A Statistical Analysis on Operation Scheduling for an Energy Network Project

Distributed power generation, using renewable energy, has been attracting attention to cope with global environment issues; a microgrid is a promising configuration for distributed power generation. To augment the stability and efficiency of the microgrid, an intelligent control, which considers the restrictions and characteristics of each unit, is indispensable. It can be achieved by constructing an efficient operation schedule for each power plant in the microgrid, depending on energy demand, and predicting passive power generation. The operation scheduling is regarded as a constrained optimization problem, which must have nonlinear characteristics in case of actual systems. Although several methods using metaheuristic optimization have been proposed, it would be trapped into a local minimum in some cases. In this paper, we statistically analyze operation schedules, computed for an actual power network of the demonstrative project. In addition, we conduct an investigation of the relationship between the input parameter space and the solution space, which can be exploited to obtain more appropriate initial solutions leading to better and faster converging solutions.

[1]  M. Y. El-Sharkh,et al.  Short term scheduling of multiple grid-parallel PEM fuel cells for microgrid applications , 2010 .

[2]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[3]  Xu Rong,et al.  A review on distributed energy resources and MicroGrid , 2008 .

[4]  Yousuke Nozaki,et al.  Forecasting electric power generation in a photovoltaic power system for an energy network , 2009 .

[5]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[6]  R.H. Lasseter,et al.  Microgrid: a conceptual solution , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[7]  S. Abu-Sharkha,et al.  Can microgrids make a major contribution to UK energy supply ? , 2005 .

[8]  Balaji Rengarajan,et al.  Operation method study based on the energy balance of an independent microgrid using solar-powered w , 2011 .

[9]  Adam Hawkes,et al.  Modelling high level system design and unit commitment for a microgrid , 2009 .

[10]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[11]  Hayashi Toshihiro,et al.  Optimal Scheduling in the Demonstrative Research for Energy Network , 2008 .

[12]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[13]  Ming-Che Hu,et al.  Optimal energy management of microgrid systems in Taiwan , 2011, ISGT 2011.

[14]  Fred Glover,et al.  Tabu Search: A Tutorial , 1990 .

[15]  Paulien M. Herder,et al.  Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems , 2008 .

[16]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .