Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.

Short-term load forecasting (STLF) is an essential component of smart grids, enabling power departments to anticipate grid operations in advance, thereby controlling electricity usage in a timely manner, allocating power resources reasonably, and ensuring the quality and reliability of grid services. In the field of electric load forecasting, existing prediction models perform poorly on load data with frequent fluctuations and have issues with prediction lag. To address these issues, this paper proposes an improved TimesNet model based on wavelet decomposition, called WaveTimes. Firstly, the model uses wavelet decomposition to decompose load data, obtaining sequences with smaller mutation amplitudes and weaker autocorrelation. Secondly, a feature extraction module is introduced, which uses Fourier transformation and computer vision models for periodic analysis, effectively capturing periodic changes in load data across different frequency domains. Finally, a residual connection and prediction module are used to complete the forecasting task. This paper evaluates the proposed method on public datasets and load data from a certain province in China, showing significant improvements compared to the baseline. The model can more effectively support relevant departments in load control and demand response, contributing to the construction of smart grids.

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment.