Grid Reliability Research Articles

Integration of electric vehicles in smart grids: Challenges and opportunities in achieving carbon neutrality goals

Abstract. The integration of electric vehicles (EVs) into smart grids represents a pivotal shift towards sustainable transportation, offering substantial environmental benefits and new challenges for grid stability and energy management. This paper proposes a dual-layer economic dispatch model emphasizing source-load collaboration for carbon reduction, utilizing carbon trading mechanisms and demand response strategies to enhance grid stability and carbon efficiency. The Vehicle-to-Grid (V2G) technology emerges as a key solution, enabling EVs to contribute to grid reliability and facilitating renewable energy integration. Through analysis of EV charging behavior and demand response mechanisms, the study underscores the critical role of EVs in achieving carbon neutrality goals and the necessity of innovative solutions to integrate EVs seamlessly into the grid. This research highlights the importance of collaborative efforts among policymakers, utilities, and stakeholders to address the challenges and seize the opportunities presented by EV integration, paving the way for a low-carbon energy future.

Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.

Innovative Microgrid Services and Applications in Electric Grids: Enhancing Energy Management and Grid Integration

Currently, microgrids are a reliable solution for integrating distributed energy resources and managing demand on electricity grids, serving as a pathway towards a responsible energy transition. However, the evolving needs of the sector require specialized approaches to enhance grid flexibility and support the increasing penetration of renewable energy sources and their rising demand. This article explores and characterizes various advanced and innovative services offered by microgrids to improve the resilience, security, and reliability of electricity grids. It analyzes technical advances and novel control methodologies that demonstrate the potential for microgrids beyond mere energy provision. These include ancillary services, services aligned with demand response programs, and advanced asset management and energy resource optimization services. A global case study is conducted to provide a framework for the services that microgrids can provide. The case study validates the efficiency and reliability of electric grids with microgrids and addresses challenges related to their stability and resilience. This research provides a comprehensive perspective on the benefits of implementing microgrids and proposes new guidelines for the deployment of these systems in both urban and rural areas within the framework of energy communities in the Colombian electricity system, emphasizing the need for collaboration among stakeholders to ensure sustainable energy solutions.

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

AbstractSmart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

Improving the efficiency and reliability of India’s power grid through targeted EHV transmission line investments: a case of UPPCL

This study analyzes the optimization of Extra High Voltage (EHV) transmission line design parameters by Uttar Pradesh Power Corporation Limited (UPPCL) in India to improve power grid efficiency and reliability. UPPCL reduced insulation specifications for 400 kV lines from 27 to 21 disc insulators and decreased live metal clearance from 2600 to 2400 mm, resulting in 7–9% cost savings per line and reduced right-of-way requirements. While initial test lines performed satisfactorily for over 5 years, longer lines experienced operational challenges related to insulator quality, bird activity, and design margins. Tripping issues emerged during adverse weather conditions, particularly in foggy winter periods. Root cause analysis revealed the need for stringent insulator quality control, regular cleaning to mitigate bird dropping contamination, and potential corona ring design improvements. This case study demonstrates how targeted EHV transmission line optimization can yield significant cost and environmental benefits, while highlighting the importance of balancing economic gains with reliability and safety considerations. The findings provide valuable insights for future EHV transmission line designs and upgrades across India’s power grid.

Impact of Impedances and Solar Inverter Grid Controls in Electric Distribution Line with Grid Voltage and Frequency Instability

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.

Streamlining Smart Grids Reliability Assessment: An Innovative Mapping Approach

Assessing smart grid reliability, considering both cyber and physical components, typically involves a mapping step, escalating complexity and computational overhead. This paper presents a pioneering mapping approach that redefines the fundamental paradigm of smart grid reliability assessment. By leveraging a defined interconnection matrix, the method optimizes computational efficiency, curtailing the array of potential system states. Evaluation employing key performance metrics - Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS) - quantitatively demonstrates the superiority of our approach. Also, we explore the ramifications of integrating a dynamic thermal rating (DTR) in the process of reliability assessment, augmenting component safety through permissible enhancements in their ratings. Results underscore a notable reduction in total system states, from 221 to 214 for the bus topology and from 222 to 216 for the ring topology. Moreover, the analysis reveal substantial enhancements (Up to 43.58% ) in reliability indices upon consideration of the DTR system.

Application of European fine-grid numerical forecasting products for deviation analysis along transmission lines

Abstract Aiming at the current problem of lack of objective prediction products for deviation prediction along transmission lines, a deviation prediction model is established based on the historical statistical characteristics of deviation of transmission lines by using European fine-grid numerical prediction products and actual observation data. The model is combined with the operation specification of transmission lines, and the European fine-grid numerical prediction product is revised by using the normalization method to obtain a more accurate deviation prediction product. By analyzing the differences between the European fine-grid numerical prediction product and the actual observation data, the deviation prediction software is established, which can effectively improve the prediction accuracy and stability of the deviation situation of transmission lines. The results show that the software has good prediction performance and can effectively reduce the false alarm rate and omission rate of transmission line deviation prediction and improve the reliability and stability of the power grid.

The output flexibility optimization control of load-side wind turbine considering client demand response

Abstract This study focuses on optimizing the output flexibility control of load-side wind turbines considering client demand response. Considering the potential and role of client demand response, a load-side wind turbine output objective function was constructed, client demand response constraints were designed, and a two-layer model for flexible optimization control of load-side wind turbine output was constructed. The top is the power allocation hierarchy, and the core objective of this layer is to achieve power optimization distribution of wind turbines under various constraint conditions. The lower layer is the frequency control layer, which optimizes and controls the dynamic frequency of load-side wind turbine output. The experimental results show that the optimized control strategy not only enhances the efficiency of wind turbines, but also effectively reduces energy loss and enhances the stability and reliability of the power grid. Through precise calculation and real-time adjustment of the double-layer model, we can more accurately predict and control the output of wind turbines, thereby ensuring the supply-demand balance of the power grid and reducing potential risks caused by frequency fluctuations and power offsets.

Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Fault Tolerant Control of Grid Connected Inverters Based on Low-Voltage Ride Through

To enhance the reliability of new energy grid connected systems, a fault-tolerant model predictive control strategy with low-voltage ride-through capability for grid connected inverter bridge arms has been proposed. Firstly, the operational principles following a single-phase bridge arm fault in the grid-tied inverter are analyzed. Then, a fault-tolerant structure for the grid connected inverter is established by creating a virtual bridge arm using direct current-side capacitors. Subsequently, a current prediction model is established, and control is implemented using redundant vectors. Additionally, by dynamically adjusting reactive current compensation commands based on the relationship between grid voltage drops and reactive current commands, the system is ensured to maintain continuous and stable operation even during grid voltage fluctuations. Finally, experimental results demonstrate that this fault-tolerant control strategy can still maintain the continuous and stable operation of the system when bridge arm faults occur due to grid voltage drops at the inverter grid connection point.

Experimental Investigation of Steel-Borne Acoustic Pulses for Fault Pinpointing in Pipe-Type Cable Systems: A Scaled-Down Model Approach.

Pipe-type cable systems, including high-pressure fluid-filled (HPFF) and high-pressure gas-filled cables, are widely used for underground high-voltage transmission. These systems consist of insulated conductor cables within steel pipes, filled with pressurized fluids or gases for insulation and cooling. Despite their reliability, faults can occur due to insulation degradation, thermal expansion, and environmental factors. As many circuits exceed their 40-year design life, efficient fault localization becomes crucial. Fault location involves prelocation and pinpointing. Therefore, a novel pinpointing approach for pipe-type cable systems is proposed, utilizing accelerometers mounted on a steel pipe to capture fault-induced acoustic signals and employing the time difference of arrival method to accurately pinpoint the location of the fault. The experimental investigations utilized a scaled-down HPFF pipe-type cable system setup, featuring a carbon steel pipe, high-frequency accelerometers, and both mechanical and capacitive discharge methods for generating acoustic pulses. The tests evaluated the propagation velocity, attenuation, and pinpointing accuracy with the pipe in various embedment conditions. The experimental results demonstrated accurate fault pinpointing in the centimeter range, even when the pipe was fully embedded, with the acoustic pulse velocities aligning closely with the theoretical values. These experimental investigation findings highlight the potential of this novel acoustic pinpointing technique to improve fault localization in underground systems, enhance grid reliability, and reduce outage duration. Further research is recommended to validate this approach in full-scale systems.

