Multicriteria Adjustable Regret Robust Optimization for Building Energy Supply Design

TL;DR

This paper introduces a multicriteria adjustable robust optimization model for building energy supply design, balancing cost, emissions, and uncertainty in future energy prices, solved via a column-and-constraint generation method.

Contribution

It combines adjustable robustness with multicriteria optimization for energy supply design, addressing price uncertainty and environmental objectives in a novel integrated framework.

Findings

Effective solution approach using column-and-constraint generation

Trade-off analysis between cost, emissions, and robustness

Application scenarios demonstrate practical relevance

Abstract

Optimizing a building's energy supply design is a task with multiple competing criteria, where not only monetary but also, for example, an environmental objective shall be taken into account. Moreover, when deciding which storages and heating and cooling units to purchase (here-and-now-decisions), there is uncertainty about future developments of prices for energy, e.g. electricity and gas. This can be accounted for later by operating the units accordingly (wait-and-see-decisions), once the uncertainty revealed itself. Therefore, the problem can be modeled as an adjustable robust optimization problem. We combine adjustable robustness and multicriteria optimization for the case of building energy supply design and solve the resulting problem using a column and constraint generation algorithm in combination with an $\varepsilon$-constraint approach. In the multicriteria adjustable robust problem, we simultaneously minimize worst-case cost regret and carbon emissions. We take into account future price uncertainties and consider the results in the light of information gap decision theory to find a trade-off between security against price fluctuations and over-conservatism. We present the model, a solution strategy and discuss different application scenarios for a case study building.