1,720,967 research outputs found
Decentral Energy production and integration in the Stavanger region - A techno-economic case study of Stavangerregionen Havns and Risavikas solar production potential and its contribution to the local energy challenges within the Elnett21 project
Get PDFThis thesis explores the solar production potential of Stavangerregionen Havn and Risavika and its possible contribution to the local energy challenges within the Elnett21 projects, that arises with the transport electrification strategy from the Norwegian government. The aim of this study is first to show the solar electricity generation potential for the given buildings and then investigate an economic long-term performance of those projects. Furthermore, will be explored how the integration of local produced electricity can be supported by battery storage systems.
The thesis uses a Mixed-Method approach which gives the option to explore qualitatively the possibilists and challenges of the concept of system decentralization, decentral solar production and battery storage. Additionally, is through the utilisation of the K2 and PVsyst software the simulated electricity generation potential explored on which bases the quantitative analysis and economic evaluation is executed.
Our analysis shows that Stavangerregionen Havn and Risavika have great electricity production potential which could be utilised. Furthermore, gives the economic long-term evaluation a positive output for the Ferry-Terminal as main case study object.
We concluded that through the development of local generated solar electricity and the utilisation of battery storage significant contribution towards Elnett21 and the challenges are possible. Dependent on the size of future solar production and battery storage capacity can the contribution be bigger or smaller
Use of ANN for monitoring application of a distributed energy generation system based on mGT
Get PDFIn today's dynamic energy landscape, renewable energy sources are steadily increasing their share of electricity on the grid. The world population recognises the benefits of going carbon neutral and is now willing to invest heavily in this cause. The biggest drawback with renewables like wind and solar is their intermittent nature, thus not being able to meet the demand timely. Retrofitting state-of-the-art machinery is a viable solution for satisfying the increasing need for electricity. Low-emission technology complementary to renewable energy production should be invested in and researched. The goal of this thesis is dedicated to precisely this, studying the potential of innovative solutions for future energy systems.
Research has been conducted at the Vrije Universiteit in Brussels on transforming a micro gas turbine into a micro humidified air turbine. The results have shown numerous benefits, including reduced levels of NOx and increased electrical efficiency. However, there are still areas that need improvement, and in cooperation with the University of Stavanger, a task has been set to develop data-driven models adapted for condition monitoring. These models are built using sensor measurements, which will be used to predict failures and contribute to reliable operation.
In this work, autoencoder models have successfully been trained and evaluated in detail. The task of denoising sensor measurements has produced satisfying results and has significantly enhanced the data quality. These results will be used as a preprocessing step to improve the performance of the multi-layered perceptron developed in association with this project. The second task was to develop an autoencoder model that should be able to give early alerts based on normal- and faulty operational data. A suitable baseline model has been identified for this purpose. However, a residual calculation has not been performed due to the lack of time. The development and analysis of such a model are suggested for future work
A simulation model for the thermodynamic performance evaluation of organic Rankine cycle and heat pump systems using a benchmarking methodology
No full textLAUREA MAGISTRALELe fonti geotermiche a bassa entalpia combinate a cicli Rankine organici (ORC) e pompe di calore, costituiscono una tecnologia in rapida crescita capace di sfruttare le proprietà fisiche di fluidi di lavoro disponibili sul mercato, al fine di fornire sia riscaldamento, sia energia elettrica, a numerosi tipi di edifici, tra cui case private, fattorie, città e aree metropolitane. Un mezzo efficace per valutare in quali condizioni possono essere implementati i progetti di questo tipo, al fine d’ottenere la massima efficenza e ridurre i costi, è rappresentato dalla simulazione computazionale di processi.
