[Objective] Modular multilevel converter based high voltage direct current technology (MMC-HVDC) with real bipolar connection is one of the mainstream technical solutions for efficient grid connection and cross-regional consumption of large-scale islanded wind farms. It has been widely adopted in HVDC projects such as Zhangbei and Rudong in Jiangsu. However, the control strategy for the sending-end positive and negative pole converters faces the dual challenge of balancing bipolar transmission power and providing stable voltage support for the wind farm. To address this, a grid-forming coordination control strategy based on active power-frequency boost / reactive power-voltage droop control for bipolar converters is proposed. [Methods] The relationship between the active power received by the sending-end bipolar converters and the phase and frequency is analyzed. An active power-frequency boost control strategy is established, clarifying the power balance and frequency coordination of bipolar converter under wind power fluctuation and the adaptive regulation mechanism under capacity-limited conditions. The coupling relationship between the voltage and reactive power of the sending end converters and wind farm is revealed. A reactive power-voltage droop control strategy with capacity and active power constraints is established. The coordinated stability mechanism of AC bus voltage under reactive power fluctuation is investigated. [Results] Simulation results demonstrate that the proposed strategy can effectively maintain dynamic power balance between the bipolar converters and provide stable voltage and frequency support for the AC system under various conditions, including system power fluctuations and single-pole capacity limitations. [Conclusions] The coordinated control strategy proposed can effectively deal with the core challenges of power balance and voltage and frequency support for the wind farm under complex working conditions, and improve the operation stability of the system.

[Objective] With the large-scale integration of flexible resources such as distributed generation (DG), energy storage systems (ESS), and soft open points (SOP) in distribution networks, the traditional “two-remote” and “three-remote” terminal configuration methods are no longer sufficient to meet the needs of automated control. There is an urgent need to introduce “four-remote” terminals for coordinated configuration. [Methods] Considering the functional characteristics of “three-remote” and “four-remote” terminals, a two-layer optimization configuration method for intelligent terminals is proposed. The upper-layer planning aims to minimize the comprehensive cost, including equipment investment and maintenance costs, wind and solar curtailment costs, and power outage losses, while ensuring the global observability of the distribution network. A terminal location optimization model is constructed. The lower-layer operation, based on the upper-layer planning results, establishes a multi-objective optimization model for N-1 fault recovery scenarios, targeting the minimization of power outage losses, wind and solar curtailment costs, and voltage deviations. The proposed model is solved using a hybrid algorithm combining the slime mould algorithm (SMA) and second-order cone programming. [Results] Validation test case shows that the proposed method reduces the comprehensive cost by 69.15% and 7.11% compared to scenarios without terminal configuration and with only “three-remote” terminal configuration, respectively. It improves power supply reliability by 0.026 1% and 0.002 2%, reduces total wind and solar curtailment by 37.79% and 4.46%, and decreases voltage deviations by 53.04% and 49.43%. [Conclusions] The proposed configuration method demonstrates significant advantages in improving investment economics, power supply reliability, renewable energy integration capability, and voltage quality. It also accommodates various flexible resource scenarios, providing a theoretical basis for the coordinated configuration of “three-remote” and “four-remote” terminals in flexible distribution networks.

[Objective] To enhance the guiding role of the capacity tariff mechanism in facilitating energy transition and address the issues of imprecise compensation and insufficient incentives for low-carbon and highly flexible coal-fired generating units under the current mechanism, this study develops a capacity tariff optimization model that incorporates energy transition objectives for coal power units. [Methods] The study first analyzes the cost pass-through path of coal-fired units and constructs a three-layer nested modeling framework consisting of a unit operation decision model, a power market clearing model, and a capacity tariff optimization model, thereby forming a dynamic feedback chain among "unit-market-region-mechanism." On this basis, an incentive model for coal-fired units targeting energy transition is proposed, in which the incentive targets and compensation levels are determined based on unit flexibility and carbon emission levels using a sorting algorithm. Simulation analyses are conducted for typical days in new energy-rich and hydropower-rich regions, considering both regional and seasonal variations. [Results] Simulation resultsshow that the flexibility-based incentive strategy can effectively promote energy transition in scenarios with high ancillary service demand, but may inhibit transition in low-demand scenarios. In contrast, the carbon-based incentive strategy demonstrates stable and positive effects on energy transition across all regions and scenarios, indicating broad applicability. All proposed incentive strategies cause only marginal increases in end-user electricity prices (within 3.8 cents/kWh), confirming their economic viability. [Conclusions] The capacity tariff mechanism for coal-fired power units should be implemented with differentiated regional and seasonal strategies based on variations in power source composition and load characteristics. It is recommended to initially adopt a carbon-based incentive scheme in the current stage. As the penetration of renewable energy continues to rise, joint incentives based on both carbon emissions and flexibility should be gradually introduced to enhance the low-carbon performance and regulation capabilities of coal units, thereby supporting the development of a new-type power system and the achievement of energy transition goals.

[Objective] In order to reduce carbon emissions in the power system and improve the absorption 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)-carbon capture and storage(CCS)-power-to-gas (P2G) (CCP) coupling mechanism and LCDR (low-carbon demand response, LCDR) is proposed, and a source load bidirectional flexible low-carbon scheduling model is constructed. [Methods] Firstly, a carbon trading 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 system's energy efficiency 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, establishing a bidirectional interaction mechanism between electricity and heat loads based on price elasticity matrices to reduce peak to valley load differences. [Results] Relevant simulation experiments were conducted using electricity related data from an administrative district in a city in southern China. The results showed that under the proposed method, the system's carbon emissions and operating costs were reduced, and the wind and solar power consumption capacity was improved. Among them, scenario 6 reduced the operating costs by 5.26% compared to the basic scenario. [Conclusions] The method proposed in the article can form a closed-loop conversion of carbon elements, effectively 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, reducing the carbon emissions of the system and putting it in a low-carbon economic operation state.

[Objective] Addressing the issue of insufficient system frequency response capability caused by the diversification and uncertainty of grid inertia sources under high renewable energy penetration, this study proposes a unit commitment framework that integrates multi-source inertia dynamics and uncertainties from both inertia and wind power. [Methods] First, an inertia uncertainty model is established by combining inertia contributions from dispatchable synchronous generators, non-dispatchable small synchronous generators, virtual inertia provided by power electronic devices, and load-side inertia. Second, a data-driven two-stage distributionally robust optimization model is developed to characterize the dual uncertainties of inertia and wind power. The model employs 1-norm and ∞-norm constraints to define the confidence set for uncertainty probability distributions, while dynamic frequency constraints are explicitly incorporated into the second-stage model. Finally, absolute-value terms in the formulation are linearized, and the two-stage problem is solved using a column-and-constraint generation algorithm. [Results] Simulation results on the IEEE 118-bus system demonstrate that, compared to the unit commitment model considering only the inertia of large synchronous generators, the proposed two-stage distributionally robust unit commitment model incorporating dual uncertainties of system inertia and wind power exhibits a more sufficient frequency response capability and reduces the total generation cost by 3.3%. Furthermore, the proposed framework exhibits superior economic performance over conventional robust optimization methods and stronger robustness compared to stochastic optimization approaches. [Conclusions] The proposed approach optimally balances power system economic efficiency and operational robustness, thereby enabling dynamic adaptability in high renewable penetration environments.

