Global sensitivity analysis for design and operation of distributed energy systems: A two-stage approach

Distributed Energy Systems (DES) can play a vital role as the energy sector faces unprecedented changes to reduce carbon emissions by increasing renewable and low-carbon energy generation. However, current operational DES models do not adequately reflect the influence of uncertain inputs on operational outputs, resulting in poor planning and performance. This paper details a methodology to analyse the effects of uncertain model inputs on the primary output, the total daily cost, of an operational model of a DES. Global Sensitivity Analysis (GSA) is used to quantify these effects, both individually and through interactions, on the variability of the output. A Mixed-Integer Linear Programming model for the DES design is presented, followed by the operational model, which incorporates Rolling Horizon Model Predictive Control. A subset of model inputs, which include electricity and heating demand, and solar irradiance, is treated as uncertain using data from a case study. Results show reductions of minimum 25% in the total annualised cost compared to a traditional design that purchases electricity from the centralised grid and meets heating demand using boilers. In terms of carbon emissions, the savings are much smaller, although the dependency on the national grid is drastically reduced. Limitations and suggestions for improving the overall DES design and operation are also discussed in detail, highlighting the importance of incorporating GSA into the DES framework.

Automated summary

Distributed Energy Systems (DES) is crucial for reducing carbon emissions and increasing renewable energy generation as the energy sector undergoes unprecedented changes. However, existing operational DES models fail to consider the impact of uncertain inputs on outputs, leading to poor planning and performance. This paper presents a methodology to analyze the effects of uncertain inputs on the total daily cost of a DES operational model. Global Sensitivity Analysis (GSA) is used to evaluate the effects of individual inputs and their interactions on output variability. The study shows significant cost reductions compared to traditional designs, although the carbon emission savings are relatively smaller.