Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting

Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid

The integration of advanced measuring technologies in distribution systems allows distribution system operators to have better observability of dynamic and transient events. In this work, the applications of distribution grid measurement technologies are explored in detail. The main contributions of this review are: (a) a comparison of eight advanced measurement devices for distribution networks, based on their technical characteristics, including reporting periods, measuring data, precision, and sample rate; (b) a review of the most recent applications of micro-Phasor Measurement Units, Smart Meters, and Power Quality Monitoring devices used in distribution systems, considering different novel methods applied for data analysis; and (c) an input-output table that relates measured quantities from micro-Phasor Measurement Units and Smart Meters needed for each specific application found in this extensive review. This paper aims to serve as an important guide for researches and engineers studying smart grids.

Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review

Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first investigated, including data collection devices and public data sets. Next, event‐based and non‐event‐based NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

A novel smart meter data compression method via stacked convolutional sparse auto-encoder

This study aimed to achieve a clear understanding of the response characteristics of soft pack battery extrusion conditions under various situations. In this study, we chose a LiCoO2 battery as the research object of the extrusion experiment. First, the repeatability of the extrusion test on the battery was verified. A quasi-static extrusion test was conducted on three groups of batteries in the same state, and the load-displacement curves of the three groups of experimental batteries were almost the same. Then, the influence of the extrusion speed on the battery thermal runaway was studied. The results show that a different extrusion speed has a certain impact on the thermal runaway performance of the battery. The peak load of the battery is lower at a lower speed. Finally, the study found that every 20% change in SOC has a greater impact on the battery response under a squeeze. The larger the SOC, the more severe the battery thermal runaway. Through an analysis of multiple experimental cases, it is possible to have a deeper understanding of the temperature and voltage characteristics of lithium batteries when a thermal runaway occurs, which provides ideas for monitoring the trend of the thermal runaway of electric vehicles.

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Jianfeng Wang

Papers

A novel smart meter data compression method via stacked convolutional sparse auto-encoder

Case Study of Repeatability, Different Speeds, and Different SOCs on Battery Squeeze Test