Optimization of Day-Ahead Gas Turbine Generation Scheduling with Gas Consumption Characteristics and Primary Energy Constraints in Consideration

doi:10.11930/j.issn.1004-9649.2017.02.101.06

Abstract

Abstract: Currently day-ahead gas turbine generation scheduling usually deviates a lot from the real generation curves, which brings much trouble to the operation and the peak load regulation of the power system. Thus, an optimization strategy for day-ahead gas turbine generation scheduling with the consideration of the gas consumption characteristics and the constraints of primary energy is proposed in this paper . The total gas supplied to the plant is fixed by the contract. The mathematical relationship between the output of a generator and its gas consumption can be acquired by linear approximation of the gas consumption characteristics. In terms of the day-ahead load forecasting, the generation curves are divided into three parts, i.e., the peak, medium , and low load periods. Then the gas consumed during the three periods can be obtained and summed up. The summed gas consumption is constrained by the total gas supplied. Therefore, an optimization model is built and solved with the bacterial colony chemotaxis algorithm to obtain the day-ahead generation scheduling. The results of two typical cases of summer peak and valley loads show that the mean squared errors between the calculated and the practical curves are less than 10 MW, which definitely meets the requirements of the real operation and proves the correctness and effectiveness of the proposed method.

Key words: gas turbine, gas consumption characteristics, day-ahead generation scheduling, bacterial colony chemotaxis, load forecasting

CLC Number:

TM611.24

LIU Lin, LUO Kaiming, ZHOU Ting, LIU Shengsong, LI Haifeng, LU Xiao. Optimization of Day-Ahead Gas Turbine Generation Scheduling with Gas Consumption Characteristics and Primary Energy Constraints in Consideration[J]. Electric Power, 2017, 50(2): 101-106.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2017.02.101.06

https://www.electricpower.com.cn/EN/Y2017/V50/I2/101

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