Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids
2. Literature Review and Contribution
- An integrated software architecture that supports a vendor- and protocol-agnostic data acquisition and control framework that enables both local and/or cloud based controls. The architecture is extensible to other building types, equipment and DERs.
- A field demonstration of this software controlling HVAC, refrigeration and DER using MPC. The software is deployed in the local area network in a real-world small commercial building.
- A proof-of-concept demonstration of a building controller that is responsive to various grid signals (time varying energy costs) and that supports demand response events.
- A description of the implementation challenges experienced during deployment and operations, intended to accelerate the effort of future researchers and practitioners who could avoid these barriers that have been identified.
3. Controller Architecture and Components
3.1. eXtensible Building Operating System (XBOS)
- WAVE and WAVEMQ: WAVE is an authentication engine that handles permissions and access control. WAVEMQ is a multi-tier publish-subscribe message bus that allows exchange of data and control signals.
- Drivers: Drivers are connectors to real devices and other data sources (e.g., web based services, emulated devices, etc.). A driver is responsible for gathering data from a device and for controlling the device in response to requests from an external controller. With the required permissions, a driver can publish and subscribe to messages on WAVEMQ.
- Data Storage: Both operational and configuration data are stored on dedicated databases. There are separate data stores for the building metadata represented using the Brick schema  and for the continuous real-time data that are being collected by the drivers.
- Applications: Developers can write applications on the XBOS platform using real-time data that is being published on the message bus (e.g., notification service and visualization dashboard) or using historical data that have been stored in the database (e.g., MPC based optimization engine and fault detection tools). Applications can publish control signals for the devices on WAVEMQ and can trigger a change in their mode of operation.
3.2. WAVE and WAVEMQ
3.4. Data Storage
3.5. Applications: Optimization Engine
3.5.2. Model Formulation
3.5.3. Optimization Configuration for Grid Interactions
3.5.4. Supervisory Control Scheme
3.5.5. Weather Forecast
4.1. Site Description
4.2. Hardware and Software Set Up
- Electricity Meters: The building has six Continental Controls Wattnode power meters  that measure the power consumption of: whole building, west zone RTU compressor, east zone RTU compressor, refrigerator compressor, refrigerator evaporator fan and freezer compressor and evaporator fan, respectively. These devices measure instantaneous power parameters and communicate over Modbus serial.
- HVAC: The two RTUs are controlled by two separate KMC Flexstat 120063CEW  thermostats, which communicate over BACnet/IP. The preferred temperatures in the west and east zone are 20.56 and 21.67 C (or 69 and 71 F, respectively), respectively. The heating and cooling setpoints of the thermostats are controlled by the optimizer.
- Refrigeration: Sporlan Parker PSK214 Modbus (serial)  refrigeration controllers are used to control the large refrigeration systems. By default the freezer is set to an indoor temperature of −21.67 C (−7 F) and the refrigerator to 0.56 C (33 F). During the experiments, the SPO controlled the indoor cabinet temperature setpoint of the equipment.
- PV and Battery: The inverters of the PV panels and the battery are interfaced with a Schweitzer Engineering Laboratories SEL-3505 Real Time Automation Controller (RTAC) . The RTAC is the short-timescale microgrid controller for this system and it handles power flows, circuit switching and safety aspects during the grid-islanded system operation. However, due to certain restrictions at this site, the RTAC only allows ’read’ operations (over Modbus(TCP)) to be performed by the SPO.
- Emulated Battery: As the Tesla battery on site only allows ’read’ operations, a software-based emulated battery is used for the experiments. This battery has been scaled down to a size that makes sense for conventional small and medium convenient stores (without power-hungry gaming machines, but HVAC and refrigeration). The emulated battery has a total capacity of 27 kWh, with a peak output of 14 kW (equivalent to two Tesla Powerwalls ).
- Weather: The current outdoor temperature, cloud cover, relative humidity and wind speed data, along with their 48-h forecasts, are collected from the DarkSky weather service’s REST API .
- Grid Signals: Provides information about the prices based on tariffs or dynamic prices and/or could also publish information about scheduled demand response events. While this is currently implemented as library function that retrieves the grid signals from a static database, retrieving the real-time or day-ahead Independent System Operator (ISO) prices or dynamic prices from a utility using protocols such as IEEE 2030.5  or OpenADR  are planned future work.
