Optimizing residential flexibility for sustainable energy management in distribution networks

In search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy.