Operation Strategies and Energy Market Structure for Hybrid Microgrid Using Intelligent Techniques

Hybrid microgrids are a promising solution to meet the challenges of modern energy systems. By combining multiple energy sources with energy storage and control technologies, hybrid microgrids provide reliable, affordable, and sustainable power to communities, businesses, and homes. They can operate in both grid-tied and stand-alone modes, providing resilience and flexibility to the power grid. Hybrid microgrids have attracted significant attention from researchers, policymakers and industry stakeholders worldwide due to their potential to transform the traditional power grid into a more resilient, decentralized and sustainable system. The impact of hybrid microgrids on the energy market and the environment will depend on various factors, such as the cost of the technology and the regulatory framework. It is important to carefully consider these factors to ensure that the development of hybrid microgrids leads to a more sustainable and equitable energy future. This research introduces a novel algorithm aimed at minimizing the operating costs of a microgrid over a 24-hour time horizon. The algorithm achieves this by optimizing the power allocation of DGs (Distributed Generators) while accounting for the inherent randomness associated with DGs, both in the presence and absence of Grid. To achieve this goal, hybrid optimization technique involves the utilization of Adaptive Particle Swarm Optimization (APSO) Algorithm to optimize the main objective function based on Net Present Value (NPV) method, and Grey Wolf Optimization (GWO) algorithm to optimize the Energy Management System (EMS) strategy cost in microgrids. The APSO algorithm optimizes the capacity of Distributed Generation (DG) units and batteries, while the EMS strategy cost is optimized based on quadratic programming by considering the constraints. These methods collectively enable the microgrid to efficiently manage its energy resources while also determining the appropriate operational strategies.