Large-scale Solar Plants Research Articles

The political economy of energy transitions in Africa: Coalitions, politics and power in Tanzania

The understanding of the political economy of energy transitions in lower-income African countries is little developed. A focus on coalitions has emerged as a promising approach, but it was largely developed based on experiences from higher-income countries. This article has two interrelated purposes. First, it explores and develops the coalition approach to the study of the prioritisation between energy sources in lower-income countries by combining it with a political settlement framework that has been adapted to analysing energy transitions. Secondly, it researches the promotion and implementation of non-hydro renewable energy in mainland Tanzania as a case. The article covers the period from 2008, when the first potent coalitions around private non-hydro renewable energy emerged, up until today. Until recently, these coalitions were overtaken by stronger coalitions around state-owned gas and hydropower. Only with a new president and administration in power and a donor that was pragmatic with regard to state ownership did a large-scale solar plant materialize. Based on the Tanzanian example, the article argues first, that large-scale energy projects are of such importance politically that the analysis of coalitions at the sector level must take into account how these coalitions are embedded in a country's broader distribution of power. Secondly, that for renewable energy policies and projects to get implemented they must fit with the priorities and ideas about broader development held by a country's ruling political elite. A number of implications for the study of the political economy of energy transitions are further unfolded in the article.

A 5-MWp grid-connected photovoltaic plant's performance analysis and challenges under Algerian Sahara conditions

ABSTRACT The proposed study presented some challenges and experimentally evaluated the performance of a five MW solar plant placed in Reggane, Algeria, southwest of the desert from 1 May 2022 to 30 April 2023, the solar system was monitored using the standard IEC 61724. This paper's basic goal is to explore how these harsh environmental conditions affect the efficiency of the photovoltaic (PV) power plant, with a focus on the impact of high-level temperature, where the examined system operates at temperatures exceeding 45°C during summer days, ranking this region among the hottest in the world. The results show that the median ratio of performance, final yield and capacity factor was 72.3%, 4.9 h/d and 20.6%, respectively. Additionally, the study examined how the output energy and performance ratio of the PV system changed with temperature and sun irradiation. It found that the PV system only generated 56% of its expected output at very high or low temperatures. These findings can be utilised in improving the prediction of future large-scale solar plants in hot desert conditions, as well as in both the planning and operation of novel connected to the grid PV systems.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12–68 times, 13–73 times, and 18–98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.

Univariate and multivariate short-term solar power forecasting of 25MWac Pasir Gudang utility-scale photovoltaic system using LSTM approach

The generation of solar photovoltaic (PV) systems in Malaysia has great potential due to the abundance of sunlight and high irradiation level. Malaysia’s unique location near the equator makes solar energy the most attractive option for future energy sources. However, the erratic weather leads to variability of power generation, especially during high feed-in of solar energy that will eventually affect grid system stability. Hereof, solar power forecasting is critical, especially in operating utility-scale photovoltaic systems or large-scale solar (LSS) plants. This paper presents an approach to forecasting solar power generation for 10-min to 180-min ahead based on univariate and multivariate using Long -Short-Term Memory (LSTM) technique. The model performances are evaluated based on a real dataset from the 25MWac Pasir Gudang LSS plant from July 2021 to May 2022. The result shows that LSTM with a univariate model outperformed the multivariate model for the short-term forecasting (10-min to 50-min ahead) by 2.09% of RMSE. However, multivariate outperformed univariate for the longer forecasting horizon at 180-min ahead by 34.87% of RMSE. The forecasting output for the multivariate model that only uses historical meteorological data is less reliable than the multivariate model that uses historical meteorological and AC power output data. This research finding is envisaged to provide benefits to the grid system operation, planning, maintenance, and scheduling, thereby improving the reliability of the LSS plant.

IoT Based Solar System Parameter Measurements

Using the Internet of Things Technology for supervising solar power generation can greatly enhance the performance, monitoring and maintenance of the plant. With advancement of technologies the cost of renewable energy equipment is going down globally encouraging large scale solar plant installations. This massive scale of solar system deployment requires sophisticated systems for automation of the plant monitoring remotely using web based interfaces as majority of them are installed in inaccessible locations and thus unable to be monitored from a dedicated location. The Project is based on implementation of new cost effective methodology based on IOT to remotely monitoring a solar plant for performance evaluation. This will facilitate preventive maintenance, fault detection of the plant in addition to real time monitoring.

