• Energy Research

Energy grids and markets are in transition. Increased use of renewable energy sources (RES) introduces new stability challenges for power grids. Despite the substantial electrical consumption of mobile networks, they are yet to harness their inherent flexibility for aiding in the stability of the power grid. A noticeable research gap exists concerning measuring full activation time for fast frequency reserve (FFR) product while using batteries from mobile network base stations. Our objective is to demonstrate that mobile operators could use their existing infrastructure to participate in the reserve market of a contemporary power grid. Furthermore, it seeks to determine if the full activation time can meet the requirements of an FFR product. The system consists of a live mobile base station site with a mobile connection to the site, local controller, an existing battery, and a power system that, in combination, can function as part of a power grid balancing system. Our main finding indicates that the rectifier reaction time within an installed base station site infrastructure ranges from 5 to 8 s, and the time when the base station is entirely off from the grid varies from 7 to 10 s. This finding is significant since the activation time is too long for the base station power system controller to be used for FFR. The required full activation time for FFR is less than 1.3 s. In conclusion, power system vendors should investigate improvements for their equipment and software products to enable fast reserve market entry for their existing customers and stay competitive.

Abstract Climate change is happening, and the information and communication technology (ICT) industry is providing solutions to enable a more sustainable future. ICT enables enhanced manufacturing processes, optimized logistics, solutions supporting environmental protection and solutions facilitating climate change adaptation, for example. The performance of ICT hardware and electronics has increased tremendously. The capabilities of microchips have increased ~100,000-fold in the past 30 years and the power efficiency has increased a million-fold. Yet, various reports show that the ICT sector increases its carbon footprint and energy consumption, while other sectors are lowering their impact on our planet. Thus, there seems to be an adverse development ongoing where most industry sectors can lower their carbon footprint while the ICT sector increases its consumption. This paper looks at the development of the ICT sector and seeks to understand the current development. We argue that a major reason for the rebound effect we are seeing within the ICT sector is the lack of understanding in energy consumption and the cost pressures of developing digital services. The results are software and services that could be much greener and energy efficient but seem to rather go toward a darker direction. The ICT industry and software engineering needs to do a clear change of course and take accountability of their activities.

Abstract The growth of computing and Internet use have attracted the attention of the general public concerning the carbon footprint of data centers (DCs). Previous research has focused on the implementations of energy efficiency activities. However, little research has been published on the economic evaluation of waste heat utilization in the DC industry. This paper aims to provide an economic investment assessment of DC waste heat utilization. The contribution of this paper is the assessment of three different sized cases with realistic input factors affecting the net present value (NPV) model. We contribute to the ongoing discussion on the energy efficiency of DCs and provide a transparent assessment model for DC and district heating operators. We identified the positive NPV cases with high probability. The medium case has an NPV of 1.04 M€ (with uncertainty, the results range from −0.332 M€ to 2.57 M€). The large case has an NPV of 16.3 M€ (with uncertainty, the results range from 4.1 M€ to 30.2 M€). Both of these are clear-cut waste heat utilization investment proposals. The small case NPV is −48.5 k€ (with uncertainty, the results range from −264 k€ to 143 k€). The small case is sensitive to input factor values.

Growing energy consumption is a global problem. The information and communications technology (ICT) industry is in a critical role as an enabler of energy savings in other sectors. However, the power consumption of the ICT sector also needs to be addressed, to contribute to the overall reduction of power consumption and carbon emissions. A new era has begun as the fifth generation (5G) mobile data connection rollouts are advancing globally and are expected to reach a 10% share of end-user devices and connections by 2023. The available references on energy consumption in global mobile networks are rather old and highly averaged – only estimates of energy consumption relative to data volumes are available. There is an information gap regarding the energy consumption of emerging 5G and advanced 4G technologies. Therefore, it has been difficult to understand the actual electricity consumption differences between generations and spatially aggregated electricity consumption once these generations are combined to offer capacity and coverage. This article fills this gap by providing a reference on the energy consumption of base transceiver stations for reported mobile data usage for different Radio Access Technologies; 3G, 4G and 5G respectively. To the best of our knowledge, there is no reference to scientific research on the comparison of energy intensity per square kilometer for 3G, 4G and 5G mobile radio technologies, using actual operator data. The objective of this research was to improve the understanding of the actual energy consumption of different Radio Access Technologies (RAT). The results also give insight to decision makers on when to modernize the operator radio access network. The article reports on the results of field measurements on data and visitor volumes and shares of different RATs. The research contains two statistical RAT combination cases, one representing the European average and the other Finnish mobile networks. The analyses were done for dense urban (DU) and suburban (SU) areas.

Abstract Data centers seek solutions to increase energy efficiency and lower costs by novel methods. Waste heat utilization is considered to be one of the major trends in the near future, especially in the Nordic countries, where heat demand is high. In this paper, waste heat utilization was analyzed from the perspectives of both the data center and district heating network operators. Timing of the data center waste heat production was considered based on an existing data center load profile. For the district heating network operator, the system level effects of increased waste heat utilization were quantified by simulating district heating production in the city of Espoo, Finland, with actual plant and heat demand data for 2013 and 2015. Results showed that with high shares of waste heat in the district heating system, i.e. 20–60 MW, the system level operational cost savings were 0.6–7.3% in the case study. Utilizing waste heat decreased utilization hours of both combined heat and power plants and heat-only boilers. The analysis showed that pricing of the procured waste heat affects the utilization level of waste heat, but operational hours of waste heat utilization were over 95% in all scenarios.

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user's decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.