Model Predictive Control for Deferrable Loads Scheduling
Read PDF →Chen, 2014
Category: EE
Overall Rating
Score Breakdown
- Latent Novelty Potential: 2/10
- Cross Disciplinary Applicability: 1/10
- Technical Timeliness: 2/10
- Obscurity Advantage: 2/5
Synthesized Summary
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While the paper tackles a relevant problem and provides theoretical analysis for its specific MPC algorithm under uncertainty, its practical applicability is hindered by reliance on restrictive assumptions and a computationally intensive approach.
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Despite some interesting analytical techniques, modern advancements in forecasting, robust/stochastic control, and data-driven methods offer more general, practical, and theoretically robust solutions for managing grid resources under uncertainty.
Optimist's View
Skeptic's View
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The paper's core model for base load uncertainty – a causal filter operating on i.i.d. random variables – feels simplistic compared to modern time series forecasting techniques.
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This paper likely faded because its theoretical analysis, while mathematically rigorous, hinges on a very strong assumption: the existence of a t-valley-filling solution.
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The proposed "shrinking horizon" MPC, optimizing over the entire remaining time horizon at each step, is computationally daunting for long horizons typical in demand response (days/weeks)...
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Contemporary control techniques, leveraging improved forecasting and more advanced optimization/learning methods, have surpassed this approach in generality, robustness, and practical applicability.
Final Takeaway / Relevance
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