The Netherlands is aiming for the roll-out of more solar PV. However like many densely populated countries, the country is running into issues of lack of space. Opportunities around infrastructural works like highways provide space without compromising the landscape. Examples of this double use are already developed and demonstrated, like for instance sound barriers and solar roads. New is the combination of solar PV with traffic barriers. This has a big potential since the Dutch main road network had 7.500 km of guiderail and the construction to put PV on is already there. In the MESH (Modular E cover for Solar Highways) project a consortium of knowledge institutes, a province and companies developed a prototype and tested it in a pilot. The consortium consists of TNO, Solliance (in which TNO is a partner, a high-end research institute for flexible thin film solar cells such as CIGS and Perovskite), Heijmans Infra (focusing mainly on the construction, improvement and maintenance of road infrastructure, including guiderails), DC Current (applying innovations with regard to power optimizers for the linear PV application), the Province of Noord-Holland (which acts as a leading customer) and the Amsterdam University of Applied Sciences (AUAS) as a knowledge institution that links education and research. In this project the theme Sustainable Energy Systems of AUAS is involved with both lecturers and student groups. In the project, Solliance investigated and developed the flexible thin film PV technology to be applied with a focus on shape and reliability. TNO and Heijmans developed a modular casing concept and a fastening system that allows quick installation on site. DC Current worked on the DC management with regard to voltage, electrical safety and minimizing failure in case of collision. At the end of the project, the partners in the consortium have validated knowledge about how to integrate PV into the guiderail and can start with the scaling up of the technology for commercial applications. In order to meet the various requirements for traffic safety on the one hand and generating electricity on the other hand, the Systems Engineering methodology was leading during the project. In the project we first built a small, but full scale prototype and invited safety experts to evaluate the design. With this feedback we made a redesign for the pilot. This pilot is placed on the highway as safety barrier and tested for a year. In a presentation at EU PVSEC18 [1] K.Sewalt reported on the design phase. This time we want to present the results of our test phase and give answers on our research questions.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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New technologies or approaches are being widely developed and proposed to be deployed in real energy systems to improve desired objectives; however, supporting decision making processes to select best solutions in terms of performance and efficiently following cost-benefit analysis require some sort of scientific evidence based tools. These tools should be reliable, robust, and capable of demonstrating the behaviour and impact of newly developed devices or algorithms in different pre- defined scenarios. Therefore, new approaches and technologies need to be tested and verified using a safe laboratory test environment.This report is about the development and realisation of some major tools and reliable methods to calculate risks and opportunities for integrating of new energy resources into the European electricity grid. Hanze University Groningen and Politecnico di Torino worked together within the STORE&GO project sharing laboratories, knowledge, hardware facilities and researchers for the realisation of the characterisation and mathematical modelling of renewable resources. Needed to realize a stable and reliable environment for remote physical hardware in the loop simulations.For this realisation we started with the local characterisation of a PV-Field and a PEM electrolyser at Entrance Groningen by logging and measuring the electric behaviour and specific device parameters to integrate and convert these into working mathematical models of a PV-Field and electrolyser prosumer. After testing and evaluating these models by comparing the results with the real-time measurements, these test and modelling is also realised from the remote laboratory in Torino. To achieve dynamical physical hardware we also realised dynamic mathematical model(s) with real-time functionality to interact directly with the remote electrolyser. To connect both the laboratories with full duplex communication functionalities between physical hardware and models we have also realized a network which is able to share network resources on both local and remote sites.
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The goal of this paper is twofold: i) to design a viable business model for community owned solar farms that will be setup in the north of the Netherlands. ii) To present the findings from this case study, and to propose generalisations that are relevant for the development of artefacts that can be used to facilitate the design of viable business models in a business ecosystem setting.USE 2015
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In Europe, green hydrogen and biogas/green gas are considered important renewable energy carriers, besides renewable electricity and heat. Still, incentives proceed slowly, and the feasibility of local green gas is questioned. A supply chain of decentralised green hydrogen production from locally generated electricity (PV or wind) and decentralised green gas production from locally collected biomass and biological power-to-methane technology was analysed and compared to a green hydrogen scenario. We developed a novel method for assessing local options. Meeting the heating demand of households was constrained by the current EU law (RED II) to reduce greenhouse gas (GHG) emissions by 80% relative to fossil (natural) gas. Levelised cost of energy (LCOE) analyses at 80% GHG emission savings indicate that locally produced green gas (LCOE = 24.0 €ct kWh−1) is more attractive for individual citizens than locally produced green hydrogen (LCOE = 43.5 €ct kWh−1). In case higher GHG emission savings are desired, both LCOEs go up. Data indicate an apparent mismatch between heat demand in winter and PV electricity generation in summer. Besides, at the current state of technology, local onshore wind turbines have less GHG emissions than PV panels. Wind turbines may therefore have advantages over PV fields despite the various concerns in society. Our study confirms that biomass availability in a dedicated region is a challenge.
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The rapid implementation of large scale floating solar panels has consequences to water quality and local ecosystems. Environmental impacts depend on the dimensions, design and proportions of the system in relation to the size of the surface water, as well as the characteristics of the water system (currents, tidal effects) and climatic conditions. There is often no time (and budget) for thorough research into these effects on ecology and water quality. A few studies have addressed the potential impacts of floating solar panels, but often rely on models without validation with in situ data. In this work, water quality sensors continuously monitored key water quality parameters at two different locations: (i) underneath a floating solar park; (ii) at a reference location positioned in open water. An underwater drone was used to obtain vertical profiles of water quality and to collect underwater images. The results showed little differences in the measured key water quality parameters below the solar panels. The temperature at the upper layers of water was lower under the solar panels, and there were less detected temperature fluctuations. A biofouling layer on the floating structure was visible in the underwater images a few months after the construction of the park
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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 application of DC grids is gaining more attention in office applications. Especially since powering an office desk would not require a high power connection to the main AC grid but could be made sustainable using solar power and battery storage. This would result in fewer converters and further advanced grid utilization. In this paper, a sustainable desk power application is described that can be used for powering typical office appliances such as computers, lighting, and telephones. The desk will be powered by a solar panel and has a battery for energy storage. The applied DC grid includes droop control for power management and can either operate stand-alone or connected to other DC-desks to create a meshed-grid system. A dynamic DC nano-grid is made using multiple self-developed half-bridge circuit boards controlled by microcontrollers. This grid is monitored and controlled using a lightweight network protocol, allowing for online integration. Droop control is used to create dynamic power management, allowing automated control for power consumption and production. Digital control is used to regulate the power flow, and drive other applications, including batteries and solar panels. The practical demonstrative setup is a small-sized desktop with applications built into it, such as a lamp, wireless charging pad, and laptop charge point for devices up to 45W. User control is added in the form of an interactive remote wireless touch panel and power consumption is monitored and stored in the cloud. The paper includes a description of technical implementation as well as power consumption measurements.
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In zijn inaugurele rede gaat Bert Plomp in op het belang van praktijkgericht onderzoek voor de verdere ontwikkeling en implementatie van zonnestroom en op het gebruik van zonnestroom voor schoon en stil vervoer en mobiliteit. Ook de ambities van het lectoraat en de hoofdlijnen en speerpunten van het onderzoek komen aan bod en de relaties met het onderwijs, het regionale bedrijfsleven en lopende projecten
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The increase in renewable energy sources will require an increase in the operational flexibility of the grid, due to the intermittent nature of these sources. This can be achieved for the gas and the electricity grid, which are integrated by means of power-to-gas and vice versa, by applying gas and other energy storages. Because renewables are applied on a decentralized scale level and syngas and biogas are produced at relatively low pressures, we study the application of a decentralized (bio)gas storage system combined withMicro Turbine Technology (MTT), Compressed Air Energy Storage (CAES) and Thermal Energy Storage (TES) units, which are designed to optimize energy efficiency.In this study we answer the following research questions:a. What is the techno-economical feasibilty of applying a decentralized (bio)gas storage with a MTT/CAES/TES system to balance the integrated renewable energy network?b. How should the decentralized (bio)gas storage with MTT/CAES/TES system be designed, so that the energy efficient application in such networks is optimized?Note that:c. We verify the calculations for the small scale MTT unit with measurements on our proof-of-principle set-up of part of the system that includes two MTTs in parallel.Based on wind speed, irradiance patterns and electricity and heat demand patterns for a case of 100 households, we found the optimum dimensions for the decentralized (bio)gas storage based on guaranteed supply. We concluded that a decentralized (bio)gas storage of 85 000 Nm3 was needed to provide the heat demand. LNG was the most energy efficient storage technology for such dimensions.The use of (bio)gas directly in a CHP (P/Q ratio = 2/3) that was mainly heat driven, resulted in a continuous overproduction of electricity due to the dominant heat demand of the 100 households in the Netherlands.This does not leave any room for the increase in the application of PV and wind generators, nor is there a purpose for electricity storage.For that reason we will further investigate the application of a decentralized (bio)gas storage with MTT/CAES/TES as a solution to balance a renewable integrated network. Using an MTT in the system offers a more useful P/Q ratio for households of 1/5.
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