An efficient hybrid structure to solve economic-environmental energy scheduling integrated with demand side management programs

  • Sobhan Dorahaki
  • Masoud Rashidinejad
  • Mojgan Mollahassani-pourEmail author
  • Alireza Bakhshai
Original Paper


Nowadays, by increasing the amount of greenhouse gases (GHGs) emitted from electricity generation sector, substantial challenges will be forced to the power system scheduling problems. However, under the smart environment, the amount of GHGs can be declined by handling the consumers’ demand. In this regard, this paper presents a two-stage framework concentrating on demand side management including energy efficiency programs and demand response programs as significant aspects of smart power system to handle system expenditures as well as pollutants. Therefore, in the first stage, investment rate on energy efficiency is specified over the midterm horizon time, and in the second one, a formulation of cost-and-emission-based generation scheduling in the presence of demand side management programs has been performed. Finally, a novel index the so-called emission mitigation index is nominated to investigate the impacts of demand side management on evaluation of GHGs emissions’ level. The IEEE 10 unit standard test system is conducted to evaluate the capability of demand side management in reduction of GHGs emissions and financial burden. Results indicate that by efficient utilization of demand side management programs, significant improvement is obtained.


Demand response Energy efficiency Generation scheduling Pollutants reduction Smart environment 

List of symbols

Indices and sets


Generating unit index


Segment index


Time index


Number of generating units

\( N_{\text{SF}} \)

Number of segments for linearized generation cost curve

\( N_{\text{SI}} \)

Number of segments for linearized incentive curve

\( N_{\text{SE}} \)

Number of segments for linearized emission curve

\( N_{t} \)

Scheduling time horizon


\( {\text{EC}}\, (\cdot ) \)

Emission function of a unit

\( {\text{EEI}} \)

Investment rate of final energy efficiency programs

\( {\text{FC}}\, (\cdot ) \)

Generation cost of a unit

\( {\text{inc}}\, (\cdot ) \)

Incentive of demand response programs

\( {\text{Inc}}_{\text{Ttl}} \, (\cdot ) \)

Total incentive to customers in a period

\( P\, (\cdot ) \)

Generated power of a unit in a period

\( p_{m} \, (\cdot ) \)

Generated power in mth segment of linearized generation cost curve

\( q_{m} \, (\cdot ) \)

Generated power in mth segment of linearized emission curve

\( u\, (\cdot ) \)

Commitment status of a unit in a period

\( y\, (\cdot ) \)

Startup status of a unit in a period

\( z\, (\cdot ) \)

Shut down status of a unit in a period

\( \rho \, (\cdot )/\rho_{0} \, (\cdot ) \)

Electricity price in a period after/before implementing demand side programs

\( \varpi_{m} \, (\cdot ) \)

Award of mth segment in linearized total incentive curve


\( a\, (\cdot ), b\, (\cdot ),C\, (\cdot ) \)

Generation cost coefficient

\( {\text{CSC}}\, (\cdot ) \)

Cold startup cost of a unit

\( {\text{CST}}\, (\cdot ) \)

Cold startup time of a unit

\( d \)

Week duration in hour

\( D\, (\cdot ) \)

Demand in a period

\( E\, (\cdot ) \)

Price elasticity of demand

\( \underline{\text{EC}} \,( \cdot ) \)

Lower limit on the emission of a unit

\( e_{m} \, (\cdot ) \)

Slope of mth segment in linearized emission curve

\( {\text{EEI}}_{0} \)

Investment rate of initial energy efficiency programs

\( \underline{\text{FC}} \, (\cdot ) \)

Lower limit on generation cost of a unit

\( {\text{HSC}}\, (\cdot ) \)

Hot startup cost of a unit

\( {\text{inc}}_{{\max} } /{\text{inc}}_{{\min} } \)

Maximum/minimum incentive level

\( {\text{invcost}} \)

Investment cost on energy efficiency programs

\( {\text{MD}}\, (\cdot ) \)

Minimum down time

\( {\text{MU}}\, (\cdot ) \)

Minimum up time

\( \underline{P} \, (\cdot )/ \bar{P}\, (\cdot ) \)

Lower/upper generation capacity of a unit

\( \bar{P}_{m} \, (\cdot ) \)

