Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas

Fan Liu, Jiandong Duan, Chen Wu and Qinxing Tian

Renewable Energy, 2024, vol. 227, issue C

Abstract: The uncertainties of wind power and photovoltaic (Wind-PV) systems pose a threat to the steady functioning of integrated electricity-gas systems and restrict their effective consumption. In this case, an optimal operation model considering the cost risk of Wind-PV uncertainties is proposed. Firstly, the cost risk of uncertainties in typical scenarios is measured by conditional value at risk (CVaR). Secondly, considering that the power system and the natural gas system as different stakeholders have barriers in information exchange, a distributed optimization operation model is established, and the uncertainty of the conversion efficiency of the coupling unit from Power-to-Gas (P2G) is considered. Then, the integrated electricity-gas systems are decoupled through consistency constraints, and the model is solved by an algorithm framework based on adaptive penalty ADMM. This work analyzed the impact of uncertainty and CVaR parameters on optimized operation, and discussed the performance of related distributed algorithms. The following conclusions were drawn: 1) the proposed model reduces costs by 1.72% and can measure the operation risk caused by uncertainty factors, which provide guidance for decision makers; 2) the total cost will increase by 3.9% when the uncertainty factor reaches the preset maximum value, which demonstrates that the uncertainty of conversion efficiency should be controlled to reduce the operation cost of P2G; 3) As the convergence accuracy gradually improves, the proposed method is easier to converge and converges faster.

Keywords: Risk-averse; Wind power and photovoltaic uncertainty; P2G conversion efficiency; Conditional value at risk(CVaR); Adaptive penalty ADMM (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:227:y:2024:i:c:s0960148124004233

DOI: 10.1016/j.renene.2024.120358

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