To avoid energy scarcity as well as climate change, a transition towards a sustainable society must be initiated. Within this context, governmental bodies and/or companies often note sustainability as an end goal, for instance as a green circular economy. However, if sustainability cannot be clearly defined as an end goal or measured uniformly and transparently, then the direction and progress towards this goal can only be roughly followed. A clear understanding of and a transparent, uniform measuring technique for sustainability are hence required for sustainable and circular (renewable) energy production pathways (REPPs), as society is asking for an integrated and understandable overview of the decision-making and planning process towards a future sustainable energy system. Therefore, within this dissertation, a new approach is proposed for measuring and optimizing the sustainability of REPPs; it is useful for the analysis, comparison, and optimization of REPP systems on all elements of sustainability. The new approach is applied and tested on a case based on farm-scale, anaerobic digestion (AD), biogas production pathways.
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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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The municipality of Apeldoorn had polled the interest among its private home-owners to turn their homes energy neutral. Based on the enthusiastic response, Apeldoorn saw the launch of the Energy Apeldoorn (#ENEXAP) in 2011. Its goal was to convert to it technically and financially possible for privately owned homes to be refurbished and to energy neutral, taking the residential needs and wishes from occupants as the starting point. The project was called an Expedition, because although the goal was clear, the road to get there wasn’t. The Expedition team comprised businesses, civil-society organisations, the local university of applied sciences, the municipality of Apeldoorn, and of course, residents in a central role. The project was supported by Platform31, as part of the Dutch government’s Energy Leap programme. The #ENEXAP involved 38 homes, spread out through Apeldoorn and surrounding villages. Even though the houses were very diverse, the group of residents was quite similar: mostly middle- aged, affluent people who highly value the environment and sustainability. An important aspect of the project was the independent and active role residents played. In collaboration with businesses and professionals, through meetings, excursions, workshops and by filling in a step- by-step plan on the website, the residents gathered information about their personal situation, the energy performance of their home and the possibilities available for them to save and generate energy themselves. Businesses were encouraged to develop an integrated approach for home-owners, and consortia were set up by businesses to develop the strategy, products and services needed to meet this demand. On top of making minimal twenty from the thirty-eight houses in the project energy neutral, the ultimate goal was to boost the local demand for energy- neutral refurbishment and encourage an appropriate supply of services, opening up the (local) market for energy neutral refurbishment. This paper will reflect on the outcomes of this collective in the period 2011-2015.
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Uit het rapport: "Deze onderzoeksagenda is tot stand gebracht door de lectoren die samenwerken in het Nationaal Lectoren Platform Urban Energy. Alle betrokkenen bij het platform zijn in staat gesteld om bij te dragen aan de tekst, speciale dank daarbij voor de bijdragen en commentaren vanuit de TKI Urban Energy en de HCA topsector Energie."
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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."
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The energy efficiency and sustainability of an anaerobic green gas production pathway was evaluated, taking into account five biomass feedstocks, optimization of the green gas production pathway, replacement of current waste management pathways by mitigation, and transport of the feedstocks. Sustainability is expressed by three main factors: efficiency in (Process) Energy Returned On Invested (P)EROI, carbon footprint in Global Warming Potential GWP(100), and environmental impact in EcoPoints. The green gas production pathway operates on a mass fraction of 50% feedstock with 50% manure. The sustainability of the analyzed feedstocks differs substantially, favoring biomass waste flows over, the specially cultivated energy crop, maize. The use of optimization, in the shape of internal energy production, green gas powered trucks, and mitigation can significantly improve the sustainability for all feedstocks, but favors waste materials. Results indicate a possible improvement from an average (P)EROI for all feedstocks of 2.3 up to an average of 7.0 GJ/GJ. The carbon footprint can potentially be reduced from an average of 40 down to 18 kgCO2eq/GJ. The environmental impact can potentially be reduced from an average of 5.6 down to 1.8 Pt/GJ. Internal energy production proved to be the most effective optimization. However, the use of optimization aforementioned will result in les green gas injected into the gas grid as it is partially consumed internally. Overall, the feedstock straw was the most energy efficient, where the feedstock harvest remains proved to be the most environmentally sustainable. Furthermore, transport distances of all feedstocks should not exceed 150 km or emissions and environmental impacts will surpass those of natural gas, used as a reference. Using green gas as a fuel can increase the acceptable transportation range to over 300 km. Within the context aforementioned and from an energy efficiency and sustainable point of view, the anaerobic digestion process should be utilized for processing locally available waste feedstocks with the added advantage of producing energy, which should first be used internally for powering the green gas production process.
