Dynamic economic dispatch of wind-storage combined system based on conditional value-at-risk

Wind power's uncertainty is from the intermittency and fluctuation of wind speed, which brings a great challenge to solving the power system's dynamic economic dispatch problem. With the wind-storage combined system, this paper proposes a dynamic economic dispatch model considering AC optimal power flow based on Conditional Value-at-Risk ( $$CVaR$$ ). Since the proposed model is hard to solve, we use the big-M method and second-order cone description technique to transform it into a trackable mixed-integer second-order conic programming (MISOCP) model. By comparing the dispatching cost of the IEEE 30-bus system and the IEEE 118-bus system at different confidence levels, it is indicated that $$CVaR$$ method can adequately estimate dispatching risk and assist decision-makers in making reasonable dispatching schedules according to their risk tolerance. Meanwhile, the optimal operational energy storage capacity and initial/final energy storage state can be determined by analyzing the dispatching cost risk under different storage capacities and initial/final states.

[1]  Ruiwei Jiang,et al.  Robust Unit Commitment With Wind Power and Pumped Storage Hydro , 2012, IEEE Transactions on Power Systems.

[2]  Stan Uryasev,et al.  Conditional value-at-risk: optimization algorithms and applications , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[3]  Silvano Martello,et al.  Decision Making under Uncertainty in Electricity Markets , 2015, J. Oper. Res. Soc..

[4]  Wang Peng,et al.  Dynamic scheduling optimization model for virtual power plant connecting with wind-photovoltaic-energy storage system , 2017, 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2).

[5]  A. K. Srivastava,et al.  Impact of battery energy storage on power system with high wind penetration , 2012, PES T&D 2012.

[6]  M. Shahidehpour,et al.  Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[7]  Jochen Gönsch,et al.  Optimizing the conditional value-at-risk in revenue management , 2013 .

[8]  J. Dupacová,et al.  Scenario reduction in stochastic programming: An approach using probability metrics , 2000 .

[9]  Wenxin Liu,et al.  Real-Time Distributed Control of Battery Energy Storage Systems for Security Constrained DC-OPF , 2018, IEEE Transactions on Smart Grid.

[10]  Li Si Security Economic Dispatch in Wind Power Integrated Systems Using a Conditional Risk Method , 2012 .

[11]  Ming Yang,et al.  Flexible Look-Ahead Dispatch Realized by Robust Optimization Considering CVaR of Wind Power , 2018, IEEE Transactions on Power Systems.

[12]  Zhiwei Xu,et al.  Risk-Averse Optimal Bidding Strategy for Demand-Side Resource Aggregators in Day-Ahead Electricity Markets Under Uncertainty , 2017, IEEE Transactions on Smart Grid.

[13]  P. Johnson,et al.  Integrated Wind, Solar, and Energy Storage: Designing Plants with a Better Generation Profile and Lower Overall Cost , 2018, IEEE Power and Energy Magazine.

[14]  Paul Denholm,et al.  Analyzing storage for wind integration in a transmission-constrained power system , 2018, Applied Energy.

[15]  Santanu S. Dey,et al.  Strong SOCP Relaxations for the Optimal Power Flow Problem , 2015, Oper. Res..

[16]  Jaehee Lee,et al.  Stochastic Method for the Operation of a Power System With Wind Generators and Superconducting Magnetic Energy Storages (SMESs) , 2011, IEEE Transactions on Applied Superconductivity.

[17]  R. Rockafellar,et al.  Conditional Value-at-Risk for General Loss Distributions , 2001 .

[18]  Stanislav Uryasev,et al.  Conditional Value-at-Risk for General Loss Distributions , 2002 .

[19]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[20]  Andrey V. Savkin,et al.  Method for planning a wind–solar–battery hybrid power plant with optimal generation‐demand matching , 2018, IET Renewable Power Generation.

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