A Mixed Integer Conic Model for Distribution Expansion Planning
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
IEEE Transactions on Smart Grid
AbstractThis paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic, to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD). The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the k-means. Since the resulting stochastic MICP is a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium-or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182-node distribution system. The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.
Distribution systems expansion planning, Energy storage device, VND-based metaheuristic algorithm, Mixed-integer conic programming, Stochastic programming
Home-Ortiz , J M , Pourakbari Kasmaei , M , Lehtonen , M & Sanches Mantovani , J R 2020 , ' A Mixed Integer Conic Model for Distribution Expansion Planning : Matheuristic Approach ' , IEEE Transactions on Smart Grid , vol. 11 , no. 5 , 9042846 , pp. 3932-3943 . https://doi.org/10.1109/TSG.2020.2982129