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Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.

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 arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.

The current transition towards renewable energies has led to an increased utilization of Photovoltaic (PV) sources and battery energy storage systems to complement the PV panels. To facilitate energy transfer among PVs, batteries, and loads, multiple converters are required. Thus, this transformation in the energy system has resulted in an increase in converter-interfaced elements. Within this context, three-port converters allow for replacing multiple converters with a single one. These three-port converters use a high-frequency AC resonant link for the bidirectional transfer of energy across the different ports. This architecture uses multiple switches and has a variable operating frequency. These characteristics make the simulation of these converters computationally heavy. Thus, averaged models are required, especially for simulating multiple converters connected in parallel or composing a microgrid. In this paper, an averaged model for this type of converter is developed. The methodology is first demonstrated and applied to a two-port DC/DC converter, and subsequently extended to the three-port DC/DC/DC version. Afterwards, control strategies for three-port DC/DC/DC converters are proposed based on the elements connected to their ports. The developed model for three-port DC/DC/DC converters is then implemented in an islanded DC microgrid to demonstrate their parallel operation. The proposed developed averaged models and the test DC microgrid are implemented in MATLAB/Simulink.

This paper presents an investigation into the virtualization of substation protection IED functions using a sophisticated co-simulation environment that integrates virtual intelligent electronic devices (vIEDs) with a real-time power grid simulation. Anchored by the IEC 61850 protocol, this study constructs a virtualized IED framework, emphasizing the encapsulation of protection schemes using the example of different types of overcurrent protection within a containerized vIED. Using open-source software, this study aims to replicate the communication and functional aspects of physical IEDs. This study uses a co-simulation environment that couples virtualized network components with a real-time power grid simulation to validate the vIEDs against real substation hardware. Simulation results from induced short-circuit events confirm the operational congruence of the vIEDs with their physical counterparts, demonstrating their potential to serve as cost-effective and adaptable testbeds for substation automation. This paper concludes that virtualized IEDs represent a cost-effective, flexible alternative for substation automation testing, with future research directed towards increasing the functional complexity and real-world applicability of these virtual systems.

Three LED drivers which can be used for illumination, but whose main task is the transmission of information (data) via the light of the LEDs, are explored in this paper. The converter circuits need no capacitors for the energy transformation and avoid an inrush current. The lack of necessity of electrolytic capacitors reduces cost and space. Dimming the illumination is also easy to achieve. The control concept of the converters and the generation of pulsing of the LEDs for transmitting the information (data) are explained. The converters can also be expanded to more stages to drive more LEDs with different types of information. All three converters are explained in detail; all presented circuits are built up and simulated with LTSpice. Several data transmission concepts are applied and demonstrated through simulations.

In this paper an efficient, compact, and cheap power source design for an off-grid PV water pumping system is investigated. The proposed system consists of a PV array, battery, three-port converter (TPC), three-phase voltage source inverter, and induction motor pump. Power is extracted from PV sources during the daytime and used to charge batteries through the three-port converter, then used later to supply load during the nighttime. An intelligent MPPT method is used to obtain PV maximum power; a jellyfish optimization technique with different control algorithms is used to optimize and tune controllers’ parameters among the system. Different modes for the TPC are discussed depending on PV power availability. The proposed system is simulated to assess system performance under different conditions; also the system is efficient with reduced number of components than conventional converters. A complete unified power management over PV input port, battery port, and load port has occurred for all operation modes. At all operation modes, the system has been feeding load without any unmet loads. A real case study in Al-Kharijah oasis is studied and simulation results are listed; for the Dom case DC bus ripple factor voltage percentage equals 0.8%, in the Dim case equals 3%, and in the Siso mode equals 9%.

The economy of South Asia is experiencing growth, yet it faces constraints due to heavy reliance on fossil fuels and frequent power outages. Access to diverse energy sources, particularly electricity, is crucial for sustaining this growth. One feasible solution involves neighbouring countries engaging in the trade of renewable electrical energy. Hydropower stands as one of the many energy sources available in South Asia. However, sectorial constraints pose significant challenges to energy trade initiatives. This study utilises the Analytic Hierarchy Process (AHP) to evaluate Nepal’s readiness and identify obstacles to its cross-border energy trade with India and Bangladesh. A comprehensive analysis of these obstacles is imperative for formulating effective strategies and policies. Additionally, this study offers recommendations for enhancing preparedness and resolving issues related to energy trading, which may apply to similar cross-border situations. This study ranks energy trading obstacles with neighbouring nations using the AHP, offering key insights for stakeholders and policymakers. Using a non-probabilistic purposeful sampling technique, 25 expert respondents from the energy sector and prominent academicians were selected as part of the data collection procedure. At every level of the interview process, their perspectives were invaluable in guaranteeing a thorough and rigorous investigation.

