User-Constrained Algorithms for Aggregate Residential Demand Response Programs with Limited Feedback.
This thesis presents novel algorithms and a revised modeling framework to evaluate residential aggregate electrical demand response performance under scenarios with limited device-state feedback. These algorithms permit the provision of balancing reserves, or the smoothing of variable renewable ener...
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| Language: | English en |
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2015
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| Online Access: | http://hdl.handle.net/1828/5937 |
| Summary: | This thesis presents novel algorithms and a revised modeling framework to evaluate residential aggregate electrical demand response performance under scenarios with limited device-state feedback. These algorithms permit the provision of balancing reserves, or the smoothing of variable renewable energy generation, via an externally supplied target trajectory. The responsive load populations utilized were home heat pumps and deferred electric vehicle charging. As fewer devices in a responsive population report their state information, the error of the demand response program increases moderately but remains below 8%. The associated error of the demand response program is minimized with responsive load populations of approximately 4500 devices; the available capacity of the demand response system scales proportionally with population size. The results indicate that demand response programs with limited device-state feedback may provide a viable option to reduce overall system costs and address privacy concerns of individuals wishing to participate in a demand response program. === Graduate |
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