Skip to main content

Advertisement

SpringerLink
Log in

A novel enviro-economic three-stage market-based energy management considering energy storage systems and demand response programs for networked smart microgrids

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Today’s power market has been influenced by the introduction of smart microgrids (MGs) to the electricity infrastructure. Furthermore, the operation of the power system has already been affected by exploiting the demand response (DR) programs and make more use of the energy storage. Therefore, the study of the simultaneous presence of the power market models, DR programs, and energy storage in networked smart microgrids is crucial. In this paper, a novel multi-stage optimization model is presented to indicate the effects of DR programs on the market-based scheduling of the smart networked microgrids’ performance. In the first stage, optimal energy management has been carried out, and each microgrid system operator in the environmental smart grid proposes its power prices and power quantities to participate in the power market. Then, in the second stage, the market-based energy model is implemented, and the independent system operator (ISO) clears the market. The market-clearing stage is led to specify the prices and the amounts of energy that each microgrid can exchange. Also, in the last stage, energy management has been implemented based on the output parameters, which are submitted from the second stage. The objective function is defined as the mixed-integer nonlinear programming (MINLP) model, which has been implemented in the GAMS software and using BARON as the solver. The results generally show that the bidding strategy of the MGs can effectively control the final operation cost and the emission. However, enjoying these benefits requires accurate pricing of MGs. It also reveals that the DR programs are useful in emission mitigation programs. As well, DR programs are promising for the market in which the bidding strategy of an MG is not successful.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

\(t\) :

Time (h)

\(A\) :

WT blade area (m2)

\({C}_{M-\mathrm{CHP}}\) :

CHP maintenance cost ($)

\({C}_{\mathrm{OP}-\mathrm{CHP}}\) :

CHP operation cost ($/kWh)

\({C}_{\mathrm{OP}-\mathrm{WT}}\) :

WT operation cost ($)

\({C}_{M-\mathrm{WT}}\) :

WT constant cost ($)

\({C}_{\mathrm{OP}-\mathrm{PV}}\) :

PV operation cost ($)

\({C}_{M-\mathrm{PV}}\) :

PV constant cost ($)

\({C}_{M-\mathrm{MT}}\) :

MT maintenance cost ($)

\({C}_{\mathrm{OP}-\mathrm{MT}}\) :

MT operation cost ($/kWh)

\({C}_{M-\mathrm{Boiler}}\) :

Boiler maintenance cost ($)

\({C}_{\mathrm{OP}-\mathrm{Boiler}}\) :

Boiler operation cost ($/kWh)

\({C}_{M-\mathrm{ES}}\) :

ES maintenance cost ($)

\({C}_{\mathrm{OP}-\mathrm{ES}}\) :

ES operation cost ($/kWh)

\({C}_{M-\mathrm{TS}}\) :

TS maintenance cost ($)

\({C}_{\mathrm{OP}-\mathrm{TS}}\) :

TS operation cost ($/kWh)

C load (t):

Critical load demand at time t (kW)

C fuel :

Fuel cost ($)

C Buy :

Buying energy cost ($)

c Sell :

Selling energy cost ($)

c Shed :

Load shedding cost ($)

EFCHP :

CHP emission factor (kg/kWh)

EFMT :

MT emission factor (kg/kWh)

EFBoiler :

Boiler emission factor (kg/kWh)

EFMG :

MG emission factor (kg/kWh)

ELload (t):

Electrical load demand at time t (kW)

\( E_{S}^{{{\text{max}}}} \) :

ES maximum energy (kWh)

\( E_{S}^{{{\text{min}}}} \) :

ES minimum energy (kWh)

\( E_{S} \left( 0 \right) \) :

ES Initial state energy (kWh)

\( G_{{T_{{{\text{STC}}}} }} \) :

Solar radiation on module surface in STC (kW/m2)

\( G_{{T_{{{\text{NOCT}}}} }} \) :

Solar radiation on module surface in NOCT (kW/m2)

\( G_{T} \left( t \right) \) :

Solar radiation on module surface (kW/m2)

\( N_{{{\text{PVs}}}} \) :

Number of series cells in PV module

\( N_{{{\text{PVp}}}} \) :

Number of parallel cells in PV module

\( n_{{{\text{DR}}}} \) :

Penetration rate of demand response

m :

Percentage of demand response

\( P_{{{\text{line}}}} \) :

Limitation of power in line transfer (kW)

\( P_{{{\text{PV,STC}}}} \) :

PV maximum power in STC (kW)

