Xiaorong Xie

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This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters,(More)
Energy management in microgrids is typically formulated as a non-linear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC) but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are(More)
A neural network-based approximate dynamic programming (ADP) method, the direct neural dynamic programming (direct NDP), is introduced in this paper. The paper covers the basic principle of this learning scheme and an illustrative example of how direct NDP can be implemented. The paper focuses on how direct NDP can be applied to power system stability(More)
Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply–demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by(More)
© 2012 Xie, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Genetic Algorithm and Simulated Annealing: A Combined(More)
Energy management in microgrids is typically formulated as a non-linear optimization problem. Solving it in a centralized manner not only requires high computational capabilities at the microgrid central controller (MGCC) but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all the generations and loads are(More)
The great scales nonlinearities and uncertainties in modern power systems mean that they are among the most intractable problems in dynamic control. In the present paper, direct neural dynamic programming (direct NDP) is introduced for a real time supplementary control application. Direct NDP is an on-line learning control paradigm that learns to improve(More)
In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of(More)