Determination of the Optimal Size of Photovoltaic Systems by Using Multi-Criteria Decision-Making Methods

Guerrero-Liquet, Guido C.; Oviedo-Casado, Santiago; Sánchez-Lozano, Juan Miguel; García-Cascales, M.; Prior, Javier; Urbina, Antonio

doi:10.3390/su10124594

Cited by 7 publications

(4 citation statements)

References 45 publications

(55 reference statements)

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“…Further sensitivity analysis is required, which can confirm the logic of the suggested model and has the potential to be widely promoted and applied [36,37]. Sensitivity analysis typically focuses on two key areas: first, assessing the effects of changes in indicator value; second, assessing the effects of changes in indicator weight on evaluation outcomes [38].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

Shao¹,

Wei²,

Liu

et al. 2023

Processes

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The power sector may reduce carbon emissions and reach carbon neutrality by accelerating the energy transition and lowering its reliance on fossil fuels. However, there are limitations on the new power system’s ability to operate safely and steadily due to the randomness, volatility, and intermittent nature of renewable energy supply. The key to solving this issue is to harness the flexible resources that energy storage systems (ESSs) represent; however, ESSs have more than a value for providing system flexibility. Thus, this study suggested a flexible, technical, economic, and environmental value index system based on multi-criteria decision-making models for evaluating the multi-dimensional value of ESSs. First, the objective and subjective weights are obtained using the CRITIC model and best–worst method; then, the weights are combined using the minimum relative entropy model. The complete values of five typical ESSs are then evaluated using the TOPSIS model. Three scenarios that reflect the traits of the new power system are then created and analyzed. The outcome demonstrates the following: (1) as the new power system continues to grow, the value of ESSs’ flexibility has become ever more crucial; (2) as the need for system flexibility increases, the value of electrochemical ESSs gradually manifests.

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Section: Sensitivity Analysismentioning

confidence: 99%

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

Shao¹,

Wei²,

Liu

et al. 2023

Processes

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“…Renewable energy-based power utilization has entered into electric power generation in this era because of increasing power demand and increased carbon emission. A small generation unit with a capacity of a few kW to 50 MW is defined as a distributed generation (DG) unit (Guerrero-liquet et al , 2018). Solar and wind energy are the naturally available resources on the living planet, and therefore, solar and wind-based power generation can supply the forthcoming energy demand.…”

Section: Introductionmentioning

confidence: 99%

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

M.L.¹,

Suja²,

2022

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Purpose The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO). Design/methodology/approach The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value. Findings The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints. Originality/value A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

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“…Therefore the access, maintenance, and detection of possible problems in the panels should be carried out by trained and qualified personnel working at heights to detect these problems. These procedures can put the integrity of people, equipment, and PV Plants at risk [6]. Manual inspection can take up to 8 h/MW, depending on the number of test modules.…”

Section: Introductionmentioning

confidence: 99%

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

2021

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Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

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Determination of the Optimal Size of Photovoltaic Systems by Using Multi-Criteria Decision-Making Methods

Cited by 7 publications

References 45 publications

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

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