Advances in Networks
Volume 7, Issue 2, December 2019, Pages: 29-36
Received: Oct. 20, 2019;
Accepted: Nov. 13, 2019;
Published: Nov. 20, 2019
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Ahmed R. Abul'Wafa, Electric Power and Machines, Ain-Shams University, Cairo, Egypt
The objective of this paper is to determine and minimizes the total operation cost and the risk of load shedding in a microgrid (μG) composed of two areas: a generation center and a load center. The system operation is formulated as an optimization problem, where the objective function minimizes the costs of the system operation and the risk of load shedding. The constraints secure the balance between generation and load. Also generation and transmission may not exceed the available capacity. Monte Carlo simulation (MCS) is used for the solution of the optimization problem giving two main outputs: loss of load occasion (LOLO) and total operation cost (TOC). A variance reduction technique is used to reduce the variance of MCS. One other objective of the paper is to study how much the simulation efficiency can be improved by introducing variance reduction techniques. Simulation results shows that, (i) the formulated optimization problem, objective function, and constraints is capable to achieve the study target, and (ii), with even a quite straightforward and simple model the proposed MCS methods show considerable variance reductions compared to Simple sampling in this model of the μG.
Ahmed R. Abul'Wafa,
Minimization of Total Operation Cost and the Risk of Shedding in Microgrids, Advances in Networks.
Vol. 7, No. 2,
2019, pp. 29-36.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Guodong Liu, Bailu Xiao, Michael Starke, Oguzhan Ceylan, and Kevin Tomsovic, A robust load shedding strategy for microgrid islanding transition, Conference: 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), May 2016, DOI: 10.1109/TDC.2016.7520055.
Sayyad Nojavan, Majid Majidi, and Kazem Zare, Stochastic multi-objective model for optimal sizing of energy storage system in a microgrid under demand response program considering reliability: A weighted sum method and fuzzy satisfying approach, Manuscript received May 10, 2017; revised June 20: accepted July 1, 2017. Paper no. JEMT-1705-1015.
Amin Khodaei, and Mohammad Shahidehpour, Microgrid-based Co-optimization of Generation and Transmission Planning in Power Systems, mysite.du.edu/.../Microgrid-based%20Co-Optimization%20of%20Generation%20and...
Sai Krishna Kanth Hari, Kaarthik Sundar, Harsha Nagarajan, Russell Bent, Scott Backhaus, Hierarchical Predictive Control Algorithms for Optimal Design and Operation of Microgrids, Cornell university library, arXiv: 1803.06705v1 [math.OC] 18 Mar 2018. Conference: 2018 Power Systems Computation Conference (PSCC), DOI: 10.23919/PSCC.2018.8442977.
Mostafa Vahedipour-Dahraie, Amjad Anvari-Moghaddam, and Josep M. Guerrero, Evaluation of Reliability in Risk-Constrained Scheduling of Autonomous Microgrids with Demand Response and Renewable Resources, IET Renewable Power Generation, 2018, pp. 1-13.
Ashkan Zeinalzadeh and Vijay Gupta, Minimizing Risk of Load Shedding and Renewable Energy Curtailment in a Microgrid with Energy Storage, 2017 American Control Conference, Sheraton Seattle Hotel, May 24–26, 2017, Seattle, USA, pp. 3412-3417.
Yeongho Choi, Yujin Lim, and Hak-Man Kim, Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid, Energies 2017, 10, 45 pp. 1-13; doi: 10.3390/en10010045.
Habib Amooshahi, Rahmat-Allah Hooshmand, Amin Khodabakhshian, and Majid Moazzami, A New Load-shedding Approach for Microgrids in the Presence of Wind Turbines, Electric Power Components and Systems, 00 (00): 1–11, 2016, DOI: 10.1080/15325008.2015.1131761.
Amin Gholami, Tohid Shekari, and Xu Andy Sun, An Adaptive Optimization-Based Load Shedding Scheme in Microgrids, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018, pp. 2660-2669.
Magnus Perninge ; Mikael Amelin ; Valerijs Knazkins, Comparing Variance Reduction Techniques for Monte Carlo Simulation of Trading and Security in a Three-Area Power System, 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, 13-15 Aug. 2008, DOI: 10.1109/TDC-LA.2008.4641763.
Matt Bonakdarpour, Inverse Transform Sampling, 2016-02-02, last updated: 2017-03-06. https://stephens999.github.io/fiveMinuteStats/inverse_transform_sampling.html
Jack P. C. Kleijnen, Ad A. N. Ridder and Reuven Y. Rubinstein, Variance Reduction Techniques in Monte Carlo Methods, pp. 1-17. https://pdfs.semanticscholar.org/f260/6f334bd4865b105005b887478003ce9bd3e2.pdf
Reuven Y. Rubinstein and Dirk P. Kroese, Simulation and the Monte Carlo Method, Third Edition, 2017 John Wiley & Sons; DOI: 10.1002/9781118631980.