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Modeling of demand response programs based on market elasticity concept

  • H. JaliliEmail author
  • M. K. Sheikh-El-Eslami
  • M. Parsa Moghaddam
  • P. Siano
Original Research
  • 94 Downloads

Abstract

Demand response programs (DRPs) are appropriate tools to improve power system operation. Applying these programs results in a reduction in reliability cost and electricity price, transmission congestion and pollution relief, and also can determine postponements in network expansion. Therefore, developing a comprehensive model for DRPs is necessary for accurate planning and encouragement of consumers to increase their participation. In this paper, by using the market elasticity concept, a comprehensive model for DRPs is developed. Market elasticity is defined as sensitivity of electricity price on the network load. The proposed model is able to increase the consumers’ participation by providing a higher awareness about their participations’ effects on their electricity cost reduction. This additional awareness is provided by creating the information about the impact of consumers’ participation on the price of the electricity market in addition to the direct impact of their participations on their cost reduction. Information about the impact of consumers’ participation on the price of the electricity market is provided by the market elasticity concept. The effectiveness of the proposed \({\rho _0}(i)\) model is demonstrated by simulation results.

Keywords

DRPs Market elasticity concept Economic model of demand 

Abbreviations

Indices

i

i-th period

j

j-th period

k

k-th criterion

l

l-th scenario

m

Criteria quantity

n

Scenarios quantity

Parametes

\({\rho _0}(i)\)

Initial electricity price in i-th period

\(\rho (i)\)

Electricity price in i-th period

\({d_0}(i)\)

Initial demand in i-th period

\(d(i)\)

Demand in i-th period

\({E_D}(i,i)\)

Price self-elasticity of demand

\({E_D}(i,j)\)

Price cross-elasticity of demand

\({E_M}(i,i)\)

Market self-elasticity

\({E_M}(i,j)\)

Market cross-elasticity

\(\alpha _{2}^{i},\beta _{2}^{i}\)

Demand function parameters before demand or electricity price change

\(\alpha _{1}^{i},\beta _{1}^{i}\)

Generation function parameters

\(\alpha _{3}^{i},\beta _{2}^{i}\)

Demand function parameters after demand or electricity price change

\(\Delta d(i)\)

Demand change

\(S(i)\)

Customer’s benefit in i-th period

\(B({d_0}(i))\)

Initial customer’s income in i-th period

\(B(d(i))\)

Customer’s income in i-th period

\(P(\Delta d(i))\)

The total amount of incentive in i-th period

\(A(i)\)

Incentive of DRPs in i-th period

\(PEN(\Delta d(i))\)

The total amount of penalty in i-th period

\(pen(i)\)

Penalty of DRPs in i-th period

\(IC(i)\)

Contract level in i-th period

\(SN\)

Scenario no.

\({r_{lk}}\)

Elements of normalized decision matrix

\({X_{lk}}\)

Elements of decision matrix

\(V\)

Best solution/Worst solution

\({W_k}\)

Weight of k-th criterion

\(C\)

Priority coefficient in TOPSIS method

\(\phi\)

Consumer’s welfare parameter

\(SS\)

Distance between each scenario and the best solution/worst solution

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Roudehen BranchIslamic Azad UniversityRoudehenIran
  2. 2.Faculty of Electrical & Computer EngineeringTarbiat Modares UniversityTehranIran
  3. 3.University of SalernoSalernoItaly

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