A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) based is proposed to solve the unit commitment (UC) problem in uncertain environment as a multi-objective optimization problem considering cost, emission, and reliability as the multiple objectives. The uncertainties occurring due to thermal generator outage and load forecast error are incorporated using expected energy not served (EENS) reliability index and EENS cost is used to reflect the reliability objective. Since, UC is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of decomposition-based MOEA such that genetic algorithm (GA) evolves the binary variables while differential evolution (DE) evolves the continuous variables. To enhance the performance of the presented algorithm, novel non-uniform weight vector distribution strategies are proposed. The effectiveness of the non-uniform weight vector distribution strategy is verified through stringent simulated results on different test systems.

[1]  Enrico Zio,et al.  A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment , 2013, IEEE Transactions on Power Systems.

[2]  M. Ortega-Vazquez,et al.  Optimizing the Spinning Reserve Requirements Using a Cost/Benefit Analysis , 2007, IEEE Transactions on Power Systems.

[3]  Narayana Prasad Padhy,et al.  Thermal unit commitment using binary/real coded artificial bee colony algorithm , 2012 .

[4]  Peter J. Fleming,et al.  Generalized decomposition and cross entropy methods for many-objective optimization , 2014, Inf. Sci..

[5]  Dipti Srinivasan,et al.  A genetic algorithm - differential evolution based hybrid framework: Case study on unit commitment scheduling problem , 2016, Inf. Sci..

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Dipti Srinivasan,et al.  Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem , 2015, Swarm Evol. Comput..

[8]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[9]  Jie Zhang,et al.  Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

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

[11]  Yanbin Yuan,et al.  Unit commitment problem using enhanced particle swarm optimization algorithm , 2011, Soft Comput..

[12]  Dipti Srinivasan,et al.  Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem , 2015, IEEE Transactions on Industrial Informatics.

[13]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[14]  Dipti Srinivasan,et al.  Improved multi-objective evolutionary algorithm for day-ahead thermal generation scheduling , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[15]  Chanan Singh,et al.  Evolutionary Multi-Objective Day-Ahead Thermal Generation Scheduling in Uncertain Environment , 2013, IEEE Transactions on Power Systems.

[16]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Fang Liu,et al.  MOEA/D with Adaptive Weight Adjustment , 2014, Evolutionary Computation.

[18]  Dilip Datta,et al.  A binary-real-coded differential evolution for unit commitment problem , 2012 .

[19]  K. Chandrasekaran,et al.  Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm , 2012, Swarm Evol. Comput..

[20]  Dipti Srinivasan,et al.  A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition , 2017, IEEE Transactions on Evolutionary Computation.

[21]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[22]  Dilip Datta.  Unit commitment problem with ramp rate constraint using a binary-real-coded genetic algorithm , 2013, Appl. Soft Comput..