Probabilistic optimal planning in active distribution networks considering non-linear loads based on data clustering method

Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non-linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm is compared with the multi-objective particle swarm optimization algorithm.

Automated summary

This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm, reducing total harmonic distortion and total costs.