A scientific and rational technical standard system for power carbon emission reduction (TSS-for-PCER) is a critical element in both of advancing the construction of a new electricity system (NES) and achieving carbon peaking and carbon neutrality goals. With the gradual construction of NES, it is clearly that the TSS-for-PCER shall cover the entire power industry chain. In this paper, based on the Hall three-dimensional structure, using the method of combining "top-down" and "bottom-up", firstly, the development status of technical standards for power carbon emission reduction was reviewed. Then, the standard requirements of various stakeholders in the industry were summarized. the standard requirements in the carbon reduction field from various stakeholders in the power industry is studied. Finally . a multi-level TSS-for-PCER architecture of "3+6+N+N" has been proposed. The TSS-for-PCER is characterized by 3 levels of basic support, core implementation, and management evaluation, 6 dimensions of basic universality, carbon emission accounting and verification, carbon emission monitoring, carbon reduction technology and equipment, carbon emission assessment and evaluation, and carbon emission management, 20 technical fields, and 33 categories. To maintain the advancement of the TSS-for-PCER, it is necessary to conduct regular evaluations of it and adjust the technical fields and corresponding subjects. Therefore, the number of both technical fields and categories are represented by N+N. Finally, specific implementation suggestions were provided from four aspects: implementation direction, path, guarantee, and development. The standard system has not only successfully filled the standard gap in the field of domestic power carbon emission reduction technology, but also achieved the coverage of the whole process of power carbon emission, which is systematic, progressiveness and scalable, providing a comprehensive and suitable standard guidance for the future development of the power industry carbon emission reduction.

To address the practical challenges of poor data quality, strong hyperparameter coupling, and significant peak positioning  errors  in  power  system  carbon  emission  peak  forecasting,  a  framework  integrating  robust  data  preprocessing with an improved grey-convolution hybrid model is proposed. Firstly, a dynamic quantile boundary-based outlier detection and multi-window weighted robust repair procedure is established, together with a random forest feature importance-based chained  multiple  imputation  method,  to  suppress  outlier  disturbances  and  high-dimensional  missingness  in  non-Gaussian data. Subsequently, an improved convolutional network incorporating variational mode decomposition, dilated convolution,and attention gating is constructed, with an embedded improved grey model for long-term trend extraction; hyperparameter optimization is achieved through a grey relational grade-guided whale optimization algorithm. Experimental results show that  compared  with  genetic  algorithm,  particle  swarm  optimization,  and  grey  wolf  optimization,  the  proposed  algorithm reduces mean absolute percentage error by 39.7%, 32.5%, and 25.4%, and peak prediction time deviation by 77.1%, 71.4%,and 60.0%, respectively. Compared with autoregressive integrated moving average (ARIMA), long short-term memory -prophet  (LSTM-Prophet),  time-series  transformer  (TST),  empirical  mode  decomposition-LSTM  (EMDE-LSTM),  and variational mode decomposition - gated recurrent unit (VMD-GRU), the proposed model reduces mean absolute percentage error to 2.89% and peak prediction time deviation to 0.7 h, while improving the inflection point capture rate to 93.8%. This study provides a new technical approach for accurate carbon emission peak prediction and offers data support for power system emission reduction strategy formulation.

To  address  the  economic,  security,  and  computational  efficiency  challenges  in  transmission-distribution coordinated dispatch with high renewable energy penetration, a multi-period optimization model and an adaptive improved grey wolf optimization (AIGWO) are proposed. First, a three-dimensional objective function framework is established by comprehensively  considering  economic  cost,  environmental  penalty,  and  security  risk,  with  complete  modeling  of  unit ramping  constraints,  energy  storage  charging-discharging  time-coupled constraints,  and  network  security  boundary conditions.  On  this  basis,  three  innovative  mechanisms  are  designed:  (1)  a  nonlinear  adaptive  convergence  factor  to dynamically  balance  global  exploration  capability,  (2)  knowledge-guided  population  initialization  to  improve  solution quality,  and  (3)  hybrid  binary-real  encoding  to cooperatively  optimize  continuous  and  discrete  variables,  thereby overcoming  the  low  convergence  efficiency  and  poor  discrete  decision  space  processing  of  conventional  algorithms.Experiments  are  performed  on  a  modified  IEEE  33-bus  system  for  verification.  Results  show  that  compared  with  the standard  grey  wolf  optimization,  particle  swarm  optimization,  mixed  integer  linear  programming,  and  deep  Q-network,AIGWO  reduces  the  total  cost  by  1.72%~5.03%,  decreases  line  overload  violations  by  89.2%~93.5%,  lowers  voltage violations  by  82.4%~90.3%,  and  shortens  the  computation  time  by  9.32%~94.22%.  The  proposed  algorithm  provides  an efficient solution for coordinated transmission-distribution resource optimization in high-fluctuation source-load scenarios.

