Automated Dynamic Demand Response Implementation on a Micro-grid

Conference · Wed Nov 16 00:00:00 EST 2016

OSTI ID:1332492

Kuppannagari, Sanmukh R.; Kannan, Rajgopal; Chelmis, Charalampos; Prasanna, Viktor K.

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D²R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D²R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability to dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D²R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.

• Research Organization: City of Los Angeles Department
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: OE0000192
• OSTI ID: 1332492
• Report Number(s): DOE-USC-00192-58
• Resource Relation: Conference: 3rd ACM International Conference on Systems for Energy-Efficient Built Environments Stanford, CA, USA November 16-17, 2016
• Country of Publication: United States
• Language: English