Studying Dynamic Pricing in Electrical Power Markets with Distributed Generation: Agent-Based Modeling and Reinforcement-Learning Approach

摘要

Practical Applications DG using PV systems has been growing in recent years. However, there are concerns that the increasing penetration of DG would start a feedback loop where the lower demand for power from the grid would push utilities and generating companies to increase electricity rates to maintain profits and cover overhead, which can again motivate consumers to install DG systems. To investigate that feedback loop, this study developed a model that simulates the interacting effect of consumer behavior to adopt DG and dynamic pricing by utility companies. The results show that low-cost generators, such as nuclear plants, would be the least affected. The results also show that the best economic decision for generating companies may be to keep electricity rates unchanged to avoid accelerating DG penetration. The framework presented in this research can be used to create simulations of wholesale power markets affected by DG, which can guide decisions related to DG regulations, consumer incentives, and future grid expansion plans.