Conventional and Hybrid Network Energy Systems Optimization for Canadian Community
Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO2 optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO2 emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO2 emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.
[1] Rad M. and Leon-Garcia A., Optimal residential load control with price prediction in real-time electricity pricing environments, Trans. Smart Grid, 2010 (1), pp. 120-133.
[2] Rong A. and Lahdelma R., An efficient linear programming model and optimization algorithm for trigeneration, Applied Energy, 2005 (82), pp. 40-63.
[3] Ashouri A, Fux S., Benz M. and Guzzella L., Optimal design and operation of building services using mixed-integer linear programming techniques, Energy, 2013 (59), pp. 365-376.
[4] Maréchal F. and Kalitventzeff B., Process integration: selection of the optimal utility system, Computers & Chemical Engineering, 1998 (22), pp. 149-156.
[5] Bozchalui M., Hashmi S., Hassen H., Canizares C. and Bhattacharya K., Optimal operation of residential energy hubs in smart grids, IEEE Trans. Smart Grid, 2012 (3), pp. 1755-1766.
[6] Chen Z., Wu L. and Fu Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization, IEEE Trans. Smart Grid, 2012 (3), pp. 1822-1831.
[7] Giorgio A. and Pimpinella L., An event driven smart home controller enabling consumer economic saving and automated demand side management, Applied Energy, 2012 (96), pp. 92-103.
[8] Ha L., Joumaa H., Ploix S. and Jacomino M., An optimal approach for electrical management problem in dwellings, 2012, Energy Building (45) pp. 1-14.
[9] Fazlollahi S. and Maréchal F., Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP), Applied Thermal Engineering, 2013 (50), pp. 1504-1513.
[10] Lu Y., Wang S., Sun Y. and Yan C. Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming, Applied Energy, 2015 (147) pp. 49–58.
[11] Babu C. and Ashok S., Peak load management in electrolytic process industries, IEEE Trans. Power System, 2008 (23), pp. 399-405.
[12] Geidl M. and Andersson G., Optimal power flow of multiple energy carriers, IEEE Trans. Power System, 2007 (22) pp. 145-155.
[13] Shirazi E. and Jadid S., Optimal residential appliance scheduling under dynamic pricing scheme via hemdas, Energy Building, 2015 (93), pp. 40-49.
[14] Weber C., Maréchal F. and Favrat D., Design and optimization of district energy systems, Computer aided chemical engineering, 2007 (24) pp. 1127-1132.
[15] Hongbo R. and Weijun G., A MILP model for integrated plan and evaluation of distributed energy systems, Applied Energy, 2010 (87), pp. 1001–1014.
[16] Eugenia M., Haralambos S., Nikolaos M. and Lazaros P., A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level, Energy, 2012 (44), pp. 96-104.
[17] Mehleri D., Sarimveis H., Markatos C. and Papageorgiou G., Optimal design and operation of distributed energy systems: application to Greek residential sector, Renewable Energy 2013 (51), pp. 331-342.
[18] Omun A., Choudhary R. and Boies A., Distributed energy resource system optimization using mixed integer linear programming, Energy Policy, 2013 (61) pp. 249–266.
[19] Yang Y., Gao W., Ruan Y., Xuan J., Zhou N., and Marnay C, Optimal Model of Distributed Energy System by Using GAMS and Case Study, conference proceedings of the International Symposium on Sustainable Development of the Asian City Environment (SDACE) 2005.
[20] Scala L., Vaccaro A., and Zobaa A., A goal programming methodology for multiobjective optimization of distributed energy hubs operation, Applied Thermal Engineering, 2014 (71) pp. 658-666.
[21] Parisio A., Vecchio C. and Vaccaro A., A robust optimization approach to energy hub management, Electrical Power and Energy Systems, 2012 (42) pp. 98–104.
[22] Brahman F., Honarmand M. and Jadid S., Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system, Energy and Buildings, 2015 (90) pp. 65–75.
[23] Ren H., Gao W. and Ruan Y., Economic optimization and sensitivity analysis of photovoltaic system in residential buildings, Renewable Energy, 2009 (34) pp. 883-889.
[24] Weber C. and Shah N., Optimization based design of a district energy system for an eco-town in the United Kingdom, 2011, Energy (36) pp. 1292-1308.
[25] Aringhieri R. and Malucelli F., Optimal operations management and network planning of a district system with a combined heat and power plant, Annals of Operations Research, 2003 (120) pp. 173-199.
[26] Hepbasli A., Thermodynamic analysis of a ground-source heat pump system for district heating, International Journal of Energy Research, 2005 (29) pp. 671-687.
[27] Rolfsman B., Combined heat and power plants and district heating in a deregulated electricity market, Applied Energy, 2004 (78) pp. 37-52.
[28] Rieder A., Christidis A. and Tsatsaronis G., Multi criteria dynamic design optimization of a small scale distributed energy system, Energy, 2014 (74), pp. 230–239.
[29] Fabrizio E., Corrado V. and Filippi M., A model to design and optimize multi-energy systems in buildings at the design concept stage, Renew Energy, 2010 (35), pp. 644–655.
[30] Maroufmashat A., Elkamel A., Fowler M., Sattari S., Roshandel R., Hajimiragha A., Walker S., Entchev E., Modeling and optimization of a network of energy hubs to improve economic and emission considerations, Energy, 2015(93), pp. 2546-2558.
[31] Enbridge Gas, http://www.enbridgegas.com. (Accessed June 2015).
[32] Hydro-one, http://www.hydroone.com. (Accessed June 2015).
[33] General Algebraic Modeling System, http://www.GAMS.com/ (GAMS website).
[1] Rad M. and Leon-Garcia A., Optimal residential load control with price prediction in real-time electricity pricing environments, Trans. Smart Grid, 2010 (1), pp. 120-133.
