Investigating Fourteen Countries to Maximum the Economy Benefit by Using Offline Reconfiguration for Medium Scale PV Array Arrangements

Over the past few years, electricity demand has been on the rise. This has resulted in renewable energy resources being used rapidly, considering the shortage as well as the environmental impacts of fossil fuel. A renewable energy source that has become increasingly popular is photovoltaic (PV) energy as it is environmentally friendly. Installing PV modules, however, has to ensure harsh environments including temperature, dust, birds drop, hotspot, and storm. Thus, the phenomena of the non-uniform aging of PV modules has become unavoidable, negatively affecting the performance of PV plants, particularly during the middle and latter duration of their service life. The idea here is to decrease the capital of maintenance and operation costs involved in medium- and large-scale PV power plants and improving the power efficiency. Hence, the present paper generated an offline PV module reconfiguration strategy considering the non-uniform aging PV array to ensure that this effect is mitigated and does not need extra sensors. To enhance the economic benefit, the offline reconfiguration takes into account labor cost and electricity price. This paper proposes a gene evolution algorithm (GEA) for determining the highest economic benefit. The proposed algorithm was verified using MATLAB software-based modeling and simulations to investigate fourteen countries to maximize the economic benefit that employed a representative 18-kW and 43-kW output and the power of 10 × 10 PV arrays in connection as a testing benchmark and considered the electricity price and workforce cost. According to the results, enhanced power output can be generated from a non-uniformly aged PV array of any size, and offers the minimum swapping/replacing times to maximize the output power and improve the electric revenue by reducing the maintenance costs.