Dynamic load shedding for an islanded microgrid with limited generation resources

When an extreme weather event strikes a distribution system, the utility power may not be available for days or even weeks. As a result, a microgrid in the affected area will be operated in the islanded mode during that period. To continuously serve critical load with available generation resources, which are usually limited, load shedding actions can be performed to gradually disconnect load with low priority. This study proposes a method to obtain the dynamic load shedding strategy for an islanded microgrid with limited generation resources. First, dynamic load shedding is formulated as a stochastic optimisation problem, where the uncertainties induced by intermittent energy sources and load are incorporated. The objective is to maximise the economic performance of the microgrid. Limits on the generation resources and operational constraints are considered. Then, a model based on Markov decision process (MDP) is developed for the problem. A solution method for the MDP model is proposed to obtain the optimal load shedding strategy. Finally, numerical simulations are performed to validate the effectiveness of the proposed method. Impacts of available generation resources, uncertainties of wind power prediction, and load shedding time period on the load shedding strategy are discussed.

[1]  S. Conti,et al.  Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators , 2007 .

[2]  Yin Xu,et al.  Placement of Remote-Controlled Switches to Enhance Distribution System Restoration Capability , 2016, IEEE Transactions on Power Systems.

[3]  A Kwasinski,et al.  Quantitative Evaluation of DC Microgrids Availability: Effects of System Architecture and Converter Topology Design Choices , 2011, IEEE Transactions on Power Electronics.

[4]  Ritwik Majumder,et al.  Some Aspects of Stability in Microgrids , 2013, IEEE Transactions on Power Systems.

[5]  T. Niknam,et al.  Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources , 2012, IEEE Transactions on Power Delivery.

[6]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[7]  Marie-Josée Cros,et al.  MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems , 2014 .

[8]  Haili Song,et al.  Optimal electricity supply bidding by Markov decision process , 2000 .

[9]  Jianhui Wang,et al.  Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids , 2015, IEEE Transactions on Power Systems.

[10]  Yih-Der Lee,et al.  Multiscenario Underfrequency Load Shedding in a Microgrid Consisting of Intermittent Renewables , 2013, IEEE Transactions on Power Delivery.

[11]  Hak-Man Kim,et al.  Distributed Load-Shedding System for Agent-Based Autonomous Microgrid Operations , 2014 .

[12]  Alexis Kwasinski,et al.  Powering Through the Storm: Microgrids Operation for More Efficient Disaster Recovery , 2014, IEEE Power and Energy Magazine.

[13]  Pierluigi Mancarella,et al.  Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies , 2015 .

[14]  Antonio Piccolo,et al.  Generation Rescheduling and Load Shedding in Distribution Systems Under Imprecise Information , 2018, IEEE Systems Journal.

[15]  Rafael Mihalic,et al.  Predictive underfrequency load shedding scheme for islanded power systems with renewable generation , 2015 .

[16]  Michael E. Webber,et al.  Combining a dynamic battery model with high-resolution smart grid data to assess microgrid islanding lifetime , 2015 .

[17]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[18]  Alexis Kwasinski,et al.  Availability Evaluation of Micro-Grids for Resistant Power Supply During Natural Disasters , 2012, IEEE Transactions on Smart Grid.

[19]  Witold Pedrycz,et al.  Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems , 2009, IEEE Transactions on Industrial Informatics.

[20]  Miao Fan,et al.  Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids , 2015 .

[21]  D. J. White,et al.  A Survey of Applications of Markov Decision Processes , 1993 .

[22]  Fang Zheng Peng,et al.  Control for Grid-Connected and Intentional Islanding Operations of Distributed Power Generation , 2011, IEEE Transactions on Industrial Electronics.

[23]  Michel Bruneau,et al.  A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities , 2003 .