Power Management in a PV-Battery Microgrid Using Hybrid ANFIS-Fuzzy Logic MPPT Control and an Adaptive Charge-Discharge Algorithm

Mohamed  Amine Atillah; Hicham  Stitou; Abdelghani  Boudaoud; Aqil  Mounaim

doi:10.51646/jsesd.v14i2.1198

المؤلفون

Mohamed Amine Atillah Engineering and Applied Physics Team (EAPT), Superior School of Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco. https://orcid.org/0009-0001-9410-9239
Hicham Stitou Engineering and Applied Physics Team (EAPT), Superior School of Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco.
Abdelghani Boudaoud Engineering and Applied Physics Team (EAPT), Superior School of Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco
Aqil Mounaim Engineering and Applied Physics Team (EAPT), Superior School of Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco

DOI:

https://doi.org/10.51646/jsesd.v14i2.1198

الكلمات المفتاحية:

PV; Battery; Microgrid; ANFIS; Fuzzy logic; MPPT; Partial shading.

الملخص

This work presents an energy management strategy (EMS) for a microgrid that integrates photovoltaic (PV) panels, a battery energy storage system (BESS), and a grid connection. The primary objective is to maintain a dynamic and reliable power balance between PV generation, local load, BESS, and the grid, while maximizing the self-consumption of solar energy. The system architecture comprises a PV generator whose power is optimized by a hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a fuzzy controller, acting as a PI
regulator. The BESS, made up of Lithium-ion batteries, is managed by a bi-directional DC-DC converter and a fuzzy PI controller, which ensures fine, adaptive regulation of power flows. The core of the innovation lies in the battery management algorithm, which relies on a set of explicit rules to direct power flows. These rules include a strict 10 kW constraint on battery charging and discharging power, crucial for preserving battery life and system safety. The system was simulated considering various cases of irradiance profiles and charging demands of 35 kW and 55 kW, including normal and critical battery state-of-charge (SOC) situations. The simulation results demonstrate the success of the ANFIS-Fuzzy Logic (FL) MPPT control in maximizing energy capture with an error of ± 0.06 KW, while the BESS keeps power balance error below ± 0.02 kW in all tested scenarios. These results confirm the algorithm’s ability to handle critical high and low SOC situations, intelligently redirecting power flows to maintain microgrid stability and power continuity.

التنزيلات

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المقاييس

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