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Απόκριση ζήτησης σε βιομηχανικό κτίριο σχεδόν μηδενικής ενεργειακής κατανάλωσης με χρήση γενετικών αλγορίθμων

Sifakis Nikolaos

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Summary

One of the primary challenges that modern society must confront, involves the decreasing of energy consumption and efficient energy utilization. A tremendous potential to increase energy efficiency and energy conservation exists in buildings while the comfort of the buildings’ occupants is not compromised. As a consequence, the operation of HVAC systems, that regulate energy demand/consumption, must take into account energy tariff, daily routines, thermal comfort and various constraints. In this direction, Demand Response offers the possibility of altering the profile of power consumption of individual buildings or building districts for economic return. This is partly due to the potential of wide scale DR in minimizing investments, otherwise necessary for modernizing the power grid, through enabling flexibility and advanced grid management options. Distributed Energy Resources (DER) and Demand Side Management (DSM) are gradually gaining attention as a valuable asset for reducing peak loads, maintaining grid balance, managing the volatility and high energy rejection associated with renewable technologies (i.e. wind, solar) thus increasing grid overall efficiency.In this Diploma thesis, the potential for Demand Response (DR) optimization of the HVAC system in a Smart Near Zero Energy Industrial Building in Ancona, Italy is investigated. The analysis involves a validated thermal building model, the model of energy cost and a methodology to establish the HVAC optimum preconditioning set point curve. Preheating and precooling is explored on hourly basis according to the dynamic state of the building, varying external climatic conditions and standard indoor comfort conditions. Optimization aims at minimization of the cost of energy using a Genetic Algorithm (GA) configuration under the current pricing scheme. These algorithms were implemented with the support of MATLAB and Energy Plus software. The GA variables represent the discrete state of the HVAC hourly set points for 8 hours prior to the working hours of the building. Results are thoroughly presented and analyzed in an attempt to evaluate the effectiveness of the developed approach and potential applications in current pricing schemes. It is concluded that the effectiveness level of the examined method, depends on the location of the building and energy pricing schemes. It is envisaged that as markets become open and more dynamic the potential of such techniques will increase in parallel with gradual Demand Response implementation.

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