[Objectice] In view of the problem that the traditional calculation method for distributed photovoltaic (PV) capacity cannot comprehensively consider the energy absorption capacity of the transmission network and the operational safety of network equipment, this paper proposes a method for constructing and optimally solving the distributed PV capacity interval considering the coordination between the transmission and distribution networks.[Methods] Firstly, at the transmission network level, considering different provincial new energy utilization rates and energy storage configuration ratios, the distributed PV capacity interval is constructed based on time-series simulation of production simulation.Secondly, at the distribution network level, with the goal of optimizing the investment economy of the distribution network, a method for optimally solving the distributed PV capacity and checking the interval is proposed, taking into account the power flow of transmission and distribution networks, power quality, and energy storage charging and discharging constraints.Finally, case studies are conducted in provinces of different regions in China. [Results] The results show that the distributed PV capacity interval and the optimal solution of distributed PV capacity can constrain each other, which can meet both the new energy absorption demand of the transmission network and the reliable operation demand of the transmission and distribution networks. [Conclusions] The proposed method incorporates distributed PV into the provincial supply-demand calculation, considers the impact of the absorption space of all power sources on capacity calculation, scientifically evaluates the accessible margin of the power grid, and provides an important reference for guiding the rational layout of distributed PV.

[Objective] The large-scale integration of renewable energy through inverters poses challenges to power system stability. Although the virtual synchronous generator (VSG) control can provide inertia and damping, excessive short-circuit current during grid faults may lead to loss of synchronism or disconnection. To address this, a fault ride-through strategy based on dynamic virtual speed regulation is proposed. [Methods] The transient characteristics of the VSG are analyzed to identify the cause of the overcurrent during grid faults. A dynamic virtual speed adjustment strategy is adopted to maintain power angle stability, an improved reactive power control loop is designed to support reactive power compensation, and a dual-loop voltage-current structure based on the balanced current method is used to ensure the three-phase balance of the output current. [Results] Matlab/Simulink simulation results demonstrate that during grid short-circuit faults, the virtual speed and power angle stabilize at rated values, the short-circuit current is limited to within 1.5 times the rated output current of the VSG, reactive power is provided according to national grid connection standards, the output current remains balanced across phases, and fault inrush currents are effectively suppressed. [Conclusions] Compared with conventional methods, the proposed strategy more effectively stabilizes virtual speed and power angle, better limits short-circuit current and suppresses inrush current, and provides faster transient response. It also ensures a balanced three-phase output current, contributing to the safe and stable operation of VSGs during power grid faults.

[Objective] In view of the problems of limited access to the Internet, low return on investment and lack of incentive mechanism in the carbon market in the current large-scale wind power and photovoltaic power station investment. In this paper, a bi-level optimal configuration model of wind and solar capacity of regional power grid considering hydropower regulation ability is constructed under the environment of electric carbon coupling. [Methods] The upper model takes the maximum return on investment of wind power and photovoltaic as the optimization goal, and formulates the configuration strategy of wind and photovoltaic capacity on the basis of comprehensive consideration of the benefits of electricity market and carbon market. In the lower model, the renewable energy group composed of small hydropower, wind power and photovoltaic participates in the clearing of the electricity market with the goal of minimizing the cost of purchasing electricity. At the same time, considering the carbon trading results of wind and solar in the China certified emission reduction (CCER) market, the joint optimization clearing of the electricity and carbon markets is realized. The model introduces cooperative game and Shapley value to quantify the respective benefits of wind, solar and water, and uses the improved particle swarm optimization (IPSO) nested CPLEX solver to realize the collaborative solution of the two-layer structure. [Results] The simulation results show that under different typical scenarios, the income of the scenery varies with the change of the hydropower regulation capacity. The income is the largest in the wet season, and the dry season is reduced accordingly, which in turn affects the optimal configuration of the scenery throughout the year. After the introduction of the electricity-carbon coupling market model, the revenue of the wind-solar system is significantly improved, and the configuration capacity is about 24% higher than that of the traditional electricity market model. The carbon market mechanism effectively inhibits the operation behavior of high-carbon emission units through price signals and promotes the optimization of carbon emission structure of thermal power units. The CCER market friction factor has a significant weakening effect on carbon returns, and the return can be reduced by up to 33.5%. [Conclusions] The model highlights the key role of hydropower regulation capacity and carbon market signal in wind and solar consumption, which is helpful for new energy development and low carbonization of power structure, and provides a theoretical basis for the optimization of carbon market policy.

[Objective] Wind turbine generators generally operate in maximum power point tracking mode and do not provide inertial response and frequency support to the power grid. To fully utilize the frequency regulation capabilities within a wind farm, this study proposes an active frequency support control strategy for wind farms that accounts for the energy discrepancy among wind turbines. [Methods] The relationship between wind energy loss and rotor speed during the frequency regulation process was derived, and the available frequency regulation energy of wind turbines was evaluated to establish a frequency response model for a wind farm. Model predictive control was subsequently employed to optimize the active power distribution among wind turbine generators. This strategy aims to satisfy the frequency regulation demands of the wind farm, fully utilize the frequency regulation energy of the turbines, and minimize wind energy loss during the process. Following the frequency support phase, a rotor speed recovery strategy is implemented to restore the generator speed. [Results] Simulation results demonstrate that the proposed control strategy effectively utilizes the frequency regulation energy of wind turbines within the farm to actively support system frequency, while also reducing energy loss during the frequency regulation process and mitigating secondary frequency drops during the rotor speed recovery phase. [Conclusions] The proposed strategy effectively enhances the frequency support capability of wind farms by optimizing the allocation of frequency regulation energy, reducing wind energy loss, improving the efficiency and economic performance of wind farm frequency regulation, and providing technical support for the large-scale integration of wind power.

[Objective] With the increase in transmission capacity of DC grids, hybrid DC circuit breakers based on fully controlled power electronic devices generally have problems such as rapid rise of fault current, high amplitude, difficulty in breaking, and high cost. To address these issues, a hybrid DC circuit breaker topology with coupled inductors for current limiting is proposed. [Method] In this topology, the primary side of the coupled inductors in the main branch limits the fault current for the first time, and the current-limiting branch limits the fault current for the second time, effectively reducing the fault current rise rate. The secondary side of the coupled inductors in the main branch pre-charges the self-charging capacitor, reducing the complexity of the equipment. Thyristors are used instead of traditional IGBTs and parallel capacitors to switch the surge arresters, improving the economy. [Result] The working principle and parameters of each main circuit of the proposed topology were analyzed and designed. A four-terminal DC transmission grid system model was established in PSCAD/EMTDC, and the proposed topology was compared with existing typical topologies through simulation analysis. When a short-circuit fault occurs, the proposed topology further reduces the peak value of the fault current limit, shortens the breaking time, reduces the energy absorption of the surge arrester, and has certain economic benefits. [Conclusion] The analysis shows that the proposed topology has good breaking performance and certain economic benefits, which can provide a certain theoretical basis for the application of high-voltage DC circuit breakers in DC transmission grid systems in the future.

