학술논문

Reliability Improvement of a Radial Distribution System Considering Load Modeling and Energy Management
Document Type
Conference
Source
2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T) Power, Control and Computing Technologies (ICPC2T), 2022 Second International Conference on. :1-6 Mar, 2022
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Schedules
Simulation
Distribution networks
Power system reliability
Reliability
Resource management
Energy management
Distribution system reliability
distributed gen-eration (DG)
energy management system
optimization
load models
Language
Abstract
Increasing energy demand and recent advancements in electrical and distributed generation (DG) technology have made power systems complex. Therefore, the reliability assess-ment is important for efficient planning and operation of distribution networks. The system reliability can be improved with the optimal DG integration and energy management schemes. This work mainly studies the impact of optimal DG planning with an energy management scheme on the reliability of a radial distribution network. Usually, the reliability of a power system is evaluated using the distribution system reliability indices, which are based on load point and customers. The voltage-dependent load model and time-varying load profile for different load classes are included in this work for pragmatic planning. Particle swarm optimization (PSO) algorithm is used to find the optimal site and size of DG units and optimal scheduling of the shiftable loads. The proposed model of optimal DG allocation with energy management is evaluated with a case-based analysis. The modified IEEE 33-bus distribution system is considered in this model to demonstrate the improvement of reliability and operational parameters. Simulation results verify the efficacy of the model.