Horizontal Global Solar Radiation Research Articles

An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

ABSTRACT An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model.

A generalized approach for the determination of optimum tilt angle for solar photovoltaic modules with selected locations in Ethiopia as illustration examples

The amount of total solar energy that strikes the photovoltaic module's surface directly affects how much power is generated. On the other hand, solar energy is influenced by the solar module's location, tilt angle, and orientation. The models presented in this study can be used to determine the seasonal and annual optimal tilt angles of photovoltaic modules at any location in Ethiopia as a function of the location's latitude and elevation without the need for solar radiation data. Both isotropic and anisotropic diffuse solar radiation models were used to determine the monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 study sites, 32 for training and 12 for testing, were retrieved from the National Aeronautical and Space Administration database. The optimal tilt angle and regression models were determined by developing and applying algorithms in the programming languages MATLAB and R. The proposed model estimates the optimal tilt angle with the fewest statistical validation errors. By using the proposed monthly optimal tilt angles, solar radiation gains of 6.76% - 8.48% (for isotropic models) and 10.16% - 26.97% (anisotropic models) were obtained for Bahir Dar city. The solar radiation gains for the cities of Adigrat, Butajira, and Gode were found to be 7.18–9.02% (isotropic) and 10.90%–28.06% (anisotropic), 5.43%–7.05% (isotropic) and 8.48%–25.99% (anisotropic), and 4.33%–5.79% (isotropic) and 6.96%–23.03% (anisotropic), respectively. The results of this study were further confirmed by comparing them to previously published papers and online PVGIS and NREL's PVWatt software tools.

Geospatial Assessment and Mapping of Suitable Sites for a Utility-scale Solar PV Farm

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.

Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors

Intra-day forecasts of global horizontal solar irradiance (GHI) are widely produced by displacing existing clouds on a geo-stationary satellite image to their future locations with cloud motion vectors (CMVs) derived from preceding images. The CMV estimation methods assume rigid cloud bodies with advective motion, which performs reasonably well in mid-latitudes but can be strained for tropical and sub-tropical climatic zones during prolonged periods of seasonal convection. We study the impact of the South Asian monsoon time convection on the accuracy of CMV based forecasts by analysing 2 years of forecasts from three commonly used CMV methods—Block-match, Farnebäck (Optical flow) and TV-L1 (Optical flow). Forecasted cloud index (CI) maps of the entire image section are validated against analysis CI maps for the period 2018–2019 for forecast lead times from 0 to 5.5 h. Site-level GHI forecasts are validated against ground measured data from two Baseline Surface Radiation Network stations—Gurgaon (GUR) and Tiruvallur (TIR), located in hot semi-arid and tropical savanna climatic zones respectively. The inter-seasonal variation of forecast accuracy is prominent and a clear link is found between the increase in convection, represented by a decrease in outgoing longwave radiation (OLR), and the decrease in forecast accuracy. The GUR site shows the highest forecast error in the southwest monsoon period and exhibits a steep rise of forecast error with the increase in convection. The highest forecast error occurs in the northeast monsoon period of December in TIR. The impact of convection on the number of erroneous time blocks of predicted photovoltaic production is also studied. Our results provide insights into the contribution of convection to errors in CMV based forecasts and shows that OLR can be used as a feature in future forecasting methods to consider the impact of convection on forecast accuracy.

An inverse artificial neural network algorithm for retrieval of sunshine hours from ground-based global solar irradiation measurements

Availability of meteorological parameter values is important in applications that require solar irradiation. Meteorological parameters such as sunshine hours are best provided by measuring equipment mainly stationed at weather stations. The cost of purchasing the measuring equipment, and setting up and maintaining weather stations is enormous and cannot be easily afforded by many developing countries like Uganda. Furthermore, in Uganda, we have some weather stations which have been measuring global horizontal solar irradiation for quite some time but lacked sunshine duration sensors, and at some others, the sunshine measuring equipment malfunctioned. In this work we present an inverse artificial neural network algorithm that predicts sunshine hours based on horizontal global solar irradiation, the algorithm caters to cases where global horizontal solar irradiation is present but sunshine hour measuring equipment is missing or malfunctioned. A radial basis function neural network (RBF-NN) was trained for forward computation of global horizontal solar irradiation from sunshine hour values. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve sunshine hours. The correlation coefficient (r) between the measured sunshine hour values and those predicted by the inverse artificial neural network algorithm was found to be 0.924. The average value of the ratios of the inverse artificial neural network algorithm computed values to the sunshine duration sensor measurements was 1.043. The algorithm predicted sunshine hour values with a mean bias and relative root mean square error of 0.043 and 0.394, respectively. The algorithm developed provides an affordable, fast and reliable method for determining sunshine hour values based on global horizontal solar irradiation. However, sunshine hour values prediction for low values of global horizontal solar irradiation was less precise, and this could be attributed to high levels of cloudiness. It is recommended that an inverse algorithm that retrieves sunshine hours under cloudy conditions be constructed

