In order to enhance the operation economics and reduce the operation risks of large-scale integrated electric heat system (IEHS), as well as to generate the planning scheme with maximum benefits for each unit of investment, a cooperative planning method for IEHS considering multi-dimensional operation risks and benefit/cost ratio (BCR) is proposed. First, a district heat system (DHS) segmented virtual heat storage model considering regulation capacity quantification is developed, which can accurately quantify the electric power regulation capacity of DHS while considering heat storage characteristics. Then, a cooperative planning model for IEHS considering multi-dimensional operation risks and BCR is constructed, where BCR indicators considering the benefits of reducing operation cost and multi-dimensional risks including adequacy, flexibility and security are designed. Next, for the planning model containing fractional objective caused by BCR indicators, a solution method based on McCormick envelope and dynamic step sequential bound tightening is proposed for effectively enhancing the solution accuracy. Finally, an IEHS containing a 12-node 500kV transmission power system and two 10-node DHSs is used for case studies. Simulation results indicate that the proposed method outperforms other existing methods in terms of increasing BCR indicators of planning scheme, utilizing heat storage characteristics and electric power regulation capacity of DHS, and enhancing solution accuracy. Compared with the state before planning, the proposed method reduces the adequacy, flexibility, and security risks of IEHS by 72.68%, 62.11%, 49.99% respectively while reducing the operation cost by 12.39%.

The widespread integration of photovoltaic (PV) power, energy storage systems, and other demand-side resources highlights the importance of optimal dispatching for the PV-storage-load virtual power plant (VPP). However, the fluctuation of the PV power generation and the uncertainty of the electricity prices exacerbate the economic operation risks of the VPP. To address these challenges, an optimal dispatching strategy for the PV-storage-load VPP is proposed, with due consideration given to the dual uncertainties of electricity prices and PV power output. Firstly, the conditional value-at-risk theory is employed to quantify the uncertainty risk of VPP revenue caused by electricity price fluctuations. Secondly, in view of the asymmetric fluctuation intervals of PV power output, a quantification method for PV uncertainty and dispatch robustness is developed using the confidence gap decision theory. Furthermore, by combining the regulation reserve model of multi-type flexible resources, a robust optimization model for the PV-storage-load VPP is constructed with the objective of maximizing comprehensive operational revenue, which includes the provision of upward and downward reserve services. Finally, case studies based on a PV-storage-load VPP in a Chinese province are conducted to validate the effectiveness and superiority of the proposed model. The simulation results indicate that the proposed robust optimization strategy effectively reflects the relationship between the uncertainty of PV power output and the risk preference of decision-maker, mitigates the fluctuation risks of electricity prices to ensure the stability of the power system, and enhances the economic efficiency and flexibility of the PV-storage-load VPP operation.

Due to the existence of coal-fired generating units, power system operation is significantly influenced by coal supply. From the perspective of coupled railway and power systems, the impact of cold waves on power supply reliability is multi-dimensional, including demand surges, overhead line failures, and disruptions to coal transportation, which have not been adequately modeled in existing power system reliability studies. This paper proposes a spatio-temporal reliability assessment framework, based on multi-factor probabilistic modeling, that integrates power system operations with coal storage and transportation dynamics. The time-space network (TSN) model is developed to represent the railway network topology, and the Coal Handling and Transportation (CHT) model is formulated to describe the dynamic coupling of coal storage and transportation. The TSN-based CHT model is further embedded into the SCUC model to optimize coal allocation among ports and power plants. Case studies show that load shedding increases markedly as temperature decreases during cold waves. In a representative cold-wave scenario with a 6 degrees C temperature drop (Delta T=- 6 degrees C), introducing CHT scheduling reduces total load shedding by 39.12% and shortens the overall interruption duration.

