Developing a Strategic Stochastic Optimization Model, Robust Solutions, and a Decision Support System for Energy-efficient Buildings

Cano, E.L. (2014). Developing a Strategic Stochastic Optimization Model, Robust Solutions, and a Decision Support System for Energy-efficient Buildings. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-14-010

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Abstract

This research is being carried out in the context of the EnRiMa project (Energy Efficiency and Risk Management in Public Buildings), funded by the European Commission (EC) within the Seventh Framework Program. Energy Systems Optimization is increasing its importance due to regulations and de-regulations of the energy sector and the setting of targets such as the European Union's 20/20/20. This raises new types of dynamic stochastic energy models incorporating both strategic and operational decisions (short-term decisions have to be made from long-term perspectives) involving standard technological as well as market-oriented financial options. Thus, buildings managers are challenged by decision making processes to achieve robust optimal energy supply portfolio and they are encouraged to adopt an active role in energy markets. Moreover, those decisions must be made under inherently uncertain conditions. The goal of this paper is to develop an integrated framework for the representation and solution of such energy systems optimization problems, to be implemented in Decision Support Systems (DSSs) for robust decision making at the building level to face rising systemic economic and environmental global challenges. As the combination of operational and strategic decisions in the same model induces risk aversion in strategic decisions, the developed approach allows easy to include quantile-based measures such as Conditional Value at Risk (CVaR). Such complex energy systems need to be accurately described in a condensed way representing a large amount of variables, parameters and constraints reflecting endogenous and exogenous interdependencies, sustainability requirements and threats. Therefore, a comprehensive Symbolic Model Specification (SMS) development is a part of the research work. Using the R statistical software and programming language, an integrated framework is proposed to cover the needs of the whole decision making process, ranging from data analysis and estimation to effective representation of models and decisions to be used by both humans and machines. Such a framework provides an environment for enforcing the necessary stakeholders dialog. Furthermore, the framework allows communicating with different types of optimization software.

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