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Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure...
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... potential of solar energy in southern Algeria is the largest in all Mediterranean basins, with 1,787,000 km 2 of Sahara desert, according to the German Aerospace Centre (DLR). The insolation time of almost all the national territory exceeds 2000 h annually and reaches 3900, as shown in Figure 1 (high plains and Sahara) [47]. Over most of the country and during the day, the energy obtained on a horizontal surface of 1 m 2 is nearly 5 kWh or about 2263 kWh/m 2 /year in the south and 1700 kWh/m 2 /year in the north [48]. ...
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... the percentage of the occupied area by PV modules in the two cases presents 99% of the available area. The arrangement of PV modules in rows within the installation area is illustrated in Figure 10 using the LCOE objective function. The length of each row changed from one row to another according to the shape of the PV plant. ...
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... the percentage of the occupied area by PV modules in the two cases presents 99% of the available area. The arrangement of PV modules in rows within the installation area is illustrated in Figure 10 using the LCOE objective function. The length of each row changed from one row to another according to the shape of the PV plant. ...
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... PV modules are installed horizontally for minimum LCOE (í µí±í µí± = 1) and vertically (í µí±í µí± = 2) for maximum annual energy. 2020,13,2776 Figure 11 illustrates the monthly energy generation by the PV power plant for the LCOE objective function. The PV plant energy generation remained high over the year, with an energy average of 65 (MWh) per month. ...
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... use of semi-hourly average time meteorological data in designing the PV plant can increase the financial benefits. Monthly produced energy (MWh) Figure 11. PV plant energy generation (MWh). ...
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... all resulting cases, the proposed HGWOSCA optimisation approach was applied successfully and showed higher efficiency than that of a single SCA technique, with high performance in determining the optimal solution and solving the PV plant complex design problem. The convergence optimisation of annual energy and LCOE is illustrated in Figures 12 and 13. Energies 2020, 13, x FOR PEER REVIEW 24 of 30 determining the optimal solution and solving the PV plant complex design problem. ...
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... 2020, 13, x FOR PEER REVIEW 24 of 30 determining the optimal solution and solving the PV plant complex design problem. The convergence optimisation of annual energy and LCOE is illustrated in Figures 12 and 13. Effect of PV Module Reduction Coefficient A sensitivity analysis was applied to evaluate the PV power plant performance. ...
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... optimisation results were obtained for different annual reduction coefficient values, from 0.3% to 0.7% per year. The annual reduction coefficient used in this study was 0.5%, as mentioned in Equation (44 SCA GWOSCA Figure 12. Convergence of the optimisation of annual energy using HGWOSCA algorithm for semi-hourly data. ...
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... optimisation results were obtained for different annual reduction coefficient values, from 0.3% to 0.7% per year. The annual reduction coefficient used in this study was 0.5%, as mentioned in Equation (44 SCA GWOSCA Figure 13. The convergence of the optimisation of LCOE using HGWOSCA algorithm for semi-hourly data. ...
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... optimum results for five different values for the annual reduction coefficient of the PV module are presented in Figures 14 and 15. According to the results, by increasing the PV module reduction coefficient, the PV plant energy production is reduced throughout its lifetime period. ...
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... contrast, the total cost of the PV power plant is not affected and has the same value for all reduction coefficient values. Energies 2020, 13, x The optimum results for five different values for the annual reduction coefficient of the PV module are presented in Figures 14 and 15. According to the results, by increasing the PV module reduction coefficient, the PV plant energy production is reduced throughout its lifetime period. ...
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... the sensitivity of the PV power plant improved by the decrement of the PV module annual reduction coefficient and vice versa. The optimum results for five different values for the annual reduction coefficient of the PV module are presented in Figures 14 and 15. According to the results, by increasing the PV module reduction coefficient, the PV plant energy production is reduced throughout its lifetime period. ...
Citations
... Other projects with a 200 MWc per year capacity ought to be finished between 2021 and 2030. Additionally, the country now has 23 functional photovoltaic power plants (Ihaddadene, Jed, Ihaddadene, & De Souza, 2022;Zidane et al., 2020) as listed in Table 1. They are distributed throughout the country, but the majority of them are in the south (Sahara). ...
