Poster presentation: decentralized gas storage.
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The future energy system could benefit from the integration of the independent gas, heat and electricity infrastructures. In addition to an increase in exergy efficiency, such a Hybrid Energy Network (HEN) 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 due to the low temperature. Therefore, we analysed the integration of a decentralized LNG regasification with a CHP (Waste-to-Energy) plant, to determine whether the integration could offer additional operational flexibility for the future energy network with intermittent renewable energy sources, under optimized exergy efficient conditions. We compared the independent system with two systems integrated by means of 1) Organic Rankine Cycle and 2) Stirling Engine using the cold of the LNG, that we analysed 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. We found that for both systems the decentralized LNG regasification integrated with the W2E plant affects the imbalance of the system for electricity and heat, due to the additional redundant paths to produced electricity. The integration of the systems offers additional operational flexibility depending on the means of integration and its availability to produce additional energy carriers. For our future work, we will extend the model, taking into account the variability and randomness in the different parameters, which may cause significant changes in the performance and reliability of the model.
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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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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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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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The increasing rate of urbanization along with its socio-environmental impact are major global challenges. Therefore, there is a need to assess the boundaries to growth for the future development of cities by the inclusion of the assessment of the environmental carrying capacity (ECC) into spatial management. The purpose is to assess the resource dependence of a given entity. ECC is usually assessed based on indicators such as the ecological footprint (EF) and biocapacity (BC). EF is a measure of the biologically productive areas demanded by human consumption and waste production. Such areas include the space needed for regenerating food and fibers as well as sequestering the generated pollution, particularly CO2 from the combustion of fossil fuels. BC reflects the biological regeneration potential of a given area to regenerate resources as well to absorb waste. The city level EF assessment has been applied to urban zones across the world, however, there is a noticeable lack of urban EF assessments in Central Eastern Europe. Therefore, the current research is a first estimate of the EF and BC for the city of Wrocław, Poland. This study estimates the Ecological Footprint of Food (EFF) through both a top-down assessment and a hybrid top-down/bottom-up assessment. Thus, this research verifies also if results from hybrid method could be comparable with top-down approach. The bottom-up component of the hybrid analysis calculated the carbon footprint of food using the life cycle assessment (LCA) method. The top-down result ofWrocław’s EFF were 1% greater than the hybrid EFF result, 0.974 and 0.963 gha per person respectively. The result indicated that the EFF exceeded the BC of the city of Wrocław 10-fold. Such assessment support efforts to increase resource efficiency and decrease the risk associated with resources—including food security. Therefore, there is a need to verify if a city is able to satisfy the resource needs of its inhabitants while maintaining the natural capital on which they depend intact. Original article at: https://doi.org/10.3390/resources7030052 © 2018 by the authors. Licensee MDPI.
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Hydrogen (H2) is a key element in the Dutch energy transition, considered a sources of flexibility to balance the variable renewable energy sources, facilitating its integration into the energy system. But also as an energy carrier. Both the gas and electricity transmission operators (TSO) have the vision to interconnect their networks with H2, by distributing the green H2 produced with offshore electrolysers into high pressure gas pipelines to relive the overload electric network. The planned compressed H2 pipelines cross the north of North-Holland region, offering a backbone for a H2 economy. Furthermore, at regional level there are already a big number of privet-public H2 developments, among them the DuWaAl, which is a H2 production-demand chain, consists of 1) An H2 mill, 2) 5 filling stations in the region and 3) a large fleet of trucks and other users. Because of these developments, the North-Holland region needs a better insight into the position that H2 could fulfil in the local energy system to contribute to the energy transition. The aim of this research is to analyse these H2 economy, from the emergent to settled, by identifying early and potential producer- consumer, considering the future infrastructure requirements, and exploring economy-environmental impacts of different supply paths
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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 project STORE&GO aims to investigate all the aspects regarding the integration of large-scale Power-to-Gas (PtG) at European level, by exploiting it as means for long term storage. One of the aspects that should be properly addressed is the beneficial impact that the integration of PtG plants may have on the electricity system.In the project framework, WP6 devoted its activities to investigate different aspects of the integration of PtG in the electricity grid, with the previous delivered reports.This deliverable focused in particular on how integrate the information about the facilities replicating the real world condition into a simulation environment. For doing this, the concept of remote Physical Hardware-in-the-Loop (PHIL) has been used and exploit.Remote simulation with physical hardware appears to be an effective means for investigating new technologies for energy transition, with the purpose of solving the issues related to the introduction of new Renewable Energy Sources (RES) into the electricity system. These solutions are making the overall energy systems to be investigated much more complex than the traditional ones, introducingnew challenges to the research. In fact:• the newly integrated technologies deal with different energy vectors and sectors, thus• requiring interoperability and multidisciplinary analysis;• the systems to be implemented often are large-scale energy systems leading to enormously complicated simulation models;• the facilities for carrying out the experiments require huge investments as well as suitable areas where to be properly installed.This may lead to the fact that a single laboratory with limited expertise, hardware/software facilities and available data has not the ability to secure satisfactory outcomes. The solution is the share of existing research infrastructures, by virtually joining different distant laboratories or facilities.This results in improvement of simulation capabilities for large-scale systems by decoupling into subsystems to be run on distant targets avoidance of replication of already existing facilities by exploiting remote hardware in the loop concept for testing of remote devices.Also confidential information of one lab, whose sharing may be either not allowed or requiring long administrative authorization procedures, can be kept confidential by simulating models locally and exchanging with the partners only proper data and simulation results through the co-simulation medium.Thanks to the realized method it is possible to real time analyse renewable devices at remotepower plants and place them in the loop of a local network simulation.The results reported show that the architecture developed is strong enough for being applied also atnew renewable power plants. This opens the possibility to use the data for research purposed, butalso to act in remote on the infrastructure in case of particular test (for example the acceptance test).
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