Structure-Model-Capacity Optimization Model of Integrated Energy System Considering Carbon Emissions

doi:10.12204/j.issn.1000-7229.2022.07.004

Abstract

Abstract:

The energy structure of the integrated energy system is diverse, and the prices and performances of different models of the same equipment vary greatly. How to determine a reasonable energy structure, equipment model combination and installed capacity in the planning stage is the premise to achieve cost savings in the operation stage. This paper studies the optimization model of "energy structure-equipment model-installed capacity" planning considering carbon dioxide emissions. Firstly, a case-based reasoning technique based on K-nearest-neighbors (KNN) to obtain the energy structure suitable for the proposed park is constructed. Considering the carbon emission ladder cost, and taking the lowest total life cycle cost as the optimization objective, the combination of ant colony algorithm and genetic algorithm is used to solve the optimal equipment model combination and the optimal installed capacity. Finally, the effectiveness of the proposed model in reducing costs is verified by example analysis.

Key words: energy structure optimization, equipment model optimization, equipment capacity optimization, case based reasoning technology, carbon emission, integrated energy system

CLC Number:

TM743

LI Guotang, SONG Fuhao, CHEN Jie, DIAO Li, WANG Yongli, GUO Yuanzheng, LIU Zhenliang. Structure-Model-Capacity Optimization Model of Integrated Energy System Considering Carbon Emissions[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 37-47.

Figures/Tables 16

Fig.1 Illustration of the collaborative optimization framework

Fig.2 Case-based reasoning technology based on KNN

Table 1 Characteristic factor

Fig.3 Optimization framework of "outer layer selection, inner layer constant volume"

Table A1 Example database of integrated energy system planning

Table A2 Example database of integrated energy system planning

Table A3 Scene similarity value

Table A4 Simulation equipment parameters

Fig.4 Iteration curve of scenarios

Table 2 Planning simulation results of scenario 1

Table 3 Planning simulation results of scenario 2

Table 4 Economic comparison of planning scenario 1 and 2

Fig.5 Carbon emissions of scenario 1 and 2

Fig.6 Analysis of energy purchase cost

Table 5 Planning simulation results of scenario 3

Table 6 Economic comparison of planning scenario 1 and 3

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