When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines, for example, high-voltage direct current, the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue, firstly, an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold, while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then, a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly, the sensitivity of the total return on investment to wind speed parameters was investigated, and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419%, and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted.

This paper adopts a hierarchical multi-time scale coordinated scheduling framework to jointly optimize occupant thermal comfort and operating cost in building microgrids. Operating cost measures system economic performance, while thermal discomfort is quantified by the time-weighted $L_2$ norm of predicted mean vote (PMV). With other thermal comfort parameters held constant, PMV is evaluated using the indoor temperature obtained from a first-order RC building thermal model. In the day-ahead stage, the optimization is carried out based on day-ahead forecasts, with the operating cost and the thermal discomfort index minimized simultaneously. In the intraday stage, short-term forecasting information is used to update the day-ahead baseline schedule through rolling optimization. To ensure continuous scheduling, a power constraint based on a state-of-charge (SOC) trajectory corridor is further proposed for the battery energy storage system, thereby guaranteeing consistency between the initial and terminal SOC. The resulting bi-objective optimization problem is solved via a reinforcement learning-assisted multi-objective particle swarm optimization (MOPSO) algorithm. Compared with fixed-parameter MOPSO under the same runtime budget, the proposed algorithm increases hypervolume by 34.64% and reduces inverted generational distance by 87.14% on average. In a tested scenario, intraday rolling optimization reduces operating cost and thermal discomfort by 6.24% and 15.91%, respectively, relative to day-ahead scheduling alone.