Data-driven worst conditional value at risk energy management model of energy station

He, Shuaijia; Gao, Hongjun; Chen, Zhe; Liu, Junyong

doi:10.1016/j.energy.2022.126421

Cited by 7 publications

(1 citation statement)

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“…Reference [24] demonstrates that the flexibility of random optimization methods allows them to adapt to various problem settings and objectives, providing optimal operational strategies to address various factors such as seasonal variations, demand fluctuations, and water resource changes. Furthermore, reference [25] effectively reduces computational complexity, especially in large-scale hydroelectric power station systems, by employing techniques such as Monte Carlo simulations. Therefore, random optimization methods provide powerful tools for addressing the challenges in the complex and dynamic environment of cascading hydroelectric power station optimization.…”

Section: Introductionmentioning

confidence: 99%

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

Jia,

Zhang,

Zhou

et al. 2024

Energies

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There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow.

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Section: Introductionmentioning

confidence: 99%