Application of Intelligent Load Control to Manage Building Loads to Support Rapid Growth of Distributed Renewable Generation

Journal Article · Mon Feb 24 04:00:00 EST 2020 · Sustainable Cities and Society

DOI:https://doi.org/10.1016/j.scs.2019.101898· OSTI ID:1615337

Kim, Woohyun ^[1]; Katipamula, Srinivas ^[2]; Lutes, Robert G. ^[2]

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1. VISITORS
2. BATTELLE (PACIFIC NW LAB)

Electricity utilities are faced with the mounting challenge of providing a stable supply of power to meet the growing demand while also integrating rapid growth in distributed variable renewable generation. Traditional means of balancing short- and long-term supply and demand imbalance will be expensive. Alternative approaches of using flexible loads in buildings are needed to mitigate the imbalance at a lower cost. This paper shows how the intelligent load control (ILC) process can be used to manage loads in buildings by dynamically prioritizing loads for curtailment using both quantitative and qualitative criteria. The ILC process can be deployed on low-cost computing platforms without the need for any additional sensing. ILC was first validated in a simulation environment to provide two grid service use cases: (1) managing monthly peak electricity demand and (2) managing buildings’ electricity consumption during a capacity bidding event. After being successfully tested in a simulation environment, ILC was deployed on real buildings to manage electricity consumption to provide two different use cases under different outdoor operating conditions. Both the simulation tests and the real building experiments were deployed using VOLTTRON™, a distributed sensing and control platform. The results from the tests and experiments showed that ILC was able to manage the controllable loads (heat pumps) in the building to maintain the electricity consumption at the desired level without a significant impact on occupant comfort. Overall, the results demonstrate that the ILC allows coordination of the controllable loads and provides a more intelligent means of load management than the traditional duty-cycling approach.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1615337
• Report Number(s): PNNL-SA-143847
• Journal Information: Sustainable Cities and Society, Journal Name: Sustainable Cities and Society Vol. 53
• Country of Publication: United States
• Language: English