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The integration of Variable Renewable Energy Sources (VRES) creates challenges for meeting load demand. The lack of on-demand power generation of these VRES effectively threatens energy security. Therefore, storage facilities, especially hydrogen, have been broadly researched for potential implementation in our energy system, enabling on-demand power "generation". This thesis adds to this research by providing a framework on how VRES and long-term storage technologies can be most optimally utilized to ensure weekly or monthly energy security. This framework is explicitly applied to the Netherlands and reviews the opportunities of a VRES dominated future energy system. The framework consists of a VRES generation model and load model and identifies the optimal load coverage using single- and multi-objective genetic algorithms. The VRES generation model is designed for a weekly and monthly timeframe by using 31 years of available weather data (1988-2018), specifically average wind speeds and solar irradiance, for a number of locations in the Netherlands. Using the available weather data, power generation per MW of onshore wind, offshore wind, and solar PV are calculated. Subsequently, this calculated VRES power generation is compared to available real-world VRES power generation data and a correction factor is determined. Applying the correction factor to the more extensive weather data set allows to effectively create a VRES power generation model, based on the weather circumstances as in these 31 years. Additionally, a weekly and monthly 31-year load profile is determined, using available data for load demand in the Netherlands. Thereafter, both a single and multi-objective algorithm is tasked to provide load coverage in two main scenarios at minimum cost. First, load coverage is achieved exclusively utilizing VRES capacity. Secondly, a variety of long-term storage facilities are introduced in combination with VRES capacity to acquire energy security. Furthermore, these two methods for achieving energy security ...

(1) The amount of plastic discharges in the environment has drastically increased in the last decades negatively affecting aquatic ecosystems, societies, and the world economy. The policies initiated to deal with this problem are insufficient and there is an urgency to initiate local actions based on a deep understanding of the factors involved. (2) This paper investigates the potential of massive open online courses (MOOCs) to spread environmental education. Therefore, the conclusions drawn from the implementation of a MOOC to combat the problem of marine litter in the world are presented. (3) This work describes the activity of 3632 participants from 64 countries taking an active role presenting useful tools, connecting them with the main world associations, and defining applied action plans in their local area. Pre- and post-questionnaires explore behavioral changes regarding the actions of participants to combat marine litter. The role of MOOCs is contrasted with social media, formal education, and informal education. (4) Findings suggest that MOOCs are useful instruments to promote environmental activism, and to develop local solutions to global problems, for example, clean beaches, supplanting plastic bottles, educational initiatives, and prohibition of single-use plastic.

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Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.

The plug-in hybrid electric vehicle allows vehicle propulsion from multiple internal power sources. Electric energy from the grid can be utilized by means of the plug-in connection. An on-line energy management (EM) strategy is proposed to minimize the costs for taking energy from each power source. Especially in a dynamical energy market, an on-line optimization algorithm is desirable since energy prices change over time. By construction, the proposed EM system can operate with, and without prediction information. If predictions are available, an electronic horizon is applied to anticipate on up-coming events and further optimize the strategy. Illustrative examples are given to explain the added value for both solutions. Also the situation where energy is transferred back to the grid is considered. © 2008 IEEE.

In this paper, we propose using marginal emission factors instead of average emission factors for determining the impact of adding variable renewable electricity to the generation mix. Average emission factors assume constant emissions over time, which does not reflect reality. Therefore, they cannot be used for e.g. accurately determining the mitigated CO2 emissions by renewables, or for scheduling shiftable loads in order to have the lowest CO2 emissions. To solve this, we provide a method to construct the marginal emission profiles via the merit order and demonstrate the method by composing these for the case of the Netherlands. Using this method, we re-evaluate the CO2 impact in 2014 of photovoltaic-generated electricity to be 0.42 Mt - compared to 0.36 Mt using the average emission factor - and for wind-generated electricity to be 3.6 Mt instead of 2.9 Mt CO2 (an increase of 14.3% and 24.2%, respectively). Furthermore, we show the impact of CO2 price on the merit order and show that even high CO2 prices of 50 to 75 €/tCO2 are not sufficient to phase-out new coal-fired power plants.

Hydrogen is becoming increasingly popular as a clean, secure, and affordable energy source for the future. This study develops an approach for designing a PV–battery–electrolyzer–fuel cell energy system that utilizes hydrogen as a long-term storage medium and battery as a short-term storage medium. The system is designed to supply load demand primarily through direct electricity generation in the summer, and indirect electricity generation through hydrogen in the winter. The sizing of system components is based on the direct electricity and indirect hydrogen demand, with a key input parameter being the load sizing factor, which determines the extent to which hydrogen is used to meet seasonal imbalance. Technical and financial indicators are used to assess the performance of the designed system. Simulation results indicate that the energy system can effectively balance the seasonal variation of renewable generation and load demand with the use of hydrogen. Additionally, guidelines for achieving self-sufficiency and system sustainability for providing enough power in the following years are provided to determine the appropriate component size. The sensitivity analysis indicates that the energy system can achieve self-sufficiency and system sustainability with a proper load sizing factor from a technical perspective. From an economic perspective, the levelized cost of energy is relatively high because of the high costs of hydrogen-related components at this moment. However, it has great economic potential for future self-sufficient energy systems with the maturity of hydrogen technologies. Intelligent Electrical Power Grids Energie and Industrie The Green Village EEMS - General