Hierarchical Decision Making In Electricity Grid Management

Gal Dalal, Elad Gilboa, Shie Mannor
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2197-2206, 2016.

Abstract

The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-dalal16, title = {Hierarchical Decision Making In Electricity Grid Management}, author = {Dalal, Gal and Gilboa, Elad and Mannor, Shie}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {2197--2206}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/dalal16.pdf}, url = {https://proceedings.mlr.press/v48/dalal16.html}, abstract = {The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method.} }
Endnote
%0 Conference Paper %T Hierarchical Decision Making In Electricity Grid Management %A Gal Dalal %A Elad Gilboa %A Shie Mannor %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-dalal16 %I PMLR %P 2197--2206 %U https://proceedings.mlr.press/v48/dalal16.html %V 48 %X The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method.
RIS
TY - CPAPER TI - Hierarchical Decision Making In Electricity Grid Management AU - Gal Dalal AU - Elad Gilboa AU - Shie Mannor BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-dalal16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 2197 EP - 2206 L1 - http://proceedings.mlr.press/v48/dalal16.pdf UR - https://proceedings.mlr.press/v48/dalal16.html AB - The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method. ER -
APA
Dalal, G., Gilboa, E. & Mannor, S.. (2016). Hierarchical Decision Making In Electricity Grid Management. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:2197-2206 Available from https://proceedings.mlr.press/v48/dalal16.html.

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