Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks

• This paper proposes an algorithm for modeling stochastically dependent renewable energy sources for proper planning of unbalanced distribution networks.
• The proposed algorithm integrates the diagonal band Copula and sequential Monte Carlo method to consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand.
• The Big Bang-Big crunch method is proposed for optimal placement of renewable energy sources in presence of dispatchable distributed generation.
• This optimization algorithm aims to minimize the energy loss in unbalanced distribution systems.

This paper proposes an algorithm for modeling stochastically dependent renewable energy based distributed generators for the purpose of proper planning of unbalanced distribution networks. The proposed algorithm integrate the diagonal band Copula and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. Secondly, an efficient algorithm based on modification of the traditional Big Bang-Big crunch method is proposed for optimal placement of renewable energy based distributed generators in the presence of dispatchable distributed generation. The proposed optimization algorithm aims to minimize the energy loss in unbalanced distribution systems by determining the optimal locations of non-dispatchable distributed generators and the optimal hourly power schedule of dispatchable distributed generators. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithms.