The development of the smart grid (SG) has the potential to bring significant improvements to the energy generation, transmission, and distribution sectors. Hence, adequate handling of fluctuating energy demands is required. This can only be achieved by implementing the concept of transactive energy. Transactive energy aims to optimize energy production, transmission, and distribution combined with next-generation hardware and software, making it a challenge for implementation at a national level, and to ensure the effective collaboration of energy exchange between consumers and producers, a serverless architecture based on functionality can make significant contributions to the smart grids advanced metering infrastructure (SG-AMI). In this paper, a scalable serverless SG-AMI architecture is proposed based on fog-edge computing, virtualization consideration, and Function as a service (FaaS) as a services model to increase the operational flexibility, increase the system performance, and reduce the total cost of ownership. The design was benchmarked against the Iraqi Ministry of Electricity (MOELC) proposed designs for the smart grid, and it was evaluated based on the MOELC traditional computing-design, and a related cloud computing-based design. The results show that our proposed design offers an improvement of 20% to 65% performance on network traffic load, latency, and time to respond, with a reduction of 50% to 67% on the total cost of ownership, lower power and cooling consumption compared to the SG design proposed by MOELC. From this paper, it can be observed that a robust roadmap for SG-AMI architecture can effectively contribute towards increasing the scalability and interoperability, automation, and standardization of the energy sector.
The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1st January 2019 to 31st December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.
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