Peer-to-peer (P2P) energy trading has been recognized as an important technology to increase the local self-consumption of photovoltaics in the local energy system. Different auction mechanisms and bidding strategies haven been investigated in previous studies. However, there has been no comparatively analysis on how different market structures influence the local energy system’s overall performance. This paper presents and compares two market structures, namely a centralized market and a decentralized market. Two pricing mechanisms in the centralized market and two bidding strategies in the decentralized market are developed. The results show that the centralized market leads to higher overall system self-consumption and profits. In the decentralized market, some electricity is directly sold to the grid due to unmatchable bids and asks. Bidding strategies based on the learning algorithm can achieve better performance compared to the random method.
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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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During the opening of the Hanze Energy Transition Centre or EnTranCe (2015-10-13) posters were on display for the King and for the public. During the opening these posters where accompanied by the researchers to explain their research in more detail if questions did arise.
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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%.
Synthetic ultra-black (UB) materials, which demonstrate exceptionally high absorbance (>99%) of visible light incident on their surface, are currently used as coatings in photovoltaic cells and numerous other applications. Most commercially available UB coatings are based on an array of carbon nanotubes, which are produced at relatively high temperature and result in numerous by-products. In addition, UB nanotube coatings require harsh application conditions and are very susceptible to abrasion. As a result, these coatings are currently obtained using a manufacturing process with relatively high costs, high energy consumption and low sustainability. Interestingly, an UB coating based on a biologically derived pigment could provide a cheaper and more sustainable alternative. Specifically, GLO Biotics proposes to create UB pigment by taking a bio-mimetic approach and replicate structures found in UB deep-sea fish. A recent study[1] has actually shown that specific fish have melanosomes in their skin with particular dimensions that allow absorption of up to 99.9% of incident light. In addition to this, recent advances in bacterial engineering have demonstrated that it is possible to create bacteria-derived melanin particles with very similar dimensions to the melanosomes in aforementioned fish. During this project, the consortium partners will combine both scientific observations in an attempt to provide the proof-of-concept for developing an ultra-black coating using bacteria-derived melanin particles as bio-based, sustainable pigment. For this, Zuyd University of Applied Sciences (Zuyd) and Maastricht University (UM) collaborate with GLO Biotics in the development of the innovative ‘BLACKTERIA’ UB coating technology. The partners will attempt at engineering an E. coli expression system and adapt its growth in order to produce melanin particles of desired dimensions. In addition, UM will utilize their expertise in industrial coating research to provide input for experimental set-up and the development of a desired UB coating using the bacteria-derived melanin particles as pigment.
In order to achieve much-needed transitions in energy and health, systemic changes are required that are firmly based on the principles of regard for others and community values, while at the same time operating in market conditions. Social entrepreneurship and community entrepreneurship (SCE) hold the promise to catalyze such transitions, as they combine bottom-up social initiatives with a focus on financially viable business models. SCE requires a facilitating ecosystem in order to be able to fully realize its potential. As yet it is unclear in which way the entrepreneurial ecosystem for social and community entrepreneurship facilitates or hinders the flourishing and scaling of such entrepreneurship. It is also unclear how exactly entrepreneurs and stakeholders influence their ecosystem to become more facilitative. This research programme addresses these questions. Conceptually it integrates entrepreneurial ecosystem frameworks with upcoming theories on civic wealth creation, collaborative governance, participative learning and collective action frameworks.This multidisciplinary research project capitalizes on a unique consortium: the Dutch City Deal ‘Impact Ondernemen’. In this collaborative research, we enhance and expand current data collection efforts and adopt a living-lab setting centered on nine local and regional cases for collaborative learning through experimenting with innovative financial and business models. We develop meaningful, participatory design and evaluation methods and state-of-the-art digital tools to increase the effectiveness of impact measurement and management. Educational modules for professionals are developed to boost the abovementioned transition. The project’s learnings on mechanisms and processes can easily be adapted and translated to a broad range of impact areas.
Lectoraat, onderdeel van NHL Stenden Hogeschool
Centre of Expertise, onderdeel van Hogeschool van Arnhem en Nijmegen
Lectoraat, onderdeel van Saxion
