Intelligent control schemes applied to Automatic Generation Control

Dingguo Chen; Lu Wang; Dingguo Chen; Lu Wang

doi:10.3934/energy.2016.3.517

AIMS Energy

2016, Volume 4, Issue 3: 517-535. doi: 10.3934/energy.2016.3.517

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Research article Topical Sections

Intelligent control schemes applied to Automatic Generation Control

Dingguo Chen ^{1
,
,},
Lu Wang ²

1.
R&D Department, Siemens Smart Grid Division, 10090 Wayzata Blvd, Suite 400, Minnetonka, MN 55305 USA
2.
Accenture, 1301 Fannin St. 1900, Houston, TX77002 USA

Received: 31 December 2015 Accepted: 18 April 2016 Published: 22 April 2016

Integrating ever increasing amount of renewable generating resources to interconnected power systems has created new challenges to the safety and reliability of today‟s power grids and posed new questions to be answered in the power system modeling, analysis and control. Automatic Generation Control (AGC) must be extended to be able to accommodate the control of renewable generating assets. In addition, AGC is mandated to operate in accordance with the NERC‟s Control Performance Standard (CPS) criteria, which represent a greater flexibility in relaxing the control of generating resources and yet assuring the stability and reliability of interconnected power systems when each balancing authority operates in full compliance. Enhancements in several aspects to the traditional AGC must be made in order to meet the aforementioned challenges. It is the intention of this paper to provide a systematic, mathematical formulation for AGC as a first attempt in the context of meeting the NERC CPS requirements and integrating renewable generating assets, which has not been seen reported in the literature to the best knowledge of the authors. Furthermore, this paper proposes neural network based predictive control schemes for AGC. The proposed controller is capable of handling complicated nonlinear dynamics in comparison with the conventional Proportional Integral (PI) controller which is typically most effective to handle linear dynamics. The neural controller is designed in such a way that it has the capability of controlling the system generation in the relaxed manner so the ACE is controlled to a desired range instead of driving it to zero which would otherwise increase the control effort and cost; and most importantly the resulting system control performance meets the NERC CPS requirements and/or the NERC Balancing Authority’s ACE Limit (BAAL) compliance requirements whichever are applicable.
- Smart Grid,
- Automatic Generation Control (AGC),
- Control Performance Standard (CPS),
- Predictive Control,
- Renewable Energy Resources,
- Neural Networks,
- Neural Controller
Citation: Dingguo Chen, Lu Wang. Intelligent control schemes applied to Automatic Generation Control[J]. AIMS Energy, 2016, 4(3): 517-535. doi: 10.3934/energy.2016.3.517

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Abstract

Integrating ever increasing amount of renewable generating resources to interconnected power systems has created new challenges to the safety and reliability of today‟s power grids and posed new questions to be answered in the power system modeling, analysis and control. Automatic Generation Control (AGC) must be extended to be able to accommodate the control of renewable generating assets. In addition, AGC is mandated to operate in accordance with the NERC‟s Control Performance Standard (CPS) criteria, which represent a greater flexibility in relaxing the control of generating resources and yet assuring the stability and reliability of interconnected power systems when each balancing authority operates in full compliance. Enhancements in several aspects to the traditional AGC must be made in order to meet the aforementioned challenges. It is the intention of this paper to provide a systematic, mathematical formulation for AGC as a first attempt in the context of meeting the NERC CPS requirements and integrating renewable generating assets, which has not been seen reported in the literature to the best knowledge of the authors. Furthermore, this paper proposes neural network based predictive control schemes for AGC. The proposed controller is capable of handling complicated nonlinear dynamics in comparison with the conventional Proportional Integral (PI) controller which is typically most effective to handle linear dynamics. The neural controller is designed in such a way that it has the capability of controlling the system generation in the relaxed manner so the ACE is controlled to a desired range instead of driving it to zero which would otherwise increase the control effort and cost; and most importantly the resulting system control performance meets the NERC CPS requirements and/or the NERC Balancing Authority’s ACE Limit (BAAL) compliance requirements whichever are applicable.

