Skip to main content
SpringerLink
Log in

Implementation of Demand Response Programs on Unit Commitment Problem

  • Chapter
  • First Online:
Demand Response Application in Smart Grids

  • 439 Accesses

Abstract

Peak electrical demand usually occurs in hot weather conditions due to the synchronous connection of the air-conditioners to local power grids. Hence, if a demand-side management (DSM) strategy is not applied on consumption patterns of customers, load-generation imbalance may lead to voltage collapse, cascaded outages, and catastrophic blackouts. This chapter presents a mathematical model for application of time-amount-based DSM program on a cost-based unit commitment problem in order to minimize the energy procurement cost. The ramp down/up rate limit, start-up and shutdown costs, generation capacity, minimum up- and downtimes, minimum and maximum time-dependent operating limits, and power balance criterion are considered as constraints. Simulations are conducted on a 10-unit test system and solved as a mixed integer nonlinear problem under general algebraic mathematical modeling system to find the optimum operating point of thermal units without and with implementation of a DSM strategic program.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

t :

Operating time interval

i :

The thermal generation station

k :

The number of the operating zone in the linearized characteristic of the thermal power plants

P :

The electrical power generation of plant i at operating time period t

FCi, t:

The operation cost of the fossil-fuel-based generating unit i at operating time period t

STCi, t:

The start-up cost of the generation station i at operating time period t

SDCi, t:

The shutdown cost of the generation station i at operating time period t

u i, t :

The binary decision variable equals to 1 if the generating unit i is on at hour t, else it is 0.

\( {P}_i^{\mathrm{max}} \), \( {P}_i^{\mathrm{min}} \):

The maximum and minimum limits of the power generation of the plant i

\( {\underline{P}}_{i,t} \), \( {\overline{P}}_{i,t} \):

The minimum and maximum time-related operating bounds for generation station i

UTi:

The minimum uptime for the power plant i

DTi:

The minimum downtime for the power plant i

yi, t, zi, t:

The start-up and shutdown modes for the power plant i at timeframe t

ai, bi, ci:

The fuel cost indices for the power plant i

\( {C}_{i,\mathrm{ini}}^k \) :

The initial operating point of the unit i in the linearized zone k

\( {C}_{i,\mathrm{fin}}^k \) :

The final operating point of the unit i in the linearized zone k

\( \Delta {P}_i^k \) :

The deviation of the power generation of power plant i in zone k

n :

Number of linear zones

\( {s}_i^k \) :

The slope of the fuel cost characteristic of power plant i in zone k

SUi, SDi:

The boundaries of the start-up and shutdown ramp rates of the power plant i

\( {L}_t^0 \) :

Base load at hour t without applying the DSM program

L t :

Energy demand at time t

DSMt:

Percentage of load decrease at time t

inct:

Percentage of demand increase at hour t

References

  1. H. Anand, N. Narang, J.S. Dhillon, Profit based unit commitment using hybrid optimization technique. Energy 148, 701–715 (2018)

    Article  Google Scholar 

  2. B. Lokeshgupta, S. Sivasubramani, Multi-objective dynamic economic and emission dispatch with demand side management. Int. J. Electr. Power Energy Syst. 97, 334–343 (2018)

    Article  Google Scholar 

  3. V.K. Tumuluru, D.H. Tsang, A two-stage approach for network constrained unit commitment problem with demand response. IEEE Trans. Smart Grid 9(2), 1175–1183 (2018)

    Article  Google Scholar 

  4. L.K. Panwar et al., Binary grey wolf optimizer for large scale unit commitment problem. Swarm Evol. Comput. 38, 251–266 (2018)

    Article  Google Scholar 

  5. K.S. Reddy et al., Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets. Eng. Optim., 1–21 (2018)

    Google Scholar 

  6. K.S. Reddy et al., A new binary variant of sine–cosine algorithm: development and application to solve profit-based unit commitment problem. Arab. J. Sci. Eng. 43(8), 4041–4056 (2018)

    Article  Google Scholar 

  7. H. Liang et al., A multiobjective hybrid bat algorithm for combined economic/emission dispatch. Int. J. Electr. Power Energy Syst. 101, 103–115 (2018)

