Advertisement

Demand Response Application in Generation, Transmission, and Distribution Expansion Planning

  • Y. Hashemi
  • H. Shayeghi
Chapter
  • 38 Downloads

Abstract

This chapter introduces a model to assess the effect of demand response (DR) technique on three important parts of power system planning. The proposed model aims to determine the optimal expansion solution for all sources in the power source direction, power network direction, and power load direction. The proposed model of expansion planning is based on three targets, investment cost, performance cost, and demand response cost. The planning problem is transformed to a multi-objective optimization framework that it is solved by multipurpose comprehensive learning bacterial foraging (MP-CLBF) algorithm. To select the best solution among Pareto-optimal set obtained by MP-CLBF, a method based on fuzzy technique for order of preference by similarity to ideal solution (fuzzy-TOPSIS) is implemented. The presented model has been employed in 6-bus, 118-bus transmission system and 36-bus distribution system. The results obtained from numerical are compared in two cases, with and without using DR method and different scenarios.

Keywords

Generation Transmission Distribution Planning Optimization 

Nomenclature

\( {\tilde{p}}_{ij} \) and \( {\tilde{h}}_j \)

Linguistic value

Bmin and Bmax

Lower and upper boundaries

LDsb

Amount of load that participates in demand response technique

PGm

Amount of power production in mth generator

Vt(i, j, k, l)

Amount of tth objective function for ith bacterium at the jth chemotaxis, kth reproduction, and lth dispersal

CDDsb

Cost of demand response program

GDCsb

Cost of demand response program

CR

Cost of power loss

PGCm

Cost of power production in mth generator

WLij

Cost related to new added lines

CVij

Cost related to new added lines

\( {A}_{\mathrm{EC}}^{\mathrm{TM}} \)

Demand response cost

\( {A}_{\mathrm{EC}}^{\mathrm{DM}} \)

Demand response cost of DEP

\( {A}_{\mathrm{EM}}^{\mathrm{GM}} \)

Demand response cost of GEP

δ(i)

Direction angle

EMCi(APi)

Emission cost of ith power plant

\( {A}_{\mathrm{PC},\mathrm{EMI}}^{\mathrm{GM}} \)

Emission cost of power plant

\( {A}_{\mathrm{PC},\mathrm{FU}}^{\mathrm{GM}} \)

Fuel cost

FUCi(APi)

Fuel cost of ith power plant

IA

Inflation amount of project in year

APi

Initial installed capacity of power plant ith

\( {A}_{\mathrm{IN}}^{\mathrm{DM}} \)

Investment cost of DEP

\( {A}_{\mathrm{IN}}^{\mathrm{GM}} \)

Investment cost of generation expansion planning

NGUi

Investment cost of power plant ith

\( {A}_{\mathrm{IN}}^{\mathrm{TM}} \)

Investment cost of TEP

hj

jth target weight

Pc

Learning probability

LP

Lifetime of project in year

LAsb

Load value contribution in DR-based generation expansion planning

\( {\varGamma}_d^i\left(j,k,l\right) \)

Location of ith bacterium at jth chemotaxis, kth reproduction, dth dimension, and lth dispersal

Me

Maximum elimination step

\( {A}_{\mathrm{PC},O\&M}^{\mathrm{GM}} \)

Operation and maintenance cost

O & Mi

Operation and maintenance cost of ith power plant

\( {A}_{\mathrm{PC}}^{\mathrm{DM}} \)

Performance cost of DEP

\( {A}_{\mathrm{PC}}^{\mathrm{GM}} \)

Performance cost of GEP

\( {A}_{\mathrm{PC}}^{\mathrm{TM}} \)

Performance cost of TEP

PDRsb

Price of one MW demand response at sbth bus

Xbest(id)

The best previous location

LVsb

Value of load change at sbth bus

PLij

Value of loss in branches between i and j

pij

Value of solution ith with respect to jth target

EVij

Vector of the new lines

NALij

Vector of the new lines

DR

Demand response

e and hi

Random numbers equal to 0 or 1

FI1 and FI2

Constant amounts

Fuzzy-TOPSIS

Fuzzy-technique for order of preference by similarity to ideal solution

GEP

Generation expansion planning

HVDC

High-voltage direct current

LOLP

Loss of load probability

MP-CLBF

Multipurpose comprehensive learning bacterial foraging

NSGA-II

Non-dominated sorting genetic algorithm-II

Pr

Constant amounts

TEP

Transmission expansion planning

uw(i)

