AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

A multi agent-based optimal control method for combined cooling and power systems with thermal energy storage

Zihao Wang3, Chaobo Zhang3, Hongbo Li1,2( ), Yang Zhao3
State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai, China
Guangdong Key Laboratory of Refrigeration Equipment and Energy Conservation Technology, Zhuhai, China
Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China
Show Author Information

Abstract

Combined cooling, heating and power (CCHP) systems have been considered as a potential energy saving technology for buildings due to their high energy efficiency and low carbon emission. Thermal energy storage (TES) can improve the energy efficiency of CCHP systems, since they reduce the mismatch between the energy supply and demand. However, it also increases the complexity of operation optimization of CCHP systems. In this study, a multi-agent system (MAS)-based optimal control method is proposed to minimize the operation cost of CCHP systems combined with TES. Four types of agents, i.e., coordinator agents, building agents, energy management agents and optimization agents, are implemented in the MAS to cooperate with each other. The operation optimization problem is solved by the genetic algorithm. A simulated system is utilized to validate the performance of the proposed method. Results show that the operation cost reductions of 10.0% on a typical summer day and 7.7% on a typical spring day are achieved compared with a rule-based control method. A sensitivity analysis is further performed and results show that the optimal operation cost does not change obviously when the rated capacity of TES exceeds a threshold.

Keywords

multi-agent systemsdemand responsethermal energy storageoperation optimizationdistributed controlcombined cooling, heating and power system

References

 
Ahmad MW, Mourshed M, Yuce B, et al. (2016). Computational intelligence techniques for HVAC systems: A review. Building Simulation, 9: 359-398.
Crossref Google Scholar
 
Anvari-Moghaddam A, Rahimi-Kian A, Mirian MS, et al. (2017). A multi-agent based energy management solution for integrated buildings and microgrid system. Applied Energy, 203: 41-56.
Crossref Google Scholar
 
ASHRAE (2007). Energy standard for buildings except low-rise residential buildings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 
Bellifemine F, Poggi A, Rimassa G (1999). JADE—A FIPA-compliant agent framework. In: Proceedings of PAAM, London, UK.
 
Bünning F, Sangi R, Müller D (2017). A Modelica library for the agent-based control of building energy systems. Applied Energy, 193: 52-59.
Crossref Google Scholar
 
Cai J, Braun JE, Kim D, et al. (2016). General approaches for determining the savings potential of optimal control for cooling in commercial buildings having both energy and demand charges. Science and Technology for the Built Environment, 22: 733-750.
Crossref Google Scholar
 
Deng N, Cai R, Gao Y, et al. (2017). A MINLP model of optimal scheduling for a district heating and cooling system: a case study of an energy station in Tianjin. Energy, 141: 1750-1763.
Crossref Google Scholar
 
GB 50189-2005 (2005). Design Standard for Energy Efficiency of Public Buildings. Ministry of Construction of China. (in Chinese)
 
González-Briones A, de la Prieta F, Mohamad M, et al. (2018). Multi- agent systems applications in energy optimization problems: a state-of-the-art review. Energies, 11(8): 1928.
Crossref Google Scholar
 
Gu Q, Ren H, Gao W, et al. (2012). Integrated assessment of combined cooling heating and power systems under different design and management options for residential buildings in Shanghai. Energy and Buildings, 51: 143-152.
Crossref Google Scholar
 
Holland J (1992). Adaptation in Natural and Artificial Systems. Cambridge, MA, USA: MIT Press.
Crossref
 
Klein L, Kwak JY, Kavulya G, et al. (2012). Coordinating occupant behavior for building energy and comfort management using multi-agent systems. Automation in Construction, 22: 525-536.
Crossref Google Scholar
 
Klein SA, Beckman WA, Mitchell JW, et al. (2004a). TRNSYS 16—A TRaNsient SYstem Simulation program, User manual. Solar Energy Laboratory, University of Wisconsin-Madison, USA.
 
Klein SA, Beckman WA, Mitchell JW, et al. (2004b). TRNSYS 16— Volume 6, Multizone Building Modeling with Type 56 and Trnbuild. Solar Energy Laboratory, University of Wisconsin- Madison, USA.
 
Lavinal E, Weiss G (1999). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA, USA: MIT Press.
 
Li F, Sun B, Zhang C, et al. (2019). A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage. Energy, 188: 115948.
Crossref Google Scholar
 
Li C, Jia X, Zhou Y, et al. (2020). A microgrids energy management model based on multi-agent system using adaptive weight and chaotic search particle swarm optimization considering demand response. Journal of Cleaner Production, 262: 121247.
Crossref Google Scholar
 
Liu M, Shi Y, Fang F (2012). A new operation strategy for CCHP systems with hybrid chillers. Applied Energy, 95: 164-173.
Crossref Google Scholar
 
Liu W, Chen G, Yan B, et al. (2015). Hourly operation strategy of a CCHP system with GSHP and thermal energy storage (TES) under variable loads: A case study. Energy and Buildings, 93: 143-153.
Crossref Google Scholar
 
Lizana J, Chacartegui R, Barrios-Padura A, et al. (2018). Advanced low-carbon energy measures based on thermal energy storage in buildings: A review. Renewable and Sustainable Energy Reviews, 82: 3705-3749.
Crossref Google Scholar
 
