Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems

The effective design of Distributed Energy Systems (DES) is subject to multiple uncertainties related to aspects like the availability of renewable energy, the building energy demands, and the energy carrier prices. Nevertheless, current practices involve the use of deterministic design models, which overlook uncertainty and can lead to suboptimal DES configurations that fail to deliver the desired performance. A necessary condition in order to obtain robust DES designs against uncertainty is the understanding of uncertainty's impacts and main drivers. Therefore, this paper presents a novel methodological framework for the investigation of uncertainty in the context of DES design, which combines optimisation-based DES models and techniques from Uncertainty Analysis (UA) and Global Sensitivity Analysis (GSA). Moreover, the application of the framework is illustrated with a case study for the optimal DES design of a Swiss urban neighbourhood. Embarking from a deterministic DES design model, first, all sources of uncertainty are identified and appropriate probabilistic descriptions are assigned to all uncertain model parameters. UA is then performed using Monte Carlo (MC) simulations to quantify the impacts of uncertainty. Results reveal substantial variations in terms of economic and carbon performance of the optimal DES, but also in terms of optimal DES configurations. Moreover, the UA results indicate that the optimal system costs are mostly higher than the deterministic cost estimates, while the inverse is observed for the case of carbon emissions. Additionally, in many of the MC simulations, the resulting optimal DES configurations deviate significantly from the deterministically obtained designs, which confirms the risk of suboptimal decisions in deterministic design processes. Moreover, the results of UA can function as decision support by identifying the DES configurations that are optimal for most realisations of uncertainty. Finally, to identify the uncertain parameters that are mostly responsible for the variation of the economic performance of the DES, a two-step GSA is launched, combining the Morris method and the variance-based Sobol method. Results of the GSA indicate the energy demand patterns and the energy carrier prices as primarily responsible for the variability of the optimal system cost, while parameters like the investment costs and the technical characteristics of the technologies exert only minimal influence. The results of GSA, besides offering a better understanding of uncertainty to DES designers, also identify the parameters for which additional effort needs to be invested to reduce their uncertainty and, as a result, the uncertainty associated with DES design.