Optimal Dynamic Reconfiguration of Distribution Networks
The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration
Power distribution networks are evolving due to the integration of DERs and increased customer participation. To maintain optimal operation, minimize losses, and meet varying load demands, frequent network reconfiguratio…
Language ModelingLanguage ModellingLarge Language ModelBatch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration
Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid
The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intellige…
ManagementFeeder Load Balancing using Neural Network
The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, …
Combinatorial OptimizationDeep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement
Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., l…
Deep Reinforcement LearningQ-Learning