The production of biogas through anaerobic digestion is one of the technological solutions to convert biomass into a readily usable fuel. Biogas can replace natural gas, if the biogas is upgraded to green gas. To contribute to the EU-target to reduce Green House Gases emissions, the installed biogas production capacity and the amount of farm-based biomass, as a feedstock, has to be increased. A model was developed to describe a green gas production chain that consists of several digesters connected by a biogas grid to anupgrading and injection facility. The model calculates costs and energy use for 1 m3 of green gas. The number of digesters in the chain can be varied to find results for different configurations. Results are presented for a chain with decentralized production of biogas, i.e. a configuration with several digesters, and a centralized green gas production chain using a single digester. The model showed that no energy advantage per produced m3 green gas can be created using a biogas grid and decentralized digesters instead of one large-scale digester. Production costs using a centralized digester are lower, in the range of5 Vct to 13 Vct per m3, than in a configuration of decentralized digesters. The model calculations also showed the financial benefit for an operator of a small-scale digester wishing to produce green gas in the cooperation with nearby other producers. E.g. subsidies and legislation based on environmental arguments could encourage the use of decentralized digesters in a biogas grid.
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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.
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A transparent and comparable understanding of the energy efficiency, carbon footprint, and environmental impacts of renewable resources are required in the decision making and planning process towards a more sustainable energy system. Therefore, a new approach is proposed for measuring the environmental sustainability of anaerobic digestion green gas production pathways. The approach is based on the industrial metabolism concept, and is expanded with three known methods. First, the Material Flow Analysis method is used to simulate the decentralized energy system. Second, the Material and Energy Flow Analysis method is used to determine the direct energy and material requirements. Finally, Life Cycle Analysis is used to calculate the indirect material and energy requirements, including the embodied energy of the components and required maintenance. Complexity will be handled through a modular approach, which allows for the simplification of the green gas production pathway while also allowing for easy modification in order to determine the environmental impacts for specific conditions and scenarios. Temporal dynamics will be introduced in the approach through the use of hourly intervals and yearly scenarios. The environmental sustainability of green gas production is expressed in (Process) Energy Returned on Energy Invested, Carbon Footprint, and EcoPoints. The proposed approach within this article can be used for generating and identifying sustainable solutions. By demanding a clear and structured Material and Energy Flow Analysis of the production pathway and clear expression for energy efficiency and environmental sustainability the analysis or model can become more transparent and therefore easier to interpret and compare. Hence, a clear ruler and measuring technique can aid in the decision making and planning process towards a more sustainable energy system.
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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.