Performance evaluation of solar power plants for excess energy based on energy production

Solar is future of the world’s energy. It will become the leading source of energy to reduce the global energy deficit over the next two decades. In this performance analysis of the solar energy production has been examined at a LSSP (large-scale solar plant) to evaluate the theoretical excess energy (EE). The novel energy-based performance ratio (PR) is used as a performance benchmark based on solar energy production and is proposed as an industrial technique for analyzing the EE of large-scale solar projects. In order to analyze the EE, the solar plant’s PR is an important PV parameter utilized by the plant operators to know the performance of solar power utility by measuring its PV performance parameters. Based on this PV performance parameter, the novel energy-based PR has been applied to determine the EE and bonus energy (BE) amount of the 100MW-Quaid-e-Azam Solar Plant (QASP) grid-connected solar project. In this case, it has been proposed a one-year onsite real-time data FY (2020–2021) for energy inputs variables such as data on PR, production, and irradiance to investigate the EE and BE. According to theoretical calculations, these energy inputs are used in an energy-based PR formula model to figure out the PR % value and how much EE or BE is remained. This paper also includes a detailed discussion of 100MW-QASP plant performance between quarterly and annual targets, and yearly performance comparisons are illustrated. At the end, the detailed information of PR, capacity factor (CF), and power generation of the plant and its comparisons with quarterly and yearly graphs are defined, as well as an overview of the difference in annual energy yield, EE, and shortfall/LD also demonstrated in the graphs.

Performance Analysis of Energy Production of Large-Scale Solar Plants Based on Artificial Intelligence (Machine Learning) Technique

Due to the continual fusion reaction, the sun generates tremendous energy. This solar energy is freely available and can be extracted by installing a large-scale solar power plant. Therefore, such PV solar plants are key contributors to cutting the energy deficit in remote areas. This study focused on predicting a 10-year performance analysis of a large-scale solar power plant by using 1 year of real-time data from the Quaid-e-Azam Solar Park (QASP) situated in Bahawalpur, Pakistan. For the purpose of prediction, the ARIMA model was developed using Python, which is one of the best tools in machine learning. Since ARIMA is a statistical technique for prediction, by using the developed model through Python, we predicted the values of the performance ratio (PR), production amount (MWh), and plan of array (POA) of the solar plant for the next 10 years using 1 year of real-time data. This machine learning prediction technique is very effective and efficient, compared with other traditional prediction and forecasting techniques, for estimating the performance of the solar power plant and the status of the solar power plant in the long-term future. The forecasting/prediction results acquired from the process show that power production during the next ten years increases to approximately 400 MW and that POA will grow from 6.8 to 7.8 W/m2. However, a decline occurred in the performance ratio, which decreased from 76.7% to 73%. Based on these results, the ARIMA model for predicting solar power generation is effective and can be utilized for any solar power plant.

Complete and versatile remote controller for PV systems

The promotion of the distributed generation and the photovoltaic (PV) energy great potential put this technology in the spotlight of investors. Thus, the optimization of its efficiency is made a priority. In this paper, a complete and versatile remote controller (C&VRC) for PV systems is presented, scalable to any kind and size of installations (with or without partial shading, with or without connection to the grid, housing, small plants or even large-scale solar plants) and valid for any weather conditions. The C&VRC involves the controller itself, which is constituted by a set of sensors and actuators connected to the converters Direct Current (DC)/DC and DC/Alternating Current (AC), if necessary, and a control unit communicated with them through the third-generation mobile telecommunications technology (3G) network. The control strategies can be changed according to the different weather conditions and installations characteristics, which guaranties the optimum performance of the system. The use of the 3G network as communication platform makes the C&VRC useful in remote and low accessibility places, because it is the most extended worldwide. The C&VRC is also based on free software, open hardware and low-cost electronic devices. Experimental results are presented in this paper to show the C&VRC optimum usability.

