Demand response (DR) has been increasingly growing in significance among the solutions to tackle climate change, along with the development of intermittent renewable energy sources in the smart grid. Many models based on mathematical optimization were developed to address the challenge of making residential customers provide flexibility services to the grid. However, comparing and applying those models is not always straightforward because of particular data handling or specific assumptions. In this work, we take advantage of the common aspects of DR models to build a metamodel, and hence an open source Python library that aims to unify the concepts and the data streaming in and out of the underlying mathematical optimization models. We demonstrate the effectiveness of the metamodel and of the Python library by using it to implement a task scheduler and to optimize the energy consumption for two dwellings.