Il presente lavoro concerne dunque simulazioni dei cicli di una pompa di calore e un ORC, ottenuta utilizzando il software IPSEpro®, in grado di elaborare bilanci di massa ed energia. La metodologia si basa sull’analisi comparativa e sull'analisi di sensibilità per cicli a bassa entalpia. L’approccio consiste nel riprodurre un modello basato su esempi esistenti nella letteratura scientifica, produrre simulazioni di cicli a condizioni assegnate, e confrontarne i risultati finali. Il confronto consente di definire un modello di riferimento, mediante il quale è possibile avviare simulazioni che differiscono di un parametro alla volta. Le variazioni apportate ai cicli nel loro punto di progetto riguardano i fluidi di lavoro e la temperatura della sorgente geotermica. Mediante l'analisi di sensibilità, si è constatato che entrambi i modelli sono affidabili e flessibili rispetto alle diverse condizioni operative, in coerenza con la letteratura. Nel primo processo d’analisi, in entrambi i cicli si è osservato che l’efficenza aumenta al crescere della temperatura. Durante la seconda analisi, la simulazione dei cicli è stata implementata con l’utilizzo di refrigeranti diversi. In conclusione, è stato riscontrato che il refrigerante risulta tanto più efficace quanto più adeguato al singolo ciclo, e che i modelli mostrano flessibilità rispetto all’applicazione dei differenti fluidi di lavoro.Low Enthalpy geothermal sources, combined with Organic Rankine Cycles (ORC) and heat pumps, are a fast-growing technology that can take advantage of the physical properties of commercial working fluids, to provide both heating and power generation to numerous types of buildings, from private houses and farms, up to small towns and districts. Process simulation is a powerful means for evaluating under which conditions these kinds of projects can be implemented, in order to achieve maximum efficiency and minimize costs.
This work would present a heat pump and ORC process simulation, using a commercial software, IPSEpro®, which computes both mass and energy balances. The methodology focuses on knowledge benchmarking, and sensitivity analysis, for low enthalpy cycles. This approach consists of creating a model from existing scientific literature’s examples, running cycles’ simulations under the same conditions, and eventually comparing results. The comparison allows defining a baseline model, from which several simulations would be carried out by changing a parameter at a time. The variations applied to the design point cycles regard the working fluids and the geothermal source temperature. With the sensitivity analysis, it has been proved that both models are reliable and adjustable to different operating parameters, consistent with the literature. Thanks to the first sensibility analysis, it has been possible to observe that for both cycles, as the geothermal temperature increases, the efficiency improves. In the second analysis, several refrigerants have been implemented into the baseline cycles. Finally, it has been found out that the best performing refrigerant depends on the given cycle; likewise, the models show flexibility when it comes to applying different working fluids
Modeling and investigation of the performance of a solar-assisted ground-coupled CO2 heat pump for space and water heating
Get PDFThe rise in the popularity of heat pumps should be accompanied by the increase in the utilization of environmentally-safe working fluids. To push for wider uptake of hybrid heat pump systems that use natural working fluids, information about their performance and operating characteristics should be made available. This study models a CO2 solar-assisted ground-coupled heat pump (SAGCHP) system and investigates its performance for space and water heating. Sensitivity analysis, parametric study, and long-term performance simulation were implemented, with the system’s seasonal performance factor (SPF), levelized cost of heating (LCOH), and ground temperature change (GTC) considered as performance indicators. It was seen that ensuring a good combination of design and operating specifications can result in an SPF (∼3.5) and LCOH (0.184 USD/kWh) that are comparable with those of SAGCHP systems that use conventional working fluids. The heat pump’s high-side pressure and output temperature exhibited notable effects on all the performance indicators. Below the optimal operating pressure, a 5% increase in the heat pump’s high-side pressure brought about a ∼7–10% improvement to the SPF, a ∼3–5% reduction to the LCOH, and a ∼6–10% increase to the GTC. Reducing the output temperature by 5% increased the SPF by ∼7–8%, decreased the LCOH by ∼3%, and increased the GTC by ∼6%. Parametric studies identified the presence of optimal heat pump discharge pressure and heat source circulation rate that should be used for operations. Long-term simulation shows that managing the GTC ensures the longevity of the system. Rather than oversizing the borehole heat exchangers (BHEs), it is more practical to reduce ground temperature decline by increasing the BHE spacing or by adding solar collectors.publishedVersio