Achieving universal energy access in remote locations using HOMER energy model: a techno-economic and environmental analysis of hybrid microgrid systems for rural electrification in northeast Nigeria

The developing world continues to face substantial obstacles to achieving affordable and dependable electricity access. This issue is especially pertinent for Nigeria, where diesel generators are widely relied upon in urban and rural regions because of an underdeveloped and unreliable national grid. The lack of grid reliability is worsened in Northeastern Nigeria, an area plagued by conflict, extreme poverty, and grid infrastructure deterioration. This study investigates the feasibility of implementing community-scale microgrids in rural areas without grid connection access. It focuses on assessing the technical, economic, and environmental aspects of utilizing these microgrids to deliver inexpensive and dependable electricity to underserved populations to increase energy access. A case study was conducted in Kabuiri, a village with an estimated population of 2,300 residents and an estimated load demand of 610 kWh per day. A hybrid microgrid system was designed and optimized to meet the community’s load demand using HOMER software, sized to produce 610 kWh/day of electricity with a renewable penetration of 99%. The optimal solar PV/battery/generator system had a levelized cost of electricity (LCOE) of $ 0.093 per kWh, a net present cost (NPC) of $266,709, and an annual operating cost of $9,110. The system contributed 1,624 kg CO2 eq/year of global warming potential and 56.81 kg O3 eq/year of smog formation during operation. Sensitivity analysis showed that the system could effectively react to or adapt to substantial increases in diesel prices, requiring only marginal increases in photovoltaic capacity and reduced generator usage to maintain the most cost-efficient operation. Additionally, the system model can be adapted based on the population of the remote community without substantially impacting the LCOE, however, the NPC increases with increase in population size. This research will aid in increasing energy access in remote locations by providing insights to stakeholders and energy access project developers.

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Assessment of the polygeneration approach in wastewater treatment plants for enhanced energy efficiency and green hydrogen/ammonia production

Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. This study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H2) and ammonia (NH3) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH3 production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH3 production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH3 production. Scenario 3, in particular, achieves competitive H2 and NH3 production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO2eq, with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

Grid Integration of Offshore Wind Energy: A Review on Fault Ride Through Techniques for MMC-HVDC Systems

Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems.

Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.

An attention fused sequence -to-sequence convolutional neural network for accurate solar irradiance forecasting and prediction using sky images

The challenge of accurately forecasting ultra-short-term solar irradiance for photovoltaic systems is complicated by rapidly changing weather, and while ground-based sky images offer potential improvements, effectively extracting spatiotemporal data from these images remains a significant hurdle for current computer vision models. A new hybrid model, "An Attention Fused Sequence-to-Sequence Convolutional Neural Network," is being developed to address this challenge. The model predicts intra-hour GHI, DNI, and DHI with a 10-min lead time by combining a Convolutional Neural Network (for spatial feature extraction from sky images), an attention mechanism (to focus on relevant regions), and a sequence-to-sequence model (for temporal feature extraction from time-series data). The proposed Model is trained using the NREL Solar Radiation Research Laboratory Dataset while evaluating the model with the Mean Bias Error, Mean Absolute Error, Root Mean Squared Error, R Squared, and forecasting skill score. The 10-min and sequence length 2 interval is considered to be the best performing across most of the evaluation metrics with an MBE value is 2.321 W/m2, MAE value of 39.490 W/m2, RMSE value of 62.086 W/m2, R2 value is 0.909 W/m2, FSS value of 23.589 W/m2 and an MBE of 4.876 W/m2, MAE of 56.887 W/m2, RMSE of 85.346 W/m2, R2 of 0.834 and FSS of 24.881 W/m2 respectively. Furthermore, the sensitivity analysis reveals that the proposed model's performance is influenced by both the sequence length and the lead time. The proposed framework outperforms other techniques for ultra-short-term PV generation forecasting, demonstrating its potential for practical deployment in PV systems to improve grid reliability and energy management.