Balancing economic scheduling and low-carbon operation in power systems while effectively leveraging the potential of source-load coordination is critical for current power system optimization. Then， a source-load coordinated low-carbon economic scheduling method considering carbon intensity-electricity price joint response mechanism was proposed. The low-carbon operational characteristics of carbon capture power plants under integrated flexible operation modes and the load regulation principles of demand response were elaborated. Additionally， their complementarity in low-carbon operation was analyzed， and a coordinated operational framework for carbon capture power plants and demand response was established. By allocating partial carbon responsibility to the load side using carbon emission flow theory， a carbon intensity-electricity price joint response model was proposed to enhance the carbon reduction capability of the load side. With the goal of minimizing system operating cost， based on the tiered carbon trading mechanism and combined with the proposed source-load coordinated operation framework， a two-stage low-carbon economic scheduling model was developed to achieve coordinated source-load optimization. Comparative analysis of multiple scenarios based on a modified IEEE 39-bus system demonstrated that the proposed method could reduce system carbon emissions by 15% and decrease comprehensive operating costs by 4%， validating its coordinated optimization in both economic and low-carbon performance.

In the context of high-penetration renewable energy integration， accurately constructing wind-solar-load joint scenarios that can capture the dynamic characteristics and complex correlations of various variables has emerged as a core requirement for power system scheduling and control. To this end， a generation method for wind-solar-load scenarios based on an adaptive multi-task diffusion model was proposed. A multi-task diffusion model learning architecture based on a joint denoising network was established. By jointly processing multivariate state vectors and integrating temporal information， realistic joint scenarios that integrated physical coupling relationships and temporal dependency patterns were generated. On this basis， an adaptive diffusion strategy module guided by the dynamic features of heterogeneous data was proposed. Dynamic statistical features of the generated data were extracted， and the noise scheduling of the diffusion process was dynamically adjusted accordingly， thereby effectively characterizing the non-stationary and time-varying dynamic characteristics of the data. Meanwhile， a training criterion guided by structured consistency was introduced， where the marginal distribution and joint dependency characteristics of the data structure were constrained within the training objectives. It effectively guided the model generation process and improved the quality of wind-solar-load scenario generation. Validation based on the IES-134 standard dataset demonstrated that the proposed model could effectively generate wind-solar-load joint scenarios with realistic physical characteristics and reasonable statistical patterns， providing a practical tool for optimal scheduling and risk assessment in power systems.

In traditional building energy management strategies， a single supply-demand adjustment mechanism struggles to balance energy efficiency improvement with carbon emission reduction. Vehicle-to-building （V2B） technology， an emerging energy management model， integrates electric vehicles into the smart building energy management system and optimizes building energy utilization through charging and discharging control. In this study， a V2B-based smart building energy management strategy was proposed that incorporated a dual incentive mechanism for electricity and carbon. The strategy guided electric vehicles to participate in the carbon trading market and designed an additional carbon-oriented adaptive time-of-use pricing model. The model comprehensively considered factors such as building heat storage characteristics and carbon emissions. It constructed a smart building energy management framework that included devices such as photovoltaics， wind power， gas turbines， and electric chillers. Simulation results demonstrated that the strategy effectively improved energy utilization efficiency and reduced carbon emissions.

Against the backdrop of the construction of a new-type power system， grid-forming technology has emerged as a crucial solution to the core challenges in microgrids， including inertia deficiency， frequency fluctuations， and voltage instability. The technical principles and development trajectories of grid-forming control strategies are systematically reviewed. Based on typical engineering cases， the three-stage evolution of grid-forming equipment from demonstration verification to intelligent development is analyzed. Focusing on three representative scenarios in microgrids， namely islanded operation， grid-connected switching， and multi-microgrid coordination， the functional advantages and implementation mechanisms of grid-forming technology in enhancing system stability are thoroughly investigated. To address existing challenges such as control real-time performance， equipment reliability， and economic efficiency， future development directions including intelligent algorithm optimization， novel topology development， and multi-energy domain coordination are proposed， which provide theoretical support for the large-scale application of grid-forming technology in microgrids.

False data injection attacks pose a severe threat that cannot be overlooked during the development of new cyber-physical power systems. These attacks can tamper with power grid data to create false grid states， mislead operators into making incorrect operational decisions， and consequently disrupt the stable operation of the power system. Moreover， existing defense methods are incapable of addressing attacks involving complex data types or pinpointing abnormal states. Therefore， a multi-scenario AC false data injection attack strategy was proposed， and an attack model better aligning with actual power grid environments and exhibiting strong stealthiness was constructed. On this basis， a defense mechanism based on a multivariate detection model was designed， effectively integrating the advantages of three detectors： extreme learning machine， extreme gradient boosting， and light gradient boosting machine. Using multi-scenario attack cases as training data， an efficient attack detection model capable of pinpointing abnormal states was formed. Both the attack and defense models were simulated in IEEE 14-bus and IEEE 57-bus systems. The experimental results verified the effectiveness， stealthiness， and diversity of the attacks， as well as the real-time performance and accuracy of the detection mechanism.

Emergency load shedding in virtual power plants（VPPs） faces command deviations due to inaccurate load identification and communication delays. Therefore， an optimized model for emergency load shedding in VPPs based on dynamic elastic grading and 5G communication delay compensation was proposed. A dynamic elastic grading mechanism was introduced to accurately identify load characteristics， providing a basis for load shedding operations. A 5G communication delay compensation mechanism was incorporated to effectively reduce command execution deviations caused by communication delays， thereby improving the execution accuracy of load shedding commands. An objective function with the goal of minimizing operational costs was constructed， and the model was solved through multi-mechanism collaborative optimization. Case study results demonstrated that the two mechanisms above helped fully leverage the advantages of the optimized model， significantly improving the execution accuracy of load shedding commands while reducing system carbon emissions. The optimized model could reduce the execution error of load shedding commands from 0.73 MW to 0.28 MW， decrease the total system cost by 13.3%， and lower carbon emissions by 32.0%. The proposed model effectively enhances the operational precision， environmental performance， and economic efficiency of the system， providing a novel solution with both theoretical and practical value for emergency load shedding decision-making in VPPs.

With the rapid development of new-type power system， customer-side interactive services are flourishing. By deeply integrating communication technology to enhance the efficiency of information interaction， the intelligence level of the customer-side interactive services in the new-type power system can be improved. However， by analyzing the customer-side interactive service requirements of the new-type power system and the current information and communication technologies， it is found that many challenges remain， including the complexity of technology integration and data security. To solve the above problems， perspectives on the development of customer-side converged communication technology are proposed from the aspects of cloud-side collaboration technology， distributed intelligence， end-to-end QoS capacity assurance， data-oriented service， and security protection system， thereby supporting the comprehensive optimization of system performance to ensure data security and reliability， further enhance the development of customer-side converged communication， and support the diversified development of customer-side interactive services.

Multi-energy flow scheduling utilizes information technology to break down the barriers between traditional energy systems， thereby forming an efficient and complementary energy supply system. However， the transmission of a large amount of measurement and control data between energy systems also increases their risk of being subjected to cyberattacks. To clarify the potential attack patterns of false data injection attacks （FDIA） in integrated energy systems （IES）， a distributed scheduling-oriented modeling method for coordinated FDIA in the electricity-gas system was proposed. This method considered the influence of different management entities of electricity and gas in the IES， and established a distributed bilevel programming model. Additionally， it designed a bilevel nested distributed solution framework by integrating the alternating direction method of multipliers， protecting the privacy of data from each entity. Meanwhile， convex relaxation techniques were used to transform the Weymouth equation in the natural gas system into a tractable convex constraint form， facilitating the efficient solution of the model. Tests were conducted in an electricity-gas IES interconnected with the IEEE 39-bus and the Belgian 20-bus systems. The results showed that the destructive effect of the coordinated FDIA could be transmitted through coupling devices in the system， forcing the system to shed nearly 70% of the load. Meanwhile， the distributed solution algorithm could converge within a limited number of iterations， and the relative error compared with the centralized method was controlled within 1.4%. The proposed method provides an effective tool for analyzing the attack patterns of FDIA in IES under distributed architectures

With the deep integration of energy internet and new-generation information technologies， digital and intelligent power grids have become a core direction for the transformation and upgrading of power systems. Driven by both national policies and market demands， the construction of charging infrastructure in China is developing rapidly， with significant increases in charging and battery swapping stations， as well as charging piles， projected by 2025. However， the extensive application of charging piles in digital and intelligent power grids brings cybersecurity issues. The cybersecurity challenges of charging piles are discussed from the aspects of boundary security， communication protocols， and operating system vulnerabilities. Furthermore， the effects of vulnerability mining methods such as static symbolic execution， code auditing， and fuzz testing on the cybersecurity of charging piles are analyzed. Future trends of charging pile cybersecurity in digital and intelligent power grids are forecasted. Additionally， the importance of complying with national laws， regulations， and standards and implementing effective measures for cybersecurity protection is emphasized.