An approach to locational marginal price based zonal congestion management in deregulated electricity market

Sarwar,; Siddiqui, Anwar Shahzad

doi:10.1007/s11708-016-0404-z

Cited by 16 publications

(12 citation statements)

References 15 publications

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“…Hojjat et al in 2016 considered power system uncertainties and proposed a chance-constrained programming method for CM [12]. In another study, Sarwar and Siddiqui mitigated congestion taking into account the locational marginal pricing differences for the placement of distributed generations (DGs) in the most congested zones [13]. Nesamalar et al in 2016 proposed a cost-efficient model for the CM comprising renewable energy sources [14].…”

Section: Literature Surveymentioning

confidence: 99%

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Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

Paul¹,

Kumar²,

Agrawal³

et al. 2019

Turk J Elec Eng & Comp Sci

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The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The potency of the proposed method is tested on the 39-bus New England System and the IEEE 30-bus system and 118-bus system, and the outcomes achieved with the GSA outperform the results reported with other algorithms.

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Section: Literature Surveymentioning

confidence: 99%

“…Power flow details (line[13][14][15][16]. Generators' price bids ($/MW day) (118-bus system).…”

mentioning

confidence: 99%

Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

Paul¹,

Kumar²,

Agrawal³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Congestion problem in an electrical network averts the coveted transaction of power resulting in buyers getting forced to buy power at higher costs from other providers [4]. Hence congestion management is immensely desirable and must be effectively eased for the system's efficient and stable operation.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

Bashir¹,

Paul²,

Ahmad³

et al. 2021

SGRE

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In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

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“…In the deregulated electricity market, the power stations and generation companies sell energy through either competitive long‐term contract with consumers or by bidding for short‐term energy supply at the spot market 3‐6 . In the deregulated electricity market, a large volume of electricity is traded between Generation Companies (GENCOs) and Distribution Companies (DISCOs) 7,8 . This massive volume of transactions leads to an unpredicted amount of power flow through some transmission corridors, which may create congestion in transmission corridors 7,9 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of distributed generation size based on line sensitivity using transmission congestion cost

Sharma

Walde

Saket

et al. 2020

Int Trans Electr Energ Syst

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Summary In this paper, a novel method is presented to study the impact of the most congested line on Locational Marginal Price (LMP), and Transmission Congestion Cost (TCC). In most congested condition, the abrupt difference in LMP between two nodes result in enormous TCC and create a huge loss for participants. The method developed in this paper is to find the most congested line on the basis of TCC value, and its sensitivity on the overall network is thoroughly studied. The LMPs computed at each node help in grouping the nodes or buses into two parts, namely, zone 1 or congested zone and zone 2 or non‐congested zone. This method helps market operators to analyze the impact of the most congested line. In addition, the method is handled by obtaining the exact location and sizing of Distributed Generation (DG). Moreover, to formulate the problem accurately, the proposed method is based on AC optimal power flow (ACOPF), which includes network losses in contrast with the lossless DCOPF. The MATLAB interior point method is used to solve the proposed method, and it is tested on IEEE Reliability Test System (IEEE‐RTS) 24 bus and IEEE 30 bus system network in different operating conditions. The results thus obtained shows the betterment of the proposed method.

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An approach to locational marginal price based zonal congestion management in deregulated electricity market

Cited by 16 publications

References 15 publications

Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

Optimization of distributed generation size based on line sensitivity using transmission congestion cost

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