This paper develops an optimization scheduling model for park-level integrated energy systems with uncertainties that synergistically coordinates hydrogen, alkane, and ammonia energy flows. The proposed system integrates hydrogen-blended combined heat and power units, ammonia co-firing retrofitted coal-fired units, and power-to-X technologies including power-to-hydrogen, hydrogen-to-gas, and hydrogen-to-ammonia. Then, a multi-energy demand response strategy is designed to coordinate electricity, heat, and hydrogen loads via interruptible, shiftable, and substitutable mechanisms, improving system-level flexibility and user-side adaptability. Furthermore, to address the uncertainty of demand, a distributionally robust joint chance-constrained model based on Wasserstein distance is formulated, allowing for the characterization of joint risks across coupled constraints without assuming a precise probability distribution. Finally, a big-M-free reformulation is adopted to enhance numerical stability and computational efficiency of the proposed model. Case studies validate that the proposed method significantly improves operational flexibility, enhances renewable energy utilization, and ensures robust operation under uncertainty.

To address the strong conservatism of conventional N-1 security constraints, the insufficient response to minute-scale fluctuations, and the heavy computational burden of rolling optimization in power systems with high wind power penetration, this paper proposes a two-stage coordinated scheduling framework considering wind power uncertainty. In the day-ahead stage, a distributionally robust security-constrained unit commitment model is developed to account for wind power uncertainty. Bilateral chance constraints are introduced to relax the conventional rigid N-1 line-flow limits. They extend the deterministic security boundaries into probabilistic constraints with adjustable risk levels. Conditional value-at-risk (CVaR) is further employed to derive a convex reformulation of these constraints. The resulting model is solved using a column-and-constraint generation (C&CG) algorithm, thereby enabling a flexible trade-off between system security and operational economy. In the intraday stage, a Bi-LSTM-driven event-triggered model predictive control (ET-MPC) rolling optimization framework is constructed. By updating minute-scale wind power and load forecasts online and using forecast deviations as triggers, the framework links forecasting with dispatch and reduces the computational burden. Finally, case studies based on a modified IEEE 39-bus system demonstrate that, under a slight relaxation of the day-ahead security margin, the proposed method reduces the total operating cost by 1.97% and wind curtailment by 40.55% compared with the conventional N-1 checking strategy. In the intraday stage, different triggering thresholds reduce the computation time by 22.75% and 42.31%, respectively. The resulting dispatch decisions remain effective. The results verify the effectiveness of the proposed two-stage scheduling framework in improving wind power accommodation and economic operation.

As one of the largest energy consumers in buildings, heating, ventilation, and air conditioning (HVAC) system possesses considerable potential for demand response (DR). However, it’s challenging to accurately quantify the power flexibility of the HVAC system, which is crucial for the efficient operation of the community microgrid (MG). To address this issue, this paper proposes a physics-informed gated recurrent units (PI-GRU) model with two different time scales to quantify the DR potential of the HVAC system, enabling its integration into both day-ahead scheduling and real-time control. Considering user satisfaction including thermal comfort and air quality requirements, the PI-GRU models are established to predict the mass flow rate of supply air, thereby quantifying the flexibility of the HVAC system. Furthermore, a two-stage operation framework for community MG including day-ahead scheduling and real-time control is developed with the incorporation of the proposed PI-GRU models. Particularly, in the real-time control, a correlation model capturing multiple uncertainties of renewable generation, load demands, electricity prices and outdoor temperature is formulated to generate representative scenarios. These scenarios are then embedded within the distributionally robust optimization (DRO)-based model to enhance decision-making under uncertainties. Finally, the predictive performance of proposed the PI-GRU models, and the two-stage hierarchical day-ahead scheduling and real-time control of community MG are verified by the simulation in this paper.

Optimal coordinated operation of Cascade Hydro-Solar-Pumped Storage System (CHSP) serves as an effective method for integrating renewable energy and fostering a low-carbon energy transition. This paper proposes a multi-objective day-ahead coordinated operation of CHSP considering the DC transmission lines and vibration zone constraints of water turbines. The model aims to maximize the overall power generation and minimize the net load variance. Theɛconstraints method is developed to obtain the pareto solution set and fuzzy decision method is adopted to determine a trade-off solution. Case study of a test system demonstrate the effectiveness of proposed method.

Stochastic distribution defense hardening (SDDH) is an effective way to safeguard the operation of the distribution systems against natural disasters, especially typhoons. The interactions between the typhoon disaster, hardening decisions, and distribution line statuses make defensing against the high-impact and low-probability typhoons extremely challenging. To this end, a comprehensive framework for SDDH against typhoons to improve system resilience is proposed. Firstly, the trajectories and the wind fields of typhoons are predicted using a proposed ARIMA-LSTM based forecasting model, with which the spatiotemporal impacts of wind speed on distribution lines can be quantified. Statuses of distribution lines are obtained by sampling according to their corresponding failure probabilities. Then, with different scenarios of a typhoon and considering different system states under the typhoon, a stochastic programming based distribution system defense hardening model considering enhancement measures including hardening lines, dispatching mobile emergency generators (MEG), and line switching is proposed. The proposed framework is validated with a modified IEEE 33-bus distribution system and historical data recorded during 2018 super typhoon “Mangkhut” that landed eastern coastal areas of China.

With the sharp growth of extreme rainfall events, it is imperative to enhance the resilience of distribution systems under extreme rainfall scenarios. To address the limitations of conventional hardening models, this paper proposes a multi-level network hardening strategy for distribution systems to reduce load loss and total operational cost while considering flood prevention measures and decision-dependent uncertainty. Firstly, through a mechanism analysis of extreme rainfall impacts on distribution systems, and by combining the digital elevation model and the Chicago hyetograph method, an urban waterlogging model based on the Integrated Terrestrial Fluxes Flood model is established, incorporating surface infiltration, storm-drain drainage, and land-use types. Secondly, a time-varying decision-dependent failure probability model for system equipment is developed based on water depth calculation results from the urban waterlogging model. Finally, a multi-level distribution network hardening model integrating flood prevention measures considering decision-dependent uncertainty is constructed, which is formulated as a mixed-integer second-order cone programming problem. Case studies on the IEEE 33-node distribution system demonstrate that, under a 100-year return-period rainstorm scenario, the proposed model can effectively reduce load loss while significantly enhancing the resilience of distribution systems against extreme rainfall events.

Frequent natural disaster promotes the critical importance of coordinating the restoration of integrated gas and electricity distribution systems (IGEDSs). A bi-level frequency calibration framework of IGEDS is proposed in this paper to ensure frequency stability, where a multi-stage optimal restoration problem with a dynamic discretized frequency reserve model is solved in the upper level, and time-domain simulation is conducted to check frequency stability and frequency reserve utilization of distributed generators (DGs)/wind turbines (WTs) in the lower level. Specifically, the multi-stage optimal restoration problem optimizes the total operation cost during the restoration process utilizing repair crew dispatch, line pack of gas pipeline, gas storage, power-to-gas (P2G), and frequency reserve of DGs and WTs, while considering both nonanticipativity constraints of outage uncertainty and distributionally robust constraints of energy uncertainty. Moreover, an improved decomposition and nested progressive hedging (ID&NPH) algorithm is presented to solve the proposed model. Numerical analysis indicates that the proposed model could effectively improve the load restoration of IGEDS under extreme disasters.

To overcome the limitations of conventional short- circuit current (SCC) suppression approaches in transmission expansion planning (TEP), this letter proposes a novel SCC-constrained hybrid AC/DC TEP model considering wind uncertainty. To quantify the maximum SCC contribution of MMC, a planning-oriented equivalent current source (ECS) model is developed with different fault-type-dependent magnitudes which could be directly embedded into the TEP. Then, a unified SCC formulation is proposed, integrating the SCC contribution of high voltage alternating current (HVAC) lines, synchronous generators (SGs) and superconducting fault current limiters (SFCLs). Based on the SCC formulation, the proposed stochastic model is cast as a mixed-integer linear programming (MILP) model. Numerical simulations suggest that the proposed formulations are effective in SCC modeling and suppression.

