Stimulating green electricity consumption is a key measure for promoting the efficient integration and utilization of renewable energy and plays a vital role in reducing carbon emissions in the energy sector and supporting the green and low-carbon transition of the energy sector. However, the key factors influencing urban and rural residents' willingness to pay for green electricity and the mechanisms underlying this willingness remain unclear, and the design of relevant incentive policies lacks a clear basis. This study utilizes survey data from 1417 urban and rural residents in the Dongting Lake Ecological Economic Zone. By comprehensively applying the double-bounded dichotomous choice contingent valuation method (DBDC-CVM), structural equation modeling (SEM), and fuzzy set qualitative comparative analysis (fsQCA), the study empirically analyzes the level of willingness to pay for green electricity among urban and rural residents, as well as its influencing factors and configuration pathways. The results indicate that: (1) the willingness to pay for green electricity among urban and rural residents is 18.20 yuan per month, a conclusion that remains valid after robustness testing. (2) Attitude, subjective norms, perceived behavioral control, level of environmental awareness, and policy environment all have significant positive effects on willingness to pay, with perceived behavioral control and subjective norms exerting stronger influences, while the effect of level of environmental awareness is relatively weaker. Tests of mediating effects further indicate that attitude mediates the relationship between level of environmental awareness and willingness to pay. (3) Configurational analysis reveals that perceived behavioral control is the core condition driving high willingness to pay. A relationship of both substitution and synergy exists between subjective norms and the policy environment, with the most significant driving effect observed when the two act synergistically. Based on these findings, it is recommended to strengthen the synergistic driving force of subjective norms and the policy environment, optimize the subscription process, and implement differentiated measures to effectively stimulate urban and rural residents' willingness to pay for green electricity.
As a key carbon-emitting industry, the steel sector urgently requires a carbon emission factor model that can reflect the dynamic operation characteristics of its multi-energy system and the structure of the electricity market for carbon emission optimization. To address these limitations, this paper proposes a time-of-use carbon emission factor modeling method for integrated electricity-heat-hydrogen systems based on energy-carbon flow coupling relationships. The model establishes carbon flow allocation mechanisms for various energy production and storage devices within the industrial park and incorporates the carbon emission attributes of electricity purchased from external markets. By disaggregating the power output into three pathways—electricity, heat, and hydrogen—the number of decision variables on the supply side increases from 120 to 360 under an hourly resolution. Building on this, a bi-level optimization scheduling model with source-load coordination is developed. The upper level minimizes the operational cost of the park, while the lower level minimizes carbon emissions. By leveraging time-varying carbon emission factors to guide the temporal response of multi-energy loads, the model achieves coordinated optimization of economic efficiency and carbon reduction. Case study results demonstrate that the time-of-use carbon emission factor curve can accurately characterize the time-varying characteristics of system carbon intensity. After introducing time-of-use carbon emission factors, the system operation tends to avoid peak and high-carbon periods, with electricity, heat, and hydrogen loads achieving time-shifted adjustments, and the overall carbon emissions significantly reduced. The cumulative carbon emissions of the system are reduced by 10.8%. Meanwhile, the inclusion of purchased electricity carbon emissions can effectively enhance the integrity of carbon optimization, avoiding the underestimation of carbon responsibility and scheduling deviation, thereby verifying the effectiveness and adaptability of the proposed model.
To address the incomplete information problems commonly existing in the actual operation of integrated energy systems (IES), such as sparse observation and missing measurement data, this paper proposes an adaptive physics-informed neural network (APINN)-driven mechanism-data fusion modeling method. Firstly, a unified mechanism-data fusion network framework is constructed, which embeds multi-energy flow physical mechanisms, including power flow of power grids, fluid dynamics of natural gas pipelines, and thermodynamics of heating pipelines, into the training objective in the form of residuals, and establishes observation consistency constraints with a small number of anchor measurements to alleviate convergence difficulties caused by non-unique solution space and trivial solutions in pure physics-informed training. Secondly, a loss amplitude-aware adaptive weight adjustment mechanism is designed to dynamically optimize gradient weights of multi-physics field learning tasks, effectively resolving model optimization imbalance caused by dimensional mismatch across multiple physics fields. Finally, numerical validation on an electricity-gas-heat coupled IES consisting of an IEEE 24-bus power system, a 20-node natural gas system and a 16-node heating system shows that the proposed method achieves stable global state reconstruction with favorable physical consistency and anti-disturbance robustness.