Building Performance Simulation of MyBox Energy Lab in Norway: Integrating the Human Dimension in Energy Use Analysis
Get PDFThe MyBox energy lab at the University of Stavanger presents an innovative integration of living and research spaces housed within six repurposed shipping containers, where energy consumption data has been logged hourly over the past five years. Employing Building Performance Simulation (BPS), this study investigates the human dimension of energy use within the facility. Focusing on modelling human-related energy use, the research explores the customisation of occupant, equipment, and lighting schedules using BPS, revealing substantial day-to-day variability in energy consumption attributed to human factors such as presence, heating preferences, and cooking habits. While validation against actual data demonstrates a reasonable correlation on yearly and monthly scales, the study highlights the limitations of BPS in capturing finer temporal resolutions, emphasising the need for enhanced methodologies in simulating human behaviour within energy models.submittedVersio
Development of a surrogate model of a trans-critical CO2 heat pump for use in operations optimization using an artificial neural network
Get PDFConventional physics-based models can demand substantial computational resources when employed for operational optimization. To allow faster system simulations that can be employed for operational optimization, a surrogate model of the CO2 heat pump has been developed using an artificial neural network (ANN). The ANN model takes in six (6) inputs: evaporator water-side mass flow, its temperature, gas cooler water-side mass flow, its temperature, set-point output temperature, and high-side heat pump pressure. The model's outputs comprise the electrical energy needed to run the heat pump, the heat from the gas coolers, the temperature of the heat pump-heated fluid, and the outlet temperature of the heat pump's evaporator. Data used for training, validating, and testing the ANN model were generated by running a calibrated Modelica model of the CO2 heat pump for various combinations of input parameters obtained from Latin hypercube sampling. The ANN model developed includes an input layer with 6 inputs, 2 hidden dense layers, each with 30 neurons, and an output layer for 4 outputs (6-30-30-3). The ReLU activation function was implemented on each hidden layer and no regularizations were imposed. The Adam optimizer was used with a learning rate of 0.001 specified. Early stopping (patience = 2000) was implemented to ensure that the training data was not overfitted. A maximum of 30000 epochs was specified. The resulting Mean Square Error (MSE) obtained for the training, validation, and testing data sets were 1.38x10−5, 2.05x10−5, and 3.65x10−5, respectively. When tested against one-week operational runs generated by Modelica, the Root Mean Square Errors (RMSEs) for coefficient of performance (COP)s for spring, summer, autumn, and winter operations obtained were 0.232, 0.346, 0.089 and 0.076, respectively. The resulting surrogate ANN model can be integrated into the system model as a functional mock-up unit within Modelica to facilitate faster simulations for operational optimization.publishedVersio
Comparison of different configurations of a solar-assisted ground-source CO2 heat pump system for space and water heating using Taguchi-Grey Relational analysis
Get PDFThis work deals with the comparison of different optimized configurations of solar-assisted ground-source CO2 heat pump systems using the Taguchi method and Grey Relational Analysis (GRA) when used for simultaneous space and water heating in cold coastal climate conditions. The configurations studied include: (1) the solar collectors (SCs) and borehole heat exchangers (BHEs) connected in series, with the working fluid flowing to the SCs first; (2) the SCs and BHEs connected in series, with the fluid flowing to the BHEs first; and (3) the SCs and BHEs connected in parallel to the heat pump. Eight parameters were considered for optimizing the systems’ design, including the BHE length, BHE spacing, BHE number, SC area, BHE-SC mass flow rate, space heating return temperature, heat pump discharge pressure, and heat pump’s outlet temperature. The seasonal performance factor (SPF), levelized cost of heating (LCOH), and the estimated maximum annual ground temperature change (GTC) were chosen as performance indicators to evaluate system performance. The system model was developed using Modelica and 27 simulation runs for every configuration were implemented according to the L27 (93) Taguchi orthogonal array. Single objective optimizations were first performed using the Taguchi method to determine the parameter combinations that would optimize the SPF, LCOH, and