Predictive optimal management method for the control of polygeneration systems

A predictive optimal control system for micro-cogeneration in domestic applications has been developed. This system aims at integrating stochastic inhabitant behavior and meteorological conditions as well as modelling imprecisions, while defining operation strategies that maximize the efficiency of the system taking into account the performances, the storage capacities and the electricity market opportunities. Numerical data of an average single family house has been taken as case study. The predictive optimal controller uses mixed-integer and linear programming where energy conversion and energy services models are defined as a set of linear constraints. Integer variables model the start-up and shut-down operations as well as the load dependent efficiency of the cogeneration unit. The proposed control system has been validated using more complex building and technology models to asses model inaccuracies. Typical demand profiles for stochastic factors have been used. The system is evaluated in the perspective of its usage in Virtual Power Plants applications.

[1]  Constantinos A. Balaras,et al.  Development of a neural network heating controller for solar buildings , 2000, Neural Networks.

[2]  Michele Zehnder.  Efficient air-water heat pumps for high temperature lift residential heating, including oil migration aspects , 2004 .

[3]  H. Rietschel,et al.  Traité de chauffage et de climatisation , 1974 .

[4]  Geoffrey Basil Leyland,et al.  Multi-objective optimisation applied to industrial energy problems , 2002 .

[5]  Steven B. Kraines,et al.  Optimization of an SOFC-based decentralized polygeneration system for providing energy services in an office-building in Tōkyō , 2006 .

[6]  Ian Paul Knight,et al.  Residential cogeneration systems: A review of the current technologies. A report of subtask A of FC+COGEN-SIM: The simulation of building-integrated fuel cell and other cogeneration systems , 2005 .

[7]  Viktor Dorer,et al.  Performance assessment of fuel cell micro-cogeneration systems for residential buildings , 2005 .

[8]  D. W. Auckland,et al.  Electricity generation in the home: modelling of single-house domestic combined heat and power , 1996 .

[9]  Evgueniy Entchev,et al.  Residential fuel cell energy systems performance optimization using “soft computing” techniques , 2003 .

[10]  Roger G. Marchand,et al.  Micro-generation technology assessment for housing technology , 2004 .

[11]  Gregor P. Henze,et al.  Impact of Forecasting Accuracy on Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory , 2003 .

[12]  Markus Gwerder,et al.  PREDICTIVE CONTROL FOR INTEGRATED ROOM AUTOMATION , 2005 .

[13]  N. Ferguson,et al.  Predictive thermal control of building systems , 1990 .

[14]  Brian W. Kernighan,et al.  AMPL: A Modeling Language for Mathematical Programming , 1993 .

[15]  Nicolas Morel,et al.  NEUROBAT, A PREDICTIVE AND ADAPTIVE HEATING CONTROL SYSTEM USING ARTIFICIAL NEURAL NETWORKS , 2001 .