Performance Ratio Analysis Based on Energy Production for Large-Scale Solar Plant

This paper presents an industrial approach to assess the performance of large-scale solar plants (LSSPs) has been developed using a novel performance ratio (PR) formula model based on energy production. The PR is an important quality indicator used for the LSSP projects with a nominal installed capacity of 20 MWdc upward. When solar power plants are put into operation on the national grid, the PR can be used as a performance indicator to ensure the energy guarantee and the plant’s efficiency required to guarantee a level of performance. Then, PR gives the real face of the plant’s efficiency. This is not an easy task because it has no constant value over the years due to the effective incident global irradiation (GlobInc) value always not being available. For this purpose, the proposed PR formula model is used to compute the correct performance guarantee of the grid-connected 100MWdc-LSSP in Pakistan’s Quaid-e-Azam Solar Park (QASP). The main purpose of this study is to incorporate a PR formula based on energy production evaluation to achieve the energy guarantee (excess energy, bonus energy, or energy shortfall) in LSSPs. The 1-year on-site plant data is utilized to investigate the plant’s performance energy inputs such as production, irradiance, PR, and the excess energy. Based on the actual annual energy output and irradiance of the plant, the following results are obtained: Actual PR is achieved at a higher level than the target benchmark, so no shortfall or liquefied damage (LD) is imposed due to excess energy. Thus, 100MWdc-LSSP’s power generation is found to be 5% higher than expected by using the theoretical calculations of PR. In FY (2018–2019), the system fed 0.34% higher PR, with an actual PR of 76.58% higher than the target PR of 76.24%, and extra energy of 710.72MWh produced, as well as bonus energy of 142,143.62 USD. The majority of LSSP projects are determined to be successfully operational and generate a significant amount of power, but they fail to meet the contractual requirements for energy guarantee because they use the incorrect PR formula and also fail to provide the desired energy to stakeholders owing to a poor theoretical assessment of the PR criteria based on energy production. As a result, solar power plants confront a great energy shortfall for many years to come.

Comprehensive Review on Renewable Energy Sources in Egypt—Current Status, Grid Codes and Future Vision

The development of the energy sector in Egypt is considered an urgent issue due to the rapid population rise rate. In particular, renewable energy sources (RESs) applications play an essential role in the coverage of energy demand. Therefore, Egypt has ambitious plans towards RESs to combine a sustainable energy future with economic growth. Egypt has high potentiality for RESs and their applications, nevertheless, the study of this modality remains below the required level. Due to the widespread use of RESs, communities are facing stability issues as the power converters-based RESs create a significant lack of power inertia, causing system instability and power blackouts as well as issues of power quality such as harmonics or resonances due to the power converters and their interactions with the system. This work presents a recent review supported by a statistical analysis about the current situation in Egypt according to the last data carried out from local/global reports. In addition, this review discusses specifications of technical design standards, terms, and equipment parameters for connecting small, medium, and large-scale solar plants, respectively to the Egyptian grid in accordance with the Electricity Distribution Code (EDC), Solar Energy Grid Connection Code (SEGCC), and the Grid Code (GC). Interestingly, the use of hydropower and emergent solar energy is considered the most promising RES variant, besides the wind energy at the coastal sites. This review characterizes the progress in Egypt and classifies interest areas for RESs recent study, e.g., photovoltaic (PV), solar chimney (SC), concentrated solar plant (CSP), and wind energy in Egypt. To maximize the RES hosting capacity in Egypt, various energy storage systems are required to be integrated into the distribution networks. Finally, a view of existing gaps, future visions and projects, and visible recommendations are defined for the Egyptian grid.