Port selection plays a critical role in the corrosion diagnosis of grounding grids. However, the lack of scientific guidance for port selection has restricted the application of network-based corrosion diagnosis methods. To address this issue, this paper proposes an iterative optimization method for port selection based on weighted coverage and multi-weight collaborative optimization. The method firstly establishes a port scoring model using the sensitivity matrix to quantify port values from three aspects: information complementarity, information richness and numerical stability. An iterative optimization strategy is then adopted, which greedily selects ports while predicting the resistance of grounding grid branches, thereby enabling recursive correction of the sensitivity matrix and gradually screening out port combinations with high-quality information. Finally, simulation experiments conducted on a simulated grounding grid with 53 branches show that, compared with the unweighted greedy selection with the same 14 selected ports, the weighted greedy selection method reduces the mean absolute percentage error from 34.83% to 5.7%. It is verified that the proposed method can effectively optimize the measuring port selection and improve the accuracy of grounding grid corrosion diagnosis. The research conclusions can provide theoretical guidance for port selection in corrosion diagnosis of large grounding grids.
In the context where wind-storage power plants are gradually participating in the electricity spot market under the "both quantity and price bidding" mode, this paper studies the decision-making problem of constructing price-quantity curves for wind-storage combined systems in the day-ahead energy market. Comprehensively considering the uncertainties of wind power output and electricity prices, energy storage lifetime degradation costs, and market rule constraints, a stochastic optimization model aimed at maximizing expected revenue is established. The model simulates the uncertainties of wind power output and market prices using the scenario method and generates optimal hourly price-quantity curves using a mixed-integer linear programming approach. Case study results show that the proposed model can effectively improve the market revenue performance of wind-storage power plants. Compared with the traditional "quantity-only bidding" mode, the price-quantity curve bidding strategy demonstrates significant advantages in reducing uncertainty costs and optimizing economic benefits. Furthermore, increasing the number of bidding segments significantly promotes revenue enhancement. The re-search results provide scientific decision support for the market-oriented operation of wind-storage power plants.
Building a new power system dominated by renewable energy constitutes an inevitable pathway toward building an energy powerhouse. However, the increasing penetration of power electronic devices such as renewable energy units significantly weakens the stability support capability of power systems, and drastically reduces system stability margins. Under these conditions, scarce support resources restrict the adjustable range of stability control as well as active/reactive power scheduling measures, thereby sharply increasing the risk of system instability. To clarify the research lineage of stability support capability planning for new power systems, this paper systematically reviews new power system planning methodologies for enhancing stability support capability. Firstly, the paper analyzes the necessity of coordinated allocation of stability support resources at the planning stage based on the stability mechanisms of frequency, voltage and rotor angle. Secondly, it reviews the key evaluation indices for three categories of stability issues and modeling methods for stability boundaries, and summarizes optimal power system planning models embedded with stability constraints. Thirdly, it outlines prevalent solution algorithms to address the computational challenges introduced by complex stability constraints. Finally, from three perspectives including stability support mechanisms, stability boundary conditions and multi-scale model construction, as well as artificial intelligence-based solution techniques, it discusses the core challenges and prospective research directions in this field.
