Demand Response Application in Smart Grids pp 163-191 | Cite as

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

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## 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

*B*_{min}and*B*_{max}Lower and upper boundaries

- LD
_{sb} Amount of load that participates in demand response technique

- PG
_{m} Amount of power production in

*m*th generator*V*_{t}(*i*,*j*,*k*,*l*)Amount of

*t*th objective function for*i*th bacterium at the*j*th chemotaxis,*k*th reproduction, and*l*th dispersal- CDD
_{sb} Cost of demand response program

- GDC
_{sb} Cost of demand response program

*C*_{R}Cost of power loss

- PGC
_{m} Cost of power production in

*m*th generator- WL
_{ij} Cost related to new added lines

- CV
_{ij} 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

- EMC
_{i}(AP_{i}) Emission cost of

*i*th power plant- \( {A}_{\mathrm{PC},\mathrm{EMI}}^{\mathrm{GM}} \)
Emission cost of power plant

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

- FUC
_{i}(AP_{i}) Fuel cost of

*i*th power plant*I*_{A}Inflation amount of project in year

- AP
_{i} Initial installed capacity of power plant

*i*th- \( {A}_{\mathrm{IN}}^{\mathrm{DM}} \)
Investment cost of DEP

- \( {A}_{\mathrm{IN}}^{\mathrm{GM}} \)
Investment cost of generation expansion planning

- NGU
_{i} Investment cost of power plant

*i*th- \( {A}_{\mathrm{IN}}^{\mathrm{TM}} \)
Investment cost of TEP

*h*_{j}*j*th target weight*P*_{c}Learning probability

*L*_{P}Lifetime of project in year

- LA
_{sb} Load value contribution in DR-based generation expansion planning

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

*i*th bacterium at*j*th chemotaxis,*k*th reproduction,*d*th dimension, and*l*th dispersal*M*_{e}Maximum elimination step

- \( {A}_{\mathrm{PC},O\&M}^{\mathrm{GM}} \)
Operation and maintenance cost

*O*&*M*_{i}Operation and maintenance cost of

*i*th 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

*PDR*_{sb}Price of one MW demand response at

*sb*th bus*X*_{best(id)}The best previous location

- LV
_{sb} Value of load change at

*sb*th bus- PL
_{ij} Value of loss in branches between

*i*and*j**p*_{ij}Value of solution

*i*th with respect to*j*th target- EV
_{ij} Vector of the new lines

- NAL
_{ij} Vector of the new lines

- DR
Demand response

*e*and*h*_{i}Random numbers equal to 0 or 1

- FI
_{1}and FI_{2} 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

*P*_{r}Constant amounts

- TEP
Transmission expansion planning

*uw(i)*Length of unit walk

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