Sage Journals: Discover world-class research

Abstract

The concept of energy hub as the interface in multi-carrier energy systems has been introduced recently. This concept motivates the researchers to concentrate on multi-carrier energy systems with the purpose of achieving more efficient performance. Multi-carrier energy systems as the upcoming energy providing systems should economically operate in comparison with conventional decoupled energy systems. Economic dispatch of a multi-carrier energy system including the combined electrical-gas network with distributed resources is studied in this paper. Applying the mentioned problem to real systems leads to a large-scale nonlinear problem which should be optimized by using the optimization techniques. In this paper, adaptive group search optimization algorithm is utilized to solve the multi-carrier economic dispatch problem. The decomposing solution is implemented in order to facilitate the optimizing procedure. Additionally, the proposed method is applied to an 11-hub test system and the obtained results are analysed. The efficiency of the proposed approach is then evaluated.

Keywords

Energy hub multi-carrier energy system economic dispatch adaptive group search optimization algorithm

Get full access to this article

View all access options for this article.

References

Geidl

Koeppel

Favre-Perrod

et al . Energy hubs for the future. IEEE Power Energ Mag 2007; 5: 24–30.

Xie

Stochastic look-ahead economic dispatch with variable generation resources. IEEE Trans Power Syst 2017; 32: 17–29.

Safta

Chen

RLY

Najm

et al . Efficient uncertainty quantification in stochastic economic dispatch. IEEE Trans Power Syst 2017; 32: 2535–2546.

Tang

Jain

Dynamic economic dispatch game: the value of storage. IEEE Trans Smart Grid 2016; 7: 2350–2358.

Liu C, Shahidehpour M and Wang J. Coordinated scheduling of electricity and natural gas infrastructures with a transient model for natural gas flow. Chaos: Interdiscip J Nonlinear Sci 2011; 21(2): 025102

Krause

Andersson

Frohlich

et al . Multiple-energy carriers: modeling of production, delivery, and consumption. Proc IEEE 2011; 99: 15–27.

Mohammadi

Noorollahi

Mohammadi-Ivatloo

et al . Energy hub: from a model to a concept – a review. Renew Sustain Energ Rev 2017; 80: 1512–1527.

Geidl

Andersson

Optimal power flow of multiple energy carriers. IEEE Trans Power Syst 2007; 22: 145–155.

Almassalkhi

Hiskens

Optimization framework for the analysis of large-scale networks of energy hubs. Power Syst Comput Conf Sweden 2011; 22.

10.

Hanjie

Baldick

Optimizing short-term natural gas supply portfolio for electric utility companies. IEEE Trans Power Syst 2007; 22: 232–239.

11.

Subbaraj

Rengaraj

Salivahanan

Enhancement of combined heat and power economic dispatch using self-adaptive real-coded genetic algorithm. Appl Energ 2009; 86: 915–921.

12.

Beigvand

Abdi

Scala

ML.

A general model for energy hub economic dispatch. Appl Energ 2017; 190: 1090–1111.

13.

Moeini-Aghtaie

Dehghanian

Fotuhi-Firuzabad

et al . Multiagent genetic algorithm: an online probabilistic view on economic dispatch of energy hubs constrained by wind availability. IEEE Trans Sustain Energ 2014; 5: 699–708.

14.

Beigvand

Abdi

Scala

ML.

Economic dispatch of multiple energy carriers. Energy 2017; 138: 861–872.

15.

Dolatabadi

Mohammadi-Ivatloo

Abapour

et al . Optimal stochastic design of wind integrated energy hub. IEEE Trans Ind Inf 2017; 13: 2379–2388.

16.

Dolatabadi

Mohammadi-Ivatloo

Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response. Appl Therm Eng 2017; 123: 40–49.

17.

Saunders

JR.

Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Computat 2009; 13: 973–990.

18.

Daryani

Zare

Multiobjective power and emission dispatch using modified group search optimization method. Ain Shams Eng J 2016; online.

19.

Hagh

Teimourzadeh

Alipour

et al . Improved group search optimization method for solving CHPED in large scale power systems. Energ Convers Manag 2014; 80: 446–456.

20.

Alipour

Teimourzadeh

Seyedi

Improved group search optimization algorithm for coordination of directional overcurrent relays. Swarm Evol Comput 2015; 23: 40–49.

21.

Hagh

Alipour

Teimourzadeh

Application of HGSO to security based optimal placement and parameter setting of UPFC. Energ Convers Manag 2014; 86: 873–885.

22.

Daryani

Hagh

Teimourzadeh

Adaptive group search optimization algorithm for multi-objective optimal power flow problem. Appl Soft Comput 2016; 38: 1012–1024.

23.

Moeini-Aghtaie

Abbaspour

Fotuhi-Firuzabad

et al . A decomposed solution to multiple-energy carriers optimal power flow. IEEE Trans Power Syst 2014; 29: 707–716.

24.

Markovic

Ciric

RM.

Efficiency analysis of grid-connected photovoltaic power plants. CSEE J Power Energ Syst 2017; 3: 269–277.

25.

KS.

Point-estimate method for calculating statistical moments. J Eng Mech 1992; 118: 1506–1511.

26.

Niknam

Golestaneh

Malekpour

Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm. Energy 2012; 43: 427–437.

27.

www.powerauthority.on.ca/sop/

28.

Blaabjerg

Yang

et al . Distributed power-generation systems and protection. Proc IEEE 2017.

29.

Atwa

El-Saadany

Salama

MMA

et al . Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 2010; 25: 360–370.

30.

http://ee.sharif.edu/∼e_hajipour/index_files/Gas_Electeric_Case.pdf

Economic dispatch of multi-carrier energy systems considering intermittent resources

Abstract

Keywords

Get full access to this article

References