A PROFIT-BASED UNIT COMMITMENT USING DIFFERENT HYBRID PARTICLE SWARM OPTIMIZATION FOR COMPETITIVE MARKET | ||||
| ERJ. Engineering Research Journal | ||||
| Article 7, Volume 31, Issue 1, January 2008, Page 49-58 PDF (163.98 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erjm.2008.69500 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| A. A. Abou El-Ela1; G. E. M. Ali2; H. A. Abd El-Ghany2 | ||||
| 1Mionufiya University, Shebin EL Kom, Egypt | ||||
| 2Tanta University, Egypt | ||||
| Abstract | ||||
| This paper proposes two approaches for optimal scheduling of unit commitment (UC) considering reserve generating for competitive market. The particle swarm optimization (PSO) technique is used to find out the solution of both optimal UC and power generation problems, simultaneously. The two proposed approaches depend on various sigmoid functions to obtain the binary values PSO. The first approach takes the fuzzification of generation costs as a sigmoid function; while the second approach takes the fuzzification of power generation as sigmoid function. A proposed objective function is presented dependent on the exponential form which leads to fast convergence of PSO solution. This objective aims to minimize the generation costs as well as maximize their own profit while all load demand and generation reserve are satisfied. Hence, the generations companies (GENCO) schedule their generators with objective maximize their own profit with regard for system social benefit. This means that, this objective helps GENCO to make a decision, how much power and reserve should be sold in markets and how to schedule generators in order to receive the maximum the profit. Different comparisons are carried out using various standard test systems to show the capability of the two proposed sigmoid approaches and the proposed objective function compared with other techniques | ||||
| Keywords | ||||
| Hybrid particle swarm optimization (HPSO); bidding strategies; competitive auction markets; Unit Commitment; optimization methods; power generation dispatch | ||||
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