Smart Net Management System Efficient Peak Load Reduction in Buildings

To facilitate an efficient energy transition and combat climate change, numerous countries around the globe are investing in the expansion of renewable energy sources. However, this development has led to increased volatility in power grids. Coupled with rising energy consumption, driven in part by ongoing digitalization, this volatility has increasingly resulted in critical grid situations, which, in extreme cases, can culminate in blackouts. A significant contributor to such risks is the occurrence of load peaks from individual consumers, which locally overload grid connection points and pose a threat to grid stability.This paper introduces a novel approach to mitigating load peaks in buildings: the Smart Net Management System (SNM). This system digitally monitors energy consumers and organizes them into smaller groups, referred to as virtual Smart Nets (SNs). The core focus of the work lies in the design of the SNM, particularly the intelligent grouping of devices into SNs and the decentralized monitoring of energy consumption within each SN. The primary objective of the SNM is to activate device flexibility and perform load shifting to prevent overloads at grid connection points. The SNM serves as an extension to existing Energy Management Systems (EMS), providing actionable recommendations for device control.The effectiveness of the SNM is demonstrated through a case study involving a hypothetical hotel equipped with a photovoltaic system and approximately 200 energy-consuming devices, including washing machines, heating systems, and industrial kitchen appliances. Realistic load profiles for these devices are simulated over the course of a sample year. Subsequently, the SNM is tested under various scenarios that consider differing levels of device flexibility.The results highlight significant potential for reducing grid overloads. In the best-case scenario, where 50% of devices are assumed to be fully flexible (available for load shifting with a 100% probability), the number of overloaded 15-minute intervals can be reduced by up to 99.24%. This transition shifts the hypothetical hotel from a critical energy consumption level to a stable and non-critical state.