Impacts of binding constraints on the planning process of renewable DG in distribution systems

This paper investigates the impacts of binding constraints of the planning algorithms on the optimal allocation and sizing of renewable based distributed generation (DG) units in distribution networks. The planning algorithm under study depends on developing multi-state probabilistic models for system components and combining these models in one comprehensive model that describes all possible system states. Several technical constraints are taken into consideration, including maximum reverse power at the substation, maximum number of renewable DG connections, voltage technical limits, thermal limits of cables and overhead lines, and voltage unbalance. In this work, the renewable DG allocation binding constraints are studied, where the effect of these constraints on the objective function, also known as shadow price, is investigated. The 123-bus IEEE test system has been utilized in a case study to show the effectiveness of the proposed algorithm. The renewable DG allocation problem is formulated as a nonlinear mixed-integer programming and solved in GAMS environment.