Optimal energy management integration for a petrochemical plant under considerations of uncertain power supplies

• Published in: IEEE transactions on power systems Vol. 20; no. 3; pp. 1431 - 1439
• Main Authors: Tung-Yun Wu, Shyan-Shu Shieh, Shi-Shang Jang, Liu, C.C.L.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cogeneration; Computer simulation; Costs; Decision making; Electric power generation; Electric rates; Electric utilities; Electricity distribution; Energy management; Fluctuations; Fuels; meta-model approach; Monte Carlo methods; Monte Carlo simulation; Neural networks; optimal contract; Optimization; Petrochemicals; Power failures; Power plants; Power supplies; Power systems; Production; Searching; Studies; uncertainty factors
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2005.852063

The electric power demands of many petrochemical plants are matched by supplies from an in-house cogeneration system and from the electric grid. However, due to the fluctuations of fuel costs, production, and electricity rates, it is necessary to balance electric supply between these two sources. In reality, uncertain effects play a very important role in this decision-making problem. One of the most important uncertainties is the occurrence of power interruptions from either one of the supply sources, which could endanger operability and reliability of plant operations. To minimize the total energy cost under consideration of unexpected power failures, we break up the solution of the problem into two layers. The outer layer is to determine the optimum contracting of three-section time-of-use rate. We use an artificial neural network regression model as a meta-model to simulate the contour plot of a nonconvex cost function. The occurrences of incidental power failures are simulated by the Monte Carlo method. The inner layer is to determine the optimum operation of the cogeneration system. Since the searching space is huge in the outer layer and the Monte Carlo simulation in the inner layer is time consuming, we implement an interactive sampling search approach to find the optimal contract capacity in this multi-local-optima problem.