1 Abstract--This paper presents a method that overcomes the problem of the confusion during fast irradiance change in the classical MPPTs as well as in model predictive control (MPC)- based MPPTs available in the literature. The previously introduced MPC-based MPPTs take into account the model of the converter only, which make them prone to the drift during fast environmental conditions. Therefore, the model of the PV array is also considered in the proposed algorithm, which allows it to be prompt during rapid environmental condition changes. It takes into account multiple previous samples of power, and based on that is able to take the correct tracking decision when the predicted and measured power differ (in case of drift issue). After the tracking decision is taken, it will be sent to a second part of the algorithm as a reference. The second part is used for following the reference provided by the first part, where the pulses are sent directly to the converter, without a modulator or a linear controller. The proposed technique is validated experimentally by using a buck converter, fed by a PV simulator. The tracking efficiency is evaluated according to EN50530 standard in static and dynamic conditions. The experimental results show that the proposed MPC-MPPT is a quick and accurate tracker under very fast changing irradiance, while maintaining high tracking efficiency even under very low irradiance. Index Terms-- Buck converter, dc-dc power conversion, Drift, Double cost function, EN50530 standard, Maximum power point tracking, MPC, Photovoltaic systems. I. INTRODUCTION HOTOVOLTAIC (PV) electricity production system is one of the most essential renewable energy systems, due to its advantageous features, primarily the clean, free, and unlimited resource. It is predicted that in 2035, the energy generated by PV systems will increase by almost 20 times, expanding to 846TWh [1]. Under each irradiance/temperature level, the PV array provides different output power vs voltage characteristic P(v). This latter, is nonlinear and in normal conditions has only one peak, indeed it has a shape close to the intersection shape “∩”. The peak of this curve is usually referred to as the maximum power point (MPP). Various algorithms have been proposed in the literature for defining and making the PV module working The authors are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: abl@et.aau.dk; des@et.aau.dk; joz@et.aau.dk). under this peak simultaneously [2]. These algorithms are named as maximum power point trackers (MPPT). The classical and the most well-known MPPT is the Perturb and Observe (P&O). In fact, this algorithm is simple and requires only the use of sensors for measuring the PV current and voltage. But, as its name denotes, this algorithm continuously perturbs the voltage (in case of voltage control) by adding/subtracting a fixed voltage increment (ΔV) to/from the PV voltage, which produces some oscillations in the output PV power. Also, its speed convergence is limited, by reason that the choice of the step size is linked to the steady state operation conditions [3], [4]. Incremental Conductance (INC) also is a well-known MPPT [5], [6], and its operational principle is very congruous to that of P&O algorithm. It therefore provides tantamount static and dynamic perfor- mances as P&O according to the investigation reported in [7]. There exist other classical methods, such as fractional open- circuit voltage (FOCV) [8], and fractional short-circuit current (FSCC) [2]. But, these methods do not converge to the true MPP, and they suffer from power loss during the measurement of the fractional variable. The relative merits of these numerous approaches are discussed and investigated in [2]. The fact that the classical MPPTs fail to pursue the MPP under rapidly changing atmospheric conditions, has raised concern of many researchers [9]-[14]. This issue is referred as drift in the literature [9]. For instance, the conventional P&O fails to track the MPP during fast environmental condition changes, because this algorithm and its rules are designed for a static PV curve, and if there is a fast change in the irradiance/temperature, the rules of this algorithm are no longer sufficient. As a scenario, if the result of the condition P pv (k)-P pv (k-1)>0 is yes, P&O considers that the operating point is approaching the MPP, and subsequently, the same decision as the previous one will be taken. However, there is another probability P&O is not designed to be aware of, which is, the increase of power caused by the increase of irradiance during one perturbation period is larger than the increase in power induced by the previous perturbation. In this case, the operating point is may be going far away from the MPP. In [9], a condition has been added to P&O by observing the change in current, which provides to P&O the knowledge when the operating point goes to the right side of the MPP. But, the operating point may go to the right or left side of the MPP, that depends upon the last action taken by P&O just prior to the irradiance change. In [10], the change of power resulted by the environmental condition changes is subtracted from the overall resulted PV power, to allow the P&O A Dual-Discrete Model Predictive Control- Based MPPT for PV Systems Abderezak Lashab, Student Member, IEEE, Dezso Sera, Senior Member, IEEE, and Josep M. Guerrero, Fellow, IEEE P