In this paper, a modified decentralized finite control set model predictive control (FCS-MPC) scheme for the distributed energy resources (DERs) is proposed to improve the power management quality of the prosumers integrated microgrids under the condition of harmonic and unbalance loads. The proposed control strategy for the microgrids mainly consists of the power droop controller, the model predictive controller delay compensation, feedback correction and the unbalance compensation mechanism. The feedback correction method is used to correct the delay compensation, which effectively reduces the average switching frequency (ASF) and voltage total harmonic distortion (THD). By sharing the negative sequence reactive power of the microgrid, power distribution between the prosumers' DERs is improved. The DERs in the prosumers can be integrated without any communication wire. The transient response and robustness to parameter changes are far superior to hierarchical cascaded control. Moreover, the proposed control strategy can better suppress harmonic and reduce and share fundamental negative sequence reactive power under microgrid unbalance and nonlinear load conditions. Finally, the effectiveness of the proposed FCS-MPC control strategy is validated by time-domain simulation results and real-time tests with RT-Lab under the condition of unbalanced and nonlinear loads in the microgrids.INDEX TERMS Microgrids, finite control set (FCS), model predictive control (MPC), voltage source converter (VSC), unbalance and harmonics control.
This paper proposes a novel maximum power point tracking (MPPT) method based on the variant of the pigeon-inspired optimization (PIO) algorithm for photovoltaic (PV) systems under partial shading conditions (PSCs). The proposed method integrates the hierarchical network behavior of pigeon flock and revises the map and compass operator of the original PIO algorithm to improve optimization efficiency. In addition, the landmark operator is used to perform a small-scale search to achieve fast tracking. Based on the combination of these mechanisms and dual-mode dynamic tracking scheme, the proposed hierarchical pigeon-inspired optimization (HPIO) MPPT method has a powerful search ability to deal with PSCs. To verify the superiority of the proposed HPIO MPPT method, it is compared with other existing advanced MPPT methods in simulation and experiments. Compared with traditional MPPT techniques based on artificial intelligence, the proposed HPIO MPPT method has a higher success rate in tracking GMPP and excellent tracking speed under PSCs. And the HPIO method also shows excellent performance under complex PSC with multiple clusters and load-variation conditions.
As renewable power penetration gradually increases in hierarchical distribution networks, certain regions have started to lack the ability to consume. How to improve the consumption capacity of a hierarchical distribution network through optimal dispatching has become a hot topic in the current research on distribution system operation. Firstly, the article makes an accurate and rapid assessment of the consumption capacity of the station area through the limit scenario analysis method; secondly, based on the assessment results, the main network renewable power sources are prioritized for consumption, while the main network and the station area; thirdly, the main network renewable power sources are prioritized for consumption, considering the assessment results while the main network and the station area reach the optimal power flow simultaneously through a multi-layer dispatching model. Then, through the flexible interconnection system, load balancing and energy optimization are performed for unconsumed distributed generation (DG) and incomplete energy storage system (ESS) in the station area to further improve the efficiency of renewable energy. Finally, the effectiveness of the proposed model is verified by simulation tests.
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