Chance constrained simultaneous optimization of substations, feeders, renewable and non-renewable distributed generations in distribution network

The penetration of distributed generators (DGs) is continually increasing in the power sector due to its ability in enhancing technical specifications as well as providing a promising future for power generation in electric networks. The aforementioned objectives will be realized if DG units are allocated optimally and coordinately simultaneous with distribution network expansion planning. On the other hand, given the stochastic nature of renewable generation and severe fluctuations of load consumption and electricity price, the DGs planning problem should be accomplished under uncertainties. To address these issues, this paper proposes a novel joint chance constrained programming (JCCP) method to fulfill an acceptable level of constraint feasibility for optimal simultaneous expansion planning of HV/MV substations and multiple-DG units along with robust MV feeder routing problem. Our design objective is to determine the optimal site and size of sub-transmission substations and various DG units associated with optimally construction of network by implementing the feeder routing problem with aim to minimize the investment costs, energy not supplied (ENS) cost and energy purchasing cost from upstream network. The diverse objectives are mathematically formulated as an MINLP model and converted into a single objective function through weighted sum method and subsequently has been minimized by adaptive genetic algorithm. Furthermore, the Taguchi method is utilized in order to furnish an efficient algorithm that can find a satisfactory solution. Finally, the effectiveness of the proposed method is investigated by applying it on the 54-bus distribution network and the obtained results are duly drawn and discussed. (C) 2018 Elsevier B.V. All rights reserved.