Spatiotemporal decomposed dispatch of integrated electricity-gas system via stochastic dual dynamic programming-based value function approximation
Spatiotemporal decomposed dispatch of integrated electricity-gas system via stochastic dual dynamic programming-based value function approximation
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DOI：
10.1016/j.energy.2023.128247
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发表时间：
2023-07
期刊：
影响因子：
9
通讯作者：
Jianquan Zhu;Haixin Liu;Ye Guo;Jiajun Chen;Yelin Zhuo;Zeshuang Wang
中科院分区：
文献类型：
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作者：
Jianquan Zhu;Haixin Liu;Ye Guo;Jiajun Chen;Yelin Zhuo;Zeshuang Wang
针对具有不确定性的电-气综合系统的时空分解调度问题，提出了一种基于随机对偶动态规划的值函数逼近方法。采用随机对偶动态规划将最优调度问题在空间和时间维度上分解为若干子问题。然后，在实时调度过程中使用弯刀来描述这些子问题之间的相互作用，从而使各个子系统能够进行分散和实时的决策。这样既能保证各子系统的信息隐私性和决策独立性，又能确定风电和负荷的未来可变性。此外，历史信息可以用于离线获取弯管机切割，这有助于在实时决策阶段省略耗时的迭代，同时仍然可以获得近最优解。通过4总线-4节点系统和118总线-20节点系统的实例研究验证了该方法的有效性。数值结果表明，与传统的实时方法相比，该方法将运行成本的平均误差降低了1个数量级。与传统的去中心化算法相比，计算时间显著减少了4个数量级。
This paper proposes a novel stochastic dual dynamic programming-based value function approximation approach for the spatiotemporal decomposed dispatch of integrated electricity-gas systems with uncertainties. Stochastic dual dynamic programming is employed to decompose the optimal dispatch problem into several subproblems in both spatial and temporal dimensions. Then, Benders cuts are used in the real-time dispatch process to describe the interaction among these subproblems, according to which each subsystem can make decentralized and real-time decisions. In this way, both the information privacy and decision independence of each subsystem can be guaranteed, while the future variability of wind power and loads can be firmed. Moreover, the historical information can be used to obtain the Benders cuts offline, which helps to omit the time-consuming iterations in the real-time decision stage, while the near-optimal solutions can still be obtained. The effectiveness of the proposed approach is verified by case studies on a 4-bus-4-node system and a 118-bus-20-node system. Numerical results demonstrate that the proposed method reduces the average error of the operation cost by 1 order of magnitude compared with the traditional real-time methods. Moreover, the computational time is significantly reduced by up to 4 orders of magnitude compared with the traditional decentralized algorithms.