Solar PV Plant Research Articles

Fuel-constrained joint heat and power dynamic economic environmental dispatch

The economical use of available fuel for producing electricity has been a very important challenge for power companies due to the continuously declining supply of fossil fuels. FCJHPDEED (fuel-constrained joint heat and power dynamic economic environmental dispatch) and JHPDEED (joint heat and power dynamic economic environmental dispatch) with DSM (demand-side management) integrating solar PV plants, WTGs (wind turbine generators), and PHS (pumped hydro storage) plants have been presented. Using SPEA 2 (strength Pareto evolutionary algorithm 2) and NSGA-II (non-dominated sorting genetic algorithm-II), FCJHPDEED and JHPDEED have been solved. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. The joint heat and power dynamic economic dispatch cost obtained with fuel constraints is approximately 2.14% more than the cost obtained without fuel constraints and joint heat and power dynamic emission dispatch, and the emission obtained with fuel constraints is approximately 6.7% more than the emission obtained without fuel constraints.

Thermal Modeling of Photovoltaic Panel for Cell Temperature and Power Output Predictions under Outdoor Climatic Conditions of Jodhpur

The rise in the temperature severely affects photovoltaic cell efficiency and hence its power output. Moreover, it also causes the development of thermal stresses that may reduce their life span. Thus, there is a need for an accurate estimation of the cell’s working temperature. In this paper, a detailed thermal model based on various heat transfer modes involved and their governing equations has been presented to estimate the cell temperature of a PV module using MATLAB software under different climatic and solar insolation conditions. In order to validate the presented model, an experimental setup has been built and operated under actual outdoor conditions of Jodhpur, a city in the Thar Desert of Rajasthan. For the peak summer month of June, the predicted glass cover outer surface temperature has been found to be within 0.2–4.5°C of experimentally measured values and the back sheet temperature is found to be within 0.5–5.5°C. The predicted and measured power outputs have been found to be within 0.85–1.2 W while the efficiency values are within 0.17–0.38%. For the early summer month of April, the variations are 0.13–4.1°C, 0.2–4.1°C, 0.44–1.65 W, and 0.1–0.5% for glass cover temperature, back sheet temperature, power output, and efficiency, respectively. Thus, the predictions of the developed thermal model have exhibited a good agreement with the experimental results. The maximum glass cover temperatures recorded were 60°C and 65.5°C when the ambient temperatures were 35°C and 42°C near the noon for the early summer and peak summer day experiments, respectively. The presented model can be used to generate a year-round cell temperature data for the known environmental data of a location, which can help in the selection or development of appropriate cooling technology at the planning stage of the installation of a solar PV plant.

An interaction model applied to optimize photovoltaic farm location: A case study of China

Given the escalating environmental challenges and the looming carbon peaking and carbon neutrality goals, developing renewable energies becomes a global imperative. The pivotal initial stage involves site selection; however, this process has been plagued by the ambiguity of subjective decision-making and the inconsistency of multi-factor decisions. In this regard, this study proposes a new evaluation system which combines Geodetector and Interaction Linear Model to minimize subjective interventions and reveal the mechanisms by which interaction terms influence siting. China is selected to unveil the model's capability through solar PV site selection. The results indicate that compared with the Basic Linear Model, the Interaction Linear Model increased the estimation accuracy by 8%, with some interactive terms showing higher determinant power than single factors. Based on this interaction model, there is a total of 13% of China's land is developable for solar PV plants, consisting of 46% highly suitable areas (581,576 km2), 39% moderately suitable areas (492,615 km2), and 15% marginally suitable areas (198,088 km2). Finally, Xinjiang was identified as the most suitable place in China. Above all, the new siting system proposed here provides a new perspective on renewables' site selection, promoting the high-quality energy transition globally.

Suitability analysis of PV solar power plant sites in Gandaki province: Application of GIS and Remote sensing

