Optimizing energy management in distribution systems using PSO and scenario analysis: insights from CHP, wind, and NG integration

This paper proposes a new optimization framework for energy distribution systems using modified PSO, inspired by the flocking behavior of avian species. In this respect, the research study will be focused on energy generation and consumption optimization in different energy hubs with diverse generation sources and demand profiles. One of the key features to be included is the integration of WT-power production, energy demand, and price data into an overall model. By making use of historical wind speed data in conjunction with the Monte Carlo simulation technique, the model generates 1000 production scenarios that undergo a filtering process to select just three of the most likely scenarios using the Kantorovich distance matrix method. It therefore makes the generated scenarios at least 25% more accurate than those from traditional models. Its PSO algorithm will lead the way through complex optimization landscapes and come up with as much as a 35% reduction in operational costs when compared to conventional models. Besides this, the production of energy under this model aligns better with demand, thus reducing the mismatch between energy supply and demand by 20%. The results therefore show that the PSO-based model significantly yields a more efficient, cost-effective energy system. This work thus gives theoretical inputs into energy optimization with practical solutions to economically feasible energy distribution systems. Results recommend the best way to utilize energy resources in realistic applications, thus constituting a theoretically and practically valuable development in energy resource management.