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| Contribution | Ref. |
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(i) The study is aimed at reducing feeder losses and enhancing voltage quality. A Stamford generator, with a rated power of 1350 kW, was integrated into the network
(ii) Research gaps: the analysis did not consider time-dependent loads and time-dependent generation. Additionally, renewable energy-based distributed generators (DGs) were not considered | [8] |
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(i) The modified voltage index (MVI) method was utilized to determine the optimal placement and size of DG units in order to enhance the voltage stability margin
(ii) Research gaps: the study adopted a single DG placement approach, considering one DG at a time and assessing its placement at different buses. Economic variables such as investment and operational costs were not taken into consideration in the analysis | [9] |
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(i) The approach is aimed at minimizing power losses in the electrical network while improving voltage stability and network security. To address the stochastic nature of solar irradiance and wind speed, suitable probabilistic models were employed, allowing for realistic representation of their variability in the analysis
(ii) Research gaps: the study focused solely on the technical aspects of optimal placement and sizing of solar and wind distributed generators (DGs) in the distribution territory; however, economic factors such as investment costs, operational costs, or financial considerations were not taken into account in the analysis | [10] |
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(i) Used weighted multiobjective voltage index to minimize real power loss and enhance the voltage profile within the system
(ii) Research gaps: the study considers a general type of distributed generators (DGs) integrated into the network without specifying a particular DG type. The focus of the analysis is solely on technical aspects | [11] |
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(i) It employs a multistate modeling approach to account for the uncertain nature of wind and solar resources. The proposed model evaluates deviations in several key parameters, including annual energy losses (AEL), total DG penetration, loss of load expectation (LOLE), and loss of energy expectation
(ii) Research gaps: the study primarily focuses on technical aspects and incorporates monthly, seasonal, and yearly models of solar and wind resources. It does not include hourly models, which means that the analysis does not account for the fine-grained variations and fluctuations in solar irradiance and wind speed throughout the day | [12] |
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(i) A methodology was proposed to optimize the allocation of different types of renewable distributed generation (DG) units within the distribution system with the objective of minimizing annual energy loss. This methodology involves the utilization of a multistate model for the hourly modeling of renewable energy sources
(ii) Research gaps: the proposed methodology only focused solely on the technical aspects of optimally allocating different types of renewable distributed generation (DG) units within the distribution system | [13] |
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(i) Crow search algorithm (CSA) was used to determine the optimal size and allocation of distributed generators (DGs). A multiobjective function was formulated to address the objectives of reducing active power losses and improving the voltage profile
(ii) Research gaps: the study solely focused on technical aspect only, and the algorithm was applied to IEEE 33-bus without incorporating real data | [14] |
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(i) Used time-varying and seasonal optimal placement and sizing of both intermittent renewable energy sources (such as wind energy) and nonintermittent renewable energy sources (such as solar energy). To account for the multistate and hourly probabilistic nature of wind speed and solar irradiance data, the study employed appropriate modelling techniques
(ii) Research gaps: does not consider the economic aspects related to cost-benefit analysis or financial feasibility | [21] |
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(i) Uses multiobjective management approach that combines network reconfiguration with the allocation and sizing of renewable distributed generations (DGs) with the aim of minimizing active power loss, annual operation costs (including installation, maintenance and active power loss costs), and pollutant gas emissions. The optimization problem is solved by considering the time sequence variance in renewable DGs and load
(ii) Research gaps: used simulation models (IEEE 33 bus) without validating the results against real-world data | [15] |
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(i) Incorporated both technical and economic aspects, with aim of minimizing power losses and maximizing profit. Additionally, it accounted for the stochastic nature of wind and solar resources
(ii) Research gaps: the DG placement study lacked the use of real data and overlooked the significance of considering voltage profiles, which are crucial in DG placement studies | [16]. |
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(i) An optimization model was developed to address the allocation of distributed generators (DGs) with the primary objective of minimizing the total planning cost
(ii) Research gaps: the optimization model presented in the study focused solely on wind energy; integration challenges with other sources such as wind and solar were not tested and validated | [17] |
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(i) Multiobjective algorithm was applied to reduce power loss, maximizing the voltage stability index, minimizing voltage deviation, lowering real power loss costs, increasing real power loss savings, and reducing CO2 emissions
(ii) Research gaps: did not incorporate the potential effects of the intermittent nature of renewable distributed generations (DGs). The intermittent nature of renewable DGs, such as wind and solar, can introduce uncertainty and variability into the power system | [18] |
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(i) Three optimization techniques, PSO, variable constraint PSO (VCPSO), and GA algorithms, are applied to find the optimal size and placement of multiple DGs integrated into electrical power network. VCPSO was offered an improved solution for the optimal placement and size of DGs in terms of the accuracy of the global optimality
(ii) Draw back: did not address the application of the hybrid PSO algorithm in real-world distribution networks | [19] |
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(i) Cost-based analysis was used on distributed generators (DGs), to determine installation costs, operational costs, and maintenance costs. The objective of the analysis was to minimize losses and maximize the loading capability of the system while ensuring that voltage stability is not compromised
(ii) Research gaps: assumed a constant factor of 0.95 as power factor for DG operation which is not the case in real-life situations; the power factor of DGs may vary depending on various factors such as load conditions, system requirements, and control strategies | [20] |
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(i) Crisscross optimization algorithm and Monte Carlo simulation method (CSO MCS), used to address the optimal distributed generation allocation (ODGA) problem. This method considers the uncertainties associated with wind, solar, and load consumption
(ii) Research gaps: does not consider an hourly resolution. This can lead to inaccurate results, as it fails to capture the time-varying nature of renewable energy generation | [22] |
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(i) PSO is used on IEEE 33 radial distribution system with different types of voltage-dependent load models. The results reveal that combination of active–reactive power DG is giving better results for power loss reduction and voltage profile improvement
(ii) Research gaps: the study did not investigate the integration of renewable energy sources or consider uncertainties related to renewable generation | [23] |
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(i) Provided a review of optimization techniques used for DG sizing and placement in a distributed network
(ii) Research gaps: did not consider different scenarios and constraints to identify the most appropriate techniques for specific applications | [24] |
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(i) MOPSO algorithm has been used to find the optimal solution of DG sizing and locating problem; this was tested on IEEE 33-bus reliability enhancement of the grid which was confirmed
(ii) Research gaps: did not consider implementing renewable DGs with uncertain output power, such as PV panels or wind turbines | [25] |
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(i) Used multiobjective bat algorithm on IEEE 69-bus; from the obtained results, it is observed that the best localization and sizing of DG unit give more flexibility to the network
(ii) Research gaps: did not cost function and the algorithm did not use real data | [26] |
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(i) A backtracking search optimization algorithm (BSOA) is developed to enhance voltage profile and reduce real network losses
(ii) Research gaps: the study assumed that the output of renewable energy (RE) sources is dispatchable, hence did not consider intermittent nature of renewable energy sources | [27] |
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