The escalating global demand for energy has triggered a shift towards cleaner alternatives due to mounting environmental concerns. Emerging as a viable substitute, solar power has gained prominence as fossil fuels' adverse impact becomes evident. Historically reliant on hydropower, Nepal is exploring alternative energy sources to mitigate seasonal output variations. Despite abundant renewable resources like biomass, wind, and solar energy, Nepal's energy sector faces funding and technical expertise challenges. Solar energy presents significant promise as a primary renewable source in Nepal, boasting ample sunlight due to its location. However, the current solar capacity remains limited at around 54.6 MW, comprising less than 2.5% of the total installed capacity. The critical factor is the identification of suitable sites for solar power plants. Geographical Information System (GIS) and Multi-Criteria Decision-Making (MCDM) methods have been employed for suitability analysis. This study employs the Analytic Hierarchy Process (AHP), a robust MCDM technique, to assess site suitability. The research is carried out in the Gandaki province, Nepal, encompassing the Himalayan, Hilly, and Terai regions. Criteria like solar radiation, slope, aspect, land use/land cover, proximity to roads, and substations are considered. These criteria are reclassified into suitability categories based on expert opinions and guidelines. The results indicate areas highly suitable for solar power generation, covering 12.40 km2 (5.64% of the study area), followed by regions least suitable, spanning 7681 km2 (34.93% of the province's area). This research contributes to the effective deployment of solar power by identifying optimal locations for solar power plant construction, thus advancing Nepal's renewable energy goals.

Assessing the Impacts of Solar PV Plants on Distribution Systems using DigSilent

The purpose of this paper is to evaluate the effects of integrating solar PV plants on distribution systems using Botswana Power Corporation greater Gaborone distribution system as a case. The study compares and analyses the system baseline to solar integrated system using DIgSILENT Powerfactory software. The system studies conducted in this paper include short circuit and effects on active and reactive power losses at different penetration factors. This research found out that integrating solar PV plants to the distribution network caused a decrease in grid losses and the extent of power losses decrease vary. Fault level contribution from the PV systems is generally low. In conclusion, the effects of these interconnections on line losses and fault level contribution differs in all circuits under study, therefore network assessment must first be thoroughly conducted before interconnecting solar PV plants with distribution systems as some areas indicated some reverse power flow that could affect the protection systems because initially the distribution system was designed to operate on a single source voltage.

Enhancing the Solar PV Plant Based on Incremental Optimization Algorithm

Enhancing the Solar PV Plant Based on Incremental Optimization Algorithm

Energy, exergy, economical and environmental analysis of photovoltaic solar panel for fixed, single and dual axis tracking systems: An experimental and theoretical study

This investigation focuses on energetic, exegetic, economical and environmental analysis of PV solar system using fixed, single- and dual-axes tracking systems under climatic weather of Zakho city/north of Iraq. Experiments are carried out on 5th September 2022. The energy and exergy analyses are used to predict the performance of three solar panels. The theoretical work includes technical, economical and environmental analysis of proposed 1 MW PV solar power plant are presented using similar characteristics of the experimental data of hourly meteorological climatic conditions during 2022. The findings display that the tracking systems have significant influences on 4–E performances. The experimental results displayed that electrical output power gain and thermal exergy output are increased when using tracking systems, where the exergy losses for single- and dual-axes tracking systems are decreased as compared to fixed solar plane. The maximum improvements in the electrical output power gain of PV solar panels using dual– and single–axes tracking systems are nearly reached to 40 % at 8 a.m., 13 % (for single) and 20 %(for dual) at 12 p.m. and 30 % at 17PM as compared to fixed solar panel. The theoretical results display that the yielded produced energy for single- and dual-axes are increased by 16.5 % and 25.5 %, respectively, as compared to fixed panel. The economic findings display that the cost of energy for single-axes, dual-axes and fixed tracking systems are 4.89, 4.41 and 8.26, respectively. Finally, the use of tracking systems reduces the CO2 emission about 4000–4500 tCO2 annually.

Dynamic Optimal Power Flow for Grid-Connected Multi-Microgrid System considering Outage of Energy Sources

Recently multi-microgrid (MMG) system has attained additional attention because of significant assimilation of renewable energy sources, enhanced efficacy, reliability and stability through harmonization as well as cooperation of power swap amongst the microgrids (MGs). In this manuscript, dynamic optimal power flow (DOPF) for grid-connected MMG integrated demand response program (DRP) with and without energy source’s outage is presented. Total cost over the entire time-period considering instinctive characteristics of different phases is minimized. Power swap between grid and each MG is bidirectional. Each MG comprises small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plants (BPPs) and plug-in electric vehicles (PEVs). MMG used here comprises MG 1 including IEEE 33-bus system, MG 2 including 15-bus system and MG 3 including IEEE 69-bus system. This DOPF problem is solved by using quasi-oppositional fast convergence real coded genetic algorithm (QOFCRCGA), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), differential evolution (DE), artificial bee colony (ABC) optimization, fast convergence real coded genetic algorithm (FCRCGA) and real coded genetic algorithm (RCGA).