\(P_{{{\text{CHP}}}}^{{{\text{max}}}} \) :

CHP maximum power (kW)

\( P_{{{\text{MT}}}}^{{{\text{max}}}} \) :

MT maximum power (kW)

\( P_{{{\text{boiler}}}}^{{{\text{max}}}} \) :

Boiler maximum power (kW)

\( P_{{{\text{WT}}}}^{{{\text{max}}}} \) :

WT maximum power (kW)

\( P_{{{\text{PV}}}}^{{{\text{max}}}} \) :

PV maximum power (kW)

\( P_{{E - {\text{dech}}}}^{{{\text{max}}}} \) :

Maximum discharge rate of ES (kW)

\( P_{{E - {\text{ch}}}}^{{{\text{max}}}} \) :

Maximum charge rate of ES (kW)

\( P_{{T - {\text{dech}}}}^{{{\text{max}}}} \) :

Maximum discharge rate of TS (kW)

\( P_{{T - {\text{ch}}}}^{{{\text{max}}}} \) :

Maximum charge rate of TS (kW)

\( T_{j} \left( t \right) \) :

PV cell temperature at time t (°C)

\( T_{{j{\text{STC}}}} \) :

PV reference temperature (°C)

\( T_{{{\text{amp}}}} \) :

PV ambient temperature (°C)

\( T_{{{\text{load}}}} \left( t \right) \) :

Thermal load demand at time t (kW)

\( {\text{TE}}_{S}^{{{\text{max}}}} \) :

TS maximum energy (kWh)

\( {\text{TE}}_{S} ^{{{\text{min}}}} \) :

TS minimum energy (kWh)

\( {\text{TF}}_{{{\text{CHP}}}} \) :

Proportion of CHP heat to CHP power

\( V^{{{\text{nom}}}} \) :

Normal wind speed (m/s)

\( V_{t} \) :

Wind speed at time t (m/s)

\(V^{{{\text{cutin}}}} \) :

Minimum wind speed (m/s)

\( V^{{{\text{cutout}}}} \) :

Maximum wind speed (m/s)

\( \eta _{{{\text{CHP}}}} \) :

Electrical efficiency of CHP

\({\eta }_{\mathrm{Boiler}}\) :

Electrical efficiency of boiler

\({\eta }_{\mathrm{MT}}\) :

Electrical efficiency of MT

\({\eta }^{W}\) :

Electrical efficiency of WT

\( \eta _{C}^{E} \) :

Charge efficiency of ES

\( \eta _{D}^{E} \) :

Discharge efficiency of ES

\( \eta _{C}^{T} \) :

Charge efficiency of TS

\( \eta _{D}^{T} \) :

Discharge efficiency of TS

\( \rho \) :

Air density (kg/m3)

\( \rho _{L} \) :

DR cost ($/kW)

\( \gamma \) :

Power-temperature coefficient

\( \theta \) :

Time interval

\( C_{{{\text{CHP}}}} \left( t \right) \) :

Total CHP cost at time t ($)

\( C_{{{\text{PV}}}} \left( t \right) \) :

Total PV cost at time t ($)

\( C_{{{\text{Boiler}}}} \left( t \right) \) :

Total boiler cost at time t ($)

\( C_{{{\text{MT}}}} \left( t \right) \) :

Total MT cost at time t ($)

\( C_{{{\text{Wind}}}} \left( t \right) \) :

Total WT cost at time t ($)

\( C_{{{\text{ES}}}} \left( t \right) \) :

Total ES cost at time t ($)

\( C_{{{\text{TS}}}} \left( t \right) \) :

Total TS cost at time t ($)

\( C_{{{\text{DR}}}} \left( t \right) \) :

Total DR cost at time t ($)

\( C_{{{\text{Shed}}}} \left( t \right) \) :

Total load shedding cost at time t ($)

\( C_{{{\text{Buy}}}} \left( t \right) \) :

Total buying cost at time t ($)

\( C_{{{\text{Sell}}}} \left( t \right) \) :

Total selling cost at time t ($)

\( D_{{{\text{DR}}}} \left( t \right) \) :

Amount of demand response at time t (kW)

\( E_{S} \left( t \right) \) :

ES energy at time t (kWh)

\( ~{\text{EL}}_{{{\text{Shed}}}} \left( t \right) \) :

Electrical load shedding at time t (kW)

\( ~{\text{EM}}_{{{\text{CHP}}}} \left( t \right) \) :

Total CHP emission at time t (kg)

