Increasing penetration of non-dispatchable renewable energy resources and greater peak power demand present growing challenges to Bulk Power System (BPS) reliability and resilience. This research investigates the use of an Internet of Things (IoT) framework for large scale Distributed Energy Resource (DER) aggregation and control to reduce energy imbalance caused by stochastic renewable generation. The aggregator developed for this research is Distributed Energy Resource Aggregation System (DERAS). DERAS comprises two AllJoyn applications written in C++. The first application is the Energy Management System (EMS), which aggregates, emulates, and controls connected DERs. The second application is the Distributed Management System (DMS), which is the interface between AllJoyn and the physical DER. The EMS runs on a cloud-based server with an allocated 8 GB of memory and an 8 thread, 2 GHz processor. Raspberry Pis host the simulated Battery Energy Storage System (BESS) or electric water heater (EWH) DMSs. Five Raspberry Pis were used to simulate 250 DMSs.
The EMS used PJM's regulation control signals, RegA and RegD, to determine DERAS performance metrics. PJM is a regional transmission organization (RTO). Their regulation control signals direct power resources to negate load and generation imbalances within the BPS.
DERAS's performance was measured by the EMS server resource usage, network data transfer, and signal delay. The regulation capability of aggregated DER was measured using PJM's resource performance assessment criteria. We found the use of an IoT framework for DER aggregation and control to be inadequate in the current network implementation. However, the emulated modes and aggregation response to the regulated control signal demonstrates an excellent opportunity for DER to benefit the BPS.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-5535 |
| Date | 18 July 2018 |
| Creators | Slay, Tylor |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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