Solar PVs Research Articles

Stochastic optimal power flow framework with incorporation of wind turbines and solar PVs using improved liver cancer algorithm

AbstractThe present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

Simultaneous sizing and energy management of multi-energy Virtual Power Plants operating in