[Objective] In distribution networks where large-scale blackouts are caused by extreme faults, coordinated restoration of transmission and distribution networks is considered essential for achieving efficient power supply restoration while ensuring the operational security of the transmission network. However, since the transmission and distribution networks are operated by different dispatch entities, complete sharing of operational data across hierarchies is difficult to be achieved. For this purpose, a coordinated distributed service restoration method for transmission and distribution networks based on boundary security region characterization is proposed. [Methods] An improved Fourier-Motzkin algorithm is proposed, which incorporates umbrella constraint identification as a pre-processing step. This approach utilizes umbrella constraint identification to extract effective constraints for the transmission network and employs an implicit optimization strategy to dynamically eliminate redundant terms during the elimination process, thereby mitigating the explosion of constraint size and effectively obtaining the low-dimensional level set of boundary power for the transmission network, that is the boundary security region. A distributed collaborative restoration strategy for transmission and distribution networks that takes the boundary security region into account is proposed. This strategy embeds the boundary security region into the iterative framework of the alternating direction method of multipliers via euclidean projection, thereby enabling distributed computation of the collaborative service restoration model for transmission and distribution networks. [Results] Simulation results indicate that the proposed security region characterization strikes a balance between computational accuracy and speed. In terms of service restoration performance, the proposed distributed strategy achieves maximum service restoration in the outages area, with the total cost deviating from the centralized global optimal solution by only 0.01%. [Conclusions] The improved projection algorithm combined with umbrella-constraint identification preprocessing significantly reduces the computational complexity of security region characterization while maintaining high accuracy, thereby effectively enhancing efficiency. Furthermore, under the premise of privacy preservation, a restoration scheme close to the global optimum can be obtained by the proposed distributed service restoration strategy for transmission and distribution networks.
[Objective]Hybrid pumped storage power stations （HPSPS），integrating dual functions of power generation and energy storage，are emerging flexible resources crucial for the secure operation of power grids and accommodating high penetration of renewable energy. However，situated within cascaded river basins and characterized by complex hydraulic-electrical couplings，their optimal operation in electricity market environments faces technical challenges. To this end，this paper proposes a joint prediction and scheduling optimization method for HPSPS. [Methods]First，an operational constraint model for HPSPS considering hydro-electrical couplings is formulated. Subsequently，a joint price prediction and operational scheduling optimization method tailored for HPSPS is proposed，and a joint loss function integrating prediction errors and decision deviations is formulated，which addresses the drawback of market revenue losses caused by the traditional paradigm of separating price forecasting from scheduling operations. Finally，simulations are conducted based on data from a cascaded hydropower hub in Zhejiang Province and day-ahead electricity prices. [Results]The results show that the proposed method increases the total revenue of the HPSPS by 3.68% and can effectively adapt to diverse market price and water inflow scenarios. [Conclusions]The proposed method provides technical and methodological support for HPSPS to participate in electricity market transactions and improve their operational profitability.
[Objective]To address the limitations of existing evaluation frameworks for new power systems， such as the inadequate representation of “generation-grid-load-storage” synergy and overly simplistic evaluation dimensions， this paper develops a comprehensive evaluation system capable of scientifically quantifying the level of four-side collaborative development. [Methods]An evaluation matrix comprising 14 indicators across 12 aspects was constructed based on the five core characteristics of the new power system. A “four-side joint calculation factor” was innovatively introduced to deeply embed the collaborative relationships of generation-grid-load-storage into the indicator calculation process. The fuzzy analytic hierarchy process， combined with reference value methods， was employed to achieve a quantitative assessment of provincial-level systems. [Results]Case studies demonstrate that the framework effectively reveals developmental disparities between provinces. Province A leads in clean and low-carbon performance； however， its renewable energy installation structure results in poor generation-load temporal matching and insufficient flexibility resources. In contrast， Province B excels in supply-demand coordination and intelligent flexibility， though its energy transition progress and economic efficiency are relatively constrained. Sensitivity analysis confirms that new energy storage configuration， wind and solar installation scales， and demand response capacity are key factors influencing the overall system performance， validating the reliability and robustness of the proposed model. [Conclusions]The proposed evaluation system， based on the “four-side joint calculation factor，” achieves a precise quantitative characterization of collaborative development across generation， grid， load， and storage. It effectively identifies regional system bottlenecks and provides a quantitative basis for the planning， construction， and differentiated policy-making of new power systems.
