Solar Photovoltaic Farms Research Articles

New models for feasibility assessment and electrolyser optimal sizing of hydrogen production from dedicated wind farms and solar photovoltaic farms, and case studies for Scotland and Vietnam

Although green hydrogen produced from dedicated wind and PV plants is considered an alternative route to achieve net zero targets, its assessment models are only being developed in recent years. Even though electrolyser is known to account for a large portion of the investment costs, there is still a lack of sizing models for this equipment, which can negatively affect the feasibility of plants. Moreover, the impacts of future technology maturity and cost reduction on the feasibility of hydrogen production plants are rarely analysed and reported. Realising the research gaps from previous studies, this paper proposes a comprehensive feasibility assessment model for green hydrogen production from dedicated offshore wind and PV plants, utilising net present value, discounted payback time, and levelised cost of energy and of hydrogen (LCOH) as economic evaluation criteria. Furthermore, a unique sizing model is presented, allowing the optimal electrolyser size with the lowest LCOH to be found. Two case studies investigating a hypothetical offshore wind farm in Scotland and a hypothetical PV farm in Vietnam to produce green hydrogen have been carried out, in which the impacts of technology maturity and cost reduction on the LCOH are analysed. It is projected that the LCOH in Scotland and Vietnam case studies, with specific conditions and assumptions of the two cases, would reduce from 3.10 and 3.05 $/kg in 2030 to the lowest values of 2.16 and 1.59 $/kg in 2050, respectively. Additionally, the optimal electrolyser capacity is found to equal 89.3% of the offshore wind plant capacity, and 57.7% of the PV plant capacity. Finally, a sensitivity analysis is conducted, which revealed the capital cost of renewable energy plants has the most significant effect on the final LCOH.

Cleaning scheduling in photovoltaic solar farms with deterministic and stochastic optimization

Soiling in solar panels causes a decrease in their ability to capturing solar irradiance, thus reducing the module’s power output. To reduce losses due to soiling, the panels are cleaned. This cleaning represents a relevant share of the operation and maintenance cost for solar farms, for which there are different types of technologies available with different costs and duration. In this context, this paper proposes a method that allows scheduling the dates on which cleaning generates greater utility in terms of income from energy sales and costs associated with cleaning. For this, two optimization models that deliver a schedule of dates where the best income-cost balance is obtained, are proposed and compared: a deterministic Mixed Integer Linear Problem and a stochastic Markov Decision Process. Numerical results show that both models outperform the baseline case by ∼ 4.6%. A simulator was built and both models were compared to the baseline case for 10,000 rainfall and irradiance scenarios. The stochastic model outperformed both models for all scenarios, thus proving that modeling rainfalls increases profitability in the face of uncertainty.

Optimal solar photovoltaic site selection using geographic information system–based modeling techniques and assessing environmental and economic impacts: The case of Kurdistan

This study explores the potential of solar photovoltaic farms in Kurdistan Province, Iran, using Geographic Information System–based site-selection methods (analytic hierarchy process, network analysis process, and technique for order of preference by similarity to ideal solution). The spatial-analysis results show that11.19% of the study area is suitable for photovoltaic farms, with medium suitability accounting for10.05% and high suitability accounting for1.14%. The high-suitability areas have the potential to generate3.2% of Iran’s total electricity consumption and100% of Kurdistan’s total electricity consumption. The construction of these farms would create an average of73,154job-years in construction and7,878permanent jobs in operation and maintenance, with transfer benefits of $201.9million during construction and $39.76million annually in operation and maintenance. Additionally, constructing photovoltaic farms would reduce100% of CO2, SO2, and NOx emissions in Kurdistan and potentially address the cross-border labor issue. The induced social benefits from replacing natural gas–generated energy with solar energy are estimated to be $154,224.3, resulting from reductions in CO2, SO2, and NOx gas emissions and lower premature mortality with an estimated benefit of $25.3million. These results can inform policy and investment strategies for renewable energy development in regions with similar environmental and economic conditions.

