Iterative Bayesian Demand Response Estimation and Sample-Based Optimization for Real-Time Pricing

Dynamic pricing will introduce significant load fluctuation in the wholesale markets than we see nowadays as customers respond to price signals in real-time. In this paper, we propose an iterative estimation-optimization approach for real-time wholesale market pricing in response to stochastic load fluctuations. For additive and multiplicative demand response models, we apply a Bayesian approach to continuously update the models. The estimated demand response models are then fed into an expectation-base or a risk-averse optimal flow problem to find bus phase angles, real power injections, and bus prices simultaneously. We also present preliminary numerical results using simulated data.

By: Pu Huang

Published in: RC25133 in 2011


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