1,720,990 research outputs found
FLEXIBILITY POTENTIAL OF HEAT PUMPS THROUGH DEMAND SIDE MANAGEMENT IN BUILDINGS
No full textEnergy transition is progressively replacing conventional fossil-based energy systems with renewable energy sources, which are by nature not flexible and cannot be managed to meet energy needs. Hence, it is of key importance to have flexibility on the demand side to ensure a balance between energy supply and demand. One possible strategy to achieve this is known as Demand Side Management (DSM), consisting of actively changing user demand to obtain a more efficient system operation. When heat is supplied by heat pumps, and therefore it is tranferred into an electricity demand, DSM allows electricity to be stored as heat within the building thermal mass (e.g. by varying the temperature set-points), in order to reduce the electricity demand in subsequent periods. This study aims to characterize the potential of heat pumps supplying heat to buildings in order to implement this DSM strategy and, therefore, to offer flexibility or balancing services to the power grid. The dynamic behavior of a binary system comprising a building and a heat pump, including the heat distribution circuit for generality, is simulated through a model in MATLAB®/Simulink®. A set of experiments is carried out by preheating the building at different hours of the day (i.e. increasing the comfort set-point) and by sensitivity analyses on the building thermal properties (i.e. time constant). The flexibility potential is assessed by defining and evaluating key performance indicators that represent the behavior of the binary system, e.g. peak energy reduction, amount of electricity used to preheat the building and avoided during its discharge, and building discharge time. The results remark the periods of the day in which it is more effective to apply DSM, leading to a potential electricity saving of up to 5 % thanks to a more efficient operation of the heat pump, when considering its variable performance with the load. The discharge time of the building is also highly variable, ranging from 2 h to 8 h. The analysis of these indicators can open up new management opportunities in communities of buildings
Development, analysis and application of a predictive controller to a small-scale district heating system
Full text linkDistrict heating and cooling networks have great potential for energy saving, efficient thermal energy distribution and renewable energy source integration. Currently, heating systems are managed on the basis of operator experience or by using adaptive controllers, however these solutions are not suitable when there are remarkable variations in boundary conditions. In this context, Model Predictive Control is a promising strategy as it optimizes control based on the prediction of the future behavior of system dynamics and disturbances by means of simplified models. This paper presents the development of a predictive controller based on a novel Dynamic Programming optimization algorithm and aimed to supply the thermal energy to entire buildings within district heating networks. The controller is exploited to operate the district heating network of a school complex in a simulation environment (i.e. Model-in-the-Loop). Each branch connected to the network is optimized by a dedicated controller according to a multi-agent strategy. The performance of the innovative controller is compared to the results obtained by using a conventional PID controller. Conservative results show that, with the innovative controller, a reduction in fuel consumption of up to more than 7% is obtained together with up to 5 h of avoided failures of the indoor comfort constraints, depending on the season. Overall, the Model-based Predictive Controller is able to fulfill comfort requirements adequately while minimizing energy consumption. Moreover, the multi-agent approach allows these results to be extended to larger networks in future studies
A REVIEW OF SMART ENERGY PRACTICES AT AIRPORTS: CHALLENGES AND OPPORTUNITIES FOR SUSTAINABLE AVIATION
No full textAirports are intricate systems comprising buildings, parking lots and land infrastructure, each with unique characteristics that influence energy consumption patterns.On the airside, airfield lighting and radio navigation systems are the primary energy users, while on the landside, the terminal building stands out as a major energy consumer due to its role in passenger and cargo handling, and the extensive facilities it houses.Heating, Ventilation and Air Conditioning (HVAC), lighting, and Information and Communication Technology (ICT) systems are often the top energy consumers within airports, making it imperative to explore innovative strategies to reduce energy expenditure in these facilities.To address this challenge, airports are adopting smart energy solutions, involving electrification, ICT integration, and energy optimization.This paper reviews the current state and future prospects of these emerging trends, covering the following aspects.Regarding electrification, the paper discusses how airports are shifting to electrical power for ground support equipment, passenger vehicles, and terminal buildings, reducing their dependence on fossil fuels and improving their environmental performance.Focusing