A multi-stage stochastic optimization model for energy systems planning and risk management

• Optimization models are used to support decision making at the building level.
• A stochastic model is developed for investments on energy-efficient technologies.
• The risk neutral stochastic model takes into account parameters variability.
• We add risk measures to the model in order to reduce risks of extreme scenarios.
• We demonstrate how variability is reduced along scenarios, hedging against risks.

Strategic decision making at the building level is gaining importance in the context of a more and more deregulated energy market. The increase of the set of available options regarding distributed and renewable energy technologies leads to a complex decision process. Importantly, such decision making process is affected by uncertainties and therefore stochastic models are needed. In this paper, a comprehensive deterministic strategic optimization model for energy systems planning at the building level is extended to a stochastic optimization framework, thereby allowing the decision maker to manage risks in addition to considering the variability of the uncertain parameters. A numerical example shows the importance of taking into account uncertainty and risk in this kind of problems.