engleski

Economical, environmental and exergetic multi- objective optimization of district heating systems on hourly level for a whole year

District heating systems are proven to be an effective way of increasing energy efficiency, reducing the en-vironmental impact and achieving higher exergy efficiency than individual heating solutions. The leaders indistrict heating integration are Scandinavian countries with more than 50% of the covered total heating demand.Nevertheless, these systems haven’t reached their full potential in most European countries. The reason for thiscould be that energy planners often study only the economic feasibility of the system, thus neglecting othercrucial aspects of the previously mentioned district heating. In research papers, district heating multi-objectiveoptimization usually takes into account the minimization of the total discounted cost and the environmentalimpact. Most times, these two objectives are studied as a single objective optimization problem through theinternalization of the cost related to carbon dioxide emissions. This paper presents the multi-objective optimi-zation method which is capable of optimizing district heating technology supply capacities and their operation, including thermal storage, for a one- year time horizon in order to satisfy the optimization goals. The model waswritten in the open-source and free programming language called Julia, while linear programming solver namedClp was used to obtain the solution. The solver is part of Julia’s optimization package called JuMP. Threeseparate objective functions are included in the model: the minimization of the total discounted cost, theminimization of carbon dioxide emissions and the minimization of exergy destruction. Since these three goalsare often in conflict, thefinal result of multi-objective optimization is the so-called Pareto surface which presentsthe compromise between all possible results. To deal with the multi- objective optimization problem, theweighted sum method in combination with the epsilon- constraint method was used. The most suitable result hasbeen chosen using the knee point method which is a solution the closest to the Utopia solution where all threegoals reach their optimal value.

district heating ; exergy ; multi-objective optimization ; linear programming ; thermal storage

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