Yazar "Mehdinejad, Mehdi" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Öğe
    Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles
    (Elsevier Ltd, 2022) Seyfi, Mohammad; Mehdinejad, Mehdi; Mohammadi-Ivatloo, Behnam; Shayanfar, Heidarali
    The virtual energy hub (VEH), a combination of virtual power plant and energy hub concepts, faces many uncertainties due to its constituent distributed energy resources. This paper presents the deep learning-based scheduling of VEH for participation in electrical and thermal markets using bidirectional long short-term memory (BLSTM) network, which offers excellent accuracy in forecasting uncertain parameters by concurrent using past and future dependencies. In addition to applying learning methods, energy storage systems can also influence the optimal management of uncertainties. To provide the required electrical storage equipment, the VEH employs plug-in hybrid CNG-electric vehicles (PHGEVs) that can use both electrical energy and compressed natural gas (CNG) to fulfill their energy needs. The alternative fuel can tackle the limitations of prolonged charging of electric vehicles and excess load caused by these vehicles at peak hours. To supply the secondary fuel of PHGEVs, the modeled VEH includes a CNG station, which compresses the natural gas imported from the natural gas grid before delivering it to the vehicles. Furthermore, phase change material-based thermal energy storage (PCMTES) is considered in the VEH configuration, which unlike other common thermal energy storage systems, operates at a constant temperature during the charging and discharging period. Lastly, the simulation of the developed system illustrates that PHGEVs can reduce the imposed cost in unforeseen situations by up to 26 percent and increase the system's flexibility.
  • Küçük Resim
    Öğe
    Designing a robust decentralized energy transactions framework for active prosumers in peer-to-peer local electricity markets
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mehdinejad, Mehdi; Shayanfar, Heidar Ali; Mohammadi-Ivatloo, Behnam; Nafisi, Hamed
    In this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution's existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players' private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method.
  • Küçük Resim
    Öğe
    Scenario-based robust energy management of CCHP-based virtual energy hub for participating in multiple energy and reserve markets
    (Elsevier, 2022) Mohammadi-Ivatloo, Behnam; Seyfi, Mohammad; Mehdinejad, Mehdi; Shayanfar, Heidarali
    The multi-energy systems can operate and schedule the distributed energy resources (DERs) locally to supply the multi-type loads and participate in the energy markets by aggregating the output power of DERs. Recently, the virtual energy hub (VEH) concept, derived from the energy hub and virtual power plant concepts, has been proposed for participating in the electrical and thermal markets. In this paper, robust self-scheduling of a VEH for participating in the energy and reserve markets is presented. The thermal reserve market is proposed to maintain the real-time thermal power balance and compensate for the effects of thermal demand uncertainty. Various types of DERs for supplying loads of each energy carrier are considered. Compressed natural gas (CNG) station is discussed and modeled linearly in the developed VEH to provide the fuel needed by Hybrid CNG and plug-in electric vehicles, which used the CNG as their secondary energy resource. A scenario-based robust approach is developed and presented to maximize the VEH profit and control the downside risk without adding surplus constraints. Finally, the proposed model is simulated in three case studies to evaluate its performance and effectiveness.