PV systems are used more and more. Not always is it possible to install them in the optimal direction for maximum energy output over the year. At the Johan Cruijff ArenA the PV panels are placed all around the roof in all possible directions. Panels oriented to the north will have a lower energy gain than those oriented to the south. The 42 panel groups are connected to 8 electricity meters. Of these 8 energy meters monthly kWh produced are available. The first assignment is to calculate the energy gains of the 42 panel groups, and connect these in the correct way with the 8 energy meter readings, so simulated data is in accordance with measured data.Of the year 2017 there are also main electricity meter readings available for every quarter of an hour. A problem with these readings is that only absolute values are given. When electricity is taken of the grid this is a positive reading, but when there is a surplus of solar energy and electricity is delivered to the grid, this is also a positive reading. To see the effect on the electricity demand of future energy measures, and to use the Seev4-City detailed CO2 savings calculation with the electricity mix of the grid, it is necessary to know the real electricity demand of the building.The second assignment is to use the calculations of the first assignment to separate the 15 minute electricity meter readings in that for real building demand and for PV production.This document first gives information for teachers (learning goals, possible activities, time needed, further reading), followed by the assignment for students.
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The traditional energy industry is transitioning from a centralised fossil fuel based industry to a decentralised renewable energy industry for several reasons including climate change, policy, and changing customer needs. Furthermore, renewable sources, such as wind and solar, are intermittent and unpredictable. This has implications for the business models of energy producers, such as increased mismatch between demand and supply, increased price volatility, shift in drivers of value creation. Due to the low marginal cost of production and the intermittent nature of renewables, the price volatility on the electricity markets, in particular the imbalance market, are expected to increase. However, there is potential for market parties operating in the electricity sector to profit from this development by providing flexibility to balance electricity supply and demand. Therefore, new business models are needed that can harness and exploit flexibility in a viable manner. In these business models, flexibility becomes the key driver of value creation.
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This report focuses on the feasibility of the power-to-ammonia concept. Power-to-ammonia uses produced excess renewable electricity to electrolyze water, and then to react the obtained hydrogen with nitrogen, which is obtained through air separation, to produce ammonia. This process may be used as a “balancing load” to consume excess electricity on the grid and maintain grid stability. The product, ammonia, plays the role of a chemical storage option for excess renewable energy. This excess energy in the form of ammonia can be stored for long periods of time using mature technologies and an existing global infrastructure, and can further be used either as a fuel or a chemical commodity. Ammonia has a higher energy density than hydrogen; it is easier to store and transport than hydrogen, and it is much easier to liquefy than methane, and offers an energy chain with low carbon emissions.The objective of this study is to analyze technical, institutional and economic aspects of power-to-ammonia and the usage of ammonia as a flexible energy carrier.
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This report has been established within the Flexiheat project. Flexiheat has focused on increasing flexibility in district heating systems. The intelligent district heating network is a dynamic network: an open network where different waste heat and renewable energy sources are connected, that has multiple producers and groups of consumers and facilitates the connection between different energy infrastructures (gas, heat and electricity). Eventually this will lead to an optimal deployment of the available heat sources and an increased cost-efficiency of district heating. Flexiheat aims to develop new concepts for these intelligent, flexible district heating networks. One of the strategies is to allow third party access to the network. A smart control system is developed to manage the heat flows across the network. This system makes use of dynamic pricing. In this exploration the concept of third party access in relation to the Flexiheat project will be discussed. The development of new business and price models based on the Flexiheat approach has led to an analysis of possible alternative price models for consumers.
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To reduce greenhouse gas emissions, countries around the world are pursuing electrification policies. In residential areas, electrification will increase electricity supply and demand, which is expected to increase grid congestion at a faster rate than grids can be reinforced. Battery energy storage (BES) has the potential to reduce grid congestion and defer grid reinforcement, thus supporting the energy transition. But, BES could equally exacerbate grid congestion. This leads to the question: What are the trade-offs between different battery control strategies, considering battery performance and battery grid impacts? This paper addresses this question using the battery energy storage evaluation method (BESEM), which interlinks a BES model with an electricity grid model to simulate the interactions between these two systems. In this paper, the BESEM is applied to a case study, wherein the relative effects of different BES control strategies are compared. The results from this case study indicate that batteries can reduce grid congestion if they are passively controlled (i.e., constraining battery power) or actively controlled (i.e., overriding normal battery operations). Using batteries to reduce congestion was found to reduce the primary benefits provided by the batteries to the battery owners, but could increase secondary benefits. Further, passive battery controls were found to be nearly as effective as active battery controls at reducing grid congestion in certain situations. These findings indicate that the trade-offs between different battery control strategies are not always obvious, and should be evaluated using a method like the BESEM.
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This article explores the applicability of smart grid concepts to the Dutch gas network by reflecting on the experience of the electricity sector.
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Far from being negligible in quantity, decentralized energy production delivers a considerable part of the renewable energy production in the Netherlands. Decentralized production takes place by individual households, companies as well as citizen groups. Grassroots initiatives have sprung up in the Netherlands in the last 5 years, in a recent inventory 313 formally instituted local energy cooperatives were found. Cooperatives’ aims are sustainability, strengthening local economy and promoting a democratic governance structure for energy production.The energy industry in the Netherlands has traditionally been dominated by large energy companies, and the Groningen gas field has resulted in a very high dependency on natural gas for both consumer and business households. The climate for grassroots initiatives has improved since the so-called Energy Covenant in 2013. This covenant pertains to an agreement between government, industry representatives, labor unions and non-governmental organizations to arrive at a substantial reduction of energy use, ambitious increase in the production of renewable energy, and new jobs in the renewable energy sector.The covenant also announced new policies to stimulate community energy activities, such as the Zip-code-rose policy . The governmental interest in new forms of energy transition, is also demonstrated by the ‘Experiments Electricity Law’ facility, which gives local business and community initiatives an opportunity to experiment with a local energy system. This policy is meant as a ‘learning facility’; experiences are expected to lead to adaptations in Dutch electricity law and regulation.
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One of the factors that differentiate honors from regular teaching at the Faculty of Geosciences at Utrecht University, the Netherlands, is the freedom that honors students enjoy, a freedom that evokes excellence because it is focused and targeted. This targeted freedom takes three different shapes in our honors program and comes with specific challenges for both students and teachers. While the attractions and advantages of such freedom are both theoretically and practically significant, our experience has also demonstrated drawbacks that need to be addressed and resolved in creating effective honors education.The challenges and struggles as well as the rewards that we have experienced might be familiar to honors educators around the world, but they are also shaped by the particular contexts of our program within the Faculty of Geosciences, within Utrecht University, and within the Netherlands, contexts that we will now introduce.
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Energy cooperatives are beginning to expand their role from stimulating small-scale electricity production to developing local energy systems, including cooperatively owned energy storage solutions. However, many technical, social and financial obstacles are encountered in this process. It is as yet unclear how new roles of citizens, building owners, grid operators and energy cooperatives will develop. Furthermore, it is difficult to assess if a feasible business case is at all possible given present context conditions in the Netherlands.
MULTIFILE
This chapter presents the currently not established and identifies design requirements for new systems to address this challenge and provide directions for possible improvement. As a result, this chapter introduces the concept of SamenMarkt®, a participatory system in which multi-agent system technology enables distributed price negotiation, distribution and communication between producers, retailers and consumers.
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