Optimization Methods on Electricity Generation and Transmission Expansion Planning Problem

Mahdi Noorizadegan*, Alireza Shokri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

As a powerful analytical method, optimisation has been used for energy planning problems for a long time. Recent developments in computational capabilities have made it possible to include complex assumptions such as integration of natural gas and power networks, and uncertainty of various parameters with reasonable details in one energy planning problem. It is important to study the integrated natural gas and power networks to reduce the impact of variation in power generation of renewable sources. Moreover, advantages of renewable energy resources have been encouraging many countries to assign a large share of their energy portfolio to these resources. Due to uncertainty, finding a reliable and secure combination of technologies including thermal and renewable sources is significantly complicated. Approaches such as chance constrained programming and robust optimisation have been used to handle reliability and uncertainty of renewable resources and demand. Non-linearity of natural gas network leads to complex energy planning problems. In order to provide a practical pathway to carry out an energy planning problem, we categorise and discuss important topics in energy planning under three main subjects: problem settings and model, uncertainty and solution methods. We suggest a relatively comprehensive optimisation model which includes key features of an integrated power generation and transmission expansion plan and natural gas network. Then, to deal with uncertainty of net load and equipment failure, we suggest robust optimisation and a cutting plane-based method. Finally, we review solution methods used to solve similar problems.
Original languageEnglish
Title of host publicationApplied Operations Research and Financial Modelling in Energy Practical Applications and Implications
PublisherSpringer
Publication statusAccepted/In press - 12 Mar 2021

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