1,720,962 research outputs found
Towards Digital Twins of buildings and smart energy networks: Current and future trends
No full textThe European Union is actively promoting the digitalization of energy systems as a mean of achieving urban decarbonization. In addition, the future expansion of distributed energy networks, prosumers (for both heating and electricity) and electric vehicles, will create the need for a smart real-time and predictive management. In this framework, Digital Twins will play a key role offering management tools and planning strategies in urban energy systems. Given the recent emergence of this technology, this work aims to elucidate the definition of Digital Twin by conducting a comprehensive review of various practical applications and methodologies outlined in the current literature. This analysis will examine different applications in the context of buildings, district heating networks, and micro-grids, exploring different scopes, such as fault detection, energy saving and support for grid operators. Additionally, several methodologies for developing Digital Twins will be highlighted including Deep Learning and physical-based algorithms. Finally, the benefits of smart networks and buildings using Digital Twins in different pilots across Europe will be demonstrated through future activities of the DigiBUILD project. Within this context the project aims to develop various innovative applications, such as Electric Vehicles smart charging to increase renewable energy consumption, optimization of heat generation in 4th generation District Heating, and pro-active management and fault detection in buildings with a focus on enhancing occupant comfort
Development of a Methodology for Assessing Indoor Punctual Thermal Comfort Conditions
No full textThe assessment of indoor thermal comfort is determined by environmental parameters and indices such as the Predictive Mean Vote (PMV) and the Predictive Percentage of Dissatisfied (PPD). Some software can calculate these parameters, but with some limits. The research presented in this paper is part of the research activity made within the project 'Network 4 Energy Sustainable Transition - NEST' UNIPA. In particular, the topic of the research is the development of innovative methods and devices for monitoring thermal comfort parameters in different zones of indoor environments. It is aimed to identify the best typology, number, and position of sensors used to control heating and cooling system for different zones and to study a control for radiant conditioner systems for the zones. In this light, this paper aims to present a methodology to assess indoor thermal conditions and, in particular able to calculate it in a precise point of the space (i.e. where the user is located), to develop, consequently, a map of the Mean Radiant Temperature (MRT) in different points and to add additional surface with different temperature (e.g. to simulate radiant conditioner systems)
Dynamic simulation of a 4th generation district heating network with the presence of prosumers
Full text linkDistrict Heating Network is identified as a promising technology for decarbonizing urban areas. Thanks to the surplus of heat available from distributed renewable energy plants, a typical heat consumer of the network could become an energy producer during the day (typically referred to as a “prosumer”). Most of the models for thermal grids developed during past years usually assumed a centralized production of the consumed heat. The increasing presence of prosumers will require accurate dynamic modelling to monitor the changes induced in the thermohydraulic parameters of the network. To fill this knowledge gap, this paper aims at developing a model of a thermal grid with prosumers in the TRNSYS environment. The model allows for the dynamic monitoring of the main thermohydraulic parameters of the network. To show these capabilities, a ring-shaped network serving a cluster of 10 residential users located in Palermo (Italy) was assumed as the case study. Different scenarios are investigated based on the presence of solar collectors, prosumers along the network, and cooling by an absorption chiller. The achievable energy and emissions savings are calculated. The results of the study show that even only decreasing the operating temperature can significantly reduce heat losses via the network pipes. In particular, a temperature drop from 100 °C to 80 °C can reduce heat losses by 27.1%. Furthermore, the heat losses can be decreased by up to 52.8% when the network temperature is lowered from 100 °C to 60 °C. Additionally, the presence of prosumers and the solar field could lead to a 31.3% reduction in the energy produced by the centralized plant and a 17.6% reduction in energy consumed for pumping
Recent advances on data-driven services for smart energy systems optimization and pro-active management
Full text linkOptimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management
Exergoeconomics as a Cost-Accounting Method in Thermal Grids with the Presence of Renewable Energy Producers
Full text linkThermal grids are efficient, reliable, and sustainable technologies for satisfying the thermal demands of buildings. The capability to operate at a low temperature allows not only for the integration of heat produced by renewable energy sources but also for the storage of surplus electricity from the grid via “power to heat” technologies. Besides, in the future, heat consumers are expected to behave increasingly as “prosumers”, supplying in some periods heat produced by renewable energy plants on site. In this scenario, it is important to propose a method for the cost allocation among producers connected to the grid. In this regard, this paper proposes Exergoeconomics as a possible tool for rational cost assignment. To show the capabilities of the method, some operating scenarios are compared for a cluster of five buildings of the tertiary sector interconnected by a thermal grid. Based on exergoeconomic indicators, such as the exergy and exergoeconomic unit costs, insights into the cost formation process of the heat consumed by users are provided. Sensitivity analyses of heat unit cost to design and operating variables are also performed. Results show that in the presence of distributed producers, the heat unit cost could be approximately 33% lower than in the case of centralized production, due to the lower amount of irreversibility generated. Capital investment accounts for 20–28% of the heat unit cost
