Design methodology of smart photovoltaic plant

In this article, we present a new methodology to design an intelligent photovoltaic power plant connected to an electrical grid with storage to supply the laying hen rearing centers. This study requires a very competent design methodology in order to optimize the production and consumption of electrical energy. Our contribution consists in proposing a robust dimensioning synthesis elaborated according to a data flow chart. To achieve this objective, the photovoltaic system was first designed using a deterministic method, then the software "Homer" was used to check the feasibility of the design. Then, controllers (fuzzy logic) were used to optimize the energy produced and consumed. The power produced by the photovoltaic generator (GPV) is optimized by two fuzzy controllers: one to extract the maximum energy and another to control the batteries. The energy consumed by the load is optimized by a fuzzy controller that regulates the internal climate of the livestock buildings. The proposed control strategies are developed and implemented using MATLAB/Simulink

OPTIMAL LOCATION AND SIZING OF MULTIPLE DISTRIBUTED GENERATORS IN RADIAL DISTRIBUTION NETWORK USING METAHEURISTIC OPTIMIZATION ALGORITHMS

. The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies more essential given its advantages such as reducing power losses and enhancing voltage profiles. This study addresses the optimal sizing and setting of Photovoltaic Distributed Generator (PVDG) connected to Radial Distribution Network (RDN) using various novel optimization algorithms. These algorithms are implemented to minimize the Multi-Objective Function (MOF), which devoted to optimize the Total Active Power Loss (TAPL), the Total Voltage Deviation (TVD), and the overcurrent protection relays (OCRs)’s Total Operation Time (TOT). The effectiveness of the proposed algorithms is validated on the test system standard IEEE 33-bus RDN. In this paper is presented a recent meta-heuristic optimization algorithm of the Slime Mould Algorithm (SMA), where the results reveal its effectiveness and robustness among all the applied optimization algorithms, in identifying the optimal allocation (locate and size) of the PVDG units into RDN for mitigating the power losses, enhance the RDN system's voltage profiles and improve the overcurrent protection system. Accordingly, the SMA approach can be a very favorable algorithm to cope with the optimal PVDG allocation problem

Evaluation of the Wind Potential and Optimal Design of a Wind Farm in The Arzew Industrial Zone in Western Algeria

This work presents an assessment of the wind potential and a design methodology for a 10 MW wind farm in the Arzew industrial region, located in northwest Algeria, to improve the quality of service of the electricity grid and increase Algeria's participation in the use of renewable energy. The hourly wind data of 10 years (2005-2015) that correspond to the wind potential of the site were analyzed, such as: dominant wind directions, probability distribution, Weibull parameters, mean wind speed and power potential. The site has a mean annual wind speed of 4.46 m/s at 10m height, and enough space to locate the wind turbines. A comparative study was carried out between four wind turbine technologies to improve the site's efficiency and select the appropriate technology: PowerWind 56/ 900 kW, Nordex N50/800 kW, Vestas V50/850 kW, NEG-Micon 44/750 kW. The estimate of the energy produced using WAsP software and the choice of the optimal architectural configuration for wind turbines installation was confirmed. A techno-economic and environmental study was carried out by HOMER software, to choose the model that produces the maximum annual net energy with a competitive cost in the global wind energy market, $ 0.068/kWh, and that provides clean energy with a reduced emission of polluting gases. Finally, this work provides a good indicator for the construction of a wind farm in Arzew

Techno-Economic and Environmental Analysis of a Hybrid PV-WT-PSH/BB Standalone System Supplying Various Loads

The Algerian power system is currently dominated by conventional (gas- and oil-fueled) power stations. A small portion of the electrical demand is covered by renewable energy sources. This work is intended to analyze two configurations of renewables-based hybrid (solar⁻wind) power stations. One configuration was equipped with batteries and the second with pumped-storage hydroelectricity as two means of overcoming: the stochastic nature of the two renewable generators and resulting mismatch between demand and supply. To perform this analysis, real hourly load data for eight different electricity consumers were obtained for the area of Mostaganem. The configuration of hybrid power stations was determined for a bi-objective optimization problem (minimization of electricity cost and maximization of reliability) based on a multi-objective grey-wolf optimizer. The results of this analysis indicate that, in the case of Algeria, renewables-based power generation is still more expensive than electricity produced from the national grid. However, using renewables reduces the overall CO2 emissions up to 9.3 times compared to the current emissions from the Algerian power system. Further analysis shows that the system performance may benefit from load aggregation

Energy Management Strategy Based on Marine Predators Algorithm for Grid-Connected Microgrid

This work aims to optimize the economic dispatch problem of a microgrid system in order to cover the load of a commercial building in Algeria. The analyzed microgrid system is connected to the power grid and composed of photovoltaic panels (PV), wind turbine, battery energy storage system (BESS) and diesel generator. To ensure energy balance and the flow of energy, we have implemented an energy management strategy based on Marine Predator Algorithm (MPA) and Multilayer Perceptron Neural Network (MLPNN), which guarantee an optimal economic operation of the system. First, using historical meteorological data, the power generation is forecasted a day-ahead using MLPNN, which allows the optimization of the microgrid operation. Second, the proposed strategy has been studied under three different microgrid configurations. Eventually, the performances of MPA are compared against well-known algorithms. The results indicate that the integration of the PV-BESS microgrid system significantly reduces the daily operating cost up to 34.5%. Due to the availability of wind resources in the studied area, the addition of a wind turbine to the microgrid minimizes the operating cost by 43.96% compared to the operating cost of the power grid. In the case of selling excess energy to the main power grid, the operating cost could be decreased as much as 49.33%