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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Global awareness on energy consumption and the environmental impacts of fossil fuels boost actions and create more supportive policies towards sustainable energy systems, in the last energy outlook, by the International Energy Agency, it was forecasted totals of 3600 GW from 2016 to 2040 of global deployment of renewables sources (RES), covering 37% of the power generation. While the Natural Gas overtake the coal demand in the energy mix, growing around 50%, manly by more efficiency system and the use of LNG for long-distance gas trades. The energy infrastructure will be more integrated, deploying decentralized and Hybrid Energy Networks (HEN).This transformation on the energy mix leads to new challenges for the energy system, related to the uncertainty and variability of RES, such as: Balancing flexibility, it means having sufficient resources to accommodate when variable production increase and load levels fall (or vice versa). And Efficiency in traditional fired plants, the often turn on and off or modify their output levels to accommodate changes in variable demand, can result in a decrease in efficiency, particularly from thermal stresses on equipment. This paper focus in the possibility to offer balancing resources from the LNG regasification, while ensure an efficient system.In order to asses this issue, using the energy Hub concept a model of a distributed HEN was developed. The HEN consist in a Waste to Energy plant (W2E), a more sustainable case of Combine Heat and Power (CHP) coupled with a LNG cold recovery regasification. To guarantee a most efficiency operation, the HEN was optimized to minimized the Exergy efficiency, additionally, the system is constrained by meeting Supply with variable demand, putting on evidence the sources of balancing flexibility. The case study show, the coupled system increases in overall exergy efficiency from 25% to 35% compared to uncoupled system; it brings additional energy between 1.75 and 3 MW, and it meets variable demand in the most exergy efficient with power from LNG reducing inputs of other energy carriers. All this indicated that LNG cold recovery in regasification coupled other energy systems is as promising tool to support the transition towards sustainable energy systems.
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Sustainability has become an important blueprint to achieve a better future for all, and as part of this process, nations are called to accelerate an energy transition towards clean energy solutions. However, an often-neglected pillar is educating individuals on the benefits and challenges of energy efficiency and renewable energy, especially among young people. Their support and willingness to use clean energies will be a significant driver in short, medium and long term. However, reality shows that attention from youth on these issues has not been sufficient yet. Formal education settings become therefore a key place to educate youth in the energy transition. In search of innovative approaches, game-based learning is gaining popularity among scholars and practitioners; it can contribute to content development of complex issues by integrating insights from different disciplines in an interactive, fun and engaging manner.In this context, we would like to present “the We-Energy Game” as an innovative educational strategy which makes use of game-based learning to create understanding on the challenges in the provision of affordable energy from renewable sources for an entire town. During the game, players negotiate, from their respective roles, which energy source they want to employ and on which location, with the goal to make a village or city energy neutral. The game has been played by students in higher education institutions in The Netherlands.In addition to introducing the game, a study is presented on the effects of the game on students´ awareness on the energy transition, self-efficacy -the feeling that they can contribute to a sustainable energy transition in their towns by themselves- and collective efficacy -the feeling that they can contribute to a sustainable energy transition in their towns together with their community-. For that purpose, we conducted a survey with 100 bachelor (Dutch and international) students aged between 18 and 30 years old, at Hanze University of Applied Sciences, before and after playing the game. We also conducted a group discussion with a smaller group of students to understand their opinion about the game. From the survey, results reveal an increase in awareness about the energy transition, as well as (slightly higher) collective efficacy compared to self-efficacy. From the group discussion, findings reveal that the game makes students reflect on the complexity of the process and need for collaboration among different stakeholders. It also shows how educational games have still a long way to go to achieve the high levels of engagement of commercial games, despite the fact that students still preferred to have this type of interactive practice rather than a traditional class characterized by a unidirectional transmission of information. Different implications must be taken into account for educators when interested in implementing game-based learning in class, including immediate feedback, appropriate length of gameplay during class, and time for a reflection and critical thinking after playing the game.
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Be energy future proof: - So, be energy future proof, you do now no how. - Include legislation in this but do not rely on legislation as a guide line. - Base your future-proof energy system on the trends and prepare for that. - But be aware: this ain’t easy.
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Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
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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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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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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.
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The future energy system could benefit from the integration of independent gas, heat and electricity infrastructures. Such a hybrid energy network could support the increase of intermittent renewable energy sources by offering increased operational flexibility. Nowadays, the expectations on Natural Gas resources forecast an increase in the application of Liquefied Natural Gas (LNG), as a means of storage and transportation, which has a high exergy value. Therefore, we analyzed the integration of decentralized LNG regasification with a Waste-to-Energy (W2E) plant for a practice-based case to get an idea on how it might affect the balancing of supply and demand, under optimized exergy efficient conditions. We compared an independent system with an integrated system that consists of the use of the LNG cold to cool the condenser of the W2E plant, as well as the expansion of the regasified LNG in an expander, using a simplified deterministic model based on the energy hub concept. We use the hourly measured electricity and heat demand patterns for 200 households with 35% of the households producing electricity from PV according to a typical measured solar insolation pattern in The Netherlands. The results indicate that the integration affects the imbalance for electricity and heat compared to the independent system. If the electricity demand is met, both the total yearly heat shortage and heat excess are reduced for the integrated system. If the heat demand is met, the total yearly electricity shortage is also reduced (with 100 MWh). However, the total yearly electricity excess is then increased (with 300 MWh). We observed that these changes are solely due to the increase in exergy efficiencies for heat and electricity of the W2E Rankine cycle. The efficiency of the expander is too low to offer a significant contribution to the electricity demand. Therefore, future research should focus on the affect that can be obtained by to other means of integration (e.g. Organic Rankine Cycle and Stirling Cycle).
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This manifesto describes the notion of sustainable development according to its basic appeal for economic, social and environmental value-creation, together with the implications of its meaning at the level of the individual (the manager), the organisation (the business) and society. As sustainable tourism is focused on the long term, foresight is used to develop four scenarios for a sustainable tourism industry in 2040: “back to the seventies”, “captured in fear”, “unique in the world”, and “shoulders to the wheel”. The implications of the scenarios are mapped for four distinct types of organisational DNA: the blue organisation focusing on quality, professionalism and efficiency, the red organisation for whom challenge, vision and change are most important, the yellow organisation addressing energy, optimism and growth, and the green organisation which is led by care, tradition and security. The manifest concludes with strategic propositions for tourism organisations in each of the four business types and each of the four scenarios.
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