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reports - Deliverable

Performance of thermal loads managed via aggregator for the provision of network services

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#Control #Flexibility

reports - Deliverable

Performance of thermal loads managed via aggregator for the provision of network services

The report describes flexibility evaluations of aggregates composed of domestic hot water devices (water heaters or boilers) with heat pump technology integrated with boost resistors, for providing demand response services. In addition, the report lays out an “intelligent” management strategy for the aggregate devices, based on Model Predictive Control (MPC), and presents example applications in a simulated environment.

This paper describes the further development of research activities on the flexibility of aggregates of domestic hot water heating systems (water heaters or boilers) for providing demand response services. The objectives pursued were the following:

1) extend flexibility evaluations of aggregates composed of heat pump water heaters;

2) develop and test an intelligent device management algorithm of a water heater aggregate.

For flexibility evaluations, integrated heat pump technology with boost resistors was considered. The result is a significant increase in the “down” reserve (load increase), while the “up” reserve does not change with the introduction of the resistors.
The aggregate management algorithm for the delivery of demand response services was developed using the Model Predictive Control (MPC) methodology, which is based on the prediction of the system behavior over an appropriate time horizon using a dynamic model. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This makes it possible to make the algorithm adaptive, i.e., able to adjust its control decisions to the system characteristics, which vary over time due to the daily distribution of domestic hot water consumption by users. Variations in the power requirements of boilers are implemented considering the comfort limits of users. The developed approach allows analyzing in detail the response time to the demand response service activation signal, the service activation and deactivation ramps, the dynamics of device activations/deactivations during service delivery, and the magnitude of the so-called rebound, i.e., the energy recovery required by the aggregate at the end of the service to return to the thermal state of the baseline profile. Analysis in a simulation environment allowed validation of the potential of the proposed algorithm.

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Related tags

#Control #Flexibility

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