Bid responsive generation rescheduling for congestion management in electricity market

This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.INDEX TERMS Manta ray forge optimization, meta-heuristic technique, optimal power flow, optimization, power rescheduling, sensitivity analysis.

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“…The congested line power flow with IMRFO has been curtailed to 198.09 MW and is below its maximum TABLE 14. Price bids for 118 bus system [48].…”

Section: B 118-bus Systemmentioning

confidence: 99%

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

Paul¹,

Sinha

Bouteraa

et al. 2023

IEEE Access

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“…Congestion management (CM) is supposed to be one of the most crucial issues of restructured power system [1]. In the emerging day-ahead hybrid power market, some effective financial tools are inevitable to manage congestion in which bid-based generation rescheduling (GR) is found to be most eminent [2]. For bid-based GR, three…”

Section: Introductionmentioning

confidence: 99%

“…One is the initial bid and the remaining two are the post congestion bids which are incremental and decremental rescheduling bids. According to them, suppliers have to amend their real power generation along with optimizing flows and thus support in mitigation of congestion [2]. In the literature, different approaches have been suggested for this purpose based on the classical method [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Agrawal

Pandey²,

Srivastava

et al. 2022

IEEE Access

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In the open-access power market environment, the continuously varying loading and accommodation of various bilateral and multilateral transactions, sometimes leads to congestion, which is not desirable. In a day ahead or spot power market, generation rescheduling (GR) is one of the most prominent techniques to be adopted by the system operator (SO) to release congestion. In this paper, a novel hybrid Deep Neural Network (NN) is developed for projecting rescheduled generation dispatches at all the generators. The proposed hybrid Deep Neural Network is a cascaded combination of modified backpropagation (BP) algorithm based ANN as screening module and Deep NN as GR module. The screening module segregates the congested and non-congested loading scenarios resulting due to bilateral/multilateral transactions, efficiently and accurately. However, the GR module projects the re-scheduled active power dispatches at all the generating units at minimum congestion cost for all unseen congested loading scenarios instantly. The present approach provides a ready/instantaneous solution to manage congestion in a spot power market. During the training, the Root Mean Square Error (RMSE) is evaluated and minimized. The effectiveness of the proposed method has been demonstrated on the IEEE 30-bus system. The maximum error incurred during the testing phase is found 1.191% which is within the acceptable accuracy limits.INDEX TERMS Bilateral/multilateral transactions, congestion management, deep neural network, generation rescheduling, modified back propagation algorithm-based ANN.

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mentioning

confidence: 99%

Hybrid Deep Neural Network Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Saket¹

2022

Preprint

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Bid responsive generation rescheduling for congestion management in electricity market

Cited by 11 publications

References 18 publications

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Hybrid Deep Neural Network Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

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