State estimation (SE) is indispensable for the situational awareness of power systems. Conventional SE is fed by measurements collected from the supervisory control and data acquisition (SCADA) system. In recent years, available data sources have been greatly enriched with the deployment of phasor measurement units (PMUs), advanced metering infrastructure (AMI), intelligent electronic devices (IEDs), etc. The integration of multiple data sources provides unprecedented opportunities for enhancing the performance of SE, but also presents major challenges to resolve, including optimal multi-type-sensor co-placement, multiple reporting rates and asynchronization, diverse types of measured quantities, correlations between measurements, integration of online and historical data sources, and system and measurement uncertainties. This paper outlines the state of the art and research opportunities in this area by providing a comprehensive literature review and extensive discussions. It starts by presenting the motivations and challenges, followed by a summary of existing data sources for SE in power systems. Subsequently, for both transmission system (static and dynamic) and distribution system SE, existing methods are systematically reviewed and categorized based on the addressed challenges. Interesting attempts of using novel measurements in SE are also studied. Finally, the paper concludes by providing a detailed discussion on the remaining research gaps and future research directions to be explored.

With the increase of solar photovoltaic (PV) permeability in power system, the impact of random fluctuation of PV power on the safe and stable operation of power grid becomes more and more serious. High-precision PV power forecasting can effectively promote the grid’s absorption of PV power generation. Cloud is the most important factor affecting the surface irradiance and PV power. For the ultra- short-term solar PV power forecast considering the influence of cloud movement, it is necessary to be able to obtain the surface irradiance according to the sky cloud observation data. Therefore, in order to accurately achieve the real-time mapping relationship between sky image and surface irradiance, a hybrid mapping model based on deep learning applied for solar PV power forecasting is proposed in this paper. First, the sky image data is clustered based on the feature extraction of convolutional autoencoder and k-means clustering algorithm after preprocess stage. Second, a hybrid mapping model based on deep learning methods are established for surface irradiance. Finally, the simulation results are compared and evaluated with different deep learning methods (CNN, LSTM and ANN). The results show that the proposed model in this paper has higher accuracy and can maintain robustness under different weather conditions.

Maintaining an accurate power system model is crucial for a variety of applications such as electricity markets. In this paper, the cyber security issue induced by critical parameters in electricity market operations is investigated. Critical parameters are a set of model parameters the errors of which cannot be detected due to the lack of local measurements. In this paper, it is first proven that the criticality property of parameters is independent from system operating points, i.e. the maliciously injected errors will always remain undetectable. Then, the cyber attack against critical parameters is formulated as a bilevel programming problem in search of the optimal attack strategy or the worst situation faced by the system operators. An effective algorithm for solving this problem is also provided. Simulation results verify that by injecting undetectable errors into critical model parameters, the locational marginal prices in electricity markets can be significantly distorted, which leads to financial benefits for the attackers.

The real-time monitoring of the grid-connected renewable energy generations is of great significance to the operation and control of active distribution systems (ADS). Considering the detailed photovoltaic (PV) system model and ambient influencing factors, this paper develops a state estimation framework for the ADS with integrated PV power plants. Firstly, suitable models of PV arrays and power converters for steady-state state estimation are developed. Based on these models, the state variables of a PV generation system are selected, and the measurement equations are derived. Finally, combined with the state estimation model of distribution networks, a framework for state estimation and bad data processing of an ADS with PV power plants is presented. A parameter estimation approach for PV power plants based on multiple measurement scans is also developed in order to address the practical situation of unknown model parameters. Simulation results show that the proposed framework can effectively capture the operating state of the distribution network and PV power plants. In addition, it enhances the estimation accuracy and bad data processing capability compared to conventional state estimation frameworks.

False data injection attacks (FDIAs) can bypass conventional bad data detection methods. Recently developed FDIA detection methods based on statistical consistency of measurement values may not work effectively when false data do not significantly deviate from historical trends. They may also mistakenly treat actual power grid events as FDIAs. In this paper, a highly discriminative FDIA detector named the ${k}$ -smallest residual similarity ( ${k}$ SRS) test is proposed. The method is based on the rationale that perfect FDIAs can hardly be achieved in AC state estimation, and real-world imperfect FDIAs always lead to subtle changes in the probability distributions of measurement residuals. Therefore, the statistical consistency of measurement residuals can be carefully portrayed to detect practical FDIAs in AC state estimation. Herein, the Jensen-Shannon distance (JSD) is used to precisely quantify the similarity of measurement residual distributions. Simulations on the IEEE 30-bus system demonstrate that the proposed method can achieve high detection rates and low false alarm rates under a variety of conditions where existing methods do not yield satisfactory results.