A hierarchical optimization framework for aggregating thermostatically controlled loads to minimize real-time thermal rating of overhead distribution lines

A hierarchical optimization algorithm is presented in this study to optimize the aggregated thermostatically controlled loads (TCLs) to ensure the thermal rating of distribution lines is not violated. The key objective of the upper-level model is to minimize the thermal temperature of distribution lines and ensure it within the thermal rating. Meanwhile, the lower-level model is intended to strictly follow the dispatch instructions from the upper-level decision-maker by designing appropriate dispatch strategies for each aggregated TCLs in a specific time horizon. The developed models are solved by two different optimization software tools. Finally, a 10 kV overhead distribution line is studied to demonstrate the performance of the developed model and methodology.

[1]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2006, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[2]  Manfred Morari,et al.  Building control and storage management with dynamic tariffs for shaping demand response , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[3]  Karen Herter Residential implementation of critical-peak pricing of electricity , 2007 .

[4]  Santiago Grijalva,et al.  Modeling for Residential Electricity Optimization in Dynamic Pricing Environments , 2012, IEEE Transactions on Smart Grid.

[5]  Gerard Ledwich,et al.  A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles , 2013, IEEE Transactions on Power Systems.

[6]  Joshua N. Cooper,et al.  Parameter identification and model based predictive control of temperature inside a house , 2011 .

[7]  V. Vittal,et al.  A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption , 2008, IEEE Transactions on Power Systems.

[8]  Gregor Verbic,et al.  Demand response through smart home energy management using thermal inertia , 2013, 2013 Australasian Universities Power Engineering Conference (AUPEC).

[9]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[10]  Man-loong Chan,et al.  Simulation-Based Load Synthesis Methodology for Evaluating Load-Management Programs , 1981, IEEE Transactions on Power Apparatus and Systems.

[11]  Qing-Shan Jia,et al.  Performance Analysis and Comparison on Energy Storage Devices for Smart Building Energy Management , 2012, IEEE Transactions on Smart Grid.