Development of multi-supply-multi-demand control strategy for combined cooling, heating and power system primed with solid oxide fuel cell-gas turbine

Abstract Combined cooling, heating and power (CCHP) system with the prime mover set of solid oxide fuel cell-gas turbine (SOFC-GT) would feature with high electrical efficiency, but contain the highly coupled equipment units for cooling, heating and electricity supplies. Due to such complex nature of multiple supplies and demands, the previously developed control strategies were not suitable, and it is a challenge to develop an appropriate control strategy for the SOFC-GT CCHP system. Therefore, a new approach, called multi-supply-multi-demand (MSMD) control strategy, is proposed in this paper. The MSMD control includes two core algorithms: rolling optimization (RO) and feedback correction (FC). RO is used to determine the operation of energy supply equipment units based on the forecasting weather and loading information of the next 24 h. FC is applied for continual mitigation in case any difference between the actual and predicted energy demands. In the SOFC-GT CCHP system with energy storages for building application, the effectiveness of the MSMD control strategy was tested. It was found that RO could determine the operating schedules of the related equipment units at lower primary energy consumption than the conventional mean, while FC could effectively rectify the prediction errors incurred from the real-time loading conditions.

[1]  Lingfeng Wang,et al.  Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization , 2006, 2006 IEEE Power Engineering Society General Meeting.

[2]  Larry E. Banta,et al.  Real-time management solutions for a smart polygeneration microgrid , 2016 .

[3]  Penyarat Saisirirat.  The Solid Oxide Fuel Cell (SOFC) and Gas Turbine (GT) Hybrid System Numerical Model , 2015 .

[4]  Amenallah Guizani,et al.  Feasibility of solar absorption air conditioning in Tunisia , 2008 .

[5]  X. J. Luo,et al.  Control Optimization of Combined Cooling and Power System with Prime Mover of Solid Oxide Fuel Cell-gas Turbine for Building Application , 2017 .

[6]  Henrik Madsen,et al.  A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system , 2014 .

[7]  K. F. Fong,et al.  Performance analysis of internal-combustion-engine primed trigeneration systems for use in high-rise office buildings in Hong Kong ☆ , 2015 .

[8]  Ahmad Fakheri,et al.  Efficiency analysis of heat exchangers and heat exchanger networks , 2014 .

[9]  Yang Shi,et al.  A Novel Optimal Operational Strategy for the CCHP System Based on Two Operating Modes , 2012, IEEE Transactions on Power Systems.

[10]  Bo Sun,et al.  Initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power , 2016, IEEE/CAA Journal of Automatica Sinica.

[11]  Romain Bourdais,et al.  Hierarchical control method applied to energy management of a residential house , 2013 .

[12]  Ryohei Yokoyama,et al.  Suitable operational strategy for power interchange operation using multiple residential SOFC (solid oxide fuel cell) cogeneration systems , 2010 .

[13]  Ruzhu Wang,et al.  COMBINED COOLING, HEATING AND POWER: A REVIEW , 2006 .

[14]  M. Yari,et al.  Multi-objective optimization of an indirectly integrated solid oxide fuel cell-gas turbine cogeneration system , 2016 .

[15]  Pedro J. Mago,et al.  Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies , 2010 .

[16]  Omar Abdelaziz,et al.  New configurations of a heat recovery absorption heat pump integrated with a natural gas boiler for boiler efficiency improvement , 2014 .

[17]  K. F. Fong,et al.  Development of 2D dynamic model for hydrogen-fed and methane-fed solid oxide fuel cells , 2016 .

[18]  Lazaros G. Papageorgiou,et al.  A Model Predictive Control Framework for Residential Microgrids , 2012 .

[19]  Y. Tian,et al.  Modelling for part-load operation of solid oxide fuel cell–gas turbine hybrid power plant , 2003 .

[20]  John Z. Lin,et al.  Generation of a typical meteorological year for Hong Kong , 2006 .

[21]  Tong Seop Kim,et al.  Comparative evaluation of viable options for combining a gas turbine and a solid oxide fuel cell for high performance , 2016 .

[22]  Gevork B. Gharehpetian,et al.  Application of Hyper-Spherical Search algorithm for optimal energy resources dispatch in residential microgrids , 2015, Appl. Soft Comput..

[23]  Na Zhang,et al.  Analytical solutions and typical characteristics of part-load performances of single shaft gas turbine and its cogeneration , 2002 .

[24]  Edoardo Amaldi,et al.  A detailed MILP optimization model for combined cooling, heat and power system operation planning , 2014 .

[25]  Jihong Wang,et al.  Overview of current development in electrical energy storage technologies and the application potential in power system operation , 2015 .

[26]  Hoay Beng Gooi,et al.  Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error , 2016 .

[27]  Chris Marnay,et al.  Modeling of Thermal Storage Systems in MILP Distributed Energy Resource Models , 2015 .

[28]  Shinji Kimijima,et al.  Performance analysis for the part-load operation of a solid oxide fuel cell–micro gas turbine hybrid system , 2008 .

[29]  F. W. Yu,et al.  Optimization of water-cooled chiller system with load-based speed control , 2008 .

[30]  P. Kriett,et al.  Optimal control of a residential microgrid , 2012 .

[31]  Zhang Chun-fa,et al.  Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China , 2010 .

[32]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[33]  Peng Sun,et al.  Analysis of combined cooling, heating, and power systems under a compromised electric–thermal load strategy , 2014 .

[34]  Zhiqiang Zhai,et al.  Performance comparison of combined cooling heating and power system in different operation modes , 2011 .

[35]  K. F. Fong,et al.  Investigation on zero grid-electricity design strategies of solid oxide fuel cell trigeneration system for high-rise building in hot and humid climate , 2014 .

[36]  Jeong L. Sohn,et al.  Performance characteristics of part-load operations of a solid oxide fuel cell/gas turbine hybrid system using air-bypass valves , 2008 .

[37]  Seyed Hossein Hosseinian,et al.  Stochastic load effect on home energy system scheduling optimization , 2015 .

[38]  Wei Gu,et al.  A two-stage optimization and control for CCHP microgrid energy management , 2017 .

[39]  R. Minciardi,et al.  A dynamic decision model for Smart Grid real time optimal control , 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

[40]  Zahra Hajabdollahi,et al.  Multi-objective based configuration optimization of SOFC-GT cogeneration plant , 2017 .

[41]  M. A. Ehyaei,et al.  Exergy, economic and environmental analysis and multi-objective optimization of a SOFC-GT power plant , 2017 .

[42]  Bo Guo,et al.  Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts , 2015 .

[43]  Pedro J. Mago,et al.  Micro-combined cooling, heating and power systems hybrid electric-thermal load following operation , 2010 .

[44]  Miao Li,et al.  Analysis and Assessments of Combined Cooling, Heating and Power Systems in Various Operation Modes for a Building in China, Dalian , 2013 .

[45]  Shahin Sirouspour,et al.  An Optimal Energy Storage Control Strategy for Grid-connected Microgrids , 2014, IEEE Transactions on Smart Grid.

[46]  Saman K. Halgamuge,et al.  Optimum sizing and tracking of combined cooling heating and power systems for bulk energy consumers , 2014 .

[47]  Xiao-Ping Zhang,et al.  Real-time Energy Control Approach for Smart Home Energy Management System , 2014 .

[48]  Jie Xu,et al.  Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach , 2015, IEEE Transactions on Smart Grid.

[49]  Jan Carmeliet,et al.  Reducing Computation Time with a Rolling Horizon Approach Applied to a MILP Formulation of Multiple Urban Energy Hub System , 2015, ICCS.