A source–grid–load coordinated power planning model considering the integration of wind power generation

Power system planning approach should meet the new requirements brought by continuous development of power systems. In recent years, wind power generation capacity keeps a rapid growth, but the problem of wind power curtailment becomes increasingly serious in some countries. The limitations of wind power integration produced by power systems, such as the inconsistency between installed generation capacity and transmission capacity and the insufficiency of regulation capacity, are drawing the attention in both academia and industry. This paper proposes a source–grid–load coordinated planning model for power systems with regulation capacity constraints being taken into account. In this novel model, traditional generation expansion planning and transmission expansion planning are integrated, and the peak-load regulation capacity and flexible regulation capability of power systems are considered. Meanwhile, the positive impacts of demand response are also taken into consideration. The numerical study verifies that the source–grid–load coordinated planning model can not only reduce the overall cost of the system, but also improve the wind power integration capacity and guarantee the sustainable development of wind power generation.

[1]  Sarah M. Ryan,et al.  Temporal Versus Stochastic Granularity in Thermal Generation Capacity Planning With Wind Power , 2014, IEEE Transactions on Power Systems.

[2]  Ning Zhang,et al.  A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power , 2015 .

[3]  Shan Jin,et al.  A Tri-Level Model of Centralized Transmission and Decentralized Generation Expansion Planning for an Electricity Market—Part II , 2014, IEEE Transactions on Power Systems.

[4]  Mahdi Raoofat,et al.  Composite generation and transmission expansion planning considering distributed generation , 2014 .

[5]  Bo Zeng,et al.  Robust unit commitment problem with demand response and wind energy , 2012, PES 2012.

[6]  Takashi Ikegami,et al.  A unit commitment model with demand response for the integration of renewable energies , 2011, 2012 IEEE Power and Energy Society General Meeting.

[7]  Michael C. Georgiadis,et al.  An Integrated Unit Commitment and Generation Expansion Planning Model , 2015 .

[8]  Jamshid Aghaei,et al.  Risk based multiobjective generation expansion planning considering renewable energy sources , 2013 .

[9]  Nikolaos E. Koltsaklis,et al.  A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints , 2015 .

[10]  Qi Zhang,et al.  An integrated model for long-term power generation planning toward future smart electricity systems , 2013 .

[11]  Habib Rajabi Mashhadi,et al.  An augmented NSGA-II technique with virtual database to solve the composite generation and transmission expansion planning problem , 2014, J. Exp. Theor. Artif. Intell..

[12]  Efstratios N. Pistikopoulos,et al.  A spatial multi-period long-term energy planning model: A case study of the Greek power system , 2014 .

[13]  Carlos Batlle,et al.  An Enhanced Screening Curves Method for Considering Thermal Cycling Operation Costs in Generation Expansion Planning , 2013, IEEE Transactions on Power Systems.

[14]  Hyewon Lee,et al.  Evaluation of the Wind Power Penetration Limit and Wind Energy Penetration in the Mongolian Central Power System , 2012 .

[15]  A. Conejo,et al.  Smart grids, renewable energy integration, and climate change mitigation - Future electric energy systems , 2012 .

[16]  E. Lannoye,et al.  Evaluation of Power System Flexibility , 2012, IEEE Transactions on Power Systems.

[17]  Nima Amjady,et al.  Generation and Transmission Expansion Planning: MILP–Based Probabilistic Model , 2014, IEEE Transactions on Power Systems.

[18]  Tapan Kumar Saha,et al.  Benefit-based expansion cost allocation for large scale remote renewable power integration into the Australian grid , 2014 .

[19]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[20]  Zhijian Liu,et al.  Evaluation of the capability of accepting large-scale wind power in China , 2013 .

[21]  Hu Zhao-guang,et al.  Intelligent Engineering Theory Expanding and Its Application in Transmission Planning , 2008 .

[22]  Rahmat-Allah Hooshmand,et al.  State-of-the-art of transmission expansion planning: Comprehensive review , 2013 .

[23]  Mohammad Hossein Javidi,et al.  Coordinated decisions for transmission and generation expansion planning in electricity markets , 2013 .

[24]  Pedro Faria,et al.  The role of demand response in future power systems , 2009, 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific.

[25]  Ming Ni,et al.  Coordinating large-scale wind integration and transmission planning , 2012, 2013 IEEE Power & Energy Society General Meeting.

[26]  Yongpei Guan,et al.  Stochastic Unit Commitment With Uncertain Demand Response , 2013, IEEE Transactions on Power Systems.

[27]  Hui Zhang,et al.  Next Generation Transmission Expansion Planning Framework: Models, Tools, and Educational Opportunities , 2014, IEEE Transactions on Power Systems.

[28]  Bo Shen,et al.  The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges , 2014 .