Lee Kiyeob, Geng Xinbo, Sivaranjani S, Xia Bainan, Ming Hao, Shakkottai Srinivas, Xie Le
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Breakthrough Energy, Kirkland, WA 98033, USA.
iScience. 2022 Jan 5;25(2):103723. doi: 10.1016/j.isci.2021.103723. eCollection 2022 Feb 18.
Demand response (DR) is rapidly gaining attention as a solution to enhance the grid reliability with deep renewable energy penetration. Although studies have demonstrated the benefits of DR in mitigating price volatility, there is limited work considering the choice of locations for DR for maximal impact. We reveal that very small load reductions at a handful of targeted locations can lead to a significant decrease in price volatility and grid congestion levels based on a synthetic Texas grid model. We achieve this through exploiting the highly nonlinear nature of congestion dynamics and by strategically selecting DR locations. We demonstrate that we can similarly place energy storage to achieve an equivalent impact. Our findings suggest that targeted DR at specific locations, rather than across-the-board DR, can have substantial benefits to the grid. These findings can inform energy policy makers and grid operators how to target DR initiatives for improving grid reliability.
需求响应（DR）作为一种在可再生能源深度渗透情况下提高电网可靠性的解决方案，正迅速受到关注。尽管研究已经证明了需求响应在缓解价格波动方面的益处，但考虑为实现最大影响而选择需求响应地点的工作却很有限。基于一个合成的德克萨斯电网模型，我们发现，在少数几个目标地点进行非常小幅度的负荷削减，就可以导致价格波动和电网拥堵水平显著降低。我们通过利用拥堵动态的高度非线性特性并战略性地选择需求响应地点来实现这一点。我们证明，同样可以通过放置储能来实现等效的影响。我们的研究结果表明，在特定地点进行有针对性的需求响应，而不是全面的需求响应，对电网可能有实质性的好处。这些研究结果可以为能源政策制定者和电网运营商提供参考，指导他们如何针对需求响应举措来提高电网可靠性。
