Renewable Energy Focus最新文献

{"title":"Mitigating emissions: energy balancing in eco-industrial zones considering renewable energy and electric vehicle uncertainties","authors":"Aminabbas Golshanfard ,&nbsp;Younes Noorollahi ,&nbsp;Hamed Hashemi-Dezaki ,&nbsp;Henrik Lund","doi":"10.1016/j.ref.2025.100768","DOIUrl":"10.1016/j.ref.2025.100768","url":null,"abstract":"<div><div>Nowadays, the industrial sector stands as the major energy consumer globally, simultaneously holding a pivotal role as a significant contributor to greenhouse gas emissions. Therefore, energy system planning and management in these systems are under heightened scrutiny due to concerns over energy, economic, and environmental challenges. This study aims to develop a comprehensive optimal model that integrates renewable potential assessment and utilizes particle swarm optimization for accurate and cost-effective planning and operation of the energy system within an industrial zone. The research proposes a novel strategy for planning and operating industrial energy hubs, offering a robust and adaptable framework tailored to industrial zones. By integrating uncertain renewable energy sources and EVs, the framework effectively manages variability and uncertainty. It holistically connects electricity, heating, cooling, and transportation sectors, enabling cross-sectoral flexibility and enhancing system adaptability. The study compares four scenarios: BAU, BAU CO₂-Aware, CO₂-Blind, and CO₂-Aware, evaluating their impact on energy costs, investment, operational cost, and environmental benefits. The results show that the CO₂-Aware and CO₂-Blind scenarios reduce overall costs by approximately 15% and 10%, respectively, compared to the BAU. Additionally, the CO₂-Aware scenario achieves a 32% reduction in CO₂ emissions. Despite higher investment and operational costs, these alternative energy systems provide substantial economic and environmental advantages. Additionally, the implementation of this smart energy system within the industrial zone has addressed certain energy challenges in the studied region, such as mitigating electricity shortages during summer and alleviating natural gas shortages in winter.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100768"},"PeriodicalIF":5.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical energy management system for coordinated operation of multiple grid-tied home microgrids","authors":"Omar Muhammed Neda ,&nbsp;Jafar Adabi ,&nbsp;Mousa Marzband ,&nbsp;Hamidreza Gholinezhadomran","doi":"10.1016/j.ref.2025.100766","DOIUrl":"10.1016/j.ref.2025.100766","url":null,"abstract":"<div><div>A smart neighborhood (SN) comprising multiple home microgrids (HMGs) can provide cost-efficient electricity to end-users while supporting the main grid through ancillary services. The integration of renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs) introduces dynamic challenges, particularly under varying EV charging behaviors. To address these challenges, this study develops a hierarchical energy management system (HEMS) formulated as an optimization problem and solved using the Aquila optimizer (AO). The proposed HEMS enables the SN to operate as a cloud-based energy storage system (cloud-based ESS), minimizing energy imports from the main grid while maximizing local self-consumption and revenue. The performance of AO is benchmarked against the Particle Swarm Optimization (PSO) algorithm under two control architectures: (i) individual operation, where each local EMS (LEMS) optimizes its own HMG, and (ii) coordinated operation, where a central EMS (CEMS) synchronizes all HMGs, enabling the SN to function collectively as a cloud-based ESS. Simulation results highlight the superior performance of AO under the coordinated CEMS framework. For standard operation, AO reduces main grid imports to 30.62 kWh compared to 61.66 kWh, maintains higher SOC levels across ESSs and EVs (up to 90%), delivers greater total revenue (£44.662 vs. £22.907), and minimizes cumulative error (10.2% vs. 18.7%). Under different EV charging behaviors, AO demonstrates robust adaptability, achieving lower grid imports (40.43 kWh vs. 49.97 kWh), maintaining higher SOC across ESSs and EVs (up to 88.5%), delivering greater total revenue (£15.311 vs. £12.101, +26.5%), and reducing cumulative error from 158.19 to 146.25 (7.6% improvement). These results confirm that the AO-based HEMS efficiently coordinates distributed energy resources, enabling the SN to function as a reliable cloud-based ESS. It improves energy efficiency, economic returns, and grid support while maintaining resilience under dynamic EV charging conditions, providing a scalable and adaptive framework for future SN energy management.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100766"},"PeriodicalIF":5.