To  address  the  issues  of  data  noise  interference,  feature  scale  discrepancy,  and  insufficient  multiscale meteorological  pattern  modeling  in  photovoltaic  power  forecasting,  a  prediction  method  based  on  dynamic  data preprocessing  and  gated  dense  multiscale  convolutional  neural  network  (GDMS-CNN)  is  proposed.  Firstly,  an  anomaly detection  mechanism  based  on  dynamic  sliding-window  Z-score  is  established,  and  missing  values  are  processed  via covariance-weighted  multivariate  interpolation.  Secondly,  an  adaptive  piecewise  normalization  algorithm  is  adopted  to eliminate  feature  dimensional  differences,  and  cloud-cover  correction  factor  and  atmospheric  attenuation  factor  are constructed to enhance physical feature representation. Finally, a GDMS-CNN is designed, wherein the feature extraction efficiency  is  optimized  by  depthwise  separable  convolution  modules,  densely  connected  dilated  convolution  blocks  are constructed to capture multiscale spatiotemporal correlation features, and an asymmetric gated channel attention mechanism is embedded to dynamically recalibrate feature weights. Experimental results demonstrate that the proposed method reduces the root mean square error (RMSE) by 16.4% compared with the optimal baseline model genetic algorithm-variational mode decomposition-echo  state  network  (GA-VMD-ESN),  and  by  43.4%  compared  with  the  traditional  random  forest.  The proposed method provides a novel solution for photovoltaic output forecasting and effectively enhances the reliability of power grid dispatching.

The  uncertainty  of  wind  and  photovoltaic  power  generation  results  in  carbon  emissions  of  low-carbon scheduling  methods  not  meeting  expectations.  Therefore,  a  multi-time-scale  low-carbon  scheduling  method  for  regional integrated energy systems under chance constrained planning is proposed. Firstly, it uses the power of wind and solar power generation  at  different  time  periods  as  random  variables,  and  introduces  confidence  level  quantification  constraints,  an improved  particle  swarm  algorithm  is  used  to  determine  the  optimal  decision  variables.  Secondly,  it  introduces  carbon capture power plants to capture, store, and reuse CO₂ , constructs a carbon cycle system, and designs a tiered carbon trading mechanism  and  user  demand  response  mechanism.  Finally,  it  designs  a  multi-time-scale  real-time  rolling  control  plan,constructs  real-time scheduling  objective  functions  and  constraints,  and  achieves  low-carbon  scheduling  of  regional integrated  energy  systems  at  multiple  time  scales.  The  experimental  results  show  that  the  designed  scheduling method reduces carbon emissions by 4 570.1 kg compared to the no strategy scenario, and the actual carbon emissions are slightly lower than the free quota by 5%. It can effectively utilize low-carbon resources while meeting the requirements of system carbon emission constraints.

To address voltage over-limit and instability issues caused by high penetration of distributed photovoltaic in distribution  grids,  this  paper  proposes  a  voltage  regulation  strategy  based  on  distributed  photovoltaic  cluster  partition.Firstly,  a  dual-criterion  modularity  function  considering  net  load  and  equivalent  electrical  distance  is  constructed.  A community  detection  algorithm  based  on  modularity  (i.e.  fast-unfolding)  is  applied  to  dynamically  partition  photovoltaic clusters. Secondly, differentiated voltage regulation is designed according to the severity of cluster over-limit conditions:intra-cluster reactive power adjustment for mild over-limits, and multi-device hierarchical collaborative control for severe over-limits, with task allocation based on response speed and economic priority. Finally, an optimization model targeting minimization  of  network  losses  and  voltage  deviation  is  established.  The  improved  multi-organization  particle  swarm optimization (MPSO) algorithm, combined with niche-based techniques, is employed to determine the optimal regulation sequence and device action levels. Simulation results demonstrate that this method effectively controls voltage fluctuations,reduces network losses, and enhances system stability in both the modified IEEE 33-node system and the IEEE 123-node system.

