An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning

•Developed a multi-objective model for the combined natural gas network and electricity network.•Taken into account the uncertainty and correlations of wind power in the proposed model.•Presented an improved point-estimation method to solve the combined optimal power and natural gas load flow. With...

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Bibliographic Details
Published in:Applied energy Vol. 167; pp. 280 - 293
Main Authors: Hu, Yuan, Bie, Zhaohong, Ding, Tao, Lin, Yanling
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2016
Subjects:
algorithms
China
Decision making
Electricity
Mathematical models
Multi-objective
Natural gas
Natural gas network expansion planning
Networks
Optimization
Pareto optimality
planning
Point-estimate method
power generation
Primal–Dual Interior-Point method
production costs
Stochasticity
Transmission expansion planning
wind power
ISEW, China, People's Rep., Hainan Prov
China
Natural gas network expansion planning
Primal–Dual Interior-Point method
Point-estimate method
Multi-objective
Transmission expansion planning
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:•Developed a multi-objective model for the combined natural gas network and electricity network.•Taken into account the uncertainty and correlations of wind power in the proposed model.•Presented an improved point-estimation method to solve the combined optimal power and natural gas load flow. With the increasing proportion of natural gas in power generation, natural gas network and electricity network are closely coupled. Therefore, planning of any individual system regardless of such interdependence will increase the total cost of the whole combined systems. Therefore, a multi-objective optimization model for the combined gas and electricity network planning is presented in this work. To be specific, the objectives of the proposed model are to minimize both investment cost and production cost of the combined system while taking into account the N−1 network security criterion. Moreover, the stochastic nature of wind power generation is addressed in the proposed model. Consequently, it leads to a mixed integer non-linear, multi-objective, stochastic programming problem. To solve this complex model, the Elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to capture the optimal Pareto front, wherein the Primal–Dual Interior-Point (PDIP) method combined with the point-estimate method is adopted to evaluate the objective functions. In addition, decision makers can use a fuzzy decision making approach based on their preference to select the final optimal solution from the optimal Pareto front. The effectiveness of the proposed model and method are validated on a modified IEEE 24-bus electricity network integrated with a 15-node natural gas system as well as a real-world system of Hainan province.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2015.10.148