... The main aim of such a combination is to address complicated design challenges by utilizing the complementing qualities of the techniques. The work reported in [113] shows that the optimum design results using hybrid grey wolf optimiser-sine cosine algorithm is more efficient than PSO. Considering this, designers can increase system profit while employing the same components by utilizing novel, powerful optimization techniques. ...
Due to photovoltaic (PV) technology advantages as a clean, secure, and pollution-free energy source, PV power plants installation have shown an essential role in the energy sector. Nevertheless, the PV power plant cost of energy must be competitive when compared to traditional energy sources. Therefore, numerous studies are continuously being conducted aiming to optimize PV power plants, including components arrangements within the installation site, the inverter topology, cables, PV modules and inverters numbers, PV module tilt angle and shadow effect. For selecting the most suitable combinations for system parameters, this study seeks to systematically analyze and synthesize the design of the PV power plant optimization from the current literature. The study also examines component sizing for PV power plants, involving PV modules tilt angle, inverter, transformer, and cables. Moreover, it provides an overview of the main components employed to install the PV power plant, which includes PV modules, inverter, transformer and wiring. It examines the different inverter topologies used in PV power plants along with a comparison between these topologies.
... The control approach was able to enhance the solar air conditioners PowerPoint tracking efficiency considerably in a case study conducted in Shanghai with the Photovoltaic Fraction (PF) and Self-Consumption Ratio (SCR) improving from 82.74 to 88.11% and 70.12 to 74.42%, respectively. Zidane et al. (2020) presented a precise design approach for maximizing advantages while minimizing PV plant installation, maintenance, and operating expenses. The procedure's goal functions were having the lowest levelized cost of energy (LCOE) and the largest yearly energy, with design factors being the number of series-parallel Photovoltaic panels, the number of Photovoltaic panels for each row, inter-row space, inclination angle and alignment, Photovoltaic module type, along with inverter framework. ...
The demand for electricity is rapidly rising, and renewable energy sources are becoming increasingly important for maintaining the electric system and servicing isolated demands. Tidal energy, wind energy, and solar energy (SE) are all forms of renewable energy. The solar power system is free of pollution, and enormous volumes of solar radiation reach the earth's surface. Photovoltaic (PV) systems are taking a leading role as solar-based energy sources because of their unique advantages. But in this, the cost of power generation is an important issue since the existing research methods aren't effective. So that this research methodology uses the Cognitive Marine Predators Algorithm (CMPA) based cost-effective PV power generation system and utilization. In this research, first, the design variables, constraints, and the PV modelling is explained. After that, the objective function is determined here. The main objective function is the generation of the cost function and the power loss function. At last, the cost-effective power generation system is designed by the CMPA. In this CMPA, the determined objective functions are considered as the fitness function and the input are the design variables.
... In [32], the proposed methodology has been applied to the development of the optimal design of a PV plant connected to the electric grid and implemented in MATLAB software. According to the presented results, the PV plant optimal design variables depend on the selected objective function. ...
The application of renewable energy sources such as Photovoltaic Systems (PV) can be effective in minimizing damage to the environment. As the use of PV systems increases, questions and concerns about higher quality and reliability have been raised. The aim of this study, which has been conducted in the high-tech electronic industry, is to select the optimal components for designing photovoltaic systems. It has been done to achieve goals such as increasing customer satisfaction and system efficiency, reducing the overall cost and procurement time of the system. In this regard, after extracting Customer Needs from the first stage of the systems engineering process, they have been interpreted to Functional Requirements using the first matrix of QFD. Then, the FRs have been prioritized by use of Analytical Network Process and entered the second matrix of QFD. They have been examined along with leveled components based on the alternatives available for each component. Also, the Design Structure Matrix has been used to evaluate the effect of elements upon each other. Finally, a mathematical model is developed to select optimal components according to the defined objective functions and constraints. After solving the model in GAMS software, the results indicate that type B of Solar Panels, a type E of Controller, a type F of Combiner Box, a type H of Inverter, type L of Batteries, type Q of Disconnects, and type T of Miscellaneous Components must be selected to achieve mentioned objectives.
