CV


FA
Ali Karimi

Ali Karimi

Associate Professor

College: Faculty of Electrical and Computer Engineering

Department: Electrical Engineering - Power

Degree: Ph.D

CV
FA
Ali Karimi

Associate Professor Ali Karimi

College: Faculty of Electrical and Computer Engineering - Department: Electrical Engineering - Power Degree: Ph.D |

Email: a.karimi@kashanu.ac.ir , ali.karimi.pe@gmail.com

Google Scholar: https://scholar.google.com/citations?hl=en&user=3jLN7gkAAAAJ

ORCID: https://orcid.org/0000-0002-7466-3531

 

Research Interests:

  • Power Systems Operation and Planning
  • Electricity Market
  • Smart Grids
  • Distribution Networks

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A novel framework for simultaneous energy, reactive power, and reserve exchange in decentralized P2P markets with renewable agents

Authorsنادر تراشنده دهقی,علی کریمی
JournalRenewable Energy
IF9.1
Paper TypeFull Paper
Published At2025-09-23
Journal GradeScientific - research
Journal TypeElectronic
Journal CountryIran, Islamic Republic Of
Journal IndexJCR ,SCOPUS
KeywordsPeer, to, peer market, Renewable agents, Flexible reserve, Reactive power exchange, Decentralized framework, Network constraints.

Abstract

In recent years, peer-to-peer (P2P) trading in electric distribution systems has gained significant attention for its potential to create resilient and efficient energy markets. While most studies focus on active energy exchanges, such as decentralization, pricing, and network constraints, integrating renewable energy sources (RES) necessitates flexible reserves to manage uncertainty and reactive power to maintain grid stability. This paper addresses these challenges by proposing a novel decentralized framework for P2P markets that enables the simultaneous exchange of active energy, reactive power, and flexible reserves under network constraints. Utilizing the Alternating Direction Method of Multipliers (ADMM), the framework minimizes the role of the distribution system operator (DSO) and allows renewable agents to calculate and provide reserves based on prediction errors. This approach not only enhances RES forecasting accuracy but also equitably distributes network regulation costs among participants. The numerical results show that the proposed framework effectively organizes the interactions between agents to maintain the power factor at energy-consuming nodes. It also ensures that there is a flexible reserve available to manage the uncertainty of RES while staying within network constraints. All of this is achieved in a short amount of time and requires minimal information exchange between agents.