Electricity Tariff Insurance Design Under Real-Time Pricing

In electricity markets, real-time pricing introduces financial uncertainties to users, particularly in the presence of price spikes. Risk hedging mechanisms contain end-customers' financial uncertainties by sharing the risks with contractual counterparts and paying a fee as the risk premium. This paper introduces a pricing model for insurance primarily designed for residential electricity bills. This model employs heavy-tailed distributions and stochastic differential equations to forecast electricity prices, accounting for price spikes, and then uses a clustering-based probability distribution model to classify and predict customers' consumption patterns. The integration of predictive data and insurance principles enables the calculation of net premium rates. The change in the coefficient of variation, employed as a measure, assesses the effectiveness of risk reduction through insurance. The proposed methodology provides guidance for both insurance companies and customers.