Micro combined heat and power: Comparison
View Latest Version
Please note this is a comparison between Version 4 by PRAVEEN CHEEKATAMARLA and Version 6 by Conner Chen.

       Micro Combined Heat and Power (µCHP) systems in a DG infrastructure can reduce a building’s primary energy consumption, reduce carbon footprint, and enhance resiliency. The simultaneous production of electrical and thermal energy from a single fuel source at a high overall energy efficiency can reduce primary energy consumption while lowering greenhouse gas (GHG) emissions. A  comprehensive overview of various modeling approaches adopted by international researchers is presented.  The key objective is to present the state-of-the-art models and approaches while identifying opportunities for further refinement to expand the capabilities of such models for versatile applications.

 

  • Micro combined heat and power
  • Cogeneration
  • Prime mover
  • Primary Energy Consumption
  • Engine
  • Modeling

 

1. Introduction

       The decentralized aspect of µCHPs can potentially reduce distribution losses while reducing the peak load burden on central power generation plants. Economic and population growth are the primary drivers of rising electricity demand and it is bound to increase further, especially as electric vehicles become commonplace. In fact, CHP technology is gaining ground as an acceptable energy provider at university campuses, industrial facilities, and as backup generation, according to a recent study [

1]. The key market drivers contributing towards the growth of CHP technologies include lower operating costs, environmental regulations, resiliency, policy support, reliability, and utility interest. CHP reduces the burden on electric grid as well as the need for new transmission and distribution infrastructure while utilizing domestically available clean energy resources such as biomass and natural gas. Some of the major hurdles for the mass deployment of CHPs include value proposition to the utilities, user awareness, permitting and siting constraints, and general market uncertainties.

       Cogeneration technologies including those of industrial and micro scale have been analyzed for their applications in buildings dating back to late 20th century [2]. The authors identified the hurdles for rapid deployment and adoption by utilities, industries, and governmental regulatory bodies. Suggestions were made for accelerating the implementation of these devices with a primary focus on research, fuels, economics, environment, industry-utility interface, and regulation. Some of these focal points are still relevant even though the suggestions were made almost four decades ago!

        Cogeneration technologies including those of industrial and micro scale have been analyzed for their applications in buildings dating back to late 20th century [2]. The authors identified the hurdles for rapid deployment and adoption by utilities, industries, and governmental regulatory bodies. Suggestions were made for accelerating the implementation of these devices with a primary focus on research, fuels, economics, environment, industry-utility interface, and regulation. Some of these focal points are still relevant even though the suggestions were made almost four decades ago!

       

Given the significant potential of µCHP in buildings, we present in this paper a review of prior work in modeling µCHPs that use internal combustion engine (ICE) as the primary mover.  Integration of these µCHP units as primary building energy resources requires good understanding of their performance in meeting the dynamic energy needs (thermal and electric loads) of the building influenced by users, seasons, climate, and the overall interaction with the grid.  The key objective is to present the state-of-the-art thermodynamic models and their advantages, while identifying opportunities for further refinement to expand the capabilities of such models for versatile applications and ability to accept different prime power ICE based µCHP products. 

2. Discussion

       Based on the comprehensive review of past two decades of work, it can be concluded that the application of µCHP has been shown to reduce primary energy consumption coupled with environmental benefits associated with lower GHG emissions. However, the true savings were shown to be greatly influenced by the control mode adopted: thermal load following vs. electrical load following. Transient heat and power demand variations influenced the overall effectiveness significantly. Therefore, accurate prediction of µCHP output under steady-state as well as transient operating conditions is critical to developing µCHP control schemes and determining the economic viability of their applications. Several researchers developed and tuned the models and approaches to predict the behavior of specific equipment and their integration into buildings. Due to the complexity of the physical processes that take place to produce power in ICE, first principle modeling of µCHP is impractical to implement for building applications. It would require level of details that are usually not available to users. Therefore, all µCHP energy simulation models fall into either grey-box or black box category. Both categories require the availability of performance data of the µCHP system under investigation for calibrating the parameters of the model. Grey-box models are more versatile. The same set of equations can be directly used for different µCHP systems and can be easily modified to accommodate differences in system topography. Black-box models on the other hand are easier to develop. However, a black-box model architecture that is developed for certain system may not apply to a different system.

