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Title: Demand response algorithms to improve electric power system stability margins Open Access Deposited
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(2019). Demand response algorithms to improve electric power system stability margins [Data set]. University of Michigan  Deep Blue. https://doi.org/10.7302/qc8t6g78
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Files (Count: 2; Size: 68.2 KB)
Thumbnail  Title  Original Upload  Last Modified  File Size  Access  Actions 

Readme.txt  20190716  20190726  4.85 KB  Open Access 


Code.zip  20190716  20190716  63.3 KB  Open Access 

Date: 14 June, 2019
Dataset Title: Demand Response Algorithms to Improve Electric Power System Stability Margins
Dataset Creators: Mengqi Yao, Johanna L. Mathieu, Ian A. Hiskens, Daniel K. Molzahn, Kasra Koorehdavoudi, Sandip Roy
Dataset Contact: Mengqi Yao mqyao@umich.edu
Funding: NSF #ECCS1549670
Key Points:
 We propose a new demand response strategy which is to shift flexible loads in space to improve system stability margin
 We focus on improving voltage stability and small signal stability using our demand response strategy
 We pose different optimization problems to determine the optimal dispatches of demand responsive loads to maximize the stability margins
Research Overview:
The increasing penetration of renewables has driven power systems to operate closer to their stability boundaries, increasing the risk of instability. In contrast to past work that employs load shedding to improve stability, our approach improves stability margins by decreasing and increasing individual loads while keeping the total loading constant to avoid fluctuation of the system frequency. Additionally, an energy payback period maintains the total energy consumption of each load at its nominal value. We consider different voltage stability margins and small signal stability margins in our work.
Methodology:
The data are Matlab codes for generating the results shown in our published paper. Matpower is used to get test systems and power flow information. The Matpower version is 5.1, which can be found in https://matpower.org/download/. The Matlab version is R2016b.
Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/150104
Descriptions of the code.zip:
Folder 'TCNS journal paper':
Suggested citation: Yao, Mengqi, Daniel Molzahn, and Johanna L. Mathieu. "An Optimal Power Flow Approach to Improve Power System Voltage Stability Using Demand Response." IEEE Transactions on Control of Network Systems (2019).
AvalosMethod_Loadshedding.m : An iterative nonlinear programming algorithm to determine the minimum load shedding to achieve the voltage stability requirement
main_multi_9.m and SSVLinear_multi.m: An iterative linear programming algorithm to solve a multiperiod OPF for the 9bus system. The objective is to maximize the SSV in period 1 and minimize the generation cost in period 2
AvalosMethod_multi_9bus.m : An iterative nonlinear programming algorithm to solve a multiperiod OPF for the 9bus system.
testcase_multi.mat: System data for the 9bus system used in AvalosMethod_multi_9bus.m.
testcase.mat: System data for the 9bus system used in AvalosMethod_Loadshedding.m.
contigency_sys.mat: System data for the 118bus system.
Folder 'Allerton':
Suggested citation: Yao, Mengqi, Daniel K. Molzahn, and Johanna L. Mathieu. "The impact of load models in an algorithm for improving voltage stability via demand response." In 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 149156. IEEE, 2017.
main_induction_motor_shuntdown.m : An iterative linear programming algorithm to solve an SSV maximization problem for the 14bus system. We model loads as the induction motors.
PF_IM.m : A function to solve the voltages and currents for the induction motor with the real and reactive power given.
main_ZIP_allerton.m : An iterative linear programming algorithm to solve an SSV maximization problem for the 14bus system. We use the ZIP load model.
basecase_bus14.mat: System data for the IEEE 14bus system
Folder 'IREP':
Suggested citation: Koorehdavoudi, Kasra, Mengqi Yao, S. Roy, and J. L. Mathieu. "Using demand response to shape the fast dynamics of the bulk power network." In Proceedings of the IREP Symposium on Bulk Power System Dynamics and Control. 2017.
brute_force_main.m : A brute force algorithm to show the relationship between the smallest damping ratio and the loading at bus 7 for the Kundur's twoarea system.
dL_dYbus_alpha_Q.m : A function to compute the sensitivity of the system matrix with respect to the real part of an eigenvalue
dL_dYbus_beta_Q.m : A function to compute the sensitivity of the system matrix with respect to the imaginary part of an eigenvalue
dL_dVi.m: A function to compute the sensitivity of tye system matrix with respect to voltage
iterative_sensitivity_damping_approach.m: An iterative nonlinear programming algorithm to solve the smallest damping ratio maximization problem for the Kundur's twoarea system.
Kundur_2area.m : System data for the Kundur's twoarea system.
Folder 'CDC':
Suggested citation: Yao, Mengqi, Ian A. Hiskens, and Johanna L. Mathieu. "Improving Power System Voltage Stability by Using Demand Response to Maximize the Distance to the Closest SaddleNode Bifurcation." In 2018 IEEE Conference on Decision and Control (CDC), pp. 23902395. IEEE, 2018.
delta_F.m : A function to compute the mismatch for the Newton method
dJ_dVi.m, dSbus_dV_dVi.m, dSbus_dV_my.m: Functions to compute the sensitivity of the Jacobian matrix of power flow equations with respect to voltage magnitudes and angles.
main_newton.m : It finds a solution that satisfies the KKT conditions of the optimization problem using the Newton method. The objective of the optimization problem is to maximize the distance to the closest SNB.
random_unit_vector.m: A function to generate a random unit vector.