[Objective]To address source-load fluctuations in systems with high-penetration renewable energy， the rational arrangement and effective dispatch of power reserves should be ensured. Given this background， a multi-area power grid reserve optimization method considering short-term dynamic line rating （SDLR） is proposed. [Methods]Firstly， a network-wide source-load fluctuation scenario based on chance constraints is constructed. By pre-scheduling regional reserve capacity to guarantee the regulation capability of each area， network-wide and regional risk coordination is achieved. Secondly， an intra-day reserve optimization method considering SDLR and demand-side resources is proposed to realize the efficient utilization of line transmission capacity and ensure the effectiveness of reserve dispatch. Finally， the proposed method is verified and analyzed using a simplified provincial transmission network. [Results]The result demonstrate that by integrating SDLR， the actual reserve dispatch ratio increases from 4.73% under traditional static security constraints to 20.06%， and the system operation risk cost is reduced by 58.3%. [Conclusions]The proposed method can mitigate line congestion and reduce the risks of load shedding and renewable energy curtailment， effectively improving the power supply reliability and operational economic efficiency of multi-area power grids.
[Objective]To address the vulnerabilities of coastal distribution networks to large-scale blackouts and the uneven allocation of restoration resources during typhoon disasters， this paper proposes a two-stage resilience enhancement strategy of “pre-disaster pre-positioning and post-disaster dynamic scheduling” considering the coordination of mobile energy storage systems （MESS）. [Methods]First， a physical wind field model is constructed based on the modified Rankine vortex model to quantitatively evaluate the spatiotemporal dynamic impacts of typhoon trajectories on the line failure probabilities and the physical vulnerability of photovoltaic （PV） units， thereby establishing a coupling mechanism from “physical hazard”to “capacity degradation”. In the pre-disaster warning phase， a two-stage robust optimization model is formulated to minimize the MESS pre-positioning cost and the operational cost under the worst-case line failure scenario. The column and constraint generation （CCG） algorithm is utilized to identify the “N-k” worst-case fault combinations to achieve the scientific pre-positioning of MESS. In the post-disaster restoration phase， a multi-period rolling heuristic dynamic scheduling model is established. Based on the principle of “load priority and proximity-based scheduling”， MESS， diesel generators （DG）， and repair crews are coordinated to form a spatiotemporal dynamic coordination mechanism of “repair-storage-scheduling”. [Results]Case study results on the modified IEEE 33-bus system demonstrate that the proposed model can effectively quantify the PV capacity degradation caused by typhoons. Under the worst-case line failure scenario， the initial operational cost of the pre-positioning scheme is significantly reduced by 56.9% compared to the scheme without optimization. For the entire process， the total cost is reduced by 41.63% compared to the unoptimized scheme， and the solution time is drastically shortened by 99.51% compared to the globally strict optimization strategy. [Conclusions]The proposed strategy achieves a full-chain integration from physical hazard mechanisms to system operation optimization. By leveraging the unique advantage of the “spatiotemporal transferability” of MESS， it effectively mitigates the uncertain impacts of typhoon disasters， significantly enhancing the disaster resilience and economic efficiency of the distribution network.
[Objective]Addressing the issues of singular business models， unclear interaction mechanisms， and low utilization rates of shared energy storage （SES） in market operations， this paper proposes a bi-level stochastic optimization configuration model for multi-park SES that accounts for multiple billing modes. [Methods]Firstly， four differentiated leasing billing modes based on usage time， energy consumption， power-energy hybrid， and peak power are designed to better adapt to the diverse needs of industrial park clusters. Secondly， a pricing method for SES based on Stackelberg game theory is adopted， establishing a deterministic “operator-park” bi-level model aimed at maximizing the benefits of all participants. Then， considering the uncertainty of photovoltaic （PV） output， a bi-level stochastic optimization model for SES configuration is developed. The upper level determines the energy storage capacity configuration and the initial pricing of different billing modes； the lower level optimizes the actual charging/discharging scheduling， grid interaction， and PV grid-connection strategies across various random scenarios. Finally， the bi-level master-slave alternating iterative method is adopted to solve the model. Through a closed-loop iteration process involving upper-level decision-making， lower-level response and feedback iteration， the global optimal decision scheme is obtained. [Results]Simulation analysis using empirical data demonstrates that the proposed multi-billing mode game strategy more accurately matches the personalized needs of different parks. Furthermore， compared to traditional single-pricing models， the annualized revenue of SES operators is significantly increased， showcasing stronger risk resistance under uncertain PV output scenarios. [Conclusions]The methodology presented in this paper effectively enhances the profitability of parks and the configuration flexibility of energy storage assets through the differentiated leasing strategies.