    Article  Google Scholar 

  8. F. Chen et al., A nonlinear fractional programming approach for environmental–economic power dispatch. Int. J. Electr. Power Energy Syst. 78, 463–469 (2016)

    Article  Google Scholar 

  9. K. Le et al., Potential impacts of clean air regulations on system operations. IEEE Trans. Power Syst. 10(2), 647–656 (1995)

    Article  MathSciNet  Google Scholar 

  10. F.P. Mahdi et al., A holistic review on optimization strategies for combined economic emission dispatch problem. Renew. Sustain. Energy Rev. 81, 3006–3020 (2018)

    Article  Google Scholar 

  11. S. Kuloor, G. Hope, O. Malik, Environmentally constrained unit commitment, in IEE Proceedings C-Generation, Transmission and Distribution (IET, 1992)

    Google Scholar 

  12. F. Jabari, M. Shamizadeh, B. Mohammadi-Ivatloo, Risk-constrained day-ahead economic and environmental dispatch of thermal units using information gap decision theory. Int. Trans. Electr. Energy Syst. 29, e2704 (2018)

    Article  Google Scholar 

  13. S. Bath, J. Dhillon, D. Kothari, Fuzzy satisfying stochastic multi-objective generation scheduling by weightage pattern search methods. Electr. Power Syst. Res. 69(2–3), 311–320 (2004)

    Article  Google Scholar 

  14. M.A. Abido, Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Trans. Power Syst. 18(4), 1529–1537 (2003)

    Article  Google Scholar 

  15. L. Wang, C. Singh, Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm. Electr. Power Syst. Res. 77(12), 1654–1664 (2007)

    Article  Google Scholar 

  16. L. Wang, C. Singh, Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm. Electr. Power Syst. Res. 78(8), 1466–1476 (2008)

    Article  Google Scholar 

  17. G. Morales-España, Á. Lorca, M.M. de Weerdt, Robust unit commitment with dispatchable wind power. Electr. Power Syst. Res. 155, 58–66 (2018)

    Article  Google Scholar 

  18. B. Ming et al., Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: a case study in China. Appl. Energy 228, 1341–1352 (2018)

    Article  Google Scholar 

  19. K. Ghahary et al., Optimal reserve market clearing considering uncertain demand response using information gap decision theory. Int. J. Electr. Power Energy Syst. 101, 213–222 (2018)

    Article  Google Scholar 

  20. A. Soroudi, A. Rabiee, A. Keane, Information gap decision theory approach to deal with wind power uncertainty in unit commitment. Electr. Power Syst. Res. 145, 137–148 (2017)

    Article  Google Scholar 

  21. H. Park, Y.G. Jin, J.-K. Park, Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating. Int. J. Electr. Power Energy Syst. 102, 211–222 (2018)

    Article  Google Scholar 

  22. B. Durga Hari Kiran, M. Sailaja Kumari, Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: a probabilistic unit commitment approach. Int. J. Electr. Power Energy Syst. 81, 114–122 (2016)

    Article  Google Scholar 

  23. Z. Soltani et al., Integration of smart grid technologies in stochastic multi-objective unit commitment: an economic emission analysis. Int. J. Electr. Power Energy Syst. 100, 565–590 (2018)

    Article  Google Scholar 

  24. S. Badakhshan, M. Kazemi, M. Ehsan, Security constrained unit commitment with flexibility in natural gas transmission delivery. J. Nat. Gas Sci. Eng. 27, 632–640 (2015)

    Article  Google Scholar 

  25. P. Siano, Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)

    Article  Google Scholar 

  26. M.H. Albadi, E.F. El-Saadany, A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008)

    Article  Google Scholar 

  27. M. Vahid-Ghavidel, N. Mahmoudi, B. Mohammadi-Ivatloo, Self-scheduling of demand response aggregators in short-term markets based on information gap decision theory. IEEE Trans. Smart Grid 10(2), 2115–2126 (2019)

