Toptan ve yerel enerji piyasaları işlemleri için Wasserstein dağılımsal olarak gürbüz optimizasyon reformülasyonları

The increasing penetration of intermittent renewable energy sources (RESs) introduces substantial uncertainty into both wholesale and local electricity markets, necessitating decision-making frameworks that are robust against distributional ambiguity. This thesis develops novel Wasserstein distributionally robust optimization reformulations for market clearing and bidding problems across multiple market layers. These reformulations encompass distributionally robust objective functions, chance constraints, and newsvendor-type decision models over Wasserstein ambiguity sets exploiting physical-and-statistical support information. At the wholesale market level, a unit commitment-based distributionally robust market clearing problem including intermittent RES uncertainty is formulated for transmission networks, in which commitment and dispatch decisions for conventional power plants are determined centrally. At the local energy market level, optimal power flow-based distributionally robust market clearing model is developed for radial distribution networks, supported by a novel lossy linearized AC power flow approximation that enables a linear mapping between real-time measurements and uncertain RES generations. Lastly, at the local energy market level, a settlement-aware newsvendor-based distributionally robust day-ahead bidding framework is proposed for intermittent RES producers under a two-price real-time imbalance settlement mechanism. To enhance bid trustworthiness, an extended formulation incorporating the effect of uncertainty-aware day-ahead prices is proposed, as well. The bidding framework admits separable Wasserstein ambiguity modeling for supply- and cost-related uncertainties and is applicable to both wholesale and local energy markets.