Dynamic sub-transmission substation expansion planning using learning automata

This paper presents a new method to solve the dynamic sub-transmission substation expansion planning (SSEP) in electric power distribution networks. The method employs a new algorithm based on learning automata for optimization process. The developed algorithm includes different electrical constraints such as voltage drops, thermal limits, power flow and radial flow constraints. Moreover, prevalent cost indices are taken into consideration. The proposed method is used to solve the dynamic SSEP for Birjand city, center of South-Khorasan province of Iran, up to the year 2024. In addition, effects of the dynamic and pseudo-dynamic planning models are investigated at the study region and the obtained results are compared to those of genetic algorithm. Detailed numerical results and comparisons presented in the paper show that the proposed approach leads to proper solutions and can be used as an effective tool for the dynamic sub-transmission substation expansion planning in an actual large scale distribution network.

► A dynamic model for the sub-transmission substation expansion planning. ► A learning automata based algorithm for optimization process. ► A real life large scale distribution system is employed for numerical studies. ► Differences of the dynamic and pseudo-dynamic planning models are illustrated.