Stochastic dynamic programming approach to managing power system uncertainty with distributed storage
Stochastic dynamic programming approach to managing power system uncertainty with distributed storage
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DOI：
10.1007/s10287-017-0297-2
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发表时间：
2017-12
影响因子：
0.9
通讯作者：
Luckny Zéphyr;C. L. Anderson
中科院分区：
文献类型：
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作者：
Luckny Zéphyr;C. L. Anderson
由于风速的不确定性，电网中的风力整合具有挑战性。预测错误可能会带来代价高昂的后果。事实上，电力可能会以最高的价格购买以满足负荷，而在剩余的情况下，电力可能会被浪费。能源储存可以为风力发电的不确定性提供一些解决办法。由于电力调度问题具有时序性，理论上可以用随机动态规划来解决。然而，由于所谓的维度诅咒，这种方案仅限于小型网络。本文通过近似动态规划，更准确地说是随机对偶动态规划，分析了由常规发电机组和风力发电机组组成的电网的管理问题。考虑了一种对传统发电机具有斜坡约束的一般电网模型。在几个不同规模的网络上对近似方法进行了测试。数值实验还包括在小型网络上与经典动态规划的比较。结果表明，结合近似技术可以在合理的时间内解决问题。
Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.