Ensemble learning based multi-modal intra-hour irradiance forecasting

Accurate intra-hour irradiance forecasting plays an important role in improving the effectiveness of photovoltaic power management. More and more sensors, for example, total sky imager and cloud sensors, are widely used in photovoltaic plants, so that various factors could be comprehensively concerned to improve the accuracy of forecast. However, the existing methods have difficulty in encoding heterogeneous multi-source data dynamically. To mitigate these problems and improve the practicality of the model, an ensemble learning based multi-modal intra-hour irradiance forecasting method is proposed to forecast the global horizontal solar irradiance in the next 10 min. First, six base learners are built by selecting different combinations of multi-modal data as inputs for considering various factors. Then, a linear ridge regressor is used to integrate the pre-trained base learners to obtain outputs. Furthermore, to improve the robustness of the ensemble models, a dynamical fine-tuning scheme based on clear sky index is proposed. The weather states are predicted by a hidden Markov model based on the clear sky index. Experiments show that the proposed forecasting method effectively improves the accuracy of intra-hour irradiance forecasting by 11.6% when compared with commonly used models.

Simplified Independent Model for Predicting Global Solar Radiation

In this investigation, five existing independent empirical models were calibrated and evaluated to calculate daily and monthly mean global solar radiation on a horizontal surface in Adrar city located in the south of Algeria, using meteorological data measured from 2013 to 2018. The measured data were divided into two periods; the first period (2015-2018) was used to calculate the empirical coefficients of the models, while the second period (2013-2014) was used to validate the correlations. Additionally, the best model (Al-Salaymeh model) was compared with five dependent empirical extreme air temperature models. In general, the results show that dependent models exhibited privileged performance than independent models. However, Al-Salaymeh regression independent model can contend with regression dependent models. Because of they use only the day number as a key input with smaller relative errors. It is found that daily statistical tests mean absolute bias error, root mean square error and coefficient of determination were equal to 2.0117 MJ/m², 2.4612 MJ/m² and 0.8014 respectively. The best independent empirical model was also compared with the modified Algerian solar atlas model to show their effectiveness. As a conclusion, the simple independent models can satisfactorily describe the horizontal global solar radiation for Adrar.

Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad

The growing threat of global climate change stemming from the huge carbon footprint left behind by fossil fuels has prompted interest in exploring and utilizing renewable energy resources. Several statistical, Machine and Deep Learning techniques exist and have been used for many years for a range of forecasting problems. This study is based on the data recorded for 4 years and 9 months using precise instruments, in Islamabad, Pakistan. For this purpose we use statistical and Deep Learning architectures for forecasting solar Global Horizontal Irradiance which not only helps in grid management and power distribution, but also brings attention towards the potential of solar power production in Pakistan and its part to play in tackling global climate change. We have used statistical methods such as Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX) and Prophet, and Machine Learning methods such as Long Short-Term Memory (LSTM) which is an extension of Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The selected forecast methods in our study are based on their ability to work with time series data and we have used different models configurations to see which performs best for our dataset. The performance of every model is studied using different error metrics such as Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The major contribution of this study is the data collected to carry out research towards the goal of renewable energy future, and from the test methods used on the data in this study, it can be intuitively determined that ANN, CNN, and LSTM architectures perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.

Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia

The power generated from the photovoltaic module is directly related to the magnitude of total incident solar radiation on the surface of the solar module. The total incident solar radiation depends on the location, tilt angle, and orientation of the solar module. In this paper, generic models were developed that determine the seasonal and annual optimal tilt angle of the Photovoltaic module at any location in Ethiopia without using meteorological data. Both isotropic and anisotropic diffuse solar radiation models were used to estimate monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 cities -32 for training and 12 for testing were obtained from the National Aeronautical and Space Administration database, and algorithms were developed and implemented using MATLAB and R programming software to obtain optimum tilt angle and regression models. The study showed that the developed model accurately estimates the optimal tilt angle with the minimum statistical validation errors. It is also found that 5.11% to 6.275% (isotropic) and 5.72% to 6.346% (anisotropic models) solar radiation energy is lost when using the yearly average fixed optimal tilt angle as compared with the monthly optimal tilt angle. The result of this study was also validated by comparing it with the previously published works, PVGIS and PVWatt online software. The graphical abstract is included in the supplementary file.

