Search Results (517)

The transition to a low-carbon energy matrix has driven the electrification of vehicles (EVs), yet charging infrastructure—particularly fast direct current (DC) chargers—can negatively impact distribution networks. This study investigates the integration of Battery Energy Storage Systems (BESSs) with the power grid, focusing on the E-Lounge project in Brazil as a strategy to mitigate these impacts. The results demonstrated a 21-fold increase in charging sessions and an energy consumption growth from 0.6 MWh to 10.36 MWh between June 2023 and March 2024. Compared to previous findings, which indicated the need for more robust systems, the integration of a 100 kW/138 kWh BESS with DC fast chargers (60 kW) and AC chargers (22 kW) proved effective in reducing peak demand, optimizing energy management, and enhancing grid stability. These findings confirm the critical role of BESSs in establishing a sustainable EV charging infrastructure, demonstrating improvements in power quality and the mitigation of grid impacts. The results presented in this study stem from a project approved under the Research and Development program of the Brazilian Electricity Regulatory Agency (ANEEL) through strategic call No. 022/2018. This initiative aimed to develop a modular EV charging infrastructure for fleet vehicles in Brazil, ensuring minimal impact on the distribution network. Full article

The variability of renewable energy sources, storage limitations, and fluctuations in residential demand affect the reliability of sustainable energy systems, resulting in energy deficits and the risk of service interruptions. Given this situation, the objective of this study is to diagnose and optimize the reliability of a residential microgrid based on photovoltaic and wind power generation and battery energy storage systems (BESSs). To this end, genetic algorithms (GAs) and ant colony optimization (ACO) are used to evaluate the performance of the system using metrics such as loss of load probability (LOLP), loss of supply probability (LPSP), and availability. The test system consists of a 3.25 kW photovoltaic (PV) system, a 1 kW wind turbine, and a 3 kWh battery. The evaluation is performed using Python-based simulations with real consumption, solar irradiation, and wind speed data to assess reliability under different optimization strategies. The initial diagnosis shows limitations in the reliability of the system with an availability of 77% and high values of LOLP (22.7%) and LPSP (26.6%). Optimization using metaheuristic algorithms significantly improves these indicators, reducing LOLP to 11% and LPSP to 16.4%, and increasing availability to 89%. Furthermore, optimization achieves a better balance between generation and consumption, especially in periods of low demand, and the ACO manages to distribute wind and photovoltaic generation more efficiently. In conclusion, the use of metaheuristics is an effective strategy for improving the reliability and efficiency of autonomous microgrids, optimizing the energy balance and operating costs. Full article

This paper presents the optimization sizing of a battery energy storage system for residential use from load forecasting using AI. The solar rooftop panel installation and charging systems for electric vehicles are connected to the low-voltage electrical system of the Metropolitan Electricity Authority (MEA). The daily electricity demand for future load forecasting used the long short-term memory (LSTM) technique in order to analyze the appropriate size of the battery energy storage system (BESS) for residences. The solar rooftop installation capacity is 5.5 kWp, which produces an average of 28.78 kWh/day. The minimum actual daily load in a month is 67.04 kWh, comprising the base load and the load from charging electric vehicles, which can determine the size of the battery energy storage system as 21.03 kWh. For this research, load forecasting will be presented to find the appropriate size of BESS by considering the minimum daily load over the month, which is equal to 102.67 kWh, which can determine the size of the BESS to be 17.84 kWh. When comparing the size of BESS from actual load values with the load from the forecast, it can significantly reduce the size and cost of BESS. Full article

Energy communities constitute the main collective form for energy consumers to participate in the current energy transition. The purpose of this research paper is to present a tool that assists energy communities to achieve fair and sustainable daily operation. In this context, the proposed algorithm (i) assesses the day-ahead operation cost (or profit) of energy communities, taking into consideration photovoltaic (PV) production, battery energy storage system (BESS), and flexible loads, as well as the potential profit from selling energy to the power system, under the net billing scheme, and (ii) compares the derived cost for each member with the cost for non-cooperative operation, as single prosumers. Taking the aforementioned costs or profits into consideration, the developed algorithm then proposes three cost-sharing options for the members, peer-to-peer (P2P), so that their participation in the community is more beneficial than individual operation. The algorithm is tested on a hypothetical energy community in Greece, highlighting the importance of the cooperation amongst the members of the community for their mutual benefit; for the simulated case of different PV shares, the cooperation can result in a 24.5% cost decrease, while having a BESS can reduce the cost by 25.0%. Full article

