IET Renewable Power Generation最新文献

Building a Greener Future: A Techno-Economic Assessment of Renewable Configurations for Energy Independence and the Net-Zero Transitions in a Community Microgrid 建设更绿色的未来：社区微电网中能源独立和净零转型的可再生能源配置的技术经济评估

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-21 DOI: 10.1049/rpg2.70207

Muhammad Adnan Khan, Abdul Haseeb, Muhammad Mansoor Ashraf, Muhammad Waseem, Muhammad Ali Mughal, Fabiano Pallonetto

{"title":"Building a Greener Future: A Techno-Economic Assessment of Renewable Configurations for Energy Independence and the Net-Zero Transitions in a Community Microgrid","authors":"Muhammad Adnan Khan, Abdul Haseeb, Muhammad Mansoor Ashraf, Muhammad Waseem, Muhammad Ali Mughal, Fabiano Pallonetto","doi":"10.1049/rpg2.70207","DOIUrl":"https://doi.org/10.1049/rpg2.70207","url":null,"abstract":"Reliable and sustainable energy is fundamental to socio-economic development; however, Pakistan faces persistent energy challenges due to rising demand and heavy reliance on costly and environmentally harmful fossil fuels, particularly in Balochistan. Given Gwadar's abundant solar irradiation and wind resources, this study evaluates the feasibility of achieving a net-zero energy city through three renewable-based power system configurations using solar, wind, and battery storage. Model 1, relying solely on solar photovoltaic (PV) cells and batteries, yields a levelised cost of energy (LCOE) of $0.441/kWh, with a total net present cost (NPC) of $182 million. Model 2, integrating solar, wind turbines, and batteries, reduces the LCOE to $0.320/kWh, with an NPC of $133 million. Model 3 incorporates solar, wind, and a diesel generator, offering the lowest LCOE of $0.219/kWh and an NPC of $90.7 million. While Model 1 is the most environmentally sustainable, it has the highest LCOE and NPC. Model 3 ensures the highest reliability with the lowest LCOE and NPC at the expense of environmental impact due to diesel. The payback rate for Model 1 is 6.2 years; for Model 2, it is 7.79 years; and Model 3 has the lowest payback rate of 4.62 years. This study provides a comprehensive techno-economic analysis, identifying optimal solutions for Gwadar's energy requirements, which can be replicated in similar off-grid areas.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solar Irradiance Prediction Based on Satellite Image Data in Different Regions: A Deep Learning–Based Approach 基于不同区域卫星图像数据的太阳辐射预测：一种基于深度学习的方法

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-20 DOI: 10.1049/rpg2.70210

Ersan Omer Yuzer, Altug Bozkurt

{"title":"Solar Irradiance Prediction Based on Satellite Image Data in Different Regions: A Deep Learning–Based Approach","authors":"Ersan Omer Yuzer, Altug Bozkurt","doi":"10.1049/rpg2.70210","DOIUrl":"https://doi.org/10.1049/rpg2.70210","url":null,"abstract":"Solar irradiance is considered a fundamental parameter for photovoltaic systems. Stations established worldwide to measure solar irradiance are limited in terms of coverage. Therefore, many solar irradiance prediction models and techniques are being developed to obtain solar irradiance data. The influence of climatic factors on solar irradiance, its non-stationary nature, random variability, and complexity make prediction challenging. This study presents a short-term (minute) approach to predict global solar irradiance on a horizontal plane from satellite images, addressing all complexities and without relying on ground stations, providing broad coverage. In the proposed approach, convolutional neural network–based deep neural network architectures are employed to extract convolutional features from satellite images for the purpose of global solar irradiance on a horizontal plane prediction. Data obtained from satellite images were trained in an artificial neural network and tested and validated in regions of Turkey with different climatic conditions. Subsequently, the performance of the model is assessed and compared using common performance metrics in the literature, such as root mean square error and correlation coefficient (R), without considering the data of the predicted regions. The obtained results show that the proposed prediction model demonstrates high performance in some regions with an R value of over 97%. The prediction model may perform better if the larger training dataset is more similar to the test dataset. Indeed, successful prediction results have been obtained in regions located close to the training dataset in some areas. These results highlight that deep learning architectures can make significant contributions to research on renewable energy sources, such as solar energy.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147323875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Bidding Strategy for Electricity-Hydrogen Coupling Virtual Power Plant Participating in Demand Response and Peak Regulation Services 参与需求响应和调峰服务的电氢耦合虚拟电厂最优竞价策略

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-20 DOI: 10.1049/rpg2.70211

Liming Zhou, Yuyang Wang, Zejian Qiu, Yawen Xie, Nian Liu, Kuan Zhang, Jianpei Han

