Adaptive optimal energy management in multi-distributed energy resources by using improved slime mould algorithm with considering demand side management

Abstract

In this article, a stochastic programming model is developed to solve a multi-renewable sources-based energy management with the help of a multi-objective improved slime mould algorithm (MOISMA). As a result, the microgrid's performance is expected to improve with this technique. However, such a fast-acting methodology reduces operating generation costs and emissions by using multiple renewable sources. Firstly, Hong's (2 m + 1) point estimate method is used to generate as well as control the uncertainty of wind speed and solar irradiance patterns. Secondly, this MOISMA-based-energy management study outperforms better when compared with multi-objective genetic algorithms (MOGA) and particle swarm optimization (MOPSO). All these performances are carried out at the time of use (ToU) based on demand response (DR) load management. Using these techniques, which can reduce the cost of generating power and increase energy resource use, a demand-side management study can be performed within 24 h of the generation of load data for analysis purposes. So, in this research by the application of demand-side management, we get a minimum generation of power costs and reduce the environmental emission by 12.62% and 7.43%, for wind energy, similarly 31.53% and 2.51% for solar energy systems with this DR technique. Thus, overall, it has been validated that using such a novel optimization-based energy management study can reduce system generation cost, energy usage, peak demand shifting, and environmental emissions.