Abstract Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems (HIES), which enhance energy sustainability through coordinated electricity, thermal,natural gas, and hydrogen utilization.This study proposes a two-stage distributionally robust optimization (DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjust flexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set, enabling robust decision-making.The column-and-constraint generation (C&CG) algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%, increases photovoltaic consumption rates by 5.44%, and significantly lowers carbon emissions compared to conventional approaches.Furthermore, the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective, low-carbon energy systems while ensuring grid stability under uncertainty.

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Abstract With the intensifying global climate crisis, carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction, attracting significant attention from government agencies and academia worldwide.As of January 2024, 28 carbon trading markets have been established globally,encompassing approximately 17%of global greenhouse gas emissions and serving approximately 1/3 of the global population.With various nations setting carbon neutrality targets and delineating carbon reduction pathways,the construction, operation, and regulatory frameworks of carbon markets are becoming increasingly refined and comprehensive.This study elucidates the importance and necessity of establishing carbon markets from the perspective of energy system transformation and sustainable economic development.Second,it provides a comparative analysis of the operational mechanisms,trading scales,and emission reduction outcomes of major carbon markets in the European Union,United States,and New Zealand,systematically summarizing their development processes and recent advancements.Finally,this study addresses issues and challenges in the construction of China’s carbon market.Drawing on the successful experiences of leading global carbon markets in institutional design and market operations, we propose development strategies and recommendations for a carbon market with Chinese characteristics.These strategies are intended to align with international standards while meeting China’s national conditions,thereby contributing insights into the global carbon market trading system.

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Abstract This study analyzes the potential impact of EU carbon border regulation mechanism (CBAM) on the export of China’s carbonintensive products.First, we summarized the main content of the CBAM.Next, based on the input-output theory, this study proposes a calculation model for the implicit carbon emissions and indirect carbon emissions from electricity consumption in export products and presents the corresponding calculation results.Based on the scenario analysis method, six carbon tariffscenarios were designed to evaluate the impact of the CBAM on the major export sectors under each scenario.The results showed that in 2021, the implicit carbon emissions in all products exported to Europe from China were approximately 375 million tons, of which the indirect carbon emissions from electricity were approximately 41.8 million tons, accounting for more than 10%.According to the current levy plan, China is expected to be subject to carbon tariffs of approximately USD 1.4 billion, accounting for 0.3% of its total export value to Europe in 2021.Finally, to reduce the adverse effects of CBAM, four measures were proposed from the perspective of the power industry.

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Abstract The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape.To address this issue,an off-grid microgrid solution integrated with energy storage systems is proposed in this study.Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents.A comprehensive review of the design,control strategies,energy management,and optimization of off-grid microgrids based on domestic and international research is presented in this study.It also explores the critical role of energy storage systems in enhancing microgrid stability and economic efficiency.Additionally,the capacity configurations of energy storage systems within off-grid networks are analyzed.Energy storage systems not only mitigate the intermittency and volatility of renewable energy generation but also supply power support during peak demand periods, thereby improving grid stability and reliability.By comparing different energy storage technologies,such as lithium-ion batteries,pumped hydro storage,and compressed air energy storage,the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study.Finally,using a typical microgrid as a case study, an empirical analysis of off-grid microgrids and energy storage integration has been conducted.The optimal configuration of energy storage systems is determined, and the impact of wind and solar power integration under various scenarios on grid balance is explored.It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy,reduce system operating costs,and strengthen grid resilience under extreme conditions.This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.

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Abstract This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo,utilizing the region’s significant solar energy potential and high irradiation levels.This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization(GWO)algorithms to maximize energy output,reduce levelised energy costs,and maximally reduce carbon emissions.Results show Tarakan as the most optimal location,generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO2e,while Samarinda is the least optimal location.Economically, electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value (NPV) with a payback period (PP) of 9-12 years, while a tariffof IDR 1,500/kWh is considered unfavorable.The findings demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project’s financial viability,with a positive NPV and reasonable PP.Implementing renewable energy systems in Kalimantan Island can improve energy effi-ciency and significantly reduce carbon emissions, supporting environmental sustainability goals.

