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

Mohammadian, Leila; Abedinzadeh, Taher; Helmi, Hamid

doi:10.22075/mseee.2026.40785.1259

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

Articles in Press

Document Type : Research Article

Authors

Leila Mohammadian

Taher Abedinzadeh

Hamid Helmi

Department of Electrical Engineering, Shab.C., Islamic Azad University, Shabestar, Iran.

10.22075/mseee.2026.40785.1259

Abstract

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.

Keywords

Active Distribution Networks

Conservation Voltage Reduction(CVR)

Demand Response Program(DRP)

Information Gap Decision Theory(IGDT)

Multi-Objective Optimization

Subjects

Power System

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