Identification and mitigation of congestion in distribution system utilizing proximity index‐based demand response

Abstract

Summary With the integration of distributed energy resources and transition towards smart grid, the existing power system has undergone a paradigm change. However, increasing flexibilities in demand and penetration of local generation to minimize the mismatch between generation and supply has changed the operational characteristics of distribution system. Integration of local generation assists bypassing congestion when placed in the proper location, but excessive generation or incorrectly placed generators can lead to congestion in the distribution networks. This paper proposes a demand response‐based technique to mitigate congestion in distribution networks using power transfer distribution factors (PTDFs) and a new network impedance‐based index known as proximity index. Power transfer distribution factor is used in case of meshed distribution system and proximity index is used in case of radial system where PTDF‐based congestion management fails. By utilizing the network topology, the optimal buses are identified; participation of these buses in DR‐based congestion management will be most effective in relieving congestion. The loads of these lines will be partially or fully shifted to reduce/get rid of congestion of the network. The proposed method is validated for both meshed type as well as radial distribution networks.

Multi‐stage energy‐risk adjustments using practical Byzantine fault tolerance consensus for blockchain‐powered peer‐to‐peer transactive markets

“…[38–40], and [2] have used PTDF as a metric to identify network congestions or to optimise the network constraints. Herein, we use a distribution network power flow model (BFS) and PTDF as an additional tool to prioritise the magnitude share of the congestion from each peer.…”

“…This method guarantees that there cannot be any real-time energy transaction without the BFS algorithm verifying that the network constraints are respected. [38][39][40], and [2] have used PTDF as a metric to identify network congestions or to optimise the network constraints. Herein, we use a distribution network power flow model (BFS) and PTDF as an additional tool to prioritise the magnitude share of the congestion from each peer.…”

Joint Feature Selection of Power Load in Time Domain and Frequency Domain Based on Whale Optimization Algorithm

“…At present, many scholars have carried out in-depth research on the problem of power load identification, and a series of research results have emerged [14][15][16]. Aiming at the shortcomings of long training time and low recognition accuracy of existing algorithms, Huang et al [17] proposed a load identification algorithm based on long-short-term memory-back propagation (LSTM-BP).…”