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Control of Complex Systems Initiative

Integrated Demonstrations

Integrated demonstrations were developed as a collaborative deliverable for multiple projects across all research focus areas. The demonstrations help:

  • Ensure that project integration is occurring within and across focus areas
  • Verify that control designs are compatible with other control designs within the initiative
  • Provide scalable test cases for testing tool and test bed functions
  • Create use cases with tangible outcomes that can be used for future market development
  • Provide active visual demonstrations of meeting the first four goals.

Jason Fuller, Principal Investigator
Jacob Hansen, Principal Investigator

FY 2017 Integrated Demonstrations

CCSI conducted two major demonstration projects meant to confirm the importance and efficiency of transactive energy control algorithms to coordinate the consumption of Thermostatically Controlled Loads (TCLs) with the goal of capping peak energy.

Wholesale-retail market integration through market-based coordination of controllable loads

2017 Integrated Demonstration

The Pacific Northwest GridWise™ Testbed Demonstration Projects and the American Electric Power (AEP), Ohio, gridSMART® Real-Time Pricing double auction project operated similarly, but at different scales, and while very successful raised several concerns related to the heuristic bidding and pricing strategies and market inefficiencies. These concerns have been addressed in CCSI by developing new advanced control algorithms to improve the TCLs bidding and price-response logic. These algorithms were evaluated on a co-simulation test bed with an integrated wholesale-retail market. Distribution system flexibility is measured and communicated to the wholesale market, which creates more efficient system operation through the dispatch of flexible loads to reduce price spikes during generation-constrained scenarios. The test bed provides capabilities to evaluate control algorithms on complex and large-scale power systems models characterized by transmission networks including both conventional and renewable generation resources, and highly detailed modeled distribution networks with a high level of flexible load penetration. With these capabilities, CCSI was able to execute a groundbreaking complex and large-scale co-simulation. This co-simulation modeled the power grid of the Western United States. The simulation included a total of 15,000 individual distribution systems, coupled with a reduced order transmission system model of the Western Electricity Coordinating Council with a total load of roughly 120GW. This simulation helped validate the wholesale-retail integration algorithm developed in CCSI and showed that large-scale adoption of transactive control algorithms is possible.

Robust Distributed Economic Dispatch with Imperfect Communication Networks

As the power system evolves, support for larger balancing markets that span larger and larger geographical areas is growing. Examples of these types of markets are CAISO’s Energy Imbalance Market (EIM) market and the PJM Midcontinent Independent System Operator (MISO) market. These markets have shown interest in solving the economic dispatch problem in a robust and distributed manner and CCSI developed a control framework for solving such problems. The designed algorithm is robust to communication imperfections such as missing messages and uneven delays. As part of previous efforts, the algorithm was tested using simple models in a single simulator portraying a distributed control model. During FY 2017 a distributed agent co-simulation framework was developed with every agent modeled as a separate simulator that can evaluate the algorithm in a truly distributed manner. Because these are fully distributed simulators, they can be installed in physically separate computation systems. Using the control theory from CCSI, a distributed co-simulation framework was used to solve the day-ahead economic dispatch. Individual agents could communicate with each other and all messages between agents were simulated in a network communication simulator, ns-3. Short and long network delays, and different types of communication infrastructure were considered, power line communication and fiber optic. These technologies were used to evaluate the impacts of communication imperfections on the control system. The analysis performed in this demonstration allowed control designers to investigate in detail communication impacts on new distributed control systems.

FY 2016 Integrated Demonstration

2016 Annual Review

Three integrated demonstrations were created involving 15 out of the 17 CCSI projects. These demonstrations showcased the different scales of control and the CCSI control theory and test capabilities for each. The first demonstration implemented distributed control of a multi-zone commercial building for providing grid services using test bed capabilities to work with real buildings as test cases. The second demonstration showcased control of populations of residential loads by leveraging both physical test bed resources in coordination with simulation tools to model a distribution system with water heater loads. The third demonstration implemented coordination of distributed generation and end-use devices in large-scale systems. It used CCSI tools to enable large-scale simulation to understand the interplay of distribution control with transmission control. The objectives of these demonstrations were both to highlight the impact of the controls being developed by CCSI and to highlight how the tools and test bed enable testing and validation of controls.

FY 2015 Integrated Demonstration

The first demonstration was created with shared deliverables coming from seven of 14 projects. It focused on merging of two control strategies and testing them within the test bed environment. The test cases focused on integration and control of distributed energy resources within a future distribution system operator (DSO) scenario in which the control system was able to adapt to changing grid conditions while minimizing utility costs and responding to constraints within the system. The demonstration also highlighted the co-simulation framework, experiment user interface, and automated software deployment capabilities prototyped in FY 2015.

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