Building Energy Flexibility Optimization Framework
Budget: $250 – $750 USD
Optimization Framework for Building Energy Flexibility During Demand Response Events
This project aims to develop an optimization framework for enhancing building energy flexibility during demand response (DR) events using dynamic building energy simulation and optimization techniques.
The study focuses on a "medium office building" modeled in OpenStudio/EnergyPlus with weather data for Xi'an, China. The objective is to determine optimal operational control strategies that maximize building energy flexibility while maintaining acceptable indoor thermal comfort and reducing energy demand during a predefined demand response event.
The project is based on one representative summer demand response scenario rather than multiple DR scenarios.
Objectives
The optimization framework should:
* Reduce peak electricity demand during the DR event.
* Improve building energy flexibility.
* Minimize energy consumption where appropriate.
* Maintain occupant thermal comfort within acceptable limits.
* Identify optimal operating strategies for the selected DR period.
Building Model
The optimization will use an EnergyPlus/OpenStudio model of a medium office building, including:
* Building geometry.
* Envelope properties.
* HVAC system.
* Occupancy schedules.
* Lighting schedules.
* Equipment schedules.
* Local weather data (Xi'an EPW file).
The baseline model should run correctly before the optimization process begins.
Decision Variables
The optimization may include one or more controllable building operational parameters, such as:
* HVAC cooling setpoint temperatures.
* HVAC operating schedules.
* Ventilation control strategies.
* Lighting schedules or lighting power reduction.
* Other operational variables that influence building flexibility and energy use.
The selected variables should be technically feasible to implement in EnergyPlus.
Objective Functions
The optimization should consider multiple objectives, including:
* Peak demand reduction.
* Building energy flexibility performance.
* Total electricity consumption.
* Occupant thermal comfort (e.g., PMV, PPD, operative temperature, or another appropriate comfort metric).
There is room to recommend the most appropriate formulation for these objectives based on current research.
Optimization Method
The optimization algorithm is open to recommendation. Suitable approaches include, but are not limited to:
* NSGA-II
* Genetic Algorithm (GA)
* Particle Swarm Optimization (PSO)
* Other multi-objective optimization techniques compatible with EnergyPlus.
The optimization workflow should automatically modify decision variables, execute EnergyPlus simulations, evaluate objective functions, and search for optimal solutions.
Preferred Tools
Experience with the following is preferred:
* EnergyPlus
* OpenStudio
* Python
* MATLAB
* EnergyPlus API, OpenStudio SDK, or other automation methods for coupling simulation and optimization.
Expected Deliverables
The completed work should include:
* A fully functional optimization workflow linked with EnergyPlus/OpenStudio.
* Automated simulation and optimization process.
* Well-documented source code.
* Description of all decision variables, constraints, and objective functions.
* Optimization results.
* Graphs showing optimization performance and trade-offs.
* Pareto front visualization if a multi-objective algorithm is used.
* Documentation explaining how to reproduce and modify the workflow.
Required Experience
Applicants should have demonstrated experience with:
* EnergyPlus or OpenStudio.
* Building energy simulation.
* Building energy flexibility.
* Demand response.
* Multi-objective optimization.
* Python or MATLAB.
* Automation of EnergyPlus simulations.
Please include examples of similar optimization or building energy simulation projects completed previously.
This project aims to develop an optimization framework for enhancing building energy flexibility during demand response (DR) events using dynamic building energy simulation and optimization techniques.
The study focuses on a "medium office building" modeled in OpenStudio/EnergyPlus with weather data for Xi'an, China. The objective is to determine optimal operational control strategies that maximize building energy flexibility while maintaining acceptable indoor thermal comfort and reducing energy demand during a predefined demand response event.
The project is based on one representative summer demand response scenario rather than multiple DR scenarios.
Objectives
The optimization framework should:
* Reduce peak electricity demand during the DR event.
* Improve building energy flexibility.
* Minimize energy consumption where appropriate.
* Maintain occupant thermal comfort within acceptable limits.
* Identify optimal operating strategies for the selected DR period.
Building Model
The optimization will use an EnergyPlus/OpenStudio model of a medium office building, including:
* Building geometry.
* Envelope properties.
* HVAC system.
* Occupancy schedules.
* Lighting schedules.
* Equipment schedules.
* Local weather data (Xi'an EPW file).
The baseline model should run correctly before the optimization process begins.
Decision Variables
The optimization may include one or more controllable building operational parameters, such as:
* HVAC cooling setpoint temperatures.
* HVAC operating schedules.
* Ventilation control strategies.
* Lighting schedules or lighting power reduction.
* Other operational variables that influence building flexibility and energy use.
The selected variables should be technically feasible to implement in EnergyPlus.
Objective Functions
The optimization should consider multiple objectives, including:
* Peak demand reduction.
* Building energy flexibility performance.
* Total electricity consumption.
* Occupant thermal comfort (e.g., PMV, PPD, operative temperature, or another appropriate comfort metric).
There is room to recommend the most appropriate formulation for these objectives based on current research.
Optimization Method
The optimization algorithm is open to recommendation. Suitable approaches include, but are not limited to:
* NSGA-II
* Genetic Algorithm (GA)
* Particle Swarm Optimization (PSO)
* Other multi-objective optimization techniques compatible with EnergyPlus.
The optimization workflow should automatically modify decision variables, execute EnergyPlus simulations, evaluate objective functions, and search for optimal solutions.
Preferred Tools
Experience with the following is preferred:
* EnergyPlus
* OpenStudio
* Python
* MATLAB
* EnergyPlus API, OpenStudio SDK, or other automation methods for coupling simulation and optimization.
Expected Deliverables
The completed work should include:
* A fully functional optimization workflow linked with EnergyPlus/OpenStudio.
* Automated simulation and optimization process.
* Well-documented source code.
* Description of all decision variables, constraints, and objective functions.
* Optimization results.
* Graphs showing optimization performance and trade-offs.
* Pareto front visualization if a multi-objective algorithm is used.
* Documentation explaining how to reproduce and modify the workflow.
Required Experience
Applicants should have demonstrated experience with:
* EnergyPlus or OpenStudio.
* Building energy simulation.
* Building energy flexibility.
* Demand response.
* Multi-objective optimization.
* Python or MATLAB.
* Automation of EnergyPlus simulations.
Please include examples of similar optimization or building energy simulation projects completed previously.