EnergyPlus is the Department of Energy’s (DOE’s) open-source, state-of-the-art building energy modeling (BEM) engine, supporting energy-efficiency code development, commercial products, and research. EnergyPlus has a broad suite of modeling capabilities. However, one of its most interesting features is EMS (Energy Management System), an integrated scripting facility that allows power users to implement custom functionality directly in EnergyPlus.
EMS exposes a significant set of EnergyPlus internal variables (e.g., date and time, sensor values, HVAC component and system settings, etc.) and allows users to write programs that manipulate these values. EnergyPlus then invokes these programs at pre-determined times (e.g., at the beginning of the zone time step, at the end of the HVAC time step, at the start of each HVAC iteration, etc.). Although initially intended to implement custom control strategies that complement EnergyPlus’s library of built-in strategies, EMS has found a range of uses—from simple tasks, such as overriding convection coefficients and altering component availability—to complete physics-based HVAC component and system models1 and, more recently, to demand response (DR) and equipment fault modeling.
To date, EMS users have had to write these programs using a minimalist language called ERL (EnergyPlus Runtime Language). On the spectrum of programming languages, ERL is analogous to circa 1980 BASIC or AWK. It has scalar variables, but no data structures like arrays or objects. It has loops, but they cannot be nested. It provides access to some internal EnergyPlus functions, but it does not support user-defined functions. It does not have general-purpose input/output facilities or libraries. These shortcomings exist because EnergyPlus is a BEM engine, not a programming language interpreter.
Released in March 2020, EnergyPlus 9.3.0 fast-forwards the EMS feature about 40 years. Now, EnergyPlus has an embedded Python interpreter, allowing users to write EMS programs in one of the world’s most popular scripting languages. Python has all the features of modern programming language and a trove of libraries for all kinds of domains from scientific calculations to machine learning (ML) to interact with web application programming interfaces (APIs) and even sense and control hardware. It will allow Python users to not only simplify the implementation of EMS’ existing use cases but enable new ones as well. Python will also allow users to organize EMS scripts into portable “packages” that can be published and used by others. “It is exciting to think about how users will take advantage of this new feature,” says Dr. Edwin Lee, NREL’s EnergyPlus project lead and primary developer of this feature.
Relationship of Python EMS to Spawn
Those who have been monitoring the development of Spawn, DOE’s next-generation BEM-controls engine, may be wondering about the relationship of Python EMS to Spawn. Isn’t Spawn intended to allow users to develop control sequences in real-world programming languages? Yes, it is! But Spawn is also a dynamic simulation engine that allows control sequences to be written in a physically realistic way and model them in, similarly, a physically realistic way. Python EMS control sequences will still have to be written to EnergyPlus’ load-based simulation approach, although once they are, they produce “correct” behavior.
Python EMS and Spawn shared significant underlying development. Much of the EnergyPlus re-engineering that enables Spawn to reuse its envelope and load modules also supports Python EMS. A key effort has been developing and exposing a rich API that allows external programs to “call into” EnergyPlus and invoke functions to read sensors, write actuators, query and calculate properties, etc. Spawn is using this API to exchange data between EnergyPlus and Modelica components. Third-party applications and services will also be able to apply this API to use EnergyPlus in a more granular “a la carte” form to implement new and enhanced functionality. Adds Lee, “Python EMS is just one use of this new API; there will be many other exciting uses.”
1 Woods, J. and Bonnema, E. “Regression-based approach to modeling emerging HVAC technologies in EnergyPlus: A case study using a Vuilleumier-cycle heat pump.” Energy and Buildings, Volume 186, 1 March 2019, Pages 195-207. https://www.sciencedirect.com/science/article/abs/pii/S0378778818331177