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Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error. [ABSTRACT FROM AUTHOR]

Abstract Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

This paper proposes a Nonlinear Backstepping Approach (NBA) to improve the control performance of a Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Generation System (WEGS) under parameter uncertainties and short circuits with fluctuations in the grid voltage. Both the rectifier and the three-phase inverter are controlled using the NBA scheme; this method ensures Maximum Power Point Tracking (MPPT), which is a very appealing control objective with unpredictable scenarios of wind speed, and regulates the active and reactive power flows to the electrical network under varying wind speeds. Also, an inverter was employed to control voltage of the DC bus and the powers. The regulator's stability is achieving using the Lyapunov approach. Simulation results with Matlab/Simulink confirm the efficiency of the presented scheme. The comparative analysis of the NBA with conventional Vector Controllers (VCs), under parameter deviations and for low voltage drop conditions, demonstrates the efficiency of the studied method. [ABSTRACT FROM AUTHOR]

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive simulation for understanding the influence of EV growth and its external factors on grid stability and offers insights into effective management strategies. To manage the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply–demand equilibrium, regulatory adjustments and subsidies can be implemented by the government. The Jawa-Madura-Bali (Jamali) electrical system, as the largest in Indonesia, is challenged with accommodating the rising number of vehicles. Following an analysis of Jamali's electricity supply using data from the National Electricity Company (RUPTL), simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, the numbers of internal combustion engine (ICE) and electric vehicles, initial charging times (ICT), slow and fast charging ratios, and BEV charge load curves are simulated. Scenario variables, including supply capacity growth rate, vehicle population growth rate, subsidy impact on EV attractiveness, ICT, and fast charging ratio, are subsequently simulated for the 2024–2029 period. Four key simulation outcomes are identified. The best-case scenario (scenario 1776) achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst-case scenario leads to a 23.12% EV percentage, necessitating 47,566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on the literature research are evaluated. This novel simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029, aiding the government in planning regulations and subsidies effectively. [ABSTRACT FROM AUTHOR]

Increasingly, electricity network managers, through their grid codes, require renewable energy production systems to participate in system services, which includes requirements such as the stability of these production systems, the quality of the energy injected into the networks, the ability to withstand voltage dips, etc. To meet these requirements, the use of appropriate commands for the control of the production systems is necessary. Various control methods have been proposed, among which direct torque control (DTC) stands out. However, several studies have highlighted the impact of parametric variations on this control method. The contribution of the work presented in this article is the improvement of DTC when combined with a fuzzy estimate applied to a wind production system based on an asynchronous machine. Robustness tests were simulated to highlight the sensitivity of this control to variations in the stator resistance of asynchronous machines. To make this command robust and stable, a fuzzy estimator was used with this command. The simulation results demonstrated that this combination (DTC with a fuzzy estimator) makes the wind system more stable. To assess the effectiveness of the proposed solution, the root mean square error index was used. [ABSTRACT FROM AUTHOR]

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the "weekly Naïve" benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management. [ABSTRACT FROM AUTHOR]

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large‐scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber‐attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop‐specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%–48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system. [ABSTRACT FROM AUTHOR]

Hybrid charging for Electric Vehicles (EVs) has been developed and put into use to increase the efficiency and sustainability of EV charging infrastructure. This paper proposes a solution that combines photovoltaic (PV) solar panels and the electrical grid to charge EV batteries. The major objective is to employ renewable energy sources as much as possible while still ensuring dependable charging. The suggested hybrid charging system employs intelligent control algorithms to manage the energy flow between the PV system and the grid. Under optimal sunny conditions, electricity from the PV panels is produced to charge the EV battery. By consuming the extra energy generated by the PV system, which is also delivered to the grid, the user may help the overall energy grid. The charging system automatically switches to the grid when the solar energy source is insufficient, ensuring uninterrupted charging and relieving EV customers of range anxiety. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources.

The Vineyard Wind offshore wind farm is going to be about 15 nautical miles out to sea from Martha's Vineyard. Especially when going from the five windmills of that experimental offshore wind farm of Block Island to the 62 of Vineyard Wind - and another 130 windmills they're planning for Vineyard Wind South. That's because these are the ones that are most economically viable right now, which is why you see fixed-bottom offshore wind turbines in the proposals for every offshore windfarm lease area. They have expertise in the actual equipment, understanding how to make an offshore wind farm happen, what surveys and observations you need to make beforehand.... But then you add the nuances of the ocean environment that we have, the continental shelf, the boulders, and the shallowness of the waters. [Extracted from the article]

Keywords: Electrical grid; infrastructure; green economy; renewables; climate change; climate policy; Greg Nemet EN Electrical grid infrastructure green economy renewables climate change climate policy Greg Nemet 307 311 5 11/18/21 20211101 NES 211101 What does the United States need to do to start a renewables revolution and get its electrical grid to 50, 60, or even 90 percent green energy? Nemet dives into whether there is anything we can learn from the experiences regarding photovoltaics that could apply to this nationwide effort to transition from fossil fuels to renewables. And storing electricity in big lithium batteries is becoming way more feasible than it once was, because battery prices have come down even more than solar - something like a 90-percent drop in lithium battery costs in the last 10 years. [Extracted from the article]

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works.

This study provides a comprehensive simulation for understanding the influence of EV growth and its external factors on grid stability and offers insights into effective management strategies. To manage the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid’s supply–demand equilibrium, regulatory adjustments and subsidies can be implemented by the government. The Jawa-Madura-Bali (Jamali) electrical system, as the largest in Indonesia, is challenged with accommodating the rising number of vehicles. Following an analysis of Jamali’s electricity supply using data from the National Electricity Company (RUPTL), simulations are constructed to model the grid’s demand side. Input variables such as Jamali’s population, the numbers of internal combustion engine (ICE) and electric vehicles, initial charging times (ICT), slow and fast charging ratios, and BEV charge load curves are simulated. Scenario variables, including supply capacity growth rate, vehicle population growth rate, subsidy impact on EV attractiveness, ICT, and fast charging ratio, are subsequently simulated for the 2024–2029 period. Four key simulation outcomes are identified. The best-case scenario (scenario 1776) achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst-case scenario leads to a 23.12% EV percentage, necessitating 47,566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on the literature research are evaluated. This novel simulation and its results provide insights into EV growth’s impact on the grid’s balance in one presidential term from 2024 to 2029, aiding the government in planning regulations and subsidies effectively.

Abstract Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large‐scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber‐attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop‐specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%–48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system.

A reduced power system's inertia represents a big issue for high penetration levels of renewable generation sources. Recently, load frequency controllers (LFCs) and their design have become crucial factors for stability and supply reliability. Thence, a new optimized multiloop fractional LFC scheme is provided in this paper. The proposed multiloop LFC scheme presents a two-degree-of-freedom (2DOF) structure using the tilt–integral–derivatives with filter (TIDN) in the first stage and the tilt–derivative with filter (TDN) in the second stage. The employment of two different loops achieves better disturbance rejection capability using the proposed 2DOF TIDN-TDN controller. The proposed 2DOF TIDN-TDN method is optimally designed using the recent powerful marine predator optimizer algorithm (MPA). The proposed design method eliminates the need for precise modeling of power systems, complex control design theories, and complex disturbance observers and filter circuits. A multisourced two-area interlinked power grid is employed as a case study in this paper by incorporating renewable generation with multifunctionality electric vehicle (EV) control and contribution within the vehicle-to-grid (V2G) concept. The proposed 2DOF TIDN-TDN LFC is compared with feature-related LFCs from the literature, such as TID, FOTID, and TID-FOPIDN controllers. Better mitigated frequency and tie-line power fluctuations, faster response, lower overshot/undershot values, and shorter settling time are the proven features of the proposed 2DOF TIDN-TDN LFC method. [ABSTRACT FROM AUTHOR]

In this study, a performance assessment and analysis of a 1 MW three-phase photovoltaic (PV) power station connected to the electrical grid of a factory in Morocco are presented. The main objective of this research is to assess the performance of the PV power station and analyze its efficiency, energy generation, and operational characteristics. To accomplish this, a combination of field measurements, data analysis, and simulation techniques are used. The study begins by providing an extensive overview of the PV power station, including a thorough description of its main elements such as solar panels, inverters, transformers, and grid interconnection infrastructure. Furthermore, field measurements are conducted to collect data on solar irradiance, ambient temperature, and PV system power, as well as electrical parameters such as voltage, current, and power factor. The findings of this study provide valuable insights into the performance and economic viability of the PV power station. These insights can serve as guidance for renewable energy stakeholders, investors, and policymakers, facilitating the development of sustainable solar energy projects and contributing to renewable energy targets. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources. [ABSTRACT FROM AUTHOR]

Increasingly, electricity network managers, through their grid codes, require renewable energy production systems to participate in system services, which includes requirements such as the stability of these production systems, the quality of the energy injected into the networks, the ability to withstand voltage dips, etc. To meet these requirements, the use of appropriate commands for the control of the production systems is necessary. Various control methods have been proposed, among which direct torque control (DTC) stands out. However, several studies have highlighted the impact of parametric variations on this control method. The contribution of the work presented in this article is the improvement of DTC when combined with a fuzzy estimate applied to a wind production system based on an asynchronous machine. Robustness tests were simulated to highlight the sensitivity of this control to variations in the stator resistance of asynchronous machines. To make this command robust and stable, a fuzzy estimator was used with this command. The simulation results demonstrated that this combination (DTC with a fuzzy estimator) makes the wind system more stable. To assess the effectiveness of the proposed solution, the root mean square error index was used.