Estimation of monthly global solar radiation over twelve major cities of Libya

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

LSTM-CNN Tabanlı Derin Öğrenme Tekniği Kullanılarak Küresel Yatay Güneş Radyasyonu ile Hava Durumu Parametrelerinin Tahmini ve Analizi

Predicting global horizontal solar irradiance (GHSI) as well as important climate parameters plays an important role in energy management and resource planning of photovoltaic panels. To further benefit from solar energy, it is necessary to obtain information regarding future values by frequently analyzing and predicting such time series parameter data. Hence, predicting long-term solar irradiance data is a challenging task. For these purposes, in this work, a hybrid method, with modeling of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) deep neural networks, is proposed to ensure the most accurate prediction of such data. The GHSI as well as temperature, relative humidity, and wind speed data obtained in the Jordan valley are used in the forecasting methodology. In the CNN block of the proposed deep architecture, the input parameters are passed through the convolution, pooling, and flattening layers, and the outputs are forwarded to the LSTM data input. With this method, it is aimed to make more effective and accurate estimations. The proposed method has been compared according to Root Mean Square Error (RMSE), Mean Absolute Deviation Error (MADE), and Mean Absolute Percentage Error (MAPE) error performance criteria in order to reveal the difference from other methods. The proposed method produces superior results compared to other algorithms, especially in GHSI estimation.

Estimation of Solar Radiation on a PV Panel Surface with an Optimal Tilt Angle Using Electric Charged Particles Optimization

Solar energy is a promising renewable energy source that can fulfill the world’s current and future energy needs. The angle at which a photovoltaic (PV) panel faces the horizon determines the incidence of solar radiation. The incident solar radiation on PV panels could be optimized by adjusting their tilt angles and increasing the power output of the PV array. In this study, solar energy model-based research was conducted in the Saudi Arabian cities of Dhahran and Makkah. This study investigated the performance of a 1 kW monocrystalline silicon PV array in these cities. Analyzing the optimal tilt angle for efficiency and performance improvement of the PV panel is challenging. The optimal tilt angle is determined by combining the data of the Sun’s diffuse, direct radiation and the global horizontal Sun radiation. This research examined the four empirical models by applying the electric charged particle optimization (ECPO) algorithm to estimate the solar radiation on sloped surfaces. The model’s results were compared to the global horizontal solar radiation based on the daily mean solar radiation value in these cities. The Hay–Davies–Klucher–Reindel model presented the maximum amount of tilted surface solar radiation in the year and at different periods. In contrast, the Badescu model exhibited the weakest results of all the isotropic and anisotropic models. Finally, using the ECPO algorithm, all models indicated that tilted surfaces (IT) received more solar radiation than horizontal surfaces (Ig).

Solar GHI Ensemble Prediction Based on a Meteorological Model and Method Kalman Filter

The intensity of light emanating from sun is determined by using a meteorological version and is altered with the numerical version, and the forecast accuracy is improved in advance by using Kalman Filter. As the accuracy of the version output related to its specific position is often questionable, group prediction constituting three members is suggested and agreed upon measurement. Also, this ensemble prediction provides an estimation of the solar global horizontal irradiance uncertainty (i.e., coverage rate of the prediction interval), which can be useful to provide flexible energy production forecasts. This article displays how the method Kalman filter could be used as an error correction way to alter the predicted irradiance value. The Kalman filter ameliorates the prediction of solar global horizontal irradiance as well as its interval. As the empirical coverage rate increases and closes to the nominal coverage rate, the interval size reduces.

Feasibility Analysis of a Mobile Microgrid Design to Support DoD Energy Resilience Goals

This research investigates the feasibility of using mobile hybrid microgrids to increase energy resilience in DoD Installations. The primary question examined is whether a standardized mobile microgrid, constrained within an International Standards Organization (ISO) Triple Container (TriCon) and not to exceed 10,000 lbs (approximately 4535 kg), can provide the necessary power for small critical sites with an average 10 kW load on DoD installations with similar resilience to a customized single load microgrid or emergency backup generator. Key assumptions for this research are that power outages may be accompanied by a fuel constrained environment (e.g., natural disaster that restricts fuel transport), an existing installation microgrid is in place, and the risk of outages does not warrant the development of redundant customized single load microgrids for each critical load. The feasibility of a mobile hybrid microgrid is investigated by constructing an architectural design that attempts to find a satisfactory combination of commercial off-the-shelf components for battery energy storage, photovoltaic power, and generator power within the constraints of an 8 ft × 6 ft 5 in × 8 ft (approximately 2.4 m × 2 m × 2.4 m) shipping container. The proposed design is modeled and simulated over a two-week period using Global Horizontal Index solar irradiance data, and a randomized average 10 kW load. Results of the model are used to analyze the feasibility of the system to meet the load while reducing dependency on fuel resources. Trade-offs between a customized single load microgrid and standardized mobile microgrid are discussed. The result of this research indicates that a standardized mobile microgrid holds significant promise for DoD and other potential users (public safety, private industry, etc.) in having a rapidly deployable solution to bring critical loads back online during an emergency situation that reduces generator usage.

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Optimal Sizing Grid-Connected Hybrid PV/Generator/Battery Systems Following the Prediction of CO2 Emission and Electricity Consumption by Machine Learning Methods (MLP and SVR): Aseer, Tabuk, and Eastern Region, Saudi Arabia

Researchers’ concentration has been on hybrid systems that can fulfill economic and environmental goals in recent years. In this study, first, the prediction of CO2 emission and electricity consumption of Saudi Arabia by 2040 is made by employing multi-layer perceptron (MLP) and support vector regression (SVR) methods to see the rate of CO2 emission and electricity consumption. In this regard, the most important parameters such as gross domestic product (GDP), population, oil consumption, natural gas consumption, and renewable consumption are considered. Estimating CO2 emission by MLP and electricity consumption by SVR showed 815 Mt/year and 475 TWh/year, respectively, where R2 for MLP and SVR was 0.99. Prediction results showed a 31% and 39% increase in CO2 emission and electricity consumption by 2040 compared to 2020. Second, the optimum combination of components for supplying demand load and desalination load in residential usages are found where 0% capacity shortage, 20–60$/t penalty for CO2 emission, sell back to the grid, and both fixed and random grid outages are considered. Load demands were considered under two winter and non-winter times so that 4,266, 2,346, and 3,300 kWh/day for Aseer, Tabuk, and the Eastern Region were shown, respectively. Results show that 0.12, 0.11, and 0.12 (kW (PV))/(kWh/day(load)) and 0.1, 0.08, and 0.08 (kW(Bat))/(kWh/day(load)) are required under the assumption of this study for Aseer, Tabuk, and the Eastern Region, respectively. Also, COEs for the proposed systems are 0.0934, 0.0915, and 0.0910 $/kWh for Aseer, Tabuk, and the Eastern Region, respectively. Also, it was found that renewable fractions (RFs) between 46% and 48% for all of the case studies could have rational COE and NPCs and fulfill the increasing rate of CO2 emission and electricity consumption. Finally, sensitivity analysis on grid CO2 emission and its penalty, load and solar Global Horizontal Irradiance (GHI), PV, and battery prices showed 45%–55%, 42%–52%, and 43%–49% RFs for Aseer, Tabuk, and the Eastern Region, respectively.

Efficient power generation using the hybrid design of wind & solar systems

Renewable energy is the most attractive source in use due to its low pollution and an ecosystem. A hybrid power generation system is always more efficient than an individual system, because if one system becomes low efficient, the other system could be high efficient. Solar and wind energies used at Perlis state in Malaysia throughout the year. Such a system can be used in portable size or large sizem, green power generation that uses a micro-wind turbine and solar panel to provide a portable AC power supply system. The solar and wind hybrid system usually contains six main components: photovoltaic, solar tracker, micro-wind turbine, hybrid controller, battery and inverter. A microcontroller controls the switching such as battery overcharge or/and undercharge. Since the solar panel produces DC electricity, the inverter converts the energy to give an AC supply. Monthly Mean Solar Global Horizontal Irradiance resulted from sunlight and wind. The maximum received daily radiation was of an average of 5 kWh/m2/day throughout the year. At the same time the wind speed was 2.3 m/s. The maximum obtained power of an average of 16 W daily between 9 AM and 5 PM. In conclusion, the model was an efficient ratio power generation as a hybrid system related to its size.

