Skip to main content
SpringerLink
Log in

Optimal Scheduling of a Renewable Integrated Combined Heat Power Microgrid with Energy Storage and Load Uncertainties

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Presently, several communities are employing renewable integrated combined heat-power (CHP) microgrids to optimally supply connected heat-power loads. Whilst microturbines are often employed in CHP microgrids, their operational flexibility as a CHP technology remains underexamined. The proposed work studies this perspective with acceptable penetrations of renewable energy sources (RES). Dynamic scheduling of combined heat-power islanded microgrid with RES and energy storage is presented for optimizing cost, emission, losses, and heat output considering RES and load uncertainties. RES uncertainties are modeled using Weibull distribution while load uncertainties are generated stochastically. To obtain the best solution for the contradictory multiple objectives and to reduce dependency on any specific tuning parameter, a fuzzy attainment module is integrated into the modified differential evolution algorithm with dynamic mutation rates. First, the operational flexibility of cogeneration units with RES and RES uncertainties is studied without storage. Secondly, with storage, and finally with storage under different load uncertainty scenarios. With both electrical and thermal storage, total operating costs are found to be reduced by 2.6%, emission by 1.2%, waste heat by 12.1%, and power losses by 25.4% per day. Random load scenarios in ± 25% uncertainty range were found to produce variations in cost in the range of − 2.49% to + 5.93% and emission from − 1.13 to 2.22% showing the capability of the proposed approach under practical conditions. This study demonstrates a cost-effective combined heat-power dispatch to do away with unwarranted auxiliary units with environmental, and climatic benefits.

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

Similar content being viewed by others

Abbreviations

\({a}_{p},{b}_{p},{c}_{p}\) :

Cost coefficients of p-th DER unit in $, $/kW and $/kW2

\({\alpha }_{p}\), \({\beta }_{p}\), \({\gamma }_{p}\) :

Emission coefficients in kg, kg/kW, and kg/kW2

\({\mathrm{Bat}}_{\mathrm{cap}}\) :

Rated battery capacity in kWh

\({\mathrm{Bat}}^{k}\) :

The current charge status of the battery in kWh

\({\mathrm{Bat}}^{k-1}\) :

Battery charge status at the previous hour in kWh

\({\mathrm{BC}}^{k}/{\mathrm{BD}}^{k}\) :

Hourly charge/discharge capacity of the battery in kW

\({\mathrm{Bat}}_{\mathrm{min}}\)/\({\mathrm{Bat}}_{\mathrm{max}}\) :

Minimum/Maximum battery state of charge limit in kWh

\(\mathrm{Dcost}\) :

Direct wind power cost in $/h

\({d}_{d}\) :

Direct cost coefficient of the wind power unit in $/kW

\({\mathrm{Fcost}}^{k}\) :

Power generation cost of cogeneration unit at hour k in $/h

\({f}_{\mathrm{W}}\left(w\right)\) :

The Weibull probability distribution function for wind power

\({H}_{\mathrm{D}}^{k} \) :

Hourly thermal load demand in kWh

\({H}_{G}\) :

Total by-product waste heat produced by cogeneration units

\({H}_{o}\) :

The heat-to-power ratio of the p-th cogeneration unit in kWh/kW

\({H}_{\mathrm{BT}}^{k}\) :

The surplus heat going to the heat buffer tank at hour k in kWh

\({I}\) :

On/off status of the solar power unit

k :

Hourly time intervals for the day

\({k}_{\mathrm{p}}\) :

Wind spillage penalty cost coefficient, $/kW

\({k}_{\mathrm{r}}\) :

Reserve scheduling penalty cost coefficient in $/kW

\({\eta }_{\mathrm{HEx},p}\) :

Heat exchanger efficiency of the p-th generation unit

\({\eta }_{gp}\) :

Thermal efficiency of the p-th cogeneration unit

\({N}_{g}\) :

Number of cogeneration units

\({\eta }_{\mathrm{ch}}/{\eta }_{\mathrm{dis}}\) :

Charging/Discharging efficiency of the battery

\({P}_{D}^{k}\) :

Hourly electrical load in kW

\({\mathrm{Pen}}_{\mathrm{cost}}\) :

\({\mathrm{Pen}}_{\mathrm{cost}}\) is the wind uncertainty costs due to wind spillage in $/kWh

\({P}_{gp}^{k}\) :

Power generated by the p-th cogeneration unit at hour k in kW

\({P}_{gp,\mathrm{min}}\) :

Lower power bounds of p-th generator unit in kW

\({P}_{gp,\mathrm{max}} \) :

Upper power bounds of p-th generator unit in kW

\({P}_{gp}^{k-1}\) :

