Distributed Robust Power System State Estimation (1204.0991v2)

Abstract: Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the communication bottleneck in real-time monitoring, regional disclosure policies, and reliability issues. In this context, distributed PSSE methods are treated here under a unified and systematic framework. A novel algorithm is developed based on the alternating direction method of multipliers. It leverages existing PSSE solvers, respects privacy policies, exhibits low communication load, and its convergence to the centralized estimates is guaranteed even in the absence of local observability. Beyond the conventional least-squares based PSSE, the decentralized framework accommodates a robust state estimator. By exploiting interesting links to the compressive sampling advances, the latter jointly estimates the state and identifies corrupted measurements. The novel algorithms are numerically evaluated using the IEEE 14-, 118-bus, and a 4,200-bus benchmarks. Simulations demonstrate that the attainable accuracy can be reached within a few inter-area exchanges, while largest residual tests are outperformed.

• The paper presents a distributed PSSE algorithm using ADMM that decouples complex state estimation tasks while ensuring convergence to centralized results.
• It introduces auxiliary variables and employs Huber’s M-estimator to robustly handle outliers and maintain data privacy across regional control areas.
• Tested on IEEE systems up to 4,200 buses, the method delivers swift, accurate state estimates, offering scalable performance for modern smart grids.

Distributed Robust Power System State Estimation

The paper entitled "Distributed Robust Power System State Estimation" addresses the critical challenge of implementing power system state estimation (PSSE) in a distributed manner. It recognizes the impracticalities of centralized estimation due to the complexity and scale of modern interconnections, communication bottlenecks, and policy constraints.

The authors propose a novel approach leveraging the Alternating Direction Method of Multipliers (ADMM) to formulate a distributed PSSE algorithm. This algorithm works within a framework that respects privacy policies and minimizes communication load. A significant contribution of this work is its ability to ensure convergence to centralized estimates, even under the absence of local observability.

The paper evaluates its methodology with the IEEE 14-, 118-bus, and a larger 4,200-bus system testimonials. These results exhibit that the distributed algorithm achieves the desired estimation accuracy swiftly within a few iterations, validating its effectiveness and efficiency compared to centralized counterparts.

Key Insights and Methodology

The distributed PSSE methodology utilizes ADMM to decouple the task into smaller, manageable computations that local control areas can solve. Each area processes its partition of the larger system's states without needing exhaustive information from adjacent areas. This is particularly advantageous as it maintains the integrity and confidentiality of regional system data.

Moreover, the method introduces auxiliary variables to ensure that estimates for shared variables between areas converge consistently. The ADMM framework provides theoretical guarantees for convergence to the global optimal estimates, assuming the underlying problem is convex.

Robustness Against Bad Data

A significant extension of the described PSSE framework is its robustness to bad data. The paper discusses the identification of outliers using a combination of least squares and compressive sampling techniques. Specifically, it leverages Huber's M-estimator in a decentralized manner to jointly estimate the state and detect corrupted measurements.

In simulated tests, this robust approach demonstrated superior performance in identifying and mitigating the impact of bad data compared to traditional methods like the Largest Normalized Residual Test. This is achieved through an iterative refinement process that effectively adjusts estimates to account for detected outliers.

Implications and Future Directions

The implications of adopting a distributed and robust PSSE framework are substantial. It enhances the resilience and accuracy of state estimation in modern power systems, which are increasingly characterized by high interconnectivity and the integration of intermittent renewable sources.

From a practical standpoint, the distributed approach aligns with ongoing efforts toward developing smart grids. It accommodates advanced metering technologies and could facilitate enhanced real-time monitoring and decision-making.

Theoretically, the paper opens avenues for further exploration in distributed optimization. Future research may focus on extending the framework's applicability to non-convex estimation problems and deploying more advanced re-weighting techniques to improve robustness against complex data attacks.

Overall, this paper lays a foundational step towards scalable, privacy-preserving, and resilient power system state estimation, crucial for the reliable operation of future power networks.