Energy Reports最新文献

{"title":"Optimizing multi-energy storage in hybrid energy hubs: A stochastic approach using an improved artificial hummingbird algorithm for renewable integration and cost reduction","authors":"Mohammed Alruwaili ,&nbsp;Mohana Alanazi ,&nbsp;Abdulaziz Alanazi","doi":"10.1016/j.egyr.2025.108930","DOIUrl":"10.1016/j.egyr.2025.108930","url":null,"abstract":"<div><div>Combining different energy sources in hybrid energy systems has attracted significant attention due to their exceptional efficiency, adaptability, and eco-friendliness. Effective operation of these systems relies heavily on optimal planning and management. This research introduces a thorough framework for the efficient planning and energy management of a hybrid energy hub (EH) that includes electric, thermal, and cold storage. The main goal is to minimize the costs associated with operation and storage utilization by framing the issue as a stochastic optimization problem. An advanced stochastic methodology is utilized to tackle uncertainties arising from variable energy sources and demand, using Monte Carlo Simulation combined with K-means clustering for scenario generation and reduction. This method integrates the Improved Artificial Hummingbird Algorithm (IAHA) with a population classification strategy, Monte Carlo simulation-based probabilistic analysis, and K-means clustering. The effectiveness of the proposed approach is confirmed through simulations conducted on a standard EH under various scenarios. These scenarios include energy management without and with multi-energy storage and uncertainty consideration. The results indicate that integrating storage into EH planning and management using IAHA leads to an 11.84 % reduction in operational costs. Additionally, solving the stochastic problem through Monte Carlo simulation and K-means clustering results in a significant 36.30 % reduction in operational costs, allowing for more precise decisions regarding EH effectiveness. This study emphasizes the IAHA's effectiveness in optimizing EH energy management, showcasing its superiority over traditional optimization algorithms such as AHA, particle swarm optimization (PSO), gray wolf optimizer (GWO), manta ray foraging optimizer (MRFO), and Kepler optimization algorithm (KOA).</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108930"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rooftop distributed photovoltaic development under the ”Whole-county promotion mode”: Based on farmers’ self-investment","authors":"Qiang Wang ,&nbsp;Yueling Yang ,&nbsp;Jiachen Wang ,&nbsp;Ye Li ,&nbsp;Liang Meng ,&nbsp;Guocai Yu","doi":"10.1016/j.egyr.2025.108980","DOIUrl":"10.1016/j.egyr.2025.108980","url":null,"abstract":"<div><div>Rural China possesses abundant rooftop resources, making the development of rooftop distributed photovoltaics (RDPV) crucial for achieving national carbon neutrality goals. Under the \"Whole-county promotion mode\", self-investment by farmers has become a key financing channel. Based on a survey of 367 rural households in Shandong Province, this study combines econometric analysis and game theory simulations to explore the factors influencing farmers' willingness to invest in self-built RDPV systems. The results show that economic returns, environmental awareness, household characteristics, and rooftop conditions significantly affect their investment decisions. The findings highlight the importance of enhancing project profitability, providing financial support, and improving farmers' cognition through policy interventions. This research offers theoretical insights into renewable energy adoption behavior and practical implications for accelerating rural PV deployment under low-carbon strategies.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108980"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-aware decision making for coordinated wind-storage system under severe price uncertainty","authors":"Swati Gupta ,&nbsp;Sandeep Chawda ,&nbsp;Bharat Bhushan Sharma ,&nbsp;Pawan Kumar Pathak ,&nbsp;Vivek Prakash ,&nbsp;Sachin Sharma","doi":"10.1016/j.egyr.2025.109005","DOIUrl":"10.1016/j.egyr.2025.109005","url":null,"abstract":"<div><div>Wind power producers (WPPs) face considerable profitability challenges in deregulated electricity markets due to the stochastic and volatile nature of wind generation and price signals. To address these challenges, this paper proposes a risk-aware decision-making framework that coordinates wind power generation with