Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

Comparing simulated demand flexibility against actual performance in commercial office buildings

“…The goal was to facilitate more field studies into this dataset with the standard data schema, and to improve the availability and consistency of these data across buildings and markets and among stakeholders. This data schema contains information about the physical characteristics, location, and DF performance of the building and serves as the core data schema of a commercial building demand flexibility database.Similar to the modeling methods used in previous studies [11,20,21], we propose to use the commercial prototype building models [42] for assessing the DF characteristics by different building types, vintages, and climates.…”

“…By comparing prototype model simulation results with the collected field data, we expected to gain insight into the validity and limitation of the prototype simulation approach for the estimation of DF potential, such as large scale simulations [20,21,11]. Figure 2 depicts the simulation data flow of DF modeling using two kinds of parametric methods for a large scale of prototype model simulations and a cross-comparison framework between simulation, laboratory testing, and field data.…”

“…The aforementioned simulated and experimental results show that building thermal loads can be shifted or reduced to provide demand flexibility with appropriate control strategies, which have inspired many follow-up simulations [18,19,20,21], laboratory [22,23] and field tests [16,24,25,26,27],…”

“…Existing large datasets are available to help estimate flexibility across the national building stock [20,34,35]. Several previous simulation studies have used EnergyPlus to perform large-scale parametric simulations of demand flexibility strategies such as global temperature adjustment (GTA) in commercial buildings [21,11], while also analyzing its relation to time of day and outdoor weather conditions such as outside air temperature (OAT). A national-level assessment study analyzed the demand response potential for seven types of small commercial buildings in different vintages and climates, including various control strategies such as pre-cooling, shading, and lighting power reduction [34].…”

Random Fuzzy Power Flow Analysis for Power System considering the Uncertainties of Renewable Energy and Load Demands

“…Te uncertainty features of renewable energy generation will also cause some problems in the construction of the dispatch region [8] and collaborative optimization of the sustainable energy system [9]. On the other hand, the type of load demands tends to be diversifed, which is no longer just a simple power load [10]. It may be coupled with gas or heating/cooling subsystems to form an integrated energy system with multienergy complementarity [11,12].…”