Distributionally Robust Chance-Constrained AC-OPF for Integrating Wind Energy Through Multi-Terminal VSC-HVDC

Using the multi-terminal VSC-HVdc (MTDC) system to collect far-distance offshore wind power into the main onshore ac grid has become one of the ideal transmission approaches for renewable energy integration. However, with traditional fixed dc droop control in MTDC system, the lack of the capability of regulating power flow might pose a great challenge to the system operation. Accordingly, a novel MTDC model has been established in this paper which establishes that the output power of onshore converters can be formulated as a linear function of the control variables, namely power initial set point of each onshore converter. Based on this model, considering the uncertainty from wind power forecast error, a distributionally robust chance-constrained AC-OPF model is proposed for the hybrid AC/MTDC system. Without any presumption on the probability distribution of uncertainty, this model ensures the secure operation by enforcing chance constraints under the worst-case probability distribution over an ambiguity set based on the Wasserstein-Moment metric. Aiming at obtaining a tractable chance-constraint reformulation, we first employ the partial linearization of ac power flow equations to yield a linear model which represents the system response under uncertainties. We then develop an efficient and scalable approach to derive the linear analytical reformulation of distributionally robust chance constraints. The performance of the proposed model is firstly tested on the 14-bus system for an illustrative purpose, and then on the 1354-bus system for demonstrating scalability.

[1]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[2]  Daniel Kuhn,et al.  Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations , 2015, Mathematical Programming.

[3]  Zhen Wang,et al.  A Distributionally Robust Co-Ordinated Reserve Scheduling Model Considering CVaR-Based Wind Power Reserve Requirements , 2016, IEEE Transactions on Sustainable Energy.

[4]  Alexander Shapiro,et al.  Convex Approximations of Chance Constrained Programs , 2006, SIAM J. Optim..

[5]  Cheng Wang,et al.  Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating , 2017, IEEE Transactions on Power Systems.

[6]  Zhao Xu,et al.  Coordinated Control of Wind Farms and MTDC Grids for System Frequency Support , 2017 .

[7]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[8]  Jun Liang,et al.  Operation and Control of Multiterminal HVDC Transmission for Offshore Wind Farms , 2011, IEEE Transactions on Power Delivery.

[9]  Anja De Waegenaere,et al.  Robust Solutions of Optimization Problems Affected by Uncertain Probabilities , 2011, Manag. Sci..

[10]  Michael Chertkov,et al.  Chance-Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty , 2012, SIAM Rev..

[11]  Maria Vrakopoulou,et al.  Probabilistic security constrained optimal power flow for a mixed HVAC and HVDC grid with stochastic infeed , 2014, 2014 Power Systems Computation Conference.

[12]  Göran Andersson,et al.  Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow , 2017, IEEE Transactions on Power Systems.

[13]  Jun Cao,et al.  An Improved Corrective Security Constrained OPF for Meshed AC/DC Grids With Multi-Terminal VSC-HVDC , 2016, IEEE Transactions on Power Systems.

[14]  J. Roland Ortt,et al.  Market strategies for offshore wind in Europe: A development and diffusion perspective , 2016 .

[15]  Ulrich Münz,et al.  Robust Optimal Power Flow for Mixed AC/DC Transmission Systems With Volatile Renewables , 2018, IEEE Transactions on Power Systems.

[16]  Yinyu Ye,et al.  Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems , 2010, Oper. Res..

[17]  Ronnie Belmans,et al.  Generalized steady-state VSC MTDC model for sequential AC/DC power flow algorithms , 2013, 2013 IEEE Power & Energy Society General Meeting.

[18]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[19]  Mehrdad Ghandhari,et al.  A Multi-Option Unified Power Flow Approach for Hybrid AC/DC Grids Incorporating Multi-Terminal VSC-HVDC , 2013, IEEE Transactions on Power Systems.

[20]  Spyros Chatzivasileiadis,et al.  Convex Relaxations of Probabilistic AC Optimal Power Flow for Interconnected AC and HVDC Grids , 2018, IEEE Transactions on Power Systems.

[21]  Yu Zheng,et al.  Hierarchical SCOPF Considering Wind Energy Integration Through Multiterminal VSC-HVDC Grids , 2017, IEEE Transactions on Power Systems.

[22]  Xiaoqing Bai,et al.  Wasserstein Metric Based Distributionally Robust Approximate Framework for Unit Commitment , 2019, IEEE Transactions on Power Systems.

[23]  R. Torres-Olguin,et al.  Offshore Wind Farm Grid Integration by VSC Technology With LCC-Based HVDC Transmission , 2012, IEEE Transactions on Sustainable Energy.

[24]  A. Kleywegt,et al.  Distributionally Robust Stochastic Optimization with Dependence Structure , 2017, 1701.04200.

[25]  Yongpei Guan,et al.  Data-driven risk-averse stochastic optimization with Wasserstein metric , 2018, Oper. Res. Lett..

[26]  Kit Po Wong,et al.  Power Flow Features and Balancing in MTDC Integrated Offshore Wind Farms , 2017 .

[27]  Wenyuan Wang,et al.  Power Flow Algorithms for Multi-Terminal VSC-HVDC With Droop Control , 2014, IEEE Transactions on Power Systems.

[28]  Lina Bertling Tjernberg,et al.  A New Approach for Benefit Evaluation of Multiterminal VSC–HVDC Using A Proposed Mixed AC/DC Optimal Power Flow , 2014, IEEE Transactions on Power Delivery.

[29]  Vishal Gupta,et al.  Data-driven robust optimization , 2013, Math. Program..

[30]  Goran Andersson,et al.  DC optimal power flow including HVDC grids , 2013, 2013 IEEE Electrical Power & Energy Conference.

[31]  Weijun Xie,et al.  On distributionally robust chance constrained programs with Wasserstein distance , 2018, Mathematical Programming.

[32]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[33]  Johanna L. Mathieu,et al.  Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads , 2017, IEEE Transactions on Power Systems.

[34]  Göran Andersson,et al.  Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms , 2017, IEEE Transactions on Power Systems.

[35]  Li Yao,et al.  Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric , 2017, IEEE Transactions on Power Systems.