Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

• Published in: Electronics (Basel) Vol. 10; no. 14; p. 1648
• Main Authors: Barukčić, Marinko, Varga, Toni, Jerković Štil, Vedrana, Benšić, Tin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.07.2021
• Subjects: Artificial intelligence; Artificial neural networks; Distributed generation; Electric power distribution; Electric power systems; Heuristic methods; Optimization; Optimization algorithms; Optimization techniques; Power control; Power management; Renewable energy sources; Sensitivity analysis; Simulation; Stability; Systems analysis
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10141648

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.