IoT based smart solar energy monitoring systems

Today our society needs more energy for day-to-day activities due to rapid globalization and industrialization. In order to minimize the stress and dependency on fossil fuel, the most sustainable way is to harness suns energy. Solar energy is characterized by low cost, environment friendly, does not require frequent maintenance and most importantly, negligible maintenance cost. However, there is a requirement of monitoring solar installation in order to maximize the output power by setting real-time angles with the suns position. This can be easily done by the adoption of IoT technologies. The Internet of Things is often utilized in the measurement of solar energy for efficiency. It's also utilized to maintain the solar plant's health. The cost of renewable energy technology is decreasing throughout the globe, which encourages the construction of large-scale solar plants. Automation of plant observation on such a large scale of preparatory deployments requires sophisticated systems reliant on Internet connections since the majority of field units are situated in isolated, remote locations and therefore are not overseen from a central office. The project is based on the use of the most up-to-date, cost-effective method for remotely monitoring a solar plant performance by the inclusion of IoT. It can assist with plant maintenance, problem diagnostics, and real-time monitoring.

Using the sun to co-generate electricity and freshwater

Using the sun to co-generate electricity and freshwater

OPTIMUM TILT ANGLE FOR MAXIMIZING LARGE SCALE SOLAR ELECTRICAL ENERGY OUTPUT

Many large-scale solar (LSS) plants that are being installed today have solar photovoltaic (PV) panels mounted on fixed structures, which limits its electrical energy production. Tracking system can be installed so that the PV panels could change its tilt angle automatically in accordance with the sun’s movement. However, it will increase the construction, operation and maintenance cost significantly. Another option is to manually adjust the tilt angle on periodically basis, but the time period and the optimum tilt angle need to be systematically determined. This paper investigates the impact of using monthly and seasonal optimum tilt angle, βopt on electrical energy production of LSS plant. The proposed strategy can be implemented by using tiltable solar panel mounting structures which is far cheaper than the tracking system. For the study, 1 MW LSS system model is used. Twelve cities around the globe with latitude angle ranging from 0º to 55º are strategically selected. The electrical energy output from the 1 MW LSS plant is simulated by using PV mathematical model that is developed in Matlab software. The overall results show that by adjusting the tilt angle of the PV modules into its optimum angle on monthly or seasonal basis, it would increase the generated energy output between 1.91% and 7.24% for monthly adjustments and between 1.59% and 6.06% for seasonal adjustments.

A first-phase screening method for site selection of large-scale solar plants with an application to Italy

Increasing the exploitation of renewable energy sources (RES) is a key element for reducing the detrimental consequences of greenhouse-gases emissions and for satisfying the growing demand for energy. Solar power plays a central role having the potential to completely satisfy the world energetic needs. Locating optimal sites for installing solar plants is therefore an important task deeply affecting the long term profitability of such plants. The present paper proposes a filtering method to select a subset of efficient locations in order to reduce the dimensionality of the original problem when a large territory is screened for locating optimal sites for large-scale solar plants. It is based on the idea of preference relations and Pareto dominance and, avoiding to operate an inter-comparison of different locations’ attributes, it can be considered assumptions-free. Moreover, the paper applies such filtering method, together with an analysis of economic viability, to Italy, a country with relatively high potentials in terms of solar energy that currently lacks an optimal site selection analysis. Once applied, the filter reduces the original set of feasible locations by more than 99%. The resulting Pareto efficient locations, evaluated through five selected criteria, are concentrated in the southern part of Italy and, particularly, in the islands of Sicily and Sardinia, where also the economic viable locations are mostly found.

Synergistic Tandem Solar Electricity-Water Generators

Summary Energy and clean water are two intertwined fundamental elements for human civilization and sustainable development. Here, we devise a monolithic tandem solar electricity-water generator that synergistically produces electricity and clean water by utilizing the full spectrum of solar irradiance. The tandem generator has two components: a top infrared-transparent photovoltaic device using above-band-gap photons, and a bottom solar water purifier using the below-band-gap photons. More strikingly, a well-designed water-proof thermal interconnecting layer (WTIL) makes these two components work synergistically: the bottom purifier serves as an evaporative cooler of the top solar cell to increase its efficiency, whereas the thermalization energy of the top cell is reutilized by the bottom purifier for producing more clean water. We experimentally demonstrate that a prototype hybrid tandem solar device with WTIL can generate electricity with a power output of 204 W m−2 and purify water at a rate of 0.80 kg m−2 h−1 under 1-sun illumination.

