Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach

Abstract Optimizing reactive power flow in electrical network is an important aspect of system study as the reactive power supports network voltage which needs to be maintained within desirable limits for system reliability. A network consisting of only conventional thermal generators has been extensively studied for optimal active and reactive power dispatch. However, increasing penetration of renewable sources into the grid necessitates power flow studies incorporating these sources. This paper presents a formulation and solution procedure for stochastic optimal reactive power dispatch (ORPD) problem with uncertainties in load demand, wind and solar power. Appropriate probability density functions (PDFs) are considered to model the stochastic load demand and the power generated from the renewable energy sources. Numerous scenarios are created running Monte-Carlo simulation and scenario reduction technique is implemented to deal with reduced number of scenarios. Real power loss and steady state voltage deviation of load buses in the network are set as the objectives of optimization. Success history based adaptive differential evolution (SHADE) is adopted as the basic search algorithm. SHADE has been successfully integrated with a constraint handling technique, called epsilon constraint (EC) handling, to handle constraints in ORPD problem. The effectiveness of a proper constraint handling technique is substantiated with case studies for deterministic ORPD on base configurations of IEEE 30-bus and 57-bus systems using SHADE-EC algorithm. The single-objective and multi-objective stochastic ORPD cases are also solved using the SHADE-EC algorithm. The results are discussed, compared and critically analyzed in this study.

[1]  Sahand Ghavidel,et al.  A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables , 2014, Appl. Soft Comput..

[2]  Abbas Rabiee,et al.  Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation , 2017 .

[3]  Nantiwat Pholdee,et al.  Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer , 2017, Expert Syst. Appl..

[4]  M. Bjelogrlic,et al.  Application of Newton's optimal power flow in voltage/reactive power control , 1989, Conference Papers Power Industry Computer Application Conference.

[5]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[6]  Ponnuthurai Nagaratnam Suganthan,et al.  Optimal power flow solutions incorporating stochastic wind and solar power , 2017 .

[7]  A. Rezaee Jordehi,et al.  Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems , 2017, Appl. Soft Comput..

[8]  Lilan Liu,et al.  Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints , 2017, Appl. Soft Comput..

[9]  Abhishek Rajan,et al.  Exchange market algorithm based optimum reactive power dispatch , 2016, Appl. Soft Comput..

[10]  Jia-Ching Wang,et al.  An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration , 2015 .

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

[12]  Ponnuthurai N. Suganthan,et al.  Minimizing harmonic distortion in power system with optimal design of hybrid active power filter using differential evolution , 2017, Appl. Soft Comput..

[13]  Hamdan Daniyal,et al.  Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique , 2017, Appl. Soft Comput..

[14]  Abbas Rabiee,et al.  A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem , 2016 .

[15]  Tian Pau Chang,et al.  Investigation on Frequency Distribution of Global Radiation Using Different Probability Density Functions , 2010 .

[16]  Tetsuyuki Takahama,et al.  Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation , 2010, IEEE Congress on Evolutionary Computation.

[17]  Ponnuthurai N. Suganthan,et al.  Optimal placement of wind turbines in a windfarm using L-SHADE algorithm , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[18]  Abbas Rabiee,et al.  Multi-objective Optimal Reactive Power Dispatch Considering Uncertainties in the Wind Integrated Power Systems , 2017 .

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

[20]  Yanyan Guo,et al.  Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems , 2016 .

[21]  Ponnuthurai N. Suganthan,et al.  Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques , 2018, Eng. Appl. Artif. Intell..

[22]  Sakti Prasad Ghoshal,et al.  Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm , 2014 .

[23]  Malabika Basu,et al.  Quasi-oppositional differential evolution for optimal reactive power dispatch , 2016 .

[24]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[25]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[26]  Abbas Rabiee,et al.  Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach , 2016 .

[27]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[28]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[29]  V. H. Quintana,et al.  Transmission power loss reduction by interior-point methods: implementation issues and practical experience , 2005 .

[30]  N. Grudinin Reactive power optimization using successive quadratic programming method , 1998 .

[31]  Behnam Mohammadi-Ivatloo,et al.  Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms , 2016 .

[32]  Ponnuthurai N. Suganthan,et al.  Efficient constraint handling for optimal reactive power dispatch problems , 2012, Swarm Evol. Comput..

[33]  Ponnuthurai Nagaratnam Suganthan,et al.  Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power , 2018 .

[34]  Mohammad Rasoul Narimani,et al.  A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch , 2017, Appl. Soft Comput..

[35]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Ponnuthurai Nagaratnam Suganthan,et al.  Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm , 2019, Renewable Energy.

[37]  Mohammad Ali Abido,et al.  Differential evolution algorithm for optimal reactive power dispatch , 2011 .

[38]  Abhishek Rajan,et al.  Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm , 2015 .

[39]  Provas Kumar Roy,et al.  Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization , 2013 .

[40]  Behnam Mohammadi-Ivatloo,et al.  Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties , 2016 .