Heuristic probabilistic power flow algorithm for microgrids operation and planning. Issue 11 (1st August 2015)

• Record Type: Journal Article
• Authors: Nikmehr, Nima; Najafi Ravadanegh, Sajad
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 11(2015)
• Page Start: 985
• Page End: 995
• Publication Date: 2015-08-01
• Subjects: load flow -- distributed power generation -- power generation planning -- power distribution planning -- statistical distributions -- power distribution reliability -- optimisation; heuristic probabilistic power flow algorithm -- microgrid operation -- microgrid planning -- future active distribution network -- renewable energy resource -- RER power production uncertainty -- power flow method -- heuristic load flow method -- probabilistic load flow algorithm -- PLF algorithm -- heuristic‐based optimisation algorithm -- IEEE 33‐bus test system -- Monte Carlo simulation method -- probability density function -- cumulative distribution function -- bus voltage; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2014.0570
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : One of the basic components of future distribution networks is renewable energy resources (RER). The uncertainty in power production of renewable resources such as wind and solar as well as load is another characteristic of such networks. Conventional power flow methods may not be suitable for active distribution networks such as microgrids. In this study, a heuristic load flow method considering the effects of intermittent behaviour of RERs and load is modelled in probabilistic load flow (PLF) algorithm. The method is suitable for both radial and weakly meshed distribution networks with RER for operation and planning of microgrids. Imperialist competitive algorithm (ICA) as heuristic‐based optimisation algorithm is applied to solve the PLF. Based on PLF technique, calculated parameters of the system such as bus voltages and feeders' current are extracted as random variables. A modified version of IEEE 33‐bus test system with RER is used to evaluate efficiency and capability of the algorithm. Results are compared with Monte Carlo simulation method. The probability density function and cumulative distribution function (CDF) of some network variable are compared. Based on the results, the presented approach can solve the PLF problem regardless of the type of distribution network.