A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty

Designing energy systems from both an economic and environmentally friendly way is a major challenge each country faces regarding regional sustainable development. In this context, Distributed Energy Systems (DES) can be developed to provide energy at local level. This paper presents an application of a multi-objective optimization model for designing a DES, using total annual cost (TAC) and carbon emissions as objective functions. Subsequently, the uncertainties of several parameters are considered, specifically energy prices, interest rate, solar radiation, wind speed and energy demand. To investigate solutions' robustness, four methods are used, (a) objective-wise worst-case uncertainty, (b) minimax regret criterion (MMR), (c) min expected regret criterion (MER) and (d) Monte Carlo simulation, in order to compare the differences in values of objective functions and resulting DES configuration. The proposed methods are presented through a case study and the results show that DES configuration varies when uncertainties in parameters are considered, enabling a decision maker (DM) to make a more informed choice. (C) 2019 Elsevier Ltd. All rights reserved.