Optimal planning of distributed generation in the power system with uncertantieis of renewable energy resources by the modified harmony search algorithm

This paper addresses the critical challenge of short-term planning in power networks, emphasizing the independent and self-sufficient optimization of network operations during a sample day enriched with renewable energy sources (RES). The integration of renewable resources is imperative for sustainable power systems, offering clean energy alternatives that mitigate environmental impact and reduce costs. The research focuses on the planning aspects and incorporation of renewable resources, considering a probabilistic model to account for uncertainties and minimize the adverse effects of inconsistent distributed generation resource installations. The study employs a harmony search algorithm as an innovative solution to the optimization problem, with a key objective function based on loss reduction within the power system. The reduction of system losses serves as a justifiable metric for investments in network efficiency, including the distribution of distributed generation (DG) resources like RES across the grid. The uncertainties associated with wind speed, solar radiation, and charge are modeled using corresponding probability distribution functions, and random variable scenarios are generated through the squared sampling method. These scenarios are clustered and applied to random programming for comprehensive evaluation. The random programming is implemented on a sample micro-grid, aiming to minimize total net present costs while ensuring expected reliability adequacy. Sensitivity analysis is conducted to determine the maximum penetration percentage and optimal number of renewable resources that can be installed in the network. The proposed model is tested on the 33-bus IEEE radial distribution network under various scenarios. Simulation results demonstrate that the efficient distribution of energy from existing resources and the reduction of grid energy losses are contingent on the location and energy sharing of distributed renewable generation sources. Additionally, the paper validates the efficacy of the proposed algorithm through comparative analysis with other existing methods.