A novel approach for congestion management using improved differential evolution algorithm

Abstract

Summary This article proposes a new methodology for Congestion Management (CM) in a deregulated power system based on Generator Rescheduling/Load curtailment. This work utilizes 4 different sensitivity factors derived from the modified Jacobian matrix, used to identify the participating generator and loads for CM effectively. Also, a novel strategy of combining sensitivity factors and bidding costs is employed for selecting the control variables namely generator real power rescheduling, generator voltage adjustment, and load curtailment at selected busses. Next, an Improved Differential Evolution Algorithm (IDE) has been recommended to solve CM with the objective of minimizing Congestion Management Cost (CMC). The standard IEEE 30‐bus system and IEEE 118‐bus system are used to evaluate the proposed scheme, and based on the obtained results, it is shown that the proposed method is superior to the other CM methods reported in the literature.

Bid responsive generation rescheduling for congestion management in electricity market

“…The problem of congestion management is formulated as a non-linear optimal power flow problem with an objective of cost minimization for active power rescheduling by the generators and is given as. 14 Minimize n g ∑ i RC i .ΔP gi (17) subject to following constraints. 1.…”

“…But, still it lacks in an efficient model on the basis of rescheduling cost. So far, many research works are reported to address this issue 11‐17 …”

“…It allows certain generators to maximize their profit. The issue is somehow addressed by Suganthi et al in References 16,17. Improved GS factor (IGSF) is introduced to reflect the effect of bid values in the selection of participating generators.…”

“…So far, many research works are reported to address this issue. [11][12][13][14][15][16][17] The management of congestion by rescheduling real power generation is presented by Yesuratnam and Thukaram. 11 The authors introduced relative electrical distance (RED) to find the rescheduling power amount.…”

Probabilistic integrated framework for AC / DC transmission congestion management considering system expansion, demand response, and renewable energy sources and load uncertainties

“…Traditional congestion management (CM) methods usually involve the construction of new power plants and/or transmission lines, which might be limited due to the high cost and the service duration for critical hours. [2][3][4] The CM problem has become more challenging in recent years due to the growth of renewable energy sources (RESs) and flexible loads. The incorporation of RESs into the power systems leads to some obstacles such as uncertainty and being far from demand centres for their efficient use.…”