Optimize Scheduling of Generating Unit for Economic Load Dispatch using ANN: A Review

Sanjay Yadav, Lavkesh Patidar

Abstract


Electrical power frameworks are structured and worked to meet the nonstop variety of intensity request. In power framework limiting, the task cost is critical. Monetary Load Dispatch (ELD) is a strategy to plan the power generator yields regarding the heap requests, and to work the power framework most financially, or at the end of the day, we can say that primary target of financial load dispatch is to distribute the ideal power age from various units at the least cost conceivable while meeting all framework imperatives Over the years, numerous endeavors have been made to tackle the ELD issue, consolidating various types of requirements or different goals through different numerical programming and advancement systems. In any case, these traditional dispatch calculations require the steady cost bends to be monotonically expanding or piece-wise straight. The information/yield qualities of present day units are inalienably profoundly nonlinear (with valve-point impact, rate limits and so forth) and having numerous nearby least focuses in the cost work. Their qualities are approximated to meet the prerequisites of traditional dispatch calculations prompting imperfect arrangements and thusly, bringing about gigantic income misfortune over the time. Thought of profoundly nonlinear qualities of the units requires exceptionally vigorous calculations to abstain from stalling out at neighborhood optima.


Full Text:

PDF

References


AbdelazizA.Y., Ali E.S. & AbdElazimS.M. Combined economic and emission dispatch solution using Flower Pollination Algorithm. Electrical Power and Energy Systems. 2016. 80;pp264–274.

Rasoul Azizipanah - Abarghooee, Niknam Taher, Amin BinaMohammad & ZareMohsen. Coordination of combined heat and power-thermal-wind photovoltaic units in economic load dispatch using chance constrained. Energy. 2015.pp. 1–18.

BanerjeeSumit, MaityDeblina & ChandaChandan Kumar. Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. IEEE Transactions on Power Systems.2016. 31(3), pp 2014–2025.

ZhangJingrui, Tang Qinghui, Chen Yalin&Lin Shuang. Genetic Algorithm based on the Lagrange Method for the Non-Convex Economic Dispatch Problem.Energy.2016. 109, pp 765–780.

Dipayan De. Economic Load Dispatch by Optimal Scheduling of Generating Units using Improved Real Coded Genetic Algorithm. IEEE Transactions on Industrial Informatics. 11(6); pp.1346–1357.

YuanXiaohui, JiBin, Yuan Yanbin, Rana M. Ikram, Xiaopan Zhang&Yuehua Huang. An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem.Energy Conversion and Management. 2015,91; pp. 225–237.

DipayanDe. Economic Load Dispatch by Optimal Scheduling of Generating Units using Improved Real Coded Genetic Algorithm. IEEE Electron Device Letters. 2014.35(2); pp. 169–171.

Pradhan Moumita. Grey wolf optimization applied to economic load dispatch problems. International Journal of Electrical Power & Energy Systems. 2014.5; pp. 222–231.

Bin Ji, Xiaohui Yuan, Xianshan Li, Yuehua Huang & Wenwu Li. Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration. Energy Conversion and Management. 2014,87; pp. 589–598.

Zheng. Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem. IEEE Transactions on Power Systems. 2013,28(1); pp. 52–63.

KambojVikram Kumar, Bath S. K.&DhillonJ.S.Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. IEEE Trans. Power Syst. 2015,30(3); pp. 1582–1592.

RestrepoJ. F. &GalianaF. D.Assessing the yearly impact of wind power through a new hybrid deterministic /stochastic unit commitment. IEEE Trans. Power Syst. 2011,26(1); pp. 401–410.

DamousisG., Bakirtzis A. G. & Dokopoulos P. S. A solution to the unit commitment problem using integer-coded genetic algorithm. IEEE Trans. Power Syst.2004,19(2); pp. 1165–1172.

ZhangX., TianY., Cheng R.&Jin Y.An efficient approach to non-dominated sorting for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 2015,19(2); pp. 201–213.

Civicioglu P. Backtracking Search Optimization Algorithm for numerical optimization problems. Appl. Math. Comput.2013, 219; pp. 8121–8144.

Azizipanah AbarghooeeR., Terzija V., GolestanehF. & Roosta A. Multi objective Dynamic optimal power flow considering fuzzy based smart utilization of mobile electric vehicles. IEEE Trans. Ind. Informat. 2016,12(2); pp. 503–514.

AzizipanahAbarghooee R., Golestaneh F., Gooi H. B., Lin J., BavafaF.,&TerzijaV.Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power. Appl. Energy. 2016, 182; pp. 634–651.

Azizipanah Abarghooee R., Dehghanian P. & Terzija V.Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm.IET Gener. Transm. Distrib(In press). 2016; pp. 1–17.

ZareM., NiknamT., Azizipanah-AbarghooeeR.&OstadiA.New stochastic bi-objective optimal cost and chance of operation management approach for smart micro-grid.IEEE Trans. Ind. Informat. 2016,PP(99); pp. 1–10.

Azizipanah-Abarghooee R., Niknam T., BinaM. A. &ZareM.Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods.Energy. 2015, 79; pp. 50–67.

Azizipanah-AbarghooeeR., NiknamT., ZareM.&GharibzadehM.Multi-objective short-term scheduling of thermoelectric power systems using a novel multi objective θ-improved cuckoo optimization algorithm.IET Gener.,Transm. Distrib. 2014,8(5); pp. 873–894.

KazarlisS. A., BakirtzisA., & Petridis V.A genetic algorithm solution to the unit commitment problem.IEEE Trans. Power Syst. 1996,11(1); pp. 83–92.


Refbacks

  • There are currently no refbacks.