Energy Efficiency in Lighting: Daylight Harvesting Optimization and Wireless Sensor Networks

openscaricabile onlin

Geothermal Energy

Introduction to geothermal energy: what is geothermal energy, main applications (heat pumping, direct use, electricity production and cogeneration), geothermal resources, geothermal power plants

Streamlining Energy Transition Scenarios to Key Policy Decisions

Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from stakeholder discussions, which can be affected by biases and infeasibilities. Leveraging decision trees, a popular machine-learning technique, we derive interpretable storylines from many quantitative scenarios and show how the key decisions in the energy transition are interlinked. Specifically, our results demonstrate that choosing a high deployment of renewables and sector coupling makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand. Also, the energy transition to a fossil-free Europe is primarily determined by choices on the roles of bioenergy, storage, and heat electrification. Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition

Uncertainty Classification for Strategic Energy Planning

Various countries and communities are defining strategic energy plans driven by concerns related to climate change and security of energy supply. The long time horizon inherent to strategic energy planning requires uncertainty to be accounted for. Uncertainty classification consists in defining the type of uncertainty involved and quantifying it. It is needed as input for uncertainty and sensitivity analyses, and optimization under uncertainty applications. In this work we define a methodology for uncertainty classification for a typical strategic energy planning problem. As an example, the methodology is applied to some representative parameters

Robust Optimization for Strategic Energy Planning

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions. A classification of uncertainty in energy systems decision-making is performed. Robust optimization is then applied to a Mixed-Integer Linear Programming problem, representing the typical trade-offs in energy planning. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal

Molecularly Imprinted Polymer as Selective Sorbent for the Extraction of Zearalenone in Edible Vegetable Oils

A method based on the selective extraction of zearalenone (ZON) from edible vegetable oils using molecularly imprinted polymer (MIP) has been developed and validated. Ultra-high-pressure liquid chromatography coupled with a fluorescence detection system was employed for the detection of zearalenone. The method was applied to the analysis of zearalenone in maize oil samples spiked at four concentration levels within the maximum permitted amount specified by the European Commission Regulation (EC) No. 1126/2007. As a result, the proposed methodology provided high recoveries (>72%) with good linearity (R2 > 0.999) in the range of 10-2000 μg/kg and a repeatability relative standard deviation below 1.8%. These findings meet the analytical performance criteria specified by the European Commission Regulation No. 401/2006 and reveal that the proposed methodology can be successfully applied for monitoring zearalenone at trace levels in different edible vegetable oils. A comparison of MIP behavior with the ones of QuEChERS and liquid-liquid extraction was also performed, showing higher extraction rates and precision of MIP. Finally, the evolution of ZON contamination during the maize oil refining process was also investigated, demonstrating how the process is unable to completely remove (60%) ZON from oil samples

Spatial clustering for district heating integration in urban energy systems: application to geothermal energy

Given the challenges related to climate change and dependency from fossil fuels, modification of the energy systems infrastructure to increase the share of renewable energy is a priority in urban energy planning. The high heating density in cities makes it more economically competitive to deploy district heating (DH), which is essential for large-scale integration of renewable energy sources. Combining georeferenced data with district heating design methods allows to improve the quality of the system design. However, increasing the spatial resolution can lead to intractable model sizes. This paper presents a methodology to spatially assess the integration of DH networks in urban energy systems. Given georeferenced data of buildings, resource availability and road networks, the methodology allows the identification of promising sites for DH deployment. First, an Integer Linear Programming (ILP) model divides the urban system into spatial clusters (of buildings). Graph theory and routing methods are then used to optimally design the DH configuration in each cluster considering the road network in the routing algorithm. A Mixed-Integer Linear Programming (MILP) model is formulated in order to economically evaluate the DH integration over the whole urban area. The proposed methodology is applied to an example case study, evaluating the use of geothermal energy (deep aquifer) for direct heat supply. The results of the optimization show the interest of deploying geothermal DH in some of the clusters. The profitability of DH integration is strongly affected by the spatial density of the heating demand

The Impact of Uncertainty in National Energy Planning

Concerns related to climate change and security of energy supply are pushing various countries to define strategic energy plans. Strategic energy planning for national energy systems involves investment decisions (selection and sizing) for energy conversion technologies over a time horizon of 20-50 years. This long time horizon requires uncertainty to be accounted for. Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favor of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on strategic energy planning decisions. A classification of uncertainty in national energy systems decision-making is performed. A Global Sensitivity Analysis (GSA) is performed in order to highlight the influence of the model uncertain parameters onto the energy strategy. Optimization under uncertainty is then applied to a general Mixed-Integer Linear Programming (MILP) problem having as objective the total annual cost and assessing as well the IPCC Global Warming Potential LCIA indicator (CO2-equivalent emissions). The application focuses on the case study of Switzerland. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal