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 high-dimensional 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 multi-class 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.

Automated summary

This paper proposes a novel deep generative network that transforms high-dimensional load curves into low-dimensional latent variables and measures the similarity between generated and real samples using maximum mean discrepancy. The proposed model does not require assuming the probability distribution of load curves, leading to stronger universality, and accurately captures the shape, frequency-domain characteristics, and temporal-spatial correlations of load curves.