{"title":"Optimal Bidding Strategy for Electricity-Hydrogen Coupling Virtual Power Plant Participating in Demand Response and Peak Regulation Services","authors":"Liming Zhou, Yuyang Wang, Zejian Qiu, Yawen Xie, Nian Liu, Kuan Zhang, Jianpei Han","doi":"10.1049/rpg2.70211","DOIUrl":"https://doi.org/10.1049/rpg2.70211","url":null,"abstract":"The large-scale integration of volatile and intermittent new energy sources into the grid causes instability and increasing peak-valley power fluctuations and demand response. To address these issues, this paper presents an optimal bidding strategy for the Electricity-Hydrogen Coupling Virtual Power Plant (EH-VPP) in demand response and peak regulation services. Firstly, feasible region models of the adjustable power space for various heterogeneous resources in EH-VPP are established. By considering the time-varying energy and power characteristics of resources like energy storage systems (ESSs), electric vehicles (EVs), hydrogen refuelling stations (HRSs), wind turbines (WTs), and photovoltaics (PVs), a high-dimensional feasible region model is proposed to accurately capture the flexibility of distributed resources. In order to overcome the curse of dimensionality in calculating the total adjustable power of the interval, an internally approximate isomorphic polyhedron model is proposed. It reduces the dimensionality of complex high-dimensional power space regions by translating and scaling the basic isomorphic polyhedra. Additionally, an optimal bidding model based on the Conditional Value at Risk (CVaR) is developed. This model takes into account the diverse demand traits of demand response and peak regulation, along with the uncertainties and adjustable power range of the EH-VPP.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147300038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zeroing Neural Frameworks for Economic Dispatch in Power Systems 电力系统经济调度的归零神经框架

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-18 DOI: 10.1049/rpg2.70199

Qingfa Li

引用次数: 0

A Bi-Level Optimization Model for Carbon Reduction in Multi-Echelon Power Equipment Supply Chains Under Carbon Quota and Trading 碳配额与交易下多级电力设备供应链碳减排的双层优化模型

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-18 DOI: 10.1049/rpg2.70202

Wei Xu, Lu Zhang, Grace Kouassi

{"title":"A Bi-Level Optimization Model for Carbon Reduction in Multi-Echelon Power Equipment Supply Chains Under Carbon Quota and Trading","authors":"Wei Xu, Lu Zhang, Grace Kouassi","doi":"10.1049/rpg2.70202","DOIUrl":"https://doi.org/10.1049/rpg2.70202","url":null,"abstract":"The decarbonization of power systems is hindered by substantial embodied carbon from critical equipment manufacturing. This paper proposes a bi-level optimization framework to coordinate carbon abatement across the multi-echelon power equipment supply chain under carbon policies. The upper level model minimizes total lifecycle emissions by setting production targets. The lower level model maximizes the supply chain's economic profit through production scaling, abatement rates, and transfer pricing. A tailored genetic algorithm solves this NP-hard problem. Simulations using transformer and inverter data validate the framework. Results show carbon price creates dual incentives, driving carbon abatement and generating revenue via quota management. The abatement cost coefficient is the dominant factor. Its superlinear escalation creates investment barriers once critical thresholds are passed. Analysis reveals distinct technological responses: manufacturers achieve the highest reduction rates (78.3%–98.5%) via system-level innovation; suppliers attain 56.2%–68.5% reductions via component-level optimization; and distributors contribute 10.7%-38.6% abatement through logistics. This work provides a quantitative tool to translate carbon policies into echelon-specific technical guidance for a cost-effective decarbonisation.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Temporal Feature Fusion Network for Short-Term Photovoltaic Forecasting Utilising Multi-Source Heterogeneous Data 基于多源异构数据的光伏短期预测时空特征融合网络

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-17 DOI: 10.1049/rpg2.70209

Ping Yang, Zhuolin Zhong, Yingxuan Sun, Yuxuan Lin

{"title":"Spatial-Temporal Feature Fusion Network for Short-Term Photovoltaic Forecasting Utilising Multi-Source Heterogeneous Data","authors":"Ping Yang, Zhuolin Zhong, Yingxuan Sun, Yuxuan Lin","doi":"10.1049/rpg2.70209","DOIUrl":"https://doi.org/10.1049/rpg2.70209","url":null,"abstract":"Accurate short-term photovoltaic (PV) power forecasting plays a key role in mitigating challenges from PV's intermittent, stochastic output — for example, grid frequency fluctuations — thereby ensuring grid stability and optimising renewable energy scheduling. However, modelling multi-source heterogeneous data and extracting spatial-temporal features remains challenging. To address this, we proposed a novel deep hybrid model integrating self-attention enhanced convolutional block attention modules (SCBAM), temporal convolutional networks (TCN) and U-net (UNet)-based feature fusion; this model is abbreviated as SCTU. It processes multi-grid/single-grid numerical weather prediction (NWP) data, alongside historical power data, via five parallel branches (tailored for spatial/temporal extraction). We evaluated SCTU on two Desert Knowledge Australia Solar Centre stations. Results show it outperforms benchmarks: normalised root mean square error (NRMSE) 3.985%/5.791% and coefficient of determination (R2) 0.9509/0.9397 on eco-kinetics (EK-PV) and service station (SS-PV), respectively. Across sunny/overcast/rainy, SCTU maintains lower NRMSE (EK-PV 3.432%–6.766%, SS-PV 5.331%–7.246%) than benchmarks. Ablation studies confirm the importance of SCBAM, UNet and multi-grid input in enhancing accuracy. SCTU exhibits strong robustness (rainy-day NRMSE: EK-PV 6.766%, SS-PV 7.246%, lower than TCN, respectively) and generalisation (EK-PV/SS-PV datasets: R2 0.9509/0.9397, NRMSE 3.985%/5.791%). Notably, it relies on high-resolution NWP — delays or reduced extreme-weather accuracy may lower performance — and its generalisation to high-latitude regions needs validation.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Management of Battery-Based Renewable Vehicles Refuelling Station to Charge of Electric Vehicles and Fuelling of Fuel-Cell Cars Considering Intelligent Distribution Network Objectives 考虑智能配电网目标的电池可再生能源汽车加气站对电动汽车充电和燃料电池汽车加油的能量管理

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-13 DOI: 10.1049/rpg2.70195

Mehdi Veisi

{"title":"Energy Management of Battery-Based Renewable Vehicles Refuelling Station to Charge of Electric Vehicles and Fuelling of Fuel-Cell Cars Considering Intelligent Distribution Network Objectives","authors":"Mehdi Veisi","doi":"10.1049/rpg2.70195","DOIUrl":"https://doi.org/10.1049/rpg2.70195","url":null,"abstract":"Electric and fuel cell vehicles represent emerging energy consumers reliant on power systems, deriving their energy through hydrogen refuelling or charging stations linked to the power grid. Ensuring these stations do not degrade the network's technical performance necessitates effective energy management strategies. This study introduces an economically oriented energy scheduling framework for vehicle refuelling stations supplied by batteries and renewable sources such as photovoltaic, bio-waste and wind within an intelligent distribution grid. The proposed setup integrates both electric vehicles charging stations and hydrogen refuelling stations; the bi-level programming is used. Upper-level objective seeks to minimise the combined energy loss and operation costs of the grid, subject to optimal power flow constraints. The lower level considers the minimisation of operational costs of the refuelling stations. Constraints account for the operational models of renewable units, batteries and various refuelling station types, with consideration for reactive power management. Uncertainties associated with loads, prices, renewable resource power and vehicle station operations are modelled using stochastic optimisation. The Karush-Kuhn-Tucker methodology is employed to identify optimal solutions. Numerical results demonstrate the framework's capability to optimise station operations while improving both operational and economic aspects of the network. Findings reveal that operating a station powered only by the grid results in a 144.6% rise in grid operation costs and a 167.6% increase in energy losses. Additionally, the voltage drop is more than 0.1 per unit and the electrical lines face an excessive load of 34.7%. The scheme enhances the grid's economic conditions by 51.3% to 74.5% and operating performance by 17.7% to 148.1%, compared to constructing a station devoid of renewable sources or batteries.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coherency Identification and Aggregation Algorithms for Parallel DAB Systems in DC Microgrids 直流微电网并联DAB系统的相干识别与聚合算法

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-12 DOI: 10.1049/rpg2.70203

Haoyu Wang, Tong Jiang, Shiwei Xia

{"title":"Coherency Identification and Aggregation Algorithms for Parallel DAB Systems in DC Microgrids","authors":"Haoyu Wang, Tong Jiang, Shiwei Xia","doi":"10.1049/rpg2.70203","DOIUrl":"https://doi.org/10.1049/rpg2.70203","url":null,"abstract":"The high complexity and long computation time in simulating dual-active-bridge (DAB)-based parallel DC microgrid systems pose significant challenges. To address this, coherency identification and aggregation algorithms (CIAA) for multi-DAB parallel DC microgrid systems (MDPDMS) are proposed. First, a correlation model between common DC bus voltage disturbances and measurable DAB output quantities is established. Using output current fluctuation similarity as the coherency criterion, a dynamic similarity index is derived. Coherent DAB groups (CDGs) are then adaptively identified using the K-Medoids algorithm, with the clustering results refined by a defined cost function. Second, under the core constraints of power conservation and dynamic response equivalence, a mapping is established between the equivalent model (EM) and the internal topology and control parameters of the CDG to accurately aggregate parameter-heterogeneous DABs. The proposed method maintains high fidelity to the dynamic characteristics of the detailed system (DS) while significantly improving simulation efficiency. The simulation time of the reduced-order system (ROS) is only 56% of that required by the detailed system.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146680414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Efficiency in PVT Solar Technology by Leveraging Artificial Intelligence in Intelligent Thermal Management 利用人工智能在智能热管理中提高PVT太阳能技术的效率

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-11 DOI: 10.1049/rpg2.70187

Mohammad Albarahati, Nan Zhao, Hassan A. Shafei, George Aggidis

{"title":"Advancing Efficiency in PVT Solar Technology by Leveraging Artificial Intelligence in Intelligent Thermal Management","authors":"Mohammad Albarahati, Nan Zhao, Hassan A. Shafei, George Aggidis","doi":"10.1049/rpg2.70187","DOIUrl":"https://doi.org/10.1049/rpg2.70187","url":null,"abstract":"Photovoltaic-Thermal (PVT) systems have a strong potential to improve solar technology in energy generation and conversion. The performance of PVT systems is, however, critically limited by the effect of elevated operating temperatures on photovoltaic efficiency under dynamic conditions. Traditional thermal management strategies limitedly address the non-linear, stochastic, and multi-objective challenges that are inherent to PVT system operation. This paper critically reviews the current application of Artificial Intelligence (AI) as a transformative technology for intelligent thermal management in PVT systems to improve PVT systems’ efficiency.We cover about 130 papers from the last decade, analysing the application of AI paradigms such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), Deep Reinforcement Learning (DRL) and Physics-Informed Neural Networks (PINNs) to solar PVT systems. The contribution of this work is its focus on thermal management that integrates modern concepts of edge AI, digital twins, and trustworthy AI. It also presents a rigorous comparative analysis of AI against traditional control methods. We also perform analysis through qualitative comparison tables of AI techniques and a visual taxonomy of AI applications. The key research gaps are identified in the study, including the scarcity of standardised validation datasets, the challenge of sim-to-real transfer and the need for a strong and computationally efficient edge deployment. The review then focuses on a strategic research roadmap which advocates for a focus on hybrid physics-AI models, verifiable digital twins, and explainable AI (XAI) to build strong, efficient, and autonomous PVT infrastructures.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Two-stage Zoned Low-carbon Dispatching Strategy for the Distribution System That Takes Into Account the Aggregation of Multiple Resources in the Distribution Station Area 考虑配站区域多种资源聚集的配电系统两阶段分区低碳调度策略

IF 2.9 4区工程技术

IET Renewable Power Generation Pub Date : 2026-02-11 DOI: 10.1049/rpg2.70204

Gan Jin, Lijuan Chen, Yang Liu, Xiaohui Xu

{"title":"A Two-stage Zoned Low-carbon Dispatching Strategy for the Distribution System That Takes Into Account the Aggregation of Multiple Resources in the Distribution Station Area","authors":"Gan Jin, Lijuan Chen, Yang Liu, Xiaohui Xu","doi":"10.1049/rpg2.70204","DOIUrl":"https://doi.org/10.1049/rpg2.70204","url":null,"abstract":"Aiming at the source-network-load interaction problem from the perspective of low-carbon transformation of the distribution system, this paper proposes a two-stage zoned low-carbon dispatching strategy for the distribution system that takes into account the multiple resource aggregations in the distribution station area (DSA). First, a feasible domain aggregation model of multiple flexible resources in DSA is constructed to describe the external equivalent characteristics of DSA in terms of power, energy, and ramp, so as to fully explore the dispatchable potential of each DSA. Second, the carbon emission of the distribution zone is analysed by using the carbon emission flow (CEF) theory, and the carbon emission responsibility of each distribution zone is apportioned based on the Shapley value method, and the load carbon emission responsibility interval is divided. Then, a two-stage zoned low-carbon dispatch model of distribution system considering source-load interaction is constructed, in which the first stage adopts the zoned coordinated operation method to carry out day-ahead low-carbon dispatch of the distribution system, and in the second stage, the ladder carbon price is used as a signal to guide the loads to respond in a low-carbon manner. Finally, the simulation is carried out using real distribution system examples, and the results show that the proposed scheduling method can effectively promote new energy consumption and reduce system operating costs and carbon emissions.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0