Battery energy storage systems (BESSs) can play a significant role in overcoming the challenges in Distribution Systems (DSs) with a high level of penetration from renewable energy sources (RESs). In this paper, the goal is to determine the optimal location, size, and charging/discharging dispatches of BESSs in DSs with a high level of photovoltaic (PV) installations. The problem of the location and size of BESSs is solved with multi-criteria optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The criteria of the multi-criteria optimization are minimal investment costs for BESS and improvement of the network performance index. The network performance index includes the reduction in annual losses of active energy in DSs and the minimization of voltage deviations. The dispatch of a BESS is determined using auxiliary optimization. Linear Programming (LP) is used for auxiliary optimization, with the aim of dispatching the BESS to smooth the load profile in DS. The proposed optimization method differs from previous studies because it takes in its calculations all days of the year. This was performed using the K-means clustering technique. The days of one year are classified by the level of consumption and PV production. The optimization was performed for five different levels of PV penetration (60%, 70%, 80%, 90%, and 100%) and for two scenarios: the first with one BESS and the second with two BESSs. The proposed methodology is applied to the IEEE 33 bus balanced radial distribution system. The results demonstrate that with an optimal choice of location and parameters of the BESS, significant improvement in network performance is achieved. This refers to a reduction in losses of active power, improvement of voltage profile, smoothing the load diagram, and reducing the peak load. For the scenario with one BESS and PV penetration of 100%, the reduction in daily energy losses reaches a value of up to 10% compared to the base case (case without a BESS). The reduction in peak load goes to 20%. Further, the highest voltage during the day is significantly lower in all buses compared to the base case. Similarly, the lowest voltage during the day is considerably higher. The methodology from this paper can be applied to any radial distribution network with a variable number of BESSs. The testing results confirm the effectiveness of the proposed method. Full article

This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. A novel approach is proposed for day-ahead weather forecasting, leveraging real-time data extraction from reliable weather websites and applying clear sky modeling to estimate photovoltaic (PV) generation with high accuracy. By automating weather data acquisition, the methodology bridges the gap between weather predictions and practical energy management, providing utilities with a reliable tool for operating and integrating renewable energy. The optimization framework focuses on minimizing the utility bill by analyzing a distribution feeder representative of Australia’s energy infrastructure, incorporating time-of-use (TOU) and flat tariff systems across eight Australian states to simulate realistic energy costs. Furthermore, voltage constraints are applied within the optimization framework to maintain system stability and improve voltage profiles, ensuring both technical reliability and economic efficiency. The proposed framework delivers actionable insights for utility industries, enhancing the scheduling of battery energy storage systems (BESS) and facilitating the integration of renewable energy into the grid. Full article

Current environmental challenges demand immediate action, especially in the transport sector, which is one of the largest CO₂ emitters. Vehicle electrification is considered an essential strategy for emission mitigation and combating global warming. This study presents methodologies for the modeling and energy management of microgrids (MGs) designed as charging stations for electric vehicles (EVs). Algorithms were developed to estimate daily energy generation and charging events in the MG. These data feed an energy management algorithm aimed at minimizing the costs associated with energy trading operations, as well as the charging and discharging cycles of the battery energy storage system (BESS). The problem constraints ensure the safe operation of the system, availability of backup energy for off-grid conditions, preference for reduced tariffs, and optimized management of the BESS charge and discharge rates, considering battery wear. The grid-connected MG used in our case study consists of a wind turbine (WT), photovoltaic system (PVS), BESS, and an electric vehicle fast charging station (EVFCS). Located on a highway, the MG was designed to provide fast charging, extending the range of EVs and reducing drivers’ range anxiety. The results of this study demonstrated the effectiveness of the proposed energy management approach, with the optimization algorithm efficiently managing energy flows within the MG while prioritizing lower operational costs. The inclusion of the battery wear model makes the optimizer more selective in terms of battery usage, operating it in cycles that minimize BESS wear and effectively prolong its lifespan. Full article

