[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.
