Demand side operational flexibility — A holistic stochastic optimization model for flexible consumers and prosumers

We propose a stochastic mixed integer optimization model to support decisions for the day-ahead scheduling problem for flexible consumers and prosumers being exposed to dynamic prices at retail side of the electricity market. A general framework is designed to cover different cases and regimes, focusing on close connection to the physical energy system. The objective is maximum value creation from dispatchable technologies in the buildings´ internal energy systems for heating, cooling and electricity specific purposes. Since some parameters may be uncertain, like loads and prices, we suggest a stochastic programming model where the uncertain parameters are represented through a scenario tree. The model is tested in a case study for a large college building in Norway being exposed to hourly elspot prices and a grid contract with a capacity fee. Results indicate significant cost savings even in current regime.