... Nowadays, the challenge is to design and build a photovoltaic power plant to achieve high benefits at low cost. Individual components of the power plant are examined and selected, and then the number of particular components and their distribution are specified [18,19]. There are created innovative management systems [20][21][22]. ...
This article describes problems related to the operation of a virtual micro power plant at the Faculty of Electrical Engineering (FEE), Czestochowa University of Technology (CUT). In the era of dynamic development of renewable energy sources, it is necessary to create alternative electricity management systems for existing power systems, including power transmission and distribution systems. Virtual power plants (VPPs) are such an alternative. So far, there has been no unified standard for a VPP operation. The article presents components that make up the VPP at the FEE and describes their physical and logical structure. The presented solution is a combination of several units operating in the internal power grid of the FEE, i.e., wind turbines, energy storage (ES), photovoltaic panels (PV) and car charging stations. Their operation is coordinated by a common control system. One of the research goals described in the article is to optimize the operation of these components to minimize consumption of the electric energy from the external supply network. An analysis of data from the VPP management system was carried out to create mathematical models for prediction of the consumed power and the power produced by the PVs. These models allowed us to achieve the assumed objective. The article also presents the VPP data processing results in terms of detecting outliers and missing values. In addition to the issues discussed above, the authors also proposed to apply the Prophet model for short-term forecasting of the PV farm electricity production. It is a statistical model that has so far been used for social and business research. The authors implemented it effectively for technical analysis purposes. It was shown that the results of the PV energy production forecasting using the Prophet model are acceptable despite occurrences of missing data in the investigated time series.
... According to the authors, the developed co-design optimization technique can achieve the maximum in the power production of a grid-connected PV system compared to the case designing PV arrays and inverters separately. A recent study in [19] presented a methodology to design PV plants to reduce installation, maintenance, and operation costs. This framework details the semi-hourly step time of meteorology data of the selected the location and analysis of the specifications of different PV plant components to determine the optimum PV module and inverter along with the suitable topology for the selected location. ...
... The impact of the step time resolution on the PV power plant design was investigated by using semi-hourly step time data and hour-by-hour data for one year in a recent study presented in [19]. Using semi-hourly step time meteorology data results in improving the PV power plant LCOE and increasing the financial benefit. ...
This paper aims to assess the impact of different key factors on the optimized design and performance of grid connected photovoltaic (PV) power plants, as such key factors can lead to re-design the PV plant and affect its optimum performance. The impact on the optimized design and performance of the PV plant is achieved by considering each factor individually. A comprehensive analysis is conducted on nine factors such as; three objectives are predefined, five recent optimization approaches, three different locations around the world, changes in solar irradiance, ambient temperature, and wind speed levels, variation in the available area, PV module type and inverters size. The performance of the PV plant is evaluated for each factor based on five performance parameters such as; energy yield, sizing ratio, performance ratio, ground cover ratio, and energy losses. The results show that the geographic location, a change in meteorological conditions levels, and an increase or decrease in the available area require the re-design of the PV plant. A change in inverter size and PV module type has a significant impact on the configuration of the PV plant leading to an increase in the cost of energy. The predefined objectives and proposed optimization methods can affect the PV plant design by producing completely different structures. Furthermore, most PV plant performance parameters are significantly changed due to the variation of these factors. The results also show the environmental benefit of the PV plant and the great potential to avoid green-house gas emissions from the atmosphere.
... Several methods in the literature proposed an optimal configuration of PV power plants using evolutionary algorithms or commercially available software tools. Generally, these methods used meteorological data, economic parameters, PV modules, and inverters components [2][3][4]. Additionally, the PV plant design was set for technical, environmental, and economic targets. PV inverter's optimum size depends on PV modules generated energy, cost ratio, and inverter performance. ...
... The nominal power of the PV array PPV(rated) can be obtained using equation (2). The PV plant losses during its operational lifetime can be calculated by subtracting the PV plant total output power from the PV array total output power as expressed in equation (3). ...