       A summary of the modeling strategies, optimization approaches, benefits and advantages of different studies discussed in Section 2 is outlined in the table below:

 
Prime Mover, (kW)Energy StorageApproach/MethodologyAdvantagesOptimizationRef
Combustion Engines, Fuel Cells (<15 kWe)Hot water storage tankControl Volume. Model calibration with empirical dataSimplicity, reliability if empirical data is utilizedThermal capacitance and conductance optimization with GenOpt[3]
ICE, 5.5 kWeSimulation in TRNSYS, ESP-r, EnergyplusAnnex 42 model-based control volume approachNon-traditional calibration

procedure—using optimization tools		single- and multi-objective optimization algorithms[4]
ICE, 6 kWeHot water tankAnnex 42 modelling approach. Electric load following modeDetailed calibration methodology, Transient mode considerationsGenOpt optimization approach[5][6]
ICE, 6 kWeVariable capacitance hot water storage tankTRNSYS dynamic platform, control volume approachParametric study similar to 14; Sensitivity of energy flow with variable thermal storage volumeElectrical and thermal load following modes of operation to optimize the savings[7]
Otto cycle Engine, 4 kWeHot water storage tank, stratified modelTRNSYS component-based model.Detailed transient test approaches and their implications on model reliabilityModel tuned to match simulated outputs with experimental results[8]
ICE, 25 kWe, CCHPTRNSYS hot water storage tank module-based modelModified Annex 42 approach with additional control volume preventing overheating via bypass loopModels ability to operate in manual, thermal priority and electrical priority modes. High level of model detail and calibration methodologyDynamic simulation model without the need for any optimization[9]
Reciprocating Gas Engine, 1.3 MWeThermal storage tanksDynamic and steady-state performance data from an operating plant was used to develop the model using engineering principlesReliable dynamic performance prediction-[10]
ICE, <50 kWeThermal and Electrical StorageSix different components (including user demand) in the CHP were independently modeledImplementation of delay subsystem yields high transient performance reliability.Optimal thermal and electrical energy storage-based configurations. Simplified representation of dynamic effects[11][12]
Otto Engine, 125 kWeStratified thermal storage moduleThree different levels of stratification were modeled along with all energy flowsInfluence of temperature level in the tank on energy efficiency and economics is modeled-[13]
ICE-ORC Hybrid, 2.5–5 MWNoneODEs representing conservation laws while using reliable heat transfer correlations such as Wiebe, Woschini, and AnnandProvides guidelines on suitable ICE designsfor waste heat recovery projectsWhole system optimization framework.[14]
Generic CHP ModelFlexible design considerationBased on Mixed-Integer Non LinearProgramming (MINLP)Generic dynamic modeling approach. Provides guidelines for system definition, and specification.Generic, low computational effort framework[15]
ICE, 15 kWeWaste heat recovery and direct utilizationModeled according to the continuity, momentum, and energy equations through 1D thermo-fluid dynamic characterizationFlexible waste heat recovery system with multiple temperature levels of thermal outputOptimal sizing of the polygeneration plant based on flexible heat recovery[16]
Otto Engine, 4 kWeStratified storage tank modelTRNSYS component based model, calibrated with empirical dataApplication of commercial software to design, optimize and validate a complete residential building CHP systemTRNSYS optimization[17]
Hybrid ICE-Stirling, 85 kWeDirect heat utilizationZero-dimensional mathematical model with single zone consisting of operating fluid as the thermodynamic systemSimplified system representation with high reliabilityElectrical output optimization via waste heat utilization in secondary power generation unit[18]
Biogas-Diesel ICE, 3.5 kWeNo thermal storageArtificial Neural Network (ANN) based approach while minimizing the RMSE valueReliable engine performance prediction showing the electrical and thermal outputsIterative selection data optimization for ANN design optimization.[19]
3MWe, polygeneration systemNoneOpen Problem Table (OPT) combining pinch analysis with MILPNovel approach for complex systems containing multiple sources and sinksMILP model with multiple decision variables[20]

