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Jul 3, 2024Keywords:
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Main Article Content
Optimal Load Shedding During Service Restoration in Electrical Secondary Distribution Network Based on Reinforcement Learning
Abstract
Increased stress in traditional power systems results in blackouts due
to voltage instability attributed to a mismatch between available
capacity and load demand, especially in distribution networks. Service
restoration schemes are designed to return power supply to the affected
parts of the networks. The availability of insufficient supply is a
complex problem that requires operational experience or an automatic
system. The stochastic nature of load demand significantly impacts
service restoration as it results in increased restored demand in case a
fault occurs during off-peak hours and helps reduce overload if the fault
occurs during peak hours. The study adopts an experimental design
methodology to develop the Reinforcement Learning-based service
restoration algorithm considering the stochastic nature of load
demand. Three reinforcement learning models were used to develop the
optimal load shedding model, including Actor-Critic (A2C), a Deep Q
Network (DQN) and Proximal Policy Optimization (PPO2), and
compared to maximize restored customers, satisfaction of operational
constraints, and balancing of power supply and demand. The Particle
Swarm Optimization (PSO) algorithm, a metaheuristic algorithm, was
also implemented to compare with the proposed approach. The
proposed solution has been tested using data from a real electrical
secondary distribution network. The proposed solution considered the
stochastic nature of load demand, resulting in more restored customers.
The computation time during restoration has been improved by 69.8%
compared to the metaheuristic approach.
