Optimization on building combined cooling, heating, and power system considering load uncertainty based on scenario generation method and two-stage stochastic programming

Abstract

The energy demand uncertainty is a complex issue for design tasks of building combined cooling, heating, and power (BCHP) systems. To overcome this problem, the characteristics of energy demand uncertainty need to be precisely described by generating massive scenarios. Here, the aim of the research is to study the effects of scenario generation methods on BCHP optimization considering energy demand uncertainty. Four scenario generation methods were compared and studied—two conventional probability-based methods using Monte-Carlo simulation and the Latin hypercube method to sample the basic scenario, as well as simulation-based methods based on building performance simulation. Two optimization models, including an independent scenario optimization (ISO) model and a two-stage stochastic programming (TSSP) model, were used to minimize the total annual cost of the BCHP system. Dynamic time warping (DTW) was applied for investigating differences between scenario generation methods. The results show that Monte Carlo building performance simulation (MCBPS) is the most similar to the basic scenario. The system cost obtained by the uncertainty based optimization method is 1.5% - 3.6% higher than that of the deterministic optimization method. This is mainly due to the capacity increase of peak shaving equipment. From the perspective of anti risk, relatively higher internal combustion engine (ICE) capacity has stronger anti risk ability, which can effectively reduce the total cost of the system. This study can provide a theoretical reference for architectural design and operation research based on uncertainty.