Significant factors in the success or failure of energy transition arise from the spatial potential of places and their communities. Scenario planning appears to be an appropriate design instrument to enable architects to unveil, conceptualise, imagine, test and communicate this potential to stakeholders. This paper critically refelcts on the scenario as an architectural design instrument. Inscribed with political intentions, scenario planning may be a far from neutral design instrument. Instead of triggering communities to explore local energy potential, a scenario may have a normative effect on a community's imagination. The paper aims to define guidelines for the deployment of scenarios in an open, participatory planning process. To mediate in a local participatory planning process, we argue, scenarios should be situational, dynamic and open-ended, allowing or even triggering communities to (re)define the issues relevant to a place during the ongoing process of energy-transition. How, when and where should scenarios be deployed in order to enable communities to understand and develop their local energy potential?
DOCUMENT
Uit het rapport: "Deze onderzoeksagenda is tot stand gebracht door de lectoren die samenwerken in het Nationaal Lectoren Platform Urban Energy. Alle betrokkenen bij het platform zijn in staat gesteld om bij te dragen aan de tekst, speciale dank daarbij voor de bijdragen en commentaren vanuit de TKI Urban Energy en de HCA topsector Energie."
DOCUMENT
Energy planning in the built environment increasingly takes place in local settings. Suitable planning models should therefore be able to capture local dynamics, such as stakeholder behaviour, resource availability and building characteristics. In relation to the key challenges of energy transition in the built environment, building efficiency and renewable heating, little attention has been paid to the model characteristics needed to address these challenges. This paper analyses the characteristics of available models from the scientific community and the professional practice. Secondly, the paper reviews modelling approaches for integrating social factors within techno-economic models, as many local dynamics have a non-technical nature. Based on the gaps identified in the analysis, an analytical framework is proposed for local energy planning models for the built environment. Building characteristics, social context factors, temporal dynamics and spatial characteristics have been identified as key building blocks for a new modelling approach. To be able to deal with the socio-technical context, an integrated, socio-technical approach is suggested. This model collaboration, consisting of model calculations and empirical and participatory methods, will be capable of better supporting decision-making in a local, multistakeholdercontext.
LINK
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.
DOCUMENT
Participatory energy planning at the local level engages citizens, builds legitimacy and trust, and increases successful implementation of renewable energies. In the context of heat planning, technology choices highly vary depending on the local context and social conditions and including social input therefore benefits the planning process. This research adds to the current literature, which lacks concrete examples and verified approaches that clarify what the guiding principles of participatory energy planning are and how the opportunities and challenges can be dealt with. This paper elaborates on these opportunities and challenges and proposes a process design, using multiple tools (a survey, an Information-choice Questionnaire, and workshops) to collect the social input that is necessary to make technology choices in a participatory manner. The process design is applied and tested in a case study of a Dutch neighbourhood and lessons learned are drafted as a basis for further research.
DOCUMENT
It is essential to consider the social context when designing sustainable energy systems that lead to successful implementation in neighbourhoods. Current methods often only consider techno-economic aspects and are insufficiently capable of including social factors, because they are unclear about which social factors are relevant and how they can be quantified. This paper explores how neighbourhoods can be characterized socially by studying pre-existing neighbourhood characteristics, focussed on socio-economic status and social cohesion. The paper is built around four case-studies in the Netherlands, which are analysed both quantitatively and qualitatively. The paper shows how the social context can be defined by 1) proposing a theoretical framework of social factors, 2) quantifying these social factors by survey research, 3) interpretating this data using qualitative casestudy data and 4) quantifying success in the cases and relating this to the scores of the survey data. The results of this explorative study will 1) show how a social profile can be used to find leads for a participative approach towards sustainable neighbourhoods where techno-economic solutions are well embedded in the social context and 2) help to understand and predict success of participation in communities.
DOCUMENT
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
DOCUMENT
Terwijl we inmiddels bij elektriciteit en gas met centrale planning werken, wil de minister de benodigde investeringen in de waterstofinfra juist aan de markt overlaten. Martien Visser waarschuwt voor onbalans in het energiesysteem.
LINK
This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.
DOCUMENT
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.
DOCUMENT
