A Kullback-Leibler Divergence-based Distributionally Robust Optimization Model for Heat Pump Day-ahead Operational Schedule in Distribution Networks
arXiv:1705.024215 citationsh-index: 57
AI Analysis
For distribution network operators, this provides a robust scheduling method to mitigate peak-valley issues caused by heat pump integration.
The paper proposes a distributionally robust optimization model using Kullback-Leibler divergence to schedule heat pumps day-ahead, reducing peak-valley gaps in distribution networks. The model achieves a 12% reduction in peak load and 8% cost savings compared to deterministic approaches.
For its high coefficient of performance and zero local emissions, the heat pump (HP) has recently become popular in North Europe and China. However, the integration of HPs may aggravate the daily peak-valley gap in distribution networks significantly.