Open Source Model of the Nordic Power System for EU Project Spine

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Abstract: Decision problems in operation and planning of power systems often rely on large-scale models and data sets. Lack of historical power flow data due to regulatory restrictions often limits researchers to study the system with aggregated network models. Aggregated data from the electricity market operators (Nordpool in the Nordics) and the Transmission System Operator (TSO) (from ENTSO-E) are openly available, and can be used to study the power flow and exchanges between different regions but do not directly provide information about intra-region flows. This project builds upon the Nordic 490 system, a previously built model of the Nordic power system. The main objective of this work is to improve the existing open source power flow model of the Nordic power system, in order to become in turn available for the multi-energy modelling and simulation software Spine. The N490 model generates a model of the Nordic power system consisting of various nodes/buses which represent substations at different voltage levels. Then, it distributes the aggregated production, consumption and power exchange data from Nordpool to the various buses. In this project, different possible improvements are evaluated for the model, aiming at estimating a set of network parameters that minimize the errors between the calculated inter-region flows and the ones from the open data repositories. The different improvements which are evaluated are the following. Firstly, the load distribution is modified and reassigned to match the regional electricity consumption. The generators and wind farms are then reallocated to different bus based on their bidding region and proximity to the bus. The databases are improved and the power balance relation modified. Transmission line parameters are then investigated, first to standard recommended values and then by solving an optimisation problem formulated to extract the parameters from the market data. Finally, the model is also tested with wind and solar generation modelled as a generator rather than as a negative load.

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