Global awareness on energy consumption and the environmental impacts of fossil fuels boost actions and create more supportive policies towards sustainable energy systems, in the last energy outlook, by the International Energy Agency, it was forecasted totals of 3600 GW from 2016 to 2040 of global deployment of renewables sources (RES), covering 37% of the power generation. While the Natural Gas overtake the coal demand in the energy mix, growing around 50%, manly by more efficiency system and the use of LNG for long-distance gas trades. The energy infrastructure will be more integrated, deploying decentralized and Hybrid Energy Networks (HEN).This transformation on the energy mix leads to new challenges for the energy system, related to the uncertainty and variability of RES, such as: Balancing flexibility, it means having sufficient resources to accommodate when variable production increase and load levels fall (or vice versa). And Efficiency in traditional fired plants, the often turn on and off or modify their output levels to accommodate changes in variable demand, can result in a decrease in efficiency, particularly from thermal stresses on equipment. This paper focus in the possibility to offer balancing resources from the LNG regasification, while ensure an efficient system.In order to asses this issue, using the energy Hub concept a model of a distributed HEN was developed. The HEN consist in a Waste to Energy plant (W2E), a more sustainable case of Combine Heat and Power (CHP) coupled with a LNG cold recovery regasification. To guarantee a most efficiency operation, the HEN was optimized to minimized the Exergy efficiency, additionally, the system is constrained by meeting Supply with variable demand, putting on evidence the sources of balancing flexibility. The case study show, the coupled system increases in overall exergy efficiency from 25% to 35% compared to uncoupled system; it brings additional energy between 1.75 and 3 MW, and it meets variable demand in the most exergy efficient with power from LNG reducing inputs of other energy carriers. All this indicated that LNG cold recovery in regasification coupled other energy systems is as promising tool to support the transition towards sustainable energy systems.
Uit de samenvatting: "Sinds medio 2017 is het Nationaal Lectorenplatform Urban Energy actief. De betrokken lectoren beogen het praktijkgericht onderzoek rond de gebouwde omgeving op hogescholen te verbinden en te stroomlijnen. Dit doen ze teneinde bij te dragen aan de energietransitie: met duurzame bronnen voorzien in onze energievoorziening. Een belangrijk instrument om de expertise van de lectoren te delen is een digitale onderzoekskaart, die beschikbaar is via: http://www.nlurbanenergy.nl. Daarnaast is er behoefte aan meer inzicht als het gaat om termen als vraagarticulatie en onderzoekssamenwerking. Meer precies wilden we achterhalen wat de behoefte is van het mkb aan praktijkgericht onderzoek van hogescholen in het domein Urban Energy. Daartoe hebben we een verkennende studie uitgevoerd naar praktijkgericht onderzoek binnen het domein Urban Energy. Hiervoor interviewden we de betrokken lectoren en ondernemers uit het innovatief MKB. Daarnaast maakten we gebruik van een enquête die we via verschillende kanalen onder de aandacht brachten bij het innovatief mkb."
