Unified probabilistic gas and power flow

The natural gas system and electricity system are coupled tightly by gas turbines in an integrated energy system. The uncertainties of one system will not only threaten its own safe operation but also be likely to have a significant impact on the other. Therefore, it is necessary to study the variation of state variables when random fluctuations emerge in the coupled system. In this paper, a multi-slack-bus model is proposed to calculate the power and gas flow in the coupled system. A unified probabilistic power and gas flow calculation, in which the cumulant method and Gram–Charlier expansion are applied, is first presented to obtain the distribution of state variables after considering the effects of uncertain factors. When the variation range of random factors is too large, a new method of piecewise linearization is put forward to achieve a better fitting precision of probability distribution. Compared to the Monte Carlo method, the proposed method can reduce computation time greatly while reaching a satisfactory accuracy. The validity of the proposed methods is verified in a coupled system that consists of a 15-node natural gas system and the IEEE case24 power system.

[1]  Carmen Borges,et al.  A simplified operation planning model considering natural gas network and reservoir constraints , 2010, IEEE PES T&D 2010.

[2]  Chun-Lien Su,et al.  Probabilistic load-flow computation using point estimate method , 2005 .

[3]  Li Sheng PROBABILISTIC LOAD FLOW ANALYSIS BASED ON MONTE-CARLO SIMULATION , 2001 .

[4]  C. R. Fuerte-Esquivel,et al.  A Unified Gas and Power Flow Analysis in Natural Gas and Electricity Coupled Networks , 2012, IEEE Transactions on Power Systems.

[5]  Hamidreza Zareipour,et al.  A Probabilistic Energy Management Scheme for Renewable-Based Residential Energy Hubs , 2017, IEEE Transactions on Smart Grid.

[6]  V. Vittal,et al.  Probabilistic Power Flow Studies for Transmission Systems With Photovoltaic Generation Using Cumulants , 2012, IEEE Transactions on Power Systems.

[7]  T. W. Gedra,et al.  Natural gas and electricity optimal power flow , 2003, 2003 IEEE PES Transmission and Distribution Conference and Exposition (IEEE Cat. No.03CH37495).

[8]  Li Wang,et al.  A Study on Generator Capacity for Wind Turbines Under Various Tower Heights and Rated Wind Speeds Using Weibull Distribution , 2008, IEEE Transactions on Energy Conversion.

[9]  S.T. Lee,et al.  Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion , 2004, IEEE Transactions on Power Systems.

[10]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[11]  J. Aguado,et al.  Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions , 2005, IEEE Transactions on Power Systems.

[12]  Federico Milano,et al.  SDE-based wind speed models with Weibull distribution and exponential autocorrelation , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[13]  R. Ramanathan,et al.  Dynamic Load Flow Technique for Power System Simulators , 1986, IEEE Transactions on Power Systems.

[14]  A.C.Z. de Souza,et al.  Modeling the Integrated Natural Gas and Electricity Optimal Power Flow , 2007, 2007 IEEE Power Engineering Society General Meeting.

[15]  Z. Hu,et al.  A probabilistic load flow method considering branch outages , 2006, IEEE Transactions on Power Systems.

[16]  Claudio R. Fuerte-Esquivel,et al.  Integrated energy flow analysis in natural gas and electricity coupled systems , 2011, 2011 North American Power Symposium.

[17]  Carlos M. Correa-Posada,et al.  Security-Constrained Optimal Power and Natural-Gas Flow , 2014, IEEE Transactions on Power Systems.