Optimal Strategies of Electricity Plans Using Latent Class Analysis Considering Renewable Energy

Demand for renewable energy is increasing. Along with this, the burden of electricity charges as levies is also increasing. There are many complaints about this levy. However, future levy prices are also expected to rise. Among these, what can be said to be important is how to get people to choose a renewable energy plan, even if there is a burden. In the research so far, there is no research that presents additional information on global environmental risk information, measures the impact of the information effect, or creates a new power plan that suits consumers. Therefore, in this study, in order to evaluate the influence of global environmental risk information on consumer perception, we extract stated preference data by selective conjoint survey and estimate consumer preference by latent class model. We decided to verify the extent to which the presentation of global environmental risk information is effective in improving consumer receptivity and increasing awareness of renewable energy plan selection. In addition to that, the purpose of this research is to derive optimal strategies of electricity plans using latent class analysis considering renewable energy for the optimization of power generation in virtual power plant environment.