GTC, separately. After that, multi-objective optimization was performed using Taguchi-GRA to determine the control factor combination that would give the optimal overall performance when all output variables are considered simultaneously and given equal importance. When the performance indicators were considered separately, simulations show that configuration 2 gave the best SPF (4.025), configuration 3 gave the best LCOH (0.124 USD/kWh), and configuration 1 gave the best GTC (100.257%), respectively. Analysis of variance (ANOVA) showed that the SPF is most sensitive to the heat pump’s discharge pressure and outlet temperature; the LCOH to the BHE length, BHE number, and SC area; and the GTC to all the BHE and SC sizing variables. Multi-objective optimization showed that configuration 1 performs the best, giving a grey relational grade of 0.6875, which is equivalent to an SPF of 3.267, and LCOH of 0.155 USD/kWh, and GTC of 100.021%. BHE length was found to be the most influential parameter to overall performance, irrespective of the configuration.publishedVersio
A novel approach based on artificial neural network for calibration of multi-hole pressure probes
Get PDFImperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.publishedVersio
The Application of an Artificial Neural Network as a Baseline Model for Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles
Get PDFDue to high penetration of renewables, the EU energy system is undergoing a transition from large-scale centralized generation toward small-scale distributed generation. The increasing share of intermittent renewables such as solar and wind has become the main driver for dispatchable distributed energy generation technologies to maintain the grid flexibility and stability. In this context, micro gas turbines (MGTs) with high fuel and operation flexibility could play a crucial role to guarantee the grid stability, enabling deeper penetration of the intermittent renewable energy sources. Despite this, the MGT market is still considered to be niche, and there are R&D&I challenges that need to be addressed to further promote this technology in distributed generation applications. Innovative MGT cycles based on a cycle humidification concept can be considered to obtain higher system performance. However, given the fact that MGTs are installed close to the consumption points, where they are operated by nontechnical prosumers with very limited access to maintenance services, they should also offer high availability and reliability to avoid unexpected outages and secure the supply. Therefore, intelligent monitoring systems are needed that can support nonexpert end-users to detect degradation and plan maintenance before a breakdown occurs. In this study, we investigated and developed advanced methods based on artificial neural networks (ANNs) for condition monitoring of a humidified MGT cycle under real-life operational conditions. To create a high-performing model, extensive data preprocessing has been conducted to remove data outliers and select optimum model features, which provide best results. Additionally, the model hyperparameters such as learning rate, momentum, and number of hidden nodes have been altered to achieve the most accurate predictions. The results of this study have provided a baseline ANN model capable of conducting condition monitoring of a micro humid air turbine (mHAT) system, which will be applied to additional studies in the future.acceptedVersio
Decarbonizing primary steel production : Techno-economic assessment of a hydrogen based green steel production plant in Norway
Get PDFHigh electricity cost is the biggest challenge faced by the steel industry in transitioning to hydrogen based steelmaking. A steel plant in Norway could have access to cheap, emission free electricity, high-quality iron ore, skilled manpower, and the European market. An open-source model for conducting techno-economic assessment of a hydrogen based steel manufacturing plant, operating in Norway has been developed in this work. Levelized cost of production (LCOP) for two plant configurations; one procuring electricity at a fixed price, and the other procuring electricity from the day-ahead electricity markets, with different electrolyzer capacity were analyzed. LCOP varied from 722/tls for the different plant configurations. Procuring electricity from the day-ahead electricity markets could reduce the LCOP by 15%. Increasing the electrolyzer capacity reduced the operational costs, but increased the capital investments, reducing the overall advantage. Sensitivity analysis revealed that electricity price and iron ore price are the major contributors to uncertainty for configurations with fixed electricity prices. For configurations with higher electrolyzer capacity, changes in the iron ore price and parameters related to capital investment were found to affect the LCOP significantly.publishedVersio
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