Begin juli 2023 verscheen het concept Nationaal Plan Energiesysteem (NPE). Voor het eerst keek de overheid met een integrale blik naar het Nederlandse energiesysteem tot en met 2050. “Door de ontwikkelpaden van energieketens en vraagsectoren in kaart te brengen, wordt helder waar deze niet op elkaar aansluiten”, aldus het toenmalige kabinet. Op Energiepodium beschreef Martien Visser het concept NPE als een aangename verrassing. “Natuurlijk was het nog niet af. Zo ontbrak de interactie met de buurlanden en terecht werd opgemerkt dat nog “aanscherping nodig was” en ook dat er in het vervolg “volle aandacht voor de uitvoering in de praktijk” zou zijn. Een half jaar later presenteerde het vorige kabinet versie twee. Ik moet bekennen dat ik teleurgesteld was. Resultaten van de aangekondigde acties waren mager. Een financiële analyse waarmee ontwikkelpaden met elkaar konden worden vergeleken en scherpe keuzes konden worden gemaakt ontbrak zelfs helemaal. Ook was er geen uitvoeringsplan. Tijdgebrek? Inmiddels zijn we een kabinet en anderhalf jaar verder. Wordt er nog aan het NPE gewerkt? Bestaat het NPE-team nog wel?”
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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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Energy efficiency has gained a lot of prominence in recent debates on urban sustainability and housing policy due to its potential consequences for climate change. At the local, national and also international level, there are numerous initiatives to promote energy savings and the use of renewable energy to reduce the environmental burden. There is a lot of literature on energy saving and other forms of energy efficiency in housing. However, how to bring this forward in the management of individual housing organisations is not often internationally explored. An international research project has been carried out to find the answers on management questions of housing organisations regarding energy efficiency. Eleven countries have been included in this study: Germany, the United Kingdom (more specifically: England), France, Sweden, Denmark, the Netherlands, Switzerland, Slovenia, the Czech Republic, Austria and Canada. The state of the art of energy efficiency in the housing management of non-profit housing organisations and the embedding of energy efficiency to improve the quality and performance of housing in management practices have been investigated, with a focus on how policy ambitions about energy efficiency are brought forward in investment decisions at the estate level. This paper presents the conclusions of the research
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The goal of a local energy community (LEC) is to create a more sustainable, resilient, and efficient energy system by reducing dependence on centralized power sources and enabling greater participation and control by local communities and individuals. LEC requires transformations in local energy systems, and strongly depends on the preferences and actions of the local actors involved. The necessity for extensive stakeholder involvement adds complexity to the energy transition, posing a significant challenge for all involved parties. The municipality of Leidschendam-Voorburg has committed to the national decision for energy transition. It has taken a strategic approach by proceeding De Heuvel/Amstelwijk as the pioneer in this initiative, leading the way for other neighborhoods to follow. It is crucial to devise strategies that effectively facilitate stakeholder engagement. To this end, a thorough stakeholder analysis is needed. Such an analysis can focus on the identification of key stakeholders, their interests, their influence, and their behavioral characteristics in relation to the energy transition. Additionally, it's crucial to uncover the challenges encountered by these stakeholders and finally develop appropriate strategies to address them hence enhance their engagement. This thesis begins with an introduction to the research background, including a presentation of the case study and a statement of the problem identified in the field, followed by the research questions underpinning the study. A thorough literature review ensues, providing a robust synthesis of existing research relating to stakeholder engagement in LECs, with a view to expediting energy transitions. The literature review not only forms the foundation for the research methods adopted in this study but also promotes in the construction of the conceptual model. Subsequent to the literature review, the research method is detailed. The filed research is conducted in five steps: Step 1 - identification of stakeholders, Step 2 - prioritization of stakeholders, Step 3 - interviewing, Step 4 - data analysis, including stakeholder profiling with mapping and addressing challenges, and finally, Step 5 - proposal of strategies for stakeholder engagement enhancement based on the expected and current levels of stakeholders engagement. This research collects necessary information to understand the profiles of stakeholders in De Heuvel/Amstelwijk, tackle challenges faced by different stakeholders, propose strategies to increase stakeholders engagement. It not only aims to enrich the depth of theoretical knowledge on the subject matter but also strives to aid in the development of a localized energy strategy that is optimally suited for the De Heuvel/Amstelwijk neighborhood as good example for other neighborhoods.
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There is an ongoing transition towards renewable energy sources in order to combat climate change. National power grids are suffering due to the rapid introduction of new energy sources and have other disadvantages. Local Energy Systems (LESs) are a beneficial example of an off-grid energy systems that can aid the energy transition. LESs are community driven and require participating and steering members. This can be achieved through empowering end-users to become active participants or steerers. End-users can be empowered to become an active participant through engagement with energy management activities. This does not work for empowering to steer, which begs the question, how to empower end-users or participants to become steerers in Local Energy Systems. Through a literature review this study explores the importance of establishing a group containing steerers with diverse skills, strong leadership, and engagement with the environment and community. Additionally, this study identifies the strategy that empowers end-users to steer. Which is training technological and managemental skills; and training capabilities in establishing relations with local participants and intermediary organisations. To apply these findings more precisely a secondary analysis is conducted on a survey with 599 participants. The original study researched willingness to participate in LESs, however the secondary analysis establishes three important factors to predict willingness to steer. These are energy independence, community trust, and community resistance. Additionally, men with a high level of education are most willing to become steerers per default, thus different demographics generally require more empowerment.
