How can physics education be designed and enacted in such a way that it is in agreement with the Nature of Science (NOS) and fosters conceptual understanding in electricity? The results of the studies may have implications for practice. Teachers and teacher educators need to develop a balanced perspective on conceptual understanding in relation to inquiry and take into account the tensions that were identified. For the topic of electricity, teachers may learn from the local instruction theory and pedagogy developed in this dissertation. Both teacher education institutes and professionalization efforts need to prepare teachers for this type of instruction. This will be fostered if teachers and teacher educators develop an understanding of NOS. A noticeable classroom impact of teacher learning may be expected if teachers work cooperatively on the same issue, related to a concern about student learning, if expertise is available on the content and pedagogy, and if classroom coaching and feedback are part of the project. The criteria to evaluate textbooks may be helpful for authors of learning materials if they intend to foster model-oriented activities and inquiry, but also for practitioners for the selection of these materials and in teacher education to prepare for a systematic evaluation of learning materials for physics.
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The authors used the INFRASTRATEGO simulation game to examine strategic behavior in a liberalizing electricity market and the effectiveness of different regulatory regimes in dealing with this strategic behavior. The game simulates the Dutch electricity market in the years 2002 to 2006. The game was played eight times with about 400 players, both professionals and students. Two regulatory regimes defined by (a) the policy-making model and (b) the regulation by negotiation model were evaluated. The authors found several patterns of strategic behavior such as regulatory capture, sometimes with rather disturbing effects with regard to the settlement of rates and long-term capacity planning.
In this report, the details of an investigation into the eect of the low induction wind turbines on the Levelised Cost of Electricity (LCoE) in a 1GW oshore wind farm is outlined. The 10 MW INNWIND.EU conventional wind turbine and its low induction variant, the 10 MW AVATAR wind turbine, are considered in a variety of 10x10 layout configurations. The Annual Energy Production (AEP) and cost of electrical infrastructure were determined using two in-house ECN software tools, namely FarmFlow and EEFarm II. Combining this information with a generalised cost model, the LCoE from these layouts were determined. The optimum LCoE for the AVATAR wind farm was determined to be 92.15 e/MWh while for the INNWIND.EU wind farm it was 93.85 e/MWh. Although the low induction wind farm oered a marginally lower LCoE, it should not be considered as definitive due to simple nature of the cost model used. The results do indicate that the AVATAR wind farms require less space to achieve this similar cost performace, with a higher optimal wind farm power density (WFPD) of 3.7 MW/km2 compared to 3 MW/km2 for the INNWIND.EU based wind farm.
