Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas

doi:10.12204/j.issn.1000-7229.2022.07.003

Abstract

Abstract:

Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaic power, and decentralized wind power, this paper designs a novel structure of a virtual power plant connected with gas-power plant carbon capture (GPPCC), power-to-gas (P2G), and waste incineration power (WI), namely, a GPW-VPP. Then, the information gap decision theory (IGDT) and fuzzy satisfaction method are applied to construct a nearly zero-carbon optimal operation model. In this model, maximizing revenue and minimizing carbon emissions are selected as the initial goals of the GPW-VPP operation, which are converted into one maximum satisfaction goal. Three uncertainty variables, namely, wind power, photovoltaic power, and user load, are described using the IGDT. Finally, the Lankao Rural Energy Revolution Pilot program in China is selected as the case study to verify the effectiveness of the proposed model. The results show that the proposed operation optimization model and benefit distribution strategy can take into account the interests of different subjects, and at the same time, promote the optimal aggregation and utilization of rural distributed energy, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.

Key words: virtual power plant, power to gas, carbon capture, optimized operation, uncertainty

CLC Number:

TM73

LI Peng, YU Xiaopeng, ZHANG Yihan, ZHOU Qingqing, TIAN Chunzheng, QIAO Huiting. Multi-objective Stochastic Scheduling Optimization Model for Rural Virtual Power Plant Considering Carbon Capture and Power-to-Gas[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 24-36.

Figures/Tables 13

Fig.1 GPW-VPP system structure

Fig.2 Structure of the flue gas treatment system of a waste incineration power plant

Table 1 Different types of loads participating in demand-response methods

Fig.3 Operation process of GPPCC carbon capture

Table 2 Equipment parameters for WPP, PV, WI, and BPG

Fig.4 WPP, PV forecast output and load demand of different users in a typical load day

Fig.5 Relationship between uncertainty degree α and predicted objective coefficients σrisk

Table 3 Comparative analysis of results under different optimization objectives

Fig.6 Operation optimization results of GPW under comprehensive optimization objectives

Fig.7 DR output for different user load

Fig.8 Flue gas flow and CO2 flow of GPW under the integrated operation mode

Table 4 Output results of each unit under different objective functions

Fig.9 Optimized operation results of GPW-VPP under the worst scenario

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