A geographical information system-based multi-criteria method for the evaluation of solar farms locations: A case study in Souss-Massa area, southern Morocco

It is now well established that solar energy is a reliable alternative way to reduce greenhouse gas emissions and to mitigate climate change effects. In Morocco, solar energy investments are rapidly rising since 2009 and are expected to grow dramatically in the next decade. Site selection is a critical issue when large-scale solar plants are being planned. In this paper, the multi-criteria evaluation method is used to identify suitable sites to host Photovoltaic projects in southern Morocco. For this purpose, geographical information systems and analytic hierarchy process methods were combined to represent a high-resolution map that compares the potential of the studied region to host large-scale Photovoltaic projects. HOMER software was then used for modeling and simulating a 10 MW grid-connected PV system. The optimum scheme is analyzed to assess the economic feasibility in the most suitable site in the Souss-Massa region. The findings from this study confirm that 24.08% of the studied area offers suitable sites to host the Photovoltaic farms. The HOMER simulation has clearly shown that the grid-connected Photovoltaic system is beneficial in the long run, although its initial capital is higher than the grid supply only. Also, the cost of electricity generated from Photovoltaic panels is affordable.

An IOT Based Solar Power Robotic Tracking & Monitoring System

Now a day’s solar power is very helpful in our everyday life. This power is used in many ways such as homemade electrical appliances, vehicles, satellites and industries etc. In simple terms this project’s objective is to have a solar panel outputting its maximum possible power all day long, this occurs when the panel tracks the sun and rotates the accordingly, to receive sunlight to the fullest extent always during the day time. Using IOT for supervising solar power generation can greatly enhance the performance, monitoring and maintenance of the plant. With advancement of technologies the cost of renewable energy equipment is going down globally encouraging large scale solar plant installations.

Economical and Technical Challenges of a Large Scale Solar Plant

Turkish government has increased the importance of Konya-Karapinar region by making the area a pilot region for solar energy production. In the near future, the government plans to have a 4 GW solar energy plant in the area. According to this master plan, a pilot solar energy production system was established in the Necmettin Erbakan University campus. A 1000W two-axis tracking and 1000W fixed panels were implemented in this system. In this paper, the performance of these panels are compared and evaluated. 25 years of solar radiation data for Konya is taken from the state meteorology institute, and the produced energy is compared with the predicted values. Tracking panels are found to be approximately 25% more efficient than the fixed panels. However, the maintenance of the tracking panels is observed to be more costly. Moreover periodic cleaning and repairs are found to be very important and another cost factor for both systems. Using the insight from this pilot system, an economic analysis of a large scale 1MW solar energy plant to be established in the campus is performed. For this analysis, fixed panels are considered due to the low investment and maintenance costs and the simplicity of the maintenance work. It is found that the 1MW solar plant will recover the investment cost within 7-8 years and will provide almost 3M$ profit after 20 years.

Large Scale Solar Plants Integration in Electro-winning Copper Recuperation Process

Abstract In this article we study the integration of a solar plant to a copper recuperation process in Northern Chile. We analyze the integration of concentrating and non-concentrating solar energy technologies, identifying their main advantages and disadvantages. Given the energy demand of the process, solar field areas over 30.000 m 2 are studied.

Control Optimization through Simulations of Large Scale Solar Plants for Industrial Heat Applications

Abstract The European FP7 Project InSun; which started in April 2012; aims to demonstrate the reliability and efficiency of three different collector technologies suitable for heat production employed in diverse industrial processes in different climatic regions. These collectors are installed and will be monitored in detail over a period of almost two years. One of the plants is installed at Fleischwaren Berger GmbH located in Sieghartskirchen, Austria, a company which produces meat and sausage products. The second solar plant is installed at the company Laterizi Gambettola SRL (SOLTIGUA) located in Gambettola, Italy, which produces highly insulating hollow brick blocks for external walls of buildings. The control optimization and commissioning of the two solar plants has been carried out as a part of the InSun project. Considering measurements and results of simulations developed with dynamic models, potential improvements of the low-level control algorithms are presented for the two solar plants.