ABSTRACT Climate warming is accelerating the decomposition of organic carbon and the release of greenhouse gases in permafrost regions, potentially creating a positive feedback effect that could further exacerbate climate change. However, the comprehensive effects of rising air temperatures on heat and moisture transfer, organic carbon decomposition, and carbon dioxide transport processes in permafrost regions remain poorly understood. This study introduces a novel coupled water‐heat‐vapor‐carbon model based on surface energy balance theory to systematically analyze the effects of rising air temperatures on soil temperature, moisture content, and soil organic carbon in permafrost regions. The main conclusions are as follows: (1) As air temperatures rise, surface net radiation, latent heat of evaporation, and soil heat flux increase, while sensible heat flux decreases; and (2) the rise in air temperature impacts the water and heat transport processes in the soil. As air temperatures increase, the liquid water flux decreases, while the water vapor flux increases. Additionally, heat transfer in the soil is enhanced with rising air temperatures; and (3) the increase in air temperature significantly enhances the transport of CO 2 in the soil, with both gaseous and liquid CO 2 diffusion fluxes increasing; and (4) rising air temperatures accelerate the decomposition of soil organic carbon in permafrost regions. However, the effect of air temperature fluctuations on soil organic carbon diminishes with increasing soil depth.

Efficient and accurate analysis to identify future power system operating modes is crucial for handling power system operation, planning, and stability analysis. This paper proposes a data-driven and deep learning-based method for analyzing typical operating modes in power systems, while also addressing the needs for identifying future operating modes. Firstly, an operating mode analysis method for power systems is developed based on an adaptive threshold Affinity Propagation (AP) clustering method with Dynamic Time Warping (DTW) that incorporating historical load sequences. The method employs a modified distance function and adaptive thresholds for clustering. Secondly, a day-ahead load forecasting method based on a Parallel Temporal Fusion Network (PTFN) model with temporal feature projection is proposed to address load uncertainty. Finally, based on the results of historical operating mode analysis and future load forecasting, a SHapley Additive exPlanation combined with Parallel Temporal Convolution Network embedded with the Squeeze-Excitation mechanism (SHAP-PTCN-SE) is proposed for identifying future operating modes of power systems. Numerical examples demonstrate the efficiency and accuracy of the proposed method in identifying future operating modes, providing guidance for system monitoring and protection.

The ability of integrated gas-electricity distribution systems (IGEDSs) to survive under extreme disasters attaches great importance. This paper proposes a multi-stage coordinated restoration model of IGEDS considering exogenous-endogenous uncertainties with initial and conditional nonanticipativity conditions under disasters. The exogenous uncertainties consist of component failure uncertainty and energy generation/ consumption uncertainty, and endogenous uncertainty represents traffic uncertainty determined by repair crew routing decisions. Network reconfiguration and frequency reserve of distributed generators (DGs)/ wind turbines (WTs) are utilized for enhancing system resilience. The optimization with constraint learning (OCL) method is applied to build linearized frequency nadir constraints via dynamic sparse neural network training and pruning. Moreover, to solve the proposed model with exogenous-endogenous uncertainties, an enhanced outer approximation (EOA) algorithm is presented. Numerical analysis indicates that the proposed model could effectively improve the load restoration of IGEDS under extreme disasters.

—The increasing penetration of renewable energy and the application of high voltage direct current (HVDC) transmission technology have gradually complicated the operation of inter-regional power systems. Under these circumstances, a two-stage moment-based distributionally robust scheduling model of inter-regional systems connected via HVDC links is proposed. The proposed model minimizes total operation cost, considering energy complementarity of generating resources. Primary frequency response (PFR) model of thermal/cascaded hydropower/pumped storage units and emergency frequency support of HVDC links is proposed to improve system frequency regulation. Then a two-stage moment-based DRO model is proposed to handle the uncertainty of photovoltaic (PV) generation. The proposed DRO-based scheduling model is then reformulated as mixed integer linear programming (MILP) for the solution. Numerical results show that the proposed model could effectively improve frequency stability with coordinated scheduling of inter-regional power systems while balancing solution robustness and economy.

