A strategic decision support system framework for energy-efficient technology investments

Emilio L. Cano (), Javier M. Moguerza, Tatiana Ermolieva and Yurii Yermoliev
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Emilio L. Cano: Rey Juan Carlos University
Javier M. Moguerza: Rey Juan Carlos University
Tatiana Ermolieva: International Institute for Applied Systems Analysis (IIASA)
Yurii Yermoliev: International Institute for Applied Systems Analysis (IIASA)

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 25, issue 2, No 6, 249-270

Abstract: Abstract Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.

Keywords: Decision support systems; Dynamic stochastic programming; Uncertainty modelling; Strategic and operational planning; 68U35; 90B50; 90C15; 91B30 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11750-016-0429-9

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