A Decentralized Optimization Strategy for Distributed Generators Power Allocation in Microgrids Based on Load Demand–Power Generation Equivalent Forecasting

• Published in: Energies (Basel) Vol. 13; no. 3; p. 648
• Main Authors: Zhang, Shicong, Yu, Zilong, Zhou, Bowen, Yang, Zhile, Yang, Dongsheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2020
• Subjects: Alternative energy sources; Communication; Computer simulation; consensus; Cooperation; Cost function; Decentralization; Distributed generation; Economic forecasting; Eigenvalues; Electric power distribution; Electrical loads; Energy resources; equivalent forecasting; Forecasting; Generators; Humidity; Load; Methods; microgrid; Neural networks; Optimization; Optimization algorithms; Perturbation theory; Power management; Power supply; Process parameters; Short term; Strategy; Utility functions
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13030648

In order to guarantee the economic and reliable operation of renewable Distributed Generators (DGs) in microgrids, a decentralized optimization strategy for DGs power allocation is proposed in this paper. According to the method, all processes and parameters are designed in a fully distributed way. To achieve decentralization and to maintain the balance between power supply and load demand, a load demand–power generation equivalent forecasting method is proposed to improve the strategy through replacing information of load demand by predicted power output, which removes the load prediction center and load sensor devices. The data of historical power generation, which is used for prediction, has already satisfied the balance constraint between power supply and load demand. Therefore, when the balance between the real power output and the predicted power output is gained, the balance constraint of power supply and load demand is achieved. Meanwhile, the uncertainty and forecasting errors of renewable generation are taken into account in the cost functions to optimize the expense of DG operation comprehensively. Then, the proposed algorithm is expounded in detail and the convergence is proved by eigenvalue perturbation theory. Finally, various cases are simulated to verify the accuracy and effectiveness of the proposed method. In summary, the proposed method are effective tools for DGs economic power allocation and the decentralization of microgrid system.