Co-Optimization of Conservation Voltage Reduction and Uncertainty-Aware Demand Response: A Hybrid IGDT-Fuzzy Programming Approach

The integration of distributed generation and demand response programs introduces significant complexity to microgrid management. This paper proposes a comprehensive, stochastic framework for the day-ahead scheduling of a microgrid that simultaneously co-optimizes Demand Response Programs (DRPs) and a Conservation Voltage Reduction (CVR) strategy. The model aims to enhance operational performance, improve reliability, and minimize costs. To address the inherent uncertainties of renewable generation, the framework employs both Time-of-Use (TOU) and incentive-based DRPs, creating a vital link between variable generation and flexible demand. This is complemented by CVR implementation, which leverages voltage-dependent load modeling to achieve further peak load reduction. A key feature of the approach is the use of Information Gap Decision Theory (IGDT) to rigorously model generation uncertainty, providing a robust decision-making foundation. The optimization problem is solved using a Genetic Algorithm (GA) on a test system. Simulation results confirm that the coordinated application of uncertainty-aware DRPs and CVR leads to a substantial reduction in operational costs while significantly improving key microgrid reliability indices, demonstrating the efficacy of the proposed integrated framework.

کلیدواژه ها:

Active Distribution Networks ، Conservation Voltage Reduction(CVR) ، Demand Response Program(DRP) ، Information Gap Decision Theory(IGDT) ، multi-objective optimization