    Article  Google Scholar 

  28. A.R. Jordehi, Optimisation of demand response in electric power systems, a review. Renew. Sustain. Energy Rev. 103, 308–319 (2019)

    Article  Google Scholar 

  29. A. Rabiee et al., Corrective voltage control scheme considering demand response and stochastic wind power. IEEE Trans. Power Syst. 29(6), 2965–2973 (2014)

    Article  Google Scholar 

  30. M. Rahmani-andebili, Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets. Electr. Power Syst. Res. 132, 115–124 (2016)

    Article  Google Scholar 

  31. M. Vahid-Pakdel et al., Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response. Energy Convers. Manage. 145, 117–128 (2017)

    Article  Google Scholar 

  32. J. Vuelvas, F. Ruiz, G. Gruosso, Limiting gaming opportunities on incentive-based demand response programs. Appl. Energy 225, 668–681 (2018)

    Article  Google Scholar 

  33. N. Ruiz, I. Cobelo, J. Oyarzabal, A direct load control model for virtual power plant management. IEEE Trans. Power Syst. 24(2), 959–966 (2009)

    Article  Google Scholar 

  34. A. Dorri, et al. A Secure and Efficient Direct Power Load Control Framework Based on Blockchain. arXiv preprint arXiv:1812.08497 (2018)

    Google Scholar 

  35. H. Aalami, M.P. Moghaddam, G. Yousefi, Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl. Energy 87(1), 243–250 (2010)

    Article  Google Scholar 

  36. L. Yao, W.H. Lim, Optimal purchase strategy for demand bidding. IEEE Trans. Power Syst. 33(3), 2754–2762 (2018)

    Article  Google Scholar 

  37. J. Iria, F. Soares, M. Matos, Optimal supply and demand bidding strategy for an aggregator of small prosumers. Appl. Energy 213, 658–669 (2018)

    Article  Google Scholar 

  38. P. Yang, G. Tang, A. Nehorai, A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Trans. Power Syst. 28(2), 884–892 (2013)

    Article  Google Scholar 

  39. E. Celebi, J.D. Fuller, Time-of-use pricing in electricity markets under different market structures. IEEE Trans. Power Syst. 27(3), 1170–1181 (2012)

    Article  Google Scholar 

  40. S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program. Appl. Energy 187, 449–464 (2017)

    Article  Google Scholar 

  41. D. Jang et al., Demand responses of Korean commercial and industrial businesses to critical peak pricing of electricity. J. Cleaner Prod. 90, 275–290 (2015)

    Article  Google Scholar 

  42. D. Jang et al., Variability of electricity load patterns and its effect on demand response: a critical peak pricing experiment on Korean commercial and industrial customers. Energy Policy 88, 11–26 (2016)

    Article  Google Scholar 

  43. M. Kazemi, B. Mohammadi-Ivatloo, M. Ehsan, Risk-based bidding of large electric utilities using information gap decision theory considering demand response. Electr. Power Syst. Res. 114, 86–92 (2014)

    Article  Google Scholar 

  44. A.J. Conejo, J.M. Morales, L. Baringo, Real-time demand response model. IEEE Trans. Smart Grid 1(3), 236–242 (2010)

    Article  Google Scholar 

  45. A. Soroudi, Power System Optimization Modeling in GAMS (Springer, 2017)

    Google Scholar 

  46. https://www.gams.com/

  47. https://www.gams.com/latest/docs/S_SBB/

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Farkhondeh Jabari & Behnam Mohammadi-Ivatloo

  2. Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran

    Mousa Mohammadpourfard

Authors
  1. Farkhondeh Jabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mousa Mohammadpourfard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Behnam Mohammadi-Ivatloo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farkhondeh Jabari .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Sayyad Nojavan

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Kazem Zare

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jabari, F., Mohammadpourfard, M., Mohammadi-Ivatloo, B. (2020). Implementation of Demand Response Programs on Unit Commitment Problem. In: Nojavan, S., Zare, K. (eds) Demand Response Application in Smart Grids. Springer, Cham. https://doi.org/10.1007/978-3-030-32104-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32104-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32103-1

  • Online ISBN: 978-3-030-32104-8

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation