Kalman filter based microgrid state estimation and control using the IoT with 5G networks

Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in smart grids, which will make the energy supply more reliable and decrease the cost and transmission losses. Unfortunately, one of the key technical challenges in power system planning, control and operation with DERs is the voltage regulation at the distribution level. This problem stimulates the deployment of smart sensors and actuators in smart grids so that the voltage regulation can be controlled at an accepted level. The observation from the multiple DERs information is transmitted to a control center via the internet of things (IoT) based fifth generation (5G) communication network. In other words, the proposed communication infrastructure provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid state information collection, estimation and control. Based on this innovative communication infrastructure, we propose a least square based Kalman filter for state estimation and a feedback control method for voltage regulation of this intermittent and weather-dependent renewable power generation. Specifically, we propose to optimize the performance index by using semidefinite programming techniques in the context of smart grid applications. At the end, the efficacy of the developed approaches is demonstrated using the linear physical model of a microgrid incorporating DERs.

[1]  Zhu Han,et al.  Smart grid sensor data collection, communication, and networking: a tutorial , 2014, Wirel. Commun. Mob. Comput..

[2]  C. D. Johnson.  Optimal initial conditions for full-order observers , 1988 .

[3]  Weihua Zhuang,et al.  Stochastic Modeling and Optimization in a Microgrid: A Survey , 2014 .

[4]  Renke Huang,et al.  Smart Grid Technologies for Autonomous Operation and Control , 2011, IEEE Transactions on Smart Grid.

[5]  Zuomin Dong,et al.  Emerging smart grid technology for mitigating global warming , 2015 .

[6]  Antonio Manzalini,et al.  Horizon 2020 and Beyond: On the 5G Operating System for a True Digital Society , 2015, IEEE Vehicular Technology Magazine.

[7]  Li Li,et al.  An Overview of Distributed Microgrid State Estimation and Control for Smart Grids , 2015, Sensors.

[8]  Hongyang Zhang,et al.  Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[9]  Husheng Li,et al.  Decoding the 'Nature Encoded' Messages for Distributed Energy Generation Control in Microgrid , 2011, 2011 IEEE International Conference on Communications (ICC).

[10]  M. Rana,et al.  Microgrid state estimation and control for smart grid and Internet of Things communication network , 2015 .

[11]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[12]  Yu Meng,et al.  A Novel Deployment Scheme for Green Internet of Things , 2014, IEEE Internet of Things Journal.

[13]  Tao Yang,et al.  A Belief Propagation Based Power Distribution System State Estimator , 2011, IEEE Computational Intelligence Magazine.

[14]  Bikash C. Pal,et al.  Stability Analysis of Networked Control in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[15]  Mihailo R. Jovanovic,et al.  On the design of optimal structured and sparse feedback gains via sequential convex programming , 2014, 2014 American Control Conference.

[16]  Qilian Liang,et al.  Stabilizing the power supply in microgrid using sensor selection , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[17]  Verica Radisavljevic-Gajic.  Linear observers design and implementation , 2014, Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education.

[18]  Jeffrey M. Voas,et al.  Imagineering an Internet of Anything , 2014, Computer.

[19]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[20]  Kenneth Stewart,et al.  Enabling technologies and architectures for 5G wireless , 2014, 2014 IEEE MTT-S International Microwave Symposium (IMS2014).

[21]  Miao Yun,et al.  Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid , 2010, 2010 International Conference on Advances in Energy Engineering.

[22]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[23]  Weihua Zhuang,et al.  Stochastic information management for voltage regulation in smart distribution systems , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[24]  Husheng Li,et al.  Nobody but You: Sensor Selection for Voltage Regulation in Smart Grid , 2011, ArXiv.