[Objective]Existing prediction methods struggle to accurately characterize the complex spatiotemporal complementarity of multi-source hydro-wind-photovoltaic series in large-scale new energy bases， thereby limiting prediction accuracy. To address this issue， this paper proposes a spatiotemporal joint prediction method based on bi-level complementarity regularization. [Methods]First， a seasonal trend decomposition spatial-temporal Transformer （STD-ST-Former） is constructed， employing a dual-channel mechanism to decouple the multi-source original sequences. Subsequently， bi-level complementarity regularization terms are introduced into the loss function to collaboratively optimize the model. At the system level， a global complementarity index is constructed based on normalized weighted aggregated sequences. This guides the model to mine multi-source complementary coupling features， thereby enhancing the overall prediction capability of the system. At the topological level， cosine similarity and a hinge function are combined to characterize the spatial correlation of electrically adjacent nodes. Furthermore， differentiated adjustments are implemented based on gating weights of real correlations to adaptively correct node prediction deviations. Finally， the STD-ST-Former model is trained and optimized using a loss function that integrates the bi-level complementarity regularization terms. This allows for the precise capturing of temporal evolution features while effectively characterizing the intrinsic complementarity mechanism， generating more accurate hydro-wind-photovoltaic joint prediction results. [Results]Experimental results based on multi-source power stations demonstrate that the proposed method exhibits higher prediction accuracy and more stable performance under complex spatiotemporal correlations. Compared to the seven baseline models， the relative Root Mean Square Error is reduced by 22.0% to 56.3%， the correlation coefficient is improved by 0.9% to 1.4% overall， and the prediction accuracy is consistently maintained above 96.9%. [Conclusions]The proposed approach based on bi-level complementarity regularization accurately reconstructs the true spatiotemporal evolutionary patterns of hydro， wind and PV power. It provides a feasible solution for multi-source collaborative forecasting in power systems with high penetration of renewable energy.
[Objective]As the core driving factors for short-term power load forecasting， multivariate meteorological elements still confront prominent constraints in the acquisition and practical application of refined meteorological data.Accordingly， this paper investigates meteorological Agents empowered by large language models （LLMs） and applies them to short‑term load forecasting. [Methods]First， a workflow agent integrated with large language models is constructed via the LLM development platform， which realizes the dynamic acquisition and intelligent parsing of structured meteorological information. This approach effectively improves the real-time response performance of the forecasting model and reduces the development and application complexity of short-term load forecasting systems. On this basis， a multimodal input framework for power system short-term load forecasting is established by integrating meteorological factors， power load time-series data， and calendar features. Combined with the inherent characteristics of time series and the gradient boosting tree algorithm， an optimized short-term power load forecasting model is further developed.Finally， the feasibility and effectiveness of the proposed model and algorithm are verified through experimental validation and case analysis. [Results]The proposed model and algorithm streamline the meteorological information‑acquisition process and improve the forecasting accuracy. [Conclusions]This research provides a novel technical idea for the low-cost acquisition and efficient utilization of meteorological data in power system operation and analysis.
[Objective]To address the issues of high cost in traditional high-capacity dynamic voltage restorer （DVR） and the difficulty of multi-tap transformer （MTT） in achieving precise compensation for grid voltage sags， a topology of dynamic voltage compensation system （DVCS） based on MTT and its voltage compensation strategy are proposed. [Methods]By designing a composite control strategy and formulating transformation ratio rules to control the on-off states of fast-action switches， the DVCS can operate in either direct compensation mode or indirect compensation mode under different operating conditions. In the direct compensation mode， only the converter module compensates for the load voltage deficit； whereas in the indirect compensation mode， the MTT and the converter module work together to maintain load voltage stability. [Results]By utilizing switch-based control to coordinate the transformer and converter， the DVCS maintains stable load voltage under conditions of a 20% voltage boost or a 70% voltage sag. Furthermore， this topology reduces the converter's rated capacity by 50% and lowers the overall cost by over 10%. [Conclusions]The proposed DVCS can meet both the compensation capacity and accuracy requirements in engineering applications while maintaining the dynamic stability of the load voltage amplitude， demonstrating a significant cost advantage.
