The energy management and economic optimization scheduling of microgrid based on Colored Petri net and Quantum-PSO algorithm

Abstract

Optimal energy scheduling is an essential part for microgrid to obtain higher efficiency no matter from technology or economy. However, the flexibility and reconfigurability of the dispatching strategies could not be achieved as the structurally complexity of the microgrid increases. To resolve this issue, a novel hierarchical model of Colored Petri Net (CPN) based dynamic scheduling scheme is first proposed for a class of wind-photovoltaic-storage microgrid, which can be better monitored from the top for the flow of information and thus facilitates energy management. Firstly, the two-layers information flow model of microgrid is established in the platform of CPN Tools, and different layers interact with each other through selected agents. Corresponding parameters of equipment are also derived under the premise of stable operation in the meanwhile. Secondly, the economic objective function is established and solved by Quantum Particle Swarm Optimization (QPSO) algorithm, which fully considers the punishment of government and reward measures for new energy generation and the consumption of electric vehicles (EV) and battery. Finally, the illustrative example on the isolated island mode is implemented by combining the CPN Tools and MATLAB platform. Note that the input data in objective function can be obtained according to the analysis of the state space in CPN tools. The results show that this management method can improve the economical efficiency, which is in line with the enterprise benefits pursued by power companies.