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

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 sce-nario 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 optimi-zation 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 com-bustion 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.

Automated summary

This study focuses on the effects of different scenario generation methods on optimizing building combined cooling, heating, and power (BCHP) systems under energy demand uncertainty. Four scenario generation methods were compared using independent scenario optimization and two-stage stochastic programming models to minimize total annual cost. Results show that the Monte Carlo building performance simulation method is closest to the base scenario, and the uncertainty-based optimization method increases system costs by 1.5% - 3.6%. Higher internal combustion engine capacity reduces total system cost and provides stronger risk resistance.