Document Type : Original Article

Author

Young Researchers and Elite club, Ardabil Branch, Islamic Azad University, Ardabil, Iran

Abstract

The combined heating, cooling, and power source (CCHP) system is a good tool for the optimal consumption of fossil fuel thermal energy. In CCHPs, the produced waste heat from the hot gases can be recycled for generating power, heat, and water cooling and oil in electrical power generation systems which can improve the efficiency of the system to more than 85%. This study presents an optimum structure for the combined heating, cooling, and power source energy flow to decrease the power demand in a building in Yazd city, Iran. In this research, a developed version of collective animal behavior optimizer is introduced to develop the combined heating, cooling, and power source system efficiency compared to the separation generation system. Two different scenarios have been studied for analyzing system efficiency. In one scenario, a constant value (670 kW) was assumed to the capacity while the electric cooling (EC) to cool load ratio (CLR) is assumed variant in a determined range and at the other scenario, an opponent condition with 0.75 constant EC to CLR were assumed. Simulation achievements of the presented technique are put in comparison with standard balanced moth search optimizer and genetic algorithm to indicate the efficiency of the algorithm.

Keywords

Gholamin R. Optimal Designing of the Capacity and the Operation of the CCHP System Using Balanced Collective Animal Behavior Algorithm: A Case Study J. Journal of Smart Energy and Sustainability, 2022; 1(2):116-129. 

