Optimizing distributed generation and energy storage in distribution networks: Harnessing metaheuristic algorithms with dynamic thermal rating technology

Renewable energy can provide a clean and intelligent solution for the continually increasing demand for electricity. In order to rationally determine the locations and capacities of DG and ESS, this paper conducts site selection analysis and capacity planning based on different objective functions and optimization methods. The site selection analysis determines the installation locations through vulnerability assessment. In this research, taking into account voltage stability, line overload probability, and line fault probability under extreme weather conditions. Using the IEEE 33-node system as an example, the vulnerability assessment conducted with the MC algorithm, along with the application of DTR technology, effectively mitigated vulnerability. Vulnerability analysis identified installation locations at nodes 11, 16, and 29. Employing the improved CALMO algorithm combined with DTR technology, capacity planning was optimized across multiple objectives based on seasonal variations, resulting in the optimal installation capacities of ESS-23 kW, WT-81 kW, and PV-124 kW. Through case analysis and comparison with the results of ALO and CALMO algorithms, the capacity planning of the proposed algorithm reduced total costs by 9.56 % and 6.94 %, increased profits by 10.03 % and 6.56 %, and decreased the WT-PV fluctuation value for stability objectives by 22.24 % and 17.28 %, respectively. Finally, improvements in the reliability objective, EENS, were also achieved, resulting in a surplus of electricity supply capacity over demand.