A virtual power plant (VPP) serves as a key platform for integrating distributed energy resources, enhancing energy  efficiency,  and  promoting  the  absorption  of  renewable  energy.  However,  a  single  VPP  has  limited  regulation
capacity and competitiveness, whereas the coordinated operation of multiple VPPs offers greater economic and low-carbon advantages.  Based  on  this,  this  paper  proposes  an  optimal  dispatch  method  for  multiple  VPPs  that  considers  integrated demand response and multi-energy interactions, and employs an improved Shapley value for benefit distribution. Firstly, a collaborative  dispatch  model  for  multiple  VPPs  incorporating  integrated  demand  response  and  energy  interactions  is established.  By  guiding  load  changes  through  demand  response,  the  model  achieves  peak  shaving  and  valley  filling.Through energy interactions among multiple VPPs, energy resources on the supply side are fully utilized, improving system economics. Secondly, in the benefit distribution process, a three-dimensional evaluation index system—covering economic,energy transaction, and environmental aspects—is introduced to enhance the traditional Shapley value method, providing a more comprehensive basis for benefit allocation. Simulation results demonstrate that the proposed model not only reduces operating costs and carbon emissions but also quantifies the contributions of each VPP member through multi-dimensional indicators, thereby more comprehensively reflecting their actual input.

With the acceleration of new power system construction, distributed photovoltaic aggregators are able to participate in the day-ahead energy and reserve joint market trading and obtain profits. However, due to differences in their interests, distributed photovoltaic aggregators, market trading centers, and distribution system operators exhibit complex market trading game behaviors. Accordingly, a single-leader multi-follower mixed-integer Stackelberg game framework is constructed for distributed photovoltaic aggregators participating in the day-ahead electricity market. The upper-level model aims to maximize the profit of the distributed photovoltaic aggregator (leader) by optimizing bidding strategies, while the lower-level model involves the market trading center (follower 1) conducting day-ahead joint market clearing, and the distribution system operator (follower 2) performing security verification on the market clearing results based on discrete control measures such as transformer tap changers and capacitor switching. To solve this multi-agent game model, the Karush-Kuhn-Tucker conditions and the Big-M method are first used to equivalently transform the follower 1 problem. Subsequently, a data-driven bilevel reconstruction algorithm is employed to solve the leader-follower game model with continuous and discrete variables. Finally, the accuracy and effectiveness of the game model and its solution algorithm are validated using a practical transmission and distribution system in a certain region.

In recent years, the rapid development of Fuel-Cell Hybrid Electric Vehicles (FCHEV) has effectively alleviated the peak-shaving challenges in new power systems caused by the "anti-peak" characteristics of renewable energy, promoting energy-transportation coupling and low-carbon emission reduction. This paper focuses on the impact of renewable energy output uncertainty and the charging response of FCHEV on microgrid optimization scheduling. By utilizing FCHEV as a link between multiple regions of the microgrid, a two-stage robust game model for the microgrid is established. To address the issue of low solving efficiency caused by continuously adding constraints to the master problem during the iteration process of the C&CG algorithm, an improved iNC&CG algorithm is proposed. Finally, simulation results demonstrate that the proposed two-stage robust game model for the microgrid can achieve a balance of interests between the microgrid and FCHEV users, effectively cope with the impact of renewable energy output uncertainty. And the advantages of the improved algorithm in solving such large-scale problems is verified.

