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Modeling of demand response programs based on market elasticity concept

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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.

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Notes

  1. Unit of Iranian currency.

Abbreviations

i:

i-th period

j:

j-th period

k:

k-th criterion

l:

l-th scenario

m:

Criteria quantity

n:

Scenarios quantity

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

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

References

  • Aalami HA (2010) Demand response modeling based on demand price elasticity coefficients (In Persian). Ph.D. dissertation, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran

  • Aalami HA, Yousefi GR, Parsa MM (2008) Demand response model considering EDRP and TOU programs. In: IEEE, PES, T&D Conference

  • Aalami HA, Parsa Moghaddam M, Yousefi GR (2010a) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87:243–250

    Article  Google Scholar 

  • Aalami HA, Parsa Moghaddam M, Yousefi GR (2010b) Modeling and prioritizing demand response programs in power markets. Electr Power Syst Res 80:426–435

    Article  Google Scholar 

  • Aalami HA, Parsa Moghaddam M, Yousefi GR (2015) Evaluation of nonlinear models for time-based rates demand response programs. Int J Electr Power Energy Syst 65:282–290

    Article  Google Scholar 

  • Aghajani GR, Shayanfar HA, Shayegani H (2015) Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers Manag 106:308–321

    Article  Google Scholar 

  • Brosdahl DJC, Carpenter JM (2010) Consumer knowledge of the environmental impacts of textile and apparel production, concern for the environment, and environmentally friendly consumption behavior. J Textile Appar Technol Manag 6:1–9

    Google Scholar 

  • Chan R (2001) Determinants of Chinese consumers’ green purchase behavior. Psychol Mark 18:389–399

    Article  Google Scholar 

  • Chan K (1999) Market segmentation of green consumers in Hong Kong. J Int Consum Mark 12:7–24

  • Cirio D, Demartini G, Massucco S, Monni A, Scaler P, Silvestvo F, Vimercati G (2003) Load control for improving system security and economics. In: IEEE, Power Tech Conference, pp 1–8

  • Deng R, Yang Z, Chen J, Asr NY, Chow MY (2014a) Residential energy consumption scheduling: a coupled-constraint game approach. IEEE Trans Smart Grid 5:1340–1350

    Article  Google Scholar 

  • Deng R, Yang Z, Chen J, Chow MY (2014b) Load scheduling with price uncertainty and temporally-coupled constraints in smart grid. IEEE Trans Power Syst 29:2823–2834

    Article  Google Scholar 

  • Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Industr Inf 11:570–582

    Article  Google Scholar 

  • Fan Z (2011) Distributed demand response and user adaption in smart grid. In: IEEE International Symposium on Integrated Network Management, pp 726–729

  • Faruqui A, Sergici S (2010) Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 38:193–225

    Article  Google Scholar 

  • IEA (2009) Strategic plan for the IEA-demand side management program 2004–2009. http://www.iea.org

  • Iran Ministry of Energy Statistical information of energy balance (2010) http://www.iranenergy.org.ir

  • Iran Power Industry Statistics (2015) http://amar.tavanir.org.ir/en/

  • Ishak S, Zabil NFM (2012) Impact of consumer awareness and knowledge to consumer effective behavior. Asian Soc Sci 8:108–114

    Google Scholar 

  • Karthikeyan S, Jacob Ragled I, Kothari D (2013) A review on market power in deregulated electricity market. Electr Power Energy Syst 48:139–147

    Article  Google Scholar 

  • Liang YP (2012) The relationship between consumer product involvement, product knowledge and impulsive buying behavior. Soc Behav Sci 57:325–330

    Article  Google Scholar 

  • Marwan M, Ledwich G, Ghosh A (2014) Demand-side response model to avoid spike of electricity price. J Process Control 24:782–789

    Article  Google Scholar 

  • Mohajeryami S, Schwarz P, Teimourzadeh Baboli P (2015) Including the behavioral aspects of customers in demand response model: Real time pricing versus peak time rebate. In: North American Power Symposium (NAPS)

  • Na L, Lijun C, Dahleh MA (2015) Demand response using linear supply function bidding. IEEE Trans Smart Grid 6:1827–1838

    Article  Google Scholar 

  • Pillay A, Karthikeyan S, Kothari D (2015) Congestion management in power systems—a review. Electr Power Energy Syst 70:83–90

    Article  Google Scholar 

  • Saaty T (1980) The analytic hierarchy processes. McGraw Hill, New York

    MATH  Google Scholar 

  • Safamehr H, Rahimi Kian A (2015) A cost-efficient and reliable energy management of a micro-grid using intelligent demand response program. Energy 91:283–293

    Article  Google Scholar 

  • Schweppe FC, Caramanis MC, Tabors RD, Bohn RE (1989) Spot pricing of electricity. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Smith V, Kiesling L (2005) A market-based model for ISO-sponsored demand response programs. In: A white paper prepared for the multi-client study

  • U. S. Department of Energy (2006) Benefits of demand response in electricity markets and recommendations for achieving them. Section 1252 of the report. Energy policy act of 2005

  • Vardakas JS, Zorba N, Verikoukis V (2015) A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Commun Surv Tutor 17:152–178

    Article  Google Scholar 

  • Ying L, Boong Loong N, Trayer M, Lingjia L (2012) Automated residential demand response: algorithmic implications of pricing models. IEEE Trans Smart Grid 3:1712–1721

    Article  Google Scholar 

  • Yousefi A, Aalami HA, Shayesteh E, Parsa Moghaddam M (2008) Enhancement of spinning reserve capacity by means of optimal utilization of EDRP program. In: Proceeding of the Fourth IASTED International Conference, Power and Energy Systems

  • Yu N, Yu J (2006) Optimal TOU decision considering demand response model. In: IEEE International Conference on Power System Technology, pp 1–5

  • Zakariazadeh A, Homaee O, Jadid S, Siano P (2014a) A new approach for real time voltage control using demand response in an automated distribution system. Appl Energy 114:157–166

    Article  Google Scholar 

  • Zakariazadeh A, Jadid S, Siano P (2014b) Multi-objective scheduling of electric vehicles in smart distribution system. Energy Convers Manag 79:43–53

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

    H. Jalili

  2. Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran

    M. K. Sheikh-El-Eslami & M. Parsa Moghaddam

  3. University of Salerno, Via Giovanni Paolo II, 132, Fisciano (SA), 84084, Salerno, Italy

    P. Siano

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  1. H. Jalili
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  2. M. K. Sheikh-El-Eslami
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  3. M. Parsa Moghaddam
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  4. P. Siano
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Jalili, H., Sheikh-El-Eslami, M.K., Moghaddam, M.P. et al. Modeling of demand response programs based on market elasticity concept. J Ambient Intell Human Comput 10, 2265–2276 (2019). https://doi.org/10.1007/s12652-018-0821-4

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