[Objective]In practical wind farms， the key control parameters of aged or imported wind turbines are often inaccessible， which hampers accurate system modeling. To address this issue， this paper proposes a data-driven identification method based on oscillation characteristics to determine these critical parameters of doubly-fed induction generator （DFIG）-based wind turbines. [Methods]Using only limited field oscillation data， the method first identifies the crucial control parameters via phase-margin sensitivity analysis， and then constructs a neural network model that takes active power output and oscillation features （frequency and damping ratio） as inputs and outputs the key control parameters， without requiring any adjustment of the control system or injection of external excitation. [Results]Simulation and field-measurement cases demonstrate that under noise conditions with a signal-to-noise ratio （SNR） of no less than 30 dB， the identification errors for all key control parameters remain below 10%， and the deviation in reproducing the oscillation characteristics for the actual case is less than 1%. [Conclusions]By exploiting the intrinsic correlation between oscillation data and control parameters， the proposed method circumvents the risks and practical constraints associated with external excitation injection， offering a novel approach for secure identification of key control parameters in “grey-box” wind turbines.
[Objective]Modular multilevel converter based high-voltage direct current （MMC-HVDC） technology serves as the optimal technical solution for power transmission of large-scale far-offshore wind farms. The grid integration of large-scale offshore wind power via MMC-HVDC leads to insufficient inertia and damping deficiency in onshore AC power grids. To solve the above problems， this paper proposes a multi-time-scale coordinated inertia support strategy. [Methods]This paper quantitatively investigates the transient power support capability of sub-module capacitors in MMC-HVDC systems， and proposes a decoupling control-based capacitor energy regulation grid-forming control strategy. By dynamically adjusting the number of inserted sub-modules in each phase， the proposed strategy eliminates the coupling between sub-module capacitor voltage and DC bus voltage. While maintaining DC voltage stability， the operating range and energy utilization potential of sub-module capacitors are significantly expanded. On this basis， a frequency deadband link is introduced， and a multi-time-scale coordinated inertia support strategy with DC voltage as the intermediate medium is designed. The proposed strategy realizes hierarchical and orderly frequency support， which preferentially utilizes capacitor energy under small disturbances and adopts coordinated wind turbine participation under large disturbances. [Results]Simulation results based on PSCAD/EMTDC verify that the system achieves inferior support performance under sudden load increase conditions compared with sudden load decrease conditions. Under identical load variation amplitudes， the maximum frequency deviation of the onshore power grid is reduced by 33.3% and 20.0% under sudden load decrease and sudden load increase scenarios， respectively. The core reason for this performance difference is the quantity limitation of bridge arm sub-modules， which results in a lower energy release margin than the energy absorption margin of sub-module capacitors. [Conclusions]The proposed control strategy effectively integrates and optimizes the utilization of inertia support resources of MMC-HVDC systems and offshore wind farms. The adoption of the frequency deadband link enables coordinated inertia support from multiple support subjects. The proposed strategy not only ensures system frequency stability， but also isolates offshore wind farms from the impact of small disturbances， thereby improving the frequency dynamic performance of offshore wind power MMC-HVDC integrated grid systems.
[Objective]With the rapid advancement of the fifth-generation （5G） mobile communication technology， large-scale deployed 5G base stations with high energy consumption characteristics exhibit considerable demand response potential， serving as an indispensable solution for the transformation of new power systems. Nevertheless， most existing studies assess the dispatchable potential of 5G base stations based on deterministic models， which fail to fully account for multiple uncertainties including communication load fluctuations and power supply reliability， thus undermining the reliability and accuracy of evaluation results. [Methods]This paper firstly reviews the traditional deterministic assessment methods and systematically analyzes their inherent limitations. Combined with the operational characteristics of 5G base stations， a credible capacity assessment framework considering multiple uncertainties is further proposed. On this basis， the credible capacity evaluation of 5G base stations is incorporated into power system optimal scheduling， and a stochastic optimal scheduling strategy constrained by credible capacity is formulated. Finally， future research directions for the collaborative scheduling of 5G base stations and power systems are prospected from three dimensions： source-load uncertainty， cross-level resource collaborative optimization， and data security protection. [Results]The results demonstrate that the proposed credible capacity assessment method can quantify the adjustable regulation capacity of 5G base stations at different confidence levels. Introducing the assessment results as credible constraints into the optimal scheduling model effectively enhances the adaptability of scheduling decisions to complex and variable operating conditions. [Conclusions]In future research， it is necessary to further deepen the collaborative research on credible capacity assessment of 5G base stations and optimal scheduling of power systems. The research findings can provide a solid theoretical foundation for the reliable participation of 5G base stations in power system scheduling， and facilitate the safe， stable operation and low-carbon transformation of new power systems.
[Objective] In recent years, the installed capacity of renewable energy in China has rapidly increased, leading to an increasingly prominent contradiction between the reverse distribution of power supply and load. To alleviate generation-side scheduling pressure, large power consumers are regarded as key flexible resources on the load side and are utilized to maintain power supply-demand balance. The current electricity market model is evolving from "two-round declaration with coordinated clearing" approach to a "coupled declaration with unified clearing" mechanism. Under this new model, large consumers are directly exposed to cascading risks caused by the uncertainty of cross-provincial renewable energy output, making traditional risk assessment methods inadequate. To address this, this paper proposes treating renewable energy output uncertainty as a single risk source and constructing a dual-dimension risk assessment model based on credit and price, to support risk management decisions of large power consumers in unified-clearing cross-provincial transactions. [Methods] Firstly, quantitatively characterizing credit risks such as power shortages, premium pricing, and curtailment caused by physical deviations using the variance method, while measuring price risks including extreme, opportunity, and volatility risks arising from market electricity price fluctuations using conditional value-at-risk (CVaR). Secondly, the entropy weight method is employed to objectively weight the aforementioned multi-dimensional risk indicators, quantifying them as risk costs and integrating them into inter-provincial-intra-provincial unified clearing to establish an optimized clearing model that accounts for consumer risk preferences. Finally, typical scenarios of inter-provincial spot trading are simulated to validate the effectiveness of the proposed risk assessment model. [Results] Compared with the generic risk indicators, the proposed method reduces electricity procurement costs for large consumers by 1.4%-2.5% under renewable penetration levels of 10%-55%. The low-price opportunity loss is effectively avoided. [Conclusions]The proposed risk evaluation indicators can effectively and accurately quantify renewable-energy-induced credit and price risks faced by large consumers in inter-provincial spot trading, thereby providing a quantitative basis for differentiated risk warning and power purchase decision-making.
[Objective]With the deepening reform of the electricity market， new market entities such as regional integrated energy operators and load aggregators（LAs） are constantly emerging. Designing trading mechanisms that coordinate internal interests and stimulate system regulation capabilities has become a critical research focus. Targeting scenarios where multiple regional integrated energy systems（RIES） coexist across industrial， commercial， and residential sectors， this paper proposes a hybrid game-based trading mechanism that accounts for regional heterogeneity. [Methods]First， a Stackelberg game model is constructed within each RIES， utilizing time-of-use pricing to guide LAs in demand response participation and achieve optimal resource allocation. Subsequently， leveraging the complementary potential among multiple RIESs， a peer-to-peer（P2P） cooperative game model is established， with benefits distributed based on asymmetric Nash bargaining. Finally， a genetic algorithm and quadratic programming are employed to solve the internal model， while the alternating direction method of multipliers is used for the distributed solution of the external game. [Results]Case studies demonstrate that the proposed mechanism reduces the total cost of LAs by 5.48%， increases the total profit of RIEOs by 11.65%， lowers total system carbon emissions by 28.23%， and decreases reliance on the external grid by 25.26%. Scalability analysis shows that the mechanism maintains high applicability across various regional types and system scales. Sensitivity analysis indicates that wheeling charges within the range of 0.02-0.06 CNY/kWh effectively stimulate P2P trading enthusiasm， and carbon price fluctuations exert heterogeneous impacts on the emissions and revenues of different regions. [Conclusions]The proposed hybrid game-based trading mechanism can effectively coordinate the interests of multiple stakeholders， unlock the value of demand-side resources， and enhance the low-carbon operation level of the system， and provide a valuable reference for power market transactions involving emerging market entities.
