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

  • Y. Hashemi
  • H. Shayeghi


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.


Generation Transmission Distribution Planning Optimization 


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

Linguistic value

Bmin and Bmax

Lower and upper boundaries


Amount of load that participates in demand response technique


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


Cost of demand response program


Cost of demand response program


Cost of power loss


Cost of power production in mth generator


Cost related to new added lines


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


Direction angle


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


Fuel cost of ith power plant


Inflation amount of project in year


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


Investment cost of power plant ith

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

Investment cost of TEP


jth target weight


Learning probability


Lifetime of project in year


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


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


Price of one MW demand response at sbth bus


The best previous location


Value of load change at sbth bus


Value of loss in branches between i and j


Value of solution ith with respect to jth target


Vector of the new lines


Vector of the new lines


Demand response

e and hi

Random numbers equal to 0 or 1

FI1 and FI2

Constant amounts


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


Generation expansion planning


High-voltage direct current


Loss of load probability


Multipurpose comprehensive learning bacterial foraging


Non-dominated sorting genetic algorithm-II


Constant amounts


Transmission expansion planning


Length of unit walk


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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

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