\( {\text{EM}}_{{{\text{MT}}}} \left( t \right) \) :

Total MT emission at time t (kg)

\( {\text{EM}}_{{{\text{Boiler}}}} \left( t \right) \) :

Total boiler emission at time t (kg)

\( {\text{EM}}_{{{\text{MG}}}} \left( t \right) \) :

Total MG emission at time t (kg)

\( P_{{{\text{MG}}}} \left( t \right) \) :

Total power of main grid at time t (kW)

\( P_{{{\text{CHP}}}} \left( t \right) \) :

Total generation power of CHP at time t (kW)

\( P_{{{\text{PV}}}} \left( t \right) \) :

Total generation power of PV at time t (kW)

\( P_{{{\text{Boiler}}}} \left( t \right) \) :

Total generation power of boiler at time t (kW)

\( P_{{{\text{MT}}}} \left( t \right) \) :

Total generation power of MT at time t (kW)

\( P_{{{\text{Wind}}}} \left( t \right) \) :

Total generation power of WT at time t (kW)

\( P_{{{\text{ES}}}} \left( t \right) \) :

Total ES power at time t (kW)

\( P_{{{\text{TS}}}} \left( t \right) \) :

Total TS power at time t (kW)

\( P_{{{\text{Buy}}}} \left( t \right) \) :

Total buying power at time t (kW)

\( P_{{{\text{Sell}}}} \left( t \right) \) :

Total selling power at time t (kW)

\( TE_{S} \left( t \right) \) :

TS energy at time t (kWh

\( u\left( t \right) \) :

Binary variable for power exchange

CHP:

Combined heat and power

CO2 :

Carbon dioxide

DES:

Distributed energy storage

DG:

Distributed generator

DR:

Demand response

ESS:

Energy storage system

ES:

Electrical storage

ISO:

Independent system operator

MC:

Market clearing

MT:

Microturbine

MG:

Main grid

NOCT:

Normal operation cell temperature

PV:

Photovoltaic

STC:

Standard test conditions

TS:

Thermal storage

WT:

Wind turbine

References

  1. Davarzani S, Pisica I, Taylor GA, Munisami KJ (2020) Residential demand response strategies and applications in active distribution network management. Renew Sustain Energy Rev 138:110567

    Article  Google Scholar 

  2. 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):3793–3802

    Article  Google Scholar 

  3. 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 97:353–362

    Article  Google Scholar 

  4. Aghajani G, Shayanfar H, Shayeghi H (2017) Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 126:622–637

    Article  Google Scholar 

  5. Majidi M, Nojavan S, Zare K (2017) A cost-emission framework for hub energy system under demand response program. Energy 134:157–166

    Article  Google Scholar 

  6. David AK, Wen F (2001) Market power in electricity supply. IEEE Trans Energy Convers 16(4):352–360

    Article  Google Scholar 

  7. Morales-España G, Ramos A, García-González J (2013) An MIP formulation for joint market-clearing of energy and reserves based on ramp scheduling. IEEE Trans Power Syst 29(1):476–488

    Article  Google Scholar 

  8. Su W, Wang J (2012) Energy management systems in microgrid operations. Electr J 25(8):45–60

    Article  Google Scholar 

  9. Wang Z, Chen B, Wang J, Chen C (2015) Networked microgrids for self-healing power systems. IEEE Trans Smart Grid 7(1):310–319

    Article  Google Scholar 

  10. Ebrahimi J, Niknam T, Firouzi BB (2021) Electrical and thermal power management in an energy hub system considering hybrid renewables. Electr Eng 103:1965–76

    Article  Google Scholar 

  11. Liu C, Wang Y, Wang L, Chen Z (2019) Load-adaptive real-time energy management strategy for battery/ultracapacitor hybrid energy storage system using dynamic programming optimization. J Power Sour 438:227024

    Article  Google Scholar 

  12. Olivares DE, Cañizares CA, Kazerani M (2011) A centralized optimal energy management system for microgrids. In: 2011 IEEE power and energy society general meeting, pp 1–6

  13. Jiang Q, Xue M, Geng G (2013) Energy management of microgrid in grid-connected and stand-alone modes. IEEE Trans Power Syst 28(3):3380–3389

    Article  Google Scholar 

  14. Elkazaz M, Sumner M, Thomas D (2020) Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation. Int J Electr Power Energy Syst 115:105483

    Article  Google Scholar 

  15. Dorahaki S, Abdollahi A, Rashidinejad M, Moghbeli M (2021) The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost. J Energy Storage 33:102022

