Formal Modeling of IoT-Based Distribution Management System for Smart Grids

An Adaptive Network Design for Advanced Metering Infrastructure in a Smart Grid

A smart grid is a next-generation intelligent power grid that can maximize energy efficiency by monitoring power information in real time and by controlling the flow of power by introducing IT communication technology to the existing power grid. In order to apply a wireless communication network to a smart grid, it is necessary to be able to efficiently process large amounts of power-related data while enabling a high level of reliability and quality of service (QoS) support. In addition, international standards-based design is essential considering compatibility and scalability. The IEEE 802.15.4 standard is considered to be the most powerful communication method for processing data through the smart grid AMI. To reduce the energy consumption, as the duty cycle of the superframe increases, the probability of the congestion increases. However, this binary exponential algorithm in IEEE 802.15.4 standard does not account for the application of traffic characteristics that essentially negatively affect the smart grid network performances in terms of packet delivery ratio and time delay. Therefore, in this paper, we propose a new transmission scheme to reduce performance degradation by excessive collisions in the content access period (CAP), when data transmission is performed in IEEE 802.15.4 applied to smart grids. In addition, we investigated the main research topics required when applying wireless networking technology to smart grids and suggested improvement measures. Simulation results showed that the proposed scheme increased the data delivery rate and reduced the latency, and it was confirmed that reliability was improved.

Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load’s consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy.

Barriers for Prosumers’ Open Business Models: A Resource-Based View on Assets and Data-Sharing in Electricity Markets

This article explores the barriers for open business models in support of sustainability in electricity markets. It puts forward privacy and data protection concerns about sharing prosumers’ physical assets as well as data due to to their critical role in decentralized modes of electricity/flexibility trading. In particular, it uses a multiple case study approach to identify actors’ resources, examine other interested actors in each resource, define their objectives, and consider privacy and data protection concerns of sharing prosumers’ physical assets and data. The findings yield new insights into sharing opportunities beyond electricity/flexibility trading. In doing so, our study contributes to theories of the firm by applying the resource-based view in a new context and to the business model literature by shedding light on barriers in applying open business models.