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Abstract The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales, leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly, we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to minimize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales: day-ahead and intraday.For the day-ahead timescale, we developed an improved particle swarm optimization (IPSO) algorithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated regulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.

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Abstract Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems.This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand.To address these issues,this study proposes a load resource management (LRM) method to cope with the sudden power disturbances.The LRM method supports primary frequency and voltage regulation,and its integration with network dynamics minimizes the established disutility function caused by load participation.For better control performance,a non-dominated sorting genetic algorithm-II(NSGA-II)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir,reduce the frequency/voltage steady-state error, and minimize the total load control efforts.To validate the effectiveness of the proposed approach,comparative experiments were conducted with three load-resource management technologies for primary regulation auxiliary services in MATLAB/Simulink.Compared to the conventional optimal load control or using LRM alone, the improved NSGA-II-based LRM demonstrates superior performance.It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.

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Abstract Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions.The findings were compared with those derived from an optimal proportional integral derivative (PID) controller designed using the artificial bee colony (ABC) algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data, it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks, emphasizing performance metrics such as disturbance rejection, response to load changes, and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation; however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.

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Abstract To address the problem of high lifespan loss and poor state of charge (SOC) balance of electric vehicles (EVs) participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving scenarios.First, considering the difference between peak and valley loads and the operating costs of EVs, a peak shaving model for EVs is constructed.Second, the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer (GEO) algorithm.Subsequently, IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next, using the k-means algorithm, EVs are dynamically divided into priority charging groups,backup groups, and priority discharging groups based on SOC differences.Finally, a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs, whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified, and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The proposed EV grouping control strategy effectively reduces the peak-valley difference in the power grid, reduces the operational life loss of EVs, and maintains a better SOC balance for EVs.

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Abstract Integrated-energy systems (IESs) are key to advancing renewable-energy utilization and addressing environmental challenges.Key components of IESs include low-carbon, economic dispatch and demand response, for maximizing renewable-energy consumption and supporting sustainable-energy systems.User participation is central to demand response; however, many users are not inclined to engage actively; therefore, the full potential of demand response remains unrealized.User satisfaction must be prioritized in demandresponse assessments.This study proposed a two-stage, capacity-optimization configuration method for user-level energy systems considering thermal inertia and user satisfaction.This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual,total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction.Indoor heating is adjusted, for optimizing device output and load profiles, with a focus on typical, daily,economic,and environmental objectives.The study findings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction.This optimization mitigates environmental concerns and enhances clean-energy integration.

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Abstract Accurate identification of unknown internal parameters in photovoltaic (PV) cells is crucial and significantly affects the subsequent system-performance analysis and control.However, noise, insufficient data acquisition, and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence, this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing, including data denoising and prediction.Furthermore, the AEO algorithm is utilized to identify unknown parameters in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM).Nine other metaheuristic algorithms(MhAs) are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing combined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance, under denoised data, the accuracies of the SDM, DDM, and TDM increase by 99.69%, 99.70%, and 99.69%, respectively, whereas their accuracy improvements increase by 66.71%, 59.65%, and 70.36%, respectively.

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Abstract When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system, voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can be used to divide the receiving system into several interconnected sub-partitions and improve the voltage support capability of the receiving system.Compared with asynchronous interconnection, which completely separates the receiving systems with VSC-HVDC, incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids.To analyze the voltage support capability of the VSC in incomplete segmentation,a micro-incremental model of the VSC was established,the operating impedance of the VSC was calculated, and the voltage support function of the VSC was quantified.The effect of the fault on the system short-circuit capacity was analyzed,and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained.A VSCsegmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform, and the validity of the microincrement model and accuracy of the proposed conclusions were verified.

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