UTJECAJ AKUMULACIJSKE MASE NA IZMIJENJENU ENERGIJU U SUSTAVU ZA TOPLINSKU PRETVORBU SUNČEVE ENERGIJE

U ovome je radu prikazan proračun i odabir optimalne mase akumulacijskog spremnika za sustav toplinske pretvorbe sunčeve energije. Rad obuhvaća proračun i provedbu numeričkog modela termoenergetskog sustava koristeći računalni algoritam pisan u programskom jeziku „Python“. Predstavljene su tri varijante sustava koje se razlikuju po strategiji rada pomoćnog izvora topline. „Sustav 1“ pomoćni izvor energije koristi po potrebi. Kod „Sustav-a 2“ pomoćni je izvor stalno uključen, a „Sustav 3“ pomoćnu energiju koristi samo noću. Za svaki je sustav napisan odgovarajući računalni algoritam koji emulira njihova ponašanja. Svaki je algoritam pokrenut s odgovarajućim ulaznim podatcima te je za cilj određivanja optimalne mase spremnika ta varijabla zadana u nizu od 500 do 8000 l. Varijabla mase spremnika u nizu išla je korakom od 50, što je rezultiralo provedbom svakog algoritma 150 puta. Nakon provedbe proračuna i analizom godišnjih troškova sustava, temeljem rezultata odabran je optimalni volumen za svaki od sustava, a u konačnici i preporučen sustav za korištenje.This thesis contains and shows the calculation and selection of an optimal tank volume for a solar thermal heating system. Also included is a numerical model analysis of the before mentioned thermal system. The analysis was written as a computer algorithm in the programming language „Python“. The whole analysis contains three individual solar thermal heating systems with obvious differences. Each system is equipped with an auxiliary power source. In the first system, „System 1”, the auxiliary power is used when needed. The second systems („System 2“) auxiliary power is always on, and the third systems („System 3“) auxiliary power is powered only at night. For each permutation, an algorithm was written which emulated the system properties. The algorithm was provided with sufficient input data, one of which was the volume of the heating tanks. The heating tank variable was set as an array from 500 to 8000 l, with an iteration step of 50. This means each algorithm was processed 150 times. This allowed the finding of an optimal tank volume after the calculation and analysis of energy consumption was made. After each systems optimal tank volume was discovered, a preferably used system was appointed