Renewable energy sources must be scheduled to manage power flow and load demand. Photovoltaic power generation is usually connected to power distribution networks and is not designed to add significant amounts of production in the event of increased electricity demand. Therefore, it is necessary to increase the generated capacity (i.e., hosting capacity) to meet the expansion in demand. This paper discussed two topics; the first is how to create an energy management strategy (EMS) for a hybrid micro-grid containing photovoltaic (PV) and battery energy storage system (BESS). A model was created within the MATLAB program through which the charging and discharging process of the BESS was managed, and the energy source was through PV. The model is connected to the leading network, where the m.file is linked to the model to control variable settings. This was carried out by using a logical–numerical modeling method. The second topic discussed how to evaluate hosting capacity (HC) without causing the network to collapse. This was achieved by choosing the best location and size for the PV. This study relied on the use of two algorithms, particle swarm optimization (PSO) and Harris hawks optimization (HHO). The fast decoupled load Flow (FDPF) method was adopted in the network analysis and finally the results of the two algorithms were compared. Full article

Despite the massive increase of renewable energy generation in Greece, large-scale battery energy storage systems (BESS) are yet to be integrated in the Greek electricity market. This paper analyzes the profitability of BESS in Greece, focusing on the Day-Ahead Market (DAM) and the Frequency Containment Reserve (FCR) market. To this end, we examine and compare the following three instances of BESS market participation with respect to the short-term uncertainty BESS participants face in terms of market prices and FCR utilization factors: (a) a theoretical perfect information instance, (b) a deterministic instance based on average historical values of the uncertain parameters, and (c) a stochastic instance based on alternative scenarios stemming from historical data. The last two instances are complemented by an out-of-sample validation representing BESS operation after uncertainty is materialized. Furthermore, for each of these three instances, we explore three cases involving participation only in the DAM, only in the FCR market, and in a combination of the DAM and FCR market, accounting for the different pricing mechanisms of these markets. The case studies employ real market and frequency data from Greece and compare the three instances and three market participation cases in terms of achieved profit and energy violation rate. Full article

Transmission flexibility is a key component of current power systems and demands a reconfiguration of alternatives to expand transmission infrastructure. This paper addresses the use of a Battery Energy Storage System (BESS) as an asset of the transmission system that provides increased transmission capacity. Furthermore, the BESS also supports operational procedures of the transmission system in the course of the re-establishment of normal operation during transients, which helps maintain the power quality requirements. A case study is presented to assess the additional capabilities that an operational 30 MW/60 MWh BESS primarily used to provide congestion relief in the state of São Paulo, Brazil, could provide to the power system. Based on a 5-year horizon transmission and generation expansion plans by local governing bodies, a set of four alternative applications for this BESS was proposed and studied, as follows: (1) increased operational flexibility under contingencies and maintenance, (2) islanded operation for increased reliability, (3) grid support during system restoration, and (4) increased hosting capacity for variable renewables. The results show that the BESS improves performance and power quality indexes while aiding the operation during contingencies. Full article

Battery Energy Storage Systems (BESSs) are critical in modernizing energy systems, addressing key challenges associated with the variability in renewable energy sources, and enhancing grid stability and resilience. This review explores the diverse applications of BESSs across different scales, from micro-scale appliance-level uses to large-scale utility and grid services, highlighting their adaptability and transformative potential. This study also includes advanced applications such as mobile energy storage, second-life battery utilization, and innovative models like Energy Storage as a Service (ESaaS) and energy storage sharing. Additionally, it discusses the integration of machine learning (ML) and large language models (LLMs), including advanced reinforcement learning (RL) algorithms, to optimize BESS operations and ensure safety through dynamic and data-driven decision-making. By examining current technologies, modeling methods, and future trends, this review provides a comprehensive overview of BESSs as a cornerstone technology for sustainable and efficient energy management, leading to a resilient energy future. Full article

