| Shrnutí: | <p>Power systems globally are significantly integrating renewable energy to achieve net zero emission targets. Nonetheless, these renewable energy sources are weather-dependent and uncertain, bringing challenges to ensure operational reliability. Additionally, extreme weather and natural disasters damage power infrastructure, leading to contingencies or large-scale blackouts occasionally. These pressing issues necessitate uncertainty-aware power system management and give rise to the main research question, how to apply the joint chance-constrained (JCC) formulation in uncertainty-aware power system operations using probabilistic forecasting information?</p>
<p>A literature review discusses different machine learning (ML) probabilistic forecasting models and uncertainty-aware formulations in power system operations. Selected ML models are then compared for weather-informed net-load probabilistic forecasting. Leveraging probabilistic forecast, this thesis investigates and applies two uncertainty-aware formulations in power system operations, the JCC and distributionally robust optimization (DRO). Case studies are demonstrated on solar-powered networked microgrids.</p>
<p>The first case study investigates solar-powered networked microgrids during utility contingencies. A distributionally robust joint chance-constrained (DRJCC) framework is developed to control this networked microgrid considering the worst-case solar forecast error distribution. The DRJCC problem is reformulated into a tractable individual chance-constrained formulation with unspecified individual violation rates. A novel evolutionary algorithm is designed to approximate the optimal individual violation rates, known as the NP-hard Bonferroni Approximation problem. The solution is proven to be reliable and less conservative in out-of-sample tests compared to other methods.</p>
<p>The second one focuses on networked microgrids in the power markets. It envisages that, due to stochastic solar power generations, the imperfect real-time reserve provisions from multiple microgrids simultaneously could result in unexpected load shedding. Lever- aging historical forecast error samples, the reserve bidding strategy of each microgrid is formulated into a two-stage Wasserstein-metrics DRO model. A JCC regulates the under-delivered reserve capacities of all microgrids in a non-cooperative game. To approximate the optimal individual violation rates of microgrids with the changing correlation, the Bayesian optimization method is introduced. This DRJCC game framework is simulated with up to 14 players in a 30-bus network. The proposed Bayesian optimization method can identify the optimal reserve violation rates, which effectively regulate the joint violation rate of the under-delivered reserve whereas securing the profit of microgrids.</p>
|
|---|