Abstract
Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure includes in detail the semi-hourly average time meteorological data from the location to maximise the accuracy and detailed characteristics of different PV modules and inverters. The minimum levelised cost of energy (LCOE) and maximum annual energy are the objective functions in this proposed procedure, whereas the design variables are the number of series and parallel PV modules, the number of PV module lines per row, tilt angle and orientation, inter-row space, PV module type, and inverter structure. The design problem was solved using a recent hybrid algorithm, namely, the grey wolf optimiser-sine cosine algorithm. The high performance for LCOE-based design optimisation in economic terms with lower installation, maintenance and operation costs than that resulting from the use of maximum annual energy objective function by 12%. Moreover, sensitivity analysis showed that the PV plant performance can be improved by decreasing the PV module annual reduction coefficient.
Highlights
Nowadays, solar photovoltaic energy is being utilised in electrical energy generation to meet the quick-growing consumption and the urgent need for power [1]
Artificial intelligence (AI) methods have been used to optimise the grid-connected PV power plant, as presented in [6], whereas the PV plant global solution is solved through particle swarm optimisation technique (PSO) and compared with a genetic algorithm (GA), based on the total net economic benefit
In sizing optimisation methodology depending on the requirements of the power plant designer, each of the two objectives can be used to produce an optimal design for the PV power plant
Summary
Solar photovoltaic energy is being utilised in electrical energy generation to meet the quick-growing consumption and the urgent need for power [1]. Grid-connected photovoltaic (PV) systems with a capacity of 3 kW PV modules could meet the electric demand of a 60–90 m2 for residential building [2]. TRNSYS software has been used to determine the optimum PV inverter sizing ratios [5]. The simulation has been carried out using three types of inverters with low, medium and high efficiency to determine the maximum total output of the PV system. The PV inverter sizing ratio of the grid-connected has been investigated for eight European locations. Artificial intelligence (AI) methods have been used to optimise the grid-connected PV power plant, as presented in [6], whereas the PV plant global solution is solved through particle swarm optimisation technique (PSO) and compared with a genetic algorithm (GA), based on the total net economic benefit. The optimisation design of the grid-connected PV system is introduced in [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