Previous hour operational capacity of p-th cogeneration unit in kW

\({P}_{gq }^{k }\) :

Power generated by the q-th cogeneration unit at hour k in kW

\({P}_{\mathrm{L}}^{k}\) :

Power loss in kW at hour k

\({P}_{\mathrm{s}}^{k}\) :

Power output in kW of the solar PV unit at hour k

\({P}_{\mathrm{s},\mathrm{r}}\) :

Rated capacity of the solar photovoltaic unit in kW

\({\mathrm{PVcost}}^{k}\) :

Power generation cost of solar PV unit at hour k in $/h

\(\mathrm{PVrate}\) :

Per unit cost of solar power in $/h

\({PV}_{\mathrm{irr}}\left(k\right)\) :

Hourly solar irradiation in W/m2 at hour k

\({P}_{\mathrm{w}}^{k}\) :

Power generated by wind power unit at hour k in kW

\({P}_{\mathrm{w},\mathrm{r}}\) :

Rated wind power in kW

\({\mathrm{Res}}_{\mathrm{cost}}\) :

\({\mathrm{Res}}_{\mathrm{cost}}\) Is wind uncertainty costs due to reserve scheduling in $/kWh

T :

The scheduling period

\({t}_{o }\) :

Atmospheric temperature in °C

\({t}_{\mathrm{amb }}\) :

Ambient temperature in °C

\({t}_{\mathrm{coeff}}\) :

Temperature coefficient of the solar panel in % °C)

\({\mathrm{UR}}_{p/}{\mathrm{DR}}_{p}\) :

Maximum ramp-up/ramp-down rate of p-th generating unit in kW/h

\({U}_{00},{U}_{0\mathrm{p}}\) and \({U}_{pq}\) :

Loss coefficients of cogeneration units in kW, dimensionless, and kW−1

\({v}_{\mathrm{in }}\) :

Cut-in speed of wind turbine in miles/h

\({v}_{\mathrm{out }}\) :

Cut-out speed of wind turbine in miles/h

\({v}_{\mathrm{r}}\) :

Rated wind speed in miles/h

\(\mathrm{W}\) :

Wind power random variable in kW

\({\mathrm{Wcost}}^{k}\) :

Wind power generation cost at hour k in $/h

References

  1. Li, Y.; Miao, S.; Yin, B.; Han, J.; Zhang, S.; Wang, J.; Luo, X.: Combined heat and power dispatch considering advanced adiabatic compressed air energy storage for wind power accommodation. Energy Convers. Manag. 200, 112091 (2019). https://doi.org/10.1016/j.enconman.2019.112091

    Article  Google Scholar 

  2. Fang, Y.; Zhao, S.: Risk-constrained optimal scheduling with combining heat and power for concentrating solar power plants. Sol. Energy 208, 937–948 (2020). https://doi.org/10.1016/j.solener.2020.08.043

    Article  Google Scholar 

  3. Basu, M.: Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm. Energy (2022). https://doi.org/10.1016/j.energy.2022.123396

    Article  Google Scholar 

  4. Younes, Z.; Alhamrouni, I.; Mekhilef, S.; Reyasudin, M.: A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Eng. J. 12, 1985–1994 (2021). https://doi.org/10.1016/j.asej.2020.10.021

    Article  Google Scholar 

  5. Wang, H.; Xing, H.; Luo, Y.; Zhang, W.: Optimal scheduling of micro-energy grid with integrated demand response based on chance-constrained programming. Int. J. Electr. Power Energy Syst. (2023). https://doi.org/10.1016/j.ijepes.2022.108602

    Article  Google Scholar 

  6. Jordehi, A.R.: Economic dispatch in grid-connected and heat network-connected CHP microgrids with storage systems and responsive loads considering reliability and uncertainties. Sustain. Cities Soc. 73, 103101 (2021). https://doi.org/10.1016/j.scs.2021.103101

    Article  Google Scholar 

  7. Eskandari, H.; Kiani, M.; Zadehbagheri, M.; Niknam, T.: Optimal scheduling of storage device, renewable resources and hydrogen storage in combined heat and power microgrids in the presence plug-in hybrid electric vehicles and their charging demand. J. Energy Stor. (2022). https://doi.org/10.1016/j.est.2022.104558

    Article  Google Scholar 

  8. Zhang, L.; Guo, Q.; Liu, M.; Yang, N.; Gao, R.; Sobhani, B.: Optimal dispatch of dynamic power and heat considering load management, water pump system, and renewable resources by grasshopper optimization algorithm. J. Energy Stor. (2023). https://doi.org/10.1016/j.est.2022.106166

