Abstract
With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.
Highlights
The increasing seriousness of the energy crisis and environment preservation create new challenges for the energy industry
The process of the proposed particle swarm optimization (PSO)-OGSA can be summarized as in Table 1; the global optimal solution has been observed by each agent and moves toward it so that the movement process always obeys the law of motion
Five canonical benchmark test functions are used to validate the performance of the proposed PSO-OGSA
Summary
The increasing seriousness of the energy crisis and environment preservation create new challenges for the energy industry. In [9], an energy management model based on day-ahead scheduling is proposed to optimize two objectives, the total operation cost and the CO2 emission cost. The power loss is considered as one of the optimization objectives to solve the MOOD problem in this paper, and the optimal solution of the MOOD problem is to improve the profit of the operator and utilize efficiency of distributed energy. The genetic algorithm (GA) [22,23], particle swarm optimization (PSO) [24,25], the strength Pareto evolutionary algorithm (SPEA) [26] and so on have been increasingly proposed for solving the optimization dispatch problem because of their non-linear mapping, simplicity and powerful search capability.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