    Article  Google Scholar 

  16. Pawar P (2019) Design and development of advanced smart energy management system integrated with IoT framework in smart grid environment. J Energy Storage 25:100846

    Article  Google Scholar 

  17. Khavari F, Badri A, Zangeneh A (2020) Energy management in multi-microgrids considering point of common coupling constraint. Int J Electr Power Energy Syst 115:105465

    Article  Google Scholar 

  18. Nunna HK, Doolla S (2012) An intelligent energy management framework to incorporate demand response and storage in microgrids. In: 2012 annual IEEE India conference (INDICON), pp 1063–1068

  19. Wang Z, Chen B, Wang J, Begovic MM, Chen C (2014) Coordinated energy management of networked microgrids in distribution systems. IEEE Trans Smart Grid 6(1):45–53

    Article  Google Scholar 

  20. Marzband M, Sumper A, Domínguez-García JL, Gumara-Ferret R (2013) Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP. Energy Convers Manag 76:314–322

    Article  Google Scholar 

  21. Tabar VS, Jirdehi MA, Hemmati R (2017) Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option. Energy 118:827–839

    Article  Google Scholar 

  22. Dorahaki S, Dashti R, Shaker HR (2020) Optimal outage management model considering emergency demand response programs for a smart distribution system. Appl Sci 10(21):7406

    Article  Google Scholar 

  23. Hecht C, Sprake D, Vagapov Y, Anuchin A (2021) Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data. Electr Eng 103(3):1791–1806

    Article  Google Scholar 

  24. Asimakopoulou GE, Dimeas AL, Hatziargyriou ND (2013) Leader-follower strategies for energy management of multi-microgrids. IEEE Trans Smart Grid 4(4):1909–1916

    Article  Google Scholar 

  25. Sardou IG, Khodayar ME, Khaledian K, Soleimani-Damaneh M, Ameli MT (2015) Energy and reserve market clearing with microgrid aggregators. IEEE Trans Smart Grid 7(6):2703–2712

    Article  Google Scholar 

  26. 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 196:185–201

    Article  Google Scholar 

  27. Tabar VS, Ghassemzadeh S, Tohidi S (2019) Energy management in hybrid microgrid with considering multiple power market and real time demand response. Energy 174:10–23

    Article  Google Scholar 

  28. Villanueva D, Feijóo A (2010) Wind power distributions: a review of their applications. Renew Sustain Energy Rev 14(5):1490–1495

    Article  Google Scholar 

  29. Riffonneau Y, Bacha S, Barruel F, Ploix S (2011) Optimal power flow management for grid connected PV systems with batteries. IEEE Trans Sustain Energy 2(3):309–320

    Article  Google Scholar 

  30. Nojavan S, allah Aalami H (2015) Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program. Energy Convers Manag 103:1008–1018

    Article  Google Scholar 

  31. Abdollahi A, Moghaddam MP, Rashidinejad M, Sheikh-El-Eslami MK (2011) Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Trans Smart Grid 3(1):12–25

    Article  Google Scholar 

  32. Afshar H, Moravej Z, Niasati M (2013) Modeling and optimization of microgrid considering emissions. In: 2013 smart grid conference (SGC), pp 225–229

  33. Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag 83:58–72

    Article  Google Scholar 

  34. Hawkes A, Leach M (2009) Modelling high level system design and unit commitment for a microgrid. Appl Energy 86(7–8):1253–1265

    Article  Google Scholar 

  35. Talari S, Yazdaninejad M, Haghifam M-R (2015) Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads. IET Gener Transm Distrib 9(12):1498–1509

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Clinical–Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, 7516913817, Iran

    Amir Reza Namjoo, Rahman Dashti & Sobhan Dorahaki

  2. Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Sothern Denmark, Odense, Denmark

    Hamid Reza Shaker

Authors
  1. Amir Reza Namjoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahman Dashti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sobhan Dorahaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamid Reza Shaker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rahman Dashti or Hamid Reza Shaker.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

See Table 10.

Table 10 Market-clearing prices (MCP) and amount of MGs power exchange for 24-h time horizon
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Namjoo, A.R., Dashti, R., Dorahaki, S. et al. A novel enviro-economic three-stage market-based energy management considering energy storage systems and demand response programs for networked smart microgrids. Electr Eng 104, 2893–2910 (2022). https://doi.org/10.1007/s00202-022-01510-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-022-01510-x

Keywords

Search

Navigation