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A comprehensive linear model for demand response optimization problem

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  • Heydarian-Forushani, Ehsan
  • Golshan, Mohamad Esmail Hamedani
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

Demand Response (DR) is known as an effective solution for many power grid problems such as high operating cost as well as high peak demand. In order to achieve full potential of DR programs, DR must be implemented optimally. On this basis, determining optimal DR location, appropriate DR program and efficient penetration rate for each DR program is of great practical interest due to the fact that it guides the system operators to choose proper DR strategies. This paper presents a novel linear framework for DR optimization problem incorporated into the network-constrained unit commitment with the aim of determining optimal location, type and penetration rate of DR programs considering several practical limitations. To this end, a number of tariff-based and incentive-based DR programs have been taken into account according to the customer’s benefit function based on the price elasticity concept. The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the applicability of the proposed model. Finally, DR optimization is analyzed with regards to different customer’s elasticity values and also different number of candidate load buses which reveal the applicability and effectiveness of the proposed methodology.

Suggested Citation

  • Heydarian-Forushani, Ehsan & Golshan, Mohamad Esmail Hamedani & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "A comprehensive linear model for demand response optimization problem," Energy, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315826
    DOI: 10.1016/j.energy.2020.118474
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    References listed on IDEAS

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    2. Rawat, Tanuj & Niazi, K.R. & Gupta, Nikhil & Sharma, Sachin, 2022. "A linearized multi-objective Bi-level approach for operation of smart distribution systems encompassing demand response," Energy, Elsevier, vol. 238(PC).
    3. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    4. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).

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