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ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs

Srete Nikolovski (), Hamid Reza Baghaee () and Dragan Mlakić ()
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Srete Nikolovski: Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, Osijek 31000, Croatia
Hamid Reza Baghaee: Department of Electrical Engineering, Amirkabir University of Technology, 15875-4413 Tehran, Iran
Dragan Mlakić: Department of Measurement and Network Management in Electrical Energy Systems, Distribution Area, Centar“, JP Elektroprivreda HZ HB“ d.d, Mostar, 88 000 Mostar, Bosnia and Herzegovina

Energies, 2018, vol. 11, issue 11, 1-23

Abstract: One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods.

Keywords: adaptive neuro-fuzzy inference system; battery energy storage; photovoltaic unit; power demand; peak power curtailment; peak shaving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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