Environmental Transmission Expansion Planning using non-linear programming and evolutionary techniques

Current energy consumption has led to an increase in the use of fossil fuels to generate electricity, which in turn produces negative impacts on the environment. Studies show that demand will continue growing and new schemes for generating power and managing resources should be developed. In the case of the transmission network, some constraints may lead to use plants with high emission levels, and therefore, appropriate planning is key to minimize environmental impact. This work proposes a methodology for solving the Transmission Expansion Planning Problem (TEPP) when emissions of CO
₂
are considered. The result is an investment plan that leads to the lowest level of emissions by means of a Chu-Beasley Genetic Algorithm (CBGA). An improvement step is carried in the CBGA in order to minimize also the cost of the plan. Non-linear Interior Point Method (NLIPM) is used to generate the initial population and Linear (LIPM) is used to solve the operative problem in the evolutionary process. The approach is validated using the IEEE-24 bus system in order to show its effectiveness.