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A robust model for the ramp-constrained economic dispatch problem with uncertain renewable energy

机译:具有不确定可再生能源的斜线约束经济调度问题的鲁棒模型

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摘要

The inherent uncertainty of renewable energy sources (RES) makes the solution to the electricity network's associated economical dispatch (ED) problem with network constraints challenging. In particular, the uncertainty in the power output of RES requires conventional generation units to ramp up and down more frequently to maintain the power balance and the reliability of the system. Typically, the RES power output uncertainty is modeled in ED problems by considering its potential future scenarios. However, this leads to an optimization problem that is difficult to solve for real-sized networks. Here, we propose an alternative way of considering the uncertainty of RES and the consequent ramping of conventional generation via a robust reformulation of the problem. In particular, we show that in typical real-world instances of the ED problem, the associated deterministic formulation of the robust problem can be solved efficiently for larger scale constrained electricity networks even when the underlying uncertainty distribution is not normal. Moreover, we show that our approach results on dispatch solutions that require less ramping than scenario based solutions, with little trade-off on the long-term expected costs of the network dispatch. These results also provide insights about how RES penetration affects cost and dispatch policies in the electricity network. To illustrate our results, we present relevant numerical experiments on IEEE test networks. (C) 2016 Elsevier B.V. All rights reserved.
机译:可再生能源(RES)的内在不确定性使解决电网约束相关的电网相关经济调度(ED)问题成为可能。特别是,RES功率输出的不确定性要求常规发电机组更频繁地上下倾斜,以保持功率平衡和系统的可靠性。通常,在ED问题中通过考虑其潜在的未来情景来对RES功率输出不确定性进行建模。但是,这导致优化问题,对于实际规模的网络来说很难解决。在这里,我们提出了一种通过考虑问题的鲁棒性来考虑RES的不确定性以及常规发电量随之增加的替代方法。尤其是,我们表明,在ED问题的典型实际情况下,即使潜在的不确定性分布不正常,对于较大规模的受限电网,也可以有效地解决鲁棒问题的相关确定性公式。而且,我们表明,我们的方法得出的调度结果要比基于方案的解决方案所需的时间要少,并且在网络调度的长期预期成本方面几乎没有取舍。这些结果还提供了有关RES渗透如何影响电网成本和调度策略的见解。为了说明我们的结果,我们在IEEE测试网络上进行了相关的数值实验。 (C)2016 Elsevier B.V.保留所有权利。

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