A Review of Smart Grid Planning Approaches and a New Proposal for Operational Planning of Smart Grid with Smart Homes

• Published in: Applications of Big Data and Artificial Intelligence in Smart Energy Systems Vol. 1; pp. 127 - 159
• Main Authors: Lakshmi, Shubh, Ganguly, Sanjib
• Format: Book Chapter
• Language: English
• Published: River Publishers 2023
• Edition: 1
• Subjects: General Engineering & Project Administration; General References; Software Engineering; smart home; IEEE Reliability Test System; Literature Review; DE Algorithm; Smart Grid Planning; DE Population; Electricity Pricing Model; Interior Point Method; Smart Grid Network; Real Time Electricity Price; PV generation; particle swarm optimization (PSO); Forward-backward sweep load flow algorithm; Bus Voltage Magnitude; Electricity Price; Optimizing Variable; smart grid; Main Grid; Smart Homes; Hybrid Renewable Energy System; PSO; Load Demand Hours; PSO Algorithm; Load Demand; Line Current Flow; Smart Grid Technologies; operational planning; Time Varying Electricity Pricing; Electricity Consumers
• ISBN: 9788770228251; 9788770229944; 8770228256; 8770229945
• DOI: 10.1201/9781003440710-6

This chapter presents a systematic review of smart grid planning approaches and a novel operational planning approach for smart grids with smart homes. A smart grid is an electrical network that utilizes communication technologies, information technologies, advanced control systems, advanced automation systems, smart meters, sensors, etc., to enable the exchange of electrical energy and data between the electricity consumers and the grid. The operational planning of a smart grid with smart homes requires the collection, processing, and storage of real-time smart meters data for making any decision. This needs the application of big data analytics. The smart grid network has two types of smart homes, i.e., with and without rooftop PV (RPV). The smart homes with RPV inject PV generation into network for the mutual benefits of the network owner and RPV owners. First, the smart grid planning approaches are classified based on the different features included in the planning. A three-level tree structure is developed. The first level is classified based on the planning type. The second level is classified based on the smart grid technologies and the third level is classified based on the solution approach. Further, the research gap is identified, and a new 128energy pricing-based smart grid operational planning approach is proposed to minimize the operational cost of smart grids with smart homes. The proposed smart grid operational planning optimization problem is solved using particle swarm optimization. The 33-bus smart grid network is used to validate the proposed approach. The computer simulation results show the effectiveness of the proposed approach in reducing the operating cost of a smart grid with smart homes. This chapter presents a systematic review of smart grid planning approaches and a novel operational planning approach for smart grids with smart homes. The operational planning of a smart grid with smart homes requires the collection, processing, and storage of real-time smart meters data for making any decision. The smart grid network has two types of smart homes, i.e., with and without rooftop photovoltaic (RPV). The smart homes with RPV inject PV generation into network for the mutual benefits of the network owner and RPV owners. The smart grid planning approaches are classified based on the different features included in the planning. Further, the research gap is identified, and a new energy pricing-based smart grid operational planning approach is proposed to minimize the operational cost of smart grids with smart homes. The proposed smart grid operational planning optimization problem is solved using particle swarm optimization.