3. Conclusions

3. Conclusions

 

       Based on the reviewed work, the authors suggest a closer look in to the following topics to help cement the µCHP as an efficient and resilient energy source to address the growing needs of the population driven by economy and new energy consumers entering the marketplace (e.g., electric vehicles). Thorough consideration of the following aspects in the model is recommended for enhancing the reliability and predictability of a global µCHP model:

  • Develop/refine models to address discrepancies associated with transient behavior—startup, cool-down, stand-by, interval between start and stop cycles, and delay time in these transient conditions. These aspects have been shown to improve the thermal efficiency of the system and are crucial for a reliable model.

  • Develop reliable schemes to analyze the performance of thermal and electric energy storage modules over a broad range of operating conditions. These models must be designed such that the integration-related discrepancies are accounted for appropriately.

  • Properly account for condensation of the flue gas exhaust stream in the PM model as well as its integration with thermal storage model

  • Simulation results have been proven to be impacted significantly by the time-step used in the model. This factor must be considered for developing the model and utilizing the calibration data in a meaningful form

  • Broader operational and experimental results need to be collected to study and characterize the PM thoroughly

  • Storage system model must balance the accuracy of the PM model

  • System design approaches focusing on cold climate applications—µCHP systems are ideal resources for cold climate applications where the heat demand is high, and the grid resources are vulnerable

  • Thorough consideration of the governing physics and chemistry of the model to improve the accuracy of complex systems

  • Expansion of the µCHP model to integrate thermally driven heat pump technology for maximizing the energy efficiency

  • Examination of co/trigeneration system models for applications in communities, non-residential buildings, and other large facilities

  • Application of these models to address commercialization issues to help wider market adoption.

 Based on the reviewed work, the authors suggest a closer look in to the following topics to help cement the µCHP as an efficient and resilient energy source to address the growing needs of the population driven by economy and new energy consumers entering the marketplace (e.g., electric vehicles). Thorough consideration of the following aspects in the model is recommended for enhancing the reliability and predictability of a  global    µCHP model:

  • Develop/refine models to address discrepancies associated with transient behavior—startup, cool-down, stand-by, interval between start and stop cycles, and delay time in these transient conditions. These aspects have been shown to improve the thermal efficiency of the system and are crucial for a reliable model.
  • Develop reliable schemes to analyze the performance of thermal and electric energy storage modules over a broad range of operating conditions. These models must be designed such that the integration-related discrepancies are accounted for appropriately.
  • Properly account for condensation of the flue gas exhaust stream in the PM model as well as its integration with thermal storage model
  • Simulation results have been proven to be impacted significantly by the time-step used in the model. This factor must be considered for developing the model and utilizing the calibration data in a meaningful form
  • Broader operational and experimental results need to be collected to study and characterize the PM thoroughly
  • Storage system model must balance the accuracy of the PM model
  • System design approaches focusing on cold climate applications—µCHP systems are ideal resources for cold climate applications where the heat demand is high, and the grid resources are vulnerable
  • Thorough consideration of the governing physics and chemistry of the model to improve the accuracy of complex systems
  • Expansion of the µCHP model to integrate thermally driven heat pump technology for maximizing the energy efficiency
  • Examination of co/trigeneration system models for applications in communities, non-residential buildings, and other large facilities
  • Application of these models to address commercialization issues to help wider market adoption.

The last two decades developing reliable mathematical models of µCHP systems, their application in real world scenarios and understanding the complexities associated with integration into the building environment. Development opportunities surrounding the modeling of prime movers and their integration with energy storage technologies were identified by several researchers. Publically available software platforms have evolved to design, improvise, and develop reliable cogeneration simulation models which will aid in further development of reliable, efficient, and resilient µCCHP products.       

