This paper develops an innovative computational model for assessing the Carbon Emission Factor (CEF) of provincial power systems that incorporates inter-provincial electricity transfers and hybrid generation portfolios combining conventional and renewable sources. A key contribution lies in evaluating how deep regulation of thermal power plants influence the carbon intensity of coal-fired generation and coal-fired generation together with high penetration renewables. Furthermore, the study quantitatively analyzes the role of renewable energy consumption and the prospective application of Carbon Capture and Storage (CCS) in reducing system-wide CEF. Based on this framework, the paper proposes phased carbon emission targets for Guangdong’s power system for key milestone years (2030, 2045, 2060), along with targeted implementation strategies. Results demonstrate that in renewable-dominant systems, deep regulation of thermal units, load peak-shaving, and deployment of flexible resources such as energy storage are effective in cutting carbon intensity. To achieve the defined targets—0.367 kg/kWh by 2030, 0.231 kg/kWh by 2045, and 0.032 kg/kWh by 2060—the following innovation-focused policy is recommended: in early stage, mainly on expansion of renewable capacity and inter-provincial transmission infrastructure along with energy storage deployment; in mid-term, mainly on enhancement of electricity market mechanisms to promote green power trading and demand-side flexibility; and in late-stage, mainly on systematic retirement of conventional coal assets coupled with large-scale CCS adoption and carbon sink mechanisms.

Carbon peak and carbon neutralization are the inevitable way to solve energy security, which shows China’s ambition and determination to unswervingly follow the green development path, and provides direction guidance and fundamental guidance for China to deal with climate change and promote green and low-carbon development. Firstly, this paper analyzes the energy development trend of low-carbon parks, by establishing a smart park model with ultra-clean energy supply, the energy demands of various low- and near-zero carbon demonstration parks-such as industrial parks, cultural tourism zones, and financial service areas-can be basically met. Finally, through the coordinated planning demonstration case of a park, based on the resource endowment and energy consumption characteristics of the park, the system framework and research are carried out, and the demonstration is carried out from the aspects of economic and social benefits, which shows the applicability of the collaborative optimization application of low-carbon energy in the park considering different scenarios, and provides thinking and direction for the subsequent large-scale application of “near zero carbon demonstration zone”.

Existing photovoltaic (PV) output simulation methods often rely on artificial neural networks for short-term forecasting, and there has been a struggle to capture long-term patterns and stochastic fluctuations when using Markov Chain Monte Carlo techniques. To address these limitations, this paper proposes an improved headroom model-based approach that enhances traditional methods in three key aspects. First, unlike traditional headroom models that ignore temporal dependencies in output fluctuations, the approach integrates probabilistic distributions with soft sequential constraints to preserve time-dependent patterns. Second, whereas previous studies often overlooked seasonal weather variations, here PV output curves are classified into representative weather types and seasonally adaptive Markov chains are constructed to model radiation dynamics and transition probabilities. Third, to address the oversimplification of sunrise and sunset transitions, the method introduces a specialized statistical correction tailored to these critical periods. The method accurately models PV output patterns and fluctuations, demonstrating < 1% deviation in annual duration (4,121 h) and utilization (1,297 h), with a 7.80%−14.59% lower root mean square error and 10.27%−14.07% reduced mean absolute error vs. conventional methods. It efficiently generates realistic long-term sequences from limited data, enhancing the accuracy and efficiency of PV power sequence simulation.

Despite the implications of winter precipitation for socioeconomic activities and transportation services, the influence of cities on winter precipitation is less studied compared to that on summer precipitation. Here we investigated the statistical relations between precipitation, temperature, and impervious surface fraction in 12 major cities across the contiguous United States. The results showed negative correlations between snowfall intensity and impervious surface fraction. The correlations depend on latitude and the distance to complex terrain features (water bodies or topography), with stronger correlations for inland cities than coastal/lakeside cities. We further selected Kansas City for modeling analyses based on the Weather Research and Forecasting model. Simulation results indicated that the heating effect of urban land occurs in the near-surface atmosphere during the precipitation period, leading to changes of different hydrometers and an overall tendency of reducing snowfall but increasing rainfall.

The harmonics generated by devices in HVDC (High Voltage Direct Current) system and the harmonics input at the grid side have a great impact on the loss assessment of the converter transformer. Therefore, this paper proposes a simplified and efficient harmonic loss algorithm based on the classical transformer harmonic model, and Fourier transform and generates the code in MATLAB software. Practical engineering examples have proved that the proposed algorithm can accurately calculate the load loss and harmonic loss of the rheology by filtering data in the HVDC system. Therefore, it can solve the problems such as unclear calculation of harmonic resistance in existing standard theoretical algorithms or difficulty in obtaining test data of stray loss and eddy current loss at the fundamental frequency.

The impact of large reservoirs on regional climate extremes, such as heatwaves, is not yet well understood, primarily due to the complex interactions of heat, moisture, and momentum over mountainous terrains and the dynamic nature of water surfaces. In this study, we investigate the role of the Miyun Reservoir (MYR), one of the largest reservoirs in northern China, covering an extensive water surface area of 180 km2, in influencing a severe heatwave event that occurred southwest of MYR from 2nd to 6th July 2010. Using high-resolution simulations from the Weather Research and Forecasting Model, our findings reveal contrasting thermodynamic effects of MYR on near-surface air temperatures, with up to three degrees of warming during night-time and up to five degrees of cooling during daytime, with a spatial extent of impact reaching up to 20 km away from the reservoir. These impacts are attributed to a combination of synergized land-lake breeze and mountain-valley winds during the night, leading to the transport of warm air plumes downwind towards the plain and intensifying the heatwave. Furthermore, we observe an increase in surface water vapor brought to higher atmospheric levels due to strong vertical mixing, resulting in lowered surface-specific humidity. Our study emphasizes the dual role of large reservoirs in exacerbating remote heatwaves while providing cooling effects in their proximity. Gaining a comprehensive understanding of the intricate interactions between reservoirs, topography, and regional climate extremes is of paramount importance for accurate climate risk assessments and the formulation of effective mitigation strategies..

With the rapid development of offshore wind power, the high permeability of renewable energy reduces the short circuit capacity of the system, and the stable operation of renewable energy grid connection has become the key factor in the development of future power system. At present, the wind turbine is basically connected to the grid through the grid side converter, and the grid control strategy has a high dependence on the voltage stability and short circuit capacity of the connection point due to its phase-locked characteristics. Therefore, this paper studies the effect of the optimized control strategy and the grid-forming control strategy to improve the grid stability of renewable system under the weak grid. Optimizing the phase-locked loop of the grid converter can improve the stability of renewable system under the weak grid, grid-forming control strategy has the best effect on improving the stability of renewable system under weak grid.

近年来随着柔性高压直流(modular multilevel converter based high voltage direct current,MMC-HVDC)输电工程的增长,主网架线损率呈上升趋势.对柔性直流工程进行准确高效的损耗计算与分析是研究损耗构成、降损措施的关键步骤.提出了一种基于有限采样的柔性直流系统损耗高效计算方法,该方法考虑脉冲波形及器件阻抗的非线性特征,并可通过改变输入数据快速计算长时段损耗电量,有效解决了长时间尺度损耗精确计算的难题.最后通过与实际工程的测量值进行对比,验证了该算法的准确性.

During the last decade, an increasing number of Modular Multilevel Converter (MMC) based HVDC projects have been operated, while the overall loss rate increased compared with the Line Commutated Converter (LCC) based HVDC system. Accurate and efficient loss calculation and analysis of HVDC systems are the key steps of studying the loss composition and loss reduction measurements. Since the losses can hardly be accurately measured for the HVDC system, the losses are often obtained by either simulation or analytical calculation, with the disadvantages of time-consuming and low accuracy, respectively. In this work, an efficient and flexible loss calculation method based on finite sampling for the MMC HVDC system is proposed, which can be used for long time-period power loss calculation utilizing the manufacturer's datasheet with device-level characteristics of power electronics switches. This method can flexibly use the finite current samples and switching pulse samples from simulation, field measurement, or calculated from power measurement as the calculation inputs. The accuracy of the algorithm is verified by comparing with the measured data of a practical hybrid HVDC project with both MMC and LCC.

To build a multi-energy cloud platform with the distributed generation, energy storage, micro-grid, flexible load, electric vehicle piles for high efficiency application is of great significance. In order to manage the resources for dispatching and trading in the cloud platform, this paper solves three problems. Firstly, to present the cloud platform planning method. The modelling and linear optimization algorithm for the prosumer’s self-balanced to minimize the cost with trading quantity and random bidding price are proposed. Secondly, the key technologies to realize the information collection and interaction, and data model management are summarized on the basis of the demonstration project, faced to be urgently solved. Thirdly, P2P trade for small and medium scaled communities affected by grid’s time-of-use tariff for prosumers are discussed. The MATLAB simulation with the bidding price following uniformly distributed sampling is taken to analyze the consumers’ benefits and behaviors. The feasibility, methodology, technology and trade mode are analyzed based on the policy background, platform, optimization algorithm and simulation. The P2P trade is a new topic, that needs more work to do in the future real project.

The cloud energy storage system (CES) is a shared distributed energy storage resource. The random disordered charging and discharging of large-scale distributed energy storage equipment has a great impact on the power grid. This paper solves two problems. On one hand, to present detailed plans for designing an orderly controlled CES system in a realistic power system. On the other hand, Monte Carlo simulation (MCS) is used for analyzing the load curves of five types of distributed energy storage systems to manage and operate the CES system. A method of its planning and the principles of CES for applied in a power grid, are presented by analyzing the impact based on five load curves including the electric vehicle (EV), the ice storage system, the demand response, the heat storage system, and the decentralized electrochemical energy storage system. The MCS simulates the random charging and discharging of the system over a five-year planned scaling of distributed energy storage from 2021 through 2025. The influence of distributed energy storage systems on power grid capacity, load characteristics, and safety margins is researched to summarize the applicable fields of CES in supporting large power grids. Finally, important conclusions are summarized and other research possibilities in this field are presented. This paper represents a significant reference for planners.

In this study, we examine the impacts of urbanization and open water surface on heavy convective rainfall based on numerical modeling experiments using the Weather Research and Forecasting model. We focus on a severe storm event over the emerging Xiong'an City in northern China. The storm event consists of two episodes and features intense moisture transport and strong large-scale forcing. A set of Weather Research and Forecasting simulations were implemented to examine the sensitivity of spatiotemporal rainfall variability in and around the urban area to different land use scenarios. Modeling results highlight contrasting roles of open water and urban surface in dictating space-time organizations of convective rainfall under strong large-scale forcing. Dynamic perturbation to atmospheric forcing dominates the impacts of open water and urban surface on spatial rainfall distribution during the second storm episode, while urban surface promotes early initiation of convection during the first storm episode through enhanced buoyant energy. Open water surface contributes to convective inhibition through evaporative cooling but can enhance moist convection when the impact of urban surface is also considered. The synergistic effect of open water and urban surface leads to rainfall enhancement both over and in the downwind urban area. Changes in rainfall accumulation with different spatial extents of urban coverage highlight strong dependence of urban-induced rainfall anomalies on urbanization stages. Our results provide improved understandings on hydrometeorological impacts due to emerging cities in complex physiographic settings and emphasize the importance of atmospheric forcing in urban rainfall modification studies.