The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Meanwhile, if an increment in the ICPT price signal can be provided to electricity providers and consumers, early preparation for operation budgeting can be realised, and energy management program development can be properly prepared. Due to this reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia that will benefit the stakeholders. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed using the mean absolute percentage error (MAPE). In light of our findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the moving average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July–December 2022 and January–June 2023 is MYR 0.21/kWh for the ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for forecasting, the ICPT price is expected to drop in the next announcement. The findings of this study may have a positive impact on the sustainability of the energy sector in Malaysia.

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analyzed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic (PV) plant distributed on some modular shelters which serves to power the same routes, is carried out. The resulting system has a power of the order of 190–200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated.

This paper proposes a single-stage direct AC to high-frequency (HF) AC resonant converter based on LLC configuration for induction heating (IH) systems or HF applications. Unlike conventional converters for IH systems, the proposed topology converts the utility frequency to HF AC in a single stage without using a DC link inductor and capacitors and takes the advantages of LLC configuration. Additionally, it improves the power factor to 0.9–1, lowers the THD (3.2% experimentally), and protects against the high-frequency components. An embedded control scheme was designed to keep the HF current oscillating at a resonant frequency, ensuring zero-voltage switching. The operating principle of the proposed topology was investigated using mathematical equations and equivalent circuits. Finally, it was verified using computer simulation, and an experimental prototype of 1.1 kW was developed to demonstrate the proposed topology’s uniqueness.

In the context of the energy transition, the share of new loads such as charging infrastructure for electromobility and electric heat pumps as well as feed-ins such as photovoltaic systems will steadily increase. This results in an increased degree of complexity for strategic network planning. In particular, the power flow analyses for the dimensioning of transformers and lines per network level currently still require different methods for the correct dimensioning of these equipment. They need to be carried out in separate data sets. For the dimensioning of the equipment simultaneity factors are predominantly used for realistic load assumptions in strategic network planning. These simultaneity factors and resulting load assumptions are determined from the planning perspective of the transformers and from the planning perspective of the lines per network level to be able to dimension the corresponding equipment. This results in different power flow results for the analysis and evaluation of different network levels in particular. This contribution presents a new concept for network modelling in which the simultaneity of the different planning perspectives of the different network levels results from a single power flow calculation in a coherent data set.

This article addresses the need for a standardized method to measure power quality in railroad systems, which differ from distribution and transmission networks. It evaluates the applicability of existing standards in detecting variations and short-term disturbances in railroad networks powered by the 50/60 Hz AC grid or the 2 × 25 kV AC network used for high-speed trains. The objective is to propose a standardized algorithm capable of accurately identifying disturbances to assess power quality on railway traction substations. A new method is proposed to characterize voltage imbalances more precisely. Practical demonstrations confirm that a short integration period, as used in existing standards, provides a more accurate estimation of disturbance amplitude and duration. Field experiments validate the proposed solution, embedded in equipment installed on the 225 kV line supplying the 2 × 25 kV AC substation for high-speed rail. Comparative analysis of results obtained during high-speed train journeys confirms the algorithm’s potential to aid standards committees in reviewing and updating existing standards, as well as expediting the creation, approval, and implementation of new standards for railway installations. Experimental comparisons of other power quality parameters, such as frequency and voltage harmonics, also underscore the algorithm’s utility in railway power quality assessment.

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm’s performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results.

Mismatched power generation is a serious issue in PV systems, resulting from unequal power generation between PV components. Solutions have been proposed to reduce or eliminate the mismatch concern. One practical strategy is individually harvesting the maximum power from each PV component; the more distributed MPPT is applied to a finer level, the more power can be obtained. This study proposes three-input single-output boost converters that are employed to effectively increase PV power generation and significantly reduce mismatch issues between the PV submodule (PV SM). Each boost converter will be controlled to harvest the maximum power from a group of PV cells inside a single PV module. The outputs of the three boost converters are connected in series to provide higher output voltage for grid integration. The cascaded power converters are linked with a forwarding diode to provide a protection feature for the system and prevent the reverse current from harming the PV module. On the grid side, a single-phase Voltage Source Inverter (VSI) is used to convert the DC power from the PV module to sinusoidal AC power. The performance of the suggested inverter has been confirmed through experimental tests.