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Anaerobic digestion (AD) can play an important role in achieving the renewable energy goals set within the European Union. Within this article the focus is placed on reaching the Dutch local renewable production goal set for the year 2020 with locally available biomass waste flows, avoiding intensive farming and long transport distances of biomass and energy carriers. The bio-energy yields, efficiency and environmental sustainability are analyzed for five municipalities in the northern part of the Netherlands, using three utilization pathways: green gas production, combined heat and power, and waste management. Literature has indicated that there is sufficient bio-energy potential in local waste streams to reach the aforementioned goal. However, the average useful energy finally produced by the AD production pathway is significantly lower, often due to poor quality biomass and difficult harvesting conditions. Furthermore, of the potential bio-energy input in the three utilization pathways considered in this article, on average: 73% can be extracted as green gas; 57% as heat and power; and 44% as green gas in the waste management pathway. This demonstrates that the Dutch renewable production goal cannot be reached. The green gas utilization pathway is preferable for reaching production goals as it retains the highest amount of energy from the feedstock. However, environmental sustainability favors the waste management pathway as it has a higher overall efficiency, and lower emissions and environmental impacts. The main lessons drawn from the aforementioned are twofold: there is a substantial gap between bio-energy potential and net energy gain; there is also a gap between top–down regulation and actual emission reduction and sustainability. Therefore, a full life cycle-based understanding of the absolute energy and environmental impact of biogas production and utilization pathways is required to help governments to develop optimal policies serving a broad set of sustainable objectives. Well-founded ideas and decisions are needed on how best to utilize the limited biomass availability most effectively and sustainably in the near and far future, as biogas can play a supportive role for integrating other renewable sources into local decentralized energy systems as a flexible and storable energy source.
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The energy transition requires the transformation of communities and neighbourhoods. It will have huge ramifications throughout society. Many cities, towns and villages have put together ambitious visions about how to achieve e.g. energy neutrality, zero-emission or zero-impact. What is happening at the local level towards realizing these ambitions? In a set of case study’s we investigate the following questions: How are self-organized local energy initiatives performing their self-set tasks? What obstacles are present in the current societal set-up that can hinder decentralized energy production? In our cases local leadership, vision, level of communication and type of organisation are important factors of the strength of the ‘local network’. (Inter)national energy policy and existing energy companies largely determine the ‘global’ or outside network. Stronger regional and national support structures, as well as an enabling environment for decentralized energy production, are needed to make decentralized sustainable energy production a success.
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This century, greenhouse gas emissions such as carbon dioxide, methane and nitrogen oxides must be significantly reduced. Greenhouse gases absorb and emit infrared radiation that contributes to global warming, which can lead to irreversible negative consequences for humans and the environment. Greenhouse gases are caused by the burning of fossil fuels such as crude oil, coal, and natural gas, but livestock farming, and agriculture are also to blame. In addition, deforestation contributes to more greenhouse gases. Of the natural greenhouse gases, water vapor is the main cause of the greenhouse effect, accounting for 90%. The remaining 10% is caused from high to low by carbon dioxide, methane, nitrogen oxides, chlorofluorocarbons, and ozone. In addition, there are industrial greenhouse gases such as fluorinated hydrocarbons, sulphurhexafluoride and nitrogen trifluoride that contribute to the greenhouse effect too. Greenhouse gases are a major cause of climate change, with far-reaching consequences for the welfare of humans and animals. In some regions, extreme weather events like rainfall are more common, while others are associated with more extreme heat waves and droughts. Sea level rise caused by melting ice and an increase in forest fires are undesirable effects of climate change. Countries in low lying areas fear that sea level rise will force their populations to move to the higher lying areas. Climate change is affecting the entire world. An estimated 30-40% o f the carbon dioxide released by the combustion of fossil fuels dissolves into the surface water resulting in an increased concentration of hydrogen ions. This causes the seawater to become more acidic, resulting in a decreasing of carbonate ions. Carbonate ions are an important building block for forming and maintaining calcium carbonate structures of organisms such as oysters, mussels, sea urchins, shallow water corals, deep sea corals and calcareous plankton.
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