Matching daily wind speed patterns with grid demand: An offshore wind assessment framework integrating source-load similarity and resource abundance

The rapid expansion of offshore wind energy is pivotal for alleviating energy pressures and advancing decarbonization. However, large-scale integration of renewable energy introduces volatility and uncertainty, posing significant challenges to grid stability and the effective management of offshore wind power to balance electricity demand. In this context, this study proposes a novel daily-scale offshore wind resource assessment and scheduling framework, integrating source load similarity (SLS) and wind resource abundance (WRA) to optimize wind power integration with grid load demand. Using the offshore area of Jiangsu Province, China, as a case study, the framework classifies different daily wind speed patterns (DWSPs) into 16 distinct scenarios, revealing their generation potential and capacity to meet regional electricity demand. By employing the shape dynamic time warping (shapeDTW) algorithm, the framework effectively captures temporal similarities between wind speed sequences and load profiles, enhancing source-load matching precision. The case results show that high-quality wind resource days are most frequent in winter and spring, while the annual generation of the optimal DWSPs closely approximates the total renewable energy share of the province for the year. In ideal scenarios, the daily power output from high-SLS wind patterns could meet up to 4.5 times Jiangsu's daily renewable energy demand. This framework provides a comprehensive tool for offshore wind farm siting and dispatch optimization from a resource-side perspective, enhancing the flexibility and stability of power systems with high renewable energy penetration and contributing to more effective renewable energy integration in low-carbon power systems.