Optimal allocation of distributed energy resources through simulation-based optimization

• Simulation-based optimization (SBO) with two layer approach is proposed to determine an optimal configuration of distributed energy resources (DER).
• The optimal location and capacity of DER is considered in the first layer to minimize the total annual cost (TAC).
• In the second layer, the dynamic optimal power flow (DOPF) is formulated as a non-linear programming (NLP) problem, taking into account various system operational constraints.
• The proposed SBO problem is solved using particle swarm optimization (PSO) to minimize the value of ATC and is tested on the 16-bus U.K. generic distribution system (GDS).

In this study, a new two-layer simulation-based optimization (SBO) approach is proposed to determine the optimal allocation and capacity of distributed energy resources (DER) in a power distribution system with an imperfect grid connection. In the first layer, a dynamic optimal power flow (DOPF) routine is embedded in a simulation algorithm that is run for each system configuration based on a set of operational rules to calculate the cost and reliability level of the system over one year. In the second layer, a particle swarm optimization (PSO) algorithm uses the outputs of the first layer to optimize the location and capacity of wind turbines, PV panels, and grid-scale batteries, in order to minimize cost while meeting reliability requirements. The proposed approach is tested on a 16-bus U.K. generic distribution system (GDS) under different grid availability conditions, and the results are reported. The merits and limitations of the proposed approach are discussed, and the differences between it and rule-free constrained optimization approaches are highlighted.