The energy sector is on the brink of a transformative shift as researchers tackle the challenges posed by the increasing integration of renewable energy sources, particularly wind and solar power. A recent study led by Jing Shi from the State Grid Jiangsu Electric Power Co., Ltd., has introduced a groundbreaking probabilistic power and energy balance risk scheduling method that could redefine how energy systems manage volatility and uncertainty.
As renewable energy sources become a larger part of the energy mix, they introduce significant fluctuations that can jeopardize the stability of power systems. By the end of 2023, China’s installed capacity of wind and photovoltaics reached an impressive 440 million kW and 610 million kW, respectively, making renewables the second-largest power source in the country. However, this rapid growth has also highlighted the urgent need for more sophisticated methods to assess and manage the risks associated with renewable energy generation.
Shi’s research proposes a novel approach that combines a probabilistic risk index with distributed robust optimization. This method allows for a clearer understanding of the risks associated with power and energy balance, particularly in the context of load shedding and energy curtailment. “Our probabilistic index not only characterizes the risk of power and energy balance but also provides a framework for decision-makers to navigate the complexities of integrating high levels of renewable energy,” said Shi.
The study introduces a risk assessment model based on the conditional value-at-risk (CVaR) theory, which is designed to minimize operational costs while ensuring reliability in energy supply. By leveraging the flexible ramp reserve capacity of thermal power, the model offers a strategic advantage for energy producers and grid operators. This is particularly crucial as energy storage and demand-side response strategies become more prevalent in modern power systems.
The implications of this research are substantial. By providing a robust framework for evaluating risks, energy companies can make more informed decisions about resource allocation and operational strategies. This could lead to enhanced reliability in energy supply, reduced curtailment of renewable resources, and ultimately, a more sustainable energy landscape. “The results of our probabilistic risk assessment can guide the rational planning of flexible resources, ensuring that we are prepared for the uncertainties that come with renewable energy generation,” Shi emphasized.
As the energy sector continues to evolve, the methodologies developed in this study may shape future developments in power system planning and operation. The integration of sophisticated risk management tools will be essential for addressing the complexities of a decarbonized energy future.
This innovative research has been published in the journal ‘Energies’, which focuses on the latest advancements in energy science and technology. For further information about the study and its implications, you can visit State Grid Jiangsu Electric Power Co., Ltd..
