Distributed energy sharing algorithm for Micro Grid energy system based on cloud computing

Abstract

Abstract The reduction of adverse environmental effects and the socioeconomic advantages of renewable energy systems promote greater integration of distributed energy systems into the traditional electrical networks. A new type of sharing economy is emerging with the sharing of energy resources to reduce transaction costs by using platform services in the cloud. Given the obstacles posed by the legacy system and various forms of renewable energy integration, Distributed Energy and Micro Grids (DE‐MG) are an efficient means of raising the quality of energy services. Rules for microgrid scalability, maintaining a budget, and security can make this difficult. Consumers are better at receiving the best renewable energy allotment price using a cloud‐based Peer‐to‐Peer (P2P) network. The main objective is to lower the daily energy cost of microgrid power in commercial buildings. In the proposed work, cloud‐based P2P for peer‐Multi Agent System (p‐MAS) optimization techniques are used to reduce system peak and integrated Demand Response (DR) with Energy Management System (EMS) in a commercial MG. To fill knowledge gaps about how various power market architectures and individual decision‐making processes impact local interactions and market outcomes, cloud‐based P2P for Modelling Leveraging Agents (MLA) is used for bill calculation. A performance measure is finally created for cost evaluation and reliability to measure the social benefits of cloud‐based P2P models for exchanging energy. For various price environments and resource types, a comparison between the proposed cloud‐based P2P model with an existing P2P model for exchanging energy is provided. The primary use of a distributed P2P model for exchanging power in a microgrid is to reduce electricity costs and increase grid environment reliability.

Keywords

• Data Structures
• Artificial Intelligence
• Data Analytics and Machine Learning
• building management systems