Machine Learning Approach for Output Power Forecasting of Grid Connected Solar PV Plant in Madurai

Machine Learning Approach for Output Power Forecasting of Grid Connected Solar PV Plant in Madurai

Multi-functional energy storage system for supporting solar PV plants and host power distribution system

Energy storage systems (ESS) will play a critical role in the ongoing development of the future electrical grid, especially as penetration of renewable energy generation increases. Since the costs of ESS are still high, it is imperative to research diverse control modes of ESS so as to use them in an effective manner, thereby offsetting their high costs. ESS technologies can diminish curtailment of renewable generators and provide much needed storage capabilities for supporting the grid, such as providing voltage regulation, relieving congestion, and improving power quality. This study develops six control modes for a battery ESS (BESS), namely, Current Limiting, Power Limiting, Load Leveling, Voltage Regulation, Power Factor Correction, and Simultaneous Real and Reactive Power Supply. The control modes are verified by simulation using a realistic utility 2.8-MW/5.6-MWh BESS and three solar PV plants connected to a power distribution grid. The study results demonstrate that the BESS functions properly in all the control modes. It can be used in all four quadrants of real and reactive power, i.e., it can provide any combination of real and reactive power amount within its rating for supporting the solar PV farms and the grid. The control modes enable effective operation of the BESS, which is a new tool to solve power system critical operational issues, thereby helping ensure the grid resilient, efficient, and reliable operation.

Comparing solar inverter design rules to subhourly solar resource simulations

The input of a solar inverter depends on multiple factors: the solar resource, weather conditions, and control strategies. Traditional design calculations specify the maximum current either as 125% of the rated module current or as the maximum 3 h average current from hourly simulations over a typical year, neglecting extreme irradiance conditions: cloud enhancement events that usually last minutes. Inverter power-limiting control strategies usually prevent extreme events to cause strong currents at the inverter, but in some cases, they can fail, leading to high currents. In this study, we aim to report how frequent and strong these high currents could be. We use 10 years of 1 min data from seven stations across the United States to estimate the photovoltaic string output through modeling the short-circuit current Isc, and the maximum-power point current Imp, and compare them to traditional inverter design values. We consider different configurations: minutely to hourly resolution; 5 min to 3 h averaging time intervals; monofacial and bifacial modules (with a case of enhanced albedo); and 3 fixed-tilt angles and horizontal single-axis tracking. The bifacial modules with enhanced albedo lead to the highest currents for 1 min data, exceeding 3 h averages by 53% for Isc and 38% for Imp. The 3 h average maxima surpass the conservative 125% design rule for bifacial modules. Inverter ratings at either a 200% of the rated current or 1.55 times the 3 h maximum could withstand all events regardless of control strategies. In summary, for some locations it is prudent to compare current design rules to subhourly simulations to guarantee the fault-free operation of solar PV plants.

Application of COOT algorithm optimized PID plus D2 controller for combined control of frequency and voltage considering renewable energy sources

This article presents the combined frequency and voltage regulation of two area interconnected power systems. In addition to conventional generations, renewable energy sources such as wind plants, solar PV plants, and geothermal power plants are considered. The COOT algorithm optimized PID + D2 controller is presented for the first time and its performance found superior to commonly employed PI and PID controllers. The individual and combined effects of the boiler dynamics (BD) non-linearity and superconducting magnetic energy storage (SMES) systems are presented. Studies confirmed that the BD non-linearity jeopardizes, and the presence of SMES suppresses the system dynamics oscillations. The COOT based PID + D2 controller performs well even for the higher magnitudes of perturbations over the PID controller. Sensitivity analysis confirms the toughness of PID + D2 controller parameter values for widely varied time constants of the geothermal plant. The COOT algorithm optimized PID + D2 controller outperforms the PID controller in arbitrary load pattern perturbation. The COOT algorithm shows better convergence and figure of demerit characteristics compared with particle swarm optimization and firefly algorithms. The obtained simulation results are also tested successfully through OPAL-RT OP4500 RT-LAB-RCP/HIL system for their real-time validation.

Evaluation and modeling of a solar photovoltaic power plant: Case of the Sakal plant

This paper proposes an evaluation and modeling of the Sakal solar PV plant. In this work, we have developed and validated a model that takes into account shading effects. The studied solar power plant is mobile on a horizontal north-south axis of east-west tracking. The results showed that the model tracked the system with a correlation coefficient of 0.993. In the following, we considered the power plant fixed to evaluate the gain between the fixed and mobile collector power plants. Subsequently, we considered the plant a two-axis mobile plant. The performance of the different configurations was evaluated. The results showed that the power plant with a two-axis mobile sensor provided a gain of 40.49 % compared to the power plant configured with a fixed sensor, and a gain of 5.378 % compared to the power plant with a single-axis mobile sensor.

Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies

Harnessing the abundant solar resources holds great potential for sustainable energy generation. This research paper delves into a comprehensive analysis of seasonal tilt and solar tracking strategy scenarios for a 15 MW grid-connected PV solar power plant situated in Kandahar province, Afghanistan. The study investigates the impact of fixed tilt, seasonal tilt, SAHST (single-axis horizontal solar tracking), and SAVST (single-axis vertical solar tracking) on energy yield, considering technical, economic, and environmental aspects. In the first scenario, a fixed tilt angle of 31 degrees was employed. The second scenario explored the use of seasonal tilt angles, with a summer tilt angle of 15 degrees and a winter tilt angle of 30 degrees. The third scenario analyzed SAHST. Finally, the fourth scenario focused on implementing SAVST. SAVST proved to be an exceptional solution, showcasing a remarkable increase in annual energy yield, and generating an additional 6680 MWh/year, 6336 MWh/year, and 5084 MWh/year compared to fixed, seasonal, and SAHST scenarios, respectively. As a result, surplus energy yielded an income of USD 554,440.00 per year compared to fixed tilt. However, the investment cost for the solar tracking system amounted to USD 1,451,932, accompanied by an annual operation and maintenance cost of 0.007 USD/W/year. The analysis revealed a promising payback period of 3 years, confirming the economic feasibility of this investment. The findings underscore the effectiveness of different strategies for optimizing solar power generation in the Kandahar region. Notably, the installation of SAVST emerged as an influential solution, significantly increasing power production. These research outcomes bear practical implications for solar tracking strategies for addressing the load challenges faced by Kandahar province and offer valuable insights for the operators and operation of solar power plants in similar regions.

Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions.

Solar parks: A review on impacts, mitigation mechanism through agrivoltaics and techno-economic analysis

Solar parks are mega solar projects to fast track renewable energy integration, while avoiding redundancy in electro-mechnical infrastruturing and land acquiring procedures. However these ground-mounted grid-integrated solar photovoltaic projects require vast land banks, which remain covered for the lifetime of the project.. The socio-economic and environmental externalities on at micro level affecting livelihoods often go unaccounted. Earlier works on impact assessment of large solar parks have considered environment, ecology, micro-climate at large while impact on livelihoods and long term externalities on socitial issues were not addressed. The effectivnes of agrivolticas as a mitigation mechanism was primarlity focused on type of crops vis-à-vis height of structures, water management and economic outputs. The current work has a reviewed agrivoltaic projects in India and identified the managaement practices, constraints, cost econmoics and policy framework. A review of works done on solar park impact assessment and mitigation mechanism by agrivoltaics are done in detail. The work has considered agrivoltaics from a social aspect and focused on impacts due to loss of livelihoods and associated externalities under social impact classification. A methodology in which agrivoltaics is taken as a self healing mechanism to environment and society is adopted. A conventional solar plant and an agrivoltaic plant are considered for study and three livelihood mechanisms namely medicinal plants, poultry and bee keeping are considered for techno-commecrcial analysis. It is found that while the medicinal plants in PV plants can improve the income by 8%, while poultry in solar parks bring additional income of 83%, considering one lifecycle, while bee keeping bring additional income of 4%. The economic analysis shows that agrivoltaic without workable business models for a captive power plant with 0.14$/kWh FiT breakeven at 3 years and 9 months while a captive plant with the same FiT without agrivoltaics breaks even in 2 years and 4 months. A captive plant with 0.14 $/kWh FiT with a workable business model will breakeven in 3 years and 3 months. A grid tied solar PV plants with a FiT of 0.03 $/kWh which has a breakeven of 13 years without agrivoltaics, may not breakeven within 25 years (plant life) without a workable business model. However, with a workable business model for agrivoltaics the grid tied solar PV plant with a FiT of 0.03 $/kWh will have a breakeven in 17 years and 8 months.The social impact assessment conclude that, livelihood impacts can lead to extinction of cultures, urban migrations, growth of uncontrolled peri‑urban regions, the long term impacts are beyond economics. Thus social impact mitigation cost (SIMC) along with environemental impact mitigation cost (EIMC) are considered as incentives or subsidied and the levelised cost of energy (LCOE) is calculated. It is found that levelised cost of energy for the conventional ground mounted solar PV plant is 0.03 $/kWh while for agrivoltaic plant without subsidies and incentives the LCOE is 0.052 $/kWh. For the agrivoltaic plant with a subsidy of 30% the LCOE is 0.046 $/kWh and with a further green incentive billing the LCOE can be brought down to 0.041 $/ kWh.