To address the problems of renewable energy accommodation and supply–demand imbalance caused by the dynamic evolution of load demand over the life cycle of western mining areas, a life-cycle dynamic configuration method for the energy system of underground coal mines is investigated. Based on the production organization in the construction, early-stage mining, mid-stage mining and late-stage mining periods, a multi-energy coupling framework for electricity–heat–cooling is constructed to reflect the differences between above-ground and underground loads. A multi-stage mixed-integer linear programming model is developed, which integrates photovoltaic (PV) generation, electrical energy storage, chillers and external power/heat supply. The objective is to minimize the total life-cycle cost consisting of investment, operation and maintenance, purchased energy and carbon emission costs by optimally sizing PV, energy storage and primary network equipment, and by representing the temporal evolution of source–load relationships through typical-day load profiles and year-by-year capacity expansion decisions. A typical western underground mine is used as a case study, and two scenarios are compared: traditional static one-shot configuration and life-cycle dynamic configuration. The results show that the dynamic configuration increases the average installed PV capacity and renewable penetration through staged PV and storage expansion in key years, while restraining the required capacity of primary network equipment and substantially reducing curtailment over the whole life cycle. Compared with the static configuration, the dynamic configuration reduces the total life-cycle cost by about 17.9% and the carbon emission cost by about 50.2%. The proposed life-cycle dynamic configuration method can satisfy secure energy supply for mining areas while balancing economic performance and low-carbon goals, and it provides a technical reference for planning clean energy systems in western mining areas and similar energy-intensive industrial parks.

To address the challenges of diversified participants in the electricity market and resolve conflicts of interest among multiple virtual power plants (VPPs), this paper proposes a three-layer hybrid game strategy involving the distribution system operator (DSO), virtual power plant operator (VPPO), and user aggregator (UA) to support collaborative optimization of multi-VPP systems. First, a UA coalition is formed by aggregating various flexible resources, including photovoltaic prosumers, electric vehicle charging stations, and integrated energy loads. Next, a 3-layer hybrid game model is constructed, encompassing hierarchical energy transactions among DSO, VPPO, and UA, as well as peer-to-peer energy trading among UA entities, structured as a "Stackelberg game - Stackelberg game - cooperative game" framework. Finally, the proposed model is solved using the bisection method, Karush-Kuhn-Tucker conditions, and the alternating direction multiplier method. Case studies demonstrate that the proposed multi-layer hybrid game strategy reduces the operating costs of the photovoltaic prosumer UA and the electric vehicle charging station UA by 4.5% and 15.3%, respectively. Meanwhile, guided by the dynamic pricing mechanism of the DSO, the total profit of the system operators increases by 2.7%. This strategy can effectively balance the interests of multiple stakeholders, optimizing the energy trading and benefit allocation mechanisms among different operators.

This study investigates the ash deposition behavior of anthracite during combustion, which significantly affects boiler safety and efficiency due to slagging tendencies. A pilot-scale one-dimensional settling furnace system was employed to conduct combustion experiments under varied operating conditions, including different loads, primary/secondary air ratios, and excess air coefficients. Ash samples were analyzed by fusion tests, SEM, XRD, XRF, and laser sizing. Results show ash composition (C, O, Si, Al) and crystalline phases (quartz, mullite, hematite) remain stable. Increasing the load from 0.2 MW to 0.3 MW raises the ash deformation temperature from 1259 °C to 1312 °C, while the slagging index increases from 1268 to 1319, indicating a significantly enhanced slagging tendency. When the mass ratio of primary air to secondary air is increased from 2/8 to 4/6, the unburned carbon content in ash increases from approximately 37.5% to 40%, and the median particle size enlarges from 22 μm to about 28 μm, resulting in a pronounced promotion of ash deposition. Excess air coefficient had limited impact on fusibility and slagging. Ash exhibited a bimodal size distribution: fine particles form an adhesive layer, while coarse particles deposit by impaction, jointly accelerating slagging. The results demonstrate that boiler load dominates slagging behavior, with air distribution affecting burnout and particle characteristics. This study provides pilot-scale experimental data and mechanistic insights for slagging prediction and combustion optimization of anthracite-fired boilers under wide-load operation.