A CRITIC–TOPSIS Multi-Criteria Decision-Making Approach for Optimum Site Selection for Solar PV Farm

This research work proposes a new hybrid framework to assess suitable sites and technical potentials for large-scale solar photovoltaic (PV) systems by integrating two multi-criteria decision-making (MCDM) techniques. The evaluation of sites for PV plants was performed using the MCDM method, taking into account a wide range of variables, including climate, technical, geographical, and economic variables, with factor weights determined using the CRITIC technique. Five Saudi Arabian cities with abundant solar radiation served as illustrations of this study’s framework. For classification, the TOPSIS method was employed to rank the five alternatives. The results show that Riyadh is ranked first with a performance score of 72%, followed by Jeddah with a performance score of 65%, and the remaining three cities, namely, Al Ahsa, Dammam, and Abha scored less than 50%. Lastly, the reliability and robustness of the results obtained were examined using sensitivity analysis. The findings of this study can be used to pinpoint possible places that could be used to build solar power plants and to promote the expansion of generating facilities and electrical grids.

Advancement in Agriculture Approaches with Agrivoltaics Natural Cooling in Large Scale Solar PV Farms

The increasing concerns about the impact of large-scale solar photovoltaic farms on the environment and the energy crisis have raised many questions. This issue is mainly addressed by the integration of agriculture advancement in solar photovoltaic systems infrastructure facilities, commonly known as agrivoltaic. Through the use of these systems, the production of crops can be increased, and the efficiency of PV panels can be improved. Accordingly, adopting such synergistic paths forward can contribute toward building resilient energy-generation and food-production systems. The utilization of cooling techniques can provide a potential solution for the excessive heating of PV cells and lower cell temperatures. Effective cooling applied to PV cells significantly improves their electrical efficiency, as well as increasing their lifespan because of decreasing thermal stresses. This paper shares an overview of both active and passive cooling approaches in solar PV applications with an emphasis on newly developed agrivoltaic natural cooling systems. Actual data analysis at the 2 MWp Puchong agrivoltaic farm shows a significant value of 3% increase of the DC generation (on average) which is most beneficial to solar farm operators.

Novel V2G regulation scheme using Dual-PSS for PV islanded microgrid

The incorporation of electric vehicles (EVs) into residential/industrial microgrids (MGs) presents some limitations such as fluctuations in voltage, frequency, and lack of power supply. According to the literature, the control methods for vehicle-to-grid (V2G) in MGs are insufficiently coordinated for charging and regulation, limited by narrow charging control constraints, complex, and computationally demanding. Moreover, the power system stabilizer (PSS) is employed only in the generation side. To this regard, a PSS is proposed in this paper along with the proportional-integral-derivative (PID) controller, which is tuned adaptively using Harris hawks optimizer (HHO) for frequency, voltage, and power-sharing regulations through the parked EVs. The considered islanded MG is consisting of a diesel generator as a backup generation source, dealing with the intermittent nature of the solar photovoltaic (PV) plant and residential load. It also includes an EV aggregator. In case of partial shading of the solar PV farm and triggering the load demand, the proposed control strategy provides a complementary signal to promote short response time during transients, boost the transient stability performance, and offer damping characteristics adequate for V2G regulation with the islanded residential MG. To examine the efficacy of the proposed scheme, a detailed simulation model using Matlab/Simulink was built. The final findings show that the proposed direct adaptive PID-PSS controller gives significantly enhanced voltage and frequency with spinning reserve compared to the conventional PI-controller both considering the inclusion of V2G on the MG regulation side and conventional controller without V2G and PSS on the regulation side. Also, it can provide power flow in a bi-directional way during the partial shading contingency that the residential MG may experience within 24 hr.

Rainfall-Retaining Ground Surface Depressions of Solar Photovoltaic Farms

Rainfall-Retaining Ground Surface Depressions of Solar Photovoltaic Farms

Refining wind and solar potential maps through spatial multicriteria assessment. Case study: Colombia

