Robust Optimization Based Optimal DG Placement in Microgrids

Abstract

This paper proposes a novel Microgrid (MG) planning methodology to decide optimal locations, sizes and mix of dispatchable and intermittent distributed generators (DGs). The long-term costs in the proposed planning model include investment, operation and maintenance (O&M), fuel and emission costs of DGs while the revenue includes payment by MG loads and utility grid. The problem is formulated as a mixed-integer program (MIP) considering the probabilistic nature of DG outputs and load consumption, wherein the costs are minimized and profits are maximized. The model is transformed to be a two-stage robust optimization problem. A column and constraint generation (CCG) framework is used to solve the problem. Compared with conventional MG planning approaches, the proposed model is more practical in that it fully considers the system uncertainties and only requires a deterministic uncertainty set, rather than a probability distribution of uncertain data which is difficult to obtain. Case studies of a MG with wind turbines, photovoltaic generators (PVs) and microturbines (MTs) demonstrate the effectiveness of the proposed methodology.