Probabilistic Integrated Planning of Primary and Secondary Distribution Networks based on a Hybrid Heuristic and GA Approach

The integrated planning of distribution system reveals a complex and non-linear problem being integrated with integer and discontinues variables. Due to these technical and modeling complexities, many researchers tend to optimize the primary and secondary distribution networks individually which depreciates the accuracy of the results. Accordingly, the integrated planning of these networks is put forward to guarantee reliable and accurate results. Different approaches are contemplated for distribution network planning studies. Genetic algorithm stands as one the widely deployed approaches. Due to the existing uncertainties in probabilistic planning of large distribution networks, the conventional genetic algorithm is aligned with high computational burden and hence, might lose its efficiency. To annihilate these issues, the ongoing study contributes to an improved approach ending to reduced computational efforts and accurate result. Being inspired based on a traditional approach for placement of distribution transformers and mixing it with the genetic algorithm, a heuristic method is devised which reduces the search space, sensibly. Accordingly, the optimal solutions are more swiftly attained in probabilistic planning of distribution networks. To represent the existing uncertainties, a set of scenarios is defined based on parameters probability distribution functions. The aggregated effects of these scenarios are introduced as the expected values of the investigated variables. Efficiency of the proposed approach is explored on two test systems within which the obtained results are discussed in depth.