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Location, location, location: optimal placement of new electricity production in the nordic energy system amidst large-scale electrification","authors":"Joel Bertilsson,&nbsp;Lisa Göransson,&nbsp;Filip Johnsson","doi":"10.1016/j.ref.2025.100765","DOIUrl":"10.1016/j.ref.2025.100765","url":null,"abstract":"<div><div>Renewable electricity generation is expected to play a pivotal role in the global shift toward electrification. However, the inherent variability of renewable energy sources, in addition to factors such as local weather patterns and grid limitations, poses a significant challenge in terms of determining the optimal size and placement of distributed generation units. This study tackles this issue by applying a novel, high-resolution energy systems model that is tailored to the Nordic region. The model is designed to capture with high accuracy local nuances in relation to grid infrastructure, weather patterns, and demand profiles. The model minimizes the total system costs, accounting for both investment and operational expenditures, through the optimal integration of variable renewable energy sources and dispatchable generation units. The findings indicate that the siting of renewable generation is primarily influenced by a combination of a high number of full-load hours and proximity to the electricity demand, with the latter becoming increasingly important under high-demand conditions. Among renewable technologies, solar photovoltaic systems exhibit the strongest correlation with demand center proximity, whereas offshore wind is mainly constrained by a high potential annual production capacity. In addition, assumptions regarding the availability of electricity grid capacity are shown to have a significant impact on the results, with up to 26% of production being relocated when 100 % thermal grid capacity is available, as compared to when 30% of grid capacity is reserved for contingency events.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100765"},"PeriodicalIF":5.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding malaysian homeowners’ intention to adopt photovoltaic systems for EV charging: An integrated theory of planned behaviour and diffusion of innovation approach","authors":"Raymond Chew Yuet Mun ,&nbsp;Cindy Chuah ,&nbsp;Stephen T. Homer","doi":"10.1016/j.ref.2025.100767","DOIUrl":"10.1016/j.ref.2025.100767","url":null,"abstract":"<div><div>This study investigates the behavioural and innovation-related factors influencing Malaysian homeowners’ intention to adopt photovoltaic (PV) systems for electric vehicle (EV) charging. Despite Malaysia’s push toward sustainability, current policies treat PV and EV technologies in isolation, missing opportunities for integrated, low-carbon solutions. By combining the Theory of Planned Behaviour and Diffusion of Innovation theory, the study examines how attitudes, subjective norms, and perceived behavioural control impact intention, and how these relationships are mediated by relative advantage and compatibility. Data from 197 EV-owning homeowners were analysed using Partial Least Squares Structural Equation Modelling. Results show that attitude and perceived behavioural control significantly influence intention, mediated by relative advantage, while subjective norms had no significant effect. Compatibility influenced intention directly but not as a mediator. The findings contribute to understanding sustainable technology co-adoption in emerging markets, offering practical and policy insights for promoting PV adoption to support EV charging in Malaysia.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100767"},"PeriodicalIF":5.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DER flexibility procurement in a centralized ancillary services market: the significance of positive TSO-DSO interaction","authors":"Rohit Vijay,&nbsp;Parul Mathuria","doi":"10.1016/j.ref.2025.100764","DOIUrl":"10.1016/j.ref.2025.100764","url":null,"abstract":"<div><div>The integration of high levels of renewables necessitates the procurement of distributed energy resources (DERs) for flexibility-based services, such as frequency, reactive power provisions, and congestion management, to ensure secure operation at both transmission and distribution levels. However, procuring flexibility-based services from DERs presents challenges due to the interdependencies in service activations between the TSO and DSO. One potential solution is to acquire DER flexibility through a centralized market jointly managed by the TSO and DSO. This presents two key challenges: i) the allocation of DER services cost between TSO and DSO based on their specific objectives; and ii) the quantification of the cross-impact cost. To address these challenges, this manuscript proposes cost allocation by considering the TSO’s objective of global balancing and the DSO’s responsibilities for congestion management and maintaining reactive power provisions. The obtained results show that the cost distribution shifts due to the cross-impact of one system operator’s actions on the other, highlighting the need for coordination, though the total cost of flexibility procurement remains largely unchanged outside peak times. Further, the reactive power provision and distribution system congestion lead to increased cost share for the DSO, despite stable overall procurement costs. This is driven by the DER’s active power adjustments to maintain the Q/P ratio, leading to subsequent opportunity cost for the DSO.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100764"},"PeriodicalIF":5.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time web inference of a BiLSTM-informer hybrid model for enhanced photovoltaic power output forecasting","authors":"Kehinde Ridwan Kamil,&nbsp;Umar F. Khan,&nbsp;Ray E. Sheriff,&nbsp;Hafeez Ullah Amin","doi":"10.1016/j.ref.2025.100763","DOIUrl":"10.1016/j.ref.2025.100763","url":null,"abstract":"<div><div>To ensure an efficient Photovoltaic (PV) renewable energy grid, it is essential to address the uncertainty inherent in power systems. An efficient energy management system must be capable of prioritising energy distribution based on an applicable and effective real-time forecasting of the generation output of the PV system. This study proposes a novel BiLSTM-Informer hybrid model that outperforms benchmarked machine and deep learning approaches in forecasting multi-step PV output by addressing their inability to capture non-linear temporal dependencies and lack of dynamic features weighting. A 39.2 kWp PV system serves as a case study, incorporating location-specific weather parameters. The proposed model integrates Fourier transformation, cyclic encoding, and autoregressive feature optimization to enhance pattern recognition and short-term variability. It achieved superior accuracy, with a mean absolute error (MAE) of 1.22 kWh, a root means square error (RMSE) of 2.21 kWh, and a coefficient of determination (R²) of 0.952. This reflects a 20.1 % increase in online forecasting accuracy. Unlike previous studies, this work integrates real-time web inferencing using a Streamlit interface on Orender, thereby validating the model’s robustness under live deployment. The model demonstrated forecasting accuracy ranging from 89 % to 97.3 % across multiple forecasting (1-hour to monthly) horizons with reduced computational overhead. These results position the BiLSTM-Informer as a novel benchmark for real-time PV forecasting and intelligent power grid management. The data and pre-trained models are available at the dedicated GitHub repository: <span><span>https://github.com/kamilkenny/EDA</span><svg><path></path></svg></span> and the Inferenced Model link is: <span><span>https://kamil-deployment-of-edgehill-durning.onrender.com/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100763"},"PeriodicalIF":5.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hourly energy demand impacts of battery electric vehicle adoption in Italy: A grid simulation and policy analysis","authors":"Hamid Safarzadeh,&nbsp;Maryam Ebrahimzadeh Sarvestani,&nbsp;Mahdi Enayati,&nbsp;Francesco Di Maria","doi":"10.1016/j.ref.2025.100761","DOIUrl":"10.1016/j.ref.2025.100761","url":null,"abstract":"<div><div>The growing adoption of Battery Electric Vehicles (BEVs) poses significant challenges to electricity grids, especially in countries aiming for rapid decarbonization. This study evaluates the hourly impact of BEV integration on Italy’s energy system using a Python-based simulation model. Two scenarios are analyzed for 2024: (1) 3.5 million BEVs and (2) 7 million BEVs. The model incorporates hourly charging profiles for household and highway fast-charging, Italy’s renewable energy mix (solar, wind, hydro, bioenergy), and a 5 GWh battery energy storage system. Results show that Scenario 1 increases daily electricity demand by 19 % (to 1.1 TWh), with peak loads of 47–49 GW, requiring 152 GWh of thermal generation and emitting 76,000 tons of CO₂ daily. Scenario 2 raises demand by 40 % (to 1.25 TWh), with peak loads of 50–53 GW, 224 GWh of thermal generation, and 112,000 tons of CO₂ emissions. Existing storage mitigates 20 % of peak load but is insufficient for Scenario 2’s 15 GW shortfall. Key demand spikes occur at 01:00 and 11:00–18:00, coinciding with home and highway charging. Policy strategies such as time-of-use tariffs, expanding storage to 15 GWh, and doubling solar capacity could reduce emissions by up to 35 % and supply 80 % of BEV charging needs during daylight hours. This hourly-resolution analysis offers critical insights for grid planning and supports the EU’s Fit for 55 targets.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100761"},"PeriodicalIF":5.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated deep MPC-enabled digital twin and multiagent learning framework for secure and scalable smart nano grid energy management","authors":"Ibrahim Sinneh Sinneh,&nbsp;Sun Yanxia","doi":"10.1016/j.ref.2025.100762","DOIUrl":"10.1016/j.ref.2025.100762","url":null,"abstract":"<div><div>This study introduces a novel Federated Secure Dynamic Optimization Framework (FSDOF) to improve smart nano grid systems’ energy management, fault tolerance, and cybersecurity. The framework suggested combines Digital Twin (DT) technology and Multiagent Reinforcement Learning (MARL) to assist in real-time decision-making and decentralized control. In essence, FSDOF integrates three major components: Federated Deep Model Predictive Control (FD-MPC) to schedule energy optimally, SecureGraph-FedNet (SG-FedNet) to communicate through Graph Neural Networks and Autoencoders securely, and Dynamic Stochastic Neuro-Evolution Optimizer (DSNEO) to adaptively handle and optimize under uncertainty. The system demonstrated 98.52 % energy efficiency, DC voltage stabilization in 0.5 seconds, and Bit Error Rate (BER) of 0.012, which is better than the traditional DRL methods. SG-FedNet guarantees a security confidence of 0.99 in federated learning with differential privacy. Scalability and resilience of the system were confirmed by large-scale simulations on more than 100 nodes with less than 4 % performance degradation. These findings make FSDOF a scalable and strong solution to next-generation smart energy networks.