Optimal Operation of Integrated Energy Systems Considering Demand Response Program

Document Type : Research paper


‎ Department of Electrical Engineering, Arak University of Technology, Arak, Iran ‎


This study presents an optimal framework for the operation of integrated energy systems using demand response programs. The main goal of integrated energy systems is to optimally supply various demands using different energy carriers such as electricity, heating, and cooling. Considering the power market price, this work investigates the effects of multiple energy storage devices and demand response programs, including the time of use pricing, real-time pricing, and integrated demand response on optimal operation of energy hub. Moreover, impacts of different optimization methods are evaluated on the optimal scheduling of multi-carrier energy systems. Maximizing profits of selling electrical energy and minimizing the purchasing cost of input carrier energies are considered as objective functions to indicate bidirectional interchanges of energy hub systems with the power grid. To minimize the generation cost of energy carriers, a new quadratic objective function is also optimized using genetic algorithm. In this study, optimal operation of the energy hub based on the proposed quadratic objective function is an economic dispatch problem where the purchasing electrical power by the energy hub is considered as a load of the upstream grid. The optimization problem is implemented in the sample energy hub to indicate the effectiveness of different energy storage roles and applied demand response programs in the optimal operation of energy hub systems.


Main Subjects

[1]  L. Ni et al., “Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices”, J. Mod. Power Syst. Clean Energy, vol. 6, pp. 423-437, 2018.
[2]  F. Jamalzadeh, A. Mirzahosseini, F. Faghihi and M. Panahi, “Optimal operation of energy hub system using hybrid stochastic-interval optimization approach”, Sustain. Cities Society, vol. 54, pp. 1-9, 2020.
[3]  A. Heidari, S. Mortazavi and R. Bansal, “Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies”, Appl. Energy,vol. 261, pp. 1-11, 2020.
[4]  F. Kalavani, M. Nazari and B. Mohammadi, “Evaluation of Peak Shifting and Saving Energy of Ice Storage Air Conditioning System in Iran”, J. Oper. Autom. Power Eng., vol. 5, pp. 163-170, 2017.
[5]  T. Ma, J. Wu and L. Hao, “Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub”, Energy Convers. Manage., vol. 133, pp. 292-306, 2017.
[6]  V. Amir, Sh. Jadid and M. Ehsan, “Operation of Multi-Carrier Microgrid (MCMG) Considering Demand Response”, J. Oper. Autom. Power Eng., vol. 7, pp. 119-128, 2019.
[7]  S. Pazouki and M. Haghifam, “Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty”, Electr. Power Energy Syst., vol. 80, pp. 219-239, 2016.
[8]  H. Yang et al., “Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response”, Appl. Energy, vol. 167, pp. 353-365, 2016.
[9]  M. Pakdel, S. Nojavan, B. Mohammadi and K. Zare, “Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response”, Energy Conv. Manag., vol. 145, pp. 117-128, 2017.
[10]  A. Pasban, M. Moeini, Z. Parvini and M. Fotuhi, “Optimal scheduling of renewable-based energy hubs considering time-of-use pricing scheme”, Smart Grid Conf. pp. 1-6, 2017.
[11]  I. Moghaddam, M. Saniei and E. Mashhour, “A comprehensive model for self-scheduling an energy hub to supply cooling, heating and electrical demands of a building”, Energy, vol. 94, pp. 157-170, 2016.
[12]  A. Ghasemi, M. Banejad and M. Rahimiyan, “Integrated energy scheduling under uncertainty in a micro energy grid”, IET Gen. Transm. Distrib., vol. 12, pp. 2887-2896, 2018.
[13]  A. Dolatabadi, M. Jadidbonab and B. Mohammadi, “Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach”, IEEE Trans. Sustain. Energy, vol. 10, pp. 438-448, 2018.
[14]  H. Mousavi, M. Jadidbonab and B. Mohammadi, “Stochastic assessment of the renewable–based multiple energy system in the presence of thermal energy market and demand response program”, J. Oper. Autom. Power Eng., vol. 8, pp. 22-31, 2020.
[15]  E. Heydarian and H. Aalami, “Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power”, J. Oper. Autom. Power Eng., vol. 4, pp. 104-116, 2016.
[16]  X. Lu et al., “A robust optimization approach for optimal load dispatch of community energy hub”, Appl. Energy, vol. 259, pp. 1-13, 2020.
[17]  T. Liu, D. Zhang, S. Wang and T. Wu, “Standardized modelling and economic optimization of multi-carrier energy systems considering energy storage and demand response,” Energy Conv. Manag., vol. 182, pp. 126-142, 2019.
[18]  M. Majidi, S. Nojavan and K. Zare, “Optimal sizing of energy storage system in a renewable-based microgrid under flexible demand side management considering reliability and uncertainties”, J. Oper. Autom. Power Eng., vol. 5, pp. 205-214, 2017.
[19]  L. He et al., “Environmental economic dispatch of integrated regional energy system considering integrated demand response”, Electr. Power Energy Syst, vol. 116, pp. 1-14, 2020.
[20]  A. Sheikhi, M. Rayati, S. Bahrami and A. M. Ranjbar, “Integrated demand side management game in smart energy hubs”, IEEE Trans. Smart Grid, vol. 6, pp. 675-683, 2015.
[21]  M. Alipour, K. Zare and M. Abapour, “MINLP probabilistic scheduling model for demand response programs integrated energy hubs”, IEEE Trans. Ind. Inf., vol. 14, pp. 79-88, 2018.
[22]  M. Rastegar, M. Fotuhi and M. Lehtonen, “Home load management in a residential energy hub”, Electr. Power Energy Res.,vol. 119, pp. 322-328, 2015.
[23]  F. Jafari, H. Samet, A. R. Seifi and M. Rastegar, “Developing a two-step method to implement residential demand response programmes in multi-carrier energy systems”, IET Gen. Transm. Distrib., vol. 12, pp. 2614-2623, 2018.
[24]  M. Kazemi, A. Badri and A. Motie, “Demand response based model for optimal decision making for distribution networks”, J. Oper. Autom. Power Eng., vol. 5, pp. 139-149, 2017.
[25]  A. Najafi, S. Nojavan, K. Zare and B. Mohammadi, “Robust scheduling of thermal, cooling and electrical hub energy system under market price uncertainty,” Appl. Therm. Eng., vol. 149, pp. 862-880, 2019.
[26]  K. Saberi et al., “Optimal performance of CCHP based microgrid considering environmental issue in the presence of real time demand response”, Sustain. Cities Society., vol. 45, 2019.