Autonomous dynamic economic dispatch with limited fuel and renewable energy sources using marine predators optimizer

Abstract This article proposes a simple and reliable Dynamic Economic Emission Dispatch (DEED) model for a power system integrating Renewable Energy Sources (RES), including photovoltaic (PV) systems and wind energy. The thermal units in the system under concern are operating under limited fuel constraints. The proposed DEED effectively manages limited fuel conditions through Dynamic Generation Capacity (DGC). The Walrus Optimization Algorithm (WaOA) and Marine Predators Algorithm (MPA) are competing for the implementation of the proposed DEED with DGC and RES. The performance of the competing optimizers is compared against a mature meta-heuristic optimizer, Particle Swarm Optimization (PSO). The MPA is the most promising candidate, significantly outperforming the contenders, WaOA and PSO. For the 1000 MW load scenario, MPA and WaOA achieved a reduction in generation cost compared to PSO. Moreover, MPA substantially reduced the variability of results, demonstrating superior consistency compared to the competing optimizers. The proposed energy management strategies are tested on a system with 10 units under various operating scenarios. The results showed that the RES for power systems with limited/shortage fuel scenarios were insufficient to meet the required load, as fuel shortages tend to occur suddenly and unpredictably, thereby reducing system security. The DEED approach, incorporating both DGC and RES, achieved a notable reduction in fuel costs and emissions compared to the full fuel scenario and the fuel shortage scenario without RES. The results highlight the potential of integrating both DGC and RES into the DEED framework, demonstrating effectiveness in reducing operational costs and emissions while simultaneously enhancing system security and contributing to the development of a sustainable, intelligent future power grid.