Agent-based model for renewable energy plant investment decisions

Abstract

The investment to encourage rapid development of Renewable Energy (RE) as well as the application of the development scheme of competitive electricity retail market is always considered to be a great challenge for investors. The uncertainty of electricity selling price results in many risks for revenue and profit planning when making decision on new plant investment. This study aims to analyze various price forecasting methods applied in power system simulation models based on data from existing agents. From there, a price forecasting method is proposed based on the optimization model and supports investors in decision making. The simulation result shows that, in an isolated electricity market with highly stylized model settings, investment decisions made by existing investment algorithms are very sensitive to assumptions related to certain parameters of the investment decision – making algorithm. The application of such agent-based model helps investors reduce potential risks and move towards a transparent competitive market in the future, in line with the competitive electricity retail market towards which Vietnam is moving.