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

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 Mathematics Subject Classification

Linking Distributed Optimization Models for Food, Water, and Energy Security Nexus Management

“…In the following, we only briefly outline the models. Further details can be found, for example, in [9][10][11][13][14][15]. The models are spatially explicit, which allows us to link the models across locations and thus control local drivers having significant implications on the overall results of models' integration.…”

“…These performance measures enable a better understanding of how systems (individually and jointly) might perform in the uncertain environment, in the presence of climate change, weather variability, market uncertainties, etc. A better understanding of how interdependent energy-water-agricultural systems may operate and how dangerous impacts of inappropriate decisions may be can motivate regional and sectoral planners, experts, and involved stakeholders in making cross-sectoral coherent and risk-adjusted robust decisions [9][10][11]14,15,22,29].…”

“…where α d ijt denotes the conversion efficiency of coal i in location j by technology t, the end product d, and D d j stands for the end product d demand. Agricultural production is required to fulfil food security constraints defined by the sufficient kilocalories and nutrients provided to the population from agricultural commodities k in location m: (11) where D A km is the required production of agricultural commodity k in location m to meet food security requirements, which can be calculated according to daily nutrients and calories requirements per capita approved by the World Health Organization (WHO).…”

“…These models account for multiple details of sectoral production planning and resource utilization, including the analysis of factors and drivers determining demand, supply, and commodity price relationships [7,8]. For example, energy sector models investigate the interactions between renewables and fossil fuels, address energy market volatilities, and analyze the effects of new technologies and policy interventions to develop energy scenarios [7][8][9][10][11][12]. Land use and agricultural models support decision making regarding agricultural policies, analyze land potentials for the production of sufficient agricultural commodities to fulfill food security and biofuels mandates [13][14][15], and assess the effects of policy responses, including export bans and high export taxes, to cope with production shortfalls and offset increasing prices [2].…”

Decision Support System for Solar Energy Adoption

“…A DSS combines models and data to solve semi-structured problems and some unstructured problems, with intense user involvement [8]. A DSS is necessarily an interactive process that requires stakeholder dialogue [9]. Carton et al [10] refer to a DSS as an organized set of people, procedures, software, databases, and devices used to help make decisions that solve problems.…”