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 effective way of increasing energy efficiency, reducing environmental impact and achieving higher exergy efficiency than individual heating solutions. The leaders in district 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 the most of European countries. The reason for this could be that energy planners often study only economical feasibility of the system thus neglecting other two crucial aspects of the district heating previously mentioned. In research papers, district heating multi-objective optimization usually takes into account minimization of the total discounted cost and environmental impact. Most of the times, these two objectives are studied as a single objective optimization problem through internalization of the cost related to the carbon dioxide emissions. This paper presents novel multi-objective optimization method which is capable of optimizing district heating technology supply capacities and their operation, including the thermal storage, for one-year time horizon in order to satisfy the optimization goals. The model was written in the open-source and free programming language called Julia, while linear programming solver named Clp was used to obtain the solution. The solver is part of the Julia’s optimization package called JuMP. Three separate objective functions are included in the model: minimization of the total discounted cost, minimization of the environmental impact in terms of the carbon dioxide emissions and minimization of exergy destruction. Since these three goals are often in conflict, the final result of multi-objective optimization is so called Pareto front which presents the compromise between all possible results. To deal with the multi-objective optimization problem, weighted sum method in combination with epsilon- constraint method was used. The most suitable result has been chosen using the knee point method which is a solution the closest to the Utopia solution where all three goals reach their optimal value.

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

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