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.
