This article proposes a generalizable nonintrusive load monitoring (NILM) framework to address nonindependent and identically distributed (Non-I.I.D.) data challenges in heterogeneous energy consumption sectors. The proposed framework uses adversarial feature augmentation based on the observed states of appliances in source sectors, and implements unsupervised domain adaptation for NILM tasks in target. A ConvNet feature extractor is built to extract the features of source samples, which are then, integrated with observed labels for adversarial data augmentation in the feature space. The generated features are used to establish a domain-invariant NILM algorithm in an unlabeled manner. Feature augmentation and domain invariant feature extractors are employed to learn effective feature mapping between the source and target sectors, thereby, accomplishing NILM tasks in Non-I.I.D. samples without additional labels. The experimental results validate the effectiveness of the proposed framework in three scenarios consisting of multiple datasets, with the best performance compared to the five state-of-the-art models.

Peer-to-peer (P2P) electricity trading in a blockchain environment has established the foundation of trust among microgrids. However, the default behaviors caused by uncertainties associated with renewable energy and microgrids profit-seeking still trigger a crisis of trust. Therefore, this study proposes a two-stage credit management strategy (TSCMS) for P2P electricity trading among microgrids to reduce the default behaviors, which contains credit-based risk control optimization stage and credit-based P2P electricity trading stage. In the first stage, a robust control optimization of credit-based risk preference is proposed to realize flexible management of renewable energy uncertainty through adaptive robustness factor, thus well balancing the reliability and economy of trading. In the second stage, a queuing priority is provided using equivalent price conversion to realize credit-based rewards and punishments. Experimental results show that the proposed TSCMS can enhance the trust of microgrids and create a reliable trading environment.

Nonintrusive load monitoring (NILM) identifies individual appliance power usage within an overall power load, supporting more refined, targeted, and efficient load management. However, feature extraction from the combined power signal may be affected by the number of appliances, appliance types, noise, and other factors. This article introduces TransUNet-NILM, an improved NILM model based on TransUNet, that significantly improves the feature extraction capability by using a residual network and an attention mechanism, improving the ability to discover power variation and temporal features. A sequence-to-subsequence method is proposed to reduce the computational complexity. After testing on the REDD and U.K.-DALE datasets, our model notably improved the F1 score by 1.1% and 8.9%, respectively, compared with the suboptimal model. Experimental results show that the proposed model was both more accurate and efficient in extracting features and identifying power consumption from the aggregate power signal, offering improvements to NILM efficiency and energy management.

Currently, an increasing number of Internet data centers (IDCs) are trying to apply distributed energy resources (DERs), such as renewable energy, battery energy storage systems (BESS), and conventional generators (CG). However, uncertain renewable energy presents significant challenges to the safe and stable operation of IDCs. A two-stage optimal operation model based on distributionally robust optimization (DRO) is proposed for an IDC with DERs to lower the total cost of the IDC, including operating cost, carbon emission cost, and re-dispatch cost under the worst-case probability distribution of renewable energy. An ambiguity set is formed by combining norm-1 and norm-inf under the given confidence level to capture the possible probability distributions of uncertain renewable energy. The proposed model can be turned into a tractable mixed-integer linear programming problem and efficiently solved by the column-and-constraint generation algorithm. Experimental results show the benefits of DERs integration and workload dispatch. Comparative analysis further demonstrates the superiority and scalability of the proposed model.

This article proposes a non-intrusive load monitoring (NILM) framework based on a deep convolutional neural network (DCNN) to profile each household applianceon/offstatus and the residential power consumption. It uses only load trajectory, which can overcome the limitations of existing voltage-current trajectory NILM techniques. The DCNN architecture with a load trajectory as the input enables the NILM to directly analyze the electricity consumption at the appliance-level. Meanwhile, the temporal feature transferring procedure improves load monitoring performance and extends its application range include monitoring appliances based on multiple and combined characteristics. Furthermore, the power variation augmentation technique enhances the load signature uniqueness. The fusion of temporal and power variation features provides rich identification information for NILM and improves the accuracy of appliance identification. Experimental results demonstrate that the proposed NILM framework is effective and superior for enhancing demand side management and energy efficiency.