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R.

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into its constituent components, allowing us to find out the impact of the system degradation, maintenance, and weather variations from the inherent seasonal patterns. Our results reveal significant trends in PV system performance, indicating the need for proactive maintenance strategies to mitigate degradation effects. Moreover, we quantify the impact of changing weather patterns and provide recommendations for optimising the system’s efficiency based on seasonally varying conditions. Hence, this study not only advances our understanding of the intricate variations within PV system performance but also provides practical guidance for enhancing the sustainability and effectiveness of solar energy utilisation in both residential and commercial settings.

The current formal common denominator of the electricity supply system in Europe has been the Harmonized Electricity Market Role Model (HEMRM) set up by ENTSO-E, ebIX, and EFET at the turn of the millennium; it introduced the concept of de-coupling and the vertical structuring of the system into the previously vertically integrated system. Since then, within demonstration projects, the system has been undergoing further changes in a controlled environment, generating bottom-up energy, caused by new technologies, business models, and new players, and extending the concept of the system to the level of energy communities and prosumers. Therefore, this paper proposes a coherent approach to the extension of HEMRM to the lowest levels in both the grid and market segments—full harmonization. This entails further structuring of both segments downwards and applying the principles of vertically nested subsystems—a system of systems approach—to a unit functional level of the electricity system, which can be the prosumer itself. At the lowest levels, the de-coupled system becomes coupled; additionally, it cross-sects with other energy vectors. Complete harmonization reduces the number of system and market segments and represents system standardization, leading to both subsystem and system-wide optimization. Prerequisites for it include the automated trading of flexibilities by the prosumers and implicit trading of energy transfer capacities along the distribution grids. The energy reservoirs, implicit and explicit, short-term, and long-term, play a vital role in techno-economic balancing.

Magnetostrictive alloys hold great promise for Energy Harvesting applications due to their inherent durability. However, their implementation often results in usable voltage ranges that fall significantly below common electronic standards like 1.6, 3.3, and 5 volts. Consequently, the utilization of electronic circuits becomes essential to amplify the voltage and enhance energy conversion efficiency. Over the past few decades, numerous conversion techniques have been devised for other intelligent materials, such as piezoelectrics, some of which have even made their way into commercial products. Surprisingly, there is a dearth of specialized techniques, if not a complete absence, tailored to magnetostrictive devices. Among potential solutions, a suitable AC–DC Boost converter stands out as a highly promising candidate for addressing this challenge, but this solution has never been fully characterized. Then, this paper presents thorough experimental validations of such a converter, driven by a real-time Arduino board equipped to measure source time periods and operate under various conditions. We present several cases demonstrating the circuit’s substantial potential for enhancing energy harvesting from magnetostrictive materials.

This paper presents the techno-economic feasibility of using grid-connected PV hybrid systems to supply power in large grid-dependent academic institutions. The study was conducted using the administration building of Moi University in Kenya. The power consumption profile of the building was collected using a PCE-360 power analyzer. The peak load demand was found to be 60 kW. Using random variability constants of 4% for day-to-day and 4% time-step load variability, a peak demand of 70.58 kW was obtained, which was used in our simulation. The solar radiation and temperature data for this site were collected from the weather station of the university. The hybrid system was simulated using HOMER Pro software. It was found from the simulation results that the optimal system was the solar PV/grid without battery storage, which had a levelized cost of energy (LCOE) of KSH 8.78/kWh (USD 0.072), net present cost (NPC) of KSH 27,974,492 (USD 230,813), capital expenditure (CAPEX) of KSH 26,300,000 (USD 216,997), and a simple payback period (SPBP) of 5.08 years for a 25-year life span. This system, when compared to the existing grid, showed an 83.94% reduction in the annual electricity bill of the administration building. These results demonstrate a reduction in energy cost by a renewable energy fraction of 67.1%.

