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《Proceedings of the CSEE》 2015-19
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A Quantum-inspired Approximate Dynamic Programming Algorithm for Large-scale Unit Commitment Problems

QIN Hua;WEI Hua;Guangxi Key Laboratory of Power System Optimization and Energy Technology (Guangxi University);  
The quantum inspired approximate dynamic programming(QI-ADP) algorithm was applied to solve the large-scale unit commitment(UC) problems. The large numbers of 0-1 states in the large-scale UC problem were expressed with the quantum superposition state effectively by means of its ability in storing massive information. Using the quantum rotation gate as the search strategy for the quantum superposition state, the QI-ADP algorithm implemented the global search in the space of the massive states of the UC problem. Meanwhile, the principle of the quantum collapsing was applied to solve the Bellman equation with a higher efficiency. To avoid the over-iteration, the average convergence probability was used to improve the iteration interrupt condition of the QI-ADP algorithm. The calculated results of the test systems from 10 units to 1000 units show that the QI-ADP algorithm can efficiently search in the space of large-scale states to generate the necessary pre-decision states for solving the Bellman equation. Furthermore, the QI-ADP algorithm can obtain the high-quality solutions within a polynomial time. The average deviations of the optimal values between the QI-ADP and the outer-inner approximation approach are less than 1%. On the other hand, the QI-ADP algorithm can solve large-scale UC problems successfully, and the size of the UC problems is over 10 times larger than that by using the classic dynamic programming. Moreover, the QI-ADP algorithm has overcome the problem of "curse of dimensionality". Therefore, it is feasible using the principle of quantum computing to overcome some practical disadvantages in ADP, such as the difficulty of searching in the state space. Consequently, the QI-ADP algorithm has very broad application prospects.
【Fund】: 国家重点基础研究发展计划项目(973计划)(2013CB 228205);; 国家自然科学基金项目(51167001)~~
【CateGory Index】: TM715
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【Co-citations】
Chinese Journal Full-text Database 10 Hits
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