Optimal Allocation of Renewable Resources in Distribution Networks Considering Uncertainty Based on Info-Gap Decision Theory Using Improved Salp Swarn Algorithm

In this paper, the optimal allocation of renewable energy resources is presented with the aim of minimizing cost of power losses and reliability improvement considering generation and load uncertainty using new approach named information gap decision theory (IGDT). Decision variables include location, size and power factor of renewable resources, also the maximum uncertainty radius of generation and load using improved salp swarm algorithm (ISSA). In the ISSA method, the performance of traditional salp swarm algorithm is improved to increase convergence speed and accuracy using evolutionary differential operators. The problem is implemented as deterministic and IGDT-based methods on 33 bus-IEEE networks with risk aversion. In the wind turbine scenario, the results showed that for the 33 bus network in the deterministic method that is increased by 20% in IGDT, the network load is increased by 7.61% and wind turbine generation is decreased by 44.06%. Also, compared to the deterministic method, the losses cost and reliability cost increased by 20.87% and 4.58%, respectively and the net saving is decreased by 6.33%.