Adaptive Operation Decisions in Net Zero Building Clusters

Abstract

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.

Comparative analysis of auction mechanisms and bidding strategies for P2P solar transactive energy markets

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A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)

“…With reference to a number of studies introducing the new electricity network [41,103,104], a generic power distribution network, which represents the future development trend of micro power network for multiple energy prosumers and the public charging stations, is summarized in this study, as shown in Figure 7. The energy prosumers are equipped with their own renewable energy systems, electrical storage, EVs and other electrical appliances.…”

“…Then, based on the optimized performances at the building cluster level, the operation of every single building inside the cluster is coordinated using a non-linear programming algorithm. Similarly, Odonkor et al, developed a control method for NZEBs using a genetic algorithm and Pareto decision making based on an adaptive bi-level decision model (with a facilitator agent at the cluster level and local systems at the single NZEB level) [104]. Considering the dynamic pricing, in [24] a similar three-step iterative demand response control algorithm is developed.…”

A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered

“…To achieve energy sharing among buildings, existing studies have developed a number of advanced controls. For example, Odonkor et al proposed a control method of ZEBs using genetic algorithm and Pareto decision making based on an adaptive bi-level decision model (with a facilitator agent at cluster level and local systems at single NZEB level) [11]. In such bi-level decision model, the individual building's systems (i.e.…”

“…𝑖 𝑗 𝑗 1 (11) (ii) For each single building, the electricity charging amount must be smaller than the remaining storage capacity of the battery, and the electricity discharging amount must be smaller than the amount of electricity stored in the battery. There are 2N inequality constraints for N buildings.…”