[Objective] Ensuring the normal operation of the price mechanism and auxiliary mechanisms is an important goal for the future regulation of China's electricity market. Summarizing the regulatory practices of mature international electricity markets in dealing with behaviors that disrupt the price mechanism and auxiliary mechanisms，and comparing the penalty rules for violations in different countries，is of great significance for improving China's electricity market system. [Methods] Grounding its analysis in penal theory，this study conducts a focused investigation into the jurisprudential rationales and operational methodologies employed by EU and U.S. electricity market regulators to sanction manipulative practices. Through a comparative examination of statutory frameworks，regulatory philosophies，and empirical enforcement cases，the research systematically contrasts jurisdictional variations in both legal thresholds for liability and gradations of punitive severity. The concluding synthesis assesses enforcement efficacy to derive comparative insights into regulatory paradigm effectiveness. [Results] While Europe and the U.S. differ significantly in specific legal provisions governing penalties，their electricity regulators apply identical thresholds for initiating enforcement actions against market manipulation. While the U.S. model presents more efficient for quantifiable illegal gains，the French model provides clearer outcomes for uncertain gains. When no illegal gains or harm have yet occurred，the French model exerts stronger deterrence against non-compliant firms. [Conclusions] In light of China's evolving electricity market landscape，this article advances three policy recommendations： excluding the element of intention for violation determination； establishing a fine base with dual deterrence and compensation functions，coupled with fines graded by harm severity； education and penalties are combined to enhance regulatory effectiveness.

[Objective] Distributed photovoltaics （PV） has developed rapidly in recent years under the guaranteed purchase policy due to the decline in costs. However，unordered growth has increased distribution network operational pressure and created new issues. Thus，an efficient，enforceable market-oriented mechanism is urgently needed to guide healthy PV industry development，coordinate flexible resources，and promote local consumption of new energy. To reduce operational and management burdens on market organizers and participants，this paper proposes a new event-driven two-stage distributed energy trading mechanism for distribution network markets. [Methods] First，the baseline operation state of the distribution network is determined via day-ahead pre-balancing，and whether to trigger distributed trading is judged based on line congestion，voltage over-limit，etc. Then，distributed trading is conducted on demand，using price signal to mobilize resources in generation，load，and storage links for optimal distribution network operation. To incentivize market entities to support system operation through trading，a value contribution evaluation method is proposed based on the "who serves，who profits" principle. Sensitivity factors measure each entity’s contributions to improving voltage quality and alleviating line congestion. Market entities' contributions can serve as the basis for allocating incentives for delaying distribution network investments，boosting their willingness to participate. [Results] Case analysis using the modified IEEE33-bus system verifies the mechanism’s feasibility and effectiveness： in the pre-balancing stage，midday PV over-generation caused branch reverse power overload，and evening peak load led to low voltage violations. after market activation，all violations were eliminated. PV curtailment decreased by 64%，improving consumption rate. With fewer adjustable resources，system improvements diminished and curtailment increased. [Conclusions] This mechanism addresses the practical issue of distributed PVs failing to perceive market signals，while deferring distribution network investments，enhancing utilization of existing equipment，and providing new ideas for future transmission and distribution pricing.

[Objective] In the context of the restart of the voluntary emission reduction market，in order to promote the coordinated construction of the electricity carbon certification market and effectively solve the decision-making problems faced by various power generation entities，a coupling mechanism of the electricity carbon certification market was proposed，and a trading decision model for power generation entities under multi market coupling was constructed. Sensitivity analysis was conducted on key parameters. [Methods] The proposed model is based on the coupling mechanism of the electricity carbon certification market and the clearing mechanism of each market. It considers the impact of the restart of the voluntary emission reduction market and sets constraints. The multi-objective particle swarm optimization algorithm is applied to solve the problem. At the same time，Morris screening and Sobol method are used to conduct sensitivity analysis around renewable energy consumption responsibility weights，baseline carbon emission factors，and certified emission reduction offset ratios. [Results] The Matlab simulation results show that the proposed model outputs a 44.55% increase in trading returns under multi market coupling compared to considering a single market. Sensitivity analysis results indicate that the total effect index of the parameter of renewable energy consumption responsibility weight is the highest at 0.79，and the rate of change in transaction costs of power generation entities significantly increases when this parameter is disturbed within the range of [23%，26%]. When set to 24.5%，the rate of change reaches its peak at 5.44%. [Conclusions] The proposed model can effectively solve the decision-making problems faced by various power generation entities at the current stage，providing scientific and reasonable trading decision-making basis for each power generation entity. At the same time，the results of sensitivity analysis can provide scientific reference for optimizing multi market trading mechanisms and adjusting assessment indicators such as renewable energy consumption responsibility weights.

