Demand-Side Flexible Resources: Modeling and Optim al Dispatch

A complete technical framework for demand-side flexibility integration As renewable energy penetration increases, power systems require additional flexibility to maintain supply-demand balance. Demand-Side Flexible Resources presents a technical framework covering flexibility modeling of DFRs tailored to specific operational characteristics, flexibility aggregation of large-scale DFRs, machine learning-based forecasting techniques, and multiple dispatch and control strategies for real-time system response. The book details flexibility aggregation with distribution network constraints and transmission-distribution coordination design, addressing both deterministic and probabilistic forecasting approaches for flexibility prediction alongside real-time flexibility activation. Coverage extends to modeling uncertainty of flexibility within dispatch operations, providing readers with strategies to coordinate DFR participation across interconnected grid levels. Readers will also find: Flexibility models tailored to the distinct operational characteristics of various demand-side resource types including electric vehicles, storage, thermostatically controlled loads, and distributed generationsTechniques for evaluating aggregated flexibility potential from large-scale distributed resourcesMethods for handling stochastic variability and its impact on the aggregated flexibility Strategies for coordinating flexibility signals between transmission system operators and distribution systemsReal time operation methods for distribution system operators and DFR aggregatorsTheoretical foundations supporting advanced optimization algorithms applied to multi-period demand-side resource scheduling problems Designed for researchers and graduate students in power systems and renewable energy, this book also serves industry professionals in sustainability engineering, including engineers and policy makers seeking advanced methods for leveraging demand-side flexibility to support grid reliability and renewable integration.