Multi-Time Scale Optimal Dispatch for AC/DC Distribution Networks Based on a Markov Chain Dynamic Scenario Method and MPC

A multi-time scale optimal dispatch model based on the scenario method and model predictive control (MPC) in the AC/DC distribution network is established due to the uncertainty of wind and load. A Markov chain dynamic scenario method is proposed, which generates scenarios by characterizing the forecast error via empirical distribution. Considering the time correlation of the forecast error, Markov chain is adopted in the Markov chain dynamic method to simulate the uncertainty and variability in wind and load with time. A multi-time scale optimal dispatch strategy based on MPC is proposed. The operation scheduling of operation units is solved in day-ahead and intraday optimal dispatch by minimizing the expected value of total cost in each scenario. In the real-time optimal dispatch, the stability and robustness of system operation are considered. MPC is adopted in the real-time optimal dispatch, taking the intraday scheduling as reference and using the roll optimization method to compute real-time optimal dispatch scheduling to smooth the output power. The simulation results in a 50-node system with uncontrollable distributed energy demonstrate that the proposed model and strategy can effectively eliminate fluctuations in wind and load in AC/DC distribution networks.

[1]  Fushuan Wen,et al.  Modeling Analysis and Control Research Framework of Cyber Physical Power Systems , 2011 .

[2]  Sun Hongbin.  Design of a Multi-time Scale Coordinated Active Power Dispatching System for Accommodating Large Scale Wind Power Penetration , 2011 .

[3]  Xi-Yuan Ma,et al.  Scenario Generation of Wind Power Based on Statistical Uncertainty and Variability , 2013, IEEE Transactions on Sustainable Energy.

[4]  Maria Isabel Garcia-Planas,et al.  Wind Profile Prediction Using Linear Markov Chains: A Linear Algebra Approach , 2018, IEEE Latin America Transactions.

[5]  Marko Bacic,et al.  Model predictive control , 2003 .

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

[7]  Canbing Li,et al.  Static Security Risk Assessment for Islanded Hybrid AC/DC Microgrid , 2019, IEEE Access.

[8]  Xia Lei,et al.  A Bi-Layer Multi-Time Coordination Method for Optimal Generation and Reserve Schedule and Dispatch of a Grid-Connected Microgrid , 2019, IEEE Access.

[9]  Hai Li,et al.  Linear three-phase power flow for unbalanced active distribution networks with PV nodes , 2017 .

[10]  Wei Wang,et al.  Reliability calculation of AC/DC hybrid distribution network with a solid‐state transformer , 2019, The Journal of Engineering.

[11]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[12]  Camila S. Gehrke,et al.  Smart Grid Infrastructure Using a Hybrid Network Architecture , 2013, IEEE Transactions on Smart Grid.

[13]  Zheng Yan,et al.  Three-Stage Distributed State Estimation for AC-DC Hybrid Distribution Network Under Mixed Measurement Environment , 2018, IEEE Access.

[14]  Ehab F. El-Saadany,et al.  A Novel Dynamic Power Routing Scheme to Maximize Loadability of Islanded Hybrid AC/DC Microgrids Under Unbalanced AC Loading , 2018, IEEE Transactions on Smart Grid.

[15]  Gustavo Valverde,et al.  Model Predictive Control of Voltages in Active Distribution Networks , 2013, IEEE Transactions on Smart Grid.

[16]  Guanqun Liu,et al.  Research on multi-objective optimal joint dispatching of wind-thermal-hydro power in multi time scales , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[17]  V. Miranda,et al.  Wind power forecasting uncertainty and unit commitment , 2011 .

[18]  A. G. Buddhika P. Jayasekara,et al.  Review of Applications of Fuzzy Logic in Multi-Agent-Based Control System of AC-DC Hybrid Microgrid , 2019, IEEE Access.

[19]  Xingzhen Bai,et al.  Unbalanced power flow algorithm for AC&DC hybrid distribution network with diverse‐controlled VSC‐MTDC converts , 2019, The Journal of Engineering.

[20]  S. Tewari,et al.  A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets , 2011, IEEE Transactions on Power Systems.

[21]  Somil Bansal,et al.  Plug-and-Play Model Predictive Control for Load Shaping and Voltage Control in Smart Grids , 2019, IEEE Transactions on Smart Grid.

[22]  Kumaraswamy Ponnambalam,et al.  Stochastic Centralized Dispatch Scheme for AC/DC Hybrid Smart Distribution Systems , 2016, IEEE Transactions on Sustainable Energy.

[23]  Ritwik Majumder,et al.  Integration of Distributed Generation in the Volt/VAR Management System for Active Distribution Networks , 2015, IEEE Transactions on Smart Grid.

[24]  Haoran Zhao,et al.  Distributed Model Predictive Control of a Wind Farm for Optimal Active Power ControlPart II: Implementation With Clustering-Based Piece-Wise Affine Wind Turbine Model , 2015, IEEE Transactions on Sustainable Energy.

[25]  W. Marsden.  I and J , 2012 .

[26]  Shan Gao,et al.  A Multi-Stage Optimization Approach for Active Distribution Network Scheduling Considering Coordinated Electrical Vehicle Charging Strategy , 2018, IEEE Access.

[27]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[28]  Wei Yuan,et al.  A Two-Stage Robust Reactive Power Optimization Considering Uncertain Wind Power Integration in Active Distribution Networks , 2016, IEEE Transactions on Sustainable Energy.