• Ph.D. (1988)

    electrical engineering

    , Tohoku, Japan

  • M.Sc. (1985)

    electrical engineering

    , Toouchi Industrial, Japan

  • B.Sc. (1980)

    electrical engineering

    , Sharif University of Technology,

  • Future Power Systems
  • Smart Grids
  • Multi Carrier Energy Systems
  • Energy Management
  • Renewable Energy in Power System
  • Planning and Control of Power Systems
  • Electrical Energy Storage
  • Data Science Application in Power Systems

    Professor Electrical Engineering (Power Systems) Faculty Member of Tarbiat Modares Univesity, Tehran, Iran, Since 1988 Date of Birth: 15. Nov. 1956 Education: B.Sc. in Electrical Engineering, Sharif University of Technology, Iran, 1980 M.Sc. in Electrical Engineering, Toyohashi University of Technology, Japan, 1985 Ph.D. in Electrical Engineering, Tohoko University, Japan, 1988 Address: Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Nasr Bridge, Tehran, I.R. Iran P.O.Box 14115-194 Phone: 98-21-82883369 Fax: 98-21-82884325


    Curriculum Vitae (CV)

    Robust Operation Planning With Participation of Flexibility Resources Both on Generation and Demand Sides Under Uncertainty of Wind-based Generation Units

    A Mansoori, A Sheikhi Fini, M Parsa Moghaddam
    Journal Paper , , {Pages }


    Flexibility-constrained operation scheduling of active distribution networks

    S Allahmoradi, MP Moghaddam, S Bahramara, P Sheikhahmadi
    Journal Paper , , {Pages }


    Power System Robust Day-ahead Scheduling with the Presence of Fast-Response Resources Both on Generation and Demand Sides under High Penetration of Wind Generation Units

    A Mansoori, AS Fini, MP Moghaddam
    Journal Paper , , {Pages }


    Fast Islanding Detection of Nested Grids Including Multiple Resources Based on Phase Criteria

    R Zamani, MP Moghaddam, H Panahi, M Sanaye-Pasand
    Journal Paper , , {Pages }


    A novel three-stage risk-based scheme to improve power system resilience against hurricane

    M Mahzarnia, MP Moghaddam, MR Haghifam
    Journal Paper , , {Pages }


    Convolutional and recurrent neural network based model for short-term load forecasting

    H Eskandari, M Imani, MP Moghaddam
    Journal Paper , , {Pages }


    Generation expansion planning in multi electricity markets considering environmental impacts

    Reza Allahdadi Mehrabadi, Mohsen Parsa Moghaddam, Mohammad Kazem Sheikh-El-Eslami
    Journal PaperJournal of Cleaner Production , Volume 243 , 2020 January 10, {Pages 118611 }


    Inter-area generation expansion planning in a multi-period timescale with minimization of expansion cost and pollution is proposed in this paper. The expansion decision is made in a deregulated environment from the viewpoint of the strategic competitor. In this regard, the GENCO can expand its generation units accessing various generation technologies as candidate expansion plans in the main area. The main area consists of several buses while the adjacent areas are modeled as a single bus. The behavior of the other competitors is considered predictable by the strategic GENCO. In this study, the uncertainties of load, electricity price and wind speed are included. To confront them, rough neural network methodology is employed to forecast the

    Regulatory-intervented sustainable generation expansion planning in multi-electricity markets

    Reza Allahdadi Mehrabadi, Mohsen Parsa Moghaddam, Mohammad Kazem Sheikh-El-Eslami
    Journal PaperSustainable Cities and Society , Volume 52 , 2020 January 1, {Pages 101794 }


    Nowadays, according to sustainable development targets in modern societies, electricity markets are gradually connecting together. In this way, in this paper, a multi-agent generation expansion planning in a joint multi-electricity market environment is proposed. The GENCO (Generation Company) agents, competing in a deregulated structure, try to obtain the most profitable investment and operation plan regarding the incentives/taxes that the regulatory sector legislates. The multi-area market assumption encourages the GENCOs to offer their energy blocks to adjacent electricity markets whenever the production capacity is higher than the local load. To confront the uncertainties such as wind speed, market price, and load demand, the Markov cha

    A new hybrid model for point and probabilistic forecasting of wind power

    Reza Tahmasebifar, Mohsen Parsa Moghaddam, Mohammad Kazem Sheikh-El-Eslami, Reza Kheirollahi
    Journal PaperEnergy , Volume 211 , 2020 November 15, {Pages 119016 }