Expeditious power system (PS) restoration is always the top priority after blackouts, which necessitates full-process restoration instead of considering different phases separately. More importantly, the discrete load restoration characteristic owing to practical switching operations is neglected in most studies. Besides, mutual interactions between the power and natural gas transmission systems (TS & NTS) during restoration are rarely studied. Therefore, a two-stage full-process integrated power-gas transmission system (IPGTS) restoration model considering discrete load restoration (DLR) is proposed in this paper. First, a two-stage restoration framework for IPGTS is presented, and a first-stage submodel is proposed to implement IPGTS zone partitioning based on connectivity and classification constraints according to the topology and resource abundance. Then, based on the zone partitioning results, the second-stage parallel full-process IPGTS restoration submodel of multiple zones considering DLR is proposed. Finally, case studies on the 125-bus TS and 49-node NTS in a Chinese city are performed to justify the effectiveness in enhancing restoration efficiency and the validity of the proposed model. Simulation results exhibit higher restoration benefits and better performances in power generation and load restorations.

IntroductionRecent years have witnessed an increasing number of Internet of Things devices (IoTDs) deployed in power grids to monitor bidirectional information and power transfer, transforming them into smart grids. The densification of IoTDs in smart grids demands communication solutions that are simultaneously secure against eavesdropping and energy-efficient for sustainable operation.MethodsThis article proposes an unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS)-assisted framework in smart grids that maximizes worst-case secrecy energy efficiency via joint optimization of the UAV’s trajectory, beamforming, and phase shifts of RIS. A twin attention-driven deep reinforcement learning algorithm, TAMRRTD3, is developed, featuring attention-based state representation and regret-aware reward design to enhance learning accuracy and convergence.Results and DiscussionSimulation results indicate that the proposed algorithm achieves a faster convergence rate and enhanced secrecy energy efficiency than the benchmark algorithms.

The integration of renewable energy sources into power systems introduces operational challenges that can be efficiently managed through grid company-led virtual power plants (VPPs), which coordinate multiple distributed resource aggregators. To enhance VPPs' short-term flexibility, demand-side distributed storage units can be aggregated by an energy storage aggregator (ESA) to provide leasing services of shared capacity. Given this background, an integrated framework for flexibility aggregation and power disaggregation of distributed shared energy storage (DSES) units to coordinate VPPs in the regulation market is proposed in this paper. First, a coordination scheme for DSES and VPP through ESA is proposed. Second, a state of charge (SoC) consistency-based flexibility aggregation method is established to determine an intuitive feasible region for the energy storage cluster and simplify interactions between ESA and VPP. Then, an SoC-consistent power disaggregation method, based on the geometric water-filling algorithm, is developed to effectively control DSES units in tracking VPP scheduling. Finally, case studies demonstrate that the proposed models significantly enhance VPP operations and profitability for both ESA and VPP, with DSES units more accurately tracking real-time VPP scheduling under normal and contingency conditions.

In response to global climate change, countries and organizations are setting ambitious carbon reduction targets, striving for net-zero emissions. The power sector, as a major source of greenhouse gas emissions, is critical to addressing climate change. A key strategy involves the integration of electricity and carbon markets, which embeds a carbon price into dispatch and investment decisions, aligning short-run operations and long-run capacity planning with emissions goals. This study develops a hybrid Coupled Electricity-enterprise Production-carbon market (CEP) model, which integrates electricity generation, enterprise production, and carbon market mechanisms. The model uses electricity and carbon prices, determined by current-year parameters and historical data, as core variables to simulate the impacts of policy adjustments (e.g., carbon quota allocation) on economic performance, power sector operations, and carbon price dynamics. The model is designed to be scalable, allowing for future application at the provincial level to capture regional heterogeneity and policy spillover effects. The CEP model showed convincing results in predicting short-period generator investment behaviours, power generation demand, and supply through experimental simulations. The CEP model also envisions future research potential in large-scale simulation scenarios.

Deliberate coordinated cyber–physical attacks, which combine cyber intrusions with physical disruptions, pose growing risks to the secure and reliable operation of power systems. To identify highly disruptive coordinated cyber–physical attack strategies under pre-attack defense allocation and post-attack emergency dispatch responses, a tri-level defender–attacker–defender (DAD)-based attack strategy optimization model is proposed. First, based on the association between substation automation control systems and transmission line operation, the mechanism of breaker-tripping attacks (BTAs) through compromised digital relays in substations is investigated. Second, a tri-level DAD model is developed to optimize coordinated BTA and physical line attack strategies while accounting for pre-attack cyber and physical defense, cascading failure propagation, and post-attack emergency dispatch responses. Then, based on duality theory, network flow models, and the column-and-constraint generation (C&CG) algorithm, an iterative solution framework consisting of a defense master problem and an attack-dispatch subproblem is constructed to capture post-attack topology updates, cascading line outages, generator redispatch and load-shedding responses. Finally, the proposed method is validated using the IEEE 39 bus and IEEE 118 bus power systems. Case study results demonstrate that the proposed model identifies the most damaging coordinated cyber–physical attack strategies and that BTA-induced topology changes and cascading failure propagation significantly affect attack target selection. The proposed solution method obtains the accurate optimal objective value while reducing computational time by 92.91% for IEEE 39 bus power system, and it successfully obtains a converged solution for IEEE 118 bus power system.

In shale gas extraction and pipeline transportation, the accurate identification of gas-liquid two-phase flow patterns within wellbores is critical for system safety and production forecasting. However, traditional long-time-series identification methods based on sensing signals face limitations such as spatial perception blind spots of a single sensor, susceptibility to optical interference, and the loss of high-frequency transient dynamic information during pre-processing. To address these challenges, this paper proposes a dual-branch multi-scale feature alignment network for edge deployment. The method takes orthogonally deployed dual-path near-infrared optical sensing signals as end-to-end inputs. Specifically, first, a multi-scale window processing module is designed to physically decouple transient high-frequency bubble perturbations and macroscopic low-frequency long-slug evolution features in the time domain; second, a one-dimensional channel attention module is introduced to adaptively reinforce key features of interface mutations while effectively suppressing environmental stray light noise; finally, a weight-sharing dual-branch structure is constructed to perform cross-branch semantic alignment in the latent feature space, deeply fusing complementary observation information from orthogonal spaces. Experimental results demonstrate that the developed network achieves an average classification accuracy of 97.63% and an F1 score of 97.28%, outperforming mainstream baseline models such as Mamba and Transformer. Furthermore, the model’s total parameter count is only 1.60 M with a single-sample inference time of 25.22 ms, exhibiting significant potential for deployment in resource-constrained edge computing environments due to its lightweight characteristics.

In scenarios involving high proportions of renewable energy grid integration, a single energy storage system struggles to simultaneously meet the demands for long-duration energy support and short-term, high-frequency fluctuation mitigation. Furthermore, the system suffers from issues such as mismatched response characteristics, poor operational economics, and significant impacts from uncertain disturbances. This paper proposes a multi-timescale rolling optimization coordination method for the operation of a hybrid energy storage system comprising a vanadium redox flow battery (VRFB) and a lithium iron phosphate battery(LFP)-flywheel energy storage (FESS). First, a comprehensive energy system framework incorporating the hybrid energy storage system is established, and operational characterization models are developed for each component to clarify the complementary roles of the three energy storage technologies: "long-duration energy, medium-duration power, and short-duration high-frequency" Second, the system is divided into three phases based on dynamic response speed: day-ahead scheduling, intraday rolling optimization, and intraday real-time adjustment ; Finally, simulation verification is conducted using actual wind and solar power generation data and electricity price data from a site in Northwest China .The results indicate that the proposed strategy demonstrates significant advantages in mitigating fluctuations and reducing operating costs.

