Zhibo Zhang

As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.

In recent years, due to the global energy crisis, increasingly more countries have recognized the importance of developing clean energy. Offshore wind energy, as a basic form of clean energy, has become one of the current research priorities. In the future, offshore wind farms will be developed in deep and distant sea areas. In these areas, there is a new trend of floating offshore wind platforms replacing fixed wind power platforms, due to their low cost, ease of installation, and independence from the water depth. However, the stability of offshore floating platforms is poor and their power fluctuations are significant; furthermore, they are more prone to failure because of sea wind, waves, and currents. This paper summarizes and analyzes the current research progress and critical technical issues of offshore floating wind power generation, such as stability control technology, integrated wind storage technology, wind power energy management, and long-distance transmission of electricity for floating wind power generation at sea. Finally, future research directions for key offshore wind power technologies are presented.