Applied Sciences

Editorial

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4 pages, 174 KiB

Open AccessEditorial

Solar Power System Planning and Design

by Yosoon Choi

Appl. Sci. 2020, 10(1), 367; https://doi.org/10.3390/app10010367 - 3 Jan 2020

Cited by 5 | Viewed by 2974

Abstract

With growing concerns about greenhouse gas emissions, the security of conventional energy supplies, and the environmental safety of conventional energy production techniques, renewable energy systems are becoming increasingly important and are receiving much political attention [...] Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

Research

Jump to: Editorial, Review

19 pages, 6627 KiB

Open AccessArticle

Net-Metering and Self-Consumption Analysis for Direct PV Groundwater Pumping in Agriculture: A Spanish Case Study

by Alvaro Rubio-Aliaga, Angel Molina-Garcia, M. Socorro Garcia-Cascales and Juan Miguel Sanchez-Lozano

Appl. Sci. 2019, 9(8), 1646; https://doi.org/10.3390/app9081646 - 20 Apr 2019

Cited by 7 | Viewed by 4224

Abstract

International policies mainly that are focused on energy-dependence reduction and climate change objectives have been widely proposed by most developed countries over the last years. These actions aim to promote the integration of renewables and the reduction of emissions in all sectors. Among [...] Read more.

International policies mainly that are focused on energy-dependence reduction and climate change objectives have been widely proposed by most developed countries over the last years. These actions aim to promote the integration of renewables and the reduction of emissions in all sectors. Among the different sectors, agriculture emerges as a remarkable opportunity to integrate these proposals. Indeed, this sector accounts for 10% of the total greenhouse gas (GHG) emissions in the EU, representing 1.5% of gross domestic product (GDP) in 2016. Within the agriculture sector, current solutions for groundwater pumping purposes are mainly based on diesel technologies, leading to a remarkable fossil fuel dependence and emissions that must be reduced to fulfill both energy and environmental requirements. Relevant actions must be proposed that are focused on sustainable strategies and initiatives. Under this scenario, the integration of photovoltaic (PV) power plants into groundwater pumping installations has recently been considered as a suitable solution. However, this approach requires a more extended analysis, including different risks and impacts related to sustainability from the economic and energy points of view, and by considering other relevant aspects such as environmental consequences. In addition, PV solar power systems connected to the grid for groundwater pumping purposes provide a relevant opportunity to optimize the power supplied by these installations in terms of self-consumption and net-metering advantages. Actually, the excess PV power might be injected to the grid, with potential profits and benefits for the agriculture sector. Under this scenario, the present paper gives a multidimensional analysis of PV solar power systems connected to the grid for groundwater pumping solutions, including net-metering conditions and benefit estimations that are focused on a Spanish case study. Extensive results based on a real aquifer (Aquifer 23) located in Castilla La Mancha (Spain) are included and discussed in detail. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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12 pages, 4368 KiB

Open AccessArticle

Wind Loads on a Solar Panel at High Tilt Angles

by Chin-Cheng Chou, Ping-Han Chung and Ray-Yeng Yang

Appl. Sci. 2019, 9(8), 1594; https://doi.org/10.3390/app9081594 - 17 Apr 2019

Cited by 27 | Viewed by 7829

Abstract

A solar photovoltaic system consists of tilted panels and is prone to extreme wind loads during hurricanes or typhoons. To ensure the proper functioning of the system, it is important to determine its aerodynamic characteristics. Offshore photovoltaic (PV) systems have been developed in [...] Read more.

A solar photovoltaic system consists of tilted panels and is prone to extreme wind loads during hurricanes or typhoons. To ensure the proper functioning of the system, it is important to determine its aerodynamic characteristics. Offshore photovoltaic (PV) systems have been developed in recent years. Wind loads are associated with wind, wave climates, and tidal regimes. In this study, the orientation of a single panel is adjusted to different angles of tilt (10°–80°) and angles of incidence for wind (0°–180°) that are pertinent to offshore PV panels. The critical wind loads on a tilted panel are observed at lower angles of incidence for the wind, when the angle of tilt for the panel is greater than 30°. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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11 pages, 1611 KiB

Open AccessArticle

An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

by Fei Mei, Qingliang Wu, Tian Shi, Jixiang Lu, Yi Pan and Jianyong Zheng

Appl. Sci. 2019, 9(7), 1487; https://doi.org/10.3390/app9071487 - 9 Apr 2019

Cited by 34 | Viewed by 4041

Abstract

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term [...] Read more.

