An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants

In this paper, a stochastic model is proposed for planning the location and operation of Molten Carbonate Fuel Cell Power Plants (MCFCPPs) in distribution networks when used for Combined Heat, Power, and Hydrogen (CHPH) simultaneously. Uncertainties of electrical and thermal loads forecasting; the pressures of hydrogen, oxygen, and carbon dioxide imported to MCFCPPs; and the nominal temperature of MCFCPPs are considered using a scenario-based method. In the method, scenarios are generated using Roulette Wheel Mechanism (RWM) based on Probability Distribution Functions (PDF) of input random variables. Using this method, probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective functions, placement, and operation of MCFCPPs as CHPH change this problem to a mixed integer nonlinear one. So, multi-objective Modified Firefly Algorithm (MFA) and Pareto optimal method are employed for solving the multi-objective problem and for compromising between the objective functions. During the simulation process, a set of non-dominated solutions are stored in a repository. The 69-bus distribution system is used for evaluating the proper function of the proposed method.

[1]  Ehab F. El-Saadany,et al.  Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation , 2013 .

[2]  Dheeraj K. Khatod,et al.  Evolutionary programming based optimal placement of renewable distributed generators , 2013, IEEE Transactions on Power Systems.

[3]  Hossein Nezamabadi-pour,et al.  An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems , 2013, IEEE Transactions on Smart Grid.

[4]  Umberto Desideri,et al.  MCFC-based CO2 capture system for small scale CHP plants , 2012 .

[5]  C.R. Philbrick,et al.  Modeling Approaches for Computational Cost Reduction in Stochastic Unit Commitment Formulations , 2010, IEEE Transactions on Power Systems.

[6]  Taher Niknam,et al.  Combined heat, power and hydrogen production optimal planning of fuel cell power plants in distribution networks , 2013 .

[7]  Taher Niknam,et al.  Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm , 2012 .

[8]  A. Dicks.  Molten carbonate fuel cells , 2004 .

[9]  M. Shahidehpour,et al.  Stochastic Security-Constrained Unit Commitment , 2007, IEEE Transactions on Power Systems.

[10]  Chonghun Han,et al.  A heuristic method of variable selection based on principal component analysis and factor analysis for monitoring in a 300 kW MCFC power plant , 2012 .

[11]  Mosayeb Bornapour,et al.  Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network , 2012 .

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

[13]  Shahram Jadid,et al.  A fuzzy environmental-technical-economic model for distributed generation planning , 2011 .

[14]  George G. Dimopoulos,et al.  Exergy analysis and optimisation of a steam methane pre-reforming system , 2013 .

[15]  Antonio José Gil Mena,et al.  Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm , 2013 .

[16]  Mohammad Hassan Moradi,et al.  An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming , 2013 .

[17]  K. Afshar,et al.  Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing , 2013 .

[18]  Ibrahim Dincer,et al.  Energy and exergy analyses of a hybrid molten carbonate fuel cell system , 2008 .

[19]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[20]  Aristide F. Massardo,et al.  Hybrid systems for distributed power generation based on pressurisation and heat recovering of an existing 100 kW molten carbonate fuel cell , 2003 .

[21]  Taher Niknam,et al.  A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch , 2012 .

[22]  M. M. Aman,et al.  A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm , 2014 .

[23]  Javad Olamaei,et al.  Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm , 2013 .

[24]  Hee Chun Lim,et al.  Effect of various stack parameters on temperature rise in molten carbonate fuel cell stack operation , 2000 .