The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

Abstract

In this paper, a study was completed investigating the financial viability of a 5 MW solar power plant in Montenegro with direct access to the market, rather than a long-term power purchase agreement. The empirical research included an econometric analysis and forecast of the prices on the exchange market, using two methods, autoregressive integrated moving average (ARIMA) and neural network auto regression (NNAR), which are compared to the forecast electricity prices. The former was used in order to obtain the electricity prices forecast, since it showed significantly better predictive performances. Consequently, the financial analysis results indicated this business strategy is a financially more viable option, even though it implies increased risks. All investigated metrics and sensitivity analysis pointed in favor of this option, which has significantly higher profitability with a shorter payback period, compared to the usual market strategy. The main conclusion and recommendation drawn from the analysis are that taking into account the entire environment and prospects for the following years, a riskier business strategy of entering the market directly, or a so-called structured PPA, is put forward to improve project returns and speed up energy-transformation processes in a developing country.

Understanding Electricity Price Evolution – Day-Ahead Market Competitiveness in Romania

“…In 2021, solar energy represented 3.6% of total electricity generation. Mišnić et al (2022) made a financial analysis of a project involving the economic viability of a 5MV solar power plant in Montenegro. The electricity price was predicted by ARIMA and neural networks.…”

Day-Ahead Electricity Price Forecasting Based on GCM Filtering and Higher-Order Pooling Feature Enhancement

“…High volatility of electricity prices results in peaks and troughs occurring unexpectedly, and supply and demand fluctuations are constantly being altered. ARIMA was suggested by Mišnić et al [10], where a linear statistical model calculates the pattern between electricity prices to make predictions. The SVM was proposed by Prahara et al [11] following the principle of structural risk minimization to predict the electricity price.…”