An Efficient Framework for Optimal Allocation of Renewable Energy Sources in Reconfigurable Distribution Systems With Variable Loads

Distribution network losses constitute over 70% of all the losses that occur in power systems, resulting in the increment of operational cost, voltage profile distortion, and power supply instability. Therefore, addressing power losses has become a major concern for power system owners and operators, leading to the development of several mechanisms for network losses minimization. Among the proposed methods for loss minimization in power systems, the placement of distributed generation (DG) in reconfigurable power networks is considered as one of the most efficient mechanisms to reduce network losses, operation costs, and generation expenses. However, load variability consideration in the DG allocation and reconfiguration problem raises the burden and computational time considerably. Consequently, the current paper proposes an effective reconfiguration and DG allocation approach for loss reduction in the presence of variable loads. In the proposed method, an equivalent load factor was appropriately calculated from an actual load profile and embedded into the constraints of DG and reconfiguration problem. The proposed model is compared with exact and conventional models in several IEEE busbar systems in a mix-integer liner programming model using A Mathematical Programming Language (AMPL) in CPLEX. Numerical results evaluation of the proposed model and the exact model shows that the proposed model is able to reduce the computational time of the exact load profile model by 9 to 1000 times with power loss error margin of between 7 and 18%. While comparing the proposed model with conventional one indicates the proposed formulation is able to reduce the systems power loss of the conventional model by up to 50%. From the results, it was concluded that the proposed model is very efficient in terms of computational time reduction as well as reducing the active losses of a distribution network.