Volume 16, Issue 3 (September 2020)                   IJEEE 2020, 16(3): 371-392 | Back to browse issues page

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Khajevand M, Fakharian A, Sedighizadeh M. Stochastic Joint Optimal Distributed Generation Scheduling and Distribution Feeder Reconfiguration of Microgrids Considering Uncertainties Modeled by Copula-Based Method. IJEEE. 2020; 16 (3) :371-392
URL: http://ijeee.iust.ac.ir/article-1-1627-en.html
Abstract:   (421 Views)
Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used.  The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.
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  • Proposing a stochastic model for MG operation with couples DFR, and optimal planning of DGs.
  • Formulating the optimization problem as a MOP considering four goals that are minimization of the operational cost, minimization of the active power loss, maximization of VSI, and minimization of emissions.
  • Using the copula-based method for modeling the stochastic pattern of wind speed, solar irradiance, and electrical demand.
  • Using a stochastic optimization based on the IMOPSO algorithm.
  • Using a fuzzy technique to select the best-compromised solution among the Pareto front solutions.

Type of Study: Research Paper | Subject: Artificial Intelligence Techniques
Received: 2019/09/30 | Revised: 2020/05/02 | Accepted: 2020/01/07

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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.