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Research and Development of Automated Demand Response Program UCLA Smart Grid Energy Research Center (SMERC)

Scope

The Automated Demand-Response (ADR) technology research program aims to showcase different levels and modalities of automation in load curtailment, control models and secure messaging schemes, leveraging multiple communication technologies and maintaining interoperability between the Smart Grid automation architecture layers. Demonstrations will leverage test beds that would include the diverse demographic distribution and consumption behavior of the partnering utility’s service area. These test beds are situated within the UCLA campus which is being used a living lab to demonstrate a variety of ADR concepts. Within the campus, UCLA has the capability of producing roughly ¾ of its consumed energy through an on-campus natural gas power plant. Such an environment within UCLA makes the campus a very desirable place to carry out ADR testing and demonstrations.


Technology Evaluation

Existing control technology components and sub-systems will be evaluated in conjunction with recommended data, messaging, protocols, security, and network standards, culminating with the aggregation of operational parameters, data formats, messaging schemes and technological synergies. In addition representative smart metering technologies (Advanced Metering Infrastructure [AMI]) will be evaluated to understand interoperability, data volume and networking aspects. An in-depth review of end-use requirements, system-wide data/metadata modeling, rate design models, and backend system integration will also be undertaken to guide the system architecture. Interoperability and security standards would be examined and investigated from the point of predominantly ‘Defense-in-Depth’ based system-wide cyber-security strategy.


System Design

The Demand-Response system would be modeled on a service-oriented architecture (SOA) that would facilitate federated data models, seamless interoperability between layers of the system and assist with integration modalities for backend utility systems based on the aggregated information obtained from the technical evaluations and requirements’ analysis. The architecture would enable real-time collaboration across the network including distribution, metering, billing, and customer premises to execute processes of the demand response programs. Built-in supervisory control would monitor customer side demand management and compliance to the load curtailment requests. The architecture would also facilitate representation of unique load profiles and service requirements for customers belonging to each of the different types (commercial, residential, industrial). To manage large number of unique pricing and load management profiles in real time the architecture would explore the creation of dynamic, instantaneous communications and feedback capabilities. For in-premise automation an open system of embedded platforms based on the aggregated automation technology protocols, data payload formats, and network communication mechanisms would be developed. The platform based on WINSmartGridTM technology would enable transactions between control nodes operating on different protocols, communicating over wireline or wireless technologies, and conveying common data payloads.

SOA in conjunction with open embedded system would attempt to support plug-and-play and secure demand-response solutions. A portal to dispense data and information to the stakeholders and an application programming interface (API) would also be designed to provide customizability and extensibility to the system.


Test Plan Design

Incremental testing of data, protocols, and communications fidelity and reliability will be carried out on the living lab test beds at UCLA. Capabilities of selected automation technologies will be demonstrated. AMI-DR models for the test bed experimentation, hardware and software interfaces for the WINSmartGridTM embedded platform, software architecture, recommended security schemes and algorithms, access control policies and desired set of optimizations would be developed.

Testing phase would cover test bed-based experimentation, proof-of-concept verification and prototype validation in different user infrastructures such as commercial and residential. Detail performance metrics for different demand response processes and technology components or sub-systems will be developed. The test plans would capture the evaluation requirement of individual system feature such as targeted load curtailment, different load profiles and customer service requirements, different facility configurations, different network technologies, and secure transactions of large data volumes, with low latency and minimal interruptions.

Each of these test plans would be executed to experiment with different operational logic for various end-use scenarios meeting the requirements of end-users, utility administration, and facility administration. The test results will be cataloged, archived and dispensed through the portal. With the guidance from the test results and performance metrics the system modules will be modified to mitigate the variances. Variances observed in the tests on account of shortcomings from representative automation technologies, control and messaging schemes or data and protocols would be recorded for incompatibility advisories.


Test Beds

Test-beds for this research would cover electric load classes of both commercial and residential infrastructures such as small loads (appliances, sprinklers, entertainment equipment, lighting, drapery controls, etc.), large loads (pool pumps, HVAC units, motors, etc.) and EV/PHEV. In-building electric load interface technologies would be both the wireline and wireless variety. Wireless and wireline broadband technologies would be part of the field area network (FAN) which would serve as the backhaul network in the immediate vicinity of the customer domain.


Demonstration

Extensive demonstrations of the demand-response system at each of the test bed facilities would be arranged. The demonstrations will execute the demand response events and processes and highlight the seamless operation of services-based architecture, granularity of load control, and compliance with customer’s load profiles and service requirements.




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