Search Results (499)

The growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability of their grids by identifying effective solutions. One promising approach to achieving this is through the deployment of battery energy storage systems, which can rapidly inject power to mitigate the impacts of network disturbances or outages. This study investigates the use of advanced genetic algorithms as a practical methodology for the optimal siting of batteries in modern distribution networks. By incorporating historical data on demand and network failures, the algorithm generates statistical models that inform the optimization process. The model integrates both the technical and economic aspects of battery systems to identify locations that minimize reliability indices such as SAIDI and SAIFI, while also reducing investment costs. Tested on a real distribution system comprising 1837 nodes, the proposed approach demonstrates the ability of genetic optimization to deliver efficient solutions compared with traditional methods, providing a high likelihood of identifying strategic battery locations that respond to variable demand, system failures, and technical constraints. Full article

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) and energy storage systems (ESSs) because of their high energy density, low self-discharge rate, good cycling performance, and environmental friendliness. Nevertheless, with the extensive utilization of LIBs, incidents of fires and explosions resulting from thermal runaway (TR) have become increasingly prevalent. The resolution of safety concerns associated with LIBs and the reduction in operational risks have become pivotal to the operation and control of ESSs. This paper proposes a model for the TR process of LIBs. By simplifying the modeling of TR reactions, it is possible to calculate the starting temperature of the battery self-heating reaction. Subsequently, this paper puts forth an operational reliability evaluation algorithm for a reconfigurable battery energy storage system (BESS). Finally, this paper develops a control algorithm for reliability improvement, with the objective of ensuring safe and stable control of the ESS. Full article

The reliance on fossil fuels as a primary global energy source has significantly impacted the environment, contributing to pollution and climate change. A shift towards renewable energy sources, particularly solar power, is underway, though these sources face challenges due to their inherent intermittency. Battery energy storage systems (BESS) play a crucial role in mitigating this intermittency, ensuring a reliable power supply when solar generation is insufficient. The objective of this paper is to accurately predict the solar irradiance for battery operation optimization in microgrids. Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. Our model outperforms previous works using the same dataset and has been validated to generalize across diverse geographical locations in Florida. This work demonstrates the potential of AI-assisted data-driven approaches to support sustainable energy management in solar-powered IoT-based microgrids. Full article

Battery energy storage systems (BESSs) play a crucial role in integrating renewable energy sources into microgrids. However, robust BESS controllers are needed to carry out this function properly. Existing controllers suffer from overshoots and slow convergence issues. Moreover, as electrical grid networks become increasingly connected, the risk of cyberattacks grows, and traditional physics-based anomaly detection methods face challenges such as reliance on predefined models, high computational demands, and limited scalability for complex, large-scale data. To address the limitations of the existing approaches, this paper first proposes a novel sigma-delta modulation (SDM) controller for BESSs in solar photovoltaic (PV)-connected microgrids. The performance of SDM has been compared with those of the proportional–integral (PI) controller and fuzzy logic controller (FLC). Also, this paper proposes an improved ensemble-based method to detect the false data injection (FDI) and denial-of-service (DoS) attacks on the BESS controller. The performance of the proposed detection method has been compared with that of the traditional ensemble-based method. Four PV-connected microgrid systems, namely the solar DC microgrid, grid-connected solar AC microgrid, hybrid AC microgrid with two BESSs, and hybrid AC microgrid with a single BESS, have been considered to show the effectiveness of the proposed control and detection methods. The MATLAB/Simulink-based results show the effectiveness and better performance of the proposed controller and detection methods. Numerical results demonstrate the improved performance of the proposed SDM controller, with a 35% reduction in AC bus voltage error compared to the conventional PI controller and FLC. Similarly, the proposed SAMME AdaBoost detection method achieves superior accuracy with an F1 score of 95%, outperforming the existing ensemble approaches. Full article