[2] Rong A. and Lahdelma R., An efficient linear programming model and optimization algorithm for trigeneration, Applied Energy, 2005 (82), pp. 40-63.
[3] Ashouri A, Fux S., Benz M. and Guzzella L., Optimal design and operation of building services using mixed-integer linear programming techniques, Energy, 2013 (59), pp. 365-376.
[4] Maréchal F. and Kalitventzeff B., Process integration: selection of the optimal utility system, Computers & Chemical Engineering, 1998 (22), pp. 149-156.
[5] Bozchalui M., Hashmi S., Hassen H., Canizares C. and Bhattacharya K., Optimal operation of residential energy hubs in smart grids, IEEE Trans. Smart Grid, 2012 (3), pp. 1755-1766.
[6] Chen Z., Wu L. and Fu Y., Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization, IEEE Trans. Smart Grid, 2012 (3), pp. 1822-1831.
[7] Giorgio A. and Pimpinella L., An event driven smart home controller enabling consumer economic saving and automated demand side management, Applied Energy, 2012 (96), pp. 92-103.
[8] Ha L., Joumaa H., Ploix S. and Jacomino M., An optimal approach for electrical management problem in dwellings, 2012, Energy Building (45) pp. 1-14.
[9] Fazlollahi S. and Maréchal F., Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP), Applied Thermal Engineering, 2013 (50), pp. 1504-1513.
[10] Lu Y., Wang S., Sun Y. and Yan C. Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming, Applied Energy, 2015 (147) pp. 49–58.
[11] Babu C. and Ashok S., Peak load management in electrolytic process industries, IEEE Trans. Power System, 2008 (23), pp. 399-405.
[12] Geidl M. and Andersson G., Optimal power flow of multiple energy carriers, IEEE Trans. Power System, 2007 (22) pp. 145-155.
[13] Shirazi E. and Jadid S., Optimal residential appliance scheduling under dynamic pricing scheme via hemdas, Energy Building, 2015 (93), pp. 40-49.
[14] Weber C., Maréchal F. and Favrat D., Design and optimization of district energy systems, Computer aided chemical engineering, 2007 (24) pp. 1127-1132.
[15] Hongbo R. and Weijun G., A MILP model for integrated plan and evaluation of distributed energy systems, Applied Energy, 2010 (87), pp. 1001–1014.
[16] Eugenia M., Haralambos S., Nikolaos M. and Lazaros P., A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level, Energy, 2012 (44), pp. 96-104.
[17] Mehleri D., Sarimveis H., Markatos C. and Papageorgiou G., Optimal design and operation of distributed energy systems: application to Greek residential sector, Renewable Energy 2013 (51), pp. 331-342.
[18] Omun A., Choudhary R. and Boies A., Distributed energy resource system optimization using mixed integer linear programming, Energy Policy, 2013 (61) pp. 249–266.
[19] Yang Y., Gao W., Ruan Y., Xuan J., Zhou N., and Marnay C, Optimal Model of Distributed Energy System by Using GAMS and Case Study, conference proceedings of the International Symposium on Sustainable Development of the Asian City Environment (SDACE) 2005.
[20] Scala L., Vaccaro A., and Zobaa A., A goal programming methodology for multiobjective optimization of distributed energy hubs operation, Applied Thermal Engineering, 2014 (71) pp. 658-666.
[21] Parisio A., Vecchio C. and Vaccaro A., A robust optimization approach to energy hub management, Electrical Power and Energy Systems, 2012 (42) pp. 98–104.
[22] Brahman F., Honarmand M. and Jadid S., Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system, Energy and Buildings, 2015 (90) pp. 65–75.
[23] Ren H., Gao W. and Ruan Y., Economic optimization and sensitivity analysis of photovoltaic system in residential buildings, Renewable Energy, 2009 (34) pp. 883-889.
[24] Weber C. and Shah N., Optimization based design of a district energy system for an eco-town in the United Kingdom, 2011, Energy (36) pp. 1292-1308.
[25] Aringhieri R. and Malucelli F., Optimal operations management and network planning of a district system with a combined heat and power plant, Annals of Operations Research, 2003 (120) pp. 173-199.
[26] Hepbasli A., Thermodynamic analysis of a ground-source heat pump system for district heating, International Journal of Energy Research, 2005 (29) pp. 671-687.
[27] Rolfsman B., Combined heat and power plants and district heating in a deregulated electricity market, Applied Energy, 2004 (78) pp. 37-52.
[28] Rieder A., Christidis A. and Tsatsaronis G., Multi criteria dynamic design optimization of a small scale distributed energy system, Energy, 2014 (74), pp. 230–239.
[29] Fabrizio E., Corrado V. and Filippi M., A model to design and optimize multi-energy systems in buildings at the design concept stage, Renew Energy, 2010 (35), pp. 644–655.
[30] Maroufmashat A., Elkamel A., Fowler M., Sattari S., Roshandel R., Hajimiragha A., Walker S., Entchev E., Modeling and optimization of a network of energy hubs to improve economic and emission considerations, Energy, 2015(93), pp. 2546-2558.
[31] Enbridge Gas, http://www.enbridgegas.com. (Accessed June 2015).
[32] Hydro-one, http://www.hydroone.com. (Accessed June 2015).
[33] General Algebraic Modeling System, http://www.GAMS.com/ (GAMS website).
@article{"International Journal of Electrical, Electronic and Communication Sciences:78395", author = "Mohamed Ghorab", title = "Conventional and Hybrid Network Energy Systems Optimization for Canadian Community", abstract = "Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO2 optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO2 emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO2 emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.
", keywords = "Distributed energy resources, network energy system, optimization, microgeneration system. ", volume = "13", number = "2", pages = "42-17", }