[Objective] DC power transmission technology plays a crucial role in the construction of new energy power systems. To expand novel or unexplored applications of DC power transmission, we need a deeper understanding and more comprehensive analysis of its underlying physical characteristics than ever before. [Methods] Based on fundamental physical principles, the inherent characteristics of DC power transmission systems can be summarized in five aspects: frequency isolation effect, fault current isolation effect, unlimited transmission distance, significantly enhanced power transmission capability, and absence of distributed capacitive current. Building on these inherent physical characteristics, the existing DC power transmission application modes have been established. [Results] Established the correspondence between the inherent physical characteristics of DC transmission and various existing application modes, providing a pathway for expanding DC technology applications in the context of new energy power systems. [Conclusions] The inherent physical characteristics of DC and AC transmission technologies have a complementary relationship. Applying DC transmission technology strategically within AC power grids can significantly enhance the operational performance of power systems. Future power systems will feature AC and DC organically integrated across all voltage levels.

[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] To address the issues of insufficient integration of traditional tool specifications and low efficiency in design validation within 3D design for power transmission and transformation projects, this study explores the collaborative mechanism between BIM and large language models to construct an intelligent design assistance system that enhances dynamic specification mapping and knowledge service capabilities. [Methods] A large language model-powered BIM 3D design expert system is proposed: 1) The Qwen2.5 model is used to parse specifications of power transmission and transformation projects, extract triplet structures, and build a knowledge graph; 2) A hybrid retrieval framework is developed to dynamically map BIM component attributes with knowledge entities, enhanced by vector-based knowledge databases for improved semantic matching; 3) RAG technology is integrated to build a question-answering system, enabling intelligent interaction between 3D models and specification data via the BIMbase platform. [Results] Application at the Xuzhou substation demonstrates: 1) Knowledge graph triplet extraction accuracy reaches 92%; 2) Coverage rate of BIM component-to-specification mapping achieves 85%; 3) The question-answering system has a response time of 1.5 seconds, with professional answer accuracy at 94% (a 31.2% improvement over the baseline). [Conclusion] This study innovatively establishes a dynamic collaboration mechanism between BIM and large language models, validating the effectiveness of knowledge graph-enhanced retrieval in power engineering design. Through bidirectional mapping between specifications and models and intelligent question-answering, the system significantly improves design quality, providing a scalable technical paradigm for smart grids.

[Objective] The large-scale integration of high-proportion renewable energy will lead to the imbalance between supply and demand of heterogeneous energy flow system (HEFS). Aiming at the problem of renewable energy consumption, the low-carbon coordinated control method of heterogeneous energy flow system with hydrogen energy flow and generalized energy storage is studied. [Methods] Firstly, considering the multi-energy flow interaction of hydrogen energy flow and the flexible regulation characteristics of generalized energy storage, the comprehensive cost, wind and solar consumption rate, carbon emissions and other indicators are constructed to evaluate the comprehensive benefits of optimal operation. Secondly, based on the system carbon emission information, a carbon flow topology model based on energy flow interaction is constructed, and the carbon flow information is used to assist in evaluating the multi-objective optimization results. Finally, a practical regional system in Northeast China is simulated to study the collaborative interaction characteristics of source-load-storage, analyze the optimization effect of hydrogen energy flow and generalized energy storage on the comprehensive benefits of the system. [Results] The simulation results show that the participation of hydrogen energy flow and generalized energy storage can effectively improve the consumption margin of heterogeneous energy flow system for renewable energy, which proves the effectiveness and feasibility of the proposed method. [Conclusions] Through the flexible interaction of electricity-heat-hydrogen energy flow, the full utilization of energy is realized, the dynamic adjustment ability of the system is enhanced, and the penetration of low-carbon energy is effectively promoted. After considering the generalized energy storage, it further promotes the consumption of low-carbon energy, realizes peak shaving and valley filling, and promotes the transformation of heterogeneous energy flow system to efficient, clean and low-carbon. Through the analysis of carbon flow in typical periods, it is shown that the participation of hydrogen energy flow and generalized energy storage can effectively alleviate the pressure of system supply and demand, which is conducive to the realization of low-carbon operation of heterogeneous energy flow system. The strategy of this paper provides new ideas and methods for heterogeneous energy flow systems to absorb renewable energy, and has good engineering prospects.

[Objective] The large-capacity SVG based on modular multi-level technology can realize various functions such as active network construction, reactive power compensation, harmonic suppression, impedance remodeling, etc., and is an important equipment to support the construction of new power systems. [Methods] This paper discuss the key technologies and innovation prospects of large-capacity voltage sourced inverter in the construction of new power systems, reviews the development history and basic topological principles of SVG, sorts out the evolutionary conversion relationship between SVG and modular multilevel converter (MMC), analyzes the quantitative evaluation formula of AC grid strength supporting by SVG, and summarizes the advantages of SVG to ensure the safe and stable operation of the new power system with a high proportion of new energy and a high proportion of power electronic equipment. Then, the key technologies such as SVG-based multi-source commutation converter technology (SLCC), active filter, grid-forming SVG, and energy storage type SVG were analyzed. Finally, the key role that SVG play in constructing new power system and the key research directions in the future are prospected. [Results] In the construction of future new power systems, large-capacity SVG holds broad application prospects in areas such as large-scale new energy island export, weak system voltage support, and wide-area harmonic suppression. [Conclusions] Further in-depth research is needed on SVG pre-charging and black start, as well as coordinated control among multiple SVGs in isolated new energy systems.

【Objective】 The high proportion of distributed photovoltaic (PV) grid integration leads to insufficient capacity for power absorption in distribution networks.Meanwhile, the development of new distribution systems imposes higher reliability requirements. Grid-forming energy storage, with its flexible power synchronization control capabilities, plays a crucial role in both enhancing the consumption of distributed PV in new distribution networks and improving system reliability. Therefore, this paper proposes an optimal configuration model for grid-forming energy storage that simultaneously addresses distributed PV consumption and reliability improvement in distribution networks. 【Methods】 First, a bi-level optimization model for the siting and sizing of grid-forming energy storage is established. The upper-level model considers fault conditions and load importance to establish an energy storage siting model designed to improve distribution network reliability. The lower-level model, considering the uncertainty of distributed PV systems, establishes an energy storage sizing model to enhance PV consumption. Specifically, the probability distribution confidence set of PV uncertainty is subject to 1-norm and ∞-norm constraints, and is solved using the Column and Constraint Generation (CCG) algorithm based on the distributionally robust optimization. Second, a comprehensive evaluation index system incorporating reliability, distributed PV consumption, and economic performance is established. The optimal configuration scheme is obtained using an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. 【Results】 The proposed algorithm is validated through a modified 33-node test system. The results show that compared with the traditional energy storage scheme, the reliability index of the proposed model is improved by more than 10%, and the light rejection rate of distributed photovoltaic is reduced by 7.44%. The optimal effect can be obtained by configuring grid-forming energy storage at four key nodes. 【Conclusions】 The proposed grid-forming energy storage optimization configuration method significantly improves the distributed photovoltaic capacity and reliability of distribution network, and provides a reference for the planning and investment of high proportion of distributed photovoltaic access to distribution network.

【Objectives】 In order to solve the problem that grid-based energy storage is difficult to adapt to the change of grid short-circuit ratio and complex fault disturbance due to the single operation mode of the converter, this paper proposes a dual-mode improved switching strategy based on amplitude and phase synchronization (APS), and uses the improved particle swarm optimization (IPSO) to identify its key parameters. 【Methods】 Firstly, this paper analyzes the shortcomings of the conventional Grid-forming and Grid-following Dual-Mode Switching Strategy, and reveals the mechanism of the voltage phase accumulation error of the power loop and the resulting reactive voltage deviation that increases the transient impact during mode switching. Based on this, an amplitude and phase synchronous compensation mechanism is proposed, which simultaneously corrects the voltage amplitude and phase signal during the mode switching process and realizes the smooth change of the inner loop current reference signal. Then, in view of the shortcomings of the tracking loop parameters in the conventional switching strategy, an IPSO algorithm based on nonlinear inertia weights and learning factors is used to adaptively identify the parameters of the four groups of tracking loops, so as to improve the tracking performance and disturbance suppression effect of the energy storage converter on the operation points of the Grid-following and Grid-forming Modes. 【Results】 Finally, a MW-level grid- forming energy storage electromagnetic transient model was built in Matlab/Simulink for verification, and the results showed that the proposed control strategy could successfully achieve a transient power impact of less than 0.02 p.u., and could operate stably in the scenarios of continuous switching and operation point fluctuation. 【Conclusions】 Compared with the conventional switching strategy, the improved strategy based on APS-IPSO can realize the small switching impact and good stability of the energy storage converter in the Grid-following-Grid-forming Modes, and provides a theoretical basis for the subsequent deployment of energy storage or new energy units with mode switching in new energy stations.

[Objective] In response to the increasingly diversified charging demands arising from the rapid development of electric vehicle (EV), this paper investigates a planning method for charging stations based on the collaboration of multiple types of charging posts. [Methods] From the perspectives of EVs, transportation networks, and power grids, a siting and sizing planning method for charging stations under vehicle-road-grid coupling is first established based on graph theory. The charging behavior characteristics of EV users are then explicitly modeled, and four types of charging posts—Slow Charging Post (SCP), Fast Charging Post (FCP), Mobile Charging Post (MCP), and Ultra-fast Charging Post (UCP)—are selected as the main facilities. A planning model is constructed with the objective of minimizing the annualized total social cost, incorporating constraints from multiple scenario conditions and multiple charging post types. The planning problem is then reformulated as a Mixed-Integer Second-Order Cone Programming (MISOCP) problem via scenario transformation and second-order cone relaxation techniques, and solved using the Gurobi optimizer. [Results] Simulation results demonstrate the high efficiency and effectiveness of the proposed model. The results indicate that the planning solution considering SCP, FCP, UCP, and MCP is optimal. Notably, the integration of MCPs provides effective emergency response during peak charging demand periods and reduces the overall planning cost by 17.82%. [Conclusions] In the proposed planning model, EV users can select among multiple types of charging posts based on specific principles. The coordinated configuration of diverse charging posts offers greater flexibility compared to single-type configurations, enabling the satisfaction of charging demands while reducing the annualized total social cost.

[Objective] The development of the new power system has paved the way for promoting the low-carbon transition of China's power grid. However, the simultaneous pursuit of safety, economic efficiency, and low-carbon emissions poses an “energy trilemma” in system operations and dispatch. As electricity and carbon markets mature and integrate, traditional dispatch methods are being reformed, making the establishment of a market mechanism tailored to new power system pivotal for addressing low-carbon dispatch challenges. [Methods] This paper, from the perspective of electro-carbon coupling, clarifies the interactive impact between the electricity market and the carbon market. It investigates low-carbon operational strategies and market mechanism designs within the dual electricity-carbon markets for the new power system. Specifically, a comprehensive survey and systematic review are conducted in five areas: carbon emission awareness, market participant behavior modeling, joint simulation of electricity and carbon markets, low-carbon dispatch strategies, and market mechanisms for new power systems, thereby dissecting five key technologies for low-carbon dispatch along with their research limitations. [Results] The findings reveal that existing research exhibits deficiencies in the aforementioned dimensions. In particular, during the integration of electricity and carbon markets, unclear coupling mechanisms and the absence of an effective quota allocation mechanism render the market coordination process static and overly simplified. Moreover, market simulation methods confront a contradiction between insufficient interpretability and overly stringent assumptions, while the integration of renewable energy units (characterized by near-zero short-term marginal costs) undermines traditional pricing models and complicates multi-period cost allocation. [Conclusions] To address these challenges, this paper proposes a research framework for low-carbon dispatch in the new power system. The framework is geared towards exploring renewable energy-dominated low-carbon operational pathways, based on enhanced carbon sensing and improved market modeling and simulation methods, thus offering fresh perspectives and insights for the low-carbon transformation of power systems.

[Objective] With the continuous increase in the proportion of renewable energy in the overall energy mix, the inherent uncertainty and variability in power generation pose challenges to the stable operation and economic efficiency of microgrid systems. Demand response strategies emerge as crucial measures to enhance the integration capacity of renewable energy in microgrids. [Methods] Firstly, to optimize the fitting ability of the demand response model to user behavior, this paper constructs a demand response model based on the endowment effect by deeply analyzing the psychological factors of users in the process of participating in demand response. Then, based on this demand response model, an economic optimization operation strategy for microgrids is proposed. Considering the comprehensive satisfaction of users and the operation cost, a microgrid economic operation model is established. The Pareto optimization technology combining -constraint and relaxation factor is adopted to solve the operation model, and the economic optimization operation of the microgrid is achieved under the constraints of equipment operation power and grid interaction. [Results] The simulation analysis verifies the effectiveness of the demand response model proposed in this paper in improving the economic benefits of microgrids and its superiority over traditional demand response models, while also enhancing user satisfaction. [Conclusions] The demand response model based on behavioral economics theory proposed in this paper can more accurately describe user demand response behavior. The introduction of the endowment effect theory provides a new perspective for understanding and predicting consumers' responses to energy price changes, enabling microgrid operators to more accurately adjust power supply strategies to cope with demand fluctuations and market changes. The demand response model proposed in this paper can effectively promote user participation in peak shaving and valley filling, and reduce the operation cost of the system.

[Objective] With the integration of large-scale distributed photovoltaics, issues like reverse power flow and over-voltage arise, posing challenges to the safe and stable operation of the power system. To address this issue, both domestically and internationally, regulations mandate that PV systems must have grid support capabilities, such as voltage-reactive control, i.e., they actively regulate their reactive power based on voltage deviations. However, such regulations are typically designed for balanced voltage scenarios, and unbalanced voltage conditions are rarely considered. Therefore, taking the voltage-reactive power control(Volt-Var control, VVC) as an example, this paper analyzes the influence of voltage imbalance on their reactive power output, and proposes an improved voltage support control method aiming at a minimal three-phase voltage deviation. [Methods] Based on the instantaneous power theory, the mathematical relationship between voltage imbalance and active/reactive power is derived, demonstrating the voltage regulation effect of reactive power and the existence of the minimum voltage point. Then, with the three-phase voltage deviation as an index, the positive sequence voltage and reactive power corresponding to such point are calculated, and dynamic adjustments to the voltage support control curve are made using this point, voltage limits, and reactive power capacity of PV inverters. [Results] Matlab/Simulink simulation results demonstrate that the proposed improved voltage support control method ensures three-phase voltages remain within limits while achieving minimal deviation from the nominal voltage under the lowest reactive power output, accomplishing economical and efficient regulation of three-phase voltage in unbalanced scenarios. [Conclusions] The proposed control strategy effectively coordinates traditional VVC control with negative-sequence current/power control. It maintains three-phase voltages within grid-connected standard limits despite changes in irradiance, temperature, or other factors causing photovoltaic power fluctuations or voltage-unbalance variations, significantly enhancing the adaptability of conventional VVC control functions.

[Objective] In order to improve the reactive response ability of photovoltaics and limit the problem of voltage over-limit, a subdivision-based reactive voltage adjustment coefficient optimisation method is proposed to consider a large number of distributed photovoltaics connected to the grid. [Methods] With the goal of limiting the voltage fluctuations of each node under a variety of scenarios, an optimisation model of nodes with reactive-voltage differential coefficient based on partitions is proposed, and the control model is simplified by using the characteristics of "strong coupling within the interval and weak coupling between partitions" to realise that photovoltaics can independently adjust the output of reactive according to external voltage fluctuations and make full use of the residual reactive. [Results] The simulation results of the IEEE33 node standard example show that the partition-based reactive voltage adjustment coefficient optimisation method can effectively use reactive to regulate node voltage, which is more conducive to meeting the requirements of accuracy and speed of reactive optimisation. [Conclusions] The method proposed in this article can effectively improve the voltage quality of the distribution grid nodes under a variety of different scenarios, giving full play to the photovoltaic reactive residual.

[Objective] With the large-scale integration of distributed wind power on the demand side, power systems are placing increasing demands on diversified and flexible demand-side resources. However, these resources are typically small in capacity and geographically dispersed, making it difficult to effectively utilize them through existing market mechanisms. To address this issue, this paper proposes a day-ahead combined electricity-flexible resources market trading mechanism based on the Nash negotiation model for typical demand-side resources. [Methods] First, in the electricity trading stage, each market participant determines its initial electricity trading plan using the Nash negotiation model. Then, based on the supply-demand balance of flexible resources, it is assessed whether flexibility trading is necessary. In the flexibility trading stage, bidding quantity and pricing principles for flexibility resources are proposed, and the corresponding trading plans are derived using the Nash negotiation model. Finally, considering the capacity coupling relationship between electricity and flexible resources, the electricity trading plans are updated to form the final combined market trading schedule. [Results] In a market involving multiple types of demand-side resources, case study results demonstrate that the proposed mechanism can effectively enhance and coordinate the benefits of multiple market participants. It achieves a reasonable allocation of market benefits after flexible resources are introduced and provides a more rational pricing mechanism for flexible resources compared to conventional methods. [Conclusions] The proposed day-ahead combined electricity-flexible resources market trading mechanism based on Nash negotiation effectively addresses the increased flexibility demands caused by the integration of distributed wind power, as well as the trading challenges posed by the small capacity and scattered distribution of demand-side flexible resources. It also improves the enthusiasm of demand-side resources to participate in the market and offers theoretical support for the market-oriented development of flexible resources.

[Objective] In a “source-grid-load-storage” system incorporating renewable energy, an electric vehicle (EV) cluster charging and discharging strategy optimization model is established to optimize the revenues of EV users and load aggregators participating in peak shaving ancillary services, considering the peak shaving demand levels across different time periods. [Methods] First, the peak shaving demand for each time period under renewable energy production scenarios is assessed. Second, based on the level of peak shaving demand in each time period, an optimization model for the charging and discharging strategy of the EV cluster is developed with the objective of maximizing the total expected revenue for both load aggregators and EV users. Finally, to prevent peak load occurrences after participating in peak shaving services, the charging and discharging strategy for subsequent time periods is optimized with the goal of minimizing load fluctuations. [Results] Case analysis demonstrates that the proposed charging and discharging strategy can assist in smoothing electricity load during peak periods, maximize the total expected revenue for load aggregators and EV users, and mitigate the occurrence of load peaks after participating in peak shaving services. [Conclusions] The proposed strategy in this paper significantly improves the benefits of EV users and load aggregators compared to the strategy without considering participation in peak shaving service, and the load fluctuations in the subsequent period of peak shaving is significantly reduced, which verifies the practicality and effectiveness of the proposed method.

[Objective] In order to realize the reasonable planning of low-voltage flexible interconnection devices, effectively solve the problem of load imbalance in distribution station areas and improve the supply capability of distribution networks, a supply capability assessment method considering the load distribution characteristics of distribution networks is proposed, and a flexible interconnection device planning model for low-voltage distribution station areas that comprehensively considers the power supply capability, construction efficiency and economy of distribution networks is established. [Methods] First, a model of distribution network supply capability with load growth is established, and an evaluation index for the efficiency of distribution network supply capability improvement is derived. Next, a flexible interconnection devices planning framework considering the improvement of supply capability, economy and load transfer constraints under distribution network reconfiguration is established and solved based on the NSGA-II algorithm. [Results] The results of the case study of IEEE 14 bus system with contact switches show that the planning model based on the proposed indicators can effectively improve the supply capability of the distribution network under actual load distribution. [Conclusions] Compared with the traditional power supply capacity indicators, the indicators proposed in this paper can effectively reflect the objective law of actual load distribution and growth in station areas. The planning method based on the proposed indicators can significantly improve the power supply capacity of the distribution network. The proposed indicators show that the access of flexible interconnection devices in low-voltage station areas can gradually eliminate the bottlenecks of power supply caused by the imbalance of the load ratio in station areas, and realize the efficient use of distribution network resources.