Lu Y, Wang S, Sun Y, Yan C (2015). Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Applied Energy, 147: 49-58.
Crossref Google Scholar
 
Luo XJ, Fong KF (2017). Development of multi-supply-multi-demand control strategy for combined cooling, heating and power system primed with solid oxide fuel cell-gas turbine. Energy Conversion and Management, 154: 538-561.
Crossref Google Scholar
 
Luo XJ, Fong KF (2019). Development of integrated demand and supply side management strategy of multi-energy system for residential building application. Applied Energy, 242: 570-587.
Crossref Google Scholar
 
MacNaughton P, Cao X, Buonocore J, et al. (2018). Energy savings, emission reductions, and health co-benefits of the green building movement. Journal of Exposure Science & Environmental Epidemiology, 28: 307-318.
Crossref Google Scholar
 
Mago PJ, Hueffed AK (2010). Evaluation of a turbine driven CCHP system for large office buildings under different operating strategies. Energy and Buildings, 42: 1628-1636.
Crossref Google Scholar
 
Mago PJ, Smith AD (2012). Evaluation of the potential emissions reductions from the use of CHP systems in different commercial buildings. Building and Environment, 53: 74-82.
Crossref Google Scholar
 
Mancarella P, Chicco G (2008). Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part II: Analysis techniques and application cases. Energy, 33: 418-430.
Crossref Google Scholar
 
Mao M, Jin P, Hatziargyriou ND, et al. (2014). Multiagent-based hybrid energy management system for microgrids. IEEE Transactions on Sustainable Energy, 5: 938-946.
Crossref Google Scholar
 
MathWorks (2015). Matlab/Simulink Documentation. Available at http://www.mathworks.com/. Accessed 30 Nov 2020.
 
Minciuc E, Le Corre O, Athanasovici V, et al. (2003). Fuel savings and CO2 emissions for tri-generation systems. Applied Thermal Engineering, 23: 1333-1346.
Crossref Google Scholar
 
Onishi VC, Antunes CH, Fraga ES, et al. (2019). Stochastic optimization of trigeneration systems for decision-making under long-term uncertainty in energy demands and prices. Energy, 175: 781-797.
Crossref Google Scholar
 
Pertzborn A (2019). Using distributed agents to optimize thermal energy storage. Journal of Energy Storage, 23: 89-97.
Crossref Google Scholar
 
Rajaeifar MA, Ghobadian B, Safa M, et al. (2014). Energy life-cycle assessment and CO2 emissions analysis of soybean-based biodiesel: A case study. Journal of Cleaner Production, 66: 233-241.
Crossref Google Scholar
 
Soetedjo A, Nakhoda YI, Saleh C (2019). An embedded platform for testbed implementation of multi-agent system in building energy management system. Energies, 12: 3655.
Crossref Google Scholar
 
Song Z, Liu T, Liu Y, et al. (2020). Study on the optimization and sensitivity analysis of CCHP systems for industrial park facilities. International Journal of Electrical Power and Energy Systems, 120: 105984.
Crossref Google Scholar
 
Su F, Huang J, Xu T, et al. (2009). An evaluation of the effects of various parameter weights on typical meteorological years used for building energy simulation. Building Simulation, 2: 19-28.
Crossref Google Scholar
 
Su B, Wang S (2020). An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks. Applied Energy, 274: 115322.
Crossref Google Scholar
 
Wang J, Jing Y, Zhang C, et al. (2011). Performance comparison of combined cooling heating and power system in different operation modes. Applied Energy, 88: 4621-4631.
Crossref Google Scholar
 
Wang J, Sui J, Jin H (2015). An improved operation strategy of combined cooling heating and power system following electrical load. Energy, 85: 654-666.
Crossref Google Scholar
 
Wu J, Wang J, Li S (2012). Multi-objective optimal operation strategy study of micro-CCHP system. Energy, 48: 472-483.
Crossref Google Scholar
 
Yang R, Wang L (2013a). Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy and Buildings, 56: 1-7.
Crossref Google Scholar
 
Yang R, Wang L (2013b). Multi-zone building energy management using intelligent control and optimization. Sustainable Cities and Society, 6: 16-21.
Crossref Google Scholar
 
Zhao Y, Lu Y, Yan C, et al. (2015). MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages. Energy and Buildings, 86: 415-426.
Crossref Google Scholar
 
Zhu G, Chow TT, Fong KF, et al. (2018). Design optimisation and performance appraisal of a combined cooling, heating and power system primed with Maisotsenko combustion turbine cycle. Energy Conversion and Management, 177: 91-106.
Crossref Google Scholar
 
Zhu G, Chow TT (2019). Design optimization and two-stage control strategy on combined cooling, heating and power system. Energy Conversion and Management, 199: 111869.
Crossref Google Scholar
Building Simulation
Volume 14 Issue 6,
December 2021
Pages 1709-1723
DOI: 10.1007/s12273-021-0768-9
Cite this article:
Wang Z, Zhang C, Li H, et al. A multi agent-based optimal control method for combined cooling and power systems with thermal energy storage. Building Simulation, 2021, 14(6): 1709-1723. https://doi.org/10.1007/s12273-021-0768-9

680

Views

19

Crossref

19

Web of Science

20

Scopus

1

CSCD

Altmetrics
Received: 25 September 2020
Revised: 07 December 2020
Accepted: 18 January 2021
Published: 27 March 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
Return