Automation of electrical power grid planning processes

Magistrsko delo obravnava avtomatizacijo procesov dolgoročnega načrtovanja elektroenergetskega omrežja. Trajnostna energija na področje elektroenergetike prinaša velike spremembe. V sklopu proizvodnje je to vključevanje obnovljivih in razpršenih virov energije, v sklopu porabe pa prehod na ogrevanje z električno energijo in električna mobilnost. Prav tako velja omeniti vedno večji pomen hranilnikov električne energije in FACTS-naprav. Vse te novosti prinašajo dodatne negotovosti, kar zahteva uvedbo novih orodij, modelov in pristopov v sklopu obratovanja in načrtovanja elektroenergetskega sistema. Eden najobetavnejših pristopov je večscenarijski pristop k načrtovanju omrežja, ki trenutne pristope načrtovanja nadgradi z vpeljavo analize množice različnih stanj za ciljno leto, kar omogoča celovit pogled in statistično analizo dogajanja v prihodnosti. Glavna cilja magistrske naloge sta zasnova algoritma in izdelava programske kode v jeziku Python, ki v kombinaciji s programsko opremo PowerFactory proizvajalca DigSILENT na podlagi modela omrežja in vhodnih podatkov iz orodij za analize trga avtomatizira izračune pretokov moči za množico scenarijev in ur/stanj. Avtomatizacija izračunov in analize pretokov moči v procese načrtovanja prinaša velike prihranke časa, pri tem pa so zaradi velike količine podatkov in časovno zamudnih izračunov ključne določene poenostavitve in optimizacije. Za predstavitev zmogljivosti in uporabnosti izdelane programske kode je izdelana večscenarijska analiza vključitve sončnih elektrarn moči 1000 MW na prenosno omrežje Republike Slovenije. Pridobljeni rezultati nazorno prikazujejo pozitiven vpliv vključevanja sončnih elektrarn. Vidni sta zmanjšanje relativnih izgub in povečanje samozadostnosti slovenskega elektroenergetskega sistema, vendar pa je za varnost obratovanja tako spremenjenega elektroenergetskega sistema potrebna dodatna okrepitev omrežja.The primary focus of the master\u27s thesis is to streamline processes in long-term power grid planning through automation. With the advent of sustainable energy, the energy industry is undergoing significant transformation. This includes the integration of renewable and dispersed energy sources in electricity production, the adoption of electric heating and electric mobility in consumption, and the growing importance of electric energy storage and FACTS devices. However, these changes introduce additional uncertainties, necessitating the development of new tools, models, and approaches to power grid operation and planning. A promising approach is the multi-scenario approach to network planning, which involves analyzing numerous states for the target year to provide a comprehensive view of the future grid through statistical analysis. This approach enhances existing methods and delivers a more precise analysis of the future grid. The central aim of this thesis is to devise an algorithm and create Python code that, when paired with DigSILENT PowerFactory software, automates power flow calculations for a range of scenarios and hours/states, using data input from market analysis tools and the network model. By automating these calculations, power grid planning processes can be greatly expedited. Due to the vast amounts of data and time-consuming calculations involved, certain simplifications and optimizations are crucial. The second part of the thesis involves a multi-scenario analysis of the effects of introducing 1,000 MW solar power plants into the power grid of the Republic of Slovenia, with the purpose of showcasing the effectiveness and practicality of the developed code. The findings of the analysis clearly indicate that the inclusion of solar power plants has a positive impact, resulting an increase in the self-sufficiency of the Slovenian power system and a reduction in relative grid losses. However, to ensure the safe operation of the altered power system, additional grid strengthening is necessary