Abstract
Development of new building HVAC control algorithms has grown due to needs for energy efficiency and operational flexibility. However, case studies demonstrating new algorithms are largely individualized, making algorithm performance difficult to compare directly. In addition, the effort and expertise required to implement case studies in real or simulated buildings limits rapid prototyping potential. Therefore, this paper presents the Building Optimization Testing Framework (BOPTEST) and associated software for simulation-based benchmarking of building HVAC control algorithms. A containerized run-time environment (RTE) enables rapid, repeatable deployment of common building emulators representing different system types. Emulators use Modelica to represent realistic physical dynamics, embed baseline control, and enable overwriting supervisory and local-loop control signals. Finally, a common set of key performance indicators are calculated within the RTE and reported to the user. This paper details the design and implementation of software and demonstrates its usage to benchmark a Model Predictive Control strategy.
Highlights
The primary use case of Building Optimization Testing Framework (BOPTEST) is to evaluate the performance of control algorithms for benchmarking purposes
From the six core Key Performance Indicators (KPI) that BOPTEST provides, we show thermal discomfort and operational cost, since these are the ones being minimized in the objective (Equation (12a))
All core KPIs for the peak heating period with dynamic price scenario and Ts = 30 minutes are shown in Figure 9, and a summary of all core KPIs obtained in this demonstration example is provided as supplemental data accompanying the online version of this article
Summary
Needs for reducing CO2 emissions, integrating renewable, variable, and distributed energy sources into electric and thermal grids, adapting to natural disasters and health emergencies, and operating complex system architectures have prompted efforts to improve upon existing control algorithms Such efforts include ASHRAE’s publishing of Guideline 36 (ASHRAE 2018), development of grid-friendly control strategies (Kim et al 2016), resolute interest in Model Predictive Control (MPC) (Drgoňa et al 2020), and rapidly growing interest in data-driven control (Vázquez-Canteli and Nagy 2019). Each new or improved algorithm is demonstrated individually through simulation or field testing on a particular application to show the benefits they promise over an alternative deemed as a baseline With such individualized studies and baseline definitions, it remains unclear how each control algorithm compares to another on a particular application in a quantitative way and whether similar benefits are observed in other applications. Such insights would enable building owners and operators to invest in the most effective solutions and the development community to identify areas of needed continued work
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