The increasing frequency of extreme weather events can significantly reduce PV generation, threatening the security of the power system. To address this issue, this study proposes a data-driven short-term PV forecasting framework based on GRU-KAN model to enhance forecasting accuracy. First, historical PV data are preprocessed and key meteorological factors are extracted via Grey Relational Analysis. Second, normal weather conditions are clustered into sunny, cloudy, and overcast categories using K-Means++. Third, a data-driven method is used to identify extreme weather conditions characterized by limited data, and a similar-day selection method is employed to construct high-quality training sets. Fourth, a GRU-KAN model which integrates temporal modeling capability of GRU with the nonlinear approximation power of KAN is proposed for photovoltaic forecasting under both normal and extreme conditions. Finally, the study simplifies the neuron structure of the feature extraction layer to enhance forecasting performance when data are limited. Experimental results demonstrate that the proposed model achieves the lowest average RMSE of 3.86 and the highest average R-2 of 96.90 % under normal conditions. In addition, the model achieves the lowest RMSEs of 0.900, 0.950, 1.157, and 1.438 across four representative extreme days.

Abstract A series of equilibrium and nonequilibrium molecular dynamics simulations were conducted to investigate the Fickian diffusion of dissolved carbon dioxide under kaolinite-slit confinement. The relationship between the chemical potential of the dissolved carbon dioxide and its molar fraction was revealed. The Fickian diffusion coefficients were also computed and were found to increase linearly with the molar fraction. The adsorption behavior of the dissolved carbon dioxide as a solute was found to be associated with the accumulation and preferential orientation of the solvent water. The radial distribution functions of the water oxygen in various regions were analyzed, revealing the spatial inhomogeneity of the solvent structure. The inhomogeneity of dissipation, quantified by local mobility, was found to be proportional to the first peak of the radial distribution function of solvent water.

With the widespread integration of new energy sources into the power grid, their inherent characteristics of uncertainty and randomness have gradually weakened the regulation capabilities on the generation side, thereby posing challenges to the reliability and stability of the power system. To effectively address the issue of new energy accommodation, this study focuses on air-conditioning loads. Given their dispatch flexibility, rapid response capabilities, and significant regulation potential, even if load adjustments or parameter changes are made in a short period of time, the impact on users is relatively limited. Based on this, this study proposes a multi-energy park economic dispatch model that comprehensively considers the demand response potential of aggregated air-conditioning loads. Firstly, a framework for the coordinated optimization of various equipment within the multi-energy park is constructed. Subsequently, aiming to minimize the total operational cost of the park, a day-ahead economic dispatch model based on mixed-integer linear programming is established. The model is solved using the Gurobi solver to optimize the operation strategies of cooling loads, energy storage devices, diesel generators, and grid power purchase plans. Finally, through case simulations, the model is verified to enhance the capacity for new energy accommodation while effectively reducing the total operational cost of the park. Additionally, the potential of multi-energy parks in improving the phenomena of "wind curtailment" and "solar curtailment" is explored.

This paper proposes a distributionally robust expansion planning method for Integrated Electricity and Natural Gas Distribution Systems (IEGDS) to enhance system resilience under earthquakes considering decision-dependent uncertainty (DDU). Based on expansion planning framework, the portfolio of coordinate planning includes power lines, tie-lines and gas pipelines, distributed generators (DG) including gas turbines (GT) and dual-fuel units (DF), power-to-gas (P2G) devices, storage devices including energy storage (ES) and gas storage (GS) devices. A modeling method of decision-dependent uncertainty is proposed to characterize the fragility curve of different models of power line and gas pipeline under earthquake damages. To further quantify different resilience enhancing effects among investing devices, inherent resilience (IR) and dynamic resilience (DR) are proposed in this paper. The IR penalty primarily evaluates the long-term load-carrying ability of IEGDS, assessing its ability to withstand disasters. In contrast, the DR metric provides a more refined short-term quantification of load shedding penalty costs by incorporating repair crew dispatch model to accelerate the restoration of critical loads. Then a two-stage distributionally robust model with Kullback-Leibler divergence-based ambiguity set is formulated. In the first stage, investment and operational costs are minimized for the base case. In the second stage, it aims to minimize the penalty of inherent resilience and expected penalty of dynamic resilience based on the planning decisions made in the first stage. To efficiently solve the proposed two-stage large-scale planning model with DDU, a progressive hedging (PH)-based column-and-constraint generation (CCG) method is developed. Numerical results demonstrate the economic benefit and resilience enhancing effect of the proposed model.