References

[1]	Jiang L, Chi Y, Qin H, et al. (2011) Wind energy in China. IEEE Power Energy M 9: 36-46. doi: 10.1109/MPE.2011.942350
[2]	Holttinen H, Orths A, Eriksen P, et al. (2011) Currents of change.IEEE Power Energy M9:47-59.
[3]	Osborn D, Henderson M, Nickell B, et al. (2011) Driving forces behind wind. IEEE Power Energy M 9: 60-74. doi: 10.1109/MPE.2011.942474
[4]	Lauby M, Ahlstrom M, Brooks D, et al. (2011) Balancing act. IEEE Power Energy M 9: 75-85.
[5]	Zavadil R, Miller N, Ellis A, et al. (2011) Models for change. IEEE Power Energy M 9: 86-96. doi: 10.1109/MPE.2011.942388
[6]	Ahlstrom M, Blatchford J, Davis M, et al. (2011) Atmospheric pressure. IEEE Power Energy M 9: 97-107.
[7]	Jaleeli N, VanSlyck L (1999) NERC’s new control performance standards. IEEE Trans Power Syst 14: 549-557.
[8]	Wang L, Chen D (2011) Extended–term dynamic simulation for AGC with smart grids. Proc IEEE PES General Meeting: 1-6.
[9]	Chen D (2010) Apparatus and method for predictive control of a power generation system. US7660640.
[10]	Golpira H, Bevrani H, Golpira H (2011) Application of GA optimization for automatic generation control design in an interconnected power system. Energ Convers Manage 52: 2247-2255.
[11]	Mohamed T, Bevrani H, Hassan A, et al. (2011) Decentralized model predictive based load frequency control in an interconnected power system. Energ Convers Manage 52: 1208-1214.
[12]	Zeynelgil H, Demiroren A, Sengor N (2002) The application of ANN technique to automatic generation control for multi-area power system. Int J Elec Power 24: 345-354.
[13]	Nanda J, Mangla A (2004) Automatic generation control of an interconnected hydro-thermal system using conventional integral and fuzzy logic controller. IEEE International Conf DRPT 1: 372-377.
[14]	Green R (1996) Transformed automatic generation control. IEEE T Power Syst 11: 1799-1804. doi: 10.1109/59.544645
[15]	Schulte R (1996) An automatic generation modification for present demands on interconnected power systems. IEEE T Power Syst 11: 1286-1294. doi: 10.1109/59.535669
[16]	Tripathy S, Juengst K (1997) Sampled data automatic generation control with superconducting magnetic energy storage in power systems. IEEE Trans Energy Conversion 12: 187-192. doi: 10.1109/60.629702
[17]	Bakken B, Grande O (1998) Automatic generation control in a deregulated power system. IEEE T Power Syst 13: 1401-1406. doi: 10.1109/59.736283
[18]	Chown G, Hartman R (1998) Design and experience with a fuzzy logic controller for automatic generation control. IEEE T Power Syst 13: 965-970. doi: 10.1109/59.709084
[19]	Shoults R, Yao M, Kelm R, et al. (1998) Improved system AGC performance with arc furnace steel mill loads. IEEE T Power Syst 13: 630-635.
[20]	Yao M, Shoults R, Kelm R (2000) AGC logic based on NERC’s new control performance standard and disturbance control standard. IEEE T Power Syst 15: 852-857. doi: 10.1109/59.867184
[21]	Hain Y, Kulessky R, Nudelman G (2000) Identification-based power unit model for load-frequency control purposes. IEEE T Power Syst 15: 1313-1321. doi: 10.1109/59.898107
[22]	Gross G, Lee J (2001) Analysis of load frequency control performance assessment criteria. IEEE T Power Syst 16: 520-525. doi: 10.1109/59.932290
[23]	Donde V, Pai M, Hiskens I (2001) Simulation and optimization in an AGC system after deregulation. IEEE T Power Syst 16: 481-489. doi: 10.1109/59.932285
[24]	Trudnowski D, McReynolds W, Johnson J (2001) Real-time very short-term load prediction for power-system automatic generation control. IEEE T Contr Syst T 9: 254-260. doi: 10.1109/87.911377
[25]	Sasaki T, Enomoto K (2002) Dynamic analysis of generation control performance standards. IEEE T Power Syst 17: 806-811. doi: 10.1109/TPWRS.2002.800946
[26]	Hoonchareon N, Ong C, Kramer R (2002) Implementation of an ACE₁ decomposition method. IEEE T Power Syst 17: 757-761.
[27]	Rodriguez-Amenedo J, Arnalte S, Burgos J (2002) Automatic generation control of a wind farm with variable speed wind turbines. IEEE T Energy Conver 17: 279-284.
[28]	Chang-Chien L, Hoonchareon N, Ong C, et al. (2003) Estimation of Beta for adaptive frequency bias setting in load frequency control. IEEE T Power Syst 18: 904-911. doi: 10.1109/TPWRS.2003.810996
[29]	Chang-Chien L, Ong C, Kramer R (2003) Field test and refinements of an ACE model. IEEE T Power Syst 18: 898-903. doi: 10.1109/TPWRS.2003.810997
[30]	Chen D, Kumar S, York M, et al. (2012) Smart Automatic Generation Control. IEEE PES General Meeting, 1-6.
[31]	Chen D, York M (2008) Neural network based approaches to very short term load prediction. IEEE PES General Meeting, 1-8.
[32]	Chen D, York M (2011) Predictive economic dispatch with time. IEEE PES General Meeting, 1-8.
[33]	Chen D (1998) Nonlinear neural control with power systems applications. PhD Dissertation, Oregon State University, Corvallis, USA.
[34]	Vedam R (1994) Nonlinear control applied to power systems. PhD Dissertation, Oregon State University, Corvallis, USA.

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