Comparative study between classical methods and genetic algorithms for sizing remote PV systems

Comparative study between classical methods and genetic algorithms for sizing remote PV systems S. Makhloufi 0 0 LEESI Laboratory, University of Adrar , Adrar , Algeria Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0 and 90 in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. Genetic algorithms and worst month methods give results close to each other between 0 and 60 but the system is largely oversized by the worst month method when the tilted angle is over 60 . The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method. Optimization; Cost; Genetic algorithms; Lighting; Photovoltaic; Worst month - & S. Makhloufi Introduction Conventional methodologies (empirical, analytical, numerical, hybrid, etc.) are used for sizing photovoltaic (PV) systems, especially when the required weather data (irradiance, temperature, humidity, clearness index, wind speed, etc.) and the information concerning the location of PV system are available [14]. These methods present a good solution for sizing PV systems under the above conditions. However, such techniques cannot be used for sizing PV systems where the required data are not available. Moreover, the majority of the above methods need long-term meteorological data, such as total solar irradiance, air temperature, and wind speed, for their operations. To overcome this situation, newer methods have been developed for sizing the parameters for PV systems based on artificial intelligence techniques [5]. However, these methods require complex implementation and powerful calculators to reduce time calculation which makes simple methods, like worst month method, still largely used in sizing simple remote PV systems. A wide range of literature is available in this area. Chen [6] proposes a sizing procedure based on the long-term trend of the observed extremes of solar radiation. In [7] the sizing and designing of a standalone photovoltaic electricity generation system for a small household load performed using the locally acclimatized simulation program is discussed. In [8] a hybrid approach, combining analytical sizing equations with long-term performance, for an optimal design of a standalone PV battery system is proposed. In [9] after the sizing of PV generator in conventional irradiation and ambient temperature conditions, the proper battery capacity has been estimated with iterative simulations. Becherif et al. [10] deal with the design, modeling, sizing and control of a photovoltaic standalone Home to Vehicle (HV) application that can fully charge the Battery Electrical Vehicles (EV) overnight at home. Brenna et al. [11] instead deals with the capability of PV and EV in gridconnected systems based on daily average solar irradiance as a function of the site coordinates. In [12] a methodology for optimum design of solar array and battery bank for a solar array-exclusive standalone photovoltaic system using energy balance concept is presented. The constraint of system cost function based on loss of power supply probability (LPSP) has been implemented using genetic algorithms (GA). In [13] one optimum sizing method based on genetic algorithm, for solar lighting system with battery banks, was recommended. In [14] the authors study the sizing and economic optimization of a standalone photovoltaicwind hybrid system with storage batteries, installed in a semi-arid region of Algeria supplying a farm. Two methods were developed. The first method is based on the average annual monthly values in which the size of photovoltaic and wind generators was determined from the average monthly contribution of each component. In the second method, the determination of these two system components size is based on the worst month. Zaninelli and Leva [15] introduces hybrid photovoltaicwinddiesel generation systems supplying a remote power load. A cost investment valuation is performed on a real plant showing the effect of sustainable economical saving. In [16] a cost investment evaluation is performed on a real plant showing the effect and the weight of sustainability economical saving. The possibility to introduce a fuel cell generation device is also investigated. Simonov et al. [17] discusses the role of evolutionary computational tools and some issues related to the variability and uncertainty in the operations where PV plants are potentially fully connected to the power grid in a future scenario. Recently, using PV lighting systems has been considerably increased in Algeria. This is motivated by the enormous potential of PV energy, especially in the south. For example, in Adrar city (27.51 N, 0.17 W), the annual mean insolation incident on a horizontal surface equals to 5.68 kWh/m2/day [18]. Consequently developing powerful methods to optimum sizing of these systems becomes very necessary. In the present study, a method for sizing remote PV systems based on GA [19] has been compared with two classical methods, worst month method [1] and LPSP method [20]. The three methods have been applied to a PV lighting systems with orientation due south and inclination angles between 0 and 90 in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. The PV lighting system studied is shown in Fig. 1. Fig. 1 Studied system Mathematical modeling Photovoltaic array output modeling The four-parameter equivalent circuit model that considers a PV cell as an ideal irradiance-dependent current source in parallel with a diode was used to model the PV module [21]. The four parameters are module photocurrent at reference conditions (IL, ref), diode reverse saturation current at reference conditions (I0, ref), empirical diode PV curve fitting factor (d1), and module series resistance (Rs) [22]. The total current (I) is calculated as follows [23]: The values of parameters (...truncated)