture tackles innovation actions on both the power plane and the management plane of the system. In the power plane, the architecture focuses on exploiting the synergies between telecommunications and innovative technologies based on power electronics managing low scale electrical storage. In the management plane, a decentralized management system is proposed based on the addition of two new agents assisting the typical Supervisory Control And Data Acquisition (SCADA) system of distribution system operators. Altogether, the proposed architecture enables operators to use more effectively—in an automated and decentralized way—weak rural distribution systems, increasing the capability to integrate new distributed energy resources. This architecture is being implemented in a real Pilot Network located in Spain, in the frame of the European Smart Rural Grid project. The paper also includes a study case showing one of the potentialities of one of the principal technologies developed in the project and underpinning the realization of the new architecture: the so-called Intelligent Distribution Power Router.

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.

The paper proposes an innovative Energy Management System (EMS) that optimizes the grid operation based on economic and technical criteria. The EMS inputs the demand and renewable generation forecasts, electricity prices and the status of the distributed storages through the network, and solves with an optimal quarter-hourly dispatch for controllable resources. The performance of the EMS is quantified through diverse proposed metrics. The analyses were based on a real rural grid from the European FP7 project Smart Rural Grid. The performance of the EMS has been evaluated through some scenarios varying the penetration of distributed generation. The obtained results demonstrate that the inclusion of the EMS from both a technical point of view and an economic perspective for the adopted grid is justified. At the technical level, the inclusion of the EMS permits us to significantly increase the power quality in weak and radial networks. At the economic level and from a certain threshold value in renewables’ penetration, the EMS reduces the energy costs for the grid participants, minimizing imports from the external grid and compensating the toll to be paid in the form of the losses incurred by including additional equipment in the network (i.e., distributed storage).

Ensuring resilient operation and control of smart grids is fundamental for empowering their deployment, but challenging at the same time. Accordingly, this study proposes a novel methodology for evaluating resilience of Information and Communication Technology (ICT) systems for smart distribution grids. Analysing how the system behaves under changing operating conditions a power system perspective allows to understand how resilient the smart distribution grid is, but the resilience of the ICT system in charge of its operation affects the overall performance of the system and does, therefore, condition its resilience. With the aim of systematising the evaluation of ICT systems’ resilience, this study proposes to combine a standardized modelling of Smart Grids, the Smart Grid Architecture Model (SGAM), with a data structured diagram, the Entity Relationship Model (ERM). The architecture of smart distribution grids is analysed through SGAM. Then, their technical characteristics and functionalities are defined and represented in a ERM diagram. Finally, the attributes or properties of the system components are used to formulate resilience indicators against different types of disturbances. This methodology is then applied to analyse the resilience of a ICT platform being developed in EMPOWER H2020 project.