A GA approach to compare ORPF objective functions including Secondary Voltage Regulation

• Published in: Electric power systems research 2012-03, Vol.84 (1), p.187-194
• Main Authors: Berizzi, A., Bovo, C., Merlo, M., Delfanti, M.
• Format: Article
• Language: English
• Subjects: Applied sciences; Disturbances. Regulation. Protection; Electrical engineering. Electrical power engineering; Electrical machines; Electrical power engineering; Exact sciences and technology; Genetic Algorithms; Loadability margin; Miscellaneous; Operation. Load control. Reliability; Optimal Reactive Power Flow; Power networks and lines; Regulation and control; Secondary voltage control; Voltage security
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2011.11.014

► Optimal Reactive Power Flow procedures can improve the operation power system in terms of economy, security and reliability. ► Different objective functions can be adopted depending on the general framework. ► The paper compares some objective functions that could be used for Optimal Reactive Power Flow procedures. ► Genetic Algorithms are used to allow a fast and reliable comparison. ► Secondary voltage control is taken into account and exploited to reduce the computational cost of Genetic Algorithm approach. In the past, with vertically integrate utilities, Optimal Reactive Power Flow (ORPF) procedures were designed to minimize power system losses, keeping the voltage profile in an acceptable range. Nowadays, in the market environment, a new formulation of the ORPF, aimed at the system security maximization, is necessary. In particular, as congestions and overloads are usually taken into account by the energy market rules, voltage security could become the main goal of ORPFs. Voltage problems are caused by the increase in power transfer among areas of interconnected systems, by the lack of reactive power support and by the increasing limitations of transmission networks. In this paper, an analysis of several ORPF Objective Functions (OFs) is reported, where the goal considered is the network security maximization. To solve the optimization problem for the considered OFs, a Genetic Algorithm (GA) approach has been adopted, together with a particular formulation of the Power Flow (PF) taking into account the Secondary Voltage Regulation (SVR). Tests are performed on a detailed model of the Italian power system, comparing the results in terms of system losses, reactive power required, loadability limits and voltage collapse indices. Furthermore, the improvements achievable with the adoption of the SVR in the new market environment have been investigated.