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The transition towards a 100% fossil-free energy system, while achieving extreme penetration levels of intermittent wind and solar power in electricity generation, requires demand-side technologies that are smart (intermittency-friendly) and efficient. The integration of Smart Grid enabling technologies has been widely acknowledged by the industry and academia as the key to achieve a more reliable, efficient and secure grid. However, there is a lack of information about real flexibility with an active participation from customers. In this paper, we combine techno-economic model results with an empirical study in order to achieve a number of recommendations with respect to technology concepts and control strategies that would allow residential compression heat pumps to support large-scale integration of intermittent renewables. The analysis is based on data gathered over a period of up to 3 years period for 283 residential heat pumps installed and operating in Denmark. The results are used to assess the flexibility of domestic heat pumps and their ability to follow production.

Power systems with high wind penetration experience increased variability and uncertainty, such that determination of the required additional operating reserve is attracting a significant amount of attention and research. This paper presents methods used in recent wind integration analyses and operating practice, with key results that compare different methods or data. Wind integration analysis over the past several years has shown that wind variability need not be seen as a contingency event. The impact of wind will be seen in the reserves for non-event operation (normal operation dealing with deviations from schedules). Wind power will also result in some events of larger variability and large forecast errors that could be categorized as slow events. The level of operating reserve that is induced by wind is not constant during all hours of the year, so that dynamic allocation of reserves will reduce the amount of reserves needed in the system for most hours. The paper concludes with recent emerging trends.

Until recently, the modelling of electricity system operations has mainly focused on hour-by-hour management. However, with the introduction of renewable energy sources such as wind power, fluctuations within the hour result in imbalances between supply and demand that are undetectable with an hourly time resolution. Ramping restrictions on production units and transmission lines contribute further to these imbalances. In this paper, we therefore propose a model for optimising electricity system operations within the hour. Taking a social welfare perspective, the model aims at reducing intra-hour costs by optimally activating so-called manual reserves based on forecasted imbalances. Since manual reserves are significantly less expensive than automatic reserves, we expect a considerable reduction in total costs of balancing. We illustrate our model in a Danish case study and investigate the effect of an expected increase in installed wind capacity. We find that the balancing costs do not outweigh the benefits of the inexpensive wind power, and that the savings from activating manual reserves are even larger for the high wind capacity case.

In recent years there has been an increased focus on sustainable farming systems. This has led to an increase in the use of farm models built to assess the environmental impact from farming. In whole-farm models including crop production it is important to consider the rotation of crops, since this has a major impact on the consequences of the crop production. The problem considered in this paper is that of finding an optimal crop rotation for a given selection of crops on a given piece of land. It is shown that techniques from network modelling can be used to model this problem, which has the advantage that special algorithms for solving network problems can be developed and applied to solve the crop rotation problem. This can save computation time and make it tractable to implement crop rotation in whole-farm models.

Experience from existing large offshore wind farms show that the geographical concentration of wind power leads to increased wind power variability, and that the response to storm front passages raises new issues: this may lead to a sudden shut down of the wind farm when the wind speed exceeds the cut-off wind speed (typically 25 m/s). Experience has shown that a large offshore wind farm in this way can be shut down from full power to zero power in less than 5 minutes. Thus, in the planned offshore development in the North Sea, several GW of wind power could be shut down within less than one hour as a result of a storm passage, which may impact the security of the whole European electric system. The storm passages will be a threat to the whole system reliability and stability, unless the wind power shut down is carefully coordinated.

This paper describes a framework and methodology for risk and vulnerability analysis including extraordinary events in power systems. The framework is based on a bow-tie structure and identifies threats, unwanted events, barriers and consequences. Application of the methodology is shown for a real case analyzing extraordinary events in a transmission system. In this case the consequence is unacceptable, but the risk is moderate due to the low probability for the event to occur. The case study and experience so far indicates that one of the most challenging parts of a risk and vulnerability analysis is how to identify the vulnerable operational states and extraordinary events.

The MEA-Scope project developed, and applied a modelling approach that allows for the ex ante assessment of sustainability impacts of new policies, technologies and market changes. Thereby, the agricultural production at farm level and its effect on social, economic and environmental assets under changing circumstances is examined. The MEA-Scope modelling approach simulates the development of regional agricultural production structures over time. Within the same analysis, the approach considers details of individual farms and soils. During the project duration, three pre-existing models were further completed and interlinked with each other. The modelling approach was applied at two different levels of detail in seven different European regions to examine the effects of five agricultural policy scenarios. The core models involved were AgriPoliS, MODAM and FASSET/Farm-N. In this chapter, the modelling approach, characteristics of the models involved and the policy scenarios are introduced while results as well as details on the different modelling applications can be found in subsequent chapters of this book.

In this paper, we describe a decision support system (DSS) to support the farmer when selecting a winter wheat variety. In Denmark, the yield characteristic has traditionally been the most important factor in variety selection. The DSS in this paper bases the selection on several characteristics and it takes future uncertain observations and decisions of relevance to the variety decision into account. We describe the decision process in detail and build a simple stochastic optimization model, representing the decision process. For each variety of winter wheat, the model calculates the expected net revenue, which is the expected gross revenue minus the expected costs for treatment of diseases and application of additional fertilization, which is allowed in certain varieties. Finally, we discuss the results from the model.

Offshore wind power has a significant development potential, especially in North Europe. The geographical concentration of offshore wind power leads to increased variability and in the case of critical weather conditions it may lead to sudden and considerable loss of production. In this context, the chances of losing several GW of wind power due to critical weather conditions in a very short time period could potentially jeopardize the whole system's reliability and stability. Forecasting such events is not trivial and the results so far are not encouraging. When assessing the impact of the variability for the 2020 Danish power system, one can see that in the worst case, up to 1500 MW of power can be lost in 30 minutes. We present results showing how this issue is partially solved by the new High Wind Storm Controller presented by Siemens in the TWENTIES project.

In 2013, offshore wind grew over 50%. This increase, concentrated in a relatively small geographical area, can lead to an increased variability of the power produced by offshore wind. The variability is one of the key issues, along transmission, in integrating offshore wind power. Hydro power is one of the fast responding sources of electricity, thus power systems with considerable amounts of flexible hydro power can potentially offer easier integration of offshore wind power. The interaction between offshore wind and hydro power can be benefic, especially when looking at how the flexibility of hydro generation can match the variability of offshore wind, allowing for larger shares of variable generation to be integrated in the power systems without decreasing its stability. The analysis includes two interrelated models, a market model and a flow-based model. The results show that hydropower systems are a very good option for balancing the natural variability of wind power production, especially when installed offshore. The flexibility of hydropower systems allows power systems with a high share of RES to maintain stability. The analysis presented indicates that the value of hydropower flexibility to the European power system is significant, consequently justifying the investment costs for transmission expansion.