Browsing by Keyword "info:eu-repo/grantAgreement/EC/H2020/768614/EU/Integrating Real-Intelligence in Energy Management Systems enabling Holistic Demand Response Optimization in Buildings and Districts/HOLISDER"
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Item Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain: Case study in Spain(2020-12) Eguiarte, O.; de Agustín-Camacho, P.; Garrido-Marijuán, A.; Romero-Amorrortu, A.; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANADynamic 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.Item Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings(2019-08-13) Noyé, Sarah; Saralegui, Unai; Rey, Raphael; Anton, Miguel Angel; Romero, Ander; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; EDIFICACIÓN DE ENERGÍA POSITIVABuildings are key actors of the electrical gird. As such they have an important role to play in grid stabilization, especially in a context where renewable energies are mandated to become an increasingly important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate more efficiently. One of the ways to obtain flexibility from building managers and building users is the introduction of variable energy prices which evolve depending on the expected load and energy generation. In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper, a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random Forest machine learning algorithm.Item Energy, Environmental and Economic Analysis of Air-to-Air Heat Pumps as an Alternative to Heating Electrification in Europe(2020-08-01) Eguiarte, Olaia; Garrido-Marijuán, Antonio; de Agustín-Camacho, Pablo; del Portillo, Luis; Romero-Amorrortu, Ander; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANAHeat pumps (HP) are an efficient alternative to non-electric heating systems (NEHS), being a cost-effective mean to support European building sector decarbonization. The paper studies HP and NEHS performance in residential buildings, under different climate conditions and energy tariffs, in six different European countries. Furthermore, a primary energy and environmental analysis is performed to evaluate if the use of HPs is more convenient than NEHS, based on different factors of the electric mix in each country. A specific HP model is developed considering the main physical phenomena occurring along its cycle. Open data from building, climatic and economic sources are used to feed the analysis. Ad hoc primary energy factors and greenhouse gas (GHG) emission coefficients are calculated for the selected countries. The costs and the environmental impact for both heating systems are then compared. The outcomes of the study suggest that, in highly fossil fuels dependent electricity mixes, the use of NEHS represents a more efficient decarbonization approach than HP, in spite of its higher efficiency. Additionally, the actual high price of the electric kWh hampers the use of HP in certain cases.Item Engaging domestic users on demand response for heating cost reduction with a recommendation tool: Case study in Belgrade: Case study in Belgrade(2022-06) Eguiarte, O.; de Agustín-Camacho, P.; Garrido-Marijuan, A.; Vukovic, M.; del Portillo, L.; Romero-Amorrortu, A.; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANAThe 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.Item Integration of Real-Intelligence in Energy Management Systems to Enable Holistic Demand Response Optimization in Buildings and Districts(IEEE, 2018-10-18) Romero, Ander; de Agustin-Camacho, Pablo; Tsitsanis, Tasos; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANAAlthough multiple trials have been conducted demonstrating that demand side flexibility works and even though technology roll-out progresses significantly fast, the business application of residential and small tertiary demand response has been slow to develop. This paper introduces a holistic demand response optimization framework that enables significant energy costs reduction at the consumer side, while introducing buildings as a major contributor to energy networks' stability in response to network constraints and conditions. The backbone of the solution consists in a modular interoperability and data management framework that enables open standards-based communication along the demand response value chain. The solution is validated in four large-scale pilot sites, incorporating diverse building types, heterogeneous home, building and district energy systems and devices, a variety of energy carriers and spanning diverse climatic conditions, demographic and cultural characteristics.