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Simulations with realistic network models and electric loads are essential for developing smart grid integration strategies such as integrating distributed generation and electric vehicles into the grids. Accurate simulations require detailed information on the electricity consumption of custom-ers connected to the grid. However, the electricity consumption data from individual customers are challenging to acquire because of data privacy concerns. Especially with the introduction of the new General Data Protection Regulation (GDPR) in the European Union, the electricity distri-bution system operators are not interested in sharing individual consumption data. Running de-tailed smart grid simulations requires individual customer load profiles and cannot be based on publicly available average load profiles such as national customer class load profiles. The aver-aged load profiles do not yield sufficiently accurate results because they do not reflect the tem-poral load variations present in actual consumption data. The study material of this thesis will consist of new type consumer load profiles as a replace-ment for the Finnish customer class load profiles, and their previously calculated statistical prop-erties by Dr.Tech. Antti Mutanen, and some thousands of real smart meter measurements. In this M.Sc. thesis, the goal is to study how those type consumer load profiles in the study material could be reverse-engineered into realistically varying individual synthetic load profiles using the top-town analysis method. This thesis develops three algorithms for generating individual load profiles based on Markov chain process. The first algorithm uses the traditional Markov chain method to generate synthetic load profiles. Then, the traditional Markov chain method is extended to improve the results, and the new algorithm (i.e. second algorithm) is called the suggested Markov chain algorithm. The third algorithm in this thesis is called the adaptive Markov chain algorithm in the literature and bor-rows several machine learning concepts to develop it. Finally, an aggregate load profile matching method is described, implemented and applied to realistically adjust and scale the synthetic load profiles generated by the above algorithms. All the algorithms described in this thesis are imple-mented using MATLAB, and a part of the adaptive Markov chain algorithm is implemented using Python. The suggested Markov chain method, combined with the aggregate load profile matching method, allows generating realistic synthetic load profiles, and meets the goal of this thesis. The results are shown and validated in the final chapters, and they confirm that the suggested Markov chain method works properly for load profile generation and it can better capture the yearly sea-sonal variations in power consumption. The MATLAB programs are designed and implemented for hourly smart meter measurement input data. These programs can later be flexibly modified for higher-resolution input data and synthetic load profiles. Furthermore, the developed adaptive Mar-kov chain algorithm can be further developed in the future with different deep learning techniques to get more realistic load profiles. |
