An Improved LCOE (Levelized Cost of Energy) Methodology: Application to Solar PV Investment Assessment for a Commercial Development Project

Zhang Ying

Abstract

Solar photovoltaic electricity is rapidly increasing renewable energy resource and playing an important role in present and future energy production. The implementation of solar photovoltaic electricity systems across Singapore is a key project for the Singapore government in meeting usage of renewable energy sources. As the solar photovoltaic (PV) matures, the economic feasibility of PV projects are increasingly being evaluated by using the Levelized Lost of Electricity (LCOE) generation in order to be compared to other electricity generation technologies. In the solar photovoltaics industry, many of the input parameters into LCOE models regarding both costs and energy production are not known with certainty [paper: Assumptions and the levelized cost of energy for photovoltaics]. Currently, these parameters are estimated on a ‘best guess’ basis and on systems already in operation. For example, the existing LCOE calculation method & calculator (NSR) only allows for one assumption at a time to be considered. It does not allow for detailed analysis of LCOE to be carried out, such as justifications, assumptions, degree of completeness, assess the reliability of result to be carried out. this produces varying and contradictory results due to some of those uncertainty factors. In this paper, we will review the methodology of properly calculating the LCOE for solar panel, and provide a more complete template for better reporting LCOE result for PV. In order to achieve this, we will conduct case study to demonstrate the clarified LCOE calculation and a correct methodology of assumption. While sensitivity analysis is performed to test how sensitive the variable influence LCOE result. Finally, we will shed light on some of key assumptions and offer a new approach to testing the risk for LCOE result for photovoltaic based on input parameter distributions by adopting Monte Carlo Simulation. As such, the investors will receive the reliable information to make better decision.