Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.

Sensitivity analysis of borehole thermal energy storage: examining key factors for system optimization

Borehole thermal energy storage(BTES)systems have garnered significant attention owing to their efficacy in storing thermal energy for heating and cooling applications.Accurate modeling is paramount for ensuring the precise design and operation of BTES systems.This study conducts a sensitivity analysis of BTES modeling by employing a comparative investigation of five distinct parameters on a wedge-shaped model,with implications extendable to a cylindrical configuration.The parameters examined included two design factors(well spacing and grout thermal conductivity),two operational variables(charging and discharging rates),and one geological attribute(soil thermal conductivity).Finite element simulations were carried out for the sensitivity analysis to evaluate the round-trip efficiency,both on a per-cycle basis and cumulatively over three years of operation,serving as performance metrics.The results showed varying degrees of sensitivity across different models to changes in these parameters.In particular,the round-trip efficiency exhibited a greater sensitivity to changes in spacing and volumetric flow rate.Furthermore,this study underscores the importance of considering the impact of the soil and grout-material thermal conductivities on the BTES-system performance over time.An optimized scenario is modelled and compared with the base case,over a comparative assessment based on a 10-year simulation.The analysis revealed that,at the end of the 10-year period,the optimized BTES model achieved a cycle efficiency of 83.4%.This sensitivity analysis provides valuable insights into the merits and constraints of diverse BTES modeling methodologies,aiding in the selection of appropriate modeling tools for BTES system design and operation.