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100762"},"PeriodicalIF":5.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bi-Level collaborative optimization strategy for power quality in distribution networks based on fuzzy triple black hole multi-objective optimization algorithm","authors":"Xiaohui Yang,&nbsp;Jiajing Xu,&nbsp;Chilv Wu,&nbsp;Lingjun Guo,&nbsp;Zhicong Wang,&nbsp;Rui Zhong,&nbsp;Zekai Tu,&nbsp;Peng Yang","doi":"10.1016/j.ref.2025.100760","DOIUrl":"10.1016/j.ref.2025.100760","url":null,"abstract":"<div><div>With the large-scale integration of renewable energy units and electric vehicles (EVs) into distribution networks, enhancing the power quality of these networks has emerged as a critical issue requiring immediate attention. Meanwhile, existing solution methods are inadequate for meeting the multi-objective optimization needs of distribution networks. This study establishes a bi-level collaborative optimization strategy for improving power quality in distribution networks. Specifically, the upper planning tier aims to minimize comprehensive costs through multi-component collaborative planning. The lower operational tier, based on the comprehensive performance evaluation decision model (CPEDM), conducts coordinated scheduling of multiple components by considering both economic benefits and power quality indicators. Furthermore, a fuzzy triple black hole multi-objective optimization algorithm (MOFTBH), which boasts high solution quality, uncertainty handling capabilities, and high adaptability, is developed and employed to solve the bi-level collaborative model. The study focuses on the IEEE-33 system as the research subject, leveraging the MOFTBH for analysis. Simulation results indicate that the optimization strategy presented in this study improves economic benefits and power quality by 45.43% and 19.90%, respectively, compared to the case without any optimization. Specifically, indices such as voltage deviation, voltage fluctuation, and harmonic distortion have improved by 39.01% , 127.45% and 113.14% , MOFTBH demonstrates a 30% faster Pareto front convergence rate compared to benchmark algorithms, with a 25% improvement in solution set uniformity. Under equivalent iteration counts, the objective function values show an optimization range of 18.7%–23.4%. This planning model aims to provide intelligent and green strategies for future smart grid construction and facilitate the commercial expansion of distribution network operators.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100760"},"PeriodicalIF":5.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing financial feasibility and equity prospects in agrivoltaics: a case study of Hachinohe, Japan","authors":"Xiao Chen ,&nbsp;Vibhas Sukhwani ,&nbsp;Bijon Kumer Mitra ,&nbsp;Anudari Batsaikhan ,&nbsp;Rajib Shaw","doi":"10.1016/j.ref.2025.100751","DOIUrl":"10.1016/j.ref.2025.100751","url":null,"abstract":"<div><div>Agrivoltaics, which combines the use of land for both agriculture and photovoltaic energy production, is emerging as a promising solution to the land use conflicts between farming and renewable energy. By simultaneously boosting crop yields, enhancing solar panel efficiency and revitalising rural incomes, agrivoltaics is attracting farmers and solar developers, prompting innovation in technology and business models. Nevertheless, the key to scaling agrivoltaics depends on its commercial viability for different stakeholders, an area that still requires further exploration. To bridge this gap, this research examines the business model of an ongoing agrivoltaic project in the Hachinohe region of Aomori Prefecture, Japan, with a particular focus on financial feasibility and equitable distribution of benefits among stakeholders. The study applies multi-criteria decision-making method to assess the project’s overall financial feasibility using NPV, IRR, and payback periods, while also exploring the equity implications through Gini coefficients. Thereafter, a sensitivity analysis is conducted to offer policy suggestions such as revenue-sharing, better lease terms, and subsidies for farmers, with the purpose of enhancing rural economic revitalization and inform equitable business model design in Japan’s energy transition. Drawing on experiences from Europe and the United States, this research emphasizes the active engagement of farm owners in the development and implementation of agrivoltaic projects to enhance financial feasibility, equity and stakeholder participation.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"56 ","pages":"Article 100751"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}