Flexible scheduling of microgrid with uncertainties considering expectation and robustness

With increasing renewable distributed energy resources integrated into a microgrid, it is difficult to realize its optimal operation due to uncertain power outputs of wind turbines, photovoltaic cells, etc. This paper presents a flexible scheduling model for microgrid optimal operation, in which both the expectation of operational cost and the robustness of scheduling solutions are taken into account. To balance the trade-off between minimizing the expectation of operational cost and enhancing robustness, we formulate the scheduling problem as a multi-objective optimization problem. By introducing a robustness preference parameter, the relationship between the cost expectation and the robustness is investigated, and the fuzzy decision making method is used for selecting a suitable solution. Simulation results based on an experimental microgrid verify the applicability and effectiveness of the proposed model.

[1]  Zhi Chen,et al.  Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization , 2012, IEEE Transactions on Smart Grid.

[2]  Zhaohao Ding,et al.  A Stochastic Microgrid Operation Scheme to Balance Between System Reliability and Greenhouse Gas Emission , 2015, IEEE Transactions on Industry Applications.

[3]  Gang Wang,et al.  Stochastic Reactive Power Management in Microgrids With Renewables , 2015 .

[4]  Wencong Su,et al.  Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources , 2014, IEEE Transactions on Smart Grid.

[5]  Soodabeh Soleymani,et al.  Scenario-based stochastic operation management of MicroGrid including Wind, Photovoltaic, Micro-Turbine, Fuel Cell and Energy Storage Devices , 2014 .

[6]  Taher Niknam,et al.  Stochastic scenario-based model and investigating size of energy storages for PEM-fuel cell unit commitment of micro-grid considering profitable strategies , 2014 .

[7]  Hoay Beng Gooi,et al.  Toward Optimal Energy Management of Microgrids via Robust Two-Stage Optimization , 2018, IEEE Transactions on Smart Grid.

[8]  Linquan Bai,et al.  Adjustable robust optimal power flow with the price of robustness for large-scale power systems , 2016 .

[9]  Jinyu Wen,et al.  A Discrete Point Estimate Method for Probabilistic Load Flow Based on the Measured Data of Wind Power , 2013 .

[10]  Wei Cao,et al.  Direct Load Control in Microgrids to Enhance the Performance of Integrated Resources Planning , 2014, IEEE Transactions on Industry Applications.

[11]  Yang Wang,et al.  Unit Commitment With Volatile Node Injections by Using Interval Optimization , 2011, IEEE Transactions on Power Systems.

[12]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[13]  Yu Zhang,et al.  Robust Energy Management for Microgrids With High-Penetration Renewables , 2012, IEEE Transactions on Sustainable Energy.

[14]  Zhao Xu,et al.  Direct Interval Forecasting of Wind Power , 2013, IEEE Transactions on Power Systems.

[15]  Yuanxiong Guo,et al.  Islanding-Aware Robust Energy Management for Microgrids , 2018, IEEE Transactions on Smart Grid.

[16]  Wei-Jen Lee,et al.  A bi-level program for the planning of an islanded microgrid including CAES , 2016, 2015 IEEE Industry Applications Society Annual Meeting.

[17]  Wei-Jen Lee,et al.  Stochastic resource planning strategy to improve the efficiency of microgrid operation , 2015, 2014 IEEE Industry Application Society Annual Meeting.

[18]  Mengshi Li,et al.  Mean-variance model for power system economic dispatch with wind power integrated , 2014 .

[19]  Manijeh Alipour,et al.  Stochastic Scheduling of Renewable and CHP-Based Microgrids , 2015, IEEE Transactions on Industrial Informatics.

[20]  Mahmoud-Reza Haghifam,et al.  Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads , 2015 .

[21]  Ram Avtar Gupta,et al.  A robust optimization based approach for microgrid operation in deregulated environment , 2015 .

[22]  Enrico Zio,et al.  Analysis of robust optimization for decentralized microgrid energy management under uncertainty , 2015 .

[23]  J. Löfberg.  Modeling and solving uncertain optimization problems in YALMIP , 2008 .

[24]  Qian Ai,et al.  Interactive Model for Energy Management of Clustered Microgrids , 2017 .

[25]  T. Nguyen,et al.  Stochastic Optimization of Renewable-Based Microgrid Operation Incorporating Battery Operating Cost , 2016, IEEE Transactions on Power Systems.

[26]  M. Smith,et al.  Key Connections: The U.S. Department of Energy?s Microgrid Initiative , 2012, IEEE Power and Energy Magazine.

[27]  Taher Niknam,et al.  An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation , 2012 .

[28]  Robin Broder Hytowitz,et al.  Managing solar uncertainty in microgrid systems with stochastic unit commitment , 2015 .

[29]  Claudio A. Cañizares,et al.  Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids , 2015, IEEE Transactions on Smart Grid.

[30]  Bo Zhao,et al.  Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization , 2016, IEEE Transactions on Smart Grid.

[31]  Jianhui Wang,et al.  Robust Optimization Based Optimal DG Placement in Microgrids , 2014, IEEE Transactions on Smart Grid.

[32]  Junyong Liu,et al.  Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load , 2016, IEEE Transactions on Smart Grid.

[33]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[34]  Zuyi Li,et al.  Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC , 2012, IEEE Transactions on Power Systems.