Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

Sirat, Ali Parsa; Mehdipourpicha, Hossein; Zendehdel, Niloofar; Mozafari, Hamid

doi:10.1109/peci48348.2020.9064636

Cited by 24 publications

(10 citation statements)

References 51 publications

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“…Then it can dedicate the level of power exchange with the upstream grid for various hours. To implement such an intelligent autonomous mechanism, a smart environment is needed that is only feasible through IoT infrastructures [39]. The following flowchart concisely describes the proposed scheme.…”

Section: Energy Management Paradigm By Microgrid's Controllermentioning

confidence: 99%

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

et al. 2021

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In this study, a microgrid scheme encompassing photovoltaic panels, an energy storage system, and a diesel generator as a backup supply source is designed, and the optimal placement for installation is suggested. The main purpose of this microgrid is to meet the intrinsic demand without being supplied by the upstream network. Thus, the main objective in the design of the microgrid is to minimize the operational cost of microgrid’s sources subject to satisfy the loads by these sources. Therefore, the considered problem in this study is to determine the optimal size and placement for generation sources simultaneously for a microgrid with the objectives of minimization of cost of generation resources along with mitigation of power losses. In order to deal with uncertainties of PV generation and load forecasting, the lognormal distribution model and Gaussian process quantile regression (GPQR) approaches are employed. In order to solve the optimization problem, the lightning attachment procedure optimization (LAPO) and artificial bee colony (ABC) methods are employed, and the results are compared. The results imply the more effectiveness and priority of the LAPO approach in comparison with ABC in convergence speed and the accuracy of solution-finding.

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Section: Energy Management Paradigm By Microgrid's Controllermentioning

confidence: 99%

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

et al. 2021

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“…The DC RNG used PV panels and an MPPT converter, which conditiones the panels' power and provides the maximum available power to the RNG. Different load models require different solar capacities [23]. In a zero net energy house, the solar capacity for each load model was obtained by matching the annual values of the generated and consumed energies, meaning that, the annual generated PV power should match the annual RNG's power consumption.…”

Section: Pvmentioning

confidence: 99%

Efficiency Assessment of a Residential DC Nanogrid with Low and High Distribution Voltages Using Realistic Data

Habibi

Rahimi

Shamsi

et al. 2021

2021 IEEE Green Technologies Conference (GreenTech)

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Direct Current (DC) power distribution has gained attention in the Residential Nanogrids (RNGs) due to the substantial increase in the number of roof-top Photovoltaic (PV) systems and internally DC appliances used in buildings. Using DC distribution improves the efficiency of the RNGs compared to AC distribution. This paper investigated the efficiency of a DC RNG for low and high distribution voltage levels by exploring reasons for power losses. The studied DC RNG consisted of various types of local loads, on-site PV generation, and battery storage systems.The realistic load, PV profiles, and converter efficiency curves were used to make the analysis more accurate. In addition, three load profiles with low, medium, and high power consumptions were considered to study the load impacts on the overall system efficiency.

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“…There is a vast amount of literature on different optimization algorithms implemented for allocation of DGs. Several methods have recently been proposed which they are mostly meta-heuristic algorithms including ant lion [7], artificial bee colony [4], crow search algorithm [8], genetic algorithms [9], grey wolf algorithm [10], harmony search algorithm [11] , tabu search [3], whale optimization algorithm [12] and several variants of particle swarm optimization [13][14] [15]. There are also several deterministic algorithms such as analytical [16], efficient analytical method [17], MILP [18] and MINPL [19].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Energy Consumption in Jaw Crusher Using artificial intelligence models

Eyüboglu¹,

Gül²

2022

REPQJ

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Climate change is the one of the most important issues faced globally and reasons of it must be reduced immediately in every area. Installing distributed power generation (DG) is one of the powerful options for reducing carbon emissions in power generation. However, improper allocation of these assets has several drawbacks. Efficient, novel and robust algorithm which is combination of both k-Means clustering and Particle Swarm Optimization is proposed in order to allocate DGs. Proposed algorithm clusters distribution network buses and selects to most proper cluster to allocate DG in this way it reduces possible buses. Furthermore, sizing and generation constraints of DGs are quite important for allocation. Therefore, several cases including different DG sizes and types are implemented to obtain the best results. Moreover, multiple DG cases are included in the study. Finally, DGs have considered as wind turbines for best cases and cases have analysed in 24 hourly bases including uncertainties both demand and production side. 33 Bus test feeder power losses are reduced up to 69%, 86%, 90% at best cases and 39%, 53%, 55% at including uncertainties by proposed algorithm for cases 1, 2, 3 DG installed, respectively.

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Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

Cited by 24 publications

References 51 publications

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

Sizing and Sitting of DERs in Active Distribution Networks Incorporating Load Prevailing Uncertainties Using Probabilistic Approaches

Efficiency Assessment of a Residential DC Nanogrid with Low and High Distribution Voltages Using Realistic Data

Simulation of Energy Consumption in Jaw Crusher Using artificial intelligence models

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