energy storage systems (ESS) for profit maximization under severe price uncertainty. The framework is built on information gap decision theory (IGDT), a non-probabilistic approach particularly suited for environments with limited historical data and unknown uncertainty distributions. A hybrid energy sale strategy is employed, wherein a portion of wind power is directly sold in the market while the remainder is stored and dispatched strategically according to prevailing market conditions. Three mathematical formulations are developed: (i) a deterministic model to establish baseline profits, (ii) a robustness model to quantify tolerance against adverse price deviations, and (iii) an opportuneness model to capture potential gains under favorable deviations. These models incorporate realistic operational constraints, including ESS dynamics, wind generation limits, and market bidding requirements. Case studies using real-world wind and price data demonstrate the efficacy of the proposed IGDT-based approach. The results reveal the trade-off between robustness and profitability, and the efficient frontiers generated provide strategic insights into decision-making under uncertainty. The robustness analysis showed that the system could tolerate profit reductions down to $15,133.81 under adverse deviations, while the opportuneness model identified profit improvements up to $46,777.22 in favorable scenarios. Overall, this study offers a comprehensive, risk-aware framework for enhancing the economic resilience of WPPs in competitive electricity markets.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 109005"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy audit and sustainability assessment of the production chain of Echium amoenum Fisch & Mey using a life cycle assessment","authors":"Mohammad Ali Ghahremani-Aghbolagh Rostam Khan,&nbsp;Majid Khanali,&nbsp;Hassan Ghasemi-Mobtaker","doi":"10.1016/j.egyr.2025.108934","DOIUrl":"10.1016/j.egyr.2025.108934","url":null,"abstract":"<div><div><em>Echium amoenum</em> Fisch &amp; Mey, or borage, is a medicinal plant widely cultivated in northern Iran for its therapeutic value and traditional use. Despite its significance, no study has examined the energy efficiency and environmental sustainability of its production. This study provides a thorough evaluation of the energy audit and environmental impacts of borage herbal tea production using the Life Cycle Assessment (LCA) approach. As one of the first studies to apply this tool to a medicinal plant, this research addresses the gap in sustainability analysis for herbal product systems. Primary data were collected through structured questionnaires, field surveys, and direct interviews with farmers and processing facilities. The system boundary spans from cultivation to processing. Energy analysis revealed a total input energy of approximately 63261 MJ ha⁻¹ and an output energy of approximately 7727 MJ ha⁻¹ , resulting in low energy use efficiency (0.12) and high specific energy (about 121.7 MJ kg⁻¹). The Global Warming Potential (GWP) was estimated at 30.70 kg CO₂ eq per kg of product, with direct emissions from chemical fertilizers especially nitrogen accounting for 77.90 % of the total greenhouse gas (GHG) emissions. Endpoint damage assessment resulted in 4.82E-05 DALY for human health, 1.57E-07 species.year for ecosystems, and 0.60 USD for resources categories. Exergy analysis showed non-renewable fossil resources dominated demand at 79.40 MJ per kg of borage herbal tea, mainly from nylon packaging, while renewable water and biomass demands were lower at 28 MJ and 15.80 MJ, respectively. The study suggests the use of organic fertilizers, biofertilizers, and nitrogen-fixing crops, along with replacing nylon packaging with biodegradable materials, to reduce environmental impacts in the post-harvest and production chain of medicinal plants.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108934"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of temperature on energy consumption and carbon emissions in Saudi Arabia","authors":"Jeyhun I. Mikayilov,&nbsp;Fateh Belaid,&nbsp;Ryan Alyamani","doi":"10.1016/j.egyr.2025.108938","DOIUrl":"10.1016/j.egyr.2025.108938","url":null,"abstract":"<div><div>In the context of accelerating climate change, we employ a general-to-specific econometric modeling approach to quantify the relationship between temperature, energy use, and CO2 emissions in Saudi Arabia through 2030. Our analysis encompasses total final energy consumption across residential, commercial, industrial, transportation, and desalination sectors, excluding petroleum feedstocks for non-energy uses. Using three Shared Socioeconomic Pathway (SSP) temperature scenarios, we project additional energy requirements and associated CO2 emissions. In the medium- and low-warming scenarios, energy demand is approximately 1 % and 2 % lower, respectively, compared to the high-warming scenario. These differences correspond to lower emissions by about 8 MtCO2eq and 12 MtCO2eq, respectively. The findings indicate that energy pricing could be viable for promoting energy efficiency and managing electricity demand in Saudi Arabia. Lower-income consumers/entities, however, are less prepared and more vulnerable to climate-induced temperature increases and higher energy prices, indicating a potentially regressive impact of the climate-energy nexus. Higher income elasticity is associated with lower sensitivity to energy prices and temperature fluctuations. These findings underscore the importance of integrating temperature-driven demand growth into energy and social policy planning. In light of Saudi Arabia’s Vision 2030 and the Saudi Green Initiative, support for energy efficiency measures among low-income households, accelerating deployment of low-cost renewable energy, and investments in climate-resilient infrastructure emerge as critical strategies to bridge the emissions gap and ensure affordable and sustainable development.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108938"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning-driven stochastic optimization and blockchain-enabled demand response to determine the techno-economic feasibility of hydrogen fueling stations in Australia","authors":"Paul C. Okonkwo ,&nbsp;Samuel Chukwujindu Nwokolo ,&nbsp;Edson L. Meyer ,&nbsp;Chinedu Christian Ahia ,&nbsp;Ibrahim B. Mansir","doi":"10.1016/j.egyr.2025.108949","DOIUrl":"10.1016/j.egyr.2025.108949","url":null,"abstract":"<div><div>This study aims to assess the feasibility of hydrogen refueling in the Central Business District (CBD) corridors of Australia, utilizing a reinforcement-learning-based stochastic framework integrated with blockchain-enabled demand response, with the objective of ensuring reliable dispatch of hybrid PV-wind-battery-electrolyze systems. The aim is to assess the techno-economic viability amidst tariff and weather uncertainties across six locations—North Terrace (Adelaide), Elizabeth Quay (Perth), Salamanca Place (Hobart), Southbank (Melbourne), Eagle Street (Brisbane), and Martin Place (Sydney)—utilizing district-scale solar and wind projections, performance-based costing, and multi-vector integration. This contribution integrates reinforcement learning (RL) control, digitally auditable demand response (DR) settlement, and spatially resolved siting into a cohesive decision framework, validated through physics-constrained simulations and bio-inspired optimizers, facilitating geography-aware configuration options and risk-sensitive investment evaluation. The scope encompasses PV–WT–Battery, PV–Battery, and WT–Battery configurations; electrolyze and tank dimensioning; demand scenarios; and sensitivity-robustness assessments. Highlights indicate that PV–WT–Battery consistently reduces lifespan metrics while achieving RF = 100 %. Elizabeth Quay represents the cost frontier (NPC ≈ $28.04 ×10 ³; LCOE ≈ $0.01747/kWh; LCOH ≈ $0.603/kg), with Eagle Street in proximity, North Terrace and Southbank at moderate elevations, and Martin Place at the upper limit; wind-dominant Salamanca Place displays a configuration inflection where 750 kW PV surpasses 1000 kW due to wind-solar covariance and part-load penalties. Reinforcement learning combined with dynamic pricing reduces LCOH variability by synchronizing electrolyze setpoints, compressor operations, and battery-tank buffers during low-marginal-cost periods, while blockchain settlement provides verifiable green-hydrogen credentials. Demand elasticity assessments reveal that −15 % scenarios yield around 10–12 % decreases in NPC/LCOE/LCOH and surpluses of about 25–30 %, whereas + 15 % scenarios result in cost increases of approximately 5–10 %. The grouping of hydrogen valleys near ports and transit hubs improves utilization and financial viability; alternative logistics (LH₂, LOHC) and subterranean storage expand site possibilities for densely populated urban areas. The strategic considerations support PV-biased trihybrids in the sun-drenched western and subtropical regions, diversified portfolios for Adelaide, Melbourne, and Sydney, and optimized wind-driven operations in Hobart, providing a replicable framework for achieving verifiable, net-zero-aligned HRS in Australian cities. Results are applicable across various climates, precinct types, and market conditions.