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Stakeholder Engagement for a Local Energy Community in Energy Transition– Case of De Heuvel/Amstelwijk

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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Stakeholder Engagement for a Local Energy Community in Energy Transition– Case of De Heuvel/Amstelwijk

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Comparative analysis of market structures of P2P energy trading in a local energy system

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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Comparative analysis of market structures of P2P energy trading in a local energy system

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Energy efficiency in housing management – conclusions from an international study

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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Energy efficiency in housing management – conclusions from an international study

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Trends and developments in the renewable energy sector in Ukraine

The government of Ukraine has adopted the Renewable Energy Directive (RED) with clear goals and a roadmap to facilitate its energy transition towards renewable sources. This is done because of both climate concerns as well as reasons related to Ukraine’s foreign policy which led the government to decide that Ukraine should work more on its own energy independence. Currently the percentage of renewable energy sources in Ukraine is among the lowest of the entire Europe and there is only slow development in terms of the growth of the sector, even though there is a lot of available biomass, given the large and flat surface of the country with a well-developed agricultural sector. As in most countries in the world, there is a quite intensive and well-developed debate in Ukraine about the energy sector, energy usage and the necessary transition towards more renewable types of energy. One of the consequences of it is that Ukraine is one of the partner countries in the Paris agreement and committed itself to reducing the amount of greenhouse gas emissions in the future. That means that a transformation towards renewable energy is needed, even though currently in Ukraine only a low percentage of energy is generated by sustainable sources. The general picture is that in Ukraine the development of the renewable energy sector is going not as fast as could have been. In other words, there are several barriers present that hinder the energy transition. One of the issues behind such a barrier may be a limited access to technology, or problems with legislation or other issues which may be unknown so far, but certainly relevant for foreign investors. The Ukrainian government adopted the so-called Renewable Energy Directive (RED), set goals for the energy transition and support the transition itself. In some areas progress was made, for example in the growing number of biomass fired boilers, but still Ukraine remains one of the European countries with the lowest percentage of renewable energy production. Therefore, in order to identify currently existing barriers and help to find possible applications of new technologies in Ukraine, the Dutch Enterprise Agency (Rijksdienst voor Ondernemerschap) commissioned this study. It was done within the framework of the Partners in Business on Bioenergy program. The focus of this study is on analysing the renewable energy sector, with special attention for biomass, in the form of biomass-based heating and biomass for biofuels. Of course, other parts of the renewable energy sector such as solar and wind energy are also taken into consideration. The second part consists of a case study to determine the business case for direct processing of sugar beets with Betaprocess as a possible application of biomass to biofuel production in Ukraine. The third study is aiming at determining the amount of biomass that can safely be taken from the fields, without negatively affecting the fertility of the soil. These sub-studies mentioned in the previous paragraph offer a better understanding of the renewable energy market in general and biomass/biofuel applications in particular. This study sheds light on several important questions that entrepreneurs and/or other foreign investors may have about investing in Ukraine. Even though it is well-known that doing business in Ukraine is challenging, it is also very important to have a clear picture of the opportunities that this country offers, within the limits that nature sets, in order to avoid negative consequences like soil degradation. The objective of this report is to find out about which opportunities and barriers exist in the Ukrainian transition towards renewable energy generation, to calculate the profitability of new biomass-processing technologies as well as finding out limitations of biomass usage.

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Role of lng in an optimized hybrid energy network

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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Role of LNG in an optimized hybrid energy network

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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Targeting Energy Management

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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Building and building systems energy prediction models to enhance energy flexibility control

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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Stakeholder Engagement for a Local Energy Community in Energy Transition– Case of De Heuvel/Amstelwijk

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Comparative analysis of market structures of P2P energy trading in a local energy system

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Energy efficiency in housing management – conclusions from an international study

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Trends and developments in the renewable energy sector in Ukraine

MULTIFILE

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Role of lng in an optimized hybrid energy network

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Role of LNG in an optimized hybrid energy network

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Targeting Energy Management

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Building and building systems energy prediction models to enhance energy flexibility control

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Products 1.263

Stakeholder Engagement for a Local Energy Community in Energy Transition– Case of De Heuvel/Amstelwijk

Comparative analysis of market structures of P2P energy trading in a local energy system

Energy efficiency in housing management – conclusions from an international study

Trends and developments in the renewable energy sector in Ukraine

Role of lng in an optimized hybrid energy network

Role of LNG in an optimized hybrid energy network

Targeting Energy Management

Building and building systems energy prediction models to enhance energy flexibility control

Shaping energy norms in digital communities

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Stakeholder Engagement for a Local Energy Community in Energy Transition– Case of De Heuvel/Amstelwijk

Comparative analysis of market structures of P2P energy trading in a local energy system

Energy efficiency in housing management – conclusions from an international study

Trends and developments in the renewable energy sector in Ukraine

Role of lng in an optimized hybrid energy network

Role of LNG in an optimized hybrid energy network

Targeting Energy Management

Building and building systems energy prediction models to enhance energy flexibility control

Shaping energy norms in digital communities