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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The energy community movement has grown considerably over the past ten years. Energy communities are also expanding their activities. However, room for new energy projects is limited because of grid congestion. Therefore, citizen energy communities (CECs) are looking for ways to reduce the pressure on the grid. Against this background we ask what new roles renewable energy communities can play in grid governance. What opportunities are available to store produced electricity or to balance electricity on a neighborhood level? Are these solutions economically and technically feasible? Our article draws on a recent research project on innovative community energy services in the Netherlands. In three case studies, we investigated local solutions to balance energy production and consumption. We organized workshops and webinars for CECs and carried out interviews and technical studies. Theoretically, we use Social Movement Theory (SMT) to understand the community energy movement. Furthermore, we employ Large Technical Systems (LTS) theories about the lifecycle of infrastructures. We investigated the technical, organizational, and economic aspects of these solutions, as well as skills and knowledge. We conclude that the community energy movement is expanding its activities to new functions in the energy system, but economic feasibility is not yet within reach.
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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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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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Local energy developments from a spatial and systemic approach are highlighted using examples from a Dutch case study. Developments in energy systems included interconnectedness of contextual factors and systems responses. The need to explore both the contextual factors and systemic aspects are illustrated. Using the case study of Energy Valley. Similarities and influences of local energy systems to the national and EU levels are also highlighted. The research used a complex adaptive approach to cluster developments to understand energy systems developments.
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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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The transformation from the current energy system to a decentralized renewable energy system requires the transformation of communities into energy neutral or even energy producing communities. Increasingly, citizens become 'prosumers' and pool their resources to start a local energy initiative. In this paper we present an in-depth study of networks that recently developed, which challenge the established way of centralized decision-making on energy resources. Many local communities are eager to promote sustainable energy production, to use local financial resources for the local community and to employ democratic governance of energy production and supply. Furthermore, we study how these co-operations are linked to local, regional and national networks for community energy. We use both Actor-Network Theory (ANT) and Social Movement Theory (SMT) to investigate the initiatives, as this allows a dynamic analysis of collective strategies. We discuss the obduracy of the energy system and how this system is challenged by new connections between communities and global networks and by new types of energy providers that are rooted in social networks. Furthermore, we draw attention to the way community energy networks provide a social innovation while realizing a decentralized and decarbonized energy system.
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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.
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The transition to a decentralized renewable energy system requires the transformation of communities. Increasingly, citizens become ‘prosumers’ and take energy production in their own hands.
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