PNNL

Abstract

Distributed, generation, demand response, distributed storage, smart appliances, electric vehicles and renewable energy resources are expected to play a key part in the transformation of the American power system. Control, coordination and compensation of these smart grid assets are inherently interlinked. Advanced control strategies to warrant large-scale penetration of distributed smart grid assets do not currently exist. While many of the smart grid technologies proposed involve assets being deployed at the distribution level, most of the significant benefits accrue at the transmission level. The development of advanced smart grid simulation tools, such as GridLAB-D, has led to a dramatic improvement in the models of smart grid assets available for design and evaluation of smart grid technology. However, one of the main challenges to quantifying the benefits of smart grid assets at the transmission level is the lack of tools and framework for integrating transmission and distribution technologies into a single simulation environment. Furthermore, given the size and complexity of the distribution system, it is crucial to be able to represent the behavior of distributed smart grid assets using reduced-order controllable models and to analyze their impacts on the bulk power system in terms of stability and reliability. The objectives of the project were to: • Develop a simulation environment for integrating transmission and distribution control, • Construct reduced-order controllable models for smart grid assets at the distribution level, • Design and validate closed-loop control strategies for distributed smart grid assets, and • Demonstrate impact of integrating thousands of smart grid assets under closed-loop control demand response strategies on the transmission system. More specifically, GridLAB-D, a distribution system tool, and PowerWorld, a transmission planning tool, are integrated into a single simulation environment. The integrated environment allows the load flow interactions between the bulk power system and end-use loads to be explicitly modeled. Power system interactions are modeled down to time intervals as short as 1-second. Another practical issue is that the size and complexity of typical distribution systems makes direct integration with transmission models computationally intractable. Hence, the focus of the next main task is to develop reduced-order controllable models for some of the smart grid assets. In particular, HVAC units, which are a type of Thermostatically Controlled Loads (TCLs), are considered. The reduced-order modeling approach can be extended to other smart grid assets, like water heaters, PVs and PHEVs. Closed-loop control strategies are designed for a population of HVAC units under realistic conditions. The proposed load controller is fully responsive and achieves the control objective without sacrificing the end-use performance. Finally, using the T&D simulation platform, the benefits to the bulk power system are demonstrated by controlling smart grid assets under different demand response closed-loop control strategies.