[Objective]Although mainstream deep learning models have achieved excellent performance in wind power forecasting，they rely heavily on large amounts of historical data. In small-sample scenarios，such as newly built wind farms or situations involving privacy protection where data availability is limited，their prediction performance often declines significantly. To address this issue，a small-sample wind power forecasting method based on a time-series large model is proposed. [Methods]First，mean scaling and uniform binning techniques are employed to perform per-sample normalization and quantization of wind power time series，converting them into discrete token sequences so that they match the input format required by large models. Second，a text-to-text transfer transformer architecture is introduced as the core forecasting model. By leveraging the general representation capability obtained through pre-training on large-scale and multi-domain time-series data，the model is able to extract deep temporal features from wind power sequences. Finally，for the small-sample data from a specific wind farm，a lightweight fine-tuning strategy based on low-rank adaptation is adopted to efficiently adapt the model while preserving the knowledge learned during pre-training. [Results]Simulation results demonstrate that under data-scarce conditions，the proposed method outperforms several mainstream deep learning models in wind power forecasting tasks across different seasons，forecasting horizons，and regions. Compared with benchmark models，the root mean square error and mean absolute error are reduced by 15.18%-33.59% and 10.18%-34.94%，respectively. [Conclusions]The proposed method effectively uses the transfer learning capability of time-series large models to achieve high-accuracy wind power forecasting under small-sample conditions. It provides a feasible and efficient technical solution for high-precision wind power forecasting in data-limited scenarios such as newly built wind farms.
[Objective] The increasing demand for green energy supply in data centers poses a critical challenge in matching the wind and photovoltaic （PV） power uncertainty with computational load requirements. To address this issue， this paper proposes an optimized two-stage stochastic scheduling method to coordinate wind and PV power uncertainty with computational flexibility. [Methods] First， considering the stochastic fluctuations and temporal coupling of wind and PV power outputs， a scenario generation model integrating first-order autoregressive process and Cholesky decomposition is developed to capture the temporal correlations and complementarity between wind and PV power outputs. Second， considering the heterogeneity of delay tolerance， a flexible computational response model is developed based on discrete-time task flows， in which the queue state equations quantify the time-dimensional migration capability and backlog constraints of workloads with different delay tolerances. Finally， in order to minimize the expected system operating cost， a two-stage stochastic optimization model incorporating computational load scheduling and multi-energy coordination is formulated to determine the optimal time-sequential operating strategy for computational tasks. [Results] Numerical case studies demonstrate that the proposed strategy shifts delay-tolerant workloads from peak price periods to off-peak periods with abundant wind and PV power. This coordination reduces the expected system operating cost by 4.1% compared with traditional rigid scheduling approaches. [Conclusions] The proposed method effectively leverages the demand response potential of computational loads to enable cost-effective data center operation.
[Objective] Under the wave of digital transformation in various sectors， the data center industry is booming and showing a new trend of clustering and wide-area interconnection. Given the high energy consumption and high carbon emissions that are common in data centers， this paper aims to guide them to actively participate in the low-carbon dispatching of the power grid based on the spatio-temporal flexibility of their computing loads， in order to achieve the goals of energy conservation， carbon reduction， and economic operation for both data centers and the power grid. [Methods] This paper proposes a high-precision dynamic carbon emission accounting method， which takes into account the spatio-temporal differences in network losses and carbon emissions. It accurately calculates the key "nodal carbon potential" signal through a power iteration traceability algorithm and， based on this signal， proposes a coordinated two-layer dispatching model for interconnected data centers and the power grid， where the upper-layer model aims to minimize the operation cost， carbon emissions， and renewable energy curtailment， while the lower-layer model aims to minimize the integrated electricity-carbon cost. The dispatching model interacts and iterates through the carbon potential and load adjustment signals between the upper and lower layers to find the optimal dispatching strategy for the system. [Results] The case study based on the improved IEEE 39-bus system shows that under the proposed coordinated dispatching strategy， data centers actively transfer online loads to low-carbon potential nodes for processing during high-carbon potential periods and delay the processing of local offline loads. During low-carbon potential periods， they take on more online loads and concentrate on processing previously delayed offline loads， achieving significant energy conservation and carbon reduction effects. [Conclusions] The two-layer optimization model proposed in this paper can effectively guide data centers to achieve economic and low-carbon coordination， providing a new technical tool for the green transition of data centers and the low-carbon dispatching of the power grid.
[Objective] Driven by the carbon peaking and carbon neutrality goals and the strategy of national computing network to synergize east and west， data centers have become crucial high-density flexible loads， making the coordinated optimization of their green transition and power systems increasingly vital. Focusing on the policy requirement for new data centers at hub nodes to achieve 80% green electricity consumption， this paper aims to explore the impact mechanisms of diverse green electricity consumption modes and computing load flexibility on the operational costs and carbon emissions of data centers， providing a decision-making basis for resource planning and coordinated dispatching in computing hubs. [Methods] A bidirectional coordinated optimization model for computing-power nodes is constructed， comprehensively considering the temporal and spatial flexibility of computing tasks alongside the physical and contractual constraints of diverse green electricity consumption modes， including direct green energy connection， power purchase agreements （PPA）， and grid power supplementation. The levelized cost of electricity （LCOE） and consequential carbon emissions are introduced as dual evaluation metrics. Using the integrated computing-power project group in the Ganzi region as a case study， simulation scenarios are designed with a gradient decrease in the direct green energy connection ratio from 80% to 20%， comparing PPA and grid supplementation strategies while exploring the operational characteristics of data centers under four schemes： no flexibility， temporal flexibility， spatial flexibility， and spatio-temporal synergistic flexibility. [Results] Simulation results indicate that under the 80% green electricity consumption constraint， the LCOE of data centers exhibits a significant "U-shaped" evolution as the direct green energy connection ratio decreases. The dispatching under the spatio-temporal synergistic flexibility achieves a "Pareto improvement" in both economic and environmental benefits through full-dimensional resource optimization， serving as the optimal dispatching paradigm for the low-carbon and economic operation of large-scale integrated computing-power clusters. [Conclusions] The proposed model and evaluation framework quantify the cost boundaries between physical connection and virtual matching and effectively reveal the impact laws of flexibility strategies， offering valuable reference for optimizing the configuration of data centers at computing hubs.
[Objective] To clarify the dynamic transmission effect of “carbon price-electricity price-hydrogen cost” triggered by the inclusion of green hydrogen under the China Certified Emission Reduction （CCER） mechanism， verify the adaptability of the 5% offset ratio， and provide a quantitative basis for multi-market coordination. [Methods] An electricity-carbon-hydrogen system dynamics model is constructed， coupling the levelized cost of hydrogen with a PEM electrolyzer learning curve modified via Bayesian inference， to simulate the evolutionary and price linkage characteristics from 2024 to 2034. [Results] Significant endogenous linkages and feedbacks exist among the three markets， indicating the inadequate adaptability of the current 5% ratio. Within a 5%-9% offset range， coupled with 1920 annual operating hours and the superimposed learning effect， green hydrogen is expected to achieve cost parity with blue and gray hydrogen by 2030 and 2035， respectively. [Conclusions] A dynamic CCER offset mechanism is crucial for balancing market operations and emission reductions. The large-scale cost reduction of green hydrogen requires dual drivers of policy and technology while averting the premium of purchased electricity. This study lays a methodological foundation for the policy design of “electricity-carbon-X” multi-market synergies.
[Objective] To address the increased operational costs and voltage violation risks in distribution networks caused by high-penetration distributed photovoltaic integration， a two-stage day-ahead and intra-day operational cost optimization and voltage violation control method for active distribution networks is proposed， combining model-driven and data-driven approaches with centralized and decentralized coordination. [Methods] The first stage is a model-driven day-ahead active-reactive power coordinated optimization. A minimum operational cost model incorporating voltage security constraints is established to coordinate adjustable resources including sources， networks， loads， and energy storage systems， thereby optimizing operational costs while enhancing hourly voltage security. The second stage is a data-driven real-time voltage/var decentralized control. Based on day-ahead power forecasts， multiple operating scenarios reflecting minute-level power fluctuations are generated. A voltage regulation training dataset for photovoltaic （PV） and battery energy storage system （BESS） units is constructed through power flow and reactive power loss optimization calculations. Knowledge-embedded adaptive network based fuzzy inference system based voltage controllers are designed for PV and BESS units and trained using the dataset to incorporate global network loss optimization. Each controller adaptively determines the optimal reactive power output using local real-time voltage and power measurements， collectively achieving real-time voltage violation correction and system loss optimization. [Results] Simulation comparisons with multiple control methods demonstrate that the proposed two-stage method effectively reduces system operational costs and significantly mitigates real-time voltage violations， outperforming traditional methods in both operational economy and voltage security control. [Conclusions] The control architecture integrating model-driven and data-driven approaches， along with centralized coordination and decentralized control， can fully exploit the regulation potential of sources， networks， loads， and storage systems， thereby enhancing the operational economy and voltage security of distribution networks with high-proportion distributed photovoltaic integration.
