Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

Author:

Zhang Yunlong1,Zhang Panhong2,Du Sheng3ORCID,Dong Hanlin1

Affiliation:

1. Power China Hubei Electric Engineering, Wuhan 430040, China

2. School of Finance, Hubei University of Economics, Wuhan 430205, China

3. School of Automation, China University of Geosciences, Wuhan 430074, China

Abstract

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Funder

Power China Hubei Electric Engineering Co., Ltd.

Publisher

MDPI AG

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