The dynamics of electrical systems have changed significantly with the increasing penetration of non-conventional loads such as hydrogen electrolysers. As a result, detailed investigations are required to quantify and characterize these loads’ effects on the dynamic response of interconnected synchronous machines after being subjected to a disturbance. Many studies have focused on the effects of conventional static and dynamic loads. However, the impact of hydrogen electrolysers on the stability of power systems’ rotor angles is rarely studied. This paper assesses the effect of proton exchange membrane (PEM) electrolysers on small-disturbance rotor-angle stability. Dynamic modelling and the control of a PEM electrolyser as a load are first studied to achieve this. Then, the proposed electrolyser model is tested in the Amercoeur plant, which is part of the Belgian power system, to study its effect on the small-signal rotor-angle stability. Two approaches are considered to examine this impact: an analytical approach and time-domain simulations. The analytical approach consists of establishing a state-space model of the Belgian test system through linearisation around an operating point of the non-linear differential and the algebraic equations of the synchronous generators, the PEM electrolyser, the loads, and the network. The obtained state-space model allows for the determination of the eigenvalues, which are useful to evaluate the effect of the PEM electrolyser on the small-signal rotor-angle stability. This impact is investigated by examining the movement of the eigenvalues in the left complex half-plane. The obtained results show that the PEM electrolyser affects the electromechanical modes of synchronous machines by increasing their oscillation frequencies. The results also show that the effect of the electrolyser on these modes can be improved by adjusting the inertial constant and the damping coefficient of the synchronous machines. These results are consolidated through time-domain simulations using the software Matlab/Simscape from the version MatlabR2022a-academic use from Mathworks.

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

In this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines faulted), islanding events, sudden load variations, and generation tripping. The proposed LSTM-based method was trained and tested using the signals produced by the events simulated in a study system with distributed generation sources via PSCAD®. Then, noise with different levels was added to the testing set for a thorough assessment, and the results were compared with other well-known methods such as convolutional and simple recurrent neuronal networks. The LSTM-based method with multi-input proved to be effective for event classification, achieving remarkable classification performance even in noisy conditions.

In young Maxwell’s eyes, electricity, magnetism and fluid mechanics present analogies that let the first be mathematically grasped in a unitary way similarly to the latter. This entry sketches the first steps of James Clerk Maxwell’s unitary view of electromagnetism and fluid mechanics, with long-lasting effects on understanding the physical world. In his very first paper on the subject, Maxwell interpreted Faraday’s concept of lines of force of both electricity and magnetism as filaments of a flowing fluid. That is, Maxwell suggested to unify the previously distinct fields of electricity and magnetism, considering them as different aspects of the same entity, the ‘electromagnetic field’, which is mathematically described by four partial differential equations. In the literature there are several comprehensive works on Maxwell’s mature exposition of his theory; however, the aim of this entry contribution is rather more limited. Indeed, it is restricted to examining the contributions of Maxwell’s first paper towards developing the idea of electric current as the motion of an incompressible fluid. Thus, this entry highlights the strong connections of Maxwell’s epistemological view with the ‘mechanistic’ approach of the time to every aspect of physical phenomenology. For this purpose, this entry mentions some historical context surrounding the emergence of Maxwell’s innovative concepts.

A significant electromotive force is induced in the rotor circuit of a doubly fed induction generator (DFIG) due to its high vulnerability to grid faults. Therefore, the system performance must be increased with appropriate control actions that can successfully offset such abnormalities in order to provide consistent and stable operations during grid disturbances. In this regard, this paper presents a solution based on a combination of an energy storage-based crowbar and a rotor-side crowbar that makes the effective transient current and voltage suppression for wind-driven DFIG possible. The core of the solution is its ability to restrict the transient rotor and stator overcurrents and DC-link overvoltages within their prescribed limits, thereby protecting the DFIG and power converters and improving the system’s ability to ride through faults. Further, the capacity of an energy storage device for transient suppression is estimated. The results confirmed that the proposed approach not only kept the transient rotor and stator currents within ±50% of their respective rated values in severe system faults but also limited the DC-link voltage variations under ±15% of its rated value, achieving transient control objectives precisely and maintaining a stable grid connection during the faults.

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets.