    The accurate and reliable forecasting of wind power is of great importance for electrical systems’ control and operation. However, the intermittent nature of wind power generation implies a complicated forecasting framework. In this paper, a new hybrid model including three steps is proposed for point and probabilistic forecasting of wind power. Within the first step, by using data preprocessing methods, proposed weighted Extreme Learning Machine (ELM) by Mutual Information, and bootstrap approach, point forecasting and variance of the model uncertainties are estimated. In the second step, by employing ELM, bootstrap approach, and an ensemble structure, the noise variance is calculated. During the final step, to improve the results of the

    Correlation based Convolutional Recurrent Network for Load Forecasting

    Hosein Eskandari, Maryam Imani, Mohsen Parsa Moghadam
    Conference Paper2020 28th Iranian Conference on Electrical Engineering (ICEE) , 2020 August 4, {Pages 05-Jan }


    The safe and economical operation of a power grid is not possible without knowing the future load. For this reason, the first step in terms of productivity and proper management of a system will be to predict the electric load in the future. In this paper, the features that exist in the history of electric load consumption are examined and they are used as a guide for designing the proposed method. We try to predict short-term electric load by extracting the characteristics of the electric load history using deep neural networks. Long Short Term Memory (LSTM), are able to hold short and long-term memory for extracting relationships between the load values from time series. On the other hand, convolution neural networks are capable of automa

    A review of the measures to enhance power systems resilience

    Maedeh Mahzarnia, Mohsen Parsa Moghaddam, Payam Teimourzadeh Baboli, Pierluigi Siano
    Journal PaperIEEE Systems Journal , 2020 January 29, {Pages }


    Rare and extreme climate events may result in wide power outages or blackouts. The concept of power system resilience has been introduced for focusing on high-impact and low-probability (HILP) events such as a hurricane, heavy snow, and floods. Power system resilience is the ability of a system to reduce the likelihood of blackout or wide power outages due to HILP events. Indeed, in a resilient power system, as the severity of HILP events increases, the rate (but not the amount) of unserved loads diminishes. Suitable measures for managing power system resilience can be classified into three categories in terms of time, known as “resilience-based planning,” “resilience-based response,” and “resilience-based restoration.” The most

    Flexibility Enhancement in Active Distribution Networks through a Risk-based Optimal Placement of Sectionalizing Switches

    Mahnaz Moradijoz, Saeed Moradijoz, Mohsen Parsa Moghaddam, Mahmoud-Reza Haghifam
    Journal PaperReliability Engineering & System Safety , 2020 May 8, {Pages 106985 }


    The advent of distributed energy resources (DERs) in distribution networks, referred to as active distribution networks (ADNs), brings in new opportunities for distribution network operators (DNOs) to improve the network reliability. The contribution of DERs in reliability enhancement generally depends on the island mode operation of DERs. Optimal placement of sectionalizing switches (SSs) enables ADNs to be operated more reliably by islanding the faulted part of the system in the form of flexible micro-grids (FMGs). However, practical methods are needed to handle the complex problem of the SS placement in ADNs. This paper presents a risk-based two-stage mixed-integer linear programming (MILP) model for the optimal placement of the network-

    A Novel Unknown Input Observer-based Measurement Fault Detection and Isolation scheme for Micro-Grid Systems

    Hassan Haes Alhelou, MEH Golshan, Nikos D Hatziargyriou, Mohsen Parsa Moghaddam
    Journal PaperIEEE Transactions on Industrial Informatics , 2020 January 29, {Pages }


    Correct measurement of different signals used in the micro-grids control center (MGCC) is crucial for their operation, control, and stability. This is especially true for micro-grids operating in island mode. To keep their operation efficient and safe, faulty sensors should be instantly isolated. To this end, novel sensor fault detection and isolation schemes are proposed in this paper that are built based on unknown input observer method (UIO). In this method, load fluctuations and output power variations of renewable energy sources are modeled as unknown inputs. Theoretical analysis and simulation scenarios are carried out on a micro-grid system which consists of different types of energy generation sources and energy storage systems to p

    An Introduction to Blockchain-based Concepts for Demand Response Considering of Electric Vehicles and Renewable Energies

