Balancing Market Integration in Northern European System - A 2020 Case Study
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- Institutt for elkraftteknikk 
The Nordic power system is a complex and interconnected system in the sense that all the Nordic countries are closely tied together when it comes to electricity trading, both in the important day-ahead market, where the majority of all electricity are purchased/sold, and the balancing market, where reserve capacity is procured and activated for balancing reasons. Sharing of power and electricity offers many advantages and great flexibility. Earlier studies show that an integrated Northern European balancing market provides huge economic gains and cost savings, both because the integrated countries can use the cheapest and most available resource, but also because the requirements for balancing reserves are folded down (imbalance netting). The achievements of the Nordic countries have been noted elsewhere in Europe and in the world, and other countries are now trying to get started similar agreements on a close electricity cooperation. In the future, the power system will consist of more unpredictable energy resources such as wind and solar, providing increased need for balancing reserves, and therefore the interconnectors stand even stronger. The purpose of this research work is to look at the impact of balancing market integration in Northern Europe. This is assessed considering simulations of an optimization model addressing an integrated system of reserve procurement in form of procurement per country and per balancing region, and FRR reservation on HVDC-lines. The optimization model for the integrated system is simulated in a 2020 scenario, which has been the case study of this thesis. The model used has earlier been formulated and simulated in a PhD for a 2010 scenario case study. A study of the balancing energy market is not included in this master s thesis. Investigation of integrated balancing market using an already refined and developed optimization model is performed through a 2020 case study. The 2020 case study is considered by creating a realistic energy system that will be similar to what we have in 2020. This involves more RES and less thermal energy, and larger HVDC-line capacities. It should be mentioned that much of the input data used in the 2010 case study is also used for 2020, and that this does not necessarily match 100% with reality. The objective of the optimization model is to minimize the total system operating costs, meaning costs related to both day-ahead market and reserve procurement market. In this case study, the cost related to day-ahead market is much higher than the cost related to reserve procurement market. The optimization model is formulated for optimal scheduling of country-vise and region-vise FRR procurement, and to optimally allocate the reservation for FRR exchange on transmission lines. The main results obtained in this master's thesis are: The Nordic balancing market is a well-functioning market structure with great results comparing to a non-integrated Nordic system. Given a considerably increased capacity, the HVDC-lines between the Nordic countries, Germany and the Netherlands are well utilized. The procurement of reserves (FRR) for balancing purpose per country increases overall sharply from 2010 to 2020. This comes as a natural response to a greater amount of unpredictable energy sources in the system. The procurement of FRR per balancing region increases as well. Throughout the year, most are procured from the Nordic region, except in early spring when the water reservoirs are empty. The procurement in the German balancing region follows an opposite curve of the Nordic region, where most of the FRR is procured in early spring. The total costs, including day-ahead and reserve procurement costs, increase when compering the 2020 scenario (case 2) with 2010 scenario (case 1). The increase is highest in the day-ahead market. The total costs, compared with two reference cases from 2010 representing no-reservation of balancing reserves and sequential market clearance option, decrease with 3 and 1 million.