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented. [ABSTRACT FROM AUTHOR]

Reaping the full benefits from cross-border interconnection typically requires reinforcement of national networks. When the relevant parts of the networks are complements, a lack of coordination between national transmission system operators results in investment below optimal levels in both interconnectors and national infrastructure. A subsidy to financially sustain interconnector building is not sufficient to restore optimality; indeed, even when possible, such subsidisation may have to be restrained so as not to encourage cross-border capacities that will not be fully utilised due to lack of investment in national systems. [ABSTRACT FROM AUTHOR]

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.

A universal analytical expression is obtained for the power of hydraulic turbines of small hydropower plants such as Francis, Pelton and Kaplan with fixed blades as a function of water flow. This expression allows us to predict the generation of active power of each of the turbines, depending on the adjusted water flow rate, which varies in the seasonal, monthly and daily periods of the year. It also allows you to solve the inverse problem, when it is necessary, depending on the load schedule dictated by the electrical system to which these small hydroelectric power stations are connected, to regulate the amount of water flow by changing the opening angles of the guiding devices of the hydraulic turbines. Studies on the proposed mathematical model containing a synchronous generator with electromagnetic excitation coupled to a Francis hydraulic turbine with a 60% change in water flow from q=1 (r.u) to q=0.4 (r.u). The value of the required active power of a small hydroelectric power station, i.e. power output of the generator will jam (either by the dispatcher, or by the value of the load schedule), i.e. it is an input value, and as the output value of the controller is the opening angle of the guiding apparatus of the turbine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low-voltage (LV) and high-voltage (HV) DC distribution systems are being investigated as alternatives due to the growth of DC distribution energy resources (DER), DC loads such as solar and wind power systems, and energy storage sources (ESSs). Furthermore, an HV/LV DC distribution system offers various advantages, including lower conversion losses, an easier connecting strategy for DC DERs, and less complex power management techniques. As renewable energy sources are increasingly incorporated into the electrical grid, it is important to create novel, effective approaches for connecting such sources and loads. It would hence be effective to merge DC distribution with AC distribution to fulfill the energy demands of both DC and AC consumers. To this end, this study proposes a multizone design with four buses: low-voltage direct current (LVDC), high-voltage direct current (HVDC), low-voltage alternating current (LVAC), and an electrical grid. A model of this system that covers crucial elements, including power systems, DER systems, and power electronic devices, to serve as a foundation for the analysis and design of this architecture is proposed. MATLAB/Simulink is used to conduct a simulation study to verify the performance of the proposed design. In this study, a hybrid electrical grid with an LVDC, HVDC, and LVAC distribution network test is used and implemented. Additionally, a transient and steady-state characteristic analysis of the test system is performed. [ABSTRACT FROM AUTHOR]

Maintaining the quality and reliability of electricity transmission lines is central to effective energy governance. However, transmission line siting is often a contentious policy decision since permitting and constructing lines may involve private and public property, residents and communities, and localized and national concerns. Yet, policy conflict in transmission siting across cases and over time has remained largely understudied. This article derives and tests hypotheses about policy conflict in the context of transmission lines completed or constructed between 2017 and 2018 in the United States. In exploring the full population of transmission lines, we find that a majority exhibit relatively low and moderate levels of conflict and attention rather than high levels. We further examine a subset of six of these cases that represent a range of conflict and attention intensity. We describe variation in the diversity of actors and frames, advocacy coalitions, and the volume of discourse associated with transmission line siting over time. As problems related to energy governance have become more complex, energy siting disputes are likely to remain a fruitful area for research on policy conflict. [ABSTRACT FROM AUTHOR]

