Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm

Soheyli, Saman; Mayam, Mohamad Hossein Shafiei; Mehrjoo, Mehri

doi:10.1016/j.apenergy.2016.09.110

Cited by 131 publications

(26 citation statements)

References 35 publications

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“…This is mainly because the mass flow rate and the output power are closely related to the thermal power transferred to the organic working fluid according to Eqs. (10) and (11), while the thermal power transferred to the organic working fluid is approximately linear with the current density according to Eqs. (8) and (9) and Fig.…”

Section: Effects Of Current Density and Operating Temperature On Orc mentioning

confidence: 99%

“…Another effective way to make the most of the low-grade thermal energy is to introduce an ORC system [6]. The low grade energy used in an ORC system can be derived from different sources, such as waste heat from internal combustion engines [7], low temperature energy of liquefied natural gas engines [8], solar energy [9,10], biomass combustion energy [11], exhaust heat from gas turbines [12] and fuel cells [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system

et al. 2017

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A combined cooling heating and power (CCHP) system based on high-temperature proton exchange membrane fuel cell (PEMFC) is proposed. This CCHP system consists of a PEMFC subsystem, an organic Rankine cycle (ORC) subsystem and a vapor compression cycle (VCC) subsystem. The electric power of the CCHP system is 8 kW under normal operating conditions, the domestic hot water power is approximately 18 kW, and the cooling and heating capacities are 12.5 kW and 20 kW, respectively. Energy and exergy performance of the CCHP system are thoroughly analyzed for six organic working fluids using Matlab coupled with REFPROP. R601 is chosen as the working fluid for ORC subsystem based on energy and exergy analysis. The results show that the average coefficient of performance (COP) of the CCHP system is 1.19 in summer and 1.42 in winter, and the average exergy efficiencies are 46% and 47% under normal operating conditions. It can also be concluded that both the current density and operating temperature have significant effects on the energy performance of the CCHP system, while only the current density affects the exergy performance noticeably. The ambient temperature can affect both the energy and exergy performance of the CCHP system. This system has the advantages of high facility availability, high efficiency, high stability, low noise and low emission; it has a good prospect for residential applications.

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Section: Effects Of Current Density and Operating Temperature On Orc mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system

et al. 2017

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“…There are also studies applying renewable energies to CCHP systems. For example, Soheyli et al [35] built a novel CCHP system with renewable energies, and applied the co-constrained multi-objective particle swarm optimization algorithm to optimize the model. Wu et al [36] proposed an optimization CHP micro-grid containing renewable energies.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

Wang

Zhang

et al. 2018

Energies

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The integration of renewable energies into combined cooling, heating, and power (CCHP) systems has become increasingly popular in recent years. However, the optimization of renewable energies integrated CCHP (RECCHP) systems (i.e., optimal component configurations) is far from being well addressed, especially in isolated mode. This study aims to fill this research gap. A multi-objective optimization model characterizing the system reliability, system cost, and environmental sustainability is constructed. In this model, the objectives include minimization of annual total cost (ATC), carbon dioxide emission (CDE), and loss of energy supply probability (LESP). The decision variables representing the configuration of the RECCHP system include the number of photovoltaic (PV) panels and wind turbines (WTs), the tilt angle of PV panels, the height of WTs, the maximum fuel consumption, and the capacity of battery and heat storage tanks (HSTs). The multi-objective model is solved by a multi-objective evolutionary algorithm, namely, the preference-inspired coevolutionary algorithm (PICEA-g), resulting in a set of Pareto optimal (trade-off) solutions. Then, a decision-making process is demonstrated, selecting a preferred solution amongst those trade-off solutions by further considering the decision-maker preferences. Furthermore, on the optimization of the RECCHP system, operational strategies (i.e., following electric load, FEL, and following thermal load, FTL) are considered, respectively. Experimental results show that the FEL and FTL strategies lead to different optimal configurations. In general, the FTL is recommended in summer and winter, while the FEL is more suitable for spring and autumn. Compared with traditional energy systems, RECCHP has better economic and environmental advantages.

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“…Particle swarm optimization algorithm and generation algorithm are used in the optimization problems of the IES in [30] and [31]. The improved particle swarm optimization algorithms are studied in [32] and [33], and [34] developed a multi-objective group search optimizer with adaptive covariance and Lévy flights to optimize the power dispatch of the large-scale IES.…”

mentioning

confidence: 99%

Optimization of Dynamic Dispatch for Multiarea Integrated Energy System Based on Hierarchical Learning Method

Tang

et al. 2020

IEEE Access

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The integrated energy system (IES) with various energy demands and distributed energy resources has been a significant approach to improve the efficiency of energy utilization. Considering the uncertainties of renewable energy sources and loads, the energy dispatch optimization for multiarea IESs is studied in this paper. Different from the most current studies, not only the electrical power and heat distribution in each area is optimized, but also the coordination power dispatch between areas. A hierarchical learning method is proposed inhere to improve the operation performance of the multiarea IESs. The proposed method with data-driven way is a model-free method which has no requirement for the accurate mathematical model. With the hierarchical structure, the electrical power dispatched between areas is optimized in upper layer, together with the dispatching optimization in each area at lower layer, to decrease the operation cost for the system and power demand from the power grid. Finite horizon discrete dynamic process model is adopted to simulate the data for learning. The simulation results show the effectiveness of the optimization policy can achieve an economic and stable operation for the multiarea IES. INDEX TERMS Integrated energy system, dispatch optimization, stochastic process, hierarchical learning, dynamic programming. NOMENCLATURE A. PARAMETERS

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Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm

Cited by 131 publications

References 35 publications

Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system

Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system

Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

Optimization of Dynamic Dispatch for Multiarea Integrated Energy System Based on Hierarchical Learning Method

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