Design optimization of community energy systems based on dual uncertainties of meteorology and load for robustness improvement

The community energy system (CES) is widely recognized for the contribution to clean and sustainable development, which allows for the integration of locally available energy sources and synergistic supply of demand. The parameter fluctuations faced by CES in operation affect the overall performance, so it is urgent to implement robustness enhancement research against uncertainty. For the purpose of improving the resilience of CES, a new design framework is proposed to facilitate more rational capacity planning in this work, considering dual uncertainties of meteorology and load. Multi-objective optimization and decision-making methods are employed, with lifecycle cost, primary energy saving rate, and pollutant reduction rate as indicators. Latin hypercube sampling and Monte Carlo simulation are coupled for the uncertainty optimization. When meteorology and load fluctuate concurrently, the comprehensive performance index have a probability of 67 % and 81 % to remain within the deviation interval of certainty optimization under following electrical load and following thermal load strategies. The robustness and comprehensive performance index of the former can be improved by 24.6 % and 3.6 % over the certainty optimization, and the values are 20.9 % and 3.9 % in the latter. The results of the case study show that dual uncertainty optimization leads to greater reliability of CES.