Multi-objective economic emission dispatch of thermal power plants based on grey relational analysis and analytic hierarchy process

Abstract

In this paper, mathematical models for a synthesized evaluation were established according to grey relational analysis and analytic hierarchy process. The models were used to select a power dispatch scheme considering hierarchies of material consumption, electrical efficiency, exergy efficiency, and environmental benefit. Four unit dispatch schemes, i.e., proportional fair allocation dispatch, conventional economic dispatch, economic emission dispatch, and economic emission dispatch with varied weights were investigated and compared. Analytic hierarchy process decision-making approach has been employed to find the optimal Pareto solution as the best tradeoff between cost and pollutant emission. The model indicated that the economic emission dispatch was preferred as the best option and could further reduce fuel consumption and pollutant emission, followed by the economic emission dispatch with varied weights. The assessment performed serves decision makers a valuable reference for policy making in the power dispatch sector.

Keywords

Grey relational analysis analytic hierarchy process thermal power plant dispatch evaluation

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References

Wicks

Keay

Can coal contribute to sustainable development?

Energy Environ 2005; 16: 767–779.

Rees

DP.

The use of naturally clean coals to achieve cleaner coal technology. Energy Environ 2003; 14: 31–38.

Kolligs

CJ.

Technological and financial aspects of coal-fired plant upgrading and optimisation. Energy Environ 2003; 14: 17–30.

Zhang

Techno-economic assessment of carbon capture and storage facilities coupled to coal-fired power plants. Energy Environ 2015; 26: 1069–1080.

Herzog

HJ.

CO₂ mitigation strategies: perspectives on the capture and sequestration option. Energy Environ 1996; 7: 223–236.

Zhang

, et al. Interactions between China’s economy, energy and the air emissions and their policy implications. Renew Sustain Energy Rev 2014; 38: 624–638.

Cohan

Douglass

Potential emissions reductions from grandfathered coal power plants in the United States. Energy Policy 2011; 39: 4816–4822.

Zhong

Xia

Chen

, et al. Energy-saving generation dispatch toward a sustainable electric power industry in China. Energy Policy 2015; 83: 14–25.

Tan

Chen

, et al. Issues and solutions of China’s generation resource utilization based on sustainable development. J Mod Power Syst Clean Energy 2016; 4: 147–160.

10.

Cheng

A hybrid method of incorporating extended priority list into equal incremental principle for energy-saving generation dispatch of thermal power systems. Energy 2014; 64: 688–696.

11.

Gao C and Yang

Evolution of China’s power dispatch principle and the new energy saving power dispatch policy. Energy Policy 2010; 38: 7346–7357.

12.

Niknam

Azizipanah-Abarghooee

Aghaei

A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch. IEEE Trans Power Syst 2013; 28: 749–763.

13.

Niu

, Wan

and Z

. A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems. J Mod Power Syst Clean Energy 2014; 2: 289–297.

14.

Li M, Wang X, Gong Y, et al. Binary glowworm swarm optimization for unit commitment. J Mod Power Syst Clean Energy 2014; 2: 357–365.

15.

Chatzimouratidis

Pilavachi

PA.

Multicriteria evaluation of power plants impact on the living standard using the analytic hierarchy process. Energy Policy 2008; 36: 1074–1089.

16.

Chatzimouratidis

Pilavachi

PA.

Technological, economic and sustainability evaluation of power plants using the analytic hierarchy process. Energy Policy 2009; 37: 778–787.

17.

Ahmad

Tahar

RM.

Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia. Renew Energy 2014; 63: 458–466.

18.

Lee

Hwang

Decision support for selecting exportable nuclear technology using the analytic hierarchy process: A Korean case. Energy Policy 2010; 38: 161–167.

19.

Kurka

Application of the analytic hierarchy process to evaluate the regional sustainability of bioenergy developments. Energy 2013; 62: 393–402.

20.

Yagmur

Multi-criteria evaluation and priority analysis for localization equipment in a thermal power plant using the AHP (analytic hierarchy process). Energy 2016; 94: 476–482.

21.

Hashemi

Karimi

Tavana

Int J Production Economics 2015; 159: 178–191.

22.

Pawlak

Rough sets. Int J Comput Inf Sci 1982; 11: 341–356.

23.

Pawlak

Rough sets and intelligent data analysis. Inf Sci 2002; 147: 1–12.

24.

Pawlak

Skowron

Rudiments of rough sets. Inf Sci 2007; 177: 3–27.

25.

Kennedy

Eberhart

(1995) Particle swarm optimization. In: IEEE international conference on neural networks. Proceedings of IEEE international conference on neural networks, pp 1942–1948, vol.1944. Piscataway, NJ: IEEE.

26.

Jadoun

Gupta

Niazi

, et al. Modulated particle swarm optimization for economic emission dispatch. Int J Electr Power Energy Syst 2015; 73: 80–88.

27.

Polprasert

Ongsakul

Dieu

VN.

Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electr Power Compon Syst 2016; 44: 518–532.

28.

Selvakumar

Thanushkodi

A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst 2007; 22: 42–51.

29.

Zhang

Yao

HH-C

, et al. Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions. J Mod Power Syst Clean Energy 2013; 1: 231–240.

30.

Cheshta

Verma

Arya

A novel statistically tracked particle swarm optimization method for automatic generation control. J Mod Power Syst Clean Energy 2014; 2: 396–410.

31.

Saaty

RW.

The analytic hierarchy process—what it is and how it is used. Math Modell 1987; 9: 161–176.

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