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

CSEE Journal of Power and Energy Systems 2022年第6期8卷 1730-1740页

作者： Wenlong Liao Yusen Wang Yuelong Wang kody powell Qi Liu Zhe Yang Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSE-10044Sweden State Grid Tianjin Chengxi Electric Power Supply Branch Tianjin 300100China Department of Chemical Engineering University of UtahUT 84112America Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China

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.

关键词： Deep learning generative moment matching networks integrated energy systems scenario generations

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Dynamic energy system modeling using hybrid physics-based and machinelearning encoder–decoder models

能源与人工智能(英文)

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能源与人工智能(英文) 2022年第3期9卷 128-138页

作者： Derek Machalek Jake Tuttle Klas Andersson kody M.powell Department of Chemical Engineering University of UtahSalt Lake CityUTUnited States of America Taber International LLCUnited States of America Department of Space Earthand EnvironmentUniversity of ChalmersGothenburgSweden Department of Mechanical Engineering University of UtahSalt Lake CityUTUnited States of America

Three model configurations are presented for multi-step time series predictions of the heat absorbed by thewater and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into thefuture, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, anovel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. Thehybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, waterwall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared totarget values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. Thehybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system.The hybrid model, compared to the pure machine learning model, is over 10% more accurate on averageover 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a localsensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in theboiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

关键词： Hybrid model Encoder-decoder Time series Automatic differentiation Thermal power plant

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