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Energiebeheer gericht aanpakken, Het analyseren van doelstellingen, resultaten en impacts van energie- en broeikasgasbeheersprogramma’s in bedrijven (met een samenvatting in het Nederlands): De wereldwijde uitstoot van broeikasgassen moet drastisch worden teruggebracht om de mondiale stijging van de temperatuur tot het relatief veilige niveau van maximaal 2 graden Celsius te beperken. In de komende decennia zal de verbetering van de energie-efficiëntie de belangrijkste strategie zijn voor het verminderen van de energiegerelateerde uitstoot van broeikasgassen. Hoewel er een enorm potentieel is voor verbetering van de energie-efficiëntie, wordt een groot deel daarvan nog niet benut. Dit wordt veroorzaakt door diverse investeringsbarrières die de invoering van maatregelen voor energie-efficiëntie verbetering verhinderen. De invoering van energiemanagement wordt vaak beschouwd als een manier om dergelijke barrières voor energiebesparing te overwinnen. De invoering van energiemanagement in bedrijven kan worden gestimuleerd door de introductie van programma's voor energie-efficiëntie verbetering en vermindering van de uitstoot van broeikasgassen. Deze programma's zijn vaak een combinatie van verschillende elementen zoals verplichtingen voor energiemanagement; (ambitieuze) doelstellingen voor energiebesparing of beperking van de uitstoot van broeikasgassen; de beschikbaarheid van regelingen voor stimulering, ondersteuning en naleving; en andere verplichtingen, zoals openbare rapportages, certificering en verificatie. Tot nu toe is er echter beperkt inzicht in het proces van het formuleren van ambitieuze doelstellingen voor energie-efficiëntie verbetering of het terugdringen van de uitstoot van broeikasgassen binnen deze programma's, in de gevolgen van de invoering van dergelijke programma's op de verbetering van het energiemanagement, en in de impact van deze programma's op energiebesparing of de vermindering van de uitstoot van broeikasgassen. De centrale onderzoeksvraag van dit proefschrift is als volgt geformuleerd: "Wat is de impact van energie- en broeikasgasmanagement programma’s op het verbeteren van het energiemanagement in de praktijk, het versnellen van de energieefficiëntie verbetering en het beperken van de uitstoot van broeikasgassen in bedrijven?".
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Citizen participation in local renewable energy projects is often promoted as many suppose it to be a panacea for the difficulties that are involved in the energy transition process. Quite evidently, it is not; there is a wide variety of visions, ideologies and interests related to an ‘energy transition’. Such a variety is actually a precondition for a stakeholder participation process, as stakeholder participation only makes sense if there is ‘something at stake’. Conflicting viewpoints, interests and debates are the essence of participation. The success of stakeholder participation implies that these differences are acknowledged, and discussed, and that this has created mutual understanding among stakeholders. It does not necessarily create ‘acceptance’. Renewable energy projects often give rise to local conflict. The successful implementation of local renewable energy systems depends on the support of the local social fabric. While at one hand decisions to construct wind turbines in specific regions trigger local resistance, the opposite also occurs! Solar parks sometimes create a similar variation: Various communities try to prevent the construction of solar parks in their vicinity, while other communities proudly present their parks. Altogether, local renewable energy initiatives create a rather chaotic picture, if regarded from the perspective of government planning. However, if we regard the successes, it appears the top down initiatives are most successful in areas with a weak social fabric, like industrial areas, or rather recently reclaimed land. Deeply rooted communities, virtually only have successful renewable energy projects that are more or less bottom up initiatives. This paper will first sketch why participation is important, and present a categorisation of processes and procedures that could be applied. It also sketches a number of myths and paradoxes that might occur in participation processes. ‘Compensating’ individuals and/or communities to accept wind turbines or solar parks is not sufficient to gain ‘acceptance’. A basic feature of many debates on local renewable energy projects is about ‘fairness’. The implication is that decision-making is neither on pros and cons of various renewable energy technologies as such, nor on what citizens are obliged to accept, but on a fair distribution of costs and benefits. Such discussions on fairness cannot be short cut by referring to legal rules, scientific evidence, or to standard financial compensations. History plays a role as old feelings of being disadvantaged, both at individual and at group level, might re-emerge in such debates. The paper will provide an overview of various local controversies on renewable energy initiatives in the Netherlands. It will argue that an open citizen participation process can be organized to work towards fair decisions, and that citizens should not be addressed as greedy subjects, trying to optimise their own private interests, but as responsible persons.
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Although there is an array of technical solutions available for retrofitting the building stock, the uptake of these by owner‐occupants in home improvement activities is lagging. Energy performance improvement is not included in maintenance, redecoration, and/or upgrading activities on a scale necessary to achieve the CO2 reduction aimed for in the built environment. Owner‐occupants usually adapt their homes in response to everyday concerns, such as having enough space available, increasing comfort levels, or adjusting arrangements to future‐proof their living conditions. Home energy improvements should be offered accordingly. Retrofit providers typically offer energy efficiency strategies and/or options for renewable energy generation only and tend to gloss over home comfort and homemaking as key considerations in decision‐making for home energy improvement. In fact, retrofit providers struggle with the tension between customisation requirements from private homeowners and demand aggregation to streamline their supply chains and upscale their retrofit projects. Customer satisfaction is studied in three different Dutch approaches to retrofit owner‐occupied dwellings to increase energy efficiency. For the analysis, a customer satisfaction framework is used that makes a distinction between satisfiers, dissatisfiers, criticals, and neutrals. This framework makes it possible to identify and structure different relevant factors from the perspective of owner‐occupants, allows visualising gaps with the professional perspective, and can assist to improve current propositions.
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The issues in our field are complex and demand a ‘unifying organisation and pioneer’ that can make a substantial contribution to a future in which there is sufficient healthy food in a healthy living environment. Our Institution Plan describes how we, HAS green academy, intend to make that contribution.
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