School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
Department of Economic Management, North China Electric Power University, Baoding, 071003, Hebei Province, China.
Environ Sci Pollut Res Int. 2023 Jul;30(33):79916-79936. doi: 10.1007/s11356-023-27955-6. Epub 2023 Jun 8.
Multi-energy virtual power plant (MEVPP) has attracted more and more attention due to its advantages in renewable energy consumption and carbon emission reduction. However, the characteristics of multi-energy coupling and the access of renewable energy may lead to some challenges in the operation of MEVPP. In this paper, a data-driven distributionally robust chance constraints optimization model (DD-DRCCO) is proposed for the dispatching of MEVPP. Firstly, the uncertainties of wind power and photovoltaic output forecasting errors are modeled as an ambiguity set based on the Wasserstein metric. Secondly, combined with the chance constraint, the expected probability of the inequality constraint with uncertain variables is limited to the lowest allowable confidence level to improve the reliability of the model. Thirdly, the forecast errors of wind power and photovoltaic are considered in the constraint conditions, so that the system can effectively resist the interference of uncertain output. Besides, based on the strong duality theory, the DD-DRCCO model is equivalent to a MILP problem which is easy to solve. Finally, simulations implemented on a typical MEVPP are delivered to show that our proposed model: 1) The model is data-driven, and the conservativeness is kept at a low level, and the solution time is about 7s~8s; 2) The MEVPP system can achieve a balance between economy and low-carbon, making the total operation cost reduced by 0.89% compared with no increase of electric boiler; 3) The CO emission during the operation of the MEVPP system was significantly reduced by about 87.33 kg.
多能虚拟电厂（MEVPP）由于其在新能源消纳和碳减排方面的优势，越来越受到关注。然而，多能耦合的特点和新能源的接入可能会给 MEVPP 的运行带来一些挑战。本文提出了一种基于数据驱动的分布鲁棒机会约束优化模型（DD-DRCCO），用于 MEVPP 的调度。首先，基于 Wasserstein 度量将风电和光伏出力预测误差的不确定性建模为一个模糊集。其次，结合机会约束，将具有不确定变量的不等式约束的期望概率限制在最低允许置信水平以下，以提高模型的可靠性。第三，在约束条件中考虑了风电和光伏的预测误差，使系统能够有效抵抗不确定输出的干扰。此外，基于强对偶理论，DD-DRCCO 模型等效于易于求解的 MILP 问题。最后，通过对典型 MEVPP 的仿真验证了所提模型的有效性：1）该模型是数据驱动的，并且保持低保守性，求解时间约为 7s~8s；2）MEVPP 系统可以在经济和低碳之间取得平衡，与不增加电锅炉相比，总运行成本降低了 0.89%；3）MEVPP 系统运行过程中的 CO2 排放量显著减少了约 87.33kg。
