State-space Approximate Dynamic Programming for Stochastic Unit Commitment

    •  Zhang, W., Nikovski, D., "State-space Approximate Dynamic Programming for Stochastic Unit Commitment", North American Power Symposium (NAPS), DOI: 10.1109/​NAPS.2011.6025113, August 2011, pp. 1-7.
      BibTeX TR2011-046 PDF
      • @inproceedings{Zhang2011aug,
      • author = {Zhang, W. and Nikovski, D.},
      • title = {State-space Approximate Dynamic Programming for Stochastic Unit Commitment},
      • booktitle = {North American Power Symposium (NAPS)},
      • year = 2011,
      • pages = {1--7},
      • month = aug,
      • doi = {10.1109/NAPS.2011.6025113},
      • url = {}
      • }
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It is known that unit commitment problems with uncertainties in power demands and the outputs of some generators can be represented as factored Markov decision process models. In this paper we propose a state space approximate dynamic programming algorithm to solve such models. The algorithm features a method to generate representative system configurations (states) and a functional metric to measure the similarity among system configurations. Experimental results show that the algorithm outperforms two deterministic approaches in resulting in both lower risks and operational costs, and that it can solve larger problems than a stochastic approach based on decision space approximate dynamic programming.


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