Optimizing Smart Grids for Distributed Energy Resource Integration and Management

Abstract

Incorporating Distributed Energy Resources, which comprise solar, wind energy, and batteries, in traditional energy supply represents a substantial opportunity and challenge regarding the maximization of efficient management of energy along with its proper stability at a grid level. Hence, rising penetrations call for advanced smart grids offering real-time monitoring, analytics based on forecasting capabilities, and control at local decentralised nodes due to high variation and uncertainties brought about by renewables. This study aims to optimize smart grids for seamless integration of DERs by harnessing advanced technologies such as the Internet of Things, machine learning algorithms, and blockchain-enabled energy transaction systems. The methodology includes data collection from a distributed grid network through IoT-enabled sensors, predictive modelling of energy demand and supply through machine learning, and implementation of a decentralized trading mechanism using blockchain technology. Prototyping a smart grid system and testing its efficiency, reliability, and scalability were simulated-based evaluations. Results include a 25% improvement in energy utilization efficiency, a 30% reduction in grid balancing costs, and improving system resilience to such power fluctuations. The study also opens a way to demonstrate the possibility of blockchain usage while allowing energy transactions to be transparent and secure to reduce administrative overhead. Findings on these issues highlight how intelligent technologies play an important role in the transition toward more sustainable and resilient energy systems. This research contributes to the broader effort of creating adaptive smart grids capable of accommodating the growing complexity of modern energy networks.