Despite the abundance of energy resource assessments for wind and solar power around the globe, the existing studies seldom integrate the environmental restrictions into the analysis, which results in overestimation of the available resources. Existing potential maps show that wind and solar resources are abundant in Colombia, but these studies do not consider the multiple restrictions developers face when a project is considered. This paper proposes a methodology to determine the areas with the best characteristics to develop wind and photovoltaic solar farms in Colombia, using Geographic Information Systems that depict the distribution of the physical, biotic, economic, cultural, and political characteristics restricting or conditioning the implementation of these projects. To consider financial performance, the LCOE associated with each location was subsequently calculated, and the environmental characteristics were assessed through interviews with a group of experts. Maps were developed where the areas with potential can be viewed, classified according to their profitability and the ease of developing a project at that location. The most promising locations for wind and solar farms for the continental territory of Colombia were determined integrating levels of difficulty in the environmental management, and financial project performance. The results were unified in a tool useful for stakeholders, decision-makers, researchers, and investors, in which basically no technical knowledge is required for its interpretation. The results allow estimating the real installable potential in each technology throughout the Colombian territory and providing signals for the national interconnected system (SIN) on where the country's new energy developments are expected to take place.

Tropical Field Observation of Weed Permanent Shading on Solar PV Surface

Solar Photovoltaic (PV) largely depends on the sun irradiation or insolation level (in W/m2) on its surface for electricity conversion process. Cloud movement especially thick cloud near to earth surface creates a non-permanent shading on solar PV farms top surface which significantly reduce the electricity yield. Improper weed management in large scale solarfarms would create a permanent shading to the PV surface especially with creeping plants. Thus, this work implies freely available application namely Pl@ntNet and Canopeo to analyse the impacts of weed surface cover in Solarfarms. Images of specified weed growing above the solar PV surface are captured and identified using Pl@ntNet application to determine the type of weed. Weed identification is the first stage of efficient weed management to aid in a fundamental understanding of the life cycle and biology of the weeds for proper control measures. The same images are used in Canopeo application to determine the surface coverage by means of Quadrat sampling. This information will be invaluable to solarfarm operators showing the significant energy reduction when the solar PV surface are covered by weeds.

Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia

Weed management in large-scale solar photovoltaic (LSS-PV) farms has become a great concern to the solar industry due to scarcity of labour and the ever-increasing price of pesticides, which opens up possibilities for integrated farming, also known as agrivoltaics. Improper weed control may have multiple negative impacts such as permanent shading of the module surface, pest housing which damages communication cables, and even bush fires. The shaded PV modules can be heated up to extreme temperatures, causing costly burn-out damage. Critical information on the types of weeds on solar farms, especially in Malaysia, has not been established to support the concept of weed management. Thus, with this study, detailed composition of the weed community was obtained via quadrat sampling between solar PV modules, near ground equipment, near perimeter fencing, and directly underneath the PV modules. Weed-control measures via high-quality weedmat installation under solar PV arrays have been implemented where this approach can be considered effective on solar farms based on the existing PV structure height and equipment constraints plus the increasing cost for labour and agricultural inputs. This work underlines the proposed Agrivoltaic for Large Scale Solar (Agrivoltaic4LSS) program to complement the solar industry in Malaysia towards an agrivoltaic, eco-friendly approach to weed management.

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.

Variational mode decomposition combined fuzzy-Twin support vector machine model with deep learning for solar photovoltaic power forecasting.

A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

Increased panel height enhances cooling for photovoltaic solar farms

Solar photovoltaic (PV) systems suffer substantial efficiency loss due to environmental and internal heating. However, increasing the canopy height of these systems promotes surface heat transfer and boosts production. This work represents the first wind tunnel experiments to explore this concept in terms of array flow behavior and relative convective heat transfer, comparing model solar arrays of varied height arrangements - a nominal height, extended height, and a staggered height configuration. Analyses of surface thermocouple data show average Nusselt number (Nu) to increase with array elevation, where panel convection at double height improved up to 1.88 times that of the nominal case. This behavior is an effect of sub-array entrainment of high velocity flow and panel interactions as evidenced through flow statistics and mean kinetic energy budgets on particle image velocimetry (PIV) data. The staggered height arrangement encourages faster sub-panel flow than in the nominal array. Despite sub-array blockage due to the lower panel interaction, heat shedding at panel surfaces promotes improvements on Nu over 1.3 times that of the nominal height case.

Optimal inverter and wire selection for solar photovoltaic fencing applications

Despite the benefits and the economic advantages of agrivoltaics, capital costs limit deployment velocity. One recent potential solution to this challenge is to radically reduce the cost of racking materials by using existing farm fencing as vertical photovoltaic (PV) racking. This type of fenced-based PV system is inherently electrically challenging because of the relatively long distances between individual modules that are not present in more densely packed conventional solar PV farms. This study provides practical insights for inverter selection and wire sizing optimization for fence-based agrivoltaic systems. Numerical simulation sensitivities on the levelized cost of electricity (LCOE) were performed for 1) distance from the fence to the AC electrical panel, 2) inverter costs, and 3) geographic locations. The results showed that microinverters had better performance when the cross-over fence length was under 30 m or when the system was designed with less than seven solar PV modules, whereas string inverters were a better selection for longer fences. The cross-over number of modules depends significantly on the cost of the inverters, which is a parameter that influences the system's design. The capital costs for a fence retrofit are far less than for any form of conventional PV racking. In addition, the LCOE of the vertical fencing solar agrivoltaic system can be competitive with conventional ground-mounted solar PV for the niche of farmers. Especially, when they are located between the latitudes of 10° and 50° in either the northern or southern hemisphere, and coupled with their ancillary benefits they represent a great alternative for conventional PV systems.

Effects of Photovoltaic Solar Farms on Microclimate and Vegetation Diversity

The need for energy and the increasing importance of climate change mitigation are leading to a conversion from conventional to renewable energy sources. Solar photovoltaic (PV) power has seen the most significant increase among all renewable energy sources. However, most of these installations are land-based, significantly changing global land use (LU). The real impacts, whether positive or negative, are poorly understood. This study was undertaken to have a better understanding of the impacts of solar parks on the microclimate and vegetation dynamics. First, different solar parks were visited to take measurements of the surface temperature (Tsurf), photosynthetic active radiation (PAR), air temperature (Tair), and humidity (RH) to quantify the microclimate and perform a vegetation relevé. The measurements were taken at different positions: underneath, in between, and outside solar panels. For vegetation, the data were first converted to diversity indices, which in turn contributed to a multi-indicator land use impact assessment that evaluated effects on vegetation, biodiversity, soil and water. Solar parks had clear effects on microclimate: if the panels were high enough from the ground, they could lower the Tsurf by providing shade and enough airflow. Additionally, the multidimensional functional diversity (FD) analysis of the vegetation indicated that there was less light at a higher humidity and lower temperature underneath the panels. Interestingly, the species underneath the panels also preferred a lower pH and a higher nitrogen level. Finally, the land use impact assessment found that the total land use impact for a wheat field was higher than that of the solar park, which suggests that the conversion of conventional intensive agriculture to a solar park would be beneficial.

Methodology for estimating the potential of ground-mounted solar photovoltaic as part of the national electricity grid: The case of Israel

Solar photovoltaic (PV) is one of the leading renewable energy technologies that has the potential to generate a large amount of electricity. However, this potential may not be fully utilized on the national level. Obstacles to its use include electrical power system restrictions designed to avoid its possible adverse effect on the stability of the system, its reliability and the quality of its supply, and the limited land areas on which to deploy it. This paper presents a GIS-based land suitability analysis that determines suitable areas for installing large-scale, ground-mounted solar photovoltaic farms and their corresponding technical potential. Our contribution is in providing a realistic assessment of how PV systems can actually be connected to existing electrical grids without disrupting the local power system's hosting capacity, capabilities and constraints. We tested our proposal using Israel's entire national power system configuration as a case study. Our results indicate that a total of 2.93 GW of installed capacity and 5.16 TWh of energy generation can theoretically be integrated into Israel's existing electrical grid, without any changes in the existing system. This potential is equivalent to approximately 15% and 7% of the country's total installed capacity and energy generation, respectively. In 2020, Israel increased its target for renewable energy from 17% to 30% of national energy production by 2030. Given that ground-mounted PV can provide only a small part of Israel's energy needs, without seeking other renewable energy solutions and making major changes, meeting this ambitious goal is unlikely.

