Assessment method for incentives and their optimization considering demand response of consumers

In a smart grid, communication technology allows short-term application of incentives in monetary form and thus dynamic pricing to the consumers. Incentives can help to reduce critical situations in grids, but for cost efficient application, they have to be optimized before the most suitable incentive is provided to the consumers. This paper introduces an assessment method for incentives taking into account demand response and the participation of individual consumers. Thereby, a model describing rational decision of individuals to incentives is presented. As the model uses adaptive neuro-fuzzy inference system (ANFIS) it can easily be trained. The assessment method includes heuristic optimization, namely the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy. MVMO can be used within the method to optimize the incentive with respects to the defined objective and given constraints. Structure of the model and procedure of the assessment method are illustrated, and performance of the method is demonstrated based on examples.