Length of unit walk

References

  1. 1.
    H. Nemati, M.A. Latify, G.R. Yousefi, Tri-level transmission expansion planning under intentional attacks: virtual attacker approach–part I: formulation. IET Gener. Transm. Dis. 13, 390–398 (2018)CrossRefGoogle Scholar
  2. 2.
    C. Dai, L. Wu, B. Zeng, C. Liu, System state model based multi-period robust generation, transmission, and demand side resource co-optimisation planning. IET Gener. Transm. Dis. 13, 345–354 (2018)CrossRefGoogle Scholar
  3. 3.
    H. Xie, Z. Bie, G. Li, Reliability-oriented networking planning for meshed VSC-HVDC grids. IEEE Trans. Power Syst. 2018Google Scholar
  4. 4.
    S.A. Rashidaee, T. Amraee, M. Fotuhi-Firuzabad, A linear model for dynamic generation expansion planning considering loss of load probability. IEEE Trans. Power Syst. 33, 6924–6934 (2018)CrossRefGoogle Scholar
  5. 5.
    H. Mavalizadeh, A. Ahmadi, F.H. Gandoman, P. Siano, H.A. Shayanfar, Multiobjective robust power system expansion planning considering generation units retirement. IEEE Syst. J. 12, 2664–2675 (2018)CrossRefGoogle Scholar
  6. 6.
    L. Baringo, A. Baringo, A stochastic adaptive robust optimization approach for the generation and transmission expansion planning. IEEE Trans. Power Syst. 33, 792–802 (2018)CrossRefGoogle Scholar
  7. 7.
    Y. Zhan, Q.P. Zheng, J. Wang, P. Pinson, Generation expansion planning with large amounts of wind power via decision-dependent stochastic programming. IEEE Trans. Power Syst. 32, 3015–3026 (2017)CrossRefGoogle Scholar
  8. 8.
    P.J. Ramírez, D. Papadaskalopoulos, G. Strbac, Co-optimization of generation expansion planning and electric vehicles flexibility. IEEE Trans. Smart Grid 7, 1609–1619 (2016)CrossRefGoogle Scholar
  9. 9.
    J. Li, Z. Li, F. Liu, H. Ye, X. Zhang, S. Mei, et al., Robust coordinated transmission and generation expansion planning considering ramping requirements and construction periods. IEEE Trans. Power Syst. 33, 268–280 (2018)CrossRefGoogle Scholar
  10. 10.
    X. Zhang, A.J. Conejo, Robust transmission expansion planning representing long-and short-term uncertainty. IEEE Trans. Power Syst. 33, 1329–1338 (2018)CrossRefGoogle Scholar
  11. 11.
    Z. Wu, Y. Liu, W. Gu, J. Zhou, J. Li, P. Liu, Decomposition method for coordinated planning of distributed generation and distribution network. IET Gener. Transm. Dis. 12, 4482–4491 (2018)CrossRefGoogle Scholar
  12. 12.
    C. Feng, W. Liu, F. Wen, Z. Li, M. Shahidehpour, X. Shen, Expansion planning for active distribution networks considering deployment of smart management technologies. IET Gener. Transm. Dis. 12, 4605–4614 (2018)CrossRefGoogle Scholar
  13. 13.
    M. Asensio, P.M. de Quevedo, G. Muñoz-Delgado, J. Contreras, Joint distribution network and renewable energy expansion planning considering demand response and energy storage—part II: numerical results. IEEE Trans. Smart Grid 9, 667–675 (2018)CrossRefGoogle Scholar
  14. 14.
    B. Niu, W. Yi, L. Tan, J. Liu, Y. Li, H. Wang, Multi-objective comprehensive learning bacterial foraging optimization for portfolio problem, in International Conference on Swarm Intelligence, 2017, pp. 69–76Google Scholar
  15. 15.
    L. Tan, H. Wang, C. Yang, B. Niu, A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch. Nat. Comput. 16, 549–565 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    P. Sirisawat, T. Kiatcharoenpol, Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Comput. Ind. Eng. 117, 303–318 (2018)CrossRefGoogle Scholar
  17. 17.
    A. Khodaei, M. Shahidehpour, S. Kamalinia, Transmission switching in expansion planning. IEEE Trans. Power Syst. 25, 1722–1733 (2010)CrossRefGoogle Scholar
  18. 18.
    C. Roldán, A.S. de la Nieta, R. García-Bertrand, R. Mínguez, Robust dynamic transmission and renewable generation expansion planning: walking towards sustainable systems. Int. J. Electr. Power Energy Syst. 96, 52–63 (2018)CrossRefGoogle Scholar
  19. 19.
    S. Ramesh, S. Kannan, S. Baskar, Application of modified NSGA-II algorithm to multi-objective reactive power planning. Appl. Soft Comput. 12, 741–753 (2012)CrossRefGoogle Scholar
  20. 20.
    E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications, PhD thesis, Swiss Federal Institute of Technology Zurich, 1999Google Scholar
  21. 21.
    S. Bandyopadhyay, S.K. Pal, B. Aruna, Multiobjective GAs, quantitative indices, and pattern classification. IEEE Trans. Syst. Man Cybern. B Cybern. 34, 2088–2099 (2004)CrossRefGoogle Scholar
  22. 22.
    C. Ye, M. Huang, Multi-objective optimal power flow considering transient stability based on parallel NSGA-II. IEEE Trans. Power Syst. 30, 857–866 (2015)CrossRefGoogle Scholar
  23. 23.
    J. Aghaei, K.M. Muttaqi, A. Azizivahed, M. Gitizadeh, Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm. Energy 65, 398–411 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Y. Hashemi
    • 1
    • 2
  • H. Shayeghi
    • 1
  1. 1.Technical Engineering DepartmentUniversity of Mohaghegh ArdabiliArdabilIran
  2. 2.Tehran Area Operating Center (TAOC), Tehran Regional Electric Company (TREC)TehranIran

Personalised recommendations