Abstract
Technical electricity generation assessment and economic analysis of six wind energy conversion systems in the categories small, medium, and large (with power ratings of 20, 35, 275, 500, 1,000, and 2,000 kW) were examined in this study. Electricity cost values were estimated based on the levelized cost of electricity (LCOE) and present value cost (PVC) methods for six locations selected across all the geopolitical zones of Nigeria. This was done using wind speed data that span between 25 and 37 years, measured at the height of 10 m. The result showed that the annual average energy output ranges from 2.242 MW h in Uyo with P10-20 turbine to 12,521.55 MW h in Kano using Vestas V80-2 MW wind turbine. Furthermore, of all the selected sites, Kano gave the least costs of electricity production per kilowatt hour with Vestas V80-2 MW model at 67-m hub heights, while the highest is obtained in Uyo with GEV-HP (1 MW) model at 70-m hub heights for the LCOE and PVC height for both the LCOE and PVC methods. In addition, sensitivity of the selected parameters to the levelized cost of electricity was also carried out.
Highlights
Photovoltaic (PV) power has proven to be one of the promising renewable energies in the recent years
A nonlinear autoregressive with exogenous inputs model (NARX) is a nonlinear autoregressive model which has exogenous inputs. This type of models relates the current value of output to both past values of the same output and current and past values of externally inputs that influence the output of interest
In this study we used a combinations of three in-situ measured parameters: the global horizontal solar irradiation (Irr) and the temperature of PV modules (Tc) as exogenous inputs U, and the PV power (P) as variable of interest Y
Summary
Photovoltaic (PV) power has proven to be one of the promising renewable energies in the recent years. This field has witnessed a significant increase in the value of investments; the production capacity reached 227 GW in 2015 compared to 5.1 GW in 2005. In the traditional grid management, the grid operator must maintain the balance between supply and demand at all times to avoid security grid problems and economic losses. From grid management point of view, solar generation variability caused generally by clouds can make it more difficult for the grid operator to predict how much additional electric generation will be required to ensure the balance between supply and demand. Renewable power forecasting imposes itself as a key solution to efficiently handle renewable energy in power grid and must be properly accounted for in the complex decision-making processes required to balance supply and demand in the power system
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