Transmission Network Planning Under Security and Environmental Constraints

• Published in: IEEE transactions on power systems Vol. 25; no. 2; pp. 1169 - 1178
• Main Authors: Kazerooni, Ali Khajeh, Mutale, Joseph
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Capacity planning; Carbon monoxide; Computer simulation; Cost function; Electric utilities; Electricity distribution; Emission; emission trading market; Environmental economics; Investments; Load flow; Mathematical models; mixed-integer optimization; Monte Carlo; Monte Carlo methods; Networks; Optimization; Power system modeling; Power system planning; Power system security; Probability density function; Security; Studies; transmission expansion; transmission losses; {\rm CO}_{2} emissions
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2009.2036800

This paper explores the impact of CO 2 emission trading on capacity planning of electric power transmission systems. Two different models for annual emission costs are assumed. The CO 2 emission price is modeled as a probability density function in the transmission network planning problem. The Monte Carlo technique is deployed to simulate the CO 2 emission price volatility. The transmission network planning problem is formulated as a mixed-integer optimization whose objective is to minimize the sum of annual generator operating costs and annuitized transmission investment costs over different demand levels subject to N-1 network security constraints as well as operating limits on system components. The overall problem is formulated within the framework of a linear dc optimal power flow incorporating binary decision variables to model the lumpy nature of transmission investment. A linear model of losses is also proposed and included in the dc power flow model. The proposed approach can be used to determine the most probable optimal transmission capacity. The methodology is demonstrated through case studies simulated on the IEEE 24-bus network.