Air conditioning load, as an important demand response resource in the power system, plays a significant role in maintaining the stability of the national power system and improving economic benefits. Therefore, this paper comprehensively considers the multifaceted costs of air conditioning load aggregation response dispatch, fully excavates the response potential of air conditioning load clusters, and forms a day-ahead dispatch plan that considers comfort and economic benefits. Firstly, based on the basic physical model of a single air conditioner, the approximate aggregated power of the air conditioning load clusters is obtained; secondly, based on users' thermal comfort, the response potential of the air conditioning load aggregation is assessed to obtain its maximum aggregated response potential; finally, a joint dispatch model is established in combination with the basic characteristics of flexible load response, fully excavating the response potential of the load side multi-types resources involved in the system, taking into account the start-up and shutdown costs of power generation units and forming a day-ahead dispatch plan with microgrids as an example, dedicated to improving economic efficiency.

To improve renewable energy consumption under the background of dual carbon, and solve problem of multi-source coordinated scheduling with DC transmission, a multi-source coordinated scheduling of receiving power system considering voltage stability based on information gap decision theory (IGDT) is proposed. Firstly, the voltage stability indicator is constructed, which indicates distance between the system voltage state and the voltage stability limit state. Then, aim at minimizing the system operation cost and wind curtailment penalty, a multi-source power receiving ability promotion model considering thermal/ cascade hydro/wind power generation, energy storage and DC transmission coordination is proposed. The models of multi-sources in the system are established, and the AC power flow and hydropower conversion constraints are linearized by Taylor series expansion and triangle approximation. On this basis, voltage stability constraints are generated through voltage stability margin threshold, and voltage stability constraints are added to multi-source coordinated optimization problem for the receiving power system through each iteration to achieve the improvement of voltage stability indicator. To consider uncertainty of wind power output and load demand, a multi-source coordinated scheduling of receiving power system considering voltage stability based on IGDT is proposed. Finally, the effectiveness of the proposed model is verified by case studies.

This study focuses on optimizing the economic dispatch of a high-permeability micro grid that incorporates hydrogen and energy storage. It integrates wind, photovoltaic, hydrogen, energy storage, and gas turbine systems to minimize operational costs. The paper establishes a carbon emission model and develops an optimal scheduling model under the constraints of system operation and power reliability. A comparative case study of four configurations reveals that the integration of hydrogen and battery systems enhances the economics and reliability of the micro grid, while carbon trading has a minimal impact on dispatch strategies.

With the sharp growth of extreme events and the tight connection between the natural gas and electricity systems, it is imperative to co-optimize the two energy systems after natural disasters. This paper proposes a multi-stage stochastic restoration model of the integrated gas-electricity distribution system (IGEDS) considering the nonanticipativity requirements of uncertain extreme events. The proposed model minimizes the total operational cost during the restoration process while considering repair crew scheduling, reconfiguration of the power distribution system, primary frequency response (PFR) of distributed generators (DGs), and gas and electricity demand response (DR). In addition, dynamic islanding via topology adjustment is proposed in this paper, which could effectively utilize the PFR reserve of DGs to supply critical loads while guaranteeing frequency stability. Moreover, uncertainties of fault branches in different stages and energy generation/consumption are taken into account to ensure nonanticipativity and all scenario feasibility. To solve the proposed model, a customized progressive hedging (PH) algorithm with improved iteration criteria is presented. Numerical results show that the proposed model could effectively improve the restoration process against extreme events.