Successful organisations prioritise product quality and customer satisfaction. Non-financial indicators are crucial for measuring performance, requiring specific financial and technology management knowledge. Effective knowledge management and entrepreneurial activity significantly impact performance, vital to the country’s economic factors. Electricity is crucial to society’s development. Renewable energy sources such as solar, wind, hydropower, and biomass can generate sustainable electricity. Managing environmental, social, and economic aspects is essential for sustainable societal and virtual development. In this study, the central element of novelty is associated with the dependent variable Nominal Labour Productivity per Employee. This research shows that effective knowledge management impacts a company’s business performance. Based on secondary data from various sources, we have used factor analysis to assess the interrelationship between the factors and econometric dimensionalities. Accompanied by this econometric approach, the research methodology aims to present hybrid models based on econometric techniques and artificial intelligence (AI) networks. Based on the principal component method analysis results, we show the interdependence of 30 variables in the micro and macro environment. The new components of the correlated variables show how knowledge and innovation are related to the economic performance of society, and nominal employee productivity is a valuable indicator for measuring economic efficiency. Nevertheless, AI, a knowledge management product, provides helpful comments on the econometric results.

This paper introduces a novel power supply voltage adjustment strategy that can determine the optimum voltage value based on the amount of absorbed power. The novel automatic voltage adjustment technique was called inverse maximum power point tracking (iMPPT). The proposed control strategy consists of a modified maximum power point tracking (MPPT) algorithm (more precisely the P&O method). In this case, the modified MPPT technique establishes the minimum value of the input absorbed power of a consumer load served by a switched-mode power supply (SMPS). The iMPPT adjusts the input power by modifying the input voltage of the main power supply. The served loads are connected to the variable power supply via an interfacing power electronics converter that performs the automatic voltage regulation function (AVR). The optimal value of the input voltage level can be achieved when the input power of the automatic voltage regulation converter is at a minimum. In that case, the energy conversion efficiency ratio is at a maximum, and the overall losses related to the front-end power stage are at a minimum. The proposed technique can also be considered a Maximum Efficiency Tracking (MET) method. By performing the inverse operation of a maximum power point tracking algorithm on the input demanded power of a switched mode power supply (SMPS), the optimum input voltage level can be determined when the maximum energy conversion ratio (related to a given load level) is achieved. The novel proposed iMPPT method can improve the energy conversion ratio from 85% up to approximately 10% in the case of an output power level of 800 W served by a synchronous buck converter at the input voltage level of 350 V. The total amount of recovered power in this situation can be approximately 100 W.

This paper aims to enhance the efficiency of biogas plants for the production of electrical and thermal energy by optimizing substrate selection and digester heating techniques. The study presents a comprehensive project of a biogas plant, including all necessary installations and a detailed methodology for equipment selection. Specific substrates used include animal flour, corn silage, and molasses, each chosen for their high dry matter content and biogas production efficiency. The digester heating technique was analyzed to maintain optimal fermentation temperatures, ensuring a stable and efficient biogas production process. The projected daily biogas production is estimated to be 5688 m3. The analysis shows that maintaining a constant digester temperature significantly improves biogas yield. Seasonal variations in thermal energy requirements were identified, highlighting the need for effective insulation and heating mechanisms, particularly during colder months. Additionally, the implementation of advanced heating techniques demonstrated a reduction in overall energy consumption for maintaining the desired digester temperature. This research provides valuable insights for the design and operation of efficient biogas plants. By optimizing substrate mixtures and digester heating, the study contributes to sustainable energy production, reduced greenhouse gas emissions, and enhanced biogas plant efficiency, supporting both environmental protection and economic development.

Power system transformation can unleash wide-ranging effects across multiple, frequently interlinked dimensions such as the environment, economy, resource systems, and biodiversity. Consequently, assessing the multidimensional impacts of power system transformation, especially under rapid transitions, has become increasingly important. Nonetheless, there is a gap in the literature when it comes to applying such an analysis to a Mediterranean country facing structural socioeconomic challenges. This paper explores the potential multifaceted implications of rapidly decarbonizing the Greek power sector by 2035, focusing on the local-level consequences. The evaluation criteria encompass the cost-optimal power mix, power costs, land use, biomass utilization, GDP, and employment. In this effort, a technology-rich cost optimization model representing Greece’s power sector is linked to a global Computable General Equilibrium (CGE) macroeconomic model focusing on the Greek economy. The results indicate that a fast decarbonization of the Greek power sector could trigger positive socioeconomic consequences in the short- and medium-term (GDP: +1.70, employees: +59,000 in 2030), although it may induce negative long-term socioeconomic effects due to increased capital investment requirements. Additionally, the impact on land use may only be trivial, with the potential to decrease over time due to the de-escalation of biomass power generation, thereby reducing the risk of harming biodiversity.

