Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power

doi:10.12204/j.issn.1000-7229.2022.12.014

Abstract

Abstract:

This paper proposes to integrate carbon emission trading into peak-shaving trading, to account for the carbon variation effects produced by thermal power peak-shaving, and proposes a multi-source low-carbon peak-shaving cost accounting method. Aiming at the uncertainty of wind power, this paper uses the information gap decision theory (IGDT) to reflect the information gap between the predicted value and the actual value of wind power, and constructs an uncertainty multi-source low-carbon peak-shaving transaction optimization model. Finally, a local power grid in northwest China is selected as the simulation system to verify the correctness and validity of the proposed model. Results show that the proposed multi-source low-carbon peak-shaving transaction model can promote the integration of wind power generation, ensure all participants obtain the cooperation incremental benefits, and establish a peak-shaving transaction plan for decision makers with different risk attitudes.

Key words: low-carbon peak-shaving, energy storage, demand response, information intermittent, optimization model

CLC Number:

TM73

LI Peng, YU Xiaopeng, ZHOU Qingqing, TAN Zhongfu, JU Liwei, QIAO Huiting. Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(12): 131-140.

Figures/Tables 10

Fig.1 Multi-source joint peak-shaving mode

Fig.2 Schematic diagram of peak-shaving strategy of thermal power units

Table 1 Operational parameters of thermal power units

Fig.3 Typical daily load demand and available output of wind power

Table 2 Optimization scheme of wind power peak-shaving transaction in scenario 1

Fig.4 Uncertainty degree of wind power and transaction cost of peak-shaving under different forecast target deviations

Fig.5 The results of the joint participation of thermal power, energy storage and DR in wind power peak-shaving transactions

Table 3 Participation of thermal power units in wind power peak-shaving under four scenarios (only start-up units are considered)

Fig.6 Wind power peak-shaving transaction results under the worst scenario

Fig.7 Wind power peak-shaving cost and uncertainty under different carbon trading price

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