A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchable and intermittent wind and solar power generations. The uncoordinated charging of PEVs imposes further challenges on the unit commitment in modern grid operations. In this paper, all these factors are comprehensively investigated for the first time within a novel hybrid unit commitment framework, namely UCsRP, which considers a wide range of scenarios in renewable generations and demand side management of dispatchable PEVs load. UCsRP is however an extremely challenging optimisation problem not only due to the large scale, mixed integer and nonlinearity, but also due to the double uncertainties relating to the renewable generations and PEV charging and discharging. In this paper, a meta-heuristic solving tool is introduced for solving the UCsRP problem. A key to improve the reliability of the unit commitment is to generate a range of scenarios based on multiple distributions of renewable generations under different prediction errors and extreme predicted value conditions. This is achieved by introducing a novel multi-zone sampling method. A comprehensive study considering four different cases of unit commitment problems with various weather and season scenarios using real power system data are conducted and solved, and smart management of charging and discharging of PEVs are incorporated into the problem. Test results confirm the efficacy of the proposed framework and new solving tool for UCsRP problem. The economic effects of various scenarios are comprehensively evaluated and compared based on the average economic cost index, and several important findings are revealed.

[1]  Qing-Shan Jia,et al.  Matching EV Charging Load With Uncertain Wind: A Simulation-Based Policy Improvement Approach , 2015, IEEE Transactions on Smart Grid.

[2]  Jong-Bae Park,et al.  A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems , 2010, IEEE Transactions on Power Systems.

[3]  Guzmán Díaz,et al.  Simulation of spatially correlated wind power in small geographic areas—Sampling methods and evaluation , 2014 .

[4]  Yongpei Guan,et al.  Expected Value and Chance Constrained Stochastic Unit Commitment Ensuring Wind Power Utilization , 2014, IEEE Transactions on Power Systems.

[5]  Masoud Rashidinejad,et al.  Evaluation of plug-in electric vehicles impact on cost-based unit commitment , 2014 .

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

[7]  Alex Q. Huang,et al.  Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty , 2014 .

[8]  J. Latorre,et al.  Tight and Compact MILP Formulation for the Thermal Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[9]  L. Soder,et al.  Importance Sampling of Injected Powers for Electric Power System Security Analysis , 2012, IEEE Transactions on Power Systems.

[10]  Bowen Zhou,et al.  An electric vehicle dispatch module for demand-side energy participation , 2016 .

[11]  J.H. Zhang,et al.  Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition , 2009, IEEE Transactions on Power Systems.

[12]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[13]  Xiaohui Yuan,et al.  Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration , 2014 .

[14]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[15]  Feng Gao,et al.  Stochastic Coordination of Plug-In Electric Vehicles and Wind Turbines in Microgrid: A Model Predictive Control Approach , 2016, IEEE Transactions on Smart Grid.

[16]  Shafaatunnur Hasan,et al.  Memetic binary particle swarm optimization for discrete optimization problems , 2015, Inf. Sci..

[17]  Zhile Yang,et al.  A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[18]  Yanbin Yuan,et al.  An improved binary particle swarm optimization for unit commitment problem , 2009, Expert Syst. Appl..

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

[20]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[21]  Zhile Yang,et al.  Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review , 2015 .

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

[23]  Ahmed Yousuf Saber,et al.  Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization , 2010 .

[24]  Fernando Magnago,et al.  Symmetry issues in mixed integer programming based Unit Commitment , 2014 .

[25]  Narayana Prasad Padhy,et al.  SCUC problem for solar/thermal power system addressing smart grid issues using FF algorithm , 2014 .

[26]  P. Jirutitijaroen,et al.  Latin Hypercube Sampling Techniques for Power Systems Reliability Analysis With Renewable Energy Sources , 2011, IEEE Transactions on Power Systems.

[27]  Hao Tian,et al.  Improved gravitational search algorithm for unit commitment considering uncertainty of wind power , 2014, Energy.

[28]  Mohammad Shahidehpour,et al.  Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC , 2012, IEEE Transactions on Smart Grid.

[29]  Jing Wu,et al.  Integrating solar PV (photovoltaics) in utility system operations: Analytical framework and Arizona case study , 2015 .

[30]  Consolación Gil,et al.  Optimization methods applied to renewable and sustainable energy: A review , 2011 .

[31]  Ahmed Yousuf Saber,et al.  Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions , 2011, IEEE Transactions on Industrial Electronics.

[32]  Yusheng XUE,et al.  A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads , 2014 .

[33]  Vikram Kumar Kamboj,et al.  Hybrid HS–random search algorithm considering ensemble and pitch violation for unit commitment problem , 2017, Neural Computing and Applications.

[34]  Joao P. S. Catalao,et al.  A new scenario generation-based method to solve the unit commitment problem with high penetration of renewable energies , 2015 .

[35]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[36]  Shahram Jadid,et al.  Multi-objective scheduling of electric vehicles in smart distribution system , 2014 .

[37]  Q. Jiang,et al.  Parallel augment Lagrangian relaxation method for transient stability constrained unit commitment , 2013, IEEE Transactions on Power Systems.

[38]  Lasantha Meegahapola,et al.  Power system steady-state analysis with large-scale electric vehicle integration , 2016, Energy.

[39]  Provas Kumar Roy,et al.  Solution of unit commitment problem using quasi-oppositional teaching learning based algorithm , 2014 .

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

[41]  Jianhui Wang,et al.  Stochastic Optimization for Unit Commitment—A Review , 2015, IEEE Transactions on Power Systems.

[42]  Seema Singh,et al.  Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem , 2016 .

[43]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[44]  Xue Li,et al.  Probabilistic load flow calculation with Latin hypercube sampling applied to grid-connected induction wind power system , 2013 .

[45]  Ahmed Yousuf Saber,et al.  Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles , 2012, IEEE Systems Journal.

[46]  Seema Singh,et al.  Clustering based unit commitment with wind power uncertainty , 2016 .

[47]  Michael Fowler,et al.  Two-layer optimization methodology for wind distributed generation planning considering plug-in electric vehicles uncertainty: A flexible active-reactive power approach , 2016 .

[48]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

[49]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[50]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[51]  A. Kai Qin,et al.  Self-adaptive differential evolution algorithm for numerical optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.