A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency

Abstract Reliable gas supply for minimum risk of supply shortage and minimum power demand for low energy cost are two fundamental objectives of natural gas pipeline networks. In this paper, a multi-objective optimization method is developed to trade-off reliability and power demand in the decision process. In the optimization, the steady state behavior of the natural gas pipeline networks is considered, but the uncertainties of the supply conditions and customer consumptions are accounted for. The multi-objective optimization regards finding operational strategies that minimize power demand and risk of gas supply shortage. To quantify the probability of supply interruption in pipeline networks, a novel limit function is introduced based on the mass conservation equation. Then, the risk of interruption is calculated by combining the probability of interruption and its consequences, measured in utility terms. The multi-objective optimization problem is solved by the NSGA-II algorithm and its effectiveness is tested on two typical pipeline networks, i.e., a tree-topology network and a loop-topology network. The results show that the developed optimization model is able to find solutions which effectively compromise the need of minimizing gas supply shortage risk and reducing power demand. Finally, a sensitivity analysis is conducted to analyze the impact of demand uncertainties on the optimization results.

[1]  Enrico Zio,et al.  An integrated framework of agent-based modelling and robust optimization for microgrid energy management , 2014 .

[2]  Ali Azadeh,et al.  Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty , 2017 .

[3]  André T. Beck,et al.  A comparison between robust and risk-based optimization under uncertainty , 2015 .

[4]  E. Andrew Boyd,et al.  Efficient operation of natural gas transmission systems: A network-based heuristic for cyclic structures , 2006, Comput. Oper. Res..

[5]  Bohong Wang,et al.  An MILP model for optimal design of multi-period natural gas transmission network , 2018 .

[6]  Ali Mohammad Ranjbar,et al.  An autonomous demand response program for electricity and natural gas networks in smart energy hubs , 2015 .

[7]  Yongtu Liang,et al.  A hybrid time MILP model for the pump scheduling of multi-product pipelines based on the rigorous description of the pipeline hydraulic loss changes , 2019, Comput. Chem. Eng..

[8]  Hesam Ahmadian Behrooz,et al.  Dynamic optimization of natural gas networks under customer demand uncertainties , 2017 .

[9]  Enrico Zio,et al.  A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems , 2011, Eur. J. Oper. Res..

[10]  Zhe Yang,et al.  A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks , 2018, Journal of Natural Gas Science and Engineering.

[11]  Sergio Bittanti,et al.  Online performance tracking and load sharing optimization for parallel operation of gas compressors , 2016, Comput. Chem. Eng..

[12]  Roger Z. Ríos-Mercado,et al.  Optimization problems in natural gas transportation systems. A state-of-the-art review , 2015 .

[13]  Halit Üster,et al.  Optimization for Design and Operation of Natural Gas Transmission Networks , 2014 .

[14]  Fredrik Wallin,et al.  Impacts of emission reduction and external cost on natural gas distribution , 2017 .

[15]  Lili Zuo,et al.  Minimizing fuel consumption of a gas pipeline in transient states by dynamic programming , 2016 .

[16]  Weihang Zhu,et al.  A multi-objective optimization model for gas pipeline operations , 2017, Comput. Chem. Eng..

[17]  Frank Pettersson,et al.  Optimization of a natural gas distribution network with potential future extensions , 2017 .

[18]  Felipe da Silva Alves,et al.  Multi-objective design optimization of natural gas transmission networks , 2016, Comput. Chem. Eng..

[19]  John Psarras,et al.  How does a natural gas supply interruption affect the EU gas security? A Monte Carlo simulation , 2015 .

[20]  Yang Nan,et al.  An integrated systemic method for supply reliability assessment of natural gas pipeline networks , 2018 .

[21]  Maryam Fasihizadeh,et al.  Improving gas transmission networks operation using simulation algorithms: Case study of the National Iranian Gas Network , 2014 .

[22]  Russell Bent,et al.  Optimal Compression in Natural Gas Networks: A Geometric Programming Approach , 2013, IEEE Transactions on Control of Network Systems.

[23]  Hesam Ahmadian Behrooz Managing demand uncertainty in natural gas transmission networks , 2016 .

[24]  Jolanta Szoplik,et al.  Improving the natural gas transporting based on the steady state simulation results , 2016 .

[25]  Dariush Jafari,et al.  Assessing and optimization of pipeline system performance using intelligent systems , 2014 .

[26]  Haoran Zhang,et al.  A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development , 2018, Comput. Chem. Eng..

[27]  Joan M. Ogden,et al.  Assessing Reliability in Energy Supply Systems , 2007 .

[28]  Carlos Pinho,et al.  Considerations About Equations for Steady State Flow in Natural Gas , 2007 .

[29]  Jiyong Kim,et al.  Bi-level optimizing operation of natural gas liquefaction process , 2017, Comput. Chem. Eng..

[30]  Omar J. Guerra,et al.  An optimization framework for the integration of water management and shale gas supply chain design , 2016, Comput. Chem. Eng..

[31]  Ottar N. Bjørnstad Transmission on Networks , 2018 .

[32]  Marcia B. H. Mantelli,et al.  Line-pack management for producing electric power on peak periods , 2011 .

[33]  Asgeir Tomasgard,et al.  Adding flexibility in a natural gas transportation network using interruptible transportation services , 2015, Eur. J. Oper. Res..

[34]  Chrysanthos E. Gounaris,et al.  Robust optimization for decision-making under endogenous uncertainty , 2018, Comput. Chem. Eng..

[35]  Gang Rong,et al.  Reprint of: Data-driven robust optimization under correlated uncertainty: A case study of production scheduling in ethylene plant , 2018, Comput. Chem. Eng..

[36]  Enrico Zio,et al.  A systematic framework of vulnerability analysis of a natural gas pipeline network , 2018, Reliab. Eng. Syst. Saf..

[37]  Yan-Fu Li,et al.  A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks , 2016 .

[38]  Enrico Zio,et al.  Challenges in the vulnerability and risk analysis of critical infrastructures , 2016, Reliab. Eng. Syst. Saf..

[39]  Gerard P.J. Dijkema,et al.  An integrated transient model for simulating the operation of natural gas transport systems , 2016 .

[40]  Amir Hesam Alinia Kashani,et al.  Techno-economical and environmental optimization of natural gas network operation , 2014 .

[41]  Marcelo Masera,et al.  Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure , 2015, Reliab. Eng. Syst. Saf..

[42]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.