A Novel Genetic Algorithm Based Dynamic Economic Dispatch With Short-Term Load Forecasting

This article proposes an optimal energy scheduling method for power transmission networks using novel genetic algorithm (nGA) for solving the dynamic economic dispatch (DED) problem combined with machine learning based short-term load forecasting (STLF). The STLF is implemented based on a multilayer artificial neural network (MANN) to estimate the day-ahead variations in the demand. The efficacy of the proposed energy scheduling model together with the STLF is verified using a modified IEEE 30-bus system using real data of the power plants located in the Ereymentau region of Kazakhstan. The simulation results suggest that the proposed model offers a cost effective, reliable, and efficient dynamic energy scheduling in power transmission systems.

[1]  S.S. Kumar,et al.  A New Dynamic Programming Based Hopfield Neural Network to Unit Commitment and Economic Dispatch , 2006, 2006 IEEE International Conference on Industrial Technology.

[2]  Wenchuan Wu,et al.  Dynamic Economic Dispatch Using Lagrangian Relaxation With Multiplier Updates Based on a Quasi-Newton Method , 2013, IEEE Transactions on Power Systems.

[3]  Liang Han,et al.  Dynamic economic dispatch based on improved particle swarm optimization and penalty function , 2014, 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific).

[4]  Batchu Rajasekhar,et al.  Heuristic approach for transactive energy management in active distribution systems , 2020, IET Smart Grid.

[5]  Adi Soeprijanto,et al.  Dynamic economic dispatch of hybrid microgrid with energy storage using quadratic programming , 2016, 2016 IEEE Region 10 Conference (TENCON).

[6]  Zhang Xiong,et al.  Restricted Boltzmann machine based stock market trend prediction , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[7]  Qiang Zhang,et al.  Security constrained economic dispatch with dynamic thermal rating technology integration , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).

[8]  Suryanarayana Doolla,et al.  Decentralized transactive energy management system for distribution systems with prosumer microgrids , 2018, 2018 19th International Carpathian Control Conference (ICCC).

[9]  B. Türkay,et al.  Dynamic Economic Dispatch with Valve Point Effect by Using GA and PSO Algorithm , 2018, 2018 6th International Conference on Control Engineering & Information Technology (CEIT).

[10]  Jiang Chuanwen,et al.  A review on the economic dispatch and risk management considering wind power in the power market , 2009 .

[11]  Xiaohua Xia,et al.  Optimal dynamic economic dispatch of generation: A review , 2010 .

[12]  Yong Liu,et al.  Dynamic Load Economic Dispatch in Electricity Market Using Improved Particle Swarm Optimization Algorithm , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[13]  Licheng Jiao,et al.  A novel genetic algorithm based on immunity , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Mingbo Liu,et al.  Multidisciplinary collaborative optimisation-based scenarios decoupling dynamic economic dispatch with wind power , 2018 .

[15]  Li Li,et al.  Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System , 2020, 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[16]  Kishan Bhushan Sahay,et al.  Economic Load Dispatch Using Genetic Algorithm Optimization Technique , 2018, 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE).

[17]  Xiaofei Wang,et al.  Bi-level robust dynamic economic emission dispatch considering wind power uncertainty , 2016 .

[18]  Florin Capitanescu,et al.  TSO–DSO interaction: Active distribution network power chart for TSO ancillary services provision , 2018, Electric Power Systems Research.

[19]  S. Sivasubramani,et al.  Multi-objective dynamic economic and emission dispatch with demand side management , 2018 .

[20]  Chinmoy Kumar Panigrahi,et al.  Dynamic economic load dispatch using classical and soft computing techniques , 2014, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).

[21]  J. Jian,et al.  A hybrid MILP and IPM approach for dynamic economic dispatch with valve-point effects , 2017, 1703.03685.

[22]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[23]  C. Abbey,et al.  Global survey on planning and operation of active distribution networks - Update of CIGRE C6.11 working group activities , 2009 .

[24]  Prashant K. Jamwal,et al.  Genetic Algorithm for Dynamic Economic Dispatch with Short-Term Load Forecasting , 2019, 2019 IEEE Industry Applications Society Annual Meeting.

[25]  Sachin Goyal,et al.  Particle swarm intelligence based dynamic economic dispatch with daily load patterns including valve point effect , 2017, 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON).

[26]  T. Aruldoss Albert Victoire,et al.  Dynamic Economic Emission Dispatch Considering Wind Uncertainty Using Non-Dominated Sorting Crisscross Optimization , 2020, IEEE Access.

[27]  James D. McCalley,et al.  An AGC Dynamics-Constrained Economic Dispatch Model , 2019, IEEE Transactions on Power Systems.

[28]  Akhtar Kalam,et al.  Electricity load forecasting for Urban area using weather forecast information , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).