Against the backdrop of coordinated advancement of the new power system and the "dual carbon" goals, the growth of China's fuel cell electric vehicle (FCEV) stock still significantly lags behind planning targets, urgently calling for a systematic analysis of its diffusion mechanisms and policy effects. Therefore, this paper adopts the system dynamics (SD) method to construct an integrated simulation model incorporating key factors including technological progress, energy economic conditions, infrastructure development and policy support. The model systematically characterizes the evolutionary pathways and complex feedback structures of the FCEV market under multi-policy interactions. A variety of scenarios are set to simulate the dynamic impacts of different policy instruments and their combinations on FCEV stock, market competitiveness and carbon emissions. The analytical results indicate that, 1) although the FCEV market exhibits certain endogenous growth potential under the benchmark scenario, the internal positive feedback mechanism of the system has not been fully activated, making it difficult to achieve large-scale diffusion spontaneously. 2) Technological progress and R&D support provide fundamental support for long-term industrial expansion by reshaping the cost reduction trajectories. Hydrogen production electricity prices and energy efficiency exert significant amplifying effects on market scale through operating cost channels. Hydrogen refueling infrastructure exhibits phased characteristics evolving from "rigid constraint" to "network effect". 3) Multi-policy synergy can significantly amplify the effectiveness of individual policy instruments, which serves as a key condition to drive the transition of FCEVs from policy-driven diffusion to market self-reinforced diffusion. This study uncovers the internal mechanism of China's FCEV industry from the perspectives of system evolution and policy synergy, and provides quantitative evidence and decision-making references for optimizing policy portfolios and improving the efficiency of low-carbon transition in the transportation sector.
In the global energy transition, hydrogen enriched compressed natural gas is efficiently transported through existing pipelines, increasing the proportion of renewable, low-carbon, and green energy in the energy consumption structure. However, hydrogen source is affected by renewable energy generation for hydrogen production, resulting in fluctuations in the hydrogen blending ratio and posing challenges to the combined cooling, heating, and power system with hydrogen-doped natural gas as the input energy source at the endpoint. Therefore, this article proposes a multi time scale distributed predictive control method for hydrogen doped natural gas electrical hydrogen coupled energy systems. Firstly, an electric-hydrogen coupled cold, heat and power system based on hydrogen-doped natural gas was constructed for exploring the dynamic characteristics of the system during operation. Then, based on the differences in dynamic response characteristics of the system, three subsystems are divided into the electric side, cold side, and hot side to achieve more accurate control. Finally, simulation experiments were conducted to verify that the proposed multi time scale distributed predictive control algorithm can improve the speed of power side tracking while meeting the tracking accuracy of the three types of loads in the system, namely cooling, heating, and power. It can also achieve stable energy supply of the system in scenarios where the hydrogen doping ratio fluctuates.
To analyze the impacts of electric vehicle(EV)charging loads on distribution network harmonics,a frequency-domain analysis framework based on harmonic extended linear modeling(HELM)is established.The cross-frequency coupling between voltage and current harmonics is characterized using a frequency coupling matrix.Sampling at 20 kHz and adopting a steady-state window of three fundamental cycles,the spectrum is extracted via discrete Fourier transform.Indicators including total harmonic distortion(THD),total demand distortion(TDD),root-mean-square value,peak value and crest factor are calculated.Furthermore,kernel density estimation,empirical distribution,the Kolmogorov-Smirnov test,principal component analysis,k-means clustering and three-dimensional visualization are jointly employed to systematically compare two scenarios:"EV loads superimposed with other loads"and"other loads only".Simulation results reveal that the voltage distributions of the two scenarios are highly consistent,while discrepancies mainly emerge in current characteristics.For the scenario incorporating EVs,the current root-mean-square value,THD and crest factor are generally lower with smaller dispersion;the energy proportion of low-order harmonics(2nd-10th orders)decreases,demonstrating a more fundamental-wave-dominated spectral feature.The proposed research provides methodological support and quantitative references for distribution network harmonic mitigation,optimization of filtering and rectification schemes for charging facilities,and improvement of assessment criteria for power quality.
As renewable energy gradually replaces traditional synchronous generators, the active voltage support it provides has become key to ensuring the security and stability of new type power systems. However, the current understanding of this critical capability remains insufficient, particularly lacking a theoretical framework and metric system for its systematic and quantitative assessment, which hinders the full exploration of its potential and its efficient utilization. To address this research gap, this paper proposes a framework for assessing the active voltage support capability of renewable energy. This framework decouples this complex capability into two core dimensions: intrinsic characterization, which reveals the converter's own control and physical characteristics, and external effects, which reflect its actual performance after dynamic interaction with the power grid. This paper reviews the development history of renewable energy from passive grid connection, grid-friendly operation, weak active support to strong active support, as well as the corresponding control strategies, based on two technical threads: simulating steady-state/quasi-steady-state voltage source characteristics and continuous improvement of fault ride-through (FRT) anti-disturbance capability. It then extracts voltage issues in four typical scenarios and summarizes the corresponding active voltage support requirements and control methods. Subsequently, from the dimension of intrinsic characterization, this paper discusses the evaluation indicators and methods for the active voltage support capability under steady-state/FRT conditions; and from the dimension of extrinsic effects, it presents the corresponding indicators and calculation methods in combination with scenario requirements. Finally, future research directions are outlined, including the aggregated evaluation of power plants' active voltage support capability and the spatiotemporal optimal coordinated control of system-wide multiple reactive power sources.
