Abstract

In view of the continuous annual increase in demand, reactive power planning (RPP) is considered one of the most significant problems to address a major challenge of the secure power system operation. In this paper, a multi-objective genetic algorithm (MOGA) for RPP is proposed, with the goals of cost minimization of power losses, new reactive power (VAR) sources, and maximizing the Total Transfer Capacity (TTC). Different optimization factors are taken into account, including generator voltages, transformer tap changers, and various operating constraints. A fuzzy min-max approach is used to identify the optimum compromise option. Studies are being conducted to compare capacitor banks, flexible ac transmission systems (FACTS), or both as a new VAR support source to improve the system performance. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. The effectiveness of the proposed algorithm is examined on the IEEE 30-bus test system where felicitous results have been acquired. From the results, the total annual cost is decreased from 3.671×106 $ before adding new VAR sources to a range between 2.02×106 and 2.486×106 $ depending on the selected type of VAR source. While the transfer capacity is increased from 458.37MW to a range between 483.084 and 539.055 MW.

Highlights

  • O ne of the most difficult aspects of contemporary power system operation is meeting ever-increasing load demand while ensuring dependable power supply to consumers and keeping voltage within acceptable limits for high-quality customer service

  • The August 2003 blackout in the US and Canada was not caused by a voltage collapse, the US– Canada Power System Outage Task Force's final report said that "insufficient reactive power was an issue in the blackout" [2]

  • The proposed multi-objective genetic algorithm (MOGA)-based approach was applied to the IEEE 30-bus system

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Summary

Constants and parameters

Transmission line susceptance between bus i and bus j (p.u) The per-unit cost of the capacitor bank ($/MVAR) The fixed installation cost of capacitor bank in ($) Transmission line conductance between bus i and bus j (p.u) Per-unit energy cost ($/MWh) The interest rate for VAR devices (%)

Variables
INTRODUCTION
MODELING OF NEW VAR SOURCE
VAR cost
Enhancement of TCC
Constraint
PROPOSED SOLUTION ALGORITHM
RESULTS AND DISCUSSION
CONCLUSION

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