Hybrid Load-Following Operation Strategy for Building Triple-Feed System Considering Energy Storage Characteristics

doi:10.12204/j.issn.1000-7229.2022.05.006

Abstract

Abstract:

As the cooling, heating and power system for traditional buildings ignores the energy storage characteristics and does not consider the participation of energy storage in decision-making, it cannot adjust the energy storage pressure, thus resulting in energy waste. Therefore, a hybrid load-following operation strategy considering energy storage characteristics is proposed in this paper. At the same time, in order to reduce the amount of model calculation and accurately select typical day of the load, a K-means clustering combined with CRITIC weighting is proposed. The system is optimized from three aspects: economy, energy and environment. The optimization results show that the K-means clustering algorithm with CRITIC weighting has higher contour coefficient and better clustering effect. At the same time, the comprehensive performance of the operation strategy proposed in this paper reaches 29.66%, which is much higher than the electric load following strategy and heat load following strategy. Compared with the traditional operation strategy, the proposed operation strategy has the best comprehensive performance, which verifies the feasibility and superiority of the proposed operation strategy.

Key words: building combined cooling, heating and power system, operation strategy, load typical day, CRITIC empowerment law, K-means clustering

CLC Number:

TM715

HOU Jianmin, XU Zhihao, YU Weijie, DING Suyun. Hybrid Load-Following Operation Strategy for Building Triple-Feed System Considering Energy Storage Characteristics[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(5): 50-62.

Figures/Tables 23

Fig.1 Energy flow diagram of BCCHP system

Fig.2 Schematic diagram of HLF-ESC operation strategy

Fig.3 Relationship between energy storage capacity change and operation mode

Table 1 Importance scale

Fig.4 Optimization framework of BCCHP system

Fig.A1 Annual cooling and heating load

Fig.A2 Annual electric load

Table A1 Efficiency of internal combustion engine generator

Table A2 Cost of equipment installation and maintenance and maximum power and climbing power

Table A3 Electricity price and natural gas price

Table A4 Carbon dioxide conversion coefficientg/(kW·h)

Table A5 Relevant parameters of particle swarm optimization algorithm

Fig.5 Iterative convergence graph

Fig.6 Contour coefficient distribution

Table 2 cluster evaluation index

Fig.7 Typical daily load and solar irradiance per unit area

Table 3

Fig.8 Annual primary energy consumption under different operation modes

Fig.9 Carbon dioxide emission under different operation modes

Table 4 Evaluation index results %

Fig.10 Percentage change of typical daily energy storage capacity and operation mode at each time in summer

Fig.11 Percentage change of typical daily energy storage capacity and operation mode at each time in winter

Fig.12 Percentage change of typical daily energy storage capacity and operation mode at each time in transition season

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