BackgroundThis research study applied the 'Integrated Energy Landscape Approach and the Ecosystem Services Framework' to formulate a pre-proposal for a Positive Energy District in the Hoogkerk Zuid neighborhood in Groningen, the Netherlands.ResultsThe proposed energy saving and energy generation interventions are sufficient to cover the energy usage of the district, while an energy surplus is generated. The pre-proposal has been developed within a participatory process, organized by the authors in close collaboration with key local stakeholders. The identification of local ecosystem services served as a crucial starting point for this study, while it also served for the basis for analysing the subsequent trade-offs and synergies derived from the proposed energy transition interventions. Then, a sustainable business case model was developed based on this Positive Energy District pre-proposal. The main outcome of the model lies in the value creation through cost savings from not using traditional energy sources and selling electricity to the grid. In addition, the economic value of the preserved ecosystem services and of the synergies generated by the pre-proposal are also included in the model.ConclusionsBeyond the local case, the results lay the groundwork for more systematic studies on merging the methodologies of Positive Energy District development, the Ecosystem Framework and the Integrated Energy Landscape approach. Finally, by adding the benefits of ecosystem services and synergies as a significant contributor in the financial analysis and decision-making process, this study opens the door to a new approach to the evaluation of sustainable projects.
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This research study applied the Integrated Energy Landscape Approach and the Ecosystem Services Framework in order to formulate a pre-proposal for a Positive Energy District in the Hoogkerk Zuid neighborhood in Groningen, the Netherlands. The proposed interventions are sufficient to cover the energy usage of the district, while an energy surplus is generated. The pre-proposal has been developed within a participatory process, organized by the authors in close collaboration with key local stakeholders. The identification of the local ecosystem services served as a crucial starting point for this study, while it also provided the transparent information base for analyzing the subsequent trade-offs and synergies derived by the proposed energy transition interventions. Then, a sustainable business case model has been developed based on this Positive Energy District pre-proposal. The main outcome of the model lies within the value creation through cost savings from foregoing traditional energy sources and sale of electricity to the grid, but also through including the economic value of ecosystem services and synergies when integrating the Renewable Energy Technologies. Beyond the local case, the findings lay the groundwork for more systematic studies on merging the methodologies of Positive Energy District development, the Ecosystem Framework and the Integrated Energy Landscape approach. Finally, by adding the benefits of ecosystem services and synergies as a significant contributor in the financial analysis and decision making process, this study opens the door for a new approach of valuing sustainable projects.
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This report relates to the Horizon 2020 project entitled ‘Making City’. The report was conducted by the Hanze University of Applied Sciences to the benefit of theMunicipality of Groningen and other consortium partners in the Making City project and addresses the legal impediments that may arise when creating and achieving a Positive Energy District (PED). In doing so, it specifically addresses the situation in the city of Groningen and the legal framework of the Netherlands.This report highlights legal developments of (upcoming) EU and mostly Dutch legislation related to a PED, such as the Collective Heat and Supply Act (Warmtewet) and the Environmental Act. Moreover, smart contracts used in the Block chain technology is discussed and a chapter on Intellectual Property legislation is included which becomes relevant when using new innovations and technologies. Furthermore, it identifies certain legal barriers that emerged in the establishment of the Groningen PED.
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This report has been established within the Flexiheat project. Flexiheat has focused on increasing flexibility in district heating systems. The intelligent district heating network is a dynamic network: an open network where different waste heat and renewable energy sources are connected, that has multiple producers and groups of consumers and facilitates the connection between different energy infrastructures (gas, heat and electricity). Eventually this will lead to an optimal deployment of the available heat sources and an increased cost-efficiency of district heating. Flexiheat aims to develop new concepts for these intelligent, flexible district heating networks. One of the strategies is to allow third party access to the network. A smart control system is developed to manage the heat flows across the network. This system makes use of dynamic pricing. In this exploration the concept of third party access in relation to the Flexiheat project will be discussed. The development of new business and price models based on the Flexiheat approach has led to an analysis of possible alternative price models for consumers.
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Positive Energy Districts (PEDs) are a promising approach to urban energy transformation, aiming to optimize local energy systems and deliver environmental, social and economic benefits. However, their effectiveness and justification for investment rely on understanding the additional value they provide (additionality) in comparison to current policies and planning methods. The additionality perspective is not used yet in current evaluations of PED demonstrations and pilots. Therefore, this paper introduces the concept of additionality in the evaluation of PEDs, focusing on the additional benefits they bring and the circumstances under which they are most effective. We discuss the additionality of PEDs in addressing the challenges of climate neutrality and energy system transformation in three European cities that are funded by the European Commission’s H2020 Programme. It should be noted that given the ongoing status of these projects, the assessment is mainly based on preliminary results, as monitoring is still ongoing and quantitative results are not yet available. The paper discusses the drivers and barriers specific to PEDs, and highlights the challenges posed by technical complexities, financing aspects and social and legal restrictions. Conclusions are drawn regarding the concept of additionality and its implications for the wider development of PEDs as a response to the challenges of climate neutrality and energy system transformation in cities. We conclude that the additionality perspective provides valuable insights into the impact and potential of PEDs for societal goals and recommend this approach for use in the final evaluation of R&I projects involving PEDs using actual monitored data on PEDs.
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It is assumed by the projects demonstrating Positive Energy District (PED) concepts in cities across Europe that citizens should want and need to be involved in the development of new energy concepts, such as PEDs for these concepts to be deployed successfully. Six different PED research and innovation projects are investigating the types and expectations of citizen engagement. They evaluate the impact of energy citizenship on the success of PED deployment across Europe.
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Positive Energy Districts (PEDs) have the potential of accelerating the decarbonization of urban areas and promoting scalability between cities. The development and real-world implementation of such innovative concepts can be enhanced through urban energy modelling. However, assessing PEDs can be challenging, and information on this topic is scarce and fragmented. The main contribution of this paper is collecting and analyzing challenges and limitations of energy modelling software for assessing PEDs through five case studies in Italy, Spain, The Netherlands, Denmark and Canada. Case studies are assessed first from a modelling approach, then the main identified challenges and limitations of modelling tools for PEDs are discussed, and finally, various ongoing trends and research needs in this field are suggested.
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To achieve the “well below 2 degrees” targets, a new ecosystem needs to be defined where citizens become more active, co-managing with relevant stakeholders, the government, and third parties. This means moving from the traditional concept of citizens-as-consumers towards energy citizenship. Positive Energy Districts (PEDs) will be the test-bed area where this transformation will take place through social, technological, and governance innovation. This paper focuses on benefits and barriers towards energy citizenships and gathers a diverse set of experiences for the definition of PEDs and Local Energy Markets from the Horizon2020 Smart Cities and Communities projects: Making City, Pocityf, and Atelier.
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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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Publicatie naar aanleiding van de door Stadslab European Urban Design Laboratory georganiseerde Master Class met als thematiek het ontwerpen van een Innovative District voor de Poolse stad Lublin. De Master Class werd gevolgd door 8 internationale deelnemers en stond onder supervisie van Didier Rebois (Europan, Parijs), Marc Glaudemans (Fontys) en Juliette van der Meijden (Fontys)
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