Biological Based Algorithms for Cost Optimization for Competitive Power System Market: A Review
Rahul Gupta1, Aparna N. Mahajan2, Anshu Mli Gaur3
1Rahul Gupta, Electrical and Electronics Engineering Department, MAIT, Maharaja Agrasen University, Baddi, India-174103.
2Aparna N. Mahajan, Electronics and Communication Engineering Department, MAIT, Maharaja Agrasen University, Baddi, India-174103.
3Anshu Mli Gaur, Electrical and Instrumentation Engineering Department , TIET, Patiala, India-147004.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 407-414 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6371068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main motivation of this article is to review the problem of cost optimization in power system by managing different parameters such as renewable resources, decision making, storage problems, etc with biological based optimization techniques. To enhance the efficiency of power system optimization, the biological optimization techniques are best suited. As the biological based artificial algorithms not only solve cost problems but also helpful in efficiently and handling productivity of power market efficiently. A comparison of different biological optimization techniques having different characteristics and mathematical parameters which can be applied in hybridization of cost parameters also been discussed. Finally, a framework is purposed for power market to solve the complexity of trading and cost emission problems based upon these biological optimization techniques. The comparison illustrates that biological based techniques are capable of not only solving load dispatch and cost problems but also helpful in controlling and restructuring the power system network
Keyword: Biological optimization, Economic dispatch, Nature inspired algorithms, Cost function.
Scope of the Article: Data Base Management System.