       Energy utilization in buildings is a challenging subject, influenced by the building’s thermal and electrical demands, and primarily impacted by mismatch between the energy demand and supply. As a result, energy storage must be an integral part of the µCHP system to fully utilize the benefits of distributed generation. A fully integrated optimal µCHP configuration is underexplored as there are numerous possible solutions, which leads to the need to utilize software programs that help design the ideal system.

4. References

  1. Combined Heat and Power (CHP) Technical Potential in the United States. Available online: https://www.energy.gov/sites/prod/files/2016/04/f30/CHP%20Technical%20Potential%20Study%203-31-2016%20Final.
  2. Icerman, L.; Staples, D. Industrial cogeneration: Problems and promise. Energy 1979, 4, 101–117.
  3. Dorer, V. Review of Existing Residential Cogeneration Systems Performance Assessments and Evaluations: A Report of Subtask C of FC+ COGEN-SIM: The Simulation of Building-integrated Fuel Cell and Other Cogeneration Systems-Annex 42 of the International Energy Agency Energy Conservation in Buildings and Community Systems Programme; , Canada: 2007.
  4. De Paepe, M.; D’Herdt, P.; Mertens, D. Micro-CHP systems for residential applications. Energy Convers. Manag. 2006, 47, 3435–3446.
  5. Dorer, V.; Weber, A. Energy and CO2 emissions performance assessment of residential micro-cogeneration systems with dynamic whole-building simulation programs. Energy Convers. Manag. 2009, 50, 648–657.
  6. Wu, D.; Wang, R. Combined cooling, heating and power: A review. Energy Combust. Sci. 2006, 32, 459–495.
  7. Few, P.; Smith, M.; Twidell, J. Modelling of a combined heat and power (CHP) plant incorporating a heat pump for domestic use. Energy 1997, 22, 651–659.
  8. Jalalzadeh-Azar, A.A. A comparison of electrical-and thermal-load-following CHP systems. ASHRAE Trans. 2004, 110, 85.
  9. Peacock, A.; Newborough, M. Controlling micro-CHP systems to modulate electrical load profiles. Energy 2007, 32, 1093–1103.
  10. Lund, H.; Münster, E. Modelling of energy systems with a high percentage of CHP and wind power. Energy 2003, 28, 2179–2193.
  11. Houwing, M.; Ajah, A.N.; Heijnen, P.W.; Bouwmans, I.; Herder, P.M. Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems. Energy 2008, 33, 1518–1536.
  12. Henning, D. Cost minimization for a local utility through CHP, heat storage and load management. J. Energy Res. 1998, 22, 691–713.
  13. Beausoleil-Morrison, I. ; International Energy Agency, Swiss Federal Laboratories, Switzerland, 2008.
  14. Ferguson, A.; Kelly, N.; Weber, A.; Griffith, B. Modelling residential-scale combustion-based cogeneration in building simulation. Build. Perform. Simul. 2009, 2, 1–14, doi:10.1080/19401490802588424.
  15. Rosato, A.; Sibilio, S. Calibration and validation of a model for simulating thermal and electric performance of an internal combustion engine-based micro-cogeneration device. Therm. Eng. 2012, 45, 79–98, doi:10.1016/j.applthermaleng.2012.04.020.
  16. Rosato, A.; Sibilio, S. Energy performance of a micro-cogeneration device during transient and steady-state operation: Experiments and simulations. Therm. Eng. 2013, 52, 478–491, doi:10.1016/j.applthermaleng.2012.12.028.
  17. Rosato, A.; Sibilio, S.; Scorpio, M. Dynamic performance assessment of a residential building-integrated cogeneration system under different boundary conditions. Part I: Energy analysis. Energy Convers. Manag. 2014, 79, 731–748, doi:10.1016/j.enconman.2013.10.001.
  18. Schütz, T.; Streblow, R.; Müller, D. A comparison of thermal energy storage models for building energy system optimization. Energy Build. 2015, 93, 23–31, doi:10.1016/j.enbuild.2015.02.031.
  19. Lee, H.; Bush, J.; Hwang, Y.; Radermacher, R. Modeling of micro-CHP (combined heat and power) unit and evaluation of system performance in building application in United States. Energy 2013, 58, 364–375, doi:10.1016/j.energy.2013.05.015.
  20. Gu, W.; Wu, Z.; Bo, R.; Liu, W.; Zhou, G.; Chen, W.; Wu, Z. Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review. J. Electr. Power Energy Syst. 2014, 54, 26–37, doi:10.1016/j.ijepes.2013.06.028.
  21. Zheng, C.Y.; Wu, J.Y.; Zhai, X.Q.; Yang, G.; Wang, R.Z. Experimental and modeling investigation of an ICE (internal combustion engine) based micro-cogeneration device considering overheat protection controls. Energy 2016, 101, 447–461, doi:10.1016/j.energy.2016.02.030.
  22. Wang, Y.; Bermukhambetova, A.; Wang, J.; Lv, J.; Gao, Q. Dynamic modelling and simulation study of a university campus CHP power plant. September 2014, Philadelphia, pp. 3–8.
  23. Rey, G.; Ulloa, C.; Cacabelos, A.; Barragáns, B. Performance analysis, model development and validation with experimental data of an ICE-based micro-CCHP system. Therm. Eng. 2015, 76, 233–244, doi:10.1016/j.applthermaleng.2014.10.087.
  24. Ippolito, F.; Venturini, M. Micro-CHP System Transient Operation in a Residential User Microgrid; July 2-6, 2017, San Diego, California, pp. 1–16.
  25. Ippolito, F.; Venturini, M. Development of a Simulation Model of Transient Operation of Micro-Combined Heat and Power Systems in a Microgrid. Eng. Gas Turbines Power 2018, 140, 032001, doi:10.1115/1.4037962.
  26. Portoraro, A.; Ruscica, G.; Badami, M. Micro-Cogenerazione nel Settore Residenziale con l’utilizzo di Motori a Combustione Interna: Sviluppo di un Modello Matematico per la Simulazione Oraria e Analisi di un Caso Reale; Report No. RdS/2010/227 (in Italian)
  27. Ippolito, F.; Venturini, M. Micro Combined Heat and Power System Transient Operation in a Residential User Microgrid. Energy Resour. Technol. 2019, 141, 042006.
  28. Campos Celador, A.; Odriozola, M.; Sala, J.M. Implications of the modelling of stratified hot water storage tanks in the simulation of CHP plants. Energy Convers. Manag. 2011, 52, 3018–3026, doi:10.1016/j.enconman.2011.04.015.
  29. Thomas, B. Experimental determination of efficiency factors for different Micro-CHP units according to the standard DIN 4709. Therm. Eng. 2014, 71, 721–728, doi:10.1016/j.applthermaleng.2013.10.068.
  30. Xie, D.; Chen, A.; Gu, C.; Tai, J. Time-domain modeling of grid-connected CHP for its interaction with the power grid. IEEE Trans. Power Syst. 2018, 33, 6430–6440.
  31. Mednikov, A.; Maksimov, A.; Tyurina, E. Mathematical modeling of mini-CHP based on biomass. In November 2018, Russia, p. 02005.
  32. Ma, X.; Cai, L.; Meng, Q.; Chen, T.; Zhang, X. Dynamic optimal control and economic analysis of a coaxial parallel-type hybrid power gas engine-driven heat pump. Therm. Eng. 2018, 131, 607–620.
  33. Chatzopoulou, M.A.; Markides, C.N. Thermodynamic optimisation of a high-electrical efficiency integrated internal combustion engine–Organic Rankine cycle combined heat and power system. Energy 2018, 226, 1229–1251.
  34. Hamzehkolaei, F.T.; Amjady, N. A techno-economic assessment for replacement of conventional fossil fuel based technologies in animal farms with biogas fueled CHP units. Energy 2018, 118, 602–614.
  35. Wang, Y.; Wang, X.; Yu, H.; Huang, Y.; Dong, H.; Qi, C.; Baptiste, N. Optimal design of integrated energy system considering economics, autonomy and carbon emissions. Clean. Prod. 2019, 225, 563–578.
  36. Mărcuş, G.; Iordache, V.; Iordache, F.; Ilie, A. Energy analysis of a CHP plant with internal combustion engines, for a district heating system, based on the information from the annual database; February 2019, Romania, Vol. 85, p. 01012.
  37. Badami, M.; Chicco, G.; Portoraro, A.; Romaniello, M. Micro-multigeneration prospects for residential applications in Italy. Energy Convers. Manag. 2018, 166, 23–36.
  38. Rech, S. Smart energy systems: Guidelines for modelling and optimizing a fleet of units of different configurations. Energies 2019, 12, 1320.
  39. Costa, M.; Rocco, V.; Caputo, C.; Cirillo, D.; Di Blasio, G.; La Villetta, M.; Martoriello, G.; Tuccillo, R. Model based optimization of the control strategy of a gasifier coupled with a spark ignition engine in a biomass powered cogeneration system. Therm. Eng. 2019, 160, 114083.
  40. Yang, Y.; Cai, L.; Chen, T.; Zhou, Q.; Zhang, X. A Study of Hybrid-Power Gas Engine-Driven Heat Pump Control Strategy Based on Instantaneous Optimization. ; August 2019, West Java, Indonesia, p. 032067.
  41. Gimelli, A.; Muccillo, M. Performance assessment of a 15 kW Micro-CHCP plant through the 0D/1D thermo-fluid dynamic characterization of a double water circuit waste heat recovery system. Energy 2019, 181, 803–814.
  42. Morrone, P.; Algieri, A.; Castiglione, T.; Perrone, D.; Bova, S. Investigation of the energy performance of multi-source integrated CHP systems for small-scale applications; July 8th, 2019, Grenoble, France, p. 020115.
  43. Jung, Y.; Kim, J.; Lee, H. Multi-criteria evaluation of medium-sized residential building with micro-CHP system in South Korea. Energy Build. 2019, 193, 201–215.
  44. Krieger, M.; Stecklair, D.; Peluso, S.; Stockar, S. Design and Verification of a Small-Scale District Heating Network Experiment. ; October 8th, 2018, Anaheim, California, p. V002T023A002.
  45. Trninic, M.; Todorovic, D.; Jovovic, A.; Stojiljkovic, D.; Skreiberg, Ø.; Wang, L.; Manic, N. Mathematical Modelling and Performance Analysis of a Small-Scale Combined Heat and Power System Based on Biomass Waste Downdraft Gasification; ; 159–173.
  46. Sheykhi, M.; Chahartaghi, M.; Balakheli, M.M.; Hashemian, S.M.; Miri, S.M.; Rafiee, N. Performance investigation of a combined heat and power system with internal and external combustion engines. Energy Convers. Manag. 2019, 185, 291–303.
  47. Akkouche, N.; Loubar, K.; Nepveu, F.; Kadi, M.E.A.; Tazerout, M. Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion. Energy Convers. Manag. 2020, 205, 112407.
  48. Pina, E.A.; Lozano, M.A.; Ramos, J.C.; Serra, L.M. Tackling thermal integration in the synthesis of polygeneration systems for buildings. Energy 2020, 269, 115115.
  49. Olympios, A.V.; Le Brun, N.; Acha, S.; Shah, N.; Markides, C.N. Stochastic real-time operation control of a combined heat and power (CHP) system under uncertainty. Energy Convers. Manag. 2020, 216, 112916.
  50. Chen, Y.; Wang, J.; Lund, P.D. Thermodynamic performance analysis and multi-criteria optimization of a hybrid combined heat and power system coupled with geothermal energy. Energy Convers. Manag. 2020, 210, 112741.
  51. Olympios, A.V.; Pantaleo, A.M.; Sapin, P.; Markides, C.N. On the value of combined heat and power (CHP) systems and heat pumps in centralised and distributed heating systems: Lessons from multi-fidelity modelling approaches. Energy 2020, 274, 115261.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Feedback