To address the optimization problem of resource aggregation and bidding decision-making for virtual power plants (VPPs) participating in power peak shaving, a decision optimization model based on resource response capability and information gap decision theory (IGDT) is proposed. Considering four dimensions—response potential,fluctuation degree, duration, and response speed—an aggregation indicator system for distributed resources is constructed. A multi-objective aggregation optimization model is established, balancing the maximization of expected response revenue and the minimization of deviation penalty risk, to screen the optimal resource portfolio. The market transaction framework and bidding decision mechanism for VPPs participating in power peak shaving are designed. The IGDT theory is introduced to characterize the uncertainty of peak shaving compensation prices, and a risk-averse (RA) model is constructed to optimize bidding strategies.The simulation results show that the multi-objective optimization model of virtual power plant aggregation can take into account both economic and risk considerations. It can provide a theoretical method for virtual power plant aggregators to screen resources and reduce the risk of deviation punishment of virtual power plants. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the virtual power plant can obtain reasonable response benefits.

system demonstrates that integrated optimization of these technologies reduces total system costs by 5.98% and curtailment rates for wind and solar power by 15.68%. The results validate that the proposed collaborative planning approach effectively integrates complementary advantages across different technologies, significantly lowering system costs and alleviating pressure on renewable energy integration. It also reveals that the flexibility regulation capacity of coal-fired power plants is influenced by their node position within the grid.

To address the issues of existing regional distributed photovoltaic (PV) power forecasting, such as heavy reliance on meteorological data, high operation and maintenance costs, poor data quality and insufficient result credibility,a joint credible forecasting method for PV power and energy is proposed. First, power measurements from smart meters and daily frozen energy data are jointly filtered, fused, and normalized to enhance data set quality. Second, a multi-time-scale, high-accuracy sequence-to-sequence (Seq2Seq) forecasting framework is developed, integrating historical and forecast data from centralized regional PV plants; a multi-time-scale loss function that jointly accounts for both power and energy is employed to optimize prediction accuracy. Finally, a model integrity verification scheme based on commit-and-prove succinct non-interactive argument of knowledge (cp-SNARKs) is designed to ensure result credibility while preserving model confidentiality. Experimental validation using real-world data from a city in North China demonstrates that the proposed method significantly reduces forecasting errors for both power and energy, thereby improving PV power prediction accuracy. Requiring no meteorological inputs or system modifications, the approach features high data quality, superior prediction accuracy, low operational cost, and strong verifiability, making it readily extensible to other time-series forecasting tasks such as load forecasting and wind power prediction.

To address the scheduling failures, power imbalances, and economic losses in virtual power plants (VPPs) caused by multisource uncertainties—including stochastic renewable generation, load fluctuations, and parameter deviations—this paper develops a multi-timescale adaptive dispatch framework incorporating multi-source uncertainty modeling and online parameter correction. The framework employs two-stage robust optimization for day-ahead scheduling to generate a robust pre-dispatch plan, and introduces a state-feedback mechanism in the intra-day stage, where an improved quantum-inspired genetic algorithm is used to recursively correct critical parameters, thereby forming a closed-loop dispatch structure. Simulation experiments validate the effectiveness of the proposed approach. Results show that, under significant forecasting errors in renewable generation and electro-thermal loads, the method improves actual operational revenue by approximately 3.2% compared to conventional deterministic dispatch. Moreover, the online parameter correction strategy reduces system balancing costs by nearly 90% during most time periods. The framework effectively balances robustness, economic efficiency, and adaptability, offering a viable technical pathway for the secure and economical operation of VPPs under high uncertainty

Vibration monitoring and condition assessment are crucial technical means and management measures for ensuring the safe operation of large wind turbines， especially in the winter freezing environment of Yunnan-Guizhou Plateau， the vibration monitoring and state assessment of the antifreezing test wind turbine is particularly important. The icing random distribution of the wind turbine blade may lead to mass imbalance and aerodynamic shape change among three blades， combined with installation differences of new equipment or auxiliary materials in three blades of wind turbine for antiicing/deicing requirement， so these factors may collectively induce severe vibration of the wind turbine to result with equipment safety hazards. This study employs the active aerothermal method to construct three 5MW test wind turbines， and the primary influencing factors of turbine vibration is analyzed deeply. The vibration monitoring data during four months from three 5MW antifreezing test turbines were employed to calculate 9 key turbine vibration condition variables and comprehensively evaluate the vibration condition in combination with characteristic limit values， and the vibration development trend of each turbine was judged by longitudinally and horizontally comparing. The assessment results indicate that the vibration intensity and its difference among three test wind turbines are smaller， which can operate safely for a long period， and the impact of blade icing and antiicing modifications on vibration is not significant.