A reduced power system’s inertia represents a big issue for high penetration levels of renewable generation sources. Recently, load frequency controllers (LFCs) and their design have become crucial factors for stability and supply reliability. Thence, a new optimized multiloop fractional LFC scheme is provided in this paper. The proposed multiloop LFC scheme presents a two-degree-of-freedom (2DOF) structure using the tilt–integral–derivatives with filter (TIDN) in the first stage and the tilt–derivative with filter (TDN) in the second stage. The employment of two different loops achieves better disturbance rejection capability using the proposed 2DOF TIDN-TDN controller. The proposed 2DOF TIDN-TDN method is optimally designed using the recent powerful marine predator optimizer algorithm (MPA). The proposed design method eliminates the need for precise modeling of power systems, complex control design theories, and complex disturbance observers and filter circuits. A multisourced two-area interlinked power grid is employed as a case study in this paper by incorporating renewable generation with multifunctionality electric vehicle (EV) control and contribution within the vehicle-to-grid (V2G) concept. The proposed 2DOF TIDN-TDN LFC is compared with feature-related LFCs from the literature, such as TID, FOTID, and TID-FOPIDN controllers. Better mitigated frequency and tie-line power fluctuations, faster response, lower overshot/undershot values, and shorter settling time are the proven features of the proposed 2DOF TIDN-TDN LFC method.

In this study, a performance assessment and analysis of a 1 MW three-phase photovoltaic (PV) power station connected to the electrical grid of a factory in Morocco are presented. The main objective of this research is to assess the performance of the PV power station and analyze its efficiency, energy generation, and operational characteristics. To accomplish this, a combination of field measurements, data analysis, and simulation techniques are used. The study begins by providing an extensive overview of the PV power station, including a thorough description of its main elements such as solar panels, inverters, transformers, and grid interconnection infrastructure. Furthermore, field measurements are conducted to collect data on solar irradiance, ambient temperature, and PV system power, as well as electrical parameters such as voltage, current, and power factor. The findings of this study provide valuable insights into the performance and economic viability of the PV power station. These insights can serve as guidance for renewable energy stakeholders, investors, and policymakers, facilitating the development of sustainable solar energy projects and contributing to renewable energy targets.

The edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.

UK plans to ban the sale of new diesel and petrol cars by 2030 to be replaced by electric vehicles (EVs). The question is, will the UK's electrical grid infrastructure ready for this change? This comparative study investigates the effect of UK green vehicles on the electrical grid and presents a new insight into improving their energy demand and carbon dioxide (CO2) emissions to the electrical grid. The results show that even when there is a very high level of market penetration of EVs, the overall effect on annual energy consumption may seem minimal. On the contrary, the effect that EVs may have on the electrical grid is dependent on the time-of-day EVs are being charged. Therefore, this study concludes that measures need to be put in place to control charging times of EVs and this would help restrict the total daily electricity and electrical energy demands. The introduction of EVs reduces the overall CO2 emissions mainly because a proportion of petrol and diesel cars are replaced by EVs. However, CO2 emissions can only reduce up to a certain level and this reduction of CO2 will have less effect due to an increasing number of EVs in the electrical grid. To reduce CO2 emissions further, the electricity that relies on high-carbon fossil fuels in the electrical grid should be set at the minimum level. [ABSTRACT FROM AUTHOR]

The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented.

This paper presents an optimized advanced control technique for a grid-connected hybrid energy system (HES) based on a solid oxide fuel cell (SOFC) and a photovoltaic panel (PV) in a distributed power generation system. A classical full sliding mode control and a Fuzzy-PI sliding mode are proposed for a system operation and power management mode (UPC mode). The latter technique which is based on a combination of a classical controller and fuzzy artificial intelligence system is utilized at power and current adjustment stage. The fuzzy system gave the optimal gain values to the PI controller to get a better performance. The obtained results show that the optimized sliding mode control (Fuzzy-PI) is better than the classical sliding one in terms of power, response time, and robustness against disturbances in production and consumption energy. [ABSTRACT FROM AUTHOR]

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As the energy transition is upon us, the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries. Therefore, it is of paramount importance to simulate a great number of hypothetical multi-variant scenarios to correctly plan the roll-out of new grids. In this paper, we deploy Generative Adversarial Networks (GANs) to swiftly reproduce the non-Gaussian and multimodal distribution of real energy-related samples, making GANs a valuable tool for data generation in the field. In particular, we propose an original dataset deriving from the aggregation of two European providers including hourly electric inland generation from several European countries. This dataset also comes along with the corresponding season, day of the week, hour of the day and macro-economic variables aiming at unequivocally describing the country’s energetic profile. Finally, we evaluate the performance of our model via dedicated metrics capable of grasping the non-Gaussian nature of the data and compare it with the state-of-the-art model for tabular data generation.