Data-Driven Chance-Constrained Planning for Distributed Generation: A Partial Sampling Approach
Data-Driven Chance-Constrained Planning for Distributed Generation: A Partial Sampling Approach
复制标题
DOI：
10.1109/tpwrs.2022.3230676
复制
发表时间：
2023-11
影响因子：
6.6
通讯作者：
Shiyi Jiang;Jianqiang Cheng;Kai Pan;F. Qiu;Boshi Yang
中科院分区：
文献类型：
--
作者：
Shiyi Jiang;Jianqiang Cheng;Kai Pan;F. Qiu;Boshi Yang
分布式能源的规划一直受到配电系统的重大不确定性和复杂性的挑战。为了保证系统的可靠性，人们经常采用机会约束规划来寻求一个高可能性的可行解，同时使一定的成本最小化。传统的样本平均近似（SAA）通常用于表示不确定性，并将机会约束规划转化为确定性优化问题。然而，SAA引入了额外的二进制变量来指示场景样本是否满足，从而给已经具有挑战性的分布式能源资源规划问题带来了很大的计算复杂性。在本文中，我们介绍了一种新的范式，即，部分样本平均近似（PSAA）使用真实的数据，以提高计算的易处理性。创新之处在于，我们只对随机参数的一部分进行采样，并在重构中只引入与样本相对应的连续变量，这是一个混合整数凸二次规划。我们在IEEE 33节点和123节点系统上的大量实验表明，PSAA方法的性能优于SAA，因为前者在样本内测试中在更短的时间内提供了更好的解决方案，并在样本外测试中提供了更好的系统可靠性保证概率。实验中使用的所有数据都是从Pecan Street Inc.获得的真实的数据。和ERCOT。更重要的是，我们提出的机会约束模型和PSAA方法是足够的一般性，可以应用于解决电力系统规划和运行中的其他有价值的问题。
The planning of distributed energy resources has been challenged by the significant uncertainties and complexities of distribution systems. To ensure system reliability, one often employs chance-constrained programs to seek a highly likely feasible solution while minimizing certain costs. The traditional sample average approximation (SAA) is commonly used to represent uncertainties and reformulate a chance-constrained program into a deterministic optimization problem. However, the SAA introduces additional binary variables to indicate whether a scenario sample is satisfied and thus brings great computational complexity to the already challenging distributed energy resource planning problems. In this paper, we introduce a new paradigm, i.e., the partial sample average approximation (PSAA) using real data, to improve computational tractability. The innovation is that we sample only a part of the random parameters and introduce only continuous variables corresponding to the samples in the reformulation, which is a mixed-integer convex quadratic program. Our extensive experiments on the IEEE 33-Bus and 123-Bus systems show that the PSAA approach performs better than the SAA because the former provides better solutions in a shorter time in in-sample tests and provides better guaranteed probability for system reliability in out-of-sample tests. All the data used in the experiments are real data acquired from Pecan Street Inc. and ERCOT. More importantly, our proposed chance-constrained model and PSAA approach are general enough and can be applied to solve other valuable problems in power system planning and operations.