Home Energy Management Retrofit Control Platform

Transcription

1 Home Energy Management Retrofit Control Platform Presenter: Ed Vineyard (ORNL) Authors: Helia Zandi (ORNL) Ed Vineyard (ORNL) Jibo Sanyal (ORNL) David Fugate (ORNL) Teja Kuruganti (ORNL) May 17, 2017 ORNL is managed by UT-Battelle for the US Department of Energy

2 Outline Goals Introduction System Hardware Setup System Software Architecture Software Components Challenges Conclusion and Future work 2 Connected Homes

3 Project Goals The overall objectives are to: develop, demonstrate, and deploy interoperable solutions that can bridge communication technologies address the main issues affecting a connected environment accelerate the adoption rate of products to enable intelligent control of devices inside a connected home 3 Connected Homes

4 HEMS and Energy Consumption Residential buildings account for 23% of U.S. primary energy consumption HVAC, water heating, and appliance loads account for approximately 83% of the total energy use in residential buildings. While HVAC systems account for a significant portion (54%), the contribution of other appliances can significantly impact peak demand A large portion of appliance energy use can be operated as dispatchable (turned on or off) loads using a Home Energy Management System (HEMS) Dispatchable loads enable responsiveness to electric grid needs and higher penetrations of renewable energy, energy storage, and electric vehicle technologies Despite the variety of residential building types, their energy use profiles are similar which facilitates a less complex solution than for commercial buildings 4 Connected Homes Buildings projected to be 80% of load growth through million commercial 117 million residential

5 Home Energy Management System HEMS are used to: Determine energy consumption patterns, providing near-real-time feedback to users Improve energy consumption by monitoring and controlling loads Support DR actions Based on literature review of existing HEMS, the following market gaps exist: Easy deployment Seamless communication between devices (interoperability) Device monitoring and control Device additions Intelligence to act on signal from utility Limited or no security 5 Connected Homes

6 Hardware Setup We evaluated retrofit-compatible, single-board computing platforms (thermostats) using VOLTTRON for potential use in the control of residential building loads VOLTTRON is a distributed agent platform based on the Multi-agent System (MAS) MAS is a community of intelligent agents which can work together and coordinate their decisions to achieve the system goal Enables the deployment of intelligent agents that can optimize the residential energy consumption while satisfying user comfort conditions Successfully demonstrated communication and control Raspberry Pi with VOLTTRON Thermostats 6 Connected Homes

7 Hardware Setup A simple Modbus read operation from the five thermostats used in the experiment was tested in four execution scenarios to evaluate the optimal single board computer to use: without VOLTTRON, using simple repeated reads from the thermostats as an agent using the VOLTTRON platform as an agent using the VOLTTRON platform and publishing values on the VOLTTRON message bus while a SMAP agent pushed the data to a remote SMAP server as an agent using the VOLTTRON platform and publishing values on the VOLTTRON message bus while a MySQL agent pushed the data to a local MySQL database Results indicated the NUC is the choice for optimal performance Lower percentages are required to enable control of multiple devices 7 Connected Homes

8 Software Architecture User Interfaces: A Web user interface and a phone/tablet application will be provided for easy interaction between the user and the software Applications and Intelligence: The implementation of many applications provided by the software is in this layer Demand Response Scheduling Notifications/alerts Device management Implementation of smart algorithms such as load shedding, price management, and energy usage pattern analysis. Database may be used for data archival and retrieval 8 Connected Homes

9 Software Architecture Framework Services: This layer provides VOLTTRON platform-centric services Software drivers to access multiple devices Access to the communication message bus (publish/subscribe) Agent services Networking services Communication Technologies and Protocols: This layer includes various communication technologies and protocols that will be supported by the software such as Wi-Fi, ZigBee, Z-Wave, Ethernet 9 Connected Homes

10 Agent Services All agent services are implemented on VOLTTRON Historian Agent is responsible for data retrieval from database Device Agent is responsible for monitoring and controlling devices DR Agent is responsible for communication with utility Intelligent Agent is responsible for goal-based scheduling and notifying appropriate devices 10 Connected Homes

11 Intelligent Agent Agent creates a plan based on system goals and DR signals received from the utility As an example, during peak, the Intelligent Agent can postpone noncritical tasks such as laundry or dishwashing and start devices such as HVAC systems or water heaters based on information regarding a homeowner s schedule The Intelligent Agent can also change settings based on a created or modified schedule and make recommendations by performing data analysis on the energy consumption history and the system goals 11 Connected Homes

12 Conclusions In this paper, we identified an open-source HEMS to support DR, provide secure and protected communication between devices and the system, and support monitoring and control for a wide number of devices The HEMS can also provide greater functionality by incorporating smart algorithms to create schedules based on system goals (reduced energy consumption, comfort) and reduced peak demand 12 Connected Homes

13 Challenges and Future work Data Management: Since the smart grid can provide this information at varying time granularity, the system should be designed to manage and analyze the tremendous amount of data provided; the amount of data for optimal control needs to be determined Intelligence: Smart algorithms need to be developed to leverage and analyze the data to make informed decisions for coordinating the energy usage and consumption, reducing peak demand, and supporting load shifting Scalability: The scalability challenge of connected homes needs to be evaluated to efficiently and robustly handle the energy usage for thousands of homes that constitute a community; two planned communities of 50 homes each underway 13 Connected Homes

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