Timber Houses in the Mediterranean Area: A Challenge to Face
No full textAbout 40% of European energy consumption and most of the environmental impacts are related to the construction sector. A key role in decarbonizing the construction sector plays the timber buildings. Wood is a sustainable resource and has excellent thermophysical and acoustic characteristics compared to traditional building materials, with short production times that affect not only the construction phase but also costs. Although wooden houses are very common in the countries of Northern Europe, in Italy, and in general in the Mediterranean countries, this type of building is not very widespread today. The hot climate, characterized by a long cooling season, has always directed builders to build massive buildings. Because today building a timber house means creating energy-efficient buildings, it is proposed to study the energy-environmental performance of timber buildings in a Mediterranean climate. In this work, the performance of a building made with traditional construction will be compared with a simulated wooden building at different latitudes and climatic conditions. At the same time, a simplified assessment of the economic aspects will be carried out. For each model, the main thermophysical and geometric characteristics necessary to achieve the energy comfort requirements will be identified using MATLAB. The first results show that a wooden house has an energy saving of around 17% with payback times of 10 years compared to a traditional house
Assessment of Performance of Hybrid Heat Pump System During Winter Season in Different Climatic Conditions
No full textA Hybrid Heat Pump (HHP) is the combination of an electric heat pump and a fossil-fuelled boiler or furnace under a single optimised control strategy. It has the possibility to switch between natural gas use and/or electricity depending on different parameters relating to the efficiency of the system under current circumstances, such as outdoor temperature, flow temperature, or price of gas/electricity. This study presents the evaluation of a commercial hybrid heat pump during the winter in different climatic conditions. To do this a case study was selected and analysed by using the simulation software TRNSYS. The system model was validated, and the results obtained from the simulation were compared with the experimental data provided by the manufacturers. Different scenarios were considered by changing the control logics and the plant typologies: a Heat Pump (HP), a boiler and an HHP. In order to compare the different scenarios hypothesized according to the comfort conditions monitored, energy performance and environmental performance indices have been introduced. An economic analysis was performed by taking into account provided incentives. Result shown that the best system solution differs for the two cities: in Palermo the HP alone allows to maintain the desired internal comfort conditions and allows the greatest savings both in terms of non-renewable primary energy (-50%) and in terms of terms of CO2-eq emissions (-40%); in Milan, HP technology does not guarantee the internal temperatures imposed therefore it is necessary to use a hybrid system that allows lower savings (-25% for both indices)
Thermodynamic-based method for supporting design and operation of thermal grids in presence of distributed energy producers
Full text linkDistrict heating networks are well-established technologies to efficiently cover the thermal demand of buildings. Recent research has been devoting large efforts to improve the design and management of these systems for integrating low-temperature heat coming from distributed sources such as industrial processes and renewable energy plants. Passing from a centralized to a decentralized approach in the heat supply, it is important to develop indicators that allow an assessment of the rational use of the available heat sources in supplying heating networks, and a quantification of the effect of inefficiencies on the unit cost of heat. To answer these questions,
Exergy Cost Theory is here proposed. Thanks to the unit exergetic cost of heat, energy managers can (i) quantify the effects of thermodynamic inefficiencies occurring in the production and distribution on the final cost of heat, (ii) compare alternative systems for heat production, and (iii) monitor the performance of buildings’ substation over time. To show the capabilities of the method, some operating scenarios are compared for a cluster of five buildings in the tertiary sector interconnected by a thermal grid, where heat is produced by a cogeneration unit, an industrial process, and distributed heat pumps. Results suggest that moving from the centralized production of heat based on fossil fuels to a decentralized production with air-to-water heat pumps, the unit cost of heat can be decreased by almost 30% thanks to the improvement of thermodynamic efficiency. In addition, the analysis reveals a great sensitivity of unit exergetic cost to the maintenance in substations. The developed tool can provide thermodynamic-sound support for the design, operation, and monitoring of innovative district heating networks
Toward a Digital Twin of a Solid Oxide Fuel Cell Microcogenerator: Data-Driven Modelling
Full text linkSolid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions
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