Multi-objective optimization of the renewable energy mix for a building

被引:66
作者
Ascione, Fabrizio [1 ]
Bianco, Nicola [1 ]
De Masi, Rosa Francesca [2 ]
De Stasio, Claudio [1 ]
Mauro, Gerardo Maria [1 ]
Vanoli, Giuseppe Peter [2 ]
机构
[1] Univ Naples Federico II, Piazzale Tecchio 80, I-80125 Naples, Italy
[2] Univ Sannio Benevento, Piazza Roma 21, I-82100 Benevento, Italy
关键词
Renewable energy systems; Building transient simulation; Cost-optimal analysis; Multi-objective optimization; Genetic algorithm; Building energy performance; PERFORMANCE; METHODOLOGY; SIMULATION;
D O I
10.1016/j.applthermaleng.2015.12.073
中图分类号
O414.1 [热力学];
学科分类号
摘要
According to the increasing worldwide attention to energy and environmental performances of the building sector, the exploitation of renewable energy sources (RESs) represents a key strategy toward sustainable buildings. However, which is the 'best' mix of RES systems in new or existing buildings? This paper proposes a novel methodology, aimed to optimize the design of the mix of renewable energy systems for the integration of building energy demand in terms of energy uses for space heating/cooling, domestic hot water and electric devices. More in detail, a multi-objective optimization is performed by considering two contrasting objectives to be minimized: primary energy demand and investment cost. The global cost is investigated too, as further criterion, in order to detect the cost-optimal solution. Moreover, the fulfillment of the minimum levels of RES integration - as provided by Italian regulations - is taken into account as constraint The optimization procedure is based on a genetic algorithm, which is performed by employing EnergyPlus and MATLAB. As case study, the methodology is applied in order to optimize the renewable energy mix for a typical new Italian residential building, located in Naples (Mediterranean area). Thermal solar systems, photovoltaic panels and efficient heat pumps are investigated as RES systems. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:612 / 621
页数:10
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