Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

A Nowcasting Central Controller with Continuous RTP for Residential Device Scheduling using Swap-Based BFPSO.

Energy management plays an important role in the residential sector allowing consumers to take control over their energy consumption w.r.t. the market fluctuations. For a long time, forecasting model-based scheduling was thought as a way to mitigate the expected versus reality electricity pricing gap. However, it does not always present a working model owing to uncertainties involved around it. This paper presents a scheduling model having a Nowcasting Central Controller. This model is designed for residential devices using continuous RTP and targets on optimizing the device schedule in the current time slot as well as the subsequent time slots. It is dependent on the current input data and less on the past dataset, making it implemen at any situation. To solve the optimization problem, four variants of PSO in conjunction with swapping operation are implemented on the proposed model by considering a normalized objective function made up of two cost metrics. The results demonstrate a quickness and reduction in costs by BFPSO at each time slot. A comparison is carried out among different pricing schemes that clearly establish the effectiveness of CRTP over DAP and TOD. With CRTP performing the best of the lot, the NCC model is found to be highly adaptable and robust to sudden changes in pricing schemes.