[Objective] With the advancement of the construction of new power systems, renewable energy units are gradually replacing traditional thermal power units. The inertia level on the generation side continues to decline, and the frequency inertia support on the load side becomes increasingly important. Therefore, it is necessary to further incorporate coordinated inertia optimization between generation and load sides into conventional unit commitment processes. [Method] In this paper, the equivalent inertia support capacity of the load side is theoretically analyzed, and a calculation method for system inertia considering load inertia response strategies is proposed. Building on this foundation, a method for assessing minimum inertia based on the initial rate of frequency change and the frequency nadir is proposed. An optimal unit commitment safety model is established based on the proposed load response strategy and the minimum inertia assessment results. [Result] Case studies demonstrate that the proposed method can satisfy system frequency security constraints while reducing reliance on conventional unit inertia and optimizing the allocation of inertia resources between generation and load sides. Consequently, the overall operational economy of the system is significantly improved without compromising grid security. [Conclusion] It is concluded that controllable resources on the load side can provide flexible equivalent inertia support through their rapid response capabilities, effectively mitigating frequency decline issues and reducing system operating costs.

[Objective] Multi-energy microgrids (MEMGs) can integrate multiple energy carriers to improve energy efficiency, thereby contributing to the achievement of the "dual carbon" goals. [Methods] The paper proposes a data-driven distributionally robust optimization scheduling method for MEMGs that accounts for uncertainty correlations and outlier data. First, an ambiguity set incorporating uncertainty correlations is introduced based on the Copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated, integrating the ambiguity set and opportunity constraints to address uncertainty correlations. Second, since the distributionally robust optimization model cannot be solved directly, a worst-case transformation method is derived for the proposed ambiguity set using dual theory, McCormick relaxation, and Conditional Value-at-Risk approximation. This transforms the distributionally robust model into a linear deterministic model, enabling efficient solution via optimization solvers. Subsequently, a sample pruning algorithm is proposed, which iteratively generates sub-samples from the original dataset by removing outliers and extreme data points. This approach mitigates the adverse effects of such data on the distributionally robust opportunity constraint scheduling results. [Results] Finally, case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set, resulting in an 8.16% reduction in out-of-sample costs. The proposed sample pruning algorithm further reduces out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances scheduling efficiency and ensuring reliability, which collectively demonstrate its clear superiority.

[Objective] Aiming at the adverse effects of source-load uncertainty in the new power system on the formulation of scheduling plans and the consumption of renewable energy power in full-guaranteed acquisition, this paper introduces a day-ahead optimal dispatch method that is founded on the concept of evolving virtual net load. [Methods] Firstly, the Informer model is used to generate the time series of wind power and load. Then, the evolution virtual net load is defined on the basis of the virtual net load, and the evolution virtual net load in the power system is calculated. Finally, considering the operating characteristics of the system, based on the guaranteed acquisition of renewable energy power, with the goal of minimizing the total system cost including the operating cost and carbon emission cost of each unit, the system flexibility constraint condition is used to reflect the demand of source-load uncertainty, and a new day-ahead optimal scheduling model of power system based on evolutionary virtual net load is established. [Results] The actual example analysis shows that compared with other scheduling models, the proposed method can effectively improve the system's ability to cope with source-load uncertainty fluctuations, reduce carbon emission costs and promote the consumption of on-grid electricity for renewable energy power generation projects. [Conclusions] The proposed method can balance the policy requirements and the economic needs of enterprises by taking into account both the guaranteed purchase of renewable energy online electricity and the market-oriented bidding incentives. While ensuring the safe operation of the system, the dual improvement of economy and low carbon can be achieved by optimizing the power generation cost and carbon income of the unit.

[Objective] In order to solve the problem of new energy system capacity allocation in rural areas, respond to the requirements of rural revitalization and low carbon, and improve the rate of renewable energy consumption, a microgrid system capacity allocation method based on the interaction between sub-microgrids and considering biogas energy generation is proposed. [Methods] Firstly, the structure of rural multi-microgrids is analyzed and a multi-microgrid system model for biogas energy generation containing inter-microgrid energy scheduling strategy is established; secondly, a two-layer model for capacity allocation and operation co-optimization of multi-microgrid system containing biogas energy generation is established, with the upper objective of the minimization of the annual integrated cost of multi-microgrids and the maximization of the utilization rate of renewable energy sources and the lower objective of the minimization of the operating cost of multi-microgrid system, which is solved by using the Particle swarm algorithm combined with Cplex solver; for the cost of the microgrid system, the Shapley value method is used to share the microgrids equitably. [Results] Matlab simulation results and example analyses show that the proposed introduction of biogas energy as a power generation unit can increase the penetration rate of new energy; the proposed rural inter-microgrid power interaction, the overall capacity allocation of new energy and energy storage have been reduced, which reduces the annual investment cost, making the cost of purchasing power from the outside of the multi-microgrid reduced by 9%, and the power sales are also on a downward trend; the microgrid cost is apportioned by using the Shapley value method, and the actual cost of each microgrid is reduced by 6.3%, 2%, and 4% respectively compared to the scenario of independent operation. Using the Shapley value method to apportion the costs of microgrids, the actual costs of each microgrid are reduced by 6.3%, 2%, and 4.4%, respectively, compared with the scenarios in which the microgrids are operated independently. [Conclusions] The proposed two-layer optimization model for capacity allocation of rural multi-microgrid system containing biogas energy generation results in lower annual integrated cost, reduced carbon emissions, increased internal consumption of new energy sources, and reduced dependence on the grid at the rural load side, which can achieve both economy and environmental friendliness.

[Objective] To alleviate the pressure of difficulties in the consumption of distributed photovoltaic power and peak shaving of the power grid that have become prominent during the transformation of China's new power system, a flexible resources and transmission and distribution collaborative planning method is proposed. [Methods] Firstly, in order to accurately describe the operational characteristics of the transmission and distribution network and simplify the complexity of the model, the refined modeling of substations is achieved at the transmission network layer. At the distribution network layer, the positions of distribution lines, the loads they carry, and distributed photovoltaic systems are treated equivalentially. Meanwhile, three types of flexible resources, namely demand response, distributed energy storage, and load transfer, are integrated at the distribution network layer. Ensure the coordinated cooperation between the transmission grid and the distribution grid; Secondly, load transfer supply constraints and reverse power flow restriction conditions of substations are introduced to meet the supply guarantee and consumption demands under N-1 faults. Finally, in view of the computational difficulty caused by the massive N-1 fault candidate set, the traditional C&CG algorithm is improved by adopting the strategies of state evaluation and cut set packaging, thereby achieving the rapid solution of the model. [Results] The XJTU-ROTS area C example shows that the system 's new energy consumption rate has increased by an average of 4.67%, and the reverse flow risk has decreased from 5.45% to 4.86%. [Conclusions] The proposed method can effectively increase the consumption rate of new energy on the basis of ensuring the reliable supply of load, providing a new idea for the planning of power systems.

[Objective] The strong randomness of hydropower, wind power and photovoltaic power exacerbates the contradiction between computational complexity and operational economy of the traditional open-loop predict-then-optimize (OPO) dispatch. [Methods] To conquer this, a two-stage dispatch method based on the improved closed-loop predict-and-optimize intertwined framework (CPO) for hydropower-wind-photovoltaic involved power systems is proposed. Firstly, a two-stage dispatch model for hydropower-wind-photovoltaic power systems involving series, parallel and hybrid-connected hydropower groups is constructed. Then, to train an economy-oriented prediction model for inflow and renewable energy generation, a loss function is established by taking absolute deviation between the system cost calculated by the ground truths and predictions of inflow and renewable energies. Finally, by combining the variance, Bollinger bands and autocorrelation function, the fluctuation intensities of renewable energy generation and hydropower inflow are quantified, such that a hybrid regularization strategy involving Elastic Net Regression is constructed to balance the training complexity and performance of CPO under multiple uncertainties. [Results] The Matlab simulation results show that, during the typical months of the wet, dry, and normal water periods, the monthly average actual system cost obtained by using the improved CPO method is reduced by 0.74%, 0.57%, and 0.66%, respectively, compared with the traditional OPO method, which verifies the effectiveness of this method in improving the economic efficiency of power dispatch. [Conclusions] The improved CPO method proposed in this paper 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 some periods. Moreover, in scenarios with a higher degree of uncertainty, the effect of this method in improving economic efficiency is even more prominent.

[Objective] Under the "dual carbon" goals, as the process of energy decarbonization and the development of renewable energy enter the express lane, low-probability, high-risk extreme weather poses significant challenges to the safe and reliable operation of new power systems with a high proportion of renewable energy. Flexible power resources such as electric vehicles and distributed generation can provide solutions to enhance system resilience during extreme weather. [Methods] This paper elaborates the conceptual characteristics of power system resilience and reveals the impact of extreme weather on new power systems. Moreover, the resilience research of new power systems under extreme weather is discussed from three aspects: system component modeling under extreme weather, system resilience analysis methods, and resilience indicator frameworks. [Results] Then, by analyzing the adjustable capacity of flexible power resources during extreme weather, strategies for enhancing the resilience of new power systems considering flexibility and extreme weather are demonstrated from four perspectives (generation, grid, load, and storage), and across three stages (prevention, emergency control, and rapid power restoration). [Conclusions] Finally, research directions for the resilience of new power systems with flexible resources under extreme weather are pointed, aiming to form a closed-loop management for risk control and resilience enhancement of new power systems under extreme weather, and providing a theoretical basis for ensuring power supply during extreme weather.

[Objective] With the integration of advanced digital information technology and power physical systems, traditional power system has evolved into more efficient and controllable Cyber-Physical System (CPS). However, this also increases the risk of cyber-physical collaborative attacks. [Methods] In response to these challenges, this paper 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 is modeled based on complex network theory and energy-information flows. A three-layer cyber-physical power system network node one-to-one dependency framework is built using the correlation matrix theory. The evolution model of cascading failures in cyber-physical power system nodes is proposed, and a weighted comprehensive centrality index is 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] providing a technical reference for security risk analysis of critical nodes and stable operation control in practical power system.

[Objective] Aiming at the uncertainty of electric vehicle users' travel mode and charging demand, a spatial-temporal distribution prediction method of electric vehicle charging load based on charging queue and real-time SOC is proposed. [Methods] The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed, and the road traffic network model and comprehensive energy consumption model are established. Based on the user's travel chain, the user's travel characteristics are analyzed, the shortest time method is used to plan the driving path, and the spatial-temporal distribution prediction model of EV charging load is built taking into account the charging queue time and real-time SOC. Finally, Monte Carlo method is used to verify the actual network structure and IEEE33-node distribution system. [Results] The analysis demonstrates that peak-hour charging queue durations surpassing 30 minutes induce partial user migration to off-peak periods, resulting in peak load reduction and off-peak load elevation compared to queuing-free models. Compared with the model without considering the charging queue, the peak load decreases and the off-peak load increases. In addition, there is a significant time difference between the charging load during holidays and that on working days. Moreover, as the penetration rate of electric vehicles increases, the overall charging load keeps growing. The significant impact of the large-scale integration of electric vehicles on the power grid has been verified. [Conclusions] The results show that the proposed method can fully consider the interaction of road network, EV and user charging behavior, and accurately predict the spatial-temporal distribution characteristics of EV charging load.

[Objective] With the strong randomness and frequent occurrence of extreme events brought about by the high proportion of new energy grid connection, the model of using deterministic methods to construct power and energy balance tables becomes unsuitable. [Methods] On the basis of the widely recognized balance table form that has been practiced for many years, this article constructs probabilistic scenarios and designs a practical method for probabilistic analysis of power and energy balance. Specifically, a probabilistic analysis framework for power and energy balance is established, and methods for constructing typical, edge, and extreme scenarios are proposed. The overall framework of the probabilistic power and energy balance table is designed, and balancing risk assessment based on indicators of balance margin and new energy consumption index is conducted. For key periods with severe balancing risks, an index system of power and energy balance analysis is designed, and a refined balance state evaluation is carried out using time-series production simulation. [Results] The results of the probabilistic power and energy balance table of the improved ROTS test system show that it is in a tight balance state throughout the year; in a typical scenario with a probability of 91.36% in November, the power and energy balance can be maintained; in the tightest supply scenario in November, the energy balance margin is 97% and the maximum power shortage is 2.66 GW, which are close to the results of the time-series production simulation. [Conclusions] The test system examples verify that the proposed method can not only adapt well to high proportion of new energy grid-connected scenarios, but also adopt different dimensions of analysis based on balancing risks, which can well meet the needs of engineering applications.

[Objective] In order to build the unified national electricity market and enhance the ability of the retail electricity market to optimize the allocation of resources, this paper carries out a study on electricity retail packages for multi-load demands, [Methods] and constructs a retail package combination optimization model for multi-load demands under marketing-based new-type power system. Firstly, a retail package trading structure with multiple load is established. And based on the daily load curves of typical users, diversified thermal and green power retail packages are designed. Secondly, a bi-level model for retail package combinations is con-structed. In the upper model, the package price is optimized with the goal of maximizing the revenue of the electricity retailer; the lower model minimizes the customer's cost of purchasing electricity and makes the decision of the package option. And genetic algorithm is applied for solving. Finally, typical users are selected for case studies to analyze package pricing results, power allocation results, and user load adjustments. [Results] Compared to the single type of package, retailer profits can improve by 60.84%, 58.76%, 62.90%, and 2.25%, respectively, and user total costs decrease by 3.24%, 4.33%, 2.14%, and 2.34%. The model proposed leads to a 7.28% reduction in user costs and a 32.60% improvement in retailer profits compared to considering only thermal power packages. In addition, the sensitivity analyses in terms of electricity demand and whole-sale tariffs are conducted. Electricity demand has a more significant impact to retailer profits and user costs. [Conclusions] The results show that the proposed model can accurately reflect the actual electricity demand of users and help to enhance the capacity of new energy consumption, fully reduce the cost of users and increase the retailer profits. It provides useful references for solving the issue of the homogenization of retail packages and forming a mutually beneficial and win-win result for both the electricity retailer and the customer.

[Objective] With the increasing penetration of renewable energy sources, the overall inertia of power systems has significantly declined due to the gradual replacement of traditional rotating machines by power electronic converters. This reduction in inertia poses serious challenges to frequency stability and system disturbance rejection. To address this issue, grid-connected converters are required to actively provide synthetic inertia support. [Methods] Based on a power-sharing control strategy, this paper analyzes the pole-zero distribution characteristics of conventional Phase-Locked Loop (PLL)-based control structures and highlights the associated stability degradation under weak grid conditions. To overcome these limitations, an inertia enhancement control scheme based on an improved Frequency-Locked Loop (FLL) structure is proposed. By proportionally integrating the frequency derivative signal with the reference active power, the proposed method effectively mitigates power oscillations and enhances the inertial response of the converter. Furthermore, a modified Model Predictive Control (MPC) strategy is employed to replace the conventional inner current loop, significantly improving the system's transient performance. [Results] Comparative studies of frequency step responses demonstrate the superiority of the proposed FLL-based structure in maintaining frequency stability under weak grid conditions. The active power control strategy under the improved FLL framework is detailed, along with the principle of inertia emulation and the tuning of associated parameters. [Conclusions] The proposed method avoids high-frequency noise issues caused by direct differentiation of frequency signals and eliminates the stability problems typically introduced by PLL. As a result, the frequency response of the system is substantially improved, and the goal of synthetic inertia enhancement is achieved. The effectiveness of the proposed control strategy is further validated through Hardware-in-the-Loop (HIL) simulation experiments.

[Objective] Nowadays, countries worldwide are actively promoting energy transition and low-carbon development, and the coupling of electricity trading, carbon trading and hydrogen trading has become an inevitable trend in developing China's energy industry. However, the complexity of energy mutualization and information interaction under multiple systems is greatly enhanced, which brings new challenges to developing the coupled electricity-carbon-hydrogen trading market. [Methods] This paper firstly introduces the research status quo of the electricity-carbon market and electricity-hydrogen market, and on this basis deeply analyzes the research methodology, coupling mechanism, trading mode and pricing and clearing mechanism of the electricity-carbon-hydrogen market; secondly, because of the double key dilemmas at the physical and information levels that constrain the development of the market, the construction of the electricity-carbon-hydrogen coupling market for the Energy Internet to solve the problem is put forward, and the participation mechanism and specific methods of the Energy Internet are analyzed; Finally, the key technologies and research points of the electricity-carbon-hydrogen market for the Energy Internet are summarized from the technical level and the market level. [Results] At the physical level, the Energy Internet solves the problem that the electricity-carbon-hydrogen market is unable to clarify the market trading mechanism and decision-making law due to a large number of resources and complicated interaction relations and realizes the clarification of the law and mechanism of energy production and marketing. At the information level, based on information technology and data platforms, the Energy Internet realizes data matching and sharing and information transparency and openness between the electricity-carbon-hydrogen markets, promotes autonomous and flexible distributed energy trading, and guides the optimal allocation of resources. [Conclusions] Exploring the key technologies and research points of the coupled electricity-carbon-hydrogen market for the Energy Internet is an effective way to break the dilemma of the development of the existing market, and to provide a feasible reference path for the improvement and development of the construction program of the electricity-carbon-hydrogen market.

[Objective] In the context of high percentage renewable energy integration, in order to meet the system flexibility and low-carbon demand, an optimization method for carbon capture reformation of thermal power plants considering the power flexible ramping demand was proposed. [Methods] Firstly, a calculation model for power system flexibility demand was established to address the ramping demand surge caused by large-scale wind and photovoltaic power integration. Secondly, a flexible operation model of thermal power units considering carbon capture reformation was established and its flexible ramping supply capacity is quantified. Then, considering the uncertainty of load and renewable energy, a flexible ramping demand constraint was introduced, and a fuzzy opportunity-constrained model was introduced to establish a carbon capture reformation planning model of thermal power plants. This model jointly solved the carbon capture reformation planning problem and the operation optimization problem of thermal power units to obtain the optimal carbon capture reformation solution. Finally, the effectiveness of the planning model was verified with examples, and the impacts of different unit carbon penalty, renewable energy integration ratios, and confidence level on the carbon capture reformation results were analyzed. [Results] The simulation results show that after the optimal carbon capture reformation of thermal power plants, the carbon emission of the power system is significantly reduced, the renewable energy curtailment rate is significantly reduced, and the total operating cost of the system is reduced by 9.44%. [Conclusions] Through the optimal carbon capture reformation of thermal power plants, the output downward regulation space and ramping capability of thermal power plants can be improved, which can promote the accommodation of renewable energy while reducing the carbon emission of the power system, thus enhancing the economic efficiency of system operation.

[Objective] To address the high operational costs and non-smooth power exchange with the grid caused by existing campus microgrids relying solely on power sources or air-conditioning loads for fluctuation mitigation, this paper proposes a multi-timescale coordinated scheduling strategy for gas turbines (micro-turbine, MT) and air-conditioning building clusters in campus microgrids considering grid curtailment uncertainties. [Methods] First, a virtual energy storage model for air-conditioned buildings is established by quantitatively analyzing the flexible energy characteristics of variable-frequency air conditioners. Combined with the MT power-generation-energy-consumption relationship, a short-timescale efficiency-coordinated control method for MT and variable-frequency air conditioners is proposed to achieve adaptive power fluctuation mitigation. Subsequently, a long-timescale coordinated operation model between MT and air-conditioned buildings is constructed based on microgrid energy supply-demand relationships. Furthermore, robust optimization and stochastic scenario methods are employed to characterize uncertainties in grid curtailment, renewable generation, and load demand, designing a flexible multi-timescale scheduling strategy compatible with grid-connected and islanded modes. Finally, the model is linearized into a mixed-integer linear programming (MILP) problem. [Results] Simulation cases demonstrate that when microgrid power fluctuates, the system strictly follows the efficiency-coordinated control method to dynamically coordinate power allocation between MT and virtual energy storage. The proposed strategy reduces total system costs by 10.1% in grid-connected mode and 5.0% in islanded mode, effectively mitigating system collapse risks while enhancing both reliability and economic performance. [Conclusion] The proposed strategy fully exploits the coordinated scheduling potential of MT and variable-frequency air-conditioning clusters, flexibly adapts to grid-connected/islanded scenarios, significantly improves microgrid economics, and provides an efficient scheduling solution with high-penetration renewable energy integration.