Multi-Objective Optimization of Combined Heat and Power Economic Emission Dispatch in Multi-Energy Systems

References

[1] S. D. A. Karki, M. Kulkarni, M. D. Mann, and H. Salehfar, ‘Efficiency improvements through combined heat and power for on-site distributed generation technologies,’ Cogeneration Distrib. Gener. J., vol. 22, no. 3, pp. 19–34, Jul. 2007. Taylor & Francis

[2] D. N. Ohaegbuchi, M. S. Olaniyi, O. Dzobo, and L. Ezemonye, “Hybrid solar PV–CHP–biomass energy system modeling and optimization with battery storage impact employing differential evolution algorithm,” ASRIC Journal on Engineering Sciences, vol. 6, no. 1, 2025.

[3] M. Alipour, B. Mohammadi-Ivatloo, and K. Zare, ‘‘Stochastic scheduling of renewable and CHP-based microgrids,’’ IEEE Trans. Ind. Informat., vol. 11, no. 5, pp. 1049–1058, Oct. 2015.

[4] D. N. Ohaegbuchi, O. I. Augustine, M. S. Olaniyi, I. F. Abiebhode, S. N. Ukagu, and E. N. Aneke, “Simulation and optimization of a hybridized solar PV-CHP-biomass energy system using the differential evolution algorithm,” in Proc. 2025 IEEE Connecting the Unconnected Europe, Middle East, and Africa Regional Summit (CTUS-EMEA), Abuja, Nigeria, 2025, pp. 1–5. IEEE

[5] P. J. Mago, N. Furno, and L. M. Chamra, "Performance analysis of CCHP systems operating following the thermal and electric load," Int. 1. Energy Res., vol.33, pp.852-864, Jul. 2009.

[6] D. N. Ohaegbuchi, O. S. Maliki, C. P. A. Okwaraoka, and H. E. Okwudiri, “Solution of combined heat and power economic dispatch problem using direct optimization algorithm,” Energy and Power Engineering, vol. 14, no. 12, pp. 737–746, 2022. Scientific Research Publishing

[7] J. Keirstead, N. Samsatli, N. Shah, and C. Weber, ‘‘The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems,’’ Energy, vol. 41, no. 1, pp. 93–103, May 2012. ScienceDirect

[8] D. N. Ohaegbuchi, M. C. Chukwuemeka, and G. Awara, “Solution of combined heat and power economic dispatch problem using genetic algorithm,” Energy and Power Engineering, vol. 14, no. 9, pp. 443–459, 2022. Scientific Research Publishing

[9] K. Le, J. Golden, C. Stansberry, R. Vice, J. Wood, J. Ballance, G. Brown, J. Kamya, E. Nielsen, H. Nakajima, M. Ookubo, I. Iyoda, and G. Cauley, ‘‘Potential impacts of clean air regulations on system operations,’’ IEEE Trans. Power Syst., vol. 10, no. 2, pp. 647–656, May 1995.

[10] D. N. Ohaegbuchi, “Solution of combined heat and power economic dispatch problem using genetic algorithm,” master’s dissertation, University of KwaZulu-Natal, Durban, South Africa, 2022. [Online]. Available: University of KwaZulu-Natal

[11] Mancarella P. MES (multi-energy systems): an overview of concepts and evaluation models. Energy 2014;65:1e17. ScienceDirect

[12] E. R. Ozioko, D. Ohaegbuchi, and L. Ezemonye, “Integrated modeling and energy generation optimization of a wind turbine system at Nsude Hill using a hybrid PSO-GA algorithm,” SSRN preprint, 2026. SSRN

[13] Mancarella P. MES (multi-energy systems): an overview of concepts and evaluation models. Energy 2014;65:1e17. ScienceDirect

[14] D. N. Ohaegbuchi, E. F. Ayetiran, L. Ezemonye, I. A. Onyegbadue, and S. N. Ukagu, “Optimal energy management of a residential CHP–PV–wind hybrid system under escalating time-of-use tariffs,” International Journal of Applied Power Engineering (IJAPE), accepted for publication, 2026.

[15] Reynolds J, Ahmad MW, Rezgui Y. Holistic modelling techniques for the operational optimisation of multi-vector energy systems. Energy Build 2018;169:397e416. ScienceDirect

[16] Huang W, Zhang N, Yang J, Wang Y, Kang C. Optimal configuration planning of multi-energy systems considering distributed renewable energy. IEEE Trans Smart Grid 2017;3053:1e12.

[17] Lan Y, Wu J, Guan X. Rollout strategies for real-time multi-energy scheduling in microgrid with storage system. IET Gener, Transm Distrib 2016;10:688e96.

[18] Srinivas N, Deb K. Multi-objective function optimization using nondominated sorting genetic algorithms. Evol Comput 1995;2(3):221–48.

[19] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002;6(2):182–97. IEEE

[20] Deb K, Agrawal RB. Simulated binary crossover for continuous search space. Complex Syst 1995;9(2):115–48.

[21] Brahman, F., Jadid, S., 2014. Optimal energy management of hybrid CCHP and PV in a residential building. In: Electrical Power Distribution Networks (EPDC), 2014 19th Conference on. IEEE, pp. 19-24.

[22] F. Y. Melhem, Z. Hammoudan, O. Grunder and N. Moubayed “Optimization and Energy Management in Smart Home Considering Photovoltaic, Wind, and Battery Storage System with Integration of Electric Vehicles’’ CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING, VOL. 40, NO. 2, JUNE 2, 2017.

[23] Measurement and Instrumentation Data Center (MIDC). nd, NREL. [Accessed 7 September 2023].

[24] OpenEI datasets NREL RSF measured data. N.d, OpenEI. [Accessed 7 September 2023].

@article{1723701,
      author = {Samuel Asoforghena Oghogbo, Onyegbadue, Ikenna Augustine, Guiawa Mathurine, Izilein Fred Abiebhode},
      title = {Multi-Objective Optimization of Combined Heat and Power Economic Emission Dispatch in Multi-Energy Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {691-713},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723701.pdf},
      abstract = {This paper presents a multi-objective energy management framework for residential multi-energy systems (MES) integrating photovoltaic (PV), wind, combined heat and power (CHP), battery energy storage system (BESS), thermal energy storage (TES), electric vehicle (EV), and grid resources. The research addresses the gap in coordinated economic–environmental optimization of highly coupled residential energy assets under renewable uncertainty and time-of-use pricing. A 24-h mathematical model minimizes energy expenditure and greenhouse-gas (GHG) emissions subject to electrical and thermal balance, generation, grid, storage, and EV operational constraints. NSGA-II and SPEA2 are implemented in Python/PyMOO and evaluated under three progressively complex scenarios. The proposed framework contributes an integrated scheduling architecture and scenario-based assessment of algorithmic performance. NSGA-II achieves −2.5592 ₦/day and 0.00040 kg/day in Scenario I, and −2.68698 ₦/day and 0.000296 kg/day in Scenario II, outperforming SPEA2 on both reported objectives. In Scenario III, NSGA-II attains 75.93173 ₦/day, while SPEA2 achieves 141,584.79 kg/day emissions. Convergence results show rapid initial NSGA-II improvement followed by stabilization, whereas SPEA2 exhibits progressive, archive-based refinement of the final non-dominated solution set.},
      keywords = {Combined heat and power (CHP), multi-energy system (MES), energy management, renewable energy, battery energy storage, thermal energy storage, electric vehicle, multi-objective optimization, NSGA-II, SPEA2.},
      month = {October},
  }