An Online Convex Optimization Approach to Real-Time Energy Pricing for Demand Response
An Online Convex Optimization Approach to Real-Time Energy Pricing for Demand Response
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DOI：
10.1109/tsg.2016.2539948
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发表时间：
2017-11
影响因子：
9.6
通讯作者：
Seung-Jun Kim;G. Giannakis
中科院分区：
文献类型：
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作者：
Seung-Jun Kim;G. Giannakis
研究实时定价策略，以供未来电网中的需求响应计划使用。主要挑战在于消费者在不同时刻对价格调整的响应程度各不相同，需求响应措施必须了解并考虑到这一点。为此，本文开发了一种在线学习方法，即使在消费者具有对抗性且采取策略性行动的情况下，该方法在对负载水平动态和消费者弹性的假设极少的情况下，也能提供强大的性能保证。所开发的算法能够依次确定电价，以引发理想的用电行为并使负载曲线平坦化，同时根据可用的反馈信息隐式地学习单个消费者的价格弹性。考虑了两种反馈结构：1）完全信息设置，其中总负载水平以及单个价格弹性参数可直接获取；2）部分信息（老虎机）情况，其中仅显示总负载水平。还通过适当的正则化项纳入了公平性和稀疏性约束。数值测试验证了所提方法的有效性。
Real-time price setting strategies are investigated for use by demand response programs in future power grids. The major challenge is that consumers have varying degrees of responsiveness to price adjustments at different time instants, which must be learned and accounted for by demand response initiatives. To this end, an online learning approach is developed here offering strong performance guarantees with minimal assumptions on the dynamics of load levels and consumer elasticity, even when consumers are adversarial and take actions strategically. The developed algorithms can determine electricity prices sequentially so as to elicit desirable usage behavior and flatten load curves, while implicitly learning individual consumers’ price elasticity based on available feedback information. Two feedback structures are considered: 1) a full information setup, where aggregate load levels as well as individual price elasticity parameters are directly available, and 2) a partial information (bandit) case, where only the aggregate load levels are revealed. Fairness and sparsity constraints are also incorporated via appropriate regularizers. Numerical tests verify the effectiveness of the proposed approach.