Yang, Jiajia, He, Yang, Zou, Bo, Shang, Jincheng, Li, Wenqi, and Wen, Fushaun (2014) A CVaR-based coal inventory optimization model for coal-fired power plants in electricity market environment. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 38 (4). pp. 51-59.

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Abstract

The coal inventory and supply arc two major issues to concern for coal-fired power plants. It is necessary to have a certain amount of coal reserves so as to maintain the stable power generation of a coal-fired power plant. In the electricity market environment, fluctuations of electricity prices and power outputs of power plants concerned arc inevitable as the results of market competition and load fluctuations. Similarly, the coal price in the coal market also exhibits fluctuating behaviours. To achieve the expected profits at the minimum conditional value at risk (CVaR), the power plant needs to make an appropriate planning for coal inventory. Given this background and based on the risk management theory developed in the financial industry, a nonlinear programming mean-CVaR model for optimizing the coal inventory is developed by taking the CVaR as a risk measuring index. In the developed optimization model, the objective is to minimize the CVaR under a given expected profit, and some uncertainties arc taken into account including the coal price, electricity price, coal consumption, the execution rates of the contracted coal and the coal purchased from the coal market. Then, the developed optimization model is transformed into a linear programming problem so as to improve the solving efficiency. Finally, a sample example is employed to demonstrate the feasibility and efficiency of the model and algorithm developed.

Item ID:	82460
Item Type:	Article (Research - C1)
ISSN:	1000-1026
Date Deposited:	23 Dec 2024 23:25
FoR Codes:	40 ENGINEERING > 4008 Electrical engineering > 400805 Electrical energy transmission, networks and systems @ 100%
SEO Codes:	17 ENERGY > 1703 Energy storage, distribution and supply > 170309 Smart grids @ 100%
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