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The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh. [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]

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]

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]

The electrification of public transport bus networks can be carried out utilizing different technological solutions, like trolley, battery or fuel cell buses. The purpose of this paper is to analyze how and to what extent existing bus networks can be electrified with fast charging battery buses. The so called opportunity chargers use mainly the regular dwell time at the stops to charge their batteries. This results in a strong linkage between the vehicle scheduling and the infrastructure planning. The analysis is based on real-world data of the bus network in Muenster, a mid-sized city in Germany. The outcomes underline the necessity to focus on entire vehicle schedules instead on individual trips. The tradeoff between required battery capacity and charging power is explained in detail. Furthermore, the impact on the electricity grid is discussed based on the load profiles of a selected charging station and a combined load profile of the entire network. [ABSTRACT FROM AUTHOR]

In this paper, a proton exchange membrane fuel cell (PEMFC) is implemented as a grid-connected electrical generator that uses hydrogen gas as fuel and air as an oxidant to produce electricity through electrochemical reactions. Analysis demonstrated that the performance of the PEMFC greatly depends on the rate of fuel supply and air supply pressure. Critical fuel and air supply pressures of the PEMFC are analysed to test its feasibility for the grid connection. Air and fuel supply pressures are varied to observe the effects on the PEMFC characteristics, efficiency, fuel supply, and air consumption over time. The PEMFC model is then implemented into an electrical power system with the aid of power electronics applications. Detailed mathematical modelling of the PEMFC is discussed with justification. The PEMFC functions as an electrical generator that is connected to the local grid through a power converter and a transformer. Modulation of the converter is controlled by means of a proportional-integral controller. The two-axis control methodology is applied to the current control of the system. The output voltage waveform and control actions of the controller on the current and frequency of the proposed system are plotted as well. Simulation results show that the PEMFC performs efficiently under certain air and fuel pressures, and it can effectively supply electrical power to the grid. [ABSTRACT FROM AUTHOR]

This paper presents a series of economic efficiency studies comparing three different investment variants: without energy storage, with energy stored in batteries and hydrogen installation with a PEM fuel cell stack for a location in Poland. To reach a target, the current solar potential in Poland, the photovoltaic (PV) productivity, the capacity of the energy storage in batteries as well as the size of the hydrogen production system were calculated. The solar potential was determined using archival meteorological data and the Krieg estimation method. A laboratory scale PV system (1 kW) was used to estimate the decrease in real solar installation power during the last 10 years of operation. All analyses were made for a 100 kW photovoltaic array located in Poland using static and dynamic methods of investment project assessment, such as Simply Bay Back Period (SPBP) or Net Present Value (NPV). The results showed that the SPBP amounted to 8.8 years and NPV 54,896 € for non-storage systems. Whereas, for systems with energy stored the economic indexes were, as follow: SPBP = never, NPV = 183,428 € for batteries and SPBP = 14.74 years, NPV = 22,639 € for hydrogen/fuel cell installation. Storage in hydrogen is more advantageous than batteries due to the smaller investment outlays. [ABSTRACT FROM AUTHOR]