Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain

Dynamic energy tariffs facilitate engaging domestic consumers on demand management, contributing to grid’s stability, but requires of informed decision enabling tools. This paper presents a domestic heating costs calculation method for different heating technologies (gas boiler, heat-pumps) and a range of energy tariffs. Based on physical modeling, effect of outdoor temperature in the COP of heat-pumps is assessed. The methodology is applied to the 2018/19 heating season in Madrid (Spain), calculating the heating costs under four diverse energy tariffs (static gas tariff, static electricity tariff, real-time-price electricity tariff, dynamic time-of-use electricity tariff) for a typical home demand. The hourly results for two representative days are detailed, along with the aggregated results for the whole season. Along the season, the continuous changes in energy wholesale market prices and weather conditions make one heating technology and/or tariff more convenient each time. For the whole season, the dynamic time-of-use tariff considered would imply heating costs up to 40% lower than the static gas tariff. The results are strongly conditioned by climate conditions and national energy market evolutions. Day-ahead information on the actual heating costs might lead to domestic end-users to adapt their behavior and consumption patterns for more cost-effective use of the energy.Research leading to these results has been supported by HOLISDER project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 768614. This paper reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information contained therein

Engaging domestic users on demand response for heating cost reduction with a recommendation tool: Case study in Belgrade

The European Union has established a legislative framework that aims to enable consumers and businesses to take information-based decisions to save energy and money. Additionally, the increase of Distributed Energy Resources (both on generation and consumption) requires additional efforts to maintain the reliability and stability of the electric grid and the need of flexibility from residential buildings. The present study introduces a domestic decision support tool for reducing heating costs. This app provides detailed recommendations to end-users based on the day-ahead hourly weather forecast, electric and district heating tariffs predictions, heating demand, and heating systems dynamic performance. The tool was tested in 6 dwellings of a neighborhood of Belgrade during the last months of 2021 heating season (March–May). Energetic results suggest that 40% of participants followed the given recommendations and changed their heating pattern. Additionally, survey results show that end-users found the lack of information and knowledge as the main barrier to actively participate in the energy market, also preferring to have automatic control in their heating system. Authors conclude that recommendation tools are key elements in user-engagement, but they should be supported by additional information and training.Research leading to these results has been supported by HOLISDER project, Spain. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 768614

Engaging domestic users on demand response for heating cost reduction with a recommendation tool: Case study in Belgrade

The European Union has established a legislative framework that aims to enable consumers and businesses to take information-based decisions to save energy and money. Additionally, the increase of Distributed Energy Resources (both on generation and consumption) requires additional efforts to maintain the reliability and stability of the electric grid and the need of flexibility from residential buildings. The present study introduces a domestic decision support tool for reducing heating costs. This app provides detailed recommendations to end-users based on the day-ahead hourly weather forecast, electric and district heating tariffs predictions, heating demand, and heating systems dynamic performance. The tool was tested in 6 dwellings of a neighborhood of Belgrade during the last months of 2021 heating season (March–May). Energetic results suggest that 40% of participants followed the given recommendations and changed their heating pattern. Additionally, survey results show that end-users found the lack of information and knowledge as the main barrier to actively participate in the energy market, also preferring to have automatic control in their heating system. Authors conclude that recommendation tools are key elements in user-engagement, but they should be supported by additional information and training