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This paper discusses how the risk of electricity grid outages is predicted using machine learning on historical data enhanced by graph embeddings of the distribution network. The process of graph creation using different embedding approaches is described. Several graph constructing strategies are used to create a graph, which is then transformed into the form acceptable for ML algorithm training. The impact of incorporating different graph embeddings on outage risk prediction is evaluated. The method used for graph embeddings is Node2Vec. The grid search is performed to find optimal hyperparameters of Node2Vec. The resulting accuracy metrics for a set of different hyperparameters are presented. The resulting metrics are compared against base scenario, where no graph embeddings were used. 

In both power system transient stability and electromagnetic transient (EMT) simulations, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential. In this paper, EMT simulation is considered and an inverse-based network solution is proposed by a hierarchical method for computing and store the approximate inverse of the conductance matrix. The proposed method can also efficiently update the inverse by modifying only local sub-matrices to reflect changes in the network, e.g., loss of a line. Experiments on a series of simplified 179-bus Western Interconnection demonstrate the advantages of the proposed methods. 

Abstract A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal. 

A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition. 

Uncertainties from deepening penetration of renewable energy resources have posed critical challenges to the secure and reliable operations of future electric grids. Among various approaches for decision making in uncertain environments, this paper focuses on chance-constrained optimization, which provides explicit probabilistic guarantees on the feasibility of optimal solutions. Although quite a few methods have been proposed to solve chance-constrained optimization problems, there is a lack of comprehensive review and comparative analysis of the proposed methods. We first review three categories of existing methods to chance-constrained optimization: (1) scenario approach; (2) sample average approximation; and (3) robust optimization based methods. Data-driven methods, which are not constrained by any particular distributions of the underlying uncertainties, are of particular interest. Key results of the analytical reformulation approach for specific distributions are briefly discussed. We then provide a comprehensive review on the applications of chance-constrained optimization in power systems. Finally, this paper provides a critical comparison of existing methods based on numerical simulations, which are conducted on standard power system test cases. 

This paper describes the implementation of a prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with a variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of the logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that the developed model manifests high accuracy and is able to differentiate an outage area from the rest of the network in 1 to 3 hours before the outage. The prediction model is integrated inside a weather testbed for real-time mapping of network outage probabilities based on incoming weather forecast.