This paper aims to select the optimum inverter size for large-scale PV power plants grid-connected based on the optimum combination between PV array and inverter, among several possible combinations. Inverters used in this proposed methodology have high-efficiency conversion in the range of 98.5% which is largely used in real large-scale PV power plants to increase the financial benefits by injecting maximum energy into the grid. To investigate the PV array-inverter sizing ratio, many PV power plants rated power are considered. The proposed method is based on the modelling of several parts of the PV power plant taking into account many design variables and constraints. The objective function is the levelized cost of energy (LCOE) and the optimization is performed by a multi-verse algorithm. The optimization method results in an optimum inverter size that depends on the PV plant rated capacity by providing an optimum number of inverters required in the installation site. The optimum sizing ratio (Rs) between PV array and inverter were found equal to 0.928, 0.904, and 0.871 for 1 MW, 1.5 MW, and more than 2 MW, respectively, whereas the total power losses reached 8% of the total energy generation during the PV power plant operational lifetime.
... Going into detail regarding the GWO algorithm, some applications in the maintenance field can be found in the literature: the majority of them focus on the cost efficiency of the maintenance processes. For example, it is applied to optimise the design and maintenance of photovoltaic power plants [34] or to minimise maintenance costs of heat and power systems [35][36][37][38]. Kumar et al. [39] focus on both the reliability and the costs of a Space Shuttle, through the implementation of a multiobjective GWO. ...
An essential step in the implementation of predictive maintenance involves the health state analysis of productive equipment in order to provide company managers with performance and degradation indicators which help to predict component condition. In this paper, a supervised approach for health indicator calculation is provided combining the Grey Wolf Optimisation method, Swarm Intelligence algorithm, and Fuzzy Cognitive Maps. The k-neighbors algorithms is used to predict the Remaining Useful Life of an item, since, in addition to its simplicity, they produce good results in a large number of domains. The approach aims to solve the problem that frequently occurs in interpolation procedures: the approximation of functions belonging to a chosen class of functions of which we have no knowledge. The proposed algorithm allows maintenance managers to distinguish different degradation profiles in depth with a consequently more precise estimate of the Remaining Useful Life of an item and, in addition, an in-depth understanding of the degradation process. Specifically, in order to show its suitability for predictive maintenance, a dataset on NASA aircraft engines has been used and results have been compared to those obtained with a neural network approach. Results highlight how all of the degradation profiles, obtained using the proposed approach, are modelled in a more detailed manner, allowing one to significantly distinguish different situations. Moreover, the physical core speed and the corrected fan speed have been identified as the main critical factors to the engine degradation.
... The main goal is finding the proper size of each technology considering both cost-effectiveness and energy efficiency, as well as the proper operating strategies [9][10][11][12][13][14]. To this purpose, different optimization procedures to define the proper system configuration and operation have been proposed in the last few years based both on single and multi-objective optimization [15][16][17][18][19]. Usually, multi-objective approaches are preferred because they provide a proper trade-off between different purposes (e.g., economic, energy and environmental goals) while single objective optimizations refer to a best solution with a specific point of view [11,20,21] and the other objectives are often degraded [22,23]. ...
The aim of the paper is the techno-economic analysis of innovative integrated combined heat and power (CHP) systems for the exploitation of different renewable sources in the residential sector. To this purpose, a biofuel-driven organic Rankine cycle (ORC) is combined with a wind turbine, a photovoltaic system and an auxiliary boiler. The subsystems work in parallel to satisfy the electric and heat demand of final users: a block of 40 dwellings in a smart community. A 12.6 kWel ORC is selected according to a thermal-driven strategy, while wind and solar subsystems are introduced to increase the global system efficiency and the electric self-consumption. The ORC can be switched-off or operated at partial load when solar and/or wind sources are significant. A multi-variable optimization has been carried out to find the proper size of the wind turbine and photovoltaic subsystems and to define the suitable operating strategy. To this purpose, several production wind turbines (1.0–60.0 kWel) and photovoltaic units (0.3–63.0 kWel) have been considered with the aim of finding the optimal trade-off between the maximum electric self-consumption and the minimum payback period and electric surplus. The multi-objective optimization suggests the integration of 12.6 kWel ORC with 10 kWel wind turbine and 6.3 kWel photovoltaic subsystem. The investigation demonstrates that the proposed multi-source integrated system offers a viable solution for smart-communities and distributed energy production with a significant improvement in the global system efficiency (+7.5%) and self-consumption (+15.0%) compared to the sole ORC apparatus.
