Di Shi

The rapid development of a new-type power system dominated by renewable energy has introduced growing complexity and variability into grid topology and dynamics, posing significant challenges for transmission interface power flow control. Traditional regulation methods based on operator experience and deterministic optimization often fail to achieve real-time optimality under such dynamic conditions. Leveraging its strong capability for autonomous learning and feature perception, deep reinforcement learning (DRL) offers a promising approach for addressing these challenges. This paper proposes a safe DRL-based control framework for online transmission interface power flow regulation. A Safe Soft Actor–Critic (SSAC) agent is developed, embedding power system security constraints directly into the decision process to ensure operational safety. A secure EMS-interactive training platform with containerized parallel learning is established to accelerate model convergence and improve adaptability to changing operating conditions. The developed SSAC agent is deployed in the Jiangsu Power Grid Energy Management System (EMS) for validation. Simulation and field test results demonstrate that the proposed method can generate control strategies online within milliseconds, achieving a 99.3% interface overload mitigation rate and 3.32% network loss reduction, outperforming conventional sensitivity-based optimization methods in both timeliness and economic efficiency. These results demonstrate strong real-time computational capability and compatibility with EMS-based dispatch workflows, indicating promising practical deployment potential for transmission interface control in renewable-dominated power systems.

Traditional statistical optimization-based state estimation (DSSE) algorithms rely on detailed grid parameters and mathematical assumptions of all possible uncertainties. Furthermore, random data missing due to communication failures, congestion, and cyberattacks, makes these methods easily infeasible. Inspired by recent advances in digital twins (DTs), this paper proposes an interactive attention-based DSSE model for robust grid monitoring by integrating three core components: physical entities, virtual modeling, and data fusion. To enable robustness against various data missing in heterogeneous measurements, we first propose physics-informed data augmentation and transfer. Moreover, a state-of-the-art attention-based spatiotemporal feature learning is proposed, followed by a novel cross-interaction feature fusion for robust voltage estimation. A case study in a real-world unbalanced 84-bus distribution system with raw data validates the accuracy and robustness of the proposed DT model in estimating voltage states, with random locational, arbitrary ratios (up to 40% of total measurements) of data missing.

This study presents a spatially explicit, multidecadal analysis of how land use and land cover (LULC) change and socio-demographic dynamics have influenced land surface temperature (LST) patterns in the Phoenix metropolitan area between 2001 and 2021. Using Landsat-derived summer LST, socio-demographic indicators, and land cover data, we quantify urban land transformation and socio-demographic changes over two decades. To account for spatial heterogeneity, we apply Multiscale Geographically Weighted Regression (MGWR), which improves upon conventional regression models by allowing for variable-specific spatial scales. Results show that the 2001–2011 period was characterized by rapid suburban expansion and widespread conversion of croplands and open space to higher-intensity development, while 2011–2021 experienced more limited infill development. Correlation analysis reveals that agricultural and open space conversions were linked to population and housing growth, whereas redevelopment of existing urban areas was often associated with socio-demographic decline. MGWR results highlight that agricultural land conversion drives localized warming, while shrub/scrub-to-developed transitions are linked to broader-scale cooling. By combining spatial sampling, area-weighted interpolation, and MGWR, this study offers a fi-ne-grained assessment of urban thermal dynamics in a fast-growing desert region. The findings provide actionable insights for planners and policymakers working toward sustainable and climate-resilient urban development in arid environments.

Load redistribution (LR) attacks represent a practical and sophisticated form of false data injection (FDI) attacks, where the attacker manipulates grid data to influence economic operations of the grid through misleading security constrained economic dispatch (SCED) decisions. Traditionally, LR attack models operate under the assumption that generator measurements are secure and immune to tampering. However, the increasing integration of solar generation into power grids challenges this assumption, exposing new vulnerabilities. This paper proposes an enhanced load redistribution attack model, addressing new vulnerabilities introduced by the increasing integration of solar generation in power grids. The study demonstrates that manipulating solar generation data significantly disrupts grid economics, with peak impacts during periods of high solar generation.

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.

In a power system, when the participation factors of generators are computed to rank their participations into an oscillatory mode, a model-based approach is conventionally used on the linearized system model by means of the corresponding right and left eigenvectors. This paper proposes a new approach for estimating participation factors directly from measurement data on generator responses under selected disturbances. The approach computes extended participation factors that coincide with accurate model-based participation factors when the measured responses satisfy an ideally symmetric condition. This paper relaxes this symmetric condition with the original measurement space by identifying and utilizing a coordinate transformation to a new space optimally recovering the symmetry. Thus, the optimal estimates of participation factors solely from measurements are achieved, and the accuracy and influencing factors are discussed. The proposed approach is first demonstrated in detail on a two-area system and then tested on an NPCC 48-machine power system. The penetration of inverter-based resources is also considered.