Catenary-powered networks are expected to play a pivotal role in urban energy transition, due to the larger deployment of electric public transport, in-motion-charging (IMC) vehicles, and catenary-backed electric vehicle chargers. However, there are technical challenges that must be overcome to ensure the successful utilization of existing networks without compromising vehicle performance or compliance with network standards. This paper aims to validate the use of battery energy storage systems (BESS) built from second-life batteries as a means of retrofitting catenary-powered traction networks. The objective is to increase the network robustness without creating a negative impact on its overall operational efficiency. Consequently, more electrification projects can be implemented using the same network infrastructure without substantial modifications. Furthermore, a power management scheme is presented which allows the voltage and current range allowed in the catenary network and the BESS maximum charging rate to be controlled from user-defined values. The proposed control scheme is adept at customizing the BESS size for the specific application under consideration. Validation is performed on a case study of the trolleybus system in Bologna, Italy. Full article

Stationary battery energy storage systems (BESS) have been developed for a variety of uses, facilitating the integration of renewables and the energy transition. Over the last decade, the installed base of BESSs has grown considerably, following an increasing trend in the number of BESS failure incidents. An in-depth analysis of these incidents provides valuable lessons for improving the safety of BESS. This paper discusses multiple safety layers at the cell, module, and rack levels to elucidate the mechanisms of battery thermal runaway and BESS failures. We further provide insights into different safety aspects of BESS, covering the system architecture, system consideration, safety standards, typical quality issues, failure statistics, and root causes. Various mitigation strategies are recommended and summarized. We highlight the importance of multi-disciplinary approaches in knowing, managing, and mitigating the risks associated with BESS. In general, this review paper serves as a guide for understanding the safety of BESS. Full article

The growing demand for clean energy transitions has become increasingly significant, with solar energy emerging as one of the most prominent clean energy resources contributing to this effort. However, there remains limited knowledge regarding the economic feasibility of solar project development across different geographic locations and scales. This study introduces a comprehensive economic analysis framework to assess the economic viability of residential- and utility-scale solar projects, using California, Tennessee, and Texas as case studies. The economic assessment is conducted through a cost–benefit analysis that adopts a full life-cycle approach, encompassing phases from permitting to demolition for both scales of solar projects. We found that utility-scale solar projects generally offer more favorable investment return ratios (IRRs) compared to residential projects, with IRRs of 0.57 to 0.61 for California, 0.63 to 0.80 for Texas, and 0.11 to 0.52 for Tennessee. Moreover, utility-scale solar projects in Texas were found to exhibit the earliest breakeven point, reaching financial viability by the seventh year. Furthermore, the incorporation of energy storage solutions, such as battery energy storage systems (BESSs), is shown to be essential for improving the efficiency of residential solar energy usage, with efficiency gains of up to 50%. Finally, region-specific strategies, including net-metering policies, electricity retail market structures, and the promotion of solar adoption, play a crucial role in enhancing the financial viability of solar projects for both residential and utility scales. Full article

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time. Full article

The energy transition toward low-carbon electricity systems has resulted in a steady increase in RESs. The expansion of RESs has been accompanied by a growing number of energy storage systems (ESSs) that smooth the demand curve or improve power quality. However, in order to investigate ESS benefits, it is necessary to determine their reliability. This article proposes a four-state reliability model of a battery ESS operating with a PV system for low-voltage grid end users: households and offices. The model assumes an integration scenario of an ESS and a PV system to maximize autoconsumption and determine generation reliability related to energy availability. The paper uses a simulation approach and proposes many variants of power source and storage capacity. Formulas to calculate the reliability parameters—the intensity of transition $\lambda$, resident time $T_i$, or stationary probabilities—are provided. The results show that increasing the BESS capacity above 80% of daily energy consumption does not improve the availability probability, but it may lead to an unnecessary cost increase; doubling the PV system capacity results in a decrease in the unavailability probability by almost half. The analysis of the results by season shows that it is impossible to achieve a high level of BESS reliability in winter in temperate climates. Full article