[Objective] To ensure the operation stability and economic requirements of the power grid under large disturbances and to incentivize market participants to engage in system protection，this paper proposes a market mechanism for emergency generator tripping and load shedding ancillary services. [Methods] First，the operational process of the proposed ancillary service market is designed，along with a two-part pricing scheme consisting of capacity and utilization fees for generator and load shedding. Then，considering specific faults，the power grid is partitioned to form a candidate set of market participants for ancillary services. Subsequently，an optimization clearing model is established with a cost-minimization objective function that incorporates expected fault occurrence frequency and average outage duration. The model also accounts for transient frequency，voltage，and synchronization stability constraints. A time-domain simulation and branch-and-bound algorithm are employed to solve the ancillary service strategy. Finally，an improved CSEE-FS case is used to validate the effectiveness of the proposed market mechanism for ancillary services. [Results] Simulation results demonstrate that the proposed ancillary service strategy can effectively restore the power grid to a stable operating state. The incorporation of fault characterization factors enables precise identification of the cost-minimizing control strategy under specific disturbance scenarios. [Conclusions] The proposed market mechanism ensures that grid security and stability requirements are met，while aligning compensation with the actual service capability of market participants. It also contributes to reducing the overall cost of ancillary services.

[Objective] "Coupling" exists widely in nature and is the fundamental physical attribute and primary characteristic of the integrated energy system （IES）. Clarifying the coupling mechanism of IES can fully leverage its multi-energy complementary advantages and tap into its potential as a "virtual energy storage" for new energy consumption. Current coupling research mainly focuses on qualitative analysis of energy coupling forms，which suffers from issues such as "incompleteness，" "unquantified，" and "difficult to analyze." [Methods] This paper reviews the analysis and application of IES coupling from both "qualitative" and "quantitative" dimensions. Firstly，starting from the widely existing coupling phenomena，the basic concept of IES coupling is proposed. Secondly，the current status of qualitative research on heterogeneous energy coupling in terms of its impact on planning and operation is sorted out. Then，in view of the limitations of qualitative analysis of IES coupling，the progress of current quantitative research on IES coupling is summarized from the dimensions of indicators and methods，and the potential of quantitative application of coupling is explored from the three dimensions of time，space and energy grade. [Results] Finally，the future research challenges of IES coupling quantification are expounded around modeling，mechanism and application. [Conclusions] Heterogeneous energy coupling involves multiple key essential issues in IES research，such as multi-time scale analysis，source-grid-load-storage coordination，and energy cascade utilization. Clarifying the coupling characteristics will help clarify the essential laws of IES operation and contribute to the formation of new theoretical achievements and application methods.

[Objective] To achieve carbon-peaking and -neutrality goals， the number of electric vehicles （EVs） in China has grown rapidly. However， the growth of EV-charging infrastructure has lagged behind the rapid increase in demand. The problem of inadequate charging capacity is particularly severe in highway service areas， where challenges such as difficulty in accessing chargers and long waiting queues have become increasingly pronounced. [Methods] First， based on a geographic information system data and actual traffic flow， a travel probability matrix is constructed and a “charging-anxiety coefficient” is introduced to enhance the model’s adaptability to real-world scenarios. Second， a “traffic-flow-to-speed impact coefficient” is proposed to improve the accuracy of predicting the state of charge of EVs upon arrival at highway service areas； this coefficient serves as the foundation for forecasting charging loads. Finally， a multi-service-area charging-pile optimization model is developed based on the forecast results. This model incorporates a dynamic weight adjustment mechanism based on traffic flow， allowing for the flexible reallocation of construction， operation and maintenance， and user-waiting-time costs under varied traffic load conditions， thereby enabling adaptive optimization of objective function weights. [Results] Simulation results show that although increasing the number of charging piles significantly reduces user waiting times， it also leads to higher investment costs. Peak charging loads during holidays increase by 26.3% compared with those during normal periods， exhibiting a bimodal pattern， with demand concentrated in service areas located in the last half of travel routes. The proposed dynamic weight adjustment mechanism adapts to traffic flow fluctuations， enabling an adaptive balance between costs and user experience. [Conclusions] Introducing a dynamic weight adjustment mechanism and optimizing charging-pile allocation across multiple service areas significantly enhance the flexibility and responsiveness of highway charging networks in matching supply with demand. Under the combined effects of traffic flow fluctuations and holiday demand peaks， the proposed approach achieves precise coupling between charging resources and user needs. This not only improves pile utilization rates and reduces queuing times but also effectively balances construction and operational costs.

[Objective] Under the context of large-scale renewable energy integration，it is crucial to quantitatively assess the power quality improvement potential of power grid for subsequent governance services and ensuring efficient and stable operation of the power grid. [Methods] Therefore，an assessment method for power quality improvement potential of power grid based on the -rung interval-valued orthopair fuzzy number and relative off-target distance is proposed. First，a comprehensive evaluation index system is constructed around four dimensions： power quality，power supply reliability，serviceability，and low-carbon. The fuzzy analytic hierarchy process method based on the -rung interval-valued orthopair fuzzy power weighted average operator is adopted to subjectively weight primary indicators，and the improved criteria importance through the intercriteria correlation method is used to objectively weigh secondary indicators. The final weights are obtained through linear weighting. Second，the technique for order preference by similarity to an ideal solution and Mahalanobis distance are introduced to establish an improved grey target decision-making model based on positive and negative target centers，and the power quality improvement potential is quantitatively analyzed using the relative off-target distance. Finally，case studies are conducted on five typical regions in a certain city. [Results] The results show that the higher the relative off-target distance，the better the level of power quality and the smaller the improvement potential. Analysis of the relative off-target distance for a single area can effectively identify the core factors that affect improvement potential. [Conclusions] The proposed model and method can analyze the impact of multidimensional factors on the improvement in power quality of regional power grid，accurately assess the overall situation of power quality，and help to improve power quality problems in a targeted manner.

[Objective] In view of the impact of various extreme weather events on complex urban distribution networks，we propose an assessment method to identify vulnerable and susceptible points in distribution networks，especially electrical transformers with distribution functions that incur high outage losses. [Methods] First，we introduce a unified and parameterized integrated model of vulnerability. By employing kernel functions，cumulative transformations，and parameterized mappings to construct a unified framework，the model achieves a synergy between mechanism- and data-driven approaches，which makes it adaptable to diverse modeling scenarios. Second，the method fully considers the influence of land-use types on the operational environment and disaster risk of transformer equipment. We developed a vulnerability index system based on land-use categories by refining the vulnerability assessment from a spatial perspective. Additionally，we also propose a scenario probability sampling and reduction approach； by utilizing Monte Carlo sampling and sample-average approximation techniques，the failure probabilities computed by the model are converted into a set of failure scenarios to improve the efficiency and accuracy of failure scenario analysis. Finally，a simulation analysis was conducted on a 10 kV distribution network in a certain area of Beijing. [Results] The results demonstrate that the proposed model and method were able to identify vulnerabilities and susceptible points in the distribution network under various extreme weather conditions. Moreover，by deploying emergency power supply vehicles at susceptible points and positioning emergency repair resources at vulnerability points，indicators such as load loss and the number of affected users can be significantly improved. [Conclusions] The experimental results validate the effectiveness of the proposed approach in guiding power grid planning，construction，operation，and maintenance.

[Objective] Traditional power systems have evolved into more efficient and controllable cyber-physical systems （CPS） with the integration of advanced digital information technology and power physical systems. However，this also increases the risk of cyber-physical collaborative attacks. [Methods] This study analyzes the sources of physical system security incidents caused by information system security threats，categorizes the types of cyber-physical collaborative attacks，and establishes two typical attack scenarios. A simplified single-sided network structure of the power CPS was modeled based on complex network theory and energy-information flows. A three-layer cyber-physical power system network node one-to-one dependency framework was built using the correlation matrix theory. The evolution model of cascading failures in cyber-physical power system nodes was proposed，and a weighted comprehensive centrality index was designed to assess the vulnerability of key nodes. [Results] By simulating different cyber-physical collaborative attack strategies，the node survival rate performance under continuous attacks or failures is analyzed. The accuracy of this method in identifying cyber-physical attack paths is validated. [Conclusions] This study provides a technical reference for the security risk analysis of critical nodes and stable operation control in practical power systems.

