ORCID Profile
0000-0002-7067-8496
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Publisher: Elsevier BV
Date: 12-2013
Publisher: Elsevier BV
Date: 11-2014
Publisher: SAGE Publications
Date: 18-05-2020
Abstract: This paper presents the application of information gap decision theory (IGDT) to deal with uncertainties associated with load forecasting in dynamic, environment constrained, coordinated generation and transmission expansion planning. Traditionally, the gaseous emissions are constrained over the whole system. Conventional methods cannot guarantee a practical expansion plan since huge emissions can still occur on some buses in the power system. This paper introduces a per-bus emission limit to avoid extreme emissions in highly populated areas. The effect of nodal emission limits is fully discussed and compared to a conventional method. The model is kept linear using the big M approach to decrease the model computational burden. Reliability is considered by limiting the estimated load not served in each year over the planning horizon. The cost of fuel transportation and fuel limits are considered in order to make the model more realistic and practical. The effectiveness of the proposed model is verified by implementation on Garver 6 bus, IEEE 30 bus, and 118 bus test systems.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Elsevier
Date: 2018
Publisher: Elsevier BV
Date: 12-2016
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 12-2015
Publisher: Springer Singapore
Date: 2019
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 02-2015
Publisher: MDPI AG
Date: 09-08-2021
DOI: 10.3390/EN14164856
Abstract: In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Elsevier BV
Date: 09-2019
Publisher: Springer Science and Business Media LLC
Date: 04-05-2012
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 05-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 14-10-2020
Publisher: Elsevier BV
Date: 08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Institution of Engineering and Technology (IET)
Date: 04-2015
Publisher: Elsevier BV
Date: 08-2012
Publisher: Informa UK Limited
Date: 04-04-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 18-01-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 10-2014
Publisher: Springer International Publishing
Date: 2019
Publisher: Informa UK Limited
Date: 28-05-2014
Publisher: Elsevier BV
Date: 04-2018
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 09-2015
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 12-2018
Publisher: MDPI AG
Date: 15-10-2022
DOI: 10.3390/EN15207614
Abstract: Demand-side management (DSM) includes various persuasive measures to improve the use of energy thus, it has been studied from various perspectives in the literature. Nowadays, the context of productivity has an important role in the evaluation of the electrical energy systems. Accordingly, this paper presents a platform to comprehensively contemplate the DSM from the productivity perspective that features its three aspects. First, the widespread indices of DSM are manifestly redefined, and a plenary index of DSM is introduced, reflecting both energy and investment productivity. Second, the modification of energy efficacy and consumption pattern is discussed, considering a general categorization of DSM modalities based on the pertaining index of each branch. Third, a modified model of demand response (DR) is developed to implement seven DR strategies in the smart microgrids. The simulation results demonstrate that the load factor can improve up to 8.12% with respect to the normal consumption pattern. Moreover, the load factor can be further enhanced at least by 4.22% in comparison with the customary model.
Publisher: Hindawi Limited
Date: 13-05-2015
DOI: 10.1002/ETEP.2082
Publisher: Elsevier BV
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 07-2015
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 06-2017
Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 12-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Elsevier BV
Date: 12-2013
Publisher: Institution of Engineering and Technology (IET)
Date: 11-04-2019
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Elsevier BV
Date: 10-2015
Publisher: Elsevier BV
Date: 02-2013
Publisher: Hindawi Limited
Date: 03-02-2015
DOI: 10.1002/ETEP.2059
Location: Iran (Islamic Republic of)
No related grants have been discovered for Abdollah Ahmadi.