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108949"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tri-objective co-optimization of waste-cooking-oil biodiesel using machine learning, NSGA-II, and life-cycle assessment","authors":"Istiyak Amin Santo ,&nbsp;Siow Chun Lim ,&nbsp;Lee It Ee ,&nbsp;Tan Wooi Haw ,&nbsp;Chai Yee Ho ,&nbsp;Nur Mazlini Razali ,&nbsp;Lukeman Yusoff","doi":"10.1016/j.egyr.2025.12.044","DOIUrl":"10.1016/j.egyr.2025.12.044","url":null,"abstract":"<div><div>Turning waste cooking oil (WCO) into biodiesel reduces waste and fossil fuel dependence, yet performance and environmental impacts are typically assessed in isolation, obscuring critical trade-offs. We identify operating conditions that simultaneously maximize biodiesel yield, increase higher heating value (HHV), and minimize process energy per kilogram of fuel. Using 500 designed experiments varying seven factors ( methanol-to-oil ratio, catalyst loading, temperature, reaction time, mixing speed, free fatty acid content, and water content). We train multiple machine learning models and apply multi-objective optimization. XGBoost delivers the best predictive accuracy across all targets; SVR performs worst for HHV. A gate-to-gate life cycle assessment (per 1 kg biodiesel) quantifies environmental impacts. The optimizer yields 58 Pareto-optimal solutions; a balanced point achieves 85.9 wt% yield, 38.2 MJ kg⁻¹ HHV, and 5.1 MJ kg⁻¹ process energy. Compared to a conventional baseline, this solution reduces global warming potential, acidification, and eutrophication by 11.3% on average. Unlike sequential approaches that optimize performance first and assess sustainability afterward, our integrated ML–optimization–LCA framework unifies prediction, search, and environmental evaluation in a single workflow, explicitly revealing trade-offs among yield, fuel quality, and energy intensity. This enables producers to select operating points aligned with priorities – higher output, better fuel, or lower energy – while quantifying environmental co-benefits. Policymakers can leverage these insights to align biodiesel production with carbon reduction and efficiency targets, supporting cleaner, scalable deployment at plant scale.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108907"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of microalgae-integrated windows on building performance: Energy, daylight, and carbon analysis using an innovative simulation method","authors":"Sarah Nazari ,&nbsp;Iman Sheikhansari ,&nbsp;Payam Keshavarz Mirzamohammadi ,&nbsp;Foroogh Mirnataj ,&nbsp;Behrang Sajadi","doi":"10.1016/j.egyr.2025.12.042","DOIUrl":"10.1016/j.egyr.2025.12.042","url":null,"abstract":"<div><div>Buildings in hot semi-arid (BSk) climates face significant challenges in managing cooling loads while optimizing daylight and decarbonizing operations. This study evaluates the performance of microalgae-integrated glazing as an adaptive façade technology. A novel co-simulation framework was developed and validated (≈3 % U-factor error), coupling EnergyPlus and Radiance with a dynamic algae module that adjusts optical and thermal properties in real-time. The framework was used to assess static and dynamic configurations of two species (Chlorella vulgaris, Chlamydomonas reinhardtii) against conventional and water-filled windows across various orientations and Window-to-Wall Ratios (WWRs). Results demonstrate that orientation is critical to system selection. Dynamic microalgae systems are most effective on south and west façades, reducing cooling loads by up to 6.8 % compared to standard double-glazing. Conversely, static systems provide superior performance on east-facing façades. Both configurations outperform water-filled glazing in cooling-load reduction while enabling co-benefits. The façades actively sequester carbon at a rate of approximately 2.4 kg-CO₂/m² annually and generate electricity, though a trade-off exists between static (≈2.0 % efficiency) and dynamic (≈1.5 % efficiency) modes. This research provides orientation-specific design guidance and quantifies the multi-objective trade-offs, positioning bio-responsive façades as a viable solution for enhancing energy performance and carbon management in the built environment.