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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20 pages, 2839 KiB

Open AccessArticle

Towards the Development of a Low-Cost Irradiance Nowcasting Sky Imager

by Luis Valentín, Manuel I. Peña-Cruz, Daniela Moctezuma, Cesar M. Peña-Martínez, Carlos A. Pineda-Arellano and Arturo Díaz-Ponce

Appl. Sci. 2019, 9(6), 1131; https://doi.org/10.3390/app9061131 - 18 Mar 2019

Cited by 11 | Viewed by 2953

Abstract

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in [...] Read more.

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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29 pages, 9597 KiB

Open AccessArticle

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

by Walter Richardson, David Cañadillas, Ariana Moncada, Ricardo Guerrero-Lemus, Les Shephard, Rolando Vega-Avila and Hariharan Krishnaswami

Appl. Sci. 2019, 9(4), 684; https://doi.org/10.3390/app9040684 - 17 Feb 2019

Cited by 12 | Viewed by 6409

Abstract

Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance [...] Read more.

Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R² values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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17 pages, 9250 KiB

Open AccessArticle

Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea

by Sung-Min Kim, Myeongchan Oh and Hyeong-Dong Park

Appl. Sci. 2019, 9(3), 395; https://doi.org/10.3390/app9030395 - 24 Jan 2019

Cited by 75 | Viewed by 9171

Abstract

Photovoltaic (PV) energy is one of the most promising renewable energies in the world due to its ubiquity and sustainability. However, installation of solar panels on the ground can cause some problems, especially in countries where there is not enough space for installation. [...] Read more.

Photovoltaic (PV) energy is one of the most promising renewable energies in the world due to its ubiquity and sustainability. However, installation of solar panels on the ground can cause some problems, especially in countries where there is not enough space for installation. As an alternative, floating PV, with advantages in terms of efficiency and environment, has attracted attention, particularly with regard to installing large-scale floating PV for dam lakes and reservoirs in Korea. In this study, the potentiality of floating PV is evaluated, and the power production is estimated for 3401 reservoirs. To select a suitable reservoir for floating PV installation, we constructed and analyzed the water depth database using OpenAPI. We also used the typical meteorological year (TMY) data and topographical information to predict the irradiance distribution. As a result, the annual power production by all possible reservoirs was estimated to be 2932 GWh, and the annual GHG reduction amount was approximately 1,294,450 tons. In particular, Jeollanam-do has many reservoirs and was evaluated as suitable for floating PV installation because of its high solar irradiance. The results can be used to estimate priorities and potentiality as a preliminary analysis for floating PV installation. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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13 pages, 2044 KiB

Open AccessArticle

Experimental Efficiency Analysis of a Photovoltaic System with Different Module Technologies under Temperate Climate Conditions

by Slawomir Gulkowski, Agata Zdyb and Piotr Dragan

Appl. Sci. 2019, 9(1), 141; https://doi.org/10.3390/app9010141 - 3 Jan 2019

Cited by 47 | Viewed by 4414

Abstract

This study presents a comparative analysis of energy production over the year 2015 by the grid connected experimental photovoltaic (PV) system composed by different technology modules, which operates under temperate climate meteorological conditions of Eastern Poland. Two thin film technologies have been taken [...] Read more.

This study presents a comparative analysis of energy production over the year 2015 by the grid connected experimental photovoltaic (PV) system composed by different technology modules, which operates under temperate climate meteorological conditions of Eastern Poland. Two thin film technologies have been taken into account: cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS). Rated power of each system is approximately equal to 3.5 kWp. In addition, the performance of a polycrystalline silicon technology system has been analyzed in order to provide comprehensive comparison of the efficiency of thin film and crystalline technologies in the same environmental conditions. The total size of the pc-Si system is equal to 17 kWp. Adequate sensors have been installed at the location of the PV system to measure solar irradiance and temperature of the modules. In real external conditions all kinds of modules exhibit lower efficiency than the values provided by manufacturers. The study reveals that CIGS technology is characterized by the highest energy production and performance ratio. The observed temperature related losses are of the lowest degree in case of CIGS modules. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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18 pages, 881 KiB

Open AccessArticle

Numerical Analysis for Thermal Performance of a Photovoltaic Thermal Solar Collector with SiO₂-Water Nanofluid

by Ali J. Chamkha and Fatih Selimefendigil

Appl. Sci. 2018, 8(11), 2223; https://doi.org/10.3390/app8112223 - 11 Nov 2018

Cited by 34 | Viewed by 4020

Abstract

Numerical analysis of a photovoltaic-thermal (PV/T) unit with SiO

_{2}

-water nanofluid was performed. The coupled heat conduction equations within the layers and convective heat transfer equations within the channel of the module were solved by using the finite volume method. Effects of [...] Read more.

