A Refined Approach Exploiting Demand Response in Distribution Grids Integrating Renewable Energy and Storage Systems Alongside Metro Facilities via LSTPA Methodology

Abstract

With the expansion of electric vehicle charging stations, renewable energy integration, and electrified subway systems, maintaining the balance between load and generation in distribution networks has become increasingly challenging. This imbalance can result in power losses, voltage instability, and additional operational costs for independent grid operators (IGOs). To address these issues, this paper proposes a novel mixed-integer nonlinear programming (MINLP) model to enhance the performance and resilience of distribution systems. The model incorporates demand response, energy storage, a battery-to-subway (B2S) system, optimal control of on-load tap changers (OLTCs) and step voltage regulators (SVRs), as well as various generation resources, capacitors, and shunt reactors. A multi-objective, scenario-based stochastic framework is employed to manage uncertainties from renewable sources. To solve the complex optimization problem, the Large-Scale Two-Population Algorithm (LSTPA) is applied, offering robust performance in large-scale scenarios. Simulation results on a standard 33-bus system demonstrate improved efficiency and reliability. Notable findings include a 50% increase in losses after the failure of three distributed generation units and a 35% emission rise following the outage of three renewable units. The proposed model ensures uninterrupted power delivery over a 24-h horizon, regardless of load variations or generation disruptions, making it highly suitable for real-time grid applications.