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
A fast growing percentage (currently 75% ) of the EU population lives in urban areas, using 70% of available energy resources. In the global competition for talent, growth and investments, quality of city life and the attractiveness of cities as environments for learning, innovation, doing business and job creation, are now the key parameters for success. Therefore cities need to provide solutions to significantly increase their overall energy and resource efficiency through actions addressing the building stock, energy systems, mobility, and air quality.The European Energy Union of 2015 aims to ensure secure, affordable and climate-friendly energy for EU citizens and businesses among others, by bringing new technologies and renewed infrastructure to cut household bills, create jobs and boost growth, for achieving a sustainable, low carbon and environmentally friendly economy, putting Europe at the forefront of renewable energy production and winning the fight against global warming.However, the retail market is not functioning properly. Many household consumers have too little choices of energy suppliers and too little control over their energy costs. An unacceptably high percentage of European households cannot afford to pay their energy bills. Energy infrastructure is ageing and is not adjusted to the increased production from renewables. As a consequence there is still a need to attract investments, with the current market design and national policies not setting the right incentives and providing insufficient predictability for potential investors. With an increasing share of renewable energy sources in the coming decades, the generation of electricity/energy will change drastically from present-day centralized production by gigawatt fossil-fueled plants towards decentralized generation, in cities mostly by local household and district level RES (e.g PV, wind turbines) systems operating in the level of micro-grids. With the intermittent nature of renewable energy, grid stress is a challenge. Therefore there is a need for more flexibility in the energy system. Technology can be of great help in linking resource efficiency and flexibility in energy supply and demand with innovative, inclusive and more efficient services for citizens and businesses. To realize the European targets for further growth of renewable energy in the energy market, and to exploit both on a European and global level the expected technological opportunities in a sustainable manner, city planners, administrators, universities, entrepreneurs, citizens, and all other relevant stakeholders, need to work together and be the key moving wheel of future EU cities development.Our SolutionIn the light of such a transiting environment, the need for strategies that help cities to smartly integrate technological solutions becomes more and more apparent. Given this condition and the fact that cities can act as large-scale demonstrators of integrated solutions, and want to contribute to the socially inclusive energy and mobility transition, IRIS offers an excellent opportunity to demonstrate and replicate the cities’ great potential. For more information see the HKU Smart Citieswebsite or check out the EU-website.
This Professional Doctorate (PD) research focuses on optimizing the intermittency of CO₂-free hydrogen production using Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM) electrolysis. The project addresses challenges arising from fluctuating renewable energy inputs, which impact system efficiency, degradation, and overall cost-effectiveness. The study aims to develop innovative control strategies and system optimizations to mitigate efficiency losses and extend the electrolyzer lifespan. By integrating dynamic modeling, lab-scale testing at HAN University’s H2Lab, and real-world validation with industry partners (Fluidwell and HyET E-Trol), the project seeks to enhance electrolyzer performance under intermittent conditions. Key areas of investigation include minimizing start-up and shutdown losses, reducing degradation effects, and optimizing power allocation for improved economic viability. Beyond technological advancements, the research contributes to workforce development by integrating new knowledge into educational programs, bridging the gap between research, industry, and education. It supports the broader transition to a CO₂-free energy system by ensuring professionals are equipped with the necessary skills. Aligned with national and European sustainability goals, the project promotes decentralized hydrogen production and strengthens the link between academia and industry. Through a combination of theoretical modeling, experimental validation, and industrial collaboration, this research aims to lower the cost of green hydrogen and accelerate its large-scale adoption.
Climate change has impacted our planet ecosystem(s) in many ways. Among other alterations, the predominance of long(er) drought periods became a point of concern for many countries. A good example is The Netherlands, a country known by its channels and abundant surface water, which has listed “drought effect mitigation” among the different topics in the last version of its “Innovation Agenda” (Kennis en Innovatie Agenda, KIA). There are many challenges to tackle in such scenario, one of them is solutions for small/decentralized communities that suffer from dry-up of surface reservoirs and have no groundwater source available. Such sites are normally far from big cities and coastal zones, which impair the supply via distribution networks. In such cases, Atmospheric Water Generation (AWG) technologies are a plausible solution. These systems have relatively small production rates (few m3 per day), but they can still provide enough volume for cities with up to 100k inhabitants. Despite having real scale systems already installed in different locations worldwide, most systems are between TRL 5 and 6. Thus need further development. SunCET proposes an in-situ evaluation of an AWG system (WaterWin) developed by two different Dutch companies (Solaq and Sustainable Eyes) in the Brazilian semi-arid state of Ceará. The cooperation with NHL Stenden will provide the necessary expertise, analytical and technical support to conduct the tests. The state government of Ceará built an infrastructure to support the realization of in-situ tests, as they want to further accelerate technology implementation in the state. Such structure will make it possible to share costs and decrease total investments for the SMEs. Finally, it is also intended to help establishing partnerships between Dutch SMEs and Brazilian end users, i.e. municipalities of the Ceará state and small agriculture companies in the region.