Numerical analysis of a photovoltaic-thermal (PV/T) unit with SiO

_{2}

-water nanofluid was performed. The coupled heat conduction equations within the layers and convective heat transfer equations within the channel of the module were solved by using the finite volume method. Effects of various particle shapes, solid volume fractions, water inlet temperature, solar irradiation and wind speed on the thermal and PV efficiency of the unit were analyzed. Correlation for the efficiencies were obtained by using radial basis function neural networks. Cylindrical shape particles were found to give best performance in terms of efficiency enhancements. Total efficiency enhances by about 7.39% at the highest volume fraction with cylindrical shape particles. Cylindrical shape particle gives 3.95% more enhancement as compared to spherical ones for the highest value of solid particle volume fraction. Thermal and total efficiency enhance for higher values of solid particle volume fraction, solar irradiation and lower values of convective heat transfer coefficient and inlet temperature. The performance characteristics of solar PV-thermal unit with radial basis function artificial neural network are found to be in excellent agreement with the results obtained from computational fluid dynamics modeling. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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24 pages, 4859 KiB

Open AccessArticle

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

by Tuo Xie, Gang Zhang, Hongchi Liu, Fuchao Liu and Peidong Du

Appl. Sci. 2018, 8(10), 1901; https://doi.org/10.3390/app8101901 - 12 Oct 2018

Cited by 57 | Viewed by 5014

Abstract

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, [...] Read more.

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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19 pages, 4249 KiB

Open AccessArticle

Evaluation Model of Demand-Side Energy Resources in Urban Power Grid Based on Geographic Information

by Feifan Chen, Shixiao Guo, Yajing Gao, Wenhai Yang, Yongchun Yang, Zheng Zhao and Ali Ehsan

Appl. Sci. 2018, 8(9), 1491; https://doi.org/10.3390/app8091491 - 29 Aug 2018

Cited by 8 | Viewed by 3649

Abstract

In the context of current energy shortage and environmental degradation, the penetration rate of demand-side energy resources (DSER) in the power grid is constantly increasing. To alleviate the problems concerning the energy and environment, it is of tremendous urgency to develop and make [...] Read more.

In the context of current energy shortage and environmental degradation, the penetration rate of demand-side energy resources (DSER) in the power grid is constantly increasing. To alleviate the problems concerning the energy and environment, it is of tremendous urgency to develop and make effective use of them. Therefore, this paper proposes the evaluation model of DSER in urban power grid based on geographic information, and a variety of demand-side energy resources in a region is evaluated. Firstly, as for five kinds of DSER, revolving wind power generation (WG), photovoltaic power generation (PV), electric vehicle (EV), energy storage (ES), and flexible load, the commonality indexes and individuality indexes of all kinds of resources are selected based on geographic information. The commonality indexes are common indexes of all DSER, and the individuality indexes are unique indexes of all DSER. Then the weight of each subindex under the commonality and individuality indexes are determined by analytic hierarchy process (AHP) and entropy weight method, respectively. Finally, weighted overlay are made according to the weights and quantized values of each index, and a comprehensive score is obtained from the commonality indexes and individuality indexes upon various demand-side energy resources in the region. The result depicts that the proposed evaluation model of demand-side energy resources is of well practicability and effectiveness, which is beneficial to the planning of the city and the power grid. Most of all, such model provides a strong support for the long-term optimization planning and the medium-term optimization aggregation of DSER. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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29 pages, 6619 KiB

Open AccessEditor’s ChoiceArticle

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

by Fei Wang, Yili Yu, Zhanyao Zhang, Jie Li, Zhao Zhen and Kangping Li

Appl. Sci. 2018, 8(8), 1286; https://doi.org/10.3390/app8081286 - 1 Aug 2018

Cited by 161 | Viewed by 11867

Abstract

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the [...] Read more.