    Mohammadreza Shekari, Mohsen Parsa Moghaddam
    Conference Paper2020 28th Iranian Conference on Electrical Engineering (ICEE) , 2020 August 4, {Pages 04-Jan }


    Future of power systems and smart grids will host modern generation and consumption units, e.g., Renewable Energies (REs) and Electric Vehicles (EVs) more rather than traditional ones. Therefore, adopting a method for managing such users in electricity markets with the implementation of Demand Response (DR) programs is required and essential. In this paper, the “Blockchain-based concepts for demand response programs by efficient use of electric vehicles and renewable energies in the electricity markets” are introduced. This process can be accomplished with the utilization of different features of blockchain technology like smart contracts. Implementation of smart contracts in demand response programs could enhance the efficiency of thes

    A comprehensive assessment of power system resilience to a hurricane using a two-stage analytical approach incorporating risk-based index

    Maedeh Mahzarnia, Mohsen Parsa Moghaddam, Pierluigi Siano, Mahmoud-Reza Haghifam
    Journal PaperSustainable Energy Technologies and Assessments , Volume 42 , 2020 December 1, {Pages 100831 }


    Sustainability of power systems is a vital need for modern societies. The occurrence of extreme weather events, such as hurricanes, may lead to blackouts. Hence, power systems resilience is a critical issue for experts. The main focus of this paper is on how to assess power system resilience comprehensively. In this regard, a two-stage framework is proposed. In the first stage, an approach is presented to evaluate power system resilience against a single intensity of a hurricane, which is called snapshot resilience assessment. The Cost of Energy Not Supplied (CENS) is regarded as a primary criterion. A risk measure called Conditional Value at Risk (CVaR) is incorporated into this approach to manage the risk of experiencing unfavorable failu

    Multi-Objective Sizing of Energy Storage Systems (ESSs) and Capacitors in a Distribution System

    H Delkhosh, MP Moghaddam, M Ghaedi
    Journal Paper , , {Pages }


    Non-Cooperative Operation of Transmission and Distribution Systems

    M Khodadadi, MEH Golshan, MP Moghaddam
    Journal Paper , , {Pages }


    Electric vehicle parking lots as a capacity expansion option in distribution systems: a mixed‐integer linear programing‐based model

    M Moradijoz, J Heidari, MP Moghaddam, MR Haghifam
    Journal Paper , , {Pages }


    Modeling of demand response programs based on market elasticity concept

    Hasan Jalili, Mohammad Kazem Sheikh-El-Eslami, M Parsa Moghaddam, Pierluigi Siano
    Journal PaperJournal of Ambient Intelligence and Humanized Computing , Volume 10 , Issue 6, 2019 June 1, {Pages 2265-2276 }


    Demand response programs (DRPs) are appropriate tools to improve power system operation. Applying these programs results in a reduction in reliability cost and electricity price, transmission congestion and pollution relief, and also can determine postponements in network expansion. Therefore, developing a comprehensive model for DRPs is necessary for accurate planning and encouragement of consumers to increase their participation. In this paper, by using the market elasticity concept, a comprehensive model for DRPs is developed. Market elasticity is defined as sensitivity of electricity price on the network load. The proposed model is able to increase the consumers’ participation by providing a higher awareness about their

    Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources

    Shahab Karamdel, Mohsen Parsa Moghaddam
    Journal PaperIET Renewable Power Generation , 2019 May 29, {Pages }


    High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three-level model for adaptive robust expansion co-planning of electricity and natural gas infrastructures in multi-energy-hub networks, which is robust against uncertainties of maximum production of wind generation and gas-fired power plants as well as estimated load levels. The proposed min–max–min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this mo

    Current Teaching

    • MS.c.

      Modern Control Principles

    Teaching History

    • Ph.D.

      Electric Energy Smart Grids

    • MS.c.

      Energy Management

    • 2021
      Alipour khezri, Sina
      Forecasting Power System Flexibility Requirements With Machine Learning Methods
    • 2021
      Moshtahi, Shirin
    • 2021
      Hashemnezhad, Mohammad
    • 2020
      Bodaghi, Maryam
      Design of Agile Flexibility Provision mechanism in power systems with high penetration renewable energy resources
    • 2020
      Mohammadi, Farzaneh
    • 2021
      Mansouri, Alireza
    • Senior Member IEEE
    • Top 1% Scientists of the World in 2021
      Data not found



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