Smart Distribution Network with Focus on Demand Side Management

Abstract

The transition toward Smart Distribution Networks (SDNs) is pivotal in enhancing energy efficiency, system reliability, and the effective integration of Distributed Energy Resources (DERs). Within this framework, Demand-Side Management (DSM) emerges as a critical mechanism for reshaping load profiles, mitigating peak demand, and optimizing the utilization of variable renewable energy sources.

This study proposes a comprehensive SDN architecture founded on DSM principles, integrating Advanced Metering Infrastructure (AMI), real-time data analytics, machine learning–based predictive modeling, and evolutionary multi-objective optimization to enable flexible and user-centric energy management.

The proposed framework employs artificial neural networks (ANNs) and support vector regression (SVR) to generate accurate short-term forecasts of both load and generation. An enhanced genetic sorting algorithm is then utilized to balance multiple conflicting objectives—namely peak reduction, cost minimization, voltage profile enhancement, and loss reduction—while adhering to power flow constraints, voltage limits, and consumer preferences.

Performance evaluation is conducted using the IEEE 33-bus radial distribution test system in a MATLAB/Simulink environment, incorporating solar generation units, controllable loads, and dynamic pricing mechanisms. The simulation outcomes demonstrate notable improvements, including:

• An 18% reduction in peak load (from 4.2 MW to 3.444 MW);
• A 28.6% decrease in active power losses (from 210 kW to 150 kW);
• An improved minimum voltage level, rising from 0.913 Pu. to 0.98 Pu;
• A 12% reduction in daily operational costs.

These results substantiate the proposed approach’s capability to enhance network resilience, increase renewable hosting capacity, and improve economic performance, offering a practical and scalable framework for next-generation integrated smart energy systems.