A Two-Stage Multi-Objective Optimal Day-Ahead Peer to Peer Energy Trade and Pricing Considering Electric Vehicles in Microgrid

Document Type : Research paper

Author

Khorasan Razavi Power Distribution Company, Mashhad, Iran.

Abstract

Due to recent developments in communications and the increasing penetration rate of distributed generation (DGs), new players in the energy market, known as prosumers, have emerged. Prosumers can both produce and consume power, offering benefits such as on-site power consumption, peak shaving, and postponing the power transmission network investment costs. This paper presents a two-stage day-ahead peer-to-peer pricing and power exchange model among local market participants, including the upstream grid, consumers, prosumers (equipped with rooftop solar panels), and electric vehicles. In the first stage, initial pricing is determined using the mid-market rate pricing method, taking into account each participant's declared demand and the forecasted solar production of prosumers. In the second stage, the random behavior of electric vehicles is modeled through scenario generation, and their dynamic behavior is incorporated into the pricing scheme. The proposed model aims to minimize two objectives: trading costs and electrical power losses due to the exchange of power among participants. This two-objective problem is reformulated as a single objective using the epsilon-constraint method. The resulting MINLP model is solved in GAMS using the DICOPT solver, and the best-compromised solution is identified through the Min-Max method. Simulation results indicate a 6.7% reduction in costs, with all participants benefiting economically. Additionally, on-site interactions led to a decrease in congestion on two lines connecting to the upstream grid by 5.02% and 6.66%, respectively.

Keywords

Main Subjects

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Journal of Operation and Automation in Power Engineering
Volume 14, Issue 3
August 2026
Pages 222-232

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History

  • Receive Date: 02 March 2024
  • Revise Date: 16 June 2024
  • Accept Date: 31 August 2024
  • First Publish Date: 26 April 2025

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