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Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems

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  • Nikzad, Mehdi
  • Samimi, Abouzar

Abstract

The aim of this work is to introduce a stochastic model for optimal day-ahead integrated heat-energy and reserve scheduling of a Microgrid (MG) that is performed by the MG central controller. In this regard, a bi-level stochastic programming is proposed for the integrated heat-energy and reserve scheduling of the smart MGs in presence of energy storage system (ESS) and demand response (DR) programs based on the maximization of total social welfare as objective function. Among available Incentive-Based DR (IBDR) programs, demand bidding/buyback and ancillary services market programs have been chosen to be included in the presented model. Moreover, optimum designing of Time-of-Use (TOU) and Real-Time Pricing (RTP) programs as Price-Based DR (PBDR) programs is embedded in the formulation. The PBDR programs are implemented using a linear function based on the concept of self and cross price elasticities of load demand and deciding on optimal PBDR tariffs is attained based on the consumers utility function embedded in the objective function. The Particle Swarm Optimization (PSO) algorithm is utilized to solve the proposed bi-level stochastic model along with to obtain the optimum PBDR programs tariffs. The proposed framework is examined on a 33-bus test MG and the simulation results are evaluated from different points of view of the model. The impact of TOU and RTP programs as well as the ESS are assessed on the results of scheduling as well as the operation cost, the total consumers utility function and the total social welfare.

Suggested Citation

  • Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315695
    DOI: 10.1016/j.apenergy.2020.116163
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Mazidi, Mohammadreza & Rezaei, Navid & Ghaderi, Abdolsalam, 2019. "Simultaneous power and heat scheduling of microgrids considering operational uncertainties: A new stochastic p-robust optimization approach," Energy, Elsevier, vol. 185(C), pages 239-253.
    3. Wang, Dan & Hu, Qing'e & Jia, Hongjie & Hou, Kai & Du, Wei & Chen, Ning & Wang, Xudong & Fan, Menghua, 2019. "Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations," Applied Energy, Elsevier, vol. 248(C), pages 656-678.
    4. Samimi, Abouzar & Nikzad, Mehdi & Siano, Pierluigi, 2017. "Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs," Renewable Energy, Elsevier, vol. 109(C), pages 22-40.
    5. Malekpour, Ahmad Reza & Tabatabaei, Sajad & Niknam, Taher, 2012. "Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm," Renewable Energy, Elsevier, vol. 39(1), pages 228-240.
    6. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    7. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    8. Nouri, Alireza & Khodaei, Hossein & Darvishan, Ayda & Sharifian, Seyedmehdi & Ghadimi, Noradin, 2018. "Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: An epsilon constraint method and fuzzy satisfying approach," Energy, Elsevier, vol. 159(C), pages 121-133.
    9. Rahmani-Andebili, Mehdi, 2017. "Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization," Renewable Energy, Elsevier, vol. 113(C), pages 1462-1471.
    10. Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    11. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
    12. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
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