Macro-Level Bottom-Up Energy Demand Modeling for Telecommunication Networks

Abstract

Rising energy costs have generated increasing interest in green networks among telecommunication operators. Purchasing or generating green energy for telecom networks can enhance energy sustainability and cost efficiency. However, the telecom industry, especially in Indonesia, has been slow to evolve owing to limited tools for telecom operators to conduct comprehensive studies and no successful business models to emulate when considering green power generation for their networks. In this study, we developed a macro-level energy demand assessment tool based on bottom-up information from the base transceiver station (BTS) to capture actual conditions as a preliminary stage for implementing a hybrid renewable energy system (HRES). We adopted energy assessment methods from the building sector and defined archetypes based on the BTS system configuration, type, and spatial information. Additionally, we used the hourly power consumption and traffic monitoring data to develop models for predicting the BTS power consumption and calculating the regional network energy demand. Our model quantifies the energy demand for the BTS network in each district of the Bali region, which accounts for 3.8% of 2022 total electricity consumption. With realistic energy demand information, we can create an HRES design that neither overestimates the energy demand, making the process too expensive, nor underestimates it. In conclusion, this study performs a macro-level analysis of energy demand calculations that consider the local characteristics of the BTS network and can be a reference for telecom operators as decision makers in implementing the HRES.

Keywords

• Archetype
• BTS network
• energy demand
• hybrid renewable energy system
• macro