    Article  Google Scholar 

  9. Wang, F.; Liao, X.; Fang, N.; Jiang, Z.: Optimal scheduling of regional combined heat and power system based on improved MFO algorithm. Energies (2022). https://doi.org/10.3390/en15093410

    Article  Google Scholar 

  10. Wu, X.; Liao, B.; Su, Y.; Li, S.: Multi-objective and multi-algorithm operation optimization of integrated energy system considering ground source energy and solar energy. Int. J. Electr. Power Energy Syst. (2023). https://doi.org/10.1016/j.ijepes.2022.108529

    Article  Google Scholar 

  11. Aghdam, F.H.; Mudiyanselage, M.W.; Mohammadi-Ivatloo, B.; Marzband, M.: Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management. Appl. Energy (2023). https://doi.org/10.1016/j.apenergy.2022.120569

    Article  Google Scholar 

  12. Salari, A.; Ahmadi, S.E.; Marzband, M.; Zeinali, M.: Fuzzy Q-learning-based approach for real-time energy management of home microgrids using cooperative multi-agent system. Sustain. Cities Soc. 95, e104528 (2023). https://doi.org/10.1016/j.scs.2023.104528

    Article  Google Scholar 

  13. Ahmadi, S.E.; Kazemi-Razi, S.M.; Marzband, M.; Ikpehai, A.; Abusorrah, A.: Multi-objective stochastic techno-economic-environmental optimization of distribution networks with G2V and V2G systems. Electr. Power Syst. Res. (2023). https://doi.org/10.1016/j.epsr.2023.109195

    Article  Google Scholar 

  14. Daramola, A.S.; Ahmadi, S.E.; Marzband, M.; Ikpehai, A.: A cost-effective and ecological stochastic optimization for integration of distributed energy resources in energy networks considering vehicle-to-grid and combined heat and power technologies. J. Energy Stor. (2023). https://doi.org/10.1016/j.est.2022.106203

    Article  Google Scholar 

  15. Shaheen, A.M.; Elsayed, A.M.; Ginidi, A.R.; El-sehiemy, R.A.; Alharthi, M.M.; Ghoneim, S.S.M.: A novel improved marine predators algorithm for combined heat and power economic dispatch problem. Alex. Eng. J. 61, 1834–1851 (2022). https://doi.org/10.1016/j.aej.2021.07.001

    Article  Google Scholar 

  16. Sharifian, Y.; Abdi, H.: Solving multi-zone combined heat and power economic emission dispatch problem considering wind uncertainty by applying grasshopper optimization algorithm. Sustain. Energy Technol. Assess. (2022). https://doi.org/10.1016/j.seta.2022.102512

    Article  Google Scholar 

  17. Kumar Jadoun, V.; Rahul Prashanth, G.; Suhas Joshi, S.; Narayanan, K.; Malik, H.; García Márquez, F.P.: Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled whale optimization algorithm. Appl. Energy (2022). https://doi.org/10.1016/j.apenergy.2022.119033

    Article  Google Scholar 

  18. Ramachandran, M.; Mirjalili, S.; Nazari-Heris, M.; Parvathysankar, D.S.; Sundaram, A.; Charles Gnanakkan, C.A.R.: A hybrid grasshopper optimization algorithm and Harris Hawks optimizer for combined heat and power economic dispatch problem. Eng. Appl. Artif. Intell. (2022). https://doi.org/10.1016/j.engappai.2022.104753

    Article  Google Scholar 

  19. Shukla, S.; Pandit, M.: Multi-objective fuzzy rank based scheduling of utility connected microgrid with high renewable energy using differential evolution with dynamic mutation. Int. Trans. Electr. Energy Syst. (2021). https://doi.org/10.1002/2050-7038.12788

    Article  Google Scholar 

  20. Pant, M.; Zaheer, H.; Garcia-Hernandez, L.; Abraham, A.: Differential evolution: a review of more than two decades of research. Eng. Appl. Artif. Intell. (2020). https://doi.org/10.1016/j.engappai.2020.103479

    Article  Google Scholar 

  21. Alomoush, M.I.: Microgrid dynamic combined power–heat economic-emission dispatch with deferrable loads and price-based energy storage elements and power exchange. Sustain. Energy Grids Netw. 26, 100479 (2021). https://doi.org/10.1016/j.segan.2021.100479

    Article  Google Scholar 

  22. Xin-gang, Z.; Ze-qi, Z.; Yi-min, X.; Jin, M.: Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization. Energy 195, 117014 (2020). https://doi.org/10.1016/j.energy.2020.117014

    Article  Google Scholar 

  23. Zhang, J.; Kong, X.; Shen, J.; Sun, L.: Day-ahead optimal scheduling of a standalone solar-wind-gas based integrated energy system with and without considering thermal inertia and user comfort. J. Energy Stor. (2023). https://doi.org/10.1016/j.est.2022.106187

    Article  Google Scholar 

  24. Basu, A.K.; Bhattacharya, A.; Chowdhury, S.; Chowdhury, S.P.: Planned scheduling for economic power sharing in a CHP-based micro-grid. IEEE Trans. Power Syst. 27, 30–38 (2012). https://doi.org/10.1109/TPWRS.2011.2162754

    Article  Google Scholar 

  25. Hetzer, J.; Yu, D.C.; Bhattarai, K.: An economic dispatch model incorporating wind power. IEEE Trans. Energy Convers. 23, 603–611 (2008). https://doi.org/10.1109/TEC.2007.914171

    Article  Google Scholar 

  26. Khan, N.A.; Awan, A.B.; Mahmood, A.; Razzaq, S.; Zafar, A.; Sidhu, G.A.S.: Combined emission economic dispatch of power system including solar photo voltaic generation. Energy Convers. Manag. 92, 82–91 (2015). https://doi.org/10.1016/j.enconman.2014.12.029

    Article  Google Scholar 

  27. Sadeghian, H.R.; Ardehali, M.M.: A novel approach for optimal economic dispatch scheduling of integrated combined heat and power systems for maximum economic profit and minimum environmental emissions based on Benders decomposition. Energy 102, 10–23 (2016). https://doi.org/10.1016/j.energy.2016.02.044

    Article  Google Scholar 

  28. Dubey, H.M.; Pandit, M.; Panigrahi, B.K.: Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renew. Energy 83, 188–202 (2015). https://doi.org/10.1016/j.renene.2015.04.034

    Article  Google Scholar 

  29. Storn, R.; Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  30. Nazari-Heris, M.; Mohammadi-Ivatloo, B.; Gharehpetian, G.B.: A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives. Renew. Sustain. Energy Rev. 81, 2128–2143 (2018). https://doi.org/10.1016/j.rser.2017.06.024

    Article  Google Scholar 

  31. Shukla, S.; Pandit, M.: Mixed-integer differential evolution algorithm for optimal static/dynamic scheduling of a microgrid with mixed generation. In: Pandit, M., Dubey, H.M., and Bansal, J.C. (eds.) Nature Inspired Optimization for Electrical Power System, pp. 83–99. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4004-2_7.

  32. dos Santos Coelho, L.; Mariani, V.C.: Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans. Power Syst. 21, 989–996 (2006). https://doi.org/10.1109/TPWRS.2006.873410

    Article  Google Scholar 

  33. Zheng, K.; Yang, R.J.; Xu, H.; Hu, J.: A new distribution metric for comparing Pareto optimal solutions. Struct. Multidiscip. Optim. 55, 53–62 (2017). https://doi.org/10.1007/s00158-016-1469-3

    Article  MathSciNet  Google Scholar 

  34. Deb, K.; Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18, 577–601 (2014). https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  35. Mirjalili, S.; Saremi, S.; Mirjalili, S.M.; Coelho, L.D.S.: Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Syst. Appl. 47, 106–119 (2016). https://doi.org/10.1016/j.eswa.2015.10.039

    Article  Google Scholar 

  36. Abido, M.A.: Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research. 79, 1105–1113 (2009). https://doi.org/10.1016/j.epsr.2009.02.005

    Article  Google Scholar 

  37. Pandit, N.; Tripathi, A.; Tapaswi, S.; Pandit, M.: An improved bacterial foraging algorithm for combined static / dynamic environmental economic dispatch. Applied Soft Computing Journal. 12, 3500–3513 (2012). https://doi.org/10.1016/j.asoc.2012.06.011

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Director and management of M.I.T.S. Gwalior, India, for providing facilities for carrying out this work.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Sunita Shukla & Manjaree Pandit

Authors
  1. Sunita Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manjaree Pandit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sunita Shukla.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

Appendix: Microgrid test system data

Appendix: Microgrid test system data

See Tables 8, 9, 10.

Table 8 Operational data of the grid-connected DERs [24]
Full size table
Table 9 Wind speed data for the DER-2
Full size table
Table 10 Data for the PV plant (DER-5)
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shukla, S., Pandit, M. Optimal Scheduling of a Renewable Integrated Combined Heat Power Microgrid with Energy Storage and Load Uncertainties. Arab J Sci Eng 49, 6883–6901 (2024). https://doi.org/10.1007/s13369-023-08309-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-08309-3

Keywords

Search

Navigation