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

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,...

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Main Authors: Guozheng Li, Rui Wang, Tao Zhang, Mengjun Ming
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Energies
Subjects:
FEL
FTL
Online Access:http://www.mdpi.com/1996-1073/11/4/743
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spelling doaj-6ed6ecc1333c4aa3ad246053bfffc97b2020-11-24T22:08:21ZengMDPI AGEnergies1996-10732018-03-0111474310.3390/en11040743en11040743Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-gGuozheng Li0Rui Wang1Tao Zhang2Mengjun Ming3College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaThe 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.http://www.mdpi.com/1996-1073/11/4/743CCHPrenewable energyFELFTLmulti-objective optimizationisolated-mode
collection DOAJ
language English
format Article
sources DOAJ
author Guozheng Li
Rui Wang
Tao Zhang
Mengjun Ming
spellingShingle Guozheng Li
Rui Wang
Tao Zhang
Mengjun Ming
Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
Energies
CCHP
renewable energy
FEL
FTL
multi-objective optimization
isolated-mode
author_facet Guozheng Li
Rui Wang
Tao Zhang
Mengjun Ming
author_sort Guozheng Li
title Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
title_short Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
title_full Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
title_fullStr Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
title_full_unstemmed Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g
title_sort multi-objective optimal design of renewable energy integrated cchp system using picea-g
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2018-03-01
description 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.
topic CCHP
renewable energy
FEL
FTL
multi-objective optimization
isolated-mode
url http://www.mdpi.com/1996-1073/11/4/743
work_keys_str_mv AT guozhengli multiobjectiveoptimaldesignofrenewableenergyintegratedcchpsystemusingpiceag
AT ruiwang multiobjectiveoptimaldesignofrenewableenergyintegratedcchpsystemusingpiceag
AT taozhang multiobjectiveoptimaldesignofrenewableenergyintegratedcchpsystemusingpiceag
AT mengjunming multiobjectiveoptimaldesignofrenewableenergyintegratedcchpsystemusingpiceag
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