A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation

This paper presents a new expansion planning model of integrated electricity and gas networks with the high penetration of renewable energy resources, using a multi-objective mixed integer linear programming approach. The proposed method concludes the generation and transmission expansion planning of electricity network and natural gas network expansion planning. Since the outage of natural gas-fired units due to gas network faults is possible, in this paper, integrated electricity and gas network expansion planning is studied by applying N-1 contingency to these systems. Photovoltaic power plants are used for generation expansion planning to reduce emission and greenhouse gases. Reduced disjunctive model is applied to decrease the calculation time of applying N-1 criterion to power systems. A scenario-based stochastic method is exploited to study the correlated uncertainty of the photovoltaic generation and electrical loads. The proposed model is applied to two case studies, including modified Garver's system & 5-nodegas network and IEEE-RTS system & Belgian gas network. The numerical results show that using photovoltaic generation in the expansion planning problem while considering contingency in both electrical and gas networks decreases costs by 9.08% and 7.08%, and reduces emission by 38.71% and 34.62% in the first and second systems, respectively.

Automated summary

This paper presents a new integrated electricity and gas networks expansion planning model using multi-objective mixed integer linear programming, aiming to reduce emissions and greenhouse gases. By incorporating photovoltaic power plants, reduced disjunctive model, and stochastic method to study correlated uncertainty, it is possible to decrease costs and emissions in the expansion planning process.