The increasing installation of Renewable Energy Sources (RES) presents significant challenges to the stability and reliability of power systems. This paper introduces an advanced control method to mitigate the adverse effects of intermittent generation from RES on the power system frequency stability. The proposed approach emphasizes the critical role of Battery Energy Storage Systems (BESS) and RES in enhancing the resilience of modern power networks. The Generation Export Management Schemes (GEMS) are employed to curtail the excessive export of RES, thereby contributing to improved frequency stability. This research involves a comprehensive analysis of the dynamic behavior of the network under various operational scenarios, particularly focusing on power exchanges between RES, BESS, and synchronous generation units. Furthermore, this paper focuses on the economic implications of integrating RES into the grid, with a detailed cost of balancing (COB) modelling and analysis conducted to assess the financial viability of the proposed frequency management solutions. The analysis encompasses both short-term and long-term perspectives, providing insights into the development of economically sustainable smart power networks that effectively integrate renewable energy and storage technologies while maintaining system stability. Full article

This paper presents the preliminary results of studies aiming to use a battery energy storage system (BESS) in the Brazilian transmission system. The main objective of the BESS is to solve congestion problems caused mainly by the large increase in variable renewable generation in certain system areas. The studies were conducted based on actual forecasted system scenarios using a full representation of the electric grid available from the Brazilian system operator data base. In this work, only the steady-state modeling was considered as this may be the first stage in the assessment of a new technology. A qualitative economic comparison of the BESS application with other possible solutions to the congestion problems is also included. Full article

Battery Energy Storage Systems (BESS) offer scalable energy storage solutions, especially valuable for remote, off-grid applications. However, traditional battery packs with fixed series-parallel configurations lack reconfigurability and are limited by the weakest cell, hindering their application for second-life batteries. The Modular Multilevel Series-Parallel Converter (MMSPC) addresses these limitations by enabling dynamic reconfiguration, optimizing cell balancing, and enhancing energy control. This paper experimentally evaluates a single-phase BESS based on the MMSPC with an output power equivalent to 2 kW and two battery units (155V), demonstrating stable output and reduced internal losses across varied battery parameters. Full article

This research developed smart integrated hybrid renewable systems for small energy communities and applied them to a real system to achieve energy self-sufficiency and promote sustainable decentralized energy generation. It compares stand-alone (SA) and grid-connected (GC) configurations using a developed optimized mathematical model and data-driven optimization, with economic analysis of various renewable combinations (PV, Wind, PHS, BESS, and Grid) to search for the optimal solution. Four cases were developed: two stand-alone (SA1: PV + Wind + PHS, SA2: PV + Wind + PHS + BESS) and two grid-connected (GC1: PV + PHS + Grid, GC2: Wind + PHS + Grid). GC2 shows the most economical with stable cash flow (−€123.2 annually), low CO₂ costs (€367.2), and 91.7% of grid independence, requiring 125 kW of installed power. While GC options had lower initial investments (between €157k to €205k), the SA configurations provided lower levelized costs of energy (LCOE) ranging from €0.039 to €0.044/kWh. The integration of pumped hydropower storage enhances energy independence, supporting peak loads for up to two days with a storage capacity of 2.17 MWh. Full article

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. Full article

Lithium-ion batteries (LIB) are prone to thermal runaway, which can potentially result in serious incidents. These challenges are more prominent in large-scale lithium-ion battery energy storage system (Li-BESS) infrastructures. The conventional risk assessment method has a limited perspective, resulting in inadequately comprehensive evaluation outcomes, which impedes the provision of dependable technical support for the scientific appraisal of intricate large-scale Li-BESS systems. This study presents a novel Li-BESS-oriented multi-scale risk-informed comprehensive assessment framework, realizing the seamless transmission of assessment information across various scales. The findings from a previous smaller-scale analysis serve as inputs for a larger scale. The evaluation process of this method is more scientifically rigorous and yields more comprehensive results compared to assessment technologies just relying on a single perspective. By utilizing the proposed comprehensive assessment methodology, this study utilized the emergency power supply of nuclear power plants (NPPs) as an application scenario, demonstrating the complete implementation process of the framework and conducting a comprehensive assessment of Li-BESS feasibility as an emergency power source for NPPs. Our findings propose a novel paradigm for the comprehensive assessment of Li-BESS, which is expected to serve as a scientific foundation for decision-making and technical guidance in practical applications. Full article