Preprint Article Version 1 This version is not peer-reviewed

A New ANFIS-based Peak Power Curtailment in Microgrids Including PV Units and BESSs

Version 1 : Received: 29 September 2018 / Approved: 30 September 2018 / Online: 30 September 2018 (04:56:58 CEST)

A peer-reviewed article of this Preprint also exists.

Nikolovski, S.; Reza Baghaee, H.; Mlakić, D. ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs. Energies 2018, 11, 2953. Nikolovski, S.; Reza Baghaee, H.; Mlakić, D. ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs. Energies 2018, 11, 2953.

Journal reference: Energies 2018, 11, 2953
DOI: 10.3390/en11112953

Abstract

One of the most crucial and economically beneficial tasks for energy customer 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 warry. 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 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, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

Subject Areas

adaptive neuro-fuzzy inference system; battery energy storage; photovoltaic unit; power demand; peak power curtailment

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