Robust optimization of an organic Rankine cycle for geothermal application

Abstract

A robust design optimization (RDO) methodology for Organic Rankine Cycles (ORC) is presented, allowing to ensure an improved, stable performance over a large range of operating conditions. In contrast with classical ORC design methods, whereby all modeling hypotheses and operating conditions are considered as perfectly known, i.e. deterministic, the RDO approach allows to account for the manifold sources of uncertainty affecting the system. For geothermal ORC, the latter are related on one hand with the ill-known properties of the geothermal source and, on the other, with intrinsically random parameters, such as the condensation temperature. The proposed RDO approach selects values of the design parameters that maximize the expected (average) performance while minimizing its variance under uncertain nominal operating conditions. The optimal design delivered by the proposed strategy outperforms the one derived from the standard deterministic approach: specifically, the expected power output is increased by 1.5%, while its standard deviation is reduced by 8.5% and the surface of the heat exchangers by 34%. • Geothermal Organic Rankine cycle performance is subject to many uncertainties. • A robust optimization strategy is devised for cycle design under uncertainty. • The design maximizes average performance while minimizing the variance. • Nested Kriging surrogates are used to efficiently speed-up the design loop. • Robust optimum has +1.5% power output and smaller variance wrt standard design.