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of 1.1×10−5. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled.

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone’s electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Tajikistan holds the eighth position globally in terms of hydropower potential, estimated at 527 terawatt-hours (TWh), with a technically exploitable capacity of 317 TWh. Only 4–6 percent of this immense potential is currently utilized. In this paper, employing a combination of the Johansen cointegration test, vector autoregression, and the Granger-causality test on annual data from 1993 to 2021, we examine the causality relationship between electricity production and key macroeconomic variables, including gross domestic product (GDP), GDP per capita, exports, imports, final consumption, capital investment, and employment, in Tajikistan. The empirical findings reveal a positive unidirectional causality from electricity production to exports and imports. A positive bidirectional or feedback causality is found between electricity production and variables such as GDP, GDP per capita, final consumption, and employment. No causality relationship between electricity production and variables such as trade openness and capital investment is observed. The exploration of complex causal relationships between electricity production and key macroeconomic variables in Tajikistan, as revealed in this study, offers a modest yet meaningful addition to academic discourse. It presents insights that may inform policymakers and stakeholders, albeit with a recognition of the limitations inherent in the findings. These insights could potentially guide the formulation of sustainable development strategies and shed light on the underutilized potential of the country’s hydropower resources.

Improving the energy efficiency of buildings is one of the main challenges facing Europe in the context of climate neutrality policy. In this article, the authors decided to investigate the impact of thermal insulation of building envelopes and the type of ventilation on the energy efficiency of multi-family buildings located in different Polish cities. In accordance with EU directives, economic analyses of the costs incurred during the lifecycle and calculations of CO2 emissions were carried out for the analyzed facilities. It was determined what measures need to be taken under Polish climatic conditions in order to meet the requirements for improving the energy efficiency of multi-family buildings. The multi-criteria analyses presented in this article provide a voice in the discussion of the issues related to the achievement of the energy performance of buildings with almost zero primary energy demand (nZEB). Based on the conclusions of the research conducted, it was possible to develop general recommendations for investors and designers of multi-family residential buildings characterized by energy efficiency and reduced environmental impact.

A lack of fresh water is one of the most significant problems currently affecting humanity. Water scarcity also affects industries, with the mining industry being one of the most affected. One possible solution to water scarcity is the recirculation of water. Water in mining is usually treated with physicochemical methods, but in metallurgical processes, reagents are added, accumulate until reaching the point of saturation, and are often not successfully removed. In this sense, electrocoagulation has shown great efficiency in the treatment of organic contaminants, heavy metals, and metallic ions, and was applied in this study to eliminate ions and undesirable organic compounds present in mining–metallurgical process water. Furthermore, this process has shown great efficacy in relation to toxic metals like arsenic because their presence reduces the efficiency of other processes such as flotation. In this study, two types of electrodes were used: stainless steel and aluminum. The best results were achieved with stainless steel electrodes, which were able to eliminate 90% of copper ions in water. The turbidity of the water during the process was measured to determine the amount of solid present in the water, and a reduction of around 95% was observed when using aluminum electrodes. The sedimentation of clots occurred in two stages: Firstly, the coagulant was formed to trap organic matter as its size increased, until a particle size that was sufficient for settling was achieved. A zero-order kinetic model was fit for this stage of the process. Secondly, the formed clots continued to settle, and a second-order kinetic model was fit for this stage. Flotation tests were carried out on the process and electrotreated water to evaluate the recovery of Zn, Pb, Ag, and Au. An increase of 1.5% was found for gold, and an increase of 2% was found for silver, while a significant improvement was identified for zinc, augmenting recuperation by 30% when electrotreated water was used. For lead, no considerable change in recovery was observed in either form of water. The formed clots were analyzed using Scanning Electronic Microscopy, and we found that metal ions were trapped in the clots. This study demonstrates the potential of electrocoagulation for clarifying mine water, which is ordinarily very difficult to clarify.

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies.

We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction.

