Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated T&D Simulation

This paper explores the differences between simulating price-responsive load (PRL) interactions with power systems using integrated transmission and distribution (T&D) models and transmission-only (T-only) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture “ISO-to-appliance” simulations using a synthetic T&D model built on the PJM 5-Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of customers and PRLs. The results show important differences in the impacts of PRLs between integrated T&D and T-only models. Experiments with the synthetic integrated T&D dataset demonstrated that integrated T&D simulation revealed notably larger differences between the PRL and no-PRL cases for load, and prices compared to T-only simulation. Similarly, differences are observed between the price response of individual buildings and distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which are difficult to capture in traditional T-only simulations.

[1]  E. Ela,et al.  Studying the Variability and Uncertainty Impacts of Variable Generation at Multiple Timescales , 2012, IEEE Transactions on Power Systems.

[2]  Kevin P. Schneider,et al.  Modern Grid Initiative Distribution Taxonomy Final Report , 2008 .

[3]  Ibrahim Krad,et al.  Studying the Impact of Distributed Solar PV on Power Systems Using Integrated Transmission and Distribution Models , 2018, 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[4]  Ned Djilali,et al.  GridLAB-D: An Agent-Based Simulation Framework for Smart Grids , 2014, J. Appl. Math..

[5]  Rob J Hyndman,et al.  The price elasticity of electricity demand in South Australia , 2011 .

[6]  Zhi Zhou,et al.  Agent-Based Electricity Market Simulation With Demand Response From Commercial Buildings , 2011, IEEE Transactions on Smart Grid.

[7]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[8]  Fangxing Li,et al.  Small test systems for power system economic studies , 2010, IEEE PES General Meeting.

[9]  Bri-Mathias Hodge,et al.  IGMS: An Integrated ISO-to-Appliance Scale Grid Modeling System , 2017, IEEE Transactions on Smart Grid.

[10]  Kathlen Sps Impacts of Responsive Load in PJM : Load Shifting and Real Time Pricing , 2007 .

[11]  L. Tesfatsion,et al.  Effects of price-responsive residential demand on retail and wholesale power market operations , 2012, 2012 IEEE Power and Energy Society General Meeting.

[12]  Xiaohong Guan,et al.  Incorporating Price-Responsive Demand in Energy Scheduling Based on Consumer Payment Minimization , 2016, IEEE Transactions on Smart Grid.

[13]  Vijay Vittal,et al.  Integrated Transmission and Distribution System Power Flow and Dynamic Simulation Using Mixed Three-Sequence/Three-Phase Modeling , 2017, IEEE Transactions on Power Systems.

[14]  N. P. Padhy,et al.  Influence of Price Responsive Demand Shifting Bidding on Congestion and LMP in Pool-Based Day-Ahead Electricity Markets , 2011, IEEE Transactions on Power Systems.

[15]  Olof M. Jarvegren,et al.  Pacific Northwest GridWise™ Testbed Demonstration Projects; Part I. Olympic Peninsula Project , 2008 .

[16]  Hongyu Wu,et al.  Effects of Home Energy Management Systems on Distribution Utilities and Feeders Under Various Market Structures: Preprint , 2015 .

[17]  Jeremy Woyak,et al.  Three-Phase Dynamic Simulation of Power Systems Using Combined Transmission and Distribution System Models , 2015, IEEE Transactions on Power Systems.

[18]  P. Siano,et al.  Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid , 2011, IEEE Transactions on Sustainable Energy.