[Objective] To reduce carbon emissions in the power system and improve the adoption rate of clean energy with the goal of minimizing total cost，a multi-energy coupling regional integrated energy systems （RIES） collaborative optimization method based on combined heat and power （CHP） with a carbon capture and storage （CCS） power-to-gas （P2G） （CCP） coupling mechanism and low-carbon demand response （LCDR） is proposed. We also introduce a source-load bidirectionally flexible low-carbon scheduling model. [Methods] First，a carbon trading and green certificate mechanism is introduced on the supply side，and a carbon emission allocation model based on the baseline method is established to incentivize the system to consume renewable energy. Secondly，based on the CHP-CCS-P2G multi-energy coupling unit，the energy efficiency of the system can be improved through carbon recycling and energy cascade conversion. Then，the load side introduces a low-carbon demand response mechanism that takes into account differences in load characteristics to establish a bidirectional interaction mechanism between electricity and heat loads based on price elasticity matrices to reduce peak-to-valley load differences. Simulation experiments were conducted using data on electricity consumption from an administrative district in a city in southern China. [Results] The results showed that the carbon emissions and operating costs of the system were reduced using the proposed method，and the wind and solar power consumption capacity of the system was improved. In particular，operating costs were reduced by up to 5.26 % compared with the basic scenario. [Conclusions] The proposed method can form a closed-loop conversion of carbon elements to reduce the output of traditional power generation units，increase the grid power of new energy，and achieve "peak shaving and valley filling" through excitation signals. Thus，the proposed approach is designed to reduce carbon emissions and support the transition to a low-carbon economy.

[Objective] Integrated energy systems （IESs） face multiple challenges in carbon and green certificate trading markets. Existing scheduling models have difficulty in balancing the dynamics of economic returns with low-carbon objectives and lack effective quantification methods for unstructured uncertainty. An optimal scheduling strategy for IES intervals that takes into account the uncertainties of green certificates and carbon trading is proposed，and a master-slave game mechanism is combined to coordinate conflicts of interest with multiple actors to enhance economic outcomes and minimize the overall carbon footprint of the system. [Methods] First，a quantitative model of uncertainty in carbon trading and green certificate markets is constructed based on interval mathematics to characterize the range of fluctuation of green certificate trades and carbon emissions in terms of the number of intervals to avoid relying on a probability distribution. Second，a master-slave game framework is established for an IES operator and an energy supplier，and a two-layer interval optimization model is designed. The upper layer dynamically optimizes an energy pricing strategy to maximize the revenue of the IES operator，and the lower layer optimizes the unit output to maximize the revenue of the energy supplier. Furthermore，the particle swarm algorithm was applied to nest the CPLEX solver to efficiently search for an equilibrium solution to the game. The performance of the model was verified through a multi-scenario comparative analysis. [Results] Simulation results show that the proposed two-layer interval optimization model increased the revenue of the integrated energy system operator and energy supplier by 2.3 % and 5.1 %，respectively，and reduced the cost of carbon emissions by 12.7 %. Narrow interval optimization combined with the dynamic pricing strategy significantly mitigated the impact of fluctuations in green certificates and carbon trading on the system，which verifies the model's economic efficacy and synergistic capability to optimize for minimal carbon consumption. [Conclusions] By integrating interval optimization and the master-slave game，the proposed model balances the conflicting interests of IESs effectively and improves the low-carbon economics of the system by taking the uncertainty of carbon trading and green certificate markets into consideration.

[Objective] To address high operational costs and non-smooth power exchange with the grid in existing campus microgrids that rely solely on power sources or air-conditioning loads to mitigate fluctuations，we propose a multi-timescale coordinated scheduling strategy for micro-turbines （MT） and air-conditioning building clusters in campus microgrids considering uncertainties in grid curtailment. [Methods] First，we established a virtual energy storage model for air-conditioned buildings by quantitatively analyzing the flexible energy characteristics of variable-frequency air conditioners. Combined with the relationship between power generation and energy consumption for MTs，we propose a short-timescale efficiency-coordinated control method for MTs and variable-frequency air conditioners to adaptively mitigate power fluctuations. Subsequently，a long-timescale coordinated operation model between MT systems and air-conditioned buildings was constructed based on supply and demand relationships relating to the microgrid. Furthermore，robust optimization methods were implemented with stochastic scenario exploration to characterize uncertainties in grid curtailment，renewable generation，and load demand. Overall，we aimed to design a flexible multi-timescale scheduling strategy compatible with grid-connected and islanded modes. Finally，we linearized the model into a mixed-integer linear programming （MILP） problem. [Results] Simulation results demonstrate that the system strictly followed the efficiency-coordinated control method to dynamically coordinate power allocation between the MT and virtual energy storage when the power provided by the microgrid fluctuated. The proposed strategy reduced the total system costs by 10.1% in the grid-connected mode and 5.0% in islanded mode. It mitigated risks of systemic collapse effectively while enhancing the reliability and economic performance of the system. [Conclusion] The proposed strategy takes advantage of the coordinated scheduling potential of MT and variable-frequency air-conditioning clusters to adapt flexibly to grid-connected and islanded scenarios. It significantly improves the economics of microgrid systems，and provides an efficient scheduling solution for high-penetration renewable energy integration.

[Objective] The high randomness of hydro，wind，and photovoltaic powers exacerbates the problem of achieving a trade-off between the computational complexity and operational economy of the traditional open-loop predict-then-optimize （OPO） dispatch. [Methods] This limitation is addressed by proposing a two-stage dispatch method based on an improved closed-loop predict-and-optimize intertwined framework （CPO） for hydro-wind-photovoltaic-involved power systems. First，a two-stage dispatch model for hydro-wind-photovoltaic power systems involving series，parallel，and hybrid-connected hydropower groups is constructed. Next，to train an economy-oriented prediction model for inflow and renewable energy generation，a loss function is established by considering the absolute deviation between the system cost calculated from the ground truths and the predictions of inflow and renewable energies. Finally，the variance，Bollinger bands，and autocorrelation function are combined to quantify the fluctuation intensities of renewable energy generation and hydropower inflow such that a hybrid regularization strategy involving elastic net regression is constructed to balance the training complexity and performance of the CPO under multiple uncertainties. [Results] MATLAB simulation results show that during the typical wet，dry，and normal months，the monthly average actual system cost obtained using the improved CPO method is reduced by 0.74%，0.57%，and 0.66%，respectively，compared with that obtained using the traditional OPO method； this verifies the effectiveness of the proposed method for improving the economic efficiency of power dispatch. [Conclusions] The improved CPO method proposed in this study significantly reduces the actual system cost and optimizes the economic efficiency of dispatch when the overall prediction accuracy of hydropower inflow，wind power，and photovoltaic power decreases slightly and the accuracy increases in certain periods. Moreover，in scenarios with high degrees of uncertainty，the effect of this method on improving economic efficiency is even more prominent.

[Objective] For the power grid with high penetration of new energy，measures such as rapid shut-off or emergency reduction of the power of new energy stations are often used to deal with DC lockout faults，but the cutting will weaken the strength of the system，which will worsen the transient overvoltage，which can easily cause chain off-grid accidents. Therefore，a wind power switching strategy at the sending end considering transient overvoltage constraints under DC lockout fault is proposed to ensure the stability of the system and prevent the voltage from exceeding the limit. [Methods] On the basis of analyzing the transient overvoltage of the converter bus after DC lockout，the principle of wind turbine switching considering the transient overvoltage constraint is clarified，and the "switching instead of cutting" mode or "switching and cutting simultaneously" mode is implemented according to the DC locking stability control instruction value and the upper limit of the total load reduction of the station. [Results] Finally，a simulation model is built on the DIgSILENT/PowerFactory platform for validation. The results demonstrate that the proposed strategy effectively mitigates DC block faults in high-penetration renewable energy sending-end grids while avoiding cascading disconnection risks caused by transient overvoltage. [Conclusions] The proposed strategy provides a specific and feasible solution to solve the contradiction between transient overvoltage and stable control in the process of DC lockout of a high proportion of new energy transmission power grid，which is helpful to improve the safe and stable operation of the power grid.

[Objective] To address the challenges of unclear wind and solar power output characterization，low accuracy of traditional clustering strategies，and poor performance in feature modeling due to the complex environment of the Sandy，Gobi，and Desert （SGD） region，this study proposes a multidimensional dynamic clustering strategy for analyzing regional wind and solar power output characteristics. The aim is to achieve precise clustering and quantitative description of wind and solar resources，providing theoretical support and data foundations for multi-source coordinated dispatch and optimization. [Methods] First，a multidimensional clustering index system is constructed，incorporating similarity，complementarity，and grid integration adaptability. Next，a clustering method based on Bidirectional Agglomerative Hierarchical Clustering （BAHC） is proposed，with Bayesian Optimization （BO） introduced to dynamically adjust index weights，enabling accurate clustering of wind and solar resources in complex environments. Finally，a "model-scenario-indicator" linkage analysis framework is established to quantitatively evaluate output characteristics and adaptability. [Results] Simulations demonstrated that the proposed multidimensional index system comprehensively characterized the wind and solar power output features in the SGD region. The BAHC-based clustering method outperformed traditional algorithms in terms of silhouette coefficient and CH index，substantially enhancing the adaptability and accuracy of clustering results. The kernel density estimation（KDE） model effectively captured the distribution patterns of wind and solar power outputs in most SGD regions，while the Weibull distribution is suitable for auxiliary description of low-output risk clusters. [Conclusions] The proposed multidimensional dynamic clustering strategy significantly enhances the accuracy and adaptability of wind and solar power output characteristic analysis in the SGD region.