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108905"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimizer for reliability improvement of a distributed energy system under uncertain climatic conditions","authors":"Adewale Obaro ,&nbsp;Josiah Munda ,&nbsp;Adedayo Yusuff","doi":"10.1016/j.egyr.2025.12.027","DOIUrl":"10.1016/j.egyr.2025.12.027","url":null,"abstract":"<div><div>The use of traditional optimization techniques in minimizing network losses and network reliability under uncertain weather conditions constitute a challenge in Distributed Energy Systems (DESs). This paper presents an innovative technique for reliability improvement and electrical power losses minimization of a DES under the dynamic nature of Renewable Energy Sources (RESs). The DES investigated is composed of a wind turbine, photovoltaic sources and energy system, in a radial network. In order to enhance power supply reliability and overcome the issues with traditional optimization techniques, a nature-inspired Flower Pollination Algorithm (FPA) which mimics the pollination characteristics of flowering plants is proposed to minimize overall power loss, improve voltage profile and improve reliability indexes while accounting for uncertainties in wind speeds and solar irradiance. The performance of the suggested scheme is tested under various scenarios of the IEEE 33 and 69 bus systems. The numerical results obtained indicates a significant minimization in total active power losses by 85.24 % and 74.91 % as well as reactive power losses by 85.07 % and 70.66 % for the IEEE33 and 69 networks respectively and improvement in various reliability metrics applied while maintaining an acceptable average voltage profile. This suggests that the proposed scheme can be adopted in addressing prevalent uncertainty issues associated with the reliability of RESs in DESs.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108890"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing UPFC compensation performance in simultaneous power and voltage control of the system with self-tuning PI controllers based on ANFIS design","authors":"Aymen Flah ,&nbsp;Habib Kraiem ,&nbsp;Masoud Dashtdar ,&nbsp;Lukas Prokop ,&nbsp;Claude Ziad El-Bayeh ,&nbsp;Mohammad Ghatasheh ,&nbsp;Maissa farhat","doi":"10.1016/j.egyr.2025.11.089","DOIUrl":"10.1016/j.egyr.2025.11.089","url":null,"abstract":"<div><div>Ensuring stability and effective oscillation damping in modern power systems remains a critical challenge, particularly under severe disturbances such as load variations or faults. Conventional stabilizers, such as the Power System Stabilizer (PSS), often exhibit limited performance across a wide range of operating conditions. To address these limitations, this paper presents an online adaptive PI–ANFIS controller for the Unified Power Flow Controller (UPFC), designed to simultaneously regulate line power flow, DC-link voltage, and generator terminal voltage. The proposed structure employs parallel ANFIS networks combined with PI controllers, where the PI gains are continuously adapted using a Recursive Least Squares (RLS)-driven learning mechanism, enabling real-time adjustment to nonlinear dynamics and parameter uncertainties. The effectiveness of the controller is validated through comprehensive simulations in MATLAB/Simulink as well as hardware-in-the-loop (HIL) experiments on a Speedgoat real-time target, ensuring both theoretical and practical feasibility. Performance is assessed under nominal and heavy-load conditions, including the most demanding Scenario 4 with 10 % power variation. Comparative evaluations against classical PI, metaheuristic-optimized controllers, and recent advanced designs (FPA–PID, O-FOPID, and NF-3DOF-PDN) demonstrate that the proposed controller achieves superior transient and steady-state responses. In particular, the results confirm significant improvements in settling time (up to 28 % faster), overshoot reduction (over 40 %), and minimized steady-state error (&lt;0.002 pu), while maintaining robustness against parameter variations. These findings establish the proposed online PI–ANFIS controller as a robust, adaptive, and real-time capable solution for enhancing UPFC performance and ensuring resilient power system operation under challenging scenarios.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"15 ","pages":"Article 108830"},"PeriodicalIF":5.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}