The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis regarding their repeatability and reliability. This study addresses this critical gap by evaluating the variability and consistency of fire-resistance tests performed on multiple battery energy storage systems (BESSs) under standardized conditions. A custom-built measurement system incorporating thermocouples, anemometers, and hygrometers provided high-resolution data on flame dynamics, ambient conditions, and pool fire efficiency. Statistical evaluations following ISO 5725 series guidelines revealed substantial inconsistencies, including unstable exposure temperatures and sensitivity to local turbulence. These findings call into question the robustness of current testing methods, and we propose an alternative approach employing LPG burners for improved precision and repeatability. By identifying significant flaws in existing standards and offering scientifically grounded enhancements, this work contributes a novel perspective to the field of EV safety, advancing global fire-resistance testing protocols. Full article

To enhance the utilization of emerging energy sources, the application of battery energy storage systems (BESSs) was increasingly explored by investors. However, the immature development of BESS technologies introduced supply–demand imbalances, complicating the establishment of standardized cost analysis frameworks for potential investments. To address this challenge, a hybrid optimization model for a user-side BESS was developed to maximize total net returns over the system’s entire life cycle. The model accounted for factors such as energy storage arbitrage revenue, government tariff subsidies, reductions in electricity transmission fees, delays in grid upgrades, and overall life cycle costs. Conditional value-at-risk (CVaR) was employed as a risk assessment metric to provide investment allocation recommendations across various risk scenarios. An example analysis was conducted to allocate and evaluate the net returns of different battery types. The results demonstrated that the model identified optimal investment strategies aligned with investors’ risk preferences, enabling informed decision-making that balanced returns with operational stability. This approach enhanced the resilience and economic viability of user-side energy storage configurations. Full article

A peak shaving approach in selected industrial loads helps minimize power usage during high demand hours, decreasing total energy expenses while improving grid stability. A battery energy storage system (BESS) can reduce peak electricity demand in distribution networks. Quasi-dynamic load flow analysis (QLFA) accurately assesses the maximum loading conditions in distribution networks by considering factors such as load profiles, system topology, and network constraints. Achieving maximum peak shaving requires optimizing battery charging and discharging cycles based on real-time energy generation and consumption patterns. Seamless integration of battery storage with solar photovoltaic (PV) systems and industrial processes is essential for effective peak shaving strategies. This paper proposes a model predictive control (MPC) scheme that can effectively perform peak shaving of the total industrial load. Adopting an MPC-based algorithm design framework enables the development of an effective control strategy for complex systems. The proposed MPC methodology was implemented and tested on the Indian Utility 29 Node Distribution Network (IU29NDN) using the DIgSILENT Power Factory environment. Additionally, the analysis encompasses technical and economic results derived from a simulated storage operation and, taking Puducherry State Electricity Department tariff details, provides significant insights into the application of this method. Full article

With the increasing emphasis on emission reduction targets, the low-carbon sustainable transformation of industrial energy supply systems is crucial. Addressing the urgent issue of reducing industrial carbon emissions, this study presents an integrated industrial energy supply system (IRE-CCUS-BESS-SPS) that incorporates renewable energy; calcium-based carbon capture, utilization, and storage (CCUS); and battery energy storage systems (BESSs) to improve energy efficiency and sustainability. The system model is designed to achieve a cost-effective and environmentally low-impact energy supply, validated through Aspen Plus V11.0 and Matlab R2019b simulations. The system’s performance is evaluated using a 4E index system encompassing economy, environment, energy, and exergy. The findings indicate that the system’s lifetime net present value (NPV) is positive, with a payback period of 6.09 years. Despite a 12.9% increase in the overall economic cost, carbon emissions are significantly reduced by 59.78%. The energy supply composition includes 48.60% from fuel oil and 22.10% from biomass, with an additional 270.04 kW of heat provided by waste heat boilers. The equalization costs for CO₂ removal (LCCR) and methanation (LCOM) are 122.95 CNY/t and 10908.35 CNY/t, respectively, both exceeding current carbon emission trading costs and methane prices. This research offers a robust framework for designing sustainable industrial energy systems that integrate renewable energy, CCUS, and energy storage technologies for low-carbon operations. The analysis also suggests that government policies, such as direct financial subsidies or tax relief, are effective in accelerating the adoption of CCUS technology. Full article