The transformation from the current energy system to a decentralized renewable energy system requires the transformation of communities into energy neutral or even energy producing communities. Increasingly, citizens become 'prosumers' and pool their resources to start a local energy initiative. In this paper we present an in-depth study of networks that recently developed, which challenge the established way of centralized decision-making on energy resources. Many local communities are eager to promote sustainable energy production, to use local financial resources for the local community and to employ democratic governance of energy production and supply. Furthermore, we study how these co-operations are linked to local, regional and national networks for community energy. We use both Actor-Network Theory (ANT) and Social Movement Theory (SMT) to investigate the initiatives, as this allows a dynamic analysis of collective strategies. We discuss the obduracy of the energy system and how this system is challenged by new connections between communities and global networks and by new types of energy providers that are rooted in social networks. Furthermore, we draw attention to the way community energy networks provide a social innovation while realizing a decentralized and decarbonized energy system.
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.
The research explored how a Dutch energy cluster embedded within a larger context of European and global developments reflected complex dynamics due to changes in its context. The case study explored Energy Valley of the Netherlands, a peripheral region that meets the challenge of energy transition, regional development and national economic interests. The research engaged complex adaptive systems approach to gain insights into complex cluster dynamics to contribute to cluster study and policy.The research captured insights into increased complexity of an energy cluster due to energy transition and other developments in the cluster context, exacerbated by differences in perceptions and responses of stakeholders to the new challenges. Findings on cluster developments included insights into cluster context, cluster condition, cluster dynamics and cluster transformations, and the interconnectedness of such developments based on Energy Valley and supplementary cases of Karlstad and Silicon Valley. The research findings led to insights into cluster systems developments and a model capturing cluster emergence.The research contributed to cluster theory by developing a CAS approach for cluster study that developed a whole systems approach to understand cluster dynamics, offering to the field of cluster study a qualitative understanding of cluster systems developments. Insights into interconnected developments at the micro, macro and inter-systemic levels, and into energy clusters in the context of energy transition were results of the research. The broad scope and nature of the study meant limitations were inherent and therefore recommendations for future research were included. EU Cluster Policy motivated the research and hence recommendations for policy developments were also part of the research contribution
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Climate change is one of the most critical global challenges nowadays. Increasing atmospheric CO2 concentration brought by anthropogenic emissions has been recognized as the primary driver of global warming. Therefore, currently, there is a strong demand within the chemical and chemical technology industry for systems that can covert, capture and reuse/recover CO2. Few examples can be seen in the literature: Hamelers et al (2013) presented systems that can use CO2 aqueous solutions to produce energy using electrochemical cells with porous electrodes; Legrand et al (2018) has proven that CDI can be used to capture CO2 without solvents; Shu et al (2020) have used electrochemical systems to desorb (recover) CO2 from an alkaline absorbent with low energy demand. Even though many efforts have been done, there is still demand for efficient and market-ready systems, especially related to solvent-free CO2 capturing systems. This project intends to assess a relatively efficient technology, with low-energy costs which can change the CO2 capturing market. This technology is called whorlpipe. The whorlpipe, developed by Viktor Schauberger, has shown already promising results in reducing the energy and CO2 emissions for water pumping. Recently, studies conducted by Wetsus and NHL Stenden (under submission), in combination with different companies (also members in this proposal) have shown that vortices like systems, like the Schauberger funnel, and thus “whorlpipe”, can be fluid dynamically represented using Taylor-Couette flows. This means that such systems have a strong tendency to form vortices like fluid-patterns close to their air-water interface. Such flow system drastically increase advection. Combined with their higher area to volume ratio, which increases diffusion, these systems can greatly enhance gas capturing (in liquids), and are, thus, a unique opportunity for CO2 uptake from the air, i.e. competing with systems like conventional scrubbers or bubble-based aeration.
Wat is de mogelijke rol van lokale duurzame energiesystemen en –initiatieven in de overgang naar een duurzame samenleving? En hoe kunnen op lokale toepassing gerichte innovaties worden ontwikkeld en toegepast op een zodanige manier dat deze bij lokale systemen en initiatieven aansluiten?Deze vragen staan centraal in dit onderzoeksproject dat zich richt op innovaties die rekening houden met een grotere rol van burgers bij een duurzame energievoorziening. Het project behelst echter meer dan het verrichten van onderzoek. Het beoogt bouwstenen te leveren voor een duurzame samenleving waarin meer ruimte is voor lokale (burger)initiatieven. We stellen drie deelprojecten voor:1. een vergelijkende studie naar energiecoöperaties en vergelijkbare innovatieve initiatieven, binnen en buiten Nederland, in heden en verleden. Daarbij hopen we lering te kunnen trekken uit de succesvolle ervaringen in Denemarken en Oostenrijk en van innovaties door coöperatiesen collectieven in het verleden.2. een analyse van energie-innovaties die beogen aan te sluiten bij lokale energiesystemen. Concreet zal het onderzoek zich richten op speciale batterijen, ontwikkeld dor het bedrijf Dr.Ten, en een soort slimme grote zoneboiler, ontwikkeld door het gelijknamige bedrijf Ecovat.3. De ontwikkeling van drie scenario’s, gebaseerd op inzichten uit studies 1 en 2. De scenario’s zullen bijvoorbeeld inhoudelijk verschillen in de mate waarin deze geïntegreerd zijn in bestaande energiesystemen. Deze zullen worden ontwikkeld en besproken met relevante stakeholders.Het onderzoek moet leiden tot een nauwkeurig overzicht van de mate van interesse en betrokkenheid van stakeholders en van de beperkingen en mogelijkheden van lokale energiesystemen en daarbij betrokken technologie. Ook leidt het tot een routemap voor duurzame energiesystemen op lokaal niveau. Het project heeft een technisch aspect, onderzoek naar verfijning en ontwikkeling van de technologie en een sociaal en normatief aspect, studies naar aansluitingsmogelijkheden bij de wensen en mogelijkheden van burgers, instanties en bedrijven in Noord-Nederland. Bovenal is het integratief en ontwerpend van karakter.This research proposal will explore new socio- technical configurations of local community-based sustainable energy systems. Energy collectives successfully combine technological and societal innovations, developing new business and organization models. A better understanding of their dynamics and needs will contribute to their continued success and thereby contribute to fulfilling the Top Sector’s Agenda. This work will also enhance the knowledge position of the Netherlands on this topic. Currently, over 500 local energy collectives are active in The Netherlands, many of them aim to produce their own sustainable energy, with thousands more in Europe. These collectives search for a new more local-based ways of organizing a sustainable society, including more direct democratic decision-making and influence on local living environment. The development of the collectives is enabled by openings in policy but –evenly important - by innovations in local energy production technologies (solar panels, windmills, biogas installations). Their future role in the sustainable energy transition can be strengthened by careful aligning new organizational and technological innovations in local energy production, storage and smart micro-grids.