Potential Assessment and Economic Analysis of Concentrated Solar Power against Solar Photovoltaic Technology

Competition between concentrated solar power and solar photovoltaic has been the subject of frequent debate in recent years based on their cost of fabrication, efficiency, storage, levelized cost of energy, reliability, and complexity of respective technologies. Taking Pakistan as a testbed, a study was conducted to determine which technology is economical in a particular location and climate. The study assesses the meteorological, orographic, and spatial factors that impact the performance and cost of both renewable energy systems. A SWOT analysis, followed by technoeconomic analyses, was conducted to determine suitable sites for setting up solar power plants in Pakistan. A detailed assessment of siting factors for solar power plants was conducted to shortlist the most suitable sites. Based on the results, economic analysis was performed to install 100 MW photovoltaic and parabolic trough power plants at selected locations. The levelized cost of energy for the 100 MW parabolic trough is 10.8 cents/kWh and 12 cents/kWh in best-case scenarios, i.e., for locations of Toba and Quetta, respectively, whereas the LCOEs of 100 MW photovoltaic systems stand comparatively low at 7.36 cents/kWh, 7.21 cents/kWh, 7.01 cents/kWh, 6.82 cents/kWh, 6.02 cents/kWh, and 5.95 cents/kWh in Multan, Bahawalpur, Rahim Yar Khan, Hyderabad, Quetta, and Toba, respectively. The results favor choosing solar PV plants over solar CSP plants in terms of finances in the selected regions. The findings will assist financiers and policymakers in creating better policies in terms of long-term goals.

Fixed head hydrothermal scheduling considering uncertainty of load demand and renewable energy sources

AbstractThis manuscript suggests quasi‐oppositional fast convergence evolutionary programming (QOFCEP) technique to determine short‐term generation scheduling of fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants (SPVPs) and wind turbine generators (WTGs) with demand response program (DRP). PSHPs are used with fixed head hydro power plants (FHPPs), thermal generating units (TGUs), SPVPs and WTGs to operate system conveniently. Uncertainties related to renewable generations and load demands have been considered. Self‐adaptive tent mapped scenarios of uncertain parameters are generated within higher level of confidence space to deal with the system uncertainties. DRP is applied to level the load demand curve and to enhance system flexibility and reduce operational cost. Numerical results of typical test system are matched with those attained by fast convergence evolutionary programming (FCEP) and evolutionary programming (EP).

Short-term pumped storage hydrothermal generation scheduling considering uncertainty of load demand and renewable energy sources

This manuscript suggests quasi-oppositional fast convergence real coded genetic algorithm for determining short-term optimum generation schedule of variable head and fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants and wind turbine generators with demand response program (DRP). PSHP is used for convenient operation. Here, uncertainty of load demand and renewable energy sources has been considered. The uncertainties are curtailed by enclosing richer estimations of variables within well defined confidence intervals. Self adaptive scenarios of uncertain parameters are generated using Tent chaos mapping within confidence intervals. DRP is applied to flatten the load demand curves and to augment system flexibility and decrease operational cost. Numerical results of typical test systems are matched with those attained by fast convergence real coded genetic algorithm and real coded genetic algorithm.

Dynamic optimal power flow for multi‐operator renewable energy‐based virtual power plants

AbstractRecently, infiltration of distributed energy resources (DERs) is augmented considerably to upsurge network flexibility, better economic indicator, and reduced power loss. But integration of different DERs may cause challenges in power grid. To overwhelmed these challenges and obtain maximum advantage of DERs, virtual power plant's concept has been emerged. Virtual power plants (VPPs) has the capacity to partake in electricity market and rivalry of VPPs to achieve more profit, deregulated multi‐operator markets are developed. This paper suggests dynamic optimum power flow (DOPF) for multi‐operator VPPs considering demand side management (DSM) and uncertainty of renewable energy sources. VPPs with different proprietorships are interconnected with each other by tie lines. Each VPP has small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plant (BPPs), and plug‐in electric vehicles (PEVs). VPP 1 comprises IEEE 33‐bus system, VPP 2 comprises 15‐bus system, and VPP 3 comprises IEEE 69‐bus system. Bottlenose dolphin optimizer (BDO), HPSO‐TVAC, and GWO have been applied to solve DOPF problem and maximize the net profit of multi‐operator VPPs.