The integration of green energy and artificial intelligence in next-generation energy supply chain: An analysis of economic, social, and environmental impacts

In the present era, addressing the uncertainty related with green energy and request side response has developed a notable challenge in microgrid (MG) management operations. Conventionally, optimizing dispatch involves establishing odds distribution functions for renewables and the linked load model. Despite numerous modeling approaches, few consider customer approval, often relying on complex parameter -solving algorithms. This paper introduces an ideal deployment strategy for a thermoelectric -coupled MG to tackle these issues. Utilizing a digital twin framework that incorporates thermoelectric technology and Renewable Energy Resources (RERs), the study employs a Modified Biogeography Enhancement Algorithm (MBOA) for effective economic dispatch. Initially, a parameter simplification method is anticipated to address RE uncertainty, incorporating normal distribution odds functions and reserve capability allocation prices. Additionally, the paper takes into account the impact of transferable and reducible loads on customer approval for request side response, considering the load tracking capability on renewables. An enhanced biogeography -driven enhancement algorithm is presented to optimize the anticipated system. Computer simulations and comparisons validate the efficiency of the strategy in dropping the MG's functioning prices, emphasizing the importance of sustainable energy management practices that integrate waste heat recovery and renewable resources in contemporary power systems.