To address the problem of insufficient frequency perception accuracy caused by active power coupling interference on frequency information mapped by DC voltage in communication-free active frequency regulation of offshore wind power systems transmitted via flexible DC transmission, this paper proposes a communication-free active frequency support control strategy. Firstly, a dynamic analytical model is constructed to quantitatively reveal the inherent coupling mechanism between active power input fluctuations and DC voltage deviations, and a voltage-power coupling analysis framework is established. Secondly, a communication-free active frequency support strategy based on real-time power-voltage observation and feedforward compensation is proposed. Finally, a simulation model of the communication-free active frequency regulation system for offshore wind power via flexible DC transmission is built in Matlab/Simulink for simulation verification. The simulation results show that the proposed strategy can effectively suppress the interference of power transmission variations on frequency signals and markedly improve the accuracy of offshore-side perception for onshore grid frequency. The proposed strategy enhances the system’s active frequency support capability while ensuring stable dynamic response, which provides a feasible solution for communication-free frequency regulation of offshore wind power.
To address the problems that the ratio-error series of current-transformers, affected by environmental disturbance, operating condition variations and random noise, exhibit nonstationary and multi-scale characteristics, and it is difficult to balance point prediction and interval estimation, this paper proposes an interval forecasting method integrating complete ensemble empirical mode decomposition (CEEMD), dual-scale temporal convolutional network (DTCN) and bias-corrected residual bootstrap (BCRB). Firstly, The CEEMD is adopted to decompose and reconstruct the original ratio-error series to suppress high-frequency noise and mode mixing. Secondly, the DTCN is used to extract short-term fluctuation and long-term drift features to realize point prediction of ratio errors. Finally, prediction intervals under different confidence levels are constructed based on the residual bootstrap method. Case studies show that the proposed method achieves a high interval coverage probability at the confidence level of 0.99.
The virtual power plant (VPP) can participate in electricity markets to provide power and frequency regulation auxiliary services for the system by aggregating resources such as distributed generation, adjustable load and energy storage. Its core objective is to maximize the benefits by optimizing the allocation of internal resources across the two markets. To address the insufficient market competitiveness of VPPs caused by the lack of specific and accurate volume bidding methods, this paper proposes a VPP bidding strategy considering the dynamic variation of regulation capacity under a multi-market environment, as well as a joint clearing mechanism of energy-frequency regulation markets incorporating VPPs. Firstly, the adjustable capacity boundary of the VPP is characterized according to the scheduling characteristics of internal resources. A segmented joint bidding strategy based on adjustable capacity partition interval is then proposed to enable full scheduling of internal resources for market participation. Secondly, in view of the overlap between the two markets, a joint clearing mechanism of energy-frequency regulation markets with VPP integrated is constructed to improve the allocation efficiency of flexible resources. Finally, case studies are carried out to verify the proposed bidding strategy and clearing mechanism. The results show that compared with the single-segment bidding strategy based on a fixed power range, the total power purchase cost of the system and VPP is reduced by 0.75% and 53.93%, respectively. It is verified that the proposed bidding strategy and clearing mechanism can maximize the social benefits while improving the VPP's own benefits.
