Distributed aggregation control of grid-interactive smart buildings for power system frequency support

Abstract Grid-interactive smart buildings with thermostatically-controlled loads can be modeled as virtual energy storage systems with dissipation, which have great potentials for providing grid ancillary services such as frequency support. In this paper, a new distributed aggregation control method is proposed for multiple grid-interactive smart buildings in one frequency control area (e.g. a residential community) to provide fast frequency support. The proposed method is based on the distributed sliding mode control via a leader-follower communication scheme. A leader control is designed to provide power and comfort/energy level references for the smart building aggregator based on the area frequency deviation, while references are tracked by each smart building using the proposed distributed sliding mode control. The stability of the proposed control method for grid-interactive smart buildings is proved by the Lyapunov method. With the proposed method, the external characteristics of the aggregated smart buildings will have good power tracking and energy recovery capability, which can effectively improve the system frequency response. In the aggregator, fair and efficient power and comfort/energy level sharing are achieved among all participating grid-interactive smart buildings. The proposed control scheme is tested on a three-area power system considering both system contingency and normal operation scenarios.

[1]  Haoran Zhao,et al.  Review of energy storage system for wind power integration support , 2015 .

[2]  William D'haeseleer,et al.  Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems , 2015 .

[3]  Andrey V. Savkin,et al.  Multi-Agent Sliding Mode Control for State of Charge Balancing Between Battery Energy Storage Systems Distributed in a DC Microgrid , 2018, IEEE Transactions on Smart Grid.

[4]  Kaveh Dehghanpour,et al.  Electrical demand side contribution to frequency control in power systems: a review on technical aspects , 2015 .

[5]  Mattia Marinelli,et al.  Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark , 2016 .

[6]  Jianhui Wang,et al.  Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response , 2015 .

[7]  Jinde Cao,et al.  Second-order leader-following consensus of nonlinear multi-agent systems via pinning control , 2010, Syst. Control. Lett..

[8]  Jianhui Wang,et al.  A Distributed Direct Load Control Approach for Large-Scale Residential Demand Response , 2014, IEEE Transactions on Power Systems.

[9]  Goran Strbac,et al.  Advanced Control of Thermostatic Loads for Rapid Frequency Response in Great Britain , 2017 .

[10]  B. Francois,et al.  Dynamic Frequency Control Support by Energy Storage to Reduce the Impact of Wind and Solar Generation on Isolated Power System's Inertia , 2012, IEEE Transactions on Sustainable Energy.

[11]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[12]  Frank L. Lewis,et al.  Cooperative Control of Multi-Agent Systems: Optimal and Adaptive Design Approaches , 2013 .

[13]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[14]  Yiguang Hong,et al.  Finite-Time Consensus for Multi-Agent Networks with Second-Order Agent Dynamics , 2008 .

[15]  P. Kundur,et al.  Power system stability and control , 1994 .

[16]  Haoran Zhao,et al.  Hierarchical Control of Thermostatically Controlled Loads for Primary Frequency Support , 2018, IEEE Transactions on Smart Grid.

[17]  Koen Vanthournout,et al.  An automated residential demand response pilot experiment, based on day-ahead dynamic pricing , 2015 .

[18]  Shahram Jadid,et al.  A new approach for real time voltage control using demand response in an automated distribution system , 2014 .

[19]  Guanghui Wen,et al.  Second-Order Consensus in Multiagent Systems via Distributed Sliding Mode Control , 2017, IEEE Transactions on Cybernetics.

[20]  Jianliang Wang,et al.  Distributed Adaptive Sliding Mode Control Strategy for Vehicle-Following Systems With Nonlinear Acceleration Uncertainties , 2017, IEEE Transactions on Vehicular Technology.

[21]  Josep M. Guerrero,et al.  Improving Frequency Stability Based on Distributed Control of Multiple Load Aggregators , 2017, IEEE Transactions on Smart Grid.

[22]  Hongxun Hui,et al.  Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals , 2018 .

[23]  Zhao Xu,et al.  Demand as Frequency Controlled Reserve , 2011, IEEE Transactions on Power Systems.

[24]  Jianzhong Wu,et al.  Power System Frequency Response From the Control of Bitumen Tanks , 2016, IEEE Transactions on Power Systems.

[25]  Subrata K. Sarker,et al.  A survey on control issues in renewable energy integration and microgrid , 2019, Protection and Control of Modern Power Systems.

[26]  Enrique Acha,et al.  On the Provision of Frequency Regulation in Low Inertia AC Grids Using HVDC Systems , 2016, IEEE Transactions on Smart Grid.

[27]  Mattia Marinelli,et al.  Impact of thermostatically controlled loads' demand response activation on aggregated power: A field experiment , 2016 .

[28]  Josep M. Guerrero,et al.  Demand Response Load Following of Source and Load Systems , 2017, IEEE Transactions on Control Systems Technology.

[29]  Ramesh C. Bansal,et al.  A review of key power system stability challenges for large-scale PV integration , 2015 .

[30]  Yuchen Zhang,et al.  Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff , 2018 .

[31]  Gerald B. Sheblé,et al.  Direct load control-A profit-based load management using linear programming , 1998 .