Advertisement

Improved real-coded genetic algorithm solution for unit commitment problem considering energy saving and emission reduction demands

  • Qian Pan (潘 谦)Email author
  • Xing He (何 星)
  • Yun-ze Cai (蔡云泽)
  • Zhi-hua Wang (王治华)
  • Fan Su (苏 凡)
Article

Abstract

Unit commitment (UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm (IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.

Key words

genetic algorithm (GA) unit commitment (UC) improved real-number encoding 

Nomenclature

ai, bi, ci

Energy consumption function parameters of the ith unit

CSCi

Cold start-up cost of the ith unit

CSTi

Cold start-up time of the ith unit

\(D_{R_i }\)

Down ramp limit of the ith unit, MW/h

Dt

Demand during hour t, MW

G

Number of units

HSCi

Hot start-up cost of the ith unit

i

Unit index (i=1, 2, ⋯, G)

Ki,t

Start-up cost of the ith unit

MDTi

Minimum down-time of the ith unit

MUTi

Minimum up-time of the ith unit

Pi,t

Power output of the ith unit for hour t

Pimin

Minimum generation limit of the ith unit, MW

Pimax

Maximum generation limit of the ith unit, MW

ri,t

Operation status of the ith unit for hour t (1 = “on”, 0 = “off”)

Ri

Power-purchasing expense of the ith unit

si

Emission efficiency factor which represents the percent of pollutants handled by environmental apparatus

\(S_{D_t }\)

Reserved requirement during hour t, MW

t

Hour index (t=1, 2, ⋯, T)

T

UC horizon

Tion

Duration during which the ith unit is continuously on

Tioff

Duration during which the ith unit is continuously off

u

Coefficient of energy consumption which represents the cost for energy compensation when consuming unit resource (e.g. coal)

\(U_{R_i }\)

Up ramp limit of the ith unit, MW/h

αi

Emission factor, kg/MW

δi

Operation status of the ith environmental equipment (1 = “on”, 0 = “off”)

ρ

Coefficient of handling pollutants

φi

Expense of environmental equipment handling pollutants

ω

Environmental value of pollutants which represents the cost for handling pollutants of each unit

CLC number

TM 732 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Salam S. Unit commitment solution methods [J]. World Academy of Science, Engineering and Technology, 2007, 1(11): 290–295.Google Scholar
  2. [2]
    Ongsakul W, Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation [J]. IEEE Transactions on Power Systems, 2004, 19(1): 620–628.CrossRefGoogle Scholar
  3. [3]
    Chuang C S, Chang G W. Lagrangian relaxationbased unit commitment considering fast response reserve constraints [J]. Energy and Power Engineering, 2013, 5: 970–974.CrossRefGoogle Scholar
  4. [4]
    Yuan X, Su A, Nie H, et al. Unit commitment problem using enhanced particle swarm optimization algorithm [J]. Soft Computing, 2011, 15(1): 139–148.CrossRefGoogle Scholar
  5. [5]
    Simopoulos D N, Kavatza S D, Vournas C D. Unit commitment by an enhanced simulated annealing algorithm [J]. IEEE Transactions on Power Systems, 2006, 21(1): 68–76.CrossRefGoogle Scholar
  6. [6]
    Dudek G. Adaptive simulated annealing schedule to the unit commitment problem [J]. Electric Power Systems Research, 2010, 80(4): 465–472.CrossRefMathSciNetGoogle Scholar
  7. [7]
    Jalilzadeh S, Pirhayati Y. An improved genetic algorithm for unit commitment problem with lowest cost [J]. Intelligent Computing and Intelligent Systems, 2009, 1: 571–575.Google Scholar
  8. [8]
    Dudek G. Unit commitment by genetic algorithm with specialized search operators [J]. Electric Power Systems Research, 2004, 72(3): 299–308.CrossRefGoogle Scholar
  9. [9]
    Datta D. Unit commitment problem with ramp rate constraint using a binary real-coded genetic algorithm [J]. Applied Soft Computing, 2013, 13(9): 3873–3883.CrossRefGoogle Scholar
  10. [10]
    Damousis I G, Bakirtzis A G, Dokopoulos P S. A solution to the unit commitment problem using integer-coded genetic algorithm [J]. IEEE Transactions on Power Systems, 2004, 19(2): 1165–1172.CrossRefGoogle Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Qian Pan (潘 谦)
    • 1
    Email author
  • Xing He (何 星)
    • 1
  • Yun-ze Cai (蔡云泽)
    • 1
  • Zhi-hua Wang (王治华)
    • 2
  • Fan Su (苏 凡)
    • 2
  1. 1.Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiaotong UniversityShanghaiChina
  2. 2.State Grid Shanghai Municipal Electric Power CompanyShanghaiChina

Personalised recommendations