The rapid proliferation of electric vehicles (EVs) and distributed photovoltaic (PV) poses significant challenges to the safe and stable operation of distribution networks. Ordered charging and vehicle-to-grid (V2G) offer effective means to alleviate the operational pressure of distribution networks. Therefore, this paper proposes an optimal planning method for electric vehicle charging stations (EVCSs) that balances the interests of multiple stakeholders. First, considering ordered charging and V2G, the flexible charging/discharging power is determined based on dynamic pricing signals and user response behavior. Second, a multi-stakeholder equilibrium optimization model is established to coordinate the benefits of multiple stakeholders, including distribution system operators, photovoltaic power producers, EV aggregators, and EV users, and is solved by NSGA-Ⅲ algorithm to obtain the optimal planned charging and discharging power. Finally, to accommodate various charging and discharging requirements across different EV types, an EV charging network composed of centralized charging station (CCS) and distributed charging station (DCS) is constructed, and optimal power allocation between CCS and DCS is achieved. Distribution network security verification as well as distribution facility upgrading are carried out to meet the operational safety requirements. Simulation results of a power grid in a city of Shandong Province validate the effectiveness of the proposed method.
Peer-to-peer (P2P) energy trading can encourage prosumers to carry out local energy trading, and guide user-side resources including distributed energy resources (DERs), energy storage, electric vehicles (EVs) and flexible loads to participate in supply-demand balancing through market-based price signals, thereby boosting the local consumption rate of renewable energy. To accurately identify the core research priorities of P2P energy trading, this paper first elaborates its system architecture, operational logic and basic characteristics. Second, it reviews the existing market mechanisms and their applicable scenarios from the perspectives of market organization models, pricing, market clearing and settlement mechanisms. Third, it summarizes state-of-the-art research on strategy optimization approaches for P2P energy trading, covering mathematical optimization, game theory, distributed optimization, reinforcement learning and other methodologies. Finally, the paper outlines future development directions of P2P energy trading targeting critical challenges such as physical constraints of distribution networks, large-scale agent coordination, privacy protection, trusted trading and engineering demonstrations. The work is expected to provide references for market-oriented trading of user-side distributed resources and the operation of new distribution systems.
With the large-scale integration of renewable energy into multi-level power grids, the traditional deterministic dispatch framework with separated operation among different grid levels faces challenges such as boundary power mismatches and insufficient reserve capacity. To address these issues, this paper proposes a coordinated robust intra-day rolling dispatch model for multi-level power grids based on renewable energy prediction intervals. By coordinating the operation of transmission, distribution, and microgrids, the proposed model exploits the flexibility potential of large-scale flexible resources on the distribution network and microgrid sides, thereby enhancing renewable energy accommodation. An affine adjustable policy is introduced to transform the original robust optimization model into an equivalent quadratic programming formulation, and a multi-parameter space projection decomposition algorithm is developed to efficiently solve the problem. While preserving the convexity of lower-level optimization problems, each grid level only needs to exchange boundary power information and optimal projection functions to achieve the global optimum. Simulation results on the T118-D33-M4 test system demonstrate that the proposed method improves computational efficiency by approximately 8 and 11 times compared with generalized Benders decomposition and the alternating direction method of multipliers, respectively, while effectively promoting renewable energy accommodation.
With the development of new power systems, the penetration of renewable energy generation keeps rising, and distributed photovoltaics (PV) are connected to the grid on a large scale, triggering a series of problems such as voltage violations and transient voltage instability. Traditional local voltage control plays a crucial role in local regulation due to its rapid response capability, while centralized control demonstrates superior potential for coordinated regulation through global optimization. However, facing the drastically increased system complexity caused by high-penetration of renewable energy integration, local control fails to achieve global coordination, and centralized control suffers from excessive computational dimensionality and heavy communication burdens. By contrast, cluster-based partition voltage control effectively balances resource utilization efficiency and control feasibility. In view of this, this paper summarizes the existing partitioning index systems. From two dimensions including static physical connection and dynamic regulation sensitivity, it analyzes the adaptability differences of indices such as electrical distance and voltage sensitivity to the spatiotemporal fluctuation characteristics of sources and loads. Secondly, three mainstream algorithms, namely traditional clustering, community detection and intelligent optimization, are compared to reveal the evolution trend of partitioning methods from static topological rigid partitioning to dynamic sequential elastic aggregation. Furthermore, voltage control strategies across different time scales are reviewed from the perspectives of cluster autonomy and multi-cluster coordination. Finally, based on a systematic review of the existing technical frameworks, future research directions including data-driven control, heterogeneous resource aggregation and resilient defense are further discussed.
The operation and planning of power systems with high penetrations of renewable energy highly depend on high-temporal-resolution meteorological data and wind-solar power output scenarios. However, most existing climate models only provide daily-scale meteorological information, which is insufficient to directly support the hourly-level operational analysis of power systems. Therefore, this paper proposes a joint scenario generation method for meteorology-wind-solar power outputs based on retrieval-augmented probabilistic downscaling (RAPD). First, a similar-day retrieval mechanism based on the rank-sum distance is constructed to extract historical high-resolution meteorological baseline sequences. Second, a Transformer-based conditional variational autoencoder (T-CVAE) learning network is built to realize the probabilistic generation of meteorological fluctuation residuals. Finally, a physical mapping model from meteorological variables to wind-solar power outputs is established to generate continuous hourly wind-solar power output scenarios for a whole year. Validations based on historical meteorological data demonstrate that the mean absolute error (MAE) values for temperature and wind speed are reduced by 30.17% and 28.84%, respectively, compared with the traditional statistical analog method. Furthermore, taking the daily-scale data from climate models as input, the corresponding hourly-level meteorology-wind-solar power output scenarios are generated through downscaling, and the consistency between the downscaled results and the daily-scale inputs is verified. The results indicate that the proposed method can effectively accomplish temporal downscaling and scenario generation, providing a high-temporal-resolution scenario foundation for renewable energy output analysis under future scenarios.
In the context of the steady advancement of the "dual carbon" goals and the construction of the new power system, distributed photovoltaic (PV) generation and other renewable energy sources are being integrated at high penetration levels and on a wide scale. Coupled with the rapid development of emerging electricity consumption forms such as electric vehicles, energy storage systems and flexible loads, distribution networks are evolving from the conventional unidirectional passive structure to a multi-source interactive, active bidirectional system. In this process, the stochastic and volatile PV output, the spatio-temporal mismatch between generation and load, and the reverse power flow and other factors have significantly altered the feeder voltage distribution patterns, and the terminal voltage rise and voltage violation issues have become increasingly prominent under high PV penetration, constituting a critical bottleneck restricting the high-quality accommodation of distributed energy resources and the safe, stable operation of distribution networks. Firstly, this paper systematically analyzes the formation mechanisms and influencing factors of voltage violation, and summarizes the technical characteristics and applicable boundaries of single mitigation approaches including conventional voltage regulation devices and emerging power electronic equipment. Secondly, from the perspective of source-network-load-storage coordination, it concludes core ideas of optimal device configuration and multi-timescale coordinated voltage control, and refines the overall technical framework for coordinated mitigation. Finally, it further analyzes the practical constraints and institutional challenges encountered in engineering implementation, and prospects future research directions such as digitally empowerment, multi-stakeholder collaborative participation, lifetime-oriented dispatch strategies, and coordinated control of AC/DC hybrid distribution networks.
In midsummer extreme heat waves occur frequently, and electricity demand surges to its annual peak, posing the greatest pressure and the highest uncertainty for power supply security. To accurately forecast the midsummer peak load and enhance prediction interpretability, this paper proposes a dual-path forecasting method that integrates mechanism analysis of the "Four Effects" with time-series large models. In the mechanism analysis path, based on load composition theory, the total load is decomposed into base load and cooling load, which respectively characterize the economic growth effect, rest-day effect, temperature-rise effect, and accumulation effect. Historical data of the summer peak load in 2025 are reviewed and decomposed, and then each effect component for midsummer 2026 is extrapolated to obtain the peak load forecast. In the data-driven path, the Chronos-2 time-series large model is adopted, integrating multi-source information including economic, meteorological, and calendar data for daily peak load forecasting. It is compared with patch time series transformer and light gradient boosting machine algorithms to select the best performer, and the mechanistic and data-driven paths are cross-validated against each other. The results show that, considering the latest economic and climatic conditions, the maximum load of a certain regional power grid in midsummer 2026 is 1305 GW, consisting of a base load of 934 GW (economic growth effect 934 GW, rest-day effect 0 GW) and a cooling load of 371 GW (temperature-rise effect 340 GW, accumulation effect 31 GW). The time-series large model performs best on the test set, with a mean absolute percentage error of only 1.64%, and forecasts a peak load of 1306 GW, which is highly consistent with the conclusion of the "Four Effects", thus verifying the reliability of the proposed method.