To address the driving discomfort caused by sudden change of regenerative braking torque of electric vehicles, this study obtains the deceleration characteristics of engine braking and regenerative braking through real vehicle experiments. Experimental data show that the engine braking presents a gradual deceleration characteristic; the peak acceleration is stable in the range of 0.08-0.10 g, and the braking jerk of the braking torque application stage is only 0.51 m/s3; however, the high-regenerative braking mode has defects such as high peak acceleration (>0.12 g) and excessive jerk (>1.35 m/s3). Based on the smooth characteristics of engine braking, a braking feature migration method is proposed: the engine downshift characteristics are simulated by adjusting the braking torque in segments, and the braking curve characteristics are extracted using the improved Savitzky-Golay interval filtering algorithm. A regenerative braking strategy that takes into account comfort is constructed, and an AVL Cruise/Simulink co-simulation platform is built to verify it. According to the real vehicle braking acceleration curve and the simulation results, the optimization strategy reduces the impact by 62.2% and maintains the energy recovery rate of 62.8% under the deceleration condition of 60-20 km/h. The results show that a multi-stage regenerative braking control strategy proposed in this paper significantly improves the dynamic response characteristics of the vehicle. On the basis of maintaining the energy recovery efficiency, braking jerk is reduced to the level close to that of the fuel vehicle, and the coordinated optimization of braking comfort and energy economy is realized.

Abstract Against the backdrop of renewable energy achieving full market integration, renewable energy forecasting deviations have become a critical bottleneck constraining the clearing efficiency of electricity spot markets, hindering the rational allocation of costs, and threatening the secure operation of the power system. A framework comprising a full‐stage governance system covering planning, medium‐ to long‐term trading, spot markets, and settlement is adopted in this paper. The experiences of global markets in addressing forecast deviations of renewable energy through multi‐stage, tiered governance are systematically reviewed in the paper. During the planning stage, a probabilistic physical defence boundary is established to address hidden physical gaps arising from random fluctuations that deterministic planning cannot account for. In the medium‐ to long‐term stage, risks are mitigated through a combination of physical resource aggregation and financial hedging tools, including bilateral contracts, forward contracts, reliability options, and parametric insurance. In the spot market, approaches such as self‐forecast verification, rolling forecast corrections, and a multi‐level substitution mechanism for abnormal data are adopted. During the settlement stage, incentive‐compatible mechanisms are used to improve the accuracy of renewable energy forecasting. Finally, suggestions on full‐stage risk management for large‐scale renewable energy participating in the spot market are proposed in this paper.

In modern power systems, optimizing energy-intensive industrial load scheduling through demand response (DR) has become vital for enhancing operational stability and economic performance. To address existing challenges like slow convergence and local optima in optimization algorithms, this study proposes an Improved Hunter-Prey Optimizer (IHPO). The algorithm integrates Sine chaos mapping, dynamic cross-boundary mechanisms, and parallel search strategies to strengthen global exploration, constraint handling, and computational efficiency. Applied to a bi-level DR optimization model for energy-intensive industries, empirical results verify that IHPO significantly improves power system efficiency and economic outcomes, providing an innovative solution for market-oriented scheduling optimization.

As renewable energy becomes widespread and power markets advance rapidly, clearing prices in electricity spot markets display high volatility, nonlinearity, and uncertainty. Accurately forecasting these prices is vital for the healthy operation and economic benefits of power markets. This paper presents a prediction model for electricity spot market clearing prices based on a Parallel Temporal Convolutional Network (P-TCN). The P-TCN model integrates multi - period features, using independent TCN structures to model different - period features like hours, days, weeks, and months. It uses dilated convolutions to expand the receptive field, effectively solving training complexity and gradient vanishing issues in traditional CNNs when dealing with long sequences. Experimental results show that this model can enhance the prediction accuracy of electricity spot market clearing prices, offering strong support for benefit optimization and stable operation in power markets.