[1]    L. Feng, X. Dai, J. Mo, and L. Shi, “Analysis of simplified CCHP users and energy-matching relations between system provision and user demands,” Applied Thermal Engineering, vol. 152, pp. 532-542, 2019.
[2]    J. Jiang, W. Gao, X. Wei, Y. Li, and S. Kuroki, “Reliability and cost analysis of the redundant design of a combined cooling, heating and power (CCHP) system,” Energy Conversion and Management, vol. 199, p. 111988, 2019.
[3]    G. Yang and X. Zhai, “Optimization and performance analysis of solar hybrid CCHP systems under different operation strategies,” Applied Thermal Engineering, vol. 133, pp. 327-340, 2018.
[4]    Y. Liu, J. Han, and H. You, “Performance analysis of a CCHP system based on SOFC/GT/CO2 cycle and ORC with LNG cold energy utilization,” International Journal of Hydrogen Energy, 2019.
[5]    M. Moghimi, M. Emadi, P. Ahmadi, and H. Moghadasi, “4E analysis and multi-objective optimization of a CCHP cycle based on gas turbine and ejector refrigeration,” Applied Thermal Engineering, vol. 141, pp. 516-530, 2018.
[6]    M. Ebrahimi, D. Ghasemi, and A. Keshavarz, “Geometrical and thermodynamical design of a micro-steam radial turbine for different organic fluids,” ZANCO Journal of Pure and Applied Sciences, vol. 31, pp. 235-242, 2019.
[7]    M. Prakash, A. Sarkar, J. Sarkar, J. Chakraborty, S. Mondal, and R. Sahoo, “Performance assessment of novel biomass gasification based CCHP systems integrated with syngas production,” Energy, vol. 167, pp. 379-390, 2019.
[8]    K. Yang, N. Zhu, Y. Ding, C. Chang, D. Wang, and T. Yuan, “Exergy and exergoeconomic analyses of a combined cooling, heating, and power (CCHP) system based on dual-fuel of biomass and natural gas,” Journal of cleaner production, vol. 206, pp. 893-906, 2019.
[9]    M. Wegener, A. Isalgué, A. Malmquist, and A. Martin, “3E-Analysis of a Bio-Solar CCHP System for the Andaman Islands, India—A Case Study,” Energies, vol. 12, p. 1113, 2019.
[10]    M. Abbasi, M. Chahartaghi, and S. M. Hashemian, “Energy, exergy, and economic evaluations of a CCHP system by using the internal combustion engines and gas turbine as prime movers,” Energy conversion and management, vol. 173, pp. 359-374, 2018.
[11]    A. Piacentino and F. Cardona, “EABOT–Energetic analysis as a basis for robust optimization of trigeneration systems by linear programming,” Energy Conversion and Management, vol. 49, pp. 3006-3016, 2008.
[12]    H. Ren and W. Gao, “A MILP model for integrated plan and evaluation of distributed energy systems,” Applied Energy, vol. 87, pp. 1001-1014, 2010.
[13]    Y. Lu, S. Wang, Y. Sun, and C. Yan, “Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming,” Applied Energy, vol. 147, pp. 49-58, 2015.
[14]    M. Liu, Y. Shi, and F. Fang, “Optimal power flow and PGU capacity of CCHP systems using a matrix modeling approach,” Applied Energy, vol. 102, pp. 794-802, 2013.
[15]    M.-T. Tsai, F.-S. Chen, and C.-C. Lo, “Multi-interval schedule of economic dispatch for cogeneration systems,” in Electrical, Control Engineering and Computer Science: Proceedings of the 2015 International Conference on Electrical, Control Engineering and Computer Science (ECECS 2015, Hong Kong, 30-31 May 2015), 2015, p. 33.
[16]    Y. Cao, Y. Wu, L. Fu, K. Jermsittiparsert, and N. Razmjooy, “Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics,” Energy Reports, vol. 5, pp. 1551-1559, 2019.
[17]    D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, “System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm,” Energy Reports, vol. 5, pp. 1365-1374, 2019.
[18]    J. C. Mayo-Maldonado, J. E. Valdez-Resendiz, V. M. Sanchez, J. C. Rosas-Caro, A. Claudio-Sanchez, and F. C. Puc, “A novel PEMFC power conditioning system based on the interleaved high gain boost converter,” International Journal of Hydrogen Energy, vol. 44, pp. 12508-12514, 2019.
[19]    J.-J. Wang, Y.-Y. Jing, and C.-F. Zhang, “Optimization of capacity and operation for CCHP system by genetic algorithm,” Applied Energy, vol. 87, pp. 1325-1335, 2010.
[20]    N. Razmjooy and M. Khalilpour, “A new design for PID controller by considering the operating points changes in Hydro-Turbine Connected to the equivalent network by using Invasive Weed Optimization (IWO) Algorithm,” International Journal of Information, Security and Systems Management, vol. 4, pp. 468-475, 2015.
[21]    A. Ahadi, N. Ghadimi, and D. Mirabbasi, “An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability,” Complexity, vol. 21, pp. 99-113, 2015.
[22]    E. Alizadeh, M. Barzegari, M. Momenifar, M. Ghadimi, and S. Saadat, “Investigation of contact pressure distribution over the active area of PEM fuel cell stack,” International Journal of Hydrogen Energy, vol. 41, pp. 3062-3071, 2016.
[23]    N. Razmjooy and M. Ramezani, “Analytical solution for optimal control by the second kind Chebyshev polynomials expansion,” Iranian Journal of Science and Technology, Transactions A: Science, vol. 41, pp. 1017-1026, 2017.
[24]    N. Ghadimi, “An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation,” Complexity, vol. 21, pp. 10-20, 2015.
[25]    N. Ghadimi, “Genetically tuning of lead-lag controller in order to control of fuel cell voltage,” Scientific Research and Essays, vol. 7, pp. 3695-3701, 2012.
[26]    N. Ghadimi, “A new hybrid algorithm based on optimal fuzzy controller in multimachine power system,” Complexity, vol. 21, pp. 78-93, 2015.
[27]    H. Hosseini, B. Tousi, and N. Razmjooy, “Application of fuzzy subtractive clustering for optimal transient performance of automatic generation control in restructured power system,” Journal of Intelligent & Fuzzy Systems, vol. 26, pp. 1155-1166, 2014.
[28]    Y. Cao, Y. Li, G. Zhang, K. Jermsittiparsert, and N. Razmjooy, “Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm,” Energy Reports, vol. 5, pp. 1616-1625, 2019.
[29]    H. Hosseini, B. Tousi, N. Razmjooy, and M. Khalilpour, “Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm,” IETE Journal of Research, vol. 59, pp. 745-752, 2013.
[30]    N. Razmjooy and M. Ramezani, “Training wavelet neural networks using hybrid particle swarm optimization and gravitational search algorithm for system identification,” International Journal of Mechatronics, Electrical and Computer Technology, vol. 6, pp. 2987-2997, 2016.
[31]    A. Namadchian, M. Ramezani, and N. Razmjooy, “A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Guassian Distribution,” Majlesi Journal of Electrical Engineering, vol. 10, p. 49, 2016.
[32]    N. Razmjooy, M. Khalilpour, and M. Ramezani, “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System,” Journal of Control, Automation and Electrical Systems, vol. 27, pp. 419-440, 2016.
[33]    G. Dhiman and V. Kumar, “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems,” Knowledge-Based Systems, vol. 159, pp. 20-50, 2018.
[34]    M. Jain, S. Maurya, A. Rani, and V. Singh, “Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization,” Journal of Intelligent & Fuzzy Systems, vol. 34, pp. 1573-1582, 2018.
[35]    Y. Kumar and P. K. Singh, “Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering,” Applied Intelligence, vol. 48, pp. 2681-2697, 2018.
[36]    S. Arora and S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft Computing, vol. 23, pp. 715-734, 2019.
[37]    G.-G. Wang, “Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems,” Memetic Computing, vol. 10, pp. 151-164, 2018.
[38]    P. S. Callahan, “Moth and candle: the candle flame as a sexual mimic of the coded infrared wavelengths from a moth sex scent (pheromone),” Applied Optics, vol. 16, pp. 3089-3097, 1977.
[39]    C. Choi and J.-J. Lee, “Chaotic local search algorithm,” Artificial Life and Robotics, vol. 2, pp. 41-47, 1998.
[40]    X. Li, P. Niu, and J. Liu, “Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm,” Applied Mathematical Modelling, vol. 58, pp. 3-18, 2018.
[41]    D. Yang, G. Li, and G. Cheng, “On the efficiency of chaos optimization algorithms for global optimization,” Chaos, Solitons & Fractals, vol. 34, pp. 1366-1375, 2007.
[42]    C. Rim, S. Piao, G. Li, and U. Pak, “A niching chaos optimization algorithm for multimodal optimization,” Soft Computing, vol. 22, pp. 621-633, 2018.