4.3. Optimization Engine Set Up
4.3.2. Controller Start-Up
4.4.1. Dynamic Prices
4.4.2. Time-Of-Use Prices
4.4.3. Demand Limiting Event
5.1. Benefits to Developers
5.2. Challenges of the Real World Deployment
5.3. Limitations and Future Work
Conflicts of Interest
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|Mode||Objective||Information in the Signal||Constraints|
|Minimize cost of|
energy and/or power
|Energy price and/or|
estimated peak power
|Minimize cost of|
energy and/or power
|Energy price and/or|
estimated peak power,
|Minimize cost of|
energy and/or power
|Energy price and/or|
estimated peak power,
baseline power ,
, start time, final time
|Minimize cost of|
energy and/or power
|Energy price and/or|
estimated peak power,
baseline power ,
start increase time,
final increase time,
start decrease time,
final decrease time
|Minimize error against|
reference power profile
|Wattnode Meter||Modbus (serial)||active power (W)|
|KMC Thermostat||BACnet/IP||indoor temperature ( C)|
|Refrigeration Controller||Modbus (serial)||cabinet temperature ( C)|
|PV production (W)|
battery charge/discharge rate (W)
battery state of charge (%)
|Battery (emulated)||-||battery charge/discharge rate (W)|
battery State of Charge (%)
|Dark Sky API||HTTP||outdoor temperature ( C)|
cloud cover (%)
relative humidity (%)
wind speed (mph)
and 48-hour forecasts of these
|Grid Signals (tariffs, dynamic prices, Demand response events)||-||price of energy ($/kWh)|
demand charge ($/kW)
amount of load limit/shed/shift (W)
|Controller||Protocol||Control Variables||Default||Lower Limit||Upper Limit|
|Thermostat East||BACnet/IP||heating setpoint|
|Thermostat West||BACnet/IP||heating setpoint|
|Freezer||Modbus (serial)||cabinet temp. setpoint||−21.67 C||−34.44 C||−18.89 C|
|Refrigerator||Modbus (serial)||cabinet temp. setpoint||0.56 C||0.56 C||3.33 C|
|Battery (emulated)||-||charge/discharge rate||0W||−14 kW||14 kW|
|Costs||SPO Optimized Load||Baseline Load|
|#||Category||Description of Challenge|
|1||Technical||Limited choice of secure connected devices with local communication interfaces|
|2||Technical||Uncertain service and support for connected products, web services and underlying libraries (e.g., discontinued services, APIs changes).|
|3||Technical||Complex interaction of advanced supervisory control with local control in each connected device (e.g., thermostat hysteresis and defrost control).|
|4||Technical||Overconstrained systems (e.g., undersized refrigerator with tight temperature control bands)|
|5||Technical||Faulty equipment and sensors that make modeling harder due to unexpected behavior and incorrect representation of the system state|
|6||Technical||Unmodeled and unmeasured effects in the systems (e.g., unknown occupancy, door opening, internal gains due to uncontrolled equipment)|
|7||Organizational||Conflicting objectives and different risk tolerance between occupants/managers and researchers (e.g., thermal comfort, refrigerator temperature swings)|
|8||Organizational||Strict site/organization procedures and requirements (e.g., cybersecurity procedures)|
|9||Logistic||Delays in equipment deliveries (e.g., components in high demand)|
|10||Logistic||Faulty new equipment that needs to be replaced|
|11||Regulatory||Long lead times to work with highly-regulated, risk-adverse entities (e.g., utilities to sign off on the interconnect agreement, receiving approval before battery and PV commissioning)|
|12||Exceptional||Unfortunate and unforeseen natural disasters (e.g., power shutoffs due to threats of wildfires, COVID-19 pandemic)|
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Krishnan Prakash, A.; Zhang, K.; Gupta, P.; Blum, D.; Marshall, M.; Fierro, G.; Alstone, P.; Zoellick, J.; Brown, R.; Pritoni, M. Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids. Energies 2020, 13, 3093. https://doi.org/10.3390/en13123093
Krishnan Prakash A, Zhang K, Gupta P, Blum D, Marshall M, Fierro G, Alstone P, Zoellick J, Brown R, Pritoni M. Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids. Energies. 2020; 13(12):3093. https://doi.org/10.3390/en13123093Chicago/Turabian Style
Krishnan Prakash, Anand, Kun Zhang, Pranav Gupta, David Blum, Marc Marshall, Gabe Fierro, Peter Alstone, James Zoellick, Richard Brown, and Marco Pritoni. 2020. "Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids" Energies 13, no. 12: 3093. https://doi.org/10.3390/en13123093