A Smart Technology Driven Framework for Improved Supply Restoration and Load Management in Urban and Semi Urban Low Voltage Distribution Networks

Consumers in urban and semi urban areas heavily depend on reliable electricity for socio economic activities. Consumers connected to traditional low voltage distribution networks (LVDNs) suffer from prolonged outages due to manual restoration procedures and limited operational control, despite the presence of alternative supply paths within the network. Furthermore, demand management is typically implemented through indiscriminate load shedding, which interrupts supply to all consumers regardless of priority. This paper proposes a smart technology driven framework for improving supply restoration and demand management in urban and semi urban low voltage distribution networks. The proposed framework integrates dual load control smart meters at consumer premises and remotely operated switches within the LV network, coordinated through an advanced Distribution Management System. A novel optimization algorithm is developed to dynamically maximize the number of consumers served during supply restoration and demand management while considering real time load forecasts, consumer priority levels, and network operational constraints such as voltage limits and feeder capacities. The algorithm is formulated as a mixed integer linear programming problem and solved using the PuLP optimization library. The proposed framework is validated using a modified IEEE 33 bus test system incorporating real consumer load data. Simulation results demonstrate significant improvements in restoration time, improved prioritization of essential loads, and compliance with system operating constraints. Additionally, the proposed framework reduces the Energy Not Supplied (ENS), resulting in notable economic benefits. The proposed approach provides a scalable pathway for transitioning from manually operated distribution networks to intelligent and resilient LVDNs capable of accommodating increasing demand and distributed renewable energy integration.