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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16 pages, 4520 KiB

Open AccessArticle

Solar Tower Power Plants of Molten Salt External Receivers in Algeria: Analysis of Direct Normal Irradiation on Performance

by Abdelkader Rouibah, Djamel Benazzouz, Rahmani Kouider, Awf Al-Kassir, Justo García-Sanz-Calcedo and K. Maghzili

Appl. Sci. 2018, 8(8), 1221; https://doi.org/10.3390/app8081221 - 25 Jul 2018

Cited by 17 | Viewed by 5960

Abstract

The increase of solar energy production has become a solution to meet the demand of electricity and reduce the greenhouse effect worldwide. This paper aims to determine the performance and viability of direct normal irradiation of three solar tower power plants in Algeria, [...] Read more.

The increase of solar energy production has become a solution to meet the demand of electricity and reduce the greenhouse effect worldwide. This paper aims to determine the performance and viability of direct normal irradiation of three solar tower power plants in Algeria, to be installed in the highlands and the Sahara (Béchar, El Oued, and Djelfa regions). The performance of the plants was obtained through a system advisor model simulator. It used real data gathered from appropriate meteorological files. A relationship between the solar multiple (SM), power generation, and thermal energy storage (TES) hours was observed. The results showed that the optimal heliostat field corresponds to 1.8 SM and 2 TES hours in Béchar, 1.2 SM and 2 TES hours for El Oued, and 1.5 SM and 4 TES hours for Djelfa. This study shows that there is an interesting relationship between the solar multiple, power generation, and storage capacity. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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Review

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27 pages, 8081 KiB

Open AccessFeature PaperReview

GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review

by Yosoon Choi, Jangwon Suh and Sung-Min Kim

Appl. Sci. 2019, 9(9), 1960; https://doi.org/10.3390/app9091960 - 13 May 2019

Cited by 95 | Viewed by 22407

Abstract

In this study, geographic information system (GIS)-based methods and their applications in solar power system planning and design were reviewed. Three types of GIS-based studies, including those on solar radiation mapping, site evaluation, and potential assessment, were considered to elucidate the role of [...] Read more.

In this study, geographic information system (GIS)-based methods and their applications in solar power system planning and design were reviewed. Three types of GIS-based studies, including those on solar radiation mapping, site evaluation, and potential assessment, were considered to elucidate the role of GISs as problem-solving tools in relation to photovoltaic and concentrated solar power systems for the conversion of solar energy into electricity. The review was performed by classifying previous GIS-based studies into several subtopics according to the complexity of the employed GIS-based methods, the type of solar power conversion technology, or the scale of the study area. Because GISs are appropriate for handling geospatial data related to solar resource and site suitability conditions on various scales, the applications of GIS-based methods in solar power system planning and design could be expanded further. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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30 pages, 1335 KiB

Open AccessReview

Photovoltaic Power Systems Optimization Research Status: A Review of Criteria, Constrains, Models, Techniques, and Software Tools

by Samer Alsadi and Tamer Khatib

Appl. Sci. 2018, 8(10), 1761; https://doi.org/10.3390/app8101761 - 29 Sep 2018

Cited by 86 | Viewed by 9084

Abstract

The photovoltaic (PV) generating system has high potential, since the system is clean, environmental friendly and has secure energy sources. There are two types of PV system, which are grid connected and standalone systems. In the grid connected photovoltaic system (GCPV), PV generator [...] Read more.

The photovoltaic (PV) generating system has high potential, since the system is clean, environmental friendly and has secure energy sources. There are two types of PV system, which are grid connected and standalone systems. In the grid connected photovoltaic system (GCPV), PV generator supplies power to the grid, whether or not the whole or a portion of the generated energy will be used to supply load demands. Meanwhile, the standalone photovoltaic system (SAPV) is used to fulfil a load demand that close to its point of use. These days, many researchers study in term of optimization sizing of photovoltaic system, in order to select optimum number of PV modules, inverter, battery storage capacity, and tilt angle. Based on that, this review aims to give explanations on approaches done by previous researchers in order to find ultimate combinations for design parameters. Moreover, the paper discusses on modelling of PV system components, which includes PV panels’ output power estimation and battery system. Finally, simulation softwares that used as sizing tools in previous studies are reviewed and studied. Full article

(This article belongs to the Special Issue Solar Power System Planning & Design: Resource Assessment, Site Evaluation, System Design, Production Forecasting and Feasibility Studies)

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