Volume 16, Issue 3 (September 2020)                   IJEEE 2020, 16(3): 393-411 | Back to browse issues page


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Sedighizadeh M, Alavi S M M, Mohammadpour A. Stochastic Optimal Scheduling of Microgrids Considering Demand Response and Commercial Parking Lot by AUGMECON Method. IJEEE. 2020; 16 (3) :393-411
URL: http://ijeee.iust.ac.ir/article-1-1584-en.html
Abstract:   (705 Views)
Regarding the advances in technology and anxieties around high and growing prices of fossil fuels, government incentives increase to produce cleaner and sustainable energy through distributed generations. This makes trends in the using microgrids which consist of electric demands and different distributed generations and energy storage systems. The optimum operation of microgrids with considering demand-side management increases efficiency and reliability and maximize the advantages of using distributed generations. In this paper, the optimal operation scheduling and unit commitment of generation units installed in a microgrid are investigated. The microgrid consists of technologies based on natural gas that are microturbine and phosphoric acid fuel cell and technologies based on renewable energy, including wind turbine and photovoltaic unit along with battery energy storage system and plug-in electric vehicle commercial parking lot. The goal of the paper is to solve a multi-objective problem of maximizing revenues of microgrid operator and minimizing emissions. This paper uses an augmented epsilon constraint method for solving the multi-objective problem in a stochastic framework and also implements a fuzzy-based decision-maker for choosing the suitable optimal solution amid Pareto front solutions. This new model implements the three type of the price-based and incentive-based demand response program. It also considers the generation reserve in order to enhance the flexibility of operations. The presented model is tested on a microgrid and the results demonstrate the efficacy of the proposed model economically and environmentally compared to other methods.
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  • Proposing a scenario-based stochastic combined model for MGs including DGs that are MT, PAFC, WT, and PV and ESSs that are BESS and PEVs.
  • Modeling the DR as the ToU program and the curtailable loads program.
  • Modeling the WT and PV generation with associated stochastic patterns.
  • Presenting an energy management pattern constituting operation cost and emissions simultaneously.
  • Enhancing the performance of MG with considering generation and load reserves.
  • Using the AUGMECON method and fuzzy-based decision-maker to minimize objective functions and choosing the most suitable solution, respectively.

Type of Study: Research Paper | Subject: Microgrids
Received: 2019/09/01 | Revised: 2019/11/08 | Accepted: 2019/11/09

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© 2020 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.