In light of the growing strain on the energy grid and the increased awareness of the significant role buildings play within the energy ecosystem, the need for building operational strategies which minimize energy consumption has never been greater. One of the major hurdles impeding this realization primarily lies not in the lack of decision strategies, but in their inherent lack of adaptability. With most operational strategies partly dictated by a dynamic trio of social, economic and environmental factors which include occupant preference, energy price and weather conditions, it is important to realize and capitalize on this dynamism to open up new avenues for energy savings. This paper extends this idea by developing a dynamic optimization mechanism for Net-zero building clusters. A bi-level operation framework is presented to study the energy tradeoffs resulting from the adaptive measures adopted in response to hourly variations in energy price, energy consumption and indoor occupant comfort preferences. The experimental results verify the need for adaptive decision frameworks and demonstrate, through Pareto analysis, that the approach is capable of exploiting the energy saving opportunities made available through fluctuations in energy price and occupant comfort preferences.

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