Both deterministic and probabilistic load forecasting (DLF and PLF) are of critical importance to reliable and economical power system operations. However, most of the widely used statistical machine learning (ML) models are trained by optimizing the global performance, without considering the local behaviour. This paper develops a two-step short-term load forecasting (STLF) model with Q-learning based dynamic model selection (QMS), which provides reinforced deterministic and probabilistic load forecasts (DLFs and PLFs). First, a deterministic forecasting model pool (DMP) and a probabilistic forecasting model pool (PMP) are built based on 10 state-of-the-art ML DLF models and 4 predictive distribution models. Then, in the first-step of each time stamp, a Q-learning agent selects the locally-best DLF model from the DMP to provide an enhanced DLF. At last, the DLF is input to the best PLF model selected from the PMP by another Q-learning agent to perform PLF in the second-step. Numerical simulations on two-year weather and smart meter data show that the developed STLF-QMS method improves DLF and PLF by 50% and 60%, respectively, compared to the state-of-the-art benchmarks.

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic wind power ramp forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors (WPFEs), a generalized Gaussian mixture model (GGMM) is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm (OpSDA) is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.