Zoekresultaten

Sustainability of biogas production from biomass waste streams

Biogas plays an important role in many future renewable energy scenarios as a source of storable and easily extracted form of renewable energy. However, there remains uncertainty as to which sources of biomass can provide a net energy gain while being harvested in a sustainable, ecologically friendly manner. This study will focus on the utilization of common, naturally occurring grass species which are cut during landscape management and typically treated as a waste stream. This waste grass can be valorized through co-digestion with cow manure in a biogas production process. Through the construction of a biogas production model based on the methodology proposed by (Pierie, Moll, van Gemert, & Benders, 2012), a life cycle analysis (LCA) has been performed which determines the impacts and viability of using common grass in a digester to produce biogas. This model performs a material and energy flow analysis (MEFA) on the biogas production process and tracks several system indicators (or impact factors), including the process energy return on energy investment ((P)EROI), the ecological impact (measured in Eco Points), and the global warming potential (GWP, measured in terms of kg of CO2 equivalent). A case study was performed for the village of Hoogkerk in the north-east Netherlands, to determine the viability of producing a portion of the village’s energy requirements by biogas production using biomass waste streams (i.e. common grass and cow manure in a co-digestion process). This study concludes that biogas production from common grass can be an effective and sustainable source of energy, while reducing greenhouse gas emissions and negative environmental impacts when compared to alternate methods of energy production, such as biogas produced from maize and natural gas production.

Building and building systems energy prediction models to enhance energy flexibility control

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.

Opdrachtenboek IWP/Krachtig MKB

Met dit opdrachtenboek wordt een brug geslagen tussen het onderwijs en externe bedrijven met onderzoeksvragen. In deze tweede versie van het IWP-opdrachtenboek in het kader van het IWP/Krachtig MKB van het Kenniscentrum Biobased Economy (KC BBE) en het Instituut voor Life Sciences & Technology (ILST) vindt u ILST-gerelateerde opdrachten voor de ILST-(honours)student of docent-onderzoeker. Deze opdrachten zijn geacquireerd in samenwerking met de Gebiedscoöperatie Westerkwartier en de Gebiedscoöperatie Oost-Groningen (i.o.); ‘opgehaald’ tijdens het door het KC BBE georganiseerde grassymposium en als kennisvraag ontstaan door eerdere verrichte onderzoeken vanuit het IWP-gebeuren.

The transformative power of community energy: between theory and practice

Contribution to the conference: International Conference on New Pathways for Community Energy and Storage, 6-7 June 2019ABSTRACTThe community renewable energy is often seen as the way to address the societal challenge of energy transition. Many scholars foresee a key role for community energy in accelerating of the energy transition from fossil to renewable energy sources. For example, some authors investigated the transformative role of community renewable energy in the energy transition process (Seyfang and Smith, 2007; Seyfang and Haxeltine 2012; Seyfang et al. 2013; Seyfang et al. 2014; Smith et al. 2017; Martiskainen, 2017; Ruggiero et al. 2018; Hasanov and Zuidema, 2018; de Boer et al. 2018). Recognising the importance of community energy many scholars studied different internal and external conditions that contribute or hinder the success of local renewable energy initiatives (Walker et al. 2007; Bomberg and McEwen, 2012; Seyfang et al. 2013; Wirth, 2014; Hasanov and Zuidema, 2018; Ruggiero et al. 2018). One of such conditions contributing to the success of community energy initiatives is the capacity to adopt and utilize new technologies, for example, in the area of energy storage, which would increase flexibility and resilience of the communal energy supply systems.However, as noted by Ruggiero et al. (2018), the scholarship remains unclear on “how a very diverse and relatively small sector such as community energy could scale up and promote a change in the dominant way of energy production”. What is then the real transformative power of local renewable energy initiatives and whether community energy can offer an alternative to the existing energy system? This paper aims to answer these questions by confronting the critical review of theory with the recent practice of community energy in the Netherlands to build and scale up independent and self-sustaining renewable energy supply structures on the local and national scale and drafting perspectives on the possible role of community energy in the new energy system.

The role of perceived contribution to the energy transition in acceptance of renewable energy sources

A tool for analysing, researching and modeling energy efficiency, sustainability and flexibility of biogas chains operating as load balancer within decentralized (smart) energy systems

Renewable energy is often suggested as a possible solution for reducing greenhouse gas emissions and decreasing dependency on fossil energy sources. The most readily available renewable energy sources in Europe, wind, solar and biomass are dispersed by nature, making them ideally suited for use within Decentralized Energy Systems. Decentralized energy grids can help integrate renewable production, short lived by-products e.g. heat, minimize transport of energy carriers and fuel sources and reduce the dependency on fossils, hence, possibly improving the overall efficiency and sustainability of the energy distribution system. Within these grids balance between local renewable production and local energy demand is an important subject. Currently, fluctuations between demand and production of energy are mainly balanced by input from conventional power stations, which operate on storable fossil energy sources e.g. coal, oil, natural gas and nuclear. Within the long term scope of transition towards a low carbon intensive energy system, sustainable systems must be found which can replace fossil energy sources as load balancer in our energy supply systems.