Optimal Operation of Power Systems With Energy Storage Under Uncertainty: A Scenario-Based Method With Strategic Sampling
Optimal Operation of Power Systems With Energy Storage Under Uncertainty: A Scenario-Based Method With Strategic Sampling
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DOI：
10.1109/tsg.2021.3127922
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发表时间：
2021-07
影响因子：
9.6
通讯作者：
Ren Hu;Qifeng Li
中科院分区：
文献类型：
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作者：
Ren Hu;Qifeng Li
储能、间歇性可再生能源发电和不可控负荷的多周期动态特性使得电力系统运行优化具有挑战性。采用机会约束优化（CCO）建模范式，其中约束包括非线性储能和交流潮流模型，制定了一个多周期的不确定性下的最优粒子群算法。基于新兴的情景优化方法，不依赖于预先已知的概率分布，本文提出了一种新的解决方法，这一具有挑战性的CCO问题。所提出的方法是计算有效的，主要有两个原因。首先，原来的交流潮流约束近似的学习辅助二次凸不等式的基础上广义最小绝对收缩和选择算子（LASSSO）。其次，考虑到数据的物理模式和基于学习的抽样的驱动下，策略抽样方法，显着减少所需的场景的数量，通过不同的抽样策略。IEEE标准系统上的仿真结果表明：1）所提出的策略采样方法显著提高了机会约束最优粒子群算法的计算效率; 2）数据驱动的潮流凸逼近方法是求解非线性和非凸交流潮流的有效方法。
The multi-period dynamics of energy storage (ES), intermittent renewable generation and uncontrollable power loads, make the optimization of power system operation (PSO) challenging. A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models. Based on the emerging scenario optimization method which does not rely on pre-known probability distribution, this paper develops a novel solution method for this challenging CCO problem. The proposed method is computationally effective for mainly two reasons. First, the original AC power flow constraints are approximated by a set of learning-assisted quadratic convex inequalities based on a generalized least absolute shrinkage and selection operator (LASSSO). Second, considering the physical patterns of data and driven by the learning-based sampling, the strategic sampling method is developed to significantly reduce the required number of scenarios by different sampling strategies. The simulation results on IEEE standard systems indicate that 1) the proposed strategic sampling significantly improves the computational efficiency of the scenario-based approach for solving the chance-constrained optimal PSO problem, 2) the data-driven convex approximation of power flow can be promising alternatives of nonlinear and nonconvex AC power flow.