الفهرس 
CHAPTER 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
The growing implementation of renewable energy sources (RES) within power systems is critical for managing supply security and mitigating the environmental crisis. However, integrating variable RES poses planning and operational challenges to ensure reliability and efficiency. Despite progress, renewable curtailment remains an obstacle for utilities, with abundant but intermittent renewable generation constrained by technical system limits. Studies show that managing load flexibility through demand response (DR) programs can notably impact future grid performance and resilience with high renewable penetration.
This thesis aims to propose a day-ahead price-based DR model to simultaneously improve distribution network operations and decrease curtailed renewable energy. Critical times when curtailment may arise from generator ramping restrictions or low generation outputs are identified. A DR program accordingly modifies demand patterns to alleviate curtailment risk. Deterministic and stochastic DR formulations address renewable and load uncertainties using probabilistic scenarios.
An optimal power flow (OPF) problem, with the objective of reducing the overall system operating cost, optimizes generation, storage, and flexible demand. Simulation on an enhanced IEEE 33-bus test system evaluates the proposed model’s performance. Four case studies are adopted to show the deterministic model and the impacts of individual and combined renewable and load uncertainties on various operational metrics.
The computational implementation of the proposed framework is conducted using the General Algebraic Modeling System (GAMS) software. Finally, results indicate improved economics, efficiency, and RES utilization through coordinated scheduling of flexible demands and variable renewable sources.