Automated Event Region Identification and Its Data-Driven Applications in Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

This paper is motivated by the fact that behind-the-meter solar farms are being increasingly deployed in California and elsewhere in recent years. The objective is to use real-world micro-PMU measurements at a 4.3 MW behind-the-meter solar Photovoltaic (PV) farm to build a foundation for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">event-based situational awareness</i> and its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven application</i> . Two essential tasks are conducted. First, through developing an automated event region identification mechanism, we identify whether an event at a behind-the-meter solar farm is “locally-induced”, i.e., it is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">caused</i> by the solar farm, thus potentially indicating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">internal</i> issues in the solar farm, or “grid-induced”, i.e., it is caused by something else on the grid, thus revealing how the solar farm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">responded</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">external</i> disturbances. We show that this is a highly challenging task in practice: the conventional impedance-based method is ineffective, the statistical method and the machine learning method each has its weaknesses. Accordingly, a novel mixed-integrated method is proposed and tested that can achieve very high performance metrics. The proposed mixed-integrated method also closes the gap between the accuracies in identifying grid-induced events versus locally-induced events. Second, the outcome of automated event region identification is used to unmask the constructive use of the proposed analysis. Practical use cases are proposed to take advantage of the situational awareness that we gain from analyzing both types of events to provide critical reporting, unmask trends and relationships, adjust control parameters, or take remedial actions when needed.

A suitability mapping for the PV solar farms in Egypt based on GIS-AHP to optimize multi-criteria feasibility

Nowadays, photovoltaic (PV) are considered a promising alternative solution as a clean energy source. The power output from the solar PV module is proportional to the amount of solar radiation received by its surface. Hence, the determination of the most convenient locations of PV systems is crucial to maximize their output power. In this paper, the most suitable sites of PV farms in Egypt are identified based on the multi-criteria decision-making (MCDM) technique using the geographic information system (GIS) software. Four categories have been taken into acount; location, environment, meteorology, and climatology, to select the suitable locations. Based on experts’ knowledge ten criteria belong to the main categiories have been weighted using the analytical hierarchy process (AHP) and combined in PV land suitabliltiy index which maped using GIS techniques. The conducted suitability map could be used to guiding installation of renewable energy projects in the future.

A framework for GIS-based site selection and technical potential evaluation of PV solar farm using Fuzzy-Boolean logic and AHP multi-criteria decision-making approach

Electricity demand is increasing mainly due to population expansion and the continuous supply of electricity in the residential, industrial, and service sectors. This energy is a critical factor of economic growth, and clean energy resources must be utilized and substituted to reduce the effects of climate change and move toward sustainable development. Site selection and construction of renewable power plants help to diversify the country's energy basket. This study aims to locate and evaluate a photovoltaic solar power plant's potential in Khuzestan province, using Fuzzy-Boolean logic and Analytical Hierarchy Process (AHP) decision analysis based on GIS. The main criteria for selecting the optimal locations include climatic (annual horizontal solar irradiance, sunshine hours, air temperature, relative humidity, wind speed), economic (the proximity to roads, transmission lines, substations, urban areas, and villages), orography (slope, orientation, and altitude) and the environment (land use). According to the results, 0.12% of the province's total area has excellent suitability, 25.66% has suitable suitability, and 59.46% lacks any suitability for installing a photovoltaic system. The annual electric power generation potential using single-crystalline silicon panels and polycrystalline silicon panels is calculated as 3189.54 and 2453.49 TWh, respectively. Izeh, Bandar-e Mahshahr, and Bagh-e Malek cities are the most suitable areas for photovoltaic solar farms in the province.

A Geospatial Approach to Energy Planning in Aid of Just Energy Transition in Small Island Communities in the Philippines

Providing electricity in off-grid island communities is a big challenge, exacerbated by the high cost of transporting fossil fuels and the non-viability of extending grid connections. Installing renewable energy systems in these areas is deemed a practical solution, especially supporting just energy transitions in these communities. However, the lack of information about resource availability and the most suitable locations hinders effective planning. This paper aims to determine the sufficiency of available renewable energy sources to meet the electricity demand of off-grid island communities. It is achieved through a three-phased approach: (1) an assessment stage; (2) geospatial analysis; and (3) technical potential estimation. The approach is applied in three island communities in Palawan, namely Araceli, Balabac, and Cuyo, where a diesel power plant currently provides electricity to its households and commercial/institutional establishments. The results indicate that the three islands can be powered by 3, 1.5, and 11 MW solar photovoltaic farms, respectively, which is sufficient to meet the projected demand until 2030. The approach can be helpful, especially for off-grid island communities, as they plan to provide universal electricity access using renewable energy sources.