Maximum generation in segment m in a period

\( {\text{RDR}}\, (\cdot ) \)

Ramp down rate

\( {\text{RUR}}\, (\cdot ) \)

Ramp up rate

\( s_{m} \)

Slope of mth segment in linearized incentive curve

\( {\text{SD}}\, (\cdot ) \)

Shutdown cost of a unit

\( {\text{SR}}\, (\cdot ) \)

Spinning reserve capacity in a period

\( {\text{SU}}\, (\cdot ) \)

Startup cost of a unit

\( {\text{TC}}\, (\cdot ) \)

Number of continuous shutdown hours of a unit

\( X\, (\cdot )^{\text{off}} \)

Continuous time of off status in a unit

\( X\, (\cdot )^{\text{on}} \)

Continuous time of on status in a unit

\( \alpha \, (\cdot ), \beta \, (\cdot ),\gamma \, (\cdot ) \)

Emission coefficient of a unit

\( \lambda \)

Penetration rate of energy efficiency programs

\( \eta \)

Penetration rate of demand response programs

\( \delta \, (\cdot ) \)

Efficiency-price cross-elasticity of demand

\( \psi \, (\cdot ) \)

Emission penalty factor of a unit

\( \tau \)

Energy efficiency elasticity of demand

\( \Lambda_{m} \,( \cdot ) \)

Slope of mth segment in linearized generation cost curve

\( \mu \, (\cdot ) \)

