Optimization for Energy System

I have applied both mathematical and evolutionary algorithms to solve my optimization problems.

Recently, I am working on an efficient optimization algorithm- alternating direction method of multipliers (ADMM). ADMM solves convex optimization problems by breaking them into smaller problems, each of which have closed form solution most of the time and therefore easier and faster to solve. ADMM is also very robust to algorithm parameters and shows excellent convergence characteristic for all positive values of its only parameter. ADMM also makes very little assumption on the property of the objective function and does not need the objective function to be differentiable .

ADMM applications:

• A. Anwar, A. Mahmood and M. Pickering, "Estimation of smart grid topology using SCADA measurements," IEEE International Conference on Smart Grid Communications (SmartGridComm), 2016.

Evolutionary algorithms:

I have improved as well as applied a wide variety of evolution algorithms. More specifically, my interest includes swarm intelligence, primarily I am focused on particle swarm optimization (PSO) algorithm and its variants. Some of my works on PSO are listed below:

1. Anwar, A., Mahmood, A. N., and Tari, Z., "Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid," in Information Systems, Elsevier, 2015.
2. Anwar, Adnan, Md. Apel Mahmud, Md. Jahangir Hossain and Himanshu Roy Pota, "Distributed Generation Capacity Planning for Distribution Networks to Minimize Energy Loss: An Unbalanced Multi-Phase Optimal Power Flow Based Approach." Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization. IGI Global, 2016. 76-95. Web. 28 Dec. 2016.
3. Anwar, A., and Mahmood, A. N., "Swarm intelligence based multi-phase OPF for peak power loss reduction in a smart grid," In PES General Meeting 2014. USA. (Flagship Conference In Power and Energy Society)
4. Anwar, A., Mahmood, A. N., and Islam, R., "CHPSO - A new collaborative hybrid particle swarm optimization algorithm," In Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
5. Anwar, A., and Mahmood, A. N., "Enhanced estimation of Autoregressive wind power prediction model using Constriction Factor Particle Swarm Optimization," In Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
6. Anwar, A., and Mahmood, A. N., "CF-PSO based loss sensitivity clustering technique to identify optimal DG allocation nodes for energy efficient smart grid operation," In Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
7. Anwar, A., and Pota, H. R., "Optimum Allocation and Sizing of DG Unit for Efficiency Enhancement of Distribution System," In IEEE International Power Engineering and Optimization Conference, 2012.
8. Anwar, A., and Pota, H. R., "Optimum Capacity Allocation of DG Units Based on Unbalanced Three-phase Optimal Power Flow," In IEEE Power and Energy Society General Meeting, USA, 2012. (Flagship Conference In Power and Energy Society)

Sparse Optimization (Sparse matrix separation):

Gross errors and missing values are common in measurement signals. I have applied sparse optimization techniques to separate the gross error and missing value matrix. Sample publications are listed below:

1. Adnan Anwar, Abdun Naser Mahmood, and Mark Pickering, "Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements," in Journal of Computer and System Sciences, Volume 83, Issue 1, February 2017, Pages 58-72.
2. A. Anwar and A. N. Mahmood, "Stealthy and blind false injection attacks on SCADA EMS in the presence of gross errors," 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, 2016.
3. Adnan Anwar, Abdun Naser Mahmood, and Mark Pickering, "Data-Driven Stealthy Injection Attacks on Smart Grid with Incomplete Measurements," In Intelligence and Security Informatics, LNCS, Springer, 2016.