Sana Charfi

Acomparative study of MPPT techniques for PV systems

Power generation of photovoltaic panels (PVP) depends mainly on the cell temperature (T) and the solar irradiance (G). Moreover, for a given climatic condition, the operating point is sensitive to the PVP connected load. To enable the PVP to generate the maximum of available power, many maximum power point trackers (MPPT) algorithms are developed. This paper presents an assessment of four main used algorithms: the Perturb & Observe, the Artificial Neural Network, the Fuzzy Logic, and the Table Look Up. Matlab based simulations had been carried out in order to compare the precision and the implementation of these algorithms procedures. The Table Look Up algorithm is consequently selected due to its performance.

The effect of batteries DOD range setting values on diesel engine generator pollution and overall cost of a hybrid solar/diesel/battery system

Depending on applied criteria, sizing optimization process of renewable energy plants produces different optimal dimensions of the system components. This work presents a sizing algorithm of hybrid solar/diesel/battery system; where the battery depth of discharge (DOD) range setting is considered as a key to guarantee low overall system cost and minimum CO2 emission. Different DOD range setting values (0–80%, 20–80% and 40–80%) are used in the investigation in order to ensure minimization adequacy between overall system cost and pollution emission. Obtained results show that CO2 emission and system cost are sensitive for DOD setting range. Following a juxtaposition of pollution rate and system cost curves function of DOD range set values, an optimum operating point is obtained which consists of a DOD range set value of 22–80%.

Energy scheduling strategy for a photovoltaic/batteries bank/diesel generator power supply system for an off-grid house

A novel algorithm is proposed to control the overall energy produced by a photovoltaic/battery bank/diesel generator renewable energy system. A renewable energy system is used to supply an off-grid connected house in Sfax, Tunisia. The algorithm computes recurrently the battery depth of discharge and diesel consumption of the diesel generator and then forecasts the photovoltaic subsystem output power every two minutes ahead. The estimated power is computed using an auto-regressive moving average model associated with a Kalman filter. During each calculation loop, hybrid system energy scheduling is accomplished considering criteria that guarantee the maximum use of generated renewable energy, minimum diesel generator operational time, and full-load need satisfaction during the whole day. Numerical simulations for two typical sunny and cloudy days in Sfax, Tunisia, are conducted in order to validate the algorithm and to compare the performance of managed system behavior with that of a standard unmanaged sys...

Hybrid PV/Batteries Bank/Diesel Generator Solar-Renewable Energy System Design, Energy Management, and Economics

Abstract Hybrid renewable energy systems that are constructed from photovoltaic (PV) panels, batteries bank, and diesel generator (DG) are investigated for an off-grid load supply. The main parts of the system are modeled and optimally sized using particle swarm optimization algorithm. Details on the optimization constraints and convergence of the optimization algorithm are presented. The optimal system size was obtained for either minimum system overall cost or minimum emitted system pollution. The hybrid renewable energy system is also optimized for different batteries depth of discharge parameter. Energy management scheme for the hybrid system is presented to enable energy saving and avoid blackouts that may result during turning ON the DG. Finally, economical evaluation for a case study of the renewable energy system is discussed. The overall cost and payback period are found for the hybrid energy system. Then they are compared with cases when only DG or only PV and batteries bank are used.

Exploiting DOD set value to manage the operation of a diesel generator in a hybrid renewable system

In off-grid mode operation, electricity supplied by photovoltaic panels and batteries are not usually efficient to meet the load power demand at any time. Thus, a diesel generator (DG) source is necessary as a backup in order to cover the load needs at any time. However, the diesel generator requires a time to reach its steady state operation. Thus, turning ON the diesel generator must not wait the depth of discharge (DOD) of batteries to reach the max set value because this will cause a blackout. A management scheme is proposed here to control the time at which the DG is turned ON to avoid black out and to minimize pollution and diesel consumption costs. The proposed method is based on predicting the load every two minutes using auto-regressive moving average (ARMA) scheme associated with a Kalman Filter. Then, an optimal DOD value is found for each load required power at which the DG must turned ON before reaching a max DOD value of 80%. This management scheme exploit better the power available in the hybrid renewable system and minimize the pollution and cost of operating the DG. As a result, lower energy lost during operation of the system.

Multi-criteria fuzzy algorithm for energy management of a stanalone hybrid system

A new energy management approach for a hybrid renewable energy system composed of photovoltaic panels, batteries bank and a diesel generator is proposed. The approach is implemented using Fuzzy-Logic algorithm. The approach depends on predicting the generated photovoltaic power and the load forecast every two minutes. This management scheme exploits efficiently all produced renewable energy and available power stored in the batteries bank in order to reduce the operation time of the diesel generator. Thus, reduce pollution and diesel operational cost.

Wind potential assessment for an efficient wind farm sizing

The effectiveness of autonomous wind plants depends basically on the characterization, sizing, and environmental design and analysis of its renewable energy conversion system. This article presents an assessment on wind potential characterization to be used to compute the size of a wind farm turbine. Different methods are adopted to estimate parameters of the Weibull distribution. The modified maximum likelihood method is selected as the most accurate with reference to comparison between many approaches output results and measurements provided by the National Institute of Meteorology. Also, an artificial neural network–based algorithm is developed to optimize the MMLM parameters. The monthly wind potential distribution is consequently computed for Sfax, Tunisia. Obtained results are used to optimize the size calculation of wind turbine blades and battery capacity for a standalone wind farm. The proposed approach profitability is evaluated upon the lost produced energy.

Charcterisation of an off grid hybrid system: Modelling and simulation

In this paper, a modelling and simulation of an hybrid system composed of photovoltaic panels, a lead-acid battery bank and a diesel generator connected in parallel, is studied. A non-linear model that depends on the solar radiation and the ambient temperature is adopted to model the photovoltaic panels. The battery is characterized by the depth of discharge deduced by a non-linear model. The genset model consists in modelling its three parts: the diesel engine, the synchronous generator and the excitation system. The system characterization and simulation demonstrate that the load is supplied during the day even in case of weather disturbs.