Demand ration to classify incentive in a period



  1. 1.
    Mollahassani-pour M, Rashidinejad M, Abdollahi A (2017) Appraisal of eco-friendly preventive maintenance scheduling strategy impacts on GHG emissions mitigation in smart grids. J Clean Prod 143:212–223CrossRefGoogle Scholar
  2. 2.
    International Energy Agency (IEA) (2016) CO2 emissions statistics, FranceGoogle Scholar
  3. 3.
    Chen Y, Ebenstein A, Greenstone M, Li H (2013) Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc Natl Acad Sci U S A 110(32):12936–12941CrossRefGoogle Scholar
  4. 4.
    Kakran S, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: a review. Renew Sustain Energy Rev 81:524–535CrossRefGoogle Scholar
  5. 5.
    Hashemi-Dezaki H, Askarian-Abyaneh H, Shams-Ansari A, DehghaniSanij M, Hejazi MA (2017) Direct cyber-power interdependencies-based reliability evaluation of smart grids including wind/solar/diesel distributed generations and plug-in hybrid electrical vehicles. Int J Electr Power Energy Syst 93(Supplement C):1–14CrossRefGoogle Scholar
  6. 6.
    Bayindir R, Colak I, Fulli G, Demirtas K (2016) Smart grid technologies and applications. Renew Sustain Energy Rev 66(Supplement C):499–516CrossRefGoogle Scholar
  7. 7.
    Tuballa ML, Abundo ML (2016) A review of the development of smart grid technologies. Renew Sustain Energy Rev 59(Supplement C):710–725CrossRefGoogle Scholar
  8. 8.
    Makhijani A (2007) Carbon-free and nuclear-free: a roadmap for U.S. Energy Policy. RDR Books, MuskegonGoogle Scholar
  9. 9.
    Dorahaki S, Rashidinejad M, Abdollahi A, Mollahassani-pour M (2018) A novel two-stage structure for coordination of energy efficiency and demand response in the smart grid environment. Int J Electr Power Energy Syst 97:353–362CrossRefGoogle Scholar
  10. 10.
    Reddy SS, Abhyankar AR, Bijwe PR (2012) Market clearing for a wind-thermal power system incorporating wind generation and load forecast uncertainties. IEEE Power Energy Soc Gen Meet 2012:1–8Google Scholar
  11. 11.
    Reddy SS, Bijwe PR, Abhyankar AR (2015) Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Syst J 9(1):152–164CrossRefGoogle Scholar
  12. 12.
    Chen J, Cheng S, Song M, Wu Y (2016) A carbon emissions reduction index: Integrating the volume and allocation of regional emissions. Appl Energy 184:1154–1164CrossRefGoogle Scholar
  13. 13.
    Saber AY, Venayagamoorthy GK (2010) Intelligent unit commitment with vehicle-to-grid—a cost-emission optimization. J Power Sources 195(3):898–911CrossRefGoogle Scholar
  14. 14.
    Nazari ME, Ardehali MM (2017) Profit-based unit commitment of integrated CHP-thermal-heat only units in energy and spinning reserve markets with considerations for environmental CO2 emission cost and valve-point effects. Energy 133(Supplement C):621–635CrossRefGoogle Scholar
  15. 15.
    Wang B, Wang S, Zhou X, Watada J (2016) Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties. Energy 111(Supplement C):18–31CrossRefGoogle Scholar
  16. 16.
    Mohammad N, Mishra Y (2018) Coordination of wind generation and demand response to minimise operation cost in day-ahead electricity markets using bi-level optimisation framework. IET Gener Transm Distrib 12(16):3793–3802CrossRefGoogle Scholar
  17. 17.
    Mohammad N, Mishra Y (2019) Retailer’s risk-aware trading framework with demand response aggregators in short-term electricity markets. IET Gener Transm Distrib 13(13):2611–2618CrossRefGoogle Scholar
  18. 18.
    Lokeshgupta B, Sivasubramani S (2018) Multi-objective dynamic economic and emission dispatch with demand side management. Int J Electr Power Energy Syst 97:334–343CrossRefGoogle Scholar
  19. 19.
    Abdollahi A, Parsa Moghaddam M, Rashidinejad M, Sheikh-El-Eslami MK (2012) Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Trans Smart Grid 3(1):12–25CrossRefGoogle Scholar
  20. 20.
    Alham MH, Elshahed M, Ibrahim DK, Abo El Zahab EED (2016) A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew. Energy 96(Part A):800–811CrossRefGoogle Scholar
  21. 21.
    Aghdam FH, Ghaemi S, Kalantari NT (2018) Evaluation of loss minimization on the energy management of multi-microgrid based smart distribution network in the presence of emission constraints and clean productions. J Clean Prod 196:185–201CrossRefGoogle Scholar
  22. 22.
    Majidi M, Nojavan S, Zare K (2017) A cost-emission framework for hub energy system under demand response program. Energy 134:157–166CrossRefGoogle Scholar
  23. 23.
    Zhang N, Hu Z, Dai D, Dang S, Yao M, Zhou Y (2015) Unit commitment model in smart grid environment considering carbon emissions trading. IEEE Trans Smart Grid 7(1):420–427CrossRefGoogle Scholar
  24. 24.
    Aghajani GR, Shayanfar HA, Shayeghi H (2017) Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 126(Supplement C):622–637CrossRefGoogle Scholar
  25. 25.
    Lee J, Yoo S, Kim J, Song D, Jeong H (2018) Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response. Energy 144:1052–1063CrossRefGoogle Scholar
  26. 26.
    National Action Plan for Energy Efficiency (2010) Coordination of energy efficiency and demand response: a resource of the national action plan for energy efficiencyGoogle Scholar
  27. 27.
    Talebizadeh E, Rashidinejad M, Abdollahi A (2014) Evaluation of plug-in electric vehicles impact on cost-based unit commitment. J Power Sources 248:545–552CrossRefGoogle Scholar
  28. 28.
    Saber AY, Venayagamoorthy GK (2011) Plug-in vehicles and renewable energy sources for cost and emission reductions. IEEE Trans Ind Electron 58(4):1229–1238CrossRefGoogle Scholar
  29. 29.
    Mollahassani-Pour M, Rashidinejad M, Pourakbari-Kasmaei M (2019) Environmentally constrained reliability-based generation maintenance scheduling considering demand-side management. IET Gener Transm Distrib 13(7):1153–1163CrossRefGoogle Scholar
  30. 30.
    Moghaddam MP, Abdollahi A, Rashidinejad M (2011) Flexible demand response programs modeling in competitive electricity markets. Appl Energy 88(9):3257–3269CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sobhan Dorahaki
    • 1
  • Masoud Rashidinejad
    • 1
  • Mojgan Mollahassani-pour
    • 2
    Email author
  • Alireza Bakhshai
    • 3
  1. 1.Department of Electrical EngineeringShahid Bahonar University of KermanKermanIran
  2. 2.Faculty of Electrical and Computer EngineeringUniversity of Sistan and BaluchestanZahedanIran
  3. 3.Electrical and Computer EngineeringQueen’s UniversityKingstonCanada

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