Akıllı merkezi ısıtma sistemlerinde termal talep optimizasyonu

This thesis proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden to decrease the carbon footprint of the residents. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of a decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we adapt three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses how the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission decrease and cost savings compared with the method that the experts of the utility company propose.