Wide-Area Power System Stabilizers (WAPSSs) are damping controllers used in power systems that employ data from Phasor Measurement Units (PMUs). WAPSSs are capable of providing high damping rates for the low-frequency oscillation modes, especially the inter-area modes. Oscillation modes can destabilize power systems if they are not correctly identified and adequately damped. However, WAPSS communication channels may be subject to failures or cyber-attacks that affect their proper operation and may even cause system instability. This research proposes a method based on an optimization model for the design of a WAPSS robust to multiple permanent communication failures. The results of applications of the proposed method in the IEEE 68-bus system show the ability of the WAPSS design to be robust to a possible number of permanent communication failures. Above this value, the combinations of failures and processing time are high and they make it difficult to obtain high damping rates for the closed-loop control system. The application and comparison of different optimization techniques are valid and showed a superior performance of the Grey Wolf Optimizer in solving the optimization problem.

This paper investigates the potential of bidirectional charging using modular multilevel inverter-based reconfigurable battery systems via grid-parallel control. The system offers several advantages such as modularity, scalability, and fault-tolerance over conventional battery electric vehicle systems. It is designed for seamless integration with the grid, allowing bidirectional power flow and efficient energy storage. Within this study, the battery system is first simulated in Matlab/Simulink and later implemented into a hardware setup. Eventually, the simulation results and the measurements have been compared and evaluated. Thereby, startup sequences and constant current scenarios were investigated. It has been shown that the system is fully capable to charge and discharge the batteries in the grid-parallel connection, thus enabling bidirectional charging with close to full drive system power. The current total harmonic distortion complies with grid regulations and can potentially improve the grid quality. The proposed system offers significant potential for grid-integrated energy storage systems, addressing the challenges associated with renewable energy integration, grid stability, and energy management. In comparison to other publications on this topic, the proposed approach does not need additional dedicated power electronic hardware and has more degrees of freedom for current control.

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events.

In isolated power systems with very high instantaneous shares of renewables, additional inertia should be used as a complementary resource to battery energy storage systems (BESSs) for improving frequency stability, which can be provided by synchronous condensers (SCs) integrated into the system. Therefore, this paper presents a methodology to infer the system dynamic security, with respect to key frequency indicators, following critical disturbances. Of particular interest is the evidence that multiple short-circuit locations should be considered as reference disturbances regarding the frequency stability in isolated power grids with high shares of renewables. Thus, an artificial neural network (ANN) structure was developed, aiming to predict the network frequency nadir and Rate of Change of Frequency (RoCoF), considering a certain operating scenario and disturbances. For the operating conditions where the system frequency indicators are violated, a methodology is proposed based on a gradient descent technique, which quantifies the minimum amount of additional synchronous inertia (SCs which need to be dispatch) that moves the system towards its dynamic security region, exploiting the trained ANN, and computing the sensitivity of its outputs with respect to the input defining the SC inertia.

The pursuit of a carbon-neutral society has emerged as a global imperative in the face of escalating environmental challenges [...]

We propose a method for the fault passage indication of earth faults in resonant-earthed networks, based on phase current measurements alone. This is particularly relevant for electricity distribution systems at medium-voltage levels. The method is based on the relative magnitudes of the phasor changes in the phase currents due to the fault. It is tested for various network types and operation configurations by simulating the network in pscad and using the simulated currents as the input for an implementation of the method in matlab. In over-compensated networks, the method shows reliable detection of the fault passage, with good selectivity and sensitivity for both homogeneous and mixed (cable and overhead line) feeders. However, for the less common under-compensated systems, it has limitations that are described further in this study. The method has good potential for being cost effective since it requires only current measurements, from a single location, at a moderate sampling rate.

Bioenergy recovery from biomass by-products is a promising approach for the circular bioeconomy transition. However, the management of agri-food by-products in stand-alone treatment facilities is a challenge for the low-capacity food processing industry. In this study, the techno-economic assessment of a small-scale anaerobic digestion process was evaluated for the management of jabuticaba by-product and the production of biomethane, electricity, heat, and fertilizer. The process was simulated for a treatment capacity of 782.2 m3 y−1 jabuticaba peel, considering the experimental methane production of 42.31 L CH4 kg−1 TVS. The results of the scaled-up simulated process demonstrated the production of biomethane (13,960.17 m3 y−1), electricity (61.76 MWh y−1), heat (197.62 GJ y−1), and fertilizer (211.47 t y−1). Economic analysis revealed that the process for biomethane recovery from biogas is not profitable, with a net margin of −19.58% and an internal rate of return of −1.77%. However, biogas application in a heat and power unit can improve project feasibility, with a net margin of 33.03%, an internal rate of return of 13.14%, and a payback of 5.03 years. In conclusion, the application of small-scale anaerobic digestion can prevent the wrongful open-air disposal of jabuticaba by-products, with the generation of renewable energy and biofertilizer supporting the green economy toward the transition to a circular economy.

This paper presents a method for integrating industrial consumers owning compensation systems as alternative reactive power sources into grid operating processes. In remuneration, they receive a market-based provision of reactive power. The aim is to analyze the potential of reactive power compensation systems of industrial companies connected to medium-voltage (10 kV–30 kV) AC grids in order to increase the reactive power ability of distribution grids. Measurement methods and reactive power potential results of six industrial companies are presented to characterize the amount and temporal availability of their reactive power potential. The presented approach for using the decentralized reactive power potential is a centralized reactive power control method and is based on optimal power flow (OPF) calculations. An optimization algorithm based on linear programming is used to coordinate a reactive power retrieval tuned to the actual demand. The influencing quantities are the current grid status (voltage and load flow capacity reserves at grid nodes and power lines) and the current reactive power potential of the reactive power sources. The compensation impact of six measured industrial companies on an exemplary medium-voltage grid is shown by an application example.

The increase in the average global temperature is a consequence of high greenhouse gas emissions. Therefore, using alternative energy carriers that can replace fossil fuels, especially for automotive applications, is of high importance. Introducing more electronics into an automotive battery pack provides more precise control and increases the available energy from the pack. Battery-integrated modular multilevel converters (BI-MMCs) have high efficiency, improved controllability, and better fault isolation capability. However, integrating the battery and inverter influences the maximum DC charging power. Therefore, the DC charging capabilities of 5 3-phase BI-MMCs for a 40-ton commercial vehicle designed for a maximum tractive power of 400 kW was investigated. Two continuous DC charging scenarios are considered for two cases: the first considers the total number of submodules during traction, and the second increases the total number of submodules to ensure a maximum DC charging voltage of 1250 V. The investigation shows that both DC charging scenarios have similar maximum power between 1 and 3 MW. Altering the number of submodules increases the maximum DC charging power at the cost of increased losses.

Transportation electrification can significantly reduce carbon footprint and accelerate the modernization of aging electric infrastructure. In the U.S., the growing adoption of electric vehicles (EVs) will significantly impact the electrical grid and associated greenhouse gas emissions, but with significant differences between the balancing regions due to the diverse characteristics of their electrical grids. This work assesses the impacts associated with the increasing penetration of EVs in the U.S., considering the characteristics of the grid in the different regions, in order to discuss the needed strategies to maximize the future decarbonization benefits. The assessment considers the variation in generation mix profiles during the day in each region, as well as different charging profiles associated with home, work, and public charging. The results show that more ambitious policies for the increasing share of carbon-free generation in the regions with the highest emissions are needed, emphasizing incentives for the use of work and public charging, and ensuring effective management of the charging flexibility.

This paper presents a novel tool for electricity planning, based on an improvement of MuSIASEM (Multiscale Integrated Analysis of the Societal and Ecological Metabolism) by incorporating a new regional analysis of the electricity metabolism across levels. An analysis of Mexico illustrates this toolkit and shows that the industry sector has economic energy intensity (EEI) with 40.3 MWh/MMXN reaching a higher value than the commerce and services sector with 0.84 MWh/MMXN. Regarding the economic labor productivity (ELP) indicator (AV/h), the industrial sector with 208.5 TMXN/Kh reached a higher value than the commercial and services sector with 114.3 TMXN/Kh. Regarding the exosomatic metabolic rate (EMR), the household sector obtained 59.3 KWh/Kh, whereas the economic sector reached 2486.4 KWh/Kh. Disaggregation of the EMR indicator into economic sectors shows that the industrial sector reached 8.4 KWh/Kh and the commercial and services sector reached 0.10 KWh/Kh. The lack of complete data for the agricultural sector does not allow us to calculate EEI, ELP, and EMR indicators accurately. This innovative approach is useful for governance because it helps us to understand and reduce asymmetries across regions in terms of electricity consumption, resulting in more social equality and a better economic equilibrium across sectors and regions.