Modelling of wind and solar power output uncertainty in power systems based on historical data: A characterisation through deterministic parameters

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The inherent uncertainty associated with wind and solar energy poses challenges in ensuring the reliability of the power system with a high penetration of renewable energy. Therefore, incorporating uncertainty is vital during the planning of power systems. Traditional analytical methods often necessitate transforming the uncertainty of large-scale power systems into deterministic optimisation planning, while those approaches tend to be overly conservative, yielding unsolvable solutions, or rely on assumptions and data fitting, creating a significant gap with real-world scenarios. To address this, this study employs upper α-quantiles originating from abundant historical data to replace power output expectations to consider uncertainty during the planning. Additionally, random sampling based on historical data generates potential wind and solar output scenarios, and Monte Carlo simulations are used to validate the proposed method's reliability and assess system investment distribution. Using China's power system as a case study, 64 quantile choices are evaluated. Compared to planning through output expectations, using quantiles can reduce the load loss rate from 10−1 to 10−5, and both the cost expectations and the extreme cost risk decrease. The use of fossil fuels has also decreased by 10%. Results underscore the necessity of choosing upper α-quantiles with higher α, such as 85, to account for the long-tailed wind power distribution. However, even overly conservative wind power output α is still challenging to mitigate residual load loss. The combination of photovoltaics and intraday energy storage ensures that the α selection of photovoltaics should not be too low, due to the reason that there is a need to balance the gradual decrease of load loss rates and the steep ascent of expected investment costs in the power system.