Congestion Management in Transmission System using PST

Verma, Deepika; Agarwal, Praveen Kumar; Jain, Prerna

doi:10.1109/powerafrica52236.2021.9543458

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…The penalty function PF is expressed in (11) and ( 12) to control the limits of all the inequality constraint variables.…”

Section: ) Inequality Constraintsmentioning

confidence: 99%

“…The GSF for active and reactive power rescheduling is stated in ( 13) and (25), respectively. Inequality and equality constraints are given in sub-sections 1 and 2 of section II(A), and the penalty function (11) and ( 12) are utilized to formulate the objective function (1) for the congestion management problem. By incorporating the specific CM problem, the problem mentioned above is mitigated using the PSO algorithm.…”

Section: A Pso Algorithm Implementation For Congestion Managementmentioning

confidence: 99%

“…Step 10: New velocity is computed using (45): Step 13: Compute penalty function for each particle using (11). This is done by finding constraint violations.…”

Section: A Pso Algorithm Implementation For Congestion Managementmentioning

confidence: 99%

“…The method was validated on a modified IEEE 24-bus test system network. [11] proposed a thyristor-controlled phase shifting transformer technique using GAMS solver to alleviate congestion. The viability of the technique was examined on the IEEE 24-bus network.…”

Section: Introductionmentioning

confidence: 99%

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Transmission Congestion Management Using Generator Sensitivity Factors for Active and Reactive Power Rescheduling Using Particle Swarm Optimization Algorithm

Ogunwole

Krishnamurthy

2022

IEEE Access

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Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work.

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“…The penalty function PF is expressed in (11) and ( 12) to control the limits of all the inequality constraint variables.…”

Section: ) Inequality Constraintsmentioning

confidence: 99%

Section: A Pso Algorithm Implementation For Congestion Managementmentioning

confidence: 99%

“…Step 10: New velocity is computed using (45): Step 13: Compute penalty function for each particle using (11). This is done by finding constraint violations.…”

Section: A Pso Algorithm Implementation For Congestion Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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