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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This article addresses European energy policy through conventional and transformative sustainability approaches. The reader is guided towards an understanding of different renewable energy options that are available on the policy making table and how the policy choices have been shaped. In arguing that so far, European energy policy has been guided by conventional sustainability framework that focuses on eco-efficiency and ‘energy mix’, this article proposes greater reliance on circular economy (CE) and Cradle to Cradle (C2C) frameworks. Exploring the current European reliance on biofuels as a source of renewable energy, this article will provide recommendations for transition to transformative energy choices. http://dx.doi.org/10.13135/2384-8677/2331 https://www.linkedin.com/in/helenkopnina/
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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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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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With the effects of climate change linked to the use of fossil fuels, as well as the prospect of their eventual depletion, becoming more noticeable, political establishment and society appear ready to switch towards using renewable energy. Solar power and wind power are considered to be the most significant source of global low-carbon energy supply. Wind energy continues to expand as it becomes cheaper and more technologically advanced. Yet, despite these expectations and developments, fossil fuels still comprise nine-tenths of the global commercial energy supply. In this article, the history, technology, and politics involved in the production and barriers to acceptance of wind energy will be explored. The central question is why, despite the problems associated with the use of fossil fuels, carbon dependency has not yet given way to the more ecologically benign forms of energy. Having briefly surveyed some literature on the role of political and corporate stakeholders, as well as theories relating to sociological and psychological factors responsible for the grassroots’ resistance (“not in my backyard” or NIMBYs) to renewable energy, the findings indicate that motivation for opposition to wind power varies. While the grassroots resistance is often fueled by the mistrust of the government, the governments’ reason for resisting renewable energy can be explained by their history of a close relationship with the industrial partners. This article develops an argument that understanding of various motivations for resistance at different stakeholder levels opens up space for better strategies for a successful energy transition. https://doi.org/10.30560/sdr.v1n1p11 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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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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VHL University of Applied Sciences (VHL) is a sustainable University of AppliedSciences that trains students to be ambitious, innovative professionals andcarries out applied research to make a significant contribution to asustainable world. Together with partners from the field, they contribute to innovative and sustainable developments through research and knowledge valorisation. Their focus is on circular agriculture, water, healthy food & nutrition, soil and biodiversity – themes that are developed within research lines in the variousapplied research groups. These themes address the challenges that are part ofthe international sustainability agenda for 2030: the sustainable developmentgoals (SDGs). This booklet contains fascinating and representative examplesof projects – completed or ongoing, from home and abroad – that are linked tothe SDGs. The project results contribute not only to the SDGs but to their teaching as well.
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This paper assesses wind resource characteristics and energy yield for micro wind turbines integrated on noise barriers. An experimental set-up with sonic anemometers placed on top of the barrier in reference positions is realized. The effect on wind speed magnitude, inflow angle and turbulence intensity is analysed. The annual energy yield of a micro wind turbine is estimated and compared using data from a micro-wind turbine wind tunnel experiment and field data. Electrical energy costs are discussed as well as structural integration cost reduction and the potential energy yield could decrease costs. It was found that instantaneous wind direction towards the barrier and the height of observation play an influential role for the results. Wind speed increases in perpendicular flows while decreases in parallel flow, by +35% down to −20% from the reference. The azimuth of the noise barrier expressed in wind field rotation angles was found to be influential resulted in 50%–130% changes with respect to annual energy yield. A micro wind turbine (0.375 kW) would produce between 100 and 600 kWh annually. Finally, cost analysis with cost reductions due to integration and the energy yield changes due to the barrier, show a LCOE reduction at 60%–90% of the reference value. https://doi.org/10.1016/j.jweia.2020.104206
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This article provides a nano (hyperlocal) view of climate change mitigation by viewing regenerative organizing through the eyes (as well as bodies and senses, etc.) of the households engaged in community-based energy projects. By showing what humans make up for in the largely absent relationship between nature and technology in these projects, we envision an incremental extension of the literature on community-based energy. The radically different contribution we aim to make is a tripartite imbrication that brings in natural agency alongside the human and the technical but specifies precisely how nano (smaller than micro) embodied practices afford mis- and realignments. https://doi.org/10.1177/1086026619886841 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Energy policies are vital tools used by countries to regulate economic and social development as well as guarantee national security. To address the problems of fragmented policy objectives, conflicting tools, and overlapping initiatives, the internal logic and evolutionary trends of energy policies must be explored using the policy content. This study uses 38,277 energy policies as a database and summarizes the four energy policy objectives: clean, low-carbon, safe, and efficient. Using the TextCNN model to classify and deconstruct policies, the LDA + Word2vec theme conceptualization and similarity calculations were compared with the EISMD evolution framework to determine the energy policy theme evolution path. Results indicate that the density of energy policies has increased. Policies have become more comprehensive, barriers between objectives have gradually been broken, and low-carbon objectives have been strengthened. The evolution types are more diversified, evolution paths are more complicated, and the evolution types are often related to technology, industry, and market maturity. Traditional energy themes evolve through inheritance and merger; emerging technology and industry themes evolve through innovation, inheritance, and splitting. Moreover, this study provides a replicable analytical framework for the study of policy evolution in other sectors and evidence for optimizing energy policy design
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