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Techno‐Economic Model for Reactive Power Planning Using Series‐Shunt Compensation Devices Under Load Demand in Power Transmission Network
Author(s) -
Karmakar Nihar,
Bhattacharyya Biplab
Publication year - 2021
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202100156
Subject(s) - ac power , computer science , benchmark (surveying) , electric power transmission , mathematical optimization , activity based costing , electric power system , power transmission , reliability engineering , industrial engineering , power (physics) , engineering , voltage , electrical engineering , economics , physics , mathematics , accounting , geodesy , quantum mechanics , geography
This research article proposes a planning strategy of reactive power (VAR) in a power transmission system, because reactive power planning (RPP) is a topical issue in the energy sector. Basically, it is a qualitative effort to understand the mathematical principles, technical facets, and economic aspects with their utility in RPP prior to executing the proposed strategy through a unique solution model. Initially, the objective is formulated by measuring different cost components, such as VAR generation cost, line charging cost, flexible AC transmission system (FACTS) device operation cost, and cost due to real power loss. The vulnerable positions in the system are defined for the placement of FACTS devices after computing a set of mathematical methods. The installation bottlenecks of these devices are wiped out depending on some technical considerations and performances. Thereafter, a hybrid intelligence approach along with fundamental algorithms is applied to determine an optimal set of controlling variables in conjunction with active and VAR demand. The code profiling of the proposed approach is rendered on two interregional practical power networks. Finally, the responses of the benchmark functions and statistical outcomes are analyzed extensively to justify the trustworthiness of the proposed solution model.