In smart grids, large amounts of private data, such as real-time electricity consumption data, are stored by smart meters. However, they can be turned into malicious nodes due to human attacks, resulting in data leakage and unreliable transactions. Additionally, significant issues in power transaction processes include excessive computational costs and restricted throughput. To enhance the trustworthiness and efficiency of power trading, we innovatively propose a real-time peer-to-peer power trading model (RMCT), integrating reputation mechanism and multi-chain technology in blockchain environment. Firstly, in RMCT, a reputation mechanism is designed based on user behavior management to effectively reduce malicious behaviors and improve transaction compliance in the distributed power trading process. Secondly, a multi-chain model is firstly proposed where the dynamic electricity unit price chain interacts in real-time with the user’s real-time electricity consumption chain, enabling efficient electricity billing settlement between the user and the main grid. The validity and performance of RMCT are demonstrated by adequate experimental results and analysis. Additionally, a comprehensive qualitative comparison between RMCT and related work is conducted to fully verify its feasibility and innovation.

Virtual power plants (VPPs) represent significant market entities in modern power system, where rational profit allocation serves as a vital impetus for their orderly development. To resolve the contradiction between fairness and computational efficiency in profit allocation between VPPs and energy storage systems, a cooperative game-theoretic framework based on Shapley value is proposed. First, a co-optimization model for market bidding strategy of VPP is established that considers the coordination of multiple distributed energy resources (DERs) including wind, solar, storage, and load participating in electricity markets. Subsequently, the optimal coalition is determined through marginal contribution pre-calculation, and revenue table for different sub coalitions are generated. Finally, case studies on a VPP and energy storage systems in a certain region of China is employed for demonstrating the effectiveness of the proposed method. The simulation results show that compared with the traditional Shapley value, the combined dimensionality reduction method based on coalition revenue features proposed effectively replaces the traditional exhaustive calculation by establishing a feature revenue mapping relationship.

With the increase of the renewable energy installed capacity, shared energy storage (SES) has played an important role in the renewable energy accommodation scenario. Decision-makers need to analyze renewable energy accommodation capacity of SES using user data, but privacy concerns prevent data sharing, and authenticity risks exist. Given this background, a Secure Sharing Scheme of Trusted Data of SES (i.e., 3STD-SES), which combines with blockchain and a fully homomorphic encryption scheme, is proposed for guaranteeing data privacy and data authenticity in the statistical process of the accommodated renewable energy. Firstly, an SES operation framework based on power blockchain is presented, and on this basis, the generation, storage and sharing mechanisms of trusted data of SES are designed, which create a traceable and verifiable trust chain for the shared data. Secondly, a homomorphic encryption-based scheme counts accommodated energy without exposing raw data, which can fully preserve the privacy of SES users. Finally, the actual data from an SES trading pilot project in Qinghai, China, is taken as an example to verify the feasibility and applicability of the proposed scheme. The results show that 3STD-SES can enable decision makers to obtain the authentic, accurate and integral accommodated renewable energy with the privacy protection of SES users, and meet security requirements in the field of data sharing for the SES business model.

Heat storage capacity of heat network in urban integrated energy system (UIES) has the potential to significantly improve the operational flexibility of the system. To obtain the optimal UIES planning scheme, a UIES station-network cooperative optimization planning (UIES-SNCOP) method considering heat storage capacity is proposed. First, a heat network operation model under constant flow-variable temperature considering flow direction depiction is established for solving the problem that the existing model cannot be directly applied to UIES-SNCOP because the flow direction of pipeline cannot be predetermined. Then, a radial structure-oriented topology model of distribution and heat networks is developed to ensure the radiality of energy supply network while also reducing planning cost. On this basis, a UIES-SNCOP model based on information gap decision theory and stochastic optimization is constructed to realize the co-optimization of the siting and sizing of energy station and topology of distribution and heat networks. Finally, a solution method of UIES-SNCOP model based on relaxation-contraction coupled McCormick envelope is proposed for effectively improving the accuracy of solution result. The planning of UIES is conducted on an urban topology containing 55 nodes to test the performance of the proposed method, and simulation results indicate that the proposed method outperforms other existing methods in terms of reducing planning cost, ensuring radial structure of the energy supply network, utilizing heat storage capacity and enhancing solution accuracy.