Akram Bati

Maximum Power Point Tracking technologies are being used in traditional PV system charge controllers to enhance the power conversion efficiency. An MPPT controller will ensure power extracted from the PV panels during varying climatic conditions is always maximum. This will ensure that maximum power is flowing between the panel and load. As both Temperature and Irradiation levels vary during the day, maximum power point trackers are an inevitable component in a PV system. As solar energy holds a major share in renewable energy in the world market, an improvement in MPPT technique makes the efficiency of the PV system increase and in turn cost reduction possible. However, the efficiency of conventional MPPT Techniques suffers from failing in tracking MPPT at fast varying climatic conditions and falling in local maxima of maximum power point than global maxima. The issues of stability, tracking speed, and accuracy can be solved using intelligent MPPT techniques methods based on soft computing tools: Therefore, this paper aims to study and provide a comparative analysis of two AI-based MPPT techniques such as ANN and ANFIS. The MPPT techniques considered in this study are ANN and ANFIS. Performance evaluation is carried out using MATLAB Simulation. Experimental results indicate that the two methods ANN and ANFIS are more efficient than conventional MPPT techniques due to its capability to avoid local MPP and partially shaded conditions.
Maximum Power Point Tracking technologies are being used in traditional PV system charge controllers to enhance the power conversion efficiency. An MPPT controller will ensure power extracted from the PV panels during varying climatic conditions is always maximum. This will ensure that maximum power is flowing between the panel and load. As both Temperature and Irradiation levels vary during the day, maximum power point trackers are an inevitable component in a PV system. The renewable energy source has a vital role in supplying sustainable power to meet the rising electricity demands. However, the PV system performance heavily depends on environmental conditions. This, in turn, causes efficiency to be less and in turn higher cost. For maximum power to be transferred from the PV panels under varying climatic conditions PV systems should operate at Maximum Power Point.

This paper presents a multi-period unit commitment active and reactive power optimal management for microgrids under different market policies. The overall optimization problem is formulated by mixed integer quadratic programming by taking into considerations the environmental costs and the battery degradation cost against a comprehensive set of constraints. A typical low-voltage microgrid, which comprises of distributed generators, renewable energy resources, storage battery, and varieties of loads, are employed to implement and examine the proposed approach. The microgrid is comprehensively investigated with both grid-connected and isolated modes under minimum operation and emission cost and maximum overall profit policies. The results have revealed that the charging and discharging operations of storage battery typically reduce the total operation and emission costs and hence maximize overall profit, even considering the battery degradation.