Microclimatic factors in urban development: The setup of an environmental observatory at the FAD STU

Abstract The presented research focuses on energy (im)balance on both global and local scale. The main emphasis is placed on microclimatic factors directly affecting public urban spaces and related physical processes regarding the city that are closely linked to energy flows and result in the formation of Urban Heat Islands (UHIs). As they are one of the main adverse effects of human activities, the paper introduces the classification of UHIs by types, describes the basic differences between the surface energy balance of rural and urban areas, and introduces climate-sensitive urban design as one of the possible ways of mitigating the undesirable anthropogenic impact on the climate change. The authors of the article present their own research, which predominantly focuses on the development of an environmental observatory situated on the rooftop of the building of the Faculty of Architecture and Design STU in Bratislava (hereafter referred to as the FAD STU). They interpret the experimental operation of sensing probe 1 and the first results and measurement data on Global Horizontal Solar Irradiation (GHSI) and their post-processing. In addition, they describe the construction of sensing probe 2, which will provide more data on the total atmospheric precipitation, wind speed and its direction, presence of dust particles and carbon dioxide in the air, or spectral characteristics of incident and reflected solar radiation. Finally, the experimental operation of a thermal and micro- camera with fisheye lenses is described. These cameras are essential for measuring the Normalized Difference Vegetation Index (NDVI) as one of the parameters used for the assessment of vegetation vitality, which also plays a key role in the formation of the UHI effect.

A Comparative Study of Regression Models and Meteorological Parameters to Estimate the Global Solar Radiation on a Horizontal Surface for Baghdad City, Iraq

In this study, data of the monthly average of daily global solar radiation falling on a horizontal surface, relative humidity, maximum temperature, and duration of sunshine for the city of Baghdad were collected through two sources. First, from the Iraqi Meteorological Authority (IMA) for a period extending from 1961 to 2016. The second is from NASA, for the period from 1984 to 2004. Then, four linear regression models, two single and two polynomials were formulated to calculate the values of the monthly average of daily global horizontal solar radiation (GHSR) incidents. The models calculated the monthly average of daily extraterrestrial radiation and day length, using some data provided by NASA and the IMA. To ensure the validity of the used models, a statistical test was performed for the performance of the proposed models, using the indicators mean bias Error (MBE), root mean square error (RMSE) as well as mean percentage error (MPE). The validation shows the relationship between the measured and computed values (through the analysis of the results), where a great convergence was found between the measured and calculated values. This means that the proposed models can be adapted to predict global solar radiation. The highest values of measured solar radiation were during the month of June, which were 28.555 and 27.280 MJ/m2/day from the IMA and NASA, respectively. The same applies to the radiation calculated using the four empirical models. The month of June was the highest in terms of solar radiation values. The radiation values were 28.947, 26.315, 29.699, and 26.716 MJ/m2/day for the first, second, third, and fourth models, respectively. The lowest values of measured and calculated radiation were during the month of December. Always, radiation measured by the IMA was greater than those of NASA, as well as the values of radiation calculated in the two IMA-based models were greater than the other two NSA-based models. In the absence of a method for measuring the diffuse and direct (beam) solar radiations, as well as the lack of such values by meteorological authorities, and its paramount importance, they were reported to mathematically calculate them in this study. The values of statistical indicators RMSE; MJ/m2/day, MBE; MJ/m2/day and MPE% were (0.4769, 0.0164, 0.2207), (0.8641, 0.1773, -0.9680), (0.6420, 0.3996, -1.1487), (0.9604, 0.218, -1.0225) for the first, second, third and fourth models, respectively. According to the results of the statistical test, it can be indicated that the single linear regression model, based on the IMA’s data (model No.1), is the most accurate to calculate global solar radiation for Baghdad City.

Optimal Configuration and Sizing of Seaport Microgrids including Renewable Energy and Cold Ironing—The Port of Aalborg Case Study

Microgrids are among the promising green transition technologies that will provide enormous benefits to the seaports to manage major concerns over energy crises, environmental challenges, and economic issues. However, creating a good design for the seaport microgrid is a challenging task, considering different objectives, constraints, and uncertainties involved. To ensure the optimal operation of the system, determining the right microgrid configuration and component size at minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system for a seaport microgrid with optimally sized components. The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system, and cold ironing facilities. The seaport architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering important parameters such as solar global horizontal irradiance, temperature, and wind resources. Finally, the best configuration is analyzed in terms of economic feasibility, energy reliability, and environmental impacts.