Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations

Uncertainties in design inputs (i.e. energy demand and energy price) and equipment degradations in operation result in that the actual performance of distributed energy systems (DESs) deviates from the design expectations significantly. To ensure that DESs designed can operate at high performance when the actual working environment and equipment performance change over a large range, a robust optimal design method based on life-cycle performance analysis is developed. This method adopts a probabilistic approach, which is based on qualifying the uncertainties of design inputs and equipment degradations, while Monte Carlo simulation method is adopted to model the uncertainty propagation and generate the probability distribution of the predicted DES performance in the design process. The “probabilistic” life-cycle performance of DES is therefore obtained and used for the robust optimal design. The method further identifies the optimum DES which has the best life-cycle performance expectation under the above conditions concerned. A case study is conducted on the DES design in a district in Hong Kong to test the application of this method. It is found that, compared with other schemes, the optimum DES has least life-cycle total cost and better robustness of performance under different operating conditions. The DES identified by this method achieves economic benefits and higher total system energy efficiency in the latter years of its life-cycle compared with the DES identified by optimal design method without considering the life-cycle performance. Conclusions of this study can be also used as references for DES life-cycle performance assessment for DES designers.