Optimization of Energy Storage Capacity and Operation in CCHP System Based on Improved Co-Evolutionary Algorithm

Combined cooling heating and power system (C-CHP) integrated renewable energy resources has been received increasing attention for its high energy utilization ratio. Considering the appropriate multi-objective optimization models and looking for efficient solution algorithms for CCHP systems are the...

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Vydáno v:2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) s. 424 - 429
Hlavní autoři: Li, Yuanmei, Wang, Kaike, Chen, Ning, Liu, Xiaofeng, Gao, Bingtuan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2019
Témata:
Cooling
Energy storage
Optimization
Renewable energy sources
Resistance heating
Sociology
Statistics
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Shrnutí:Combined cooling heating and power system (C-CHP) integrated renewable energy resources has been received increasing attention for its high energy utilization ratio. Considering the appropriate multi-objective optimization models and looking for efficient solution algorithms for CCHP systems are the main challenge tasks. This paper proposes an improved coevolutionary algorithm (ICEA) to optimize the energy storage capacity and operation for a CCHP system. The optimization objective is to minimize the cost including investment, fuel charge and maintenance. The proposed algorithm selects the particle swarm optimization (PSO) as the fundamental algorithm and employs strategy of dual population co-evolution and strategic synergy. In the algorithm, two subgroups are parallelly performed with the different initialization operators, including average entropy initialization and tent chaos initialization. The adaptive inertia weight strategy and definite weight coefficient are adopted in two populations. The final evolution of algorithm is achieved through collaboration of populations, strategies and algorithm. Case study based on the CCHP system verifies the feasibility of the proposed algorithm. Comparing with traditional PSO and GA, the economic results shows the proposed algorithm has better performances, which can reduce 5.4% and 5.2% of the costs, respectively.
DOI:10.1109/CYBER46603.2019.9066763