on ICT integration, the paper examines how airports are using advanced energy management systems, data analytics, and predictive modeling tools to monitor and optimize their energy consumption patterns, achieving significant energy savings, and operational efficiency.Finally for energy optimization, the paper explores how airports are implementing energy optimization measures, such as HVAC optimization and waste heat recovery, enhancing their overall sustainability and resilience.The paper also analyzes the potential and challenges of using renewable energy sources at airports, such as solar and wind power, highlighting the technical, safety (e.g.glaring for photovoltaic panels or interference with communication and trajectories for wind generators) and regulatory issues that need to be addressed.The paper concludes with a vision of the future of smart energy at airports, emphasizing the role of innovation, collaboration, and stakeholder engagement in driving the transition to a more sustainable aviation sector
A control-oriented scalable model for demand side management in district heating aggregated communities
Full text linkDistrict heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. Additional benefits can derive from novel demand side management strategies, which can efficiently balance demand and supply. However, their implementation requires detailed knowledge of heating network characteristics, which vary remarkably depending on urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. For this purpose, the present work proposes a novel procedure to develop a fast scale-free model of large-scale district heating networks for system optimization and control. Each network community is represented and modeled as an aggregated region. Its physics-based model is identified starting from a limited amount of data available at the main substations and includes heat capacity and heat loss coefficients. The procedure is demonstrated and validated on the network of Västerås, Sweden, showing results that are in agreement with data from the literature. Thus, the model is well suited for real-time optimization and predictive control. In particular, the possibility to easily estimate the heat storage potential of network communities allows demand side management solutions to be applied in several conditions
Dynamic Adsorptive Carbon Capture in Power-to-Gas Plants
No full textEnergy transition can be addressed in the very near future with low investment costs by utilizing already-available technologies and infrastructures. In this regard, among innovative energy carriers, green synthetic methane can tackle the issue by taking advantage of natural gas facilities. Power-to-gas systems enable methane synthesis by combining electrolytic hydrogen and captured carbon dioxide. This work investigates the adsorptive carbon capture in the context of a power-togas system. Carbon dioxide is trapped onto the porous surface of a packed bed by adsorption and is then released during bed regeneration. The alternating process operation is analyzed by means of a dynamic model capable of reproducing both adsorption and desorption. The other system components are dynamically modeled as well to simulate their interaction during the cyclic operation. The whole cycle is analyzed. Bed regeneration by means of a hydrogen purge flow is evaluated considering the possibility of utilizing the mixture of hydrogen and desorbed carbon dioxide as reactants in a subsequent catalytic methanation process. The boundary limits for the pressure of the hydrogen purge source are identified in order to obtain the desired reactants proportion. Regarding adsorption, different post-combustion flue gases are evaluated as carbon dioxide sources from a plant-management perspective
THE ROLE OF METHANATION MODELING IN THE SIMULATION OF POWER-TO-GAS SYSTEMS
No full textThe role that green hydrogen can play in the future energy systems has been undergoing huge investigations in recent years due to its versatility of use and its exiguous greenhouse impact. However, this energy carrier exhibits low volumetric energy density, difficulties in transportation, and high storage costs. Therefore, if not used at the site of production, it needs to be converted into other carriers for transportation and use. In this framework, synthetic methane can represent a valid option if used as a natural gas substitute taking advantage of its transportation infrastructures. Renewable methane can be synthesized from green hydrogen and captured carbon dioxide in power-to-gas systems. In order to optimize the efficiency of methane production, these systems need to be integrated into larger energy networks where it is possible to benefit from the exploitation of energy in its variety of forms (e.g. electrical, thermal, and chemical). For this reason, the employment of mathematical models is essential for optimal process development. Depending on the purpose of a specific simulation and the time horizon, it is necessary to approach the study with the most suitable level of modeling detail. In this work, the methanation technology is analyzed in the context of power-to-gas on multiple level modeling. Indeed, in several cases, methanation modeling requires the capability to capture characteristics of the input hydrogen such as mass flow rate, moisture content, or pressure. However, in other cases, the focus can regard the whole system operation and control. In such circumstances, the need to analyze specific component characteristics leaves room for considerations of mass and energy transfers, and component operating conditions and efficiencies. The paper at hand proposes and combines two modeling approaches. The choice of a specific approach is suggested considering the obtainment of all the information to be gathered from a specific simulation. The results of the interaction of the methanation reactor with other power-to-gas components are provided
Robust control of a cogeneration plant supplying a district heating system to enable grid flexibility
No full textIn recent years, the flexibility of energy systems has become essential due to the growing penetration of renewable energy sources. The producers and consumers can enhance this flexibility by enabling a given amount of power that they can produce or consume in every condition. This is made available to the grid operator to globally optimize the dispatch management and to stabilize the grid. However, this can interfere with the operation of production units such as cogeneration plants, which also have to meet thermal demand. Therefore, producers and consumers require smart controllers to comply with grid operator requests at any time. This paper proposes a robust control strategy based on Model Predictive Control, which manages distribution networks and production plants by considering the uncertainty of the requirements for flexibility from the grid operator. The simulation case study is the district heating network of a school complex supplied by a Combined Heat and Power plant and a Thermal Energy Storage tank. The robustness of the proposed optimization is investigated by simulating several scenarios with different degrees of uncertainty about the request for electricity from the grid operator. The results show that the plant operator is able to comply with the electricity requirements to different extents depending on the degree of uncertainty and on system design choices. These considerations make it possible to improve the plant design and production planning from the perspective of grid flexibility
Enabling smart control by optimally managing the State of Charge of district heating networks
Full text linkDigitalization and smart control of district heating networks are emerging as key features to make these systems flexible and optimal. However, since effective and scalable methods for large-scale systems are currently unavailable, the implementation of smart controllers can be challenging and time-consuming. This is addressed herein by proposing a novel approach to include the thermal capacity of the connected buildings in the optimal control of large-scale heating networks. A reduced-order model of the aggregated communities supplied by a large-scale network is used to define their State of Charge, which is exploited to store or retrieve energy when convenient, while maintaining indoor comfort. This concept is included in a Model Predictive Controller that optimizes power plant management and heat distribution. The results show that the controller successfully shaves heat supply peaks to different regions up to 16% and reduces the difference between distribution and soil temperature up to 20%. At the same time, the return temperature is kept close to the set-point of 35 °C, which is lower than the historical operation and further reduces distribution heat losses. The procedure can be easily replicated to optimize systems of different sizes and to support their transition to efficient, smart district heating networks
An Integrated Artificial Intelligence Approach for Building Energy Demand Forecasting
No full textBuildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global greenhouse gas emissions. Consequently, substantial effort has been made to forecast their energy needs, with the scope of optimizing their economic and environmental impact. In this regard, the available literature focuses mainly on short-term modeling through the implementation of sets of physics-based equations (i.e., white-box), functional relationships between input and output variables (i.e., black-box), or a combination of both (i.e., grey-box). On the other hand, more research is required on long-term forecast models with the aim of reducing the energy needs. Within this context, this article presents an original automatic procedure for forecasting the energy needs of buildings in short- and long-term time horizons. This is accomplished by scaling an unknown facility from a similar facility that is already known and by executing a black-box approach based on machine learning algorithms. The proposed method is implemented in real case studies in Italy, predicting the energy needs (i.e., heating, cooling, and electricity) of Sant’Anna Hospital in Ferrara using the historical data of Ca’ Foncello Hospital in Treviso. The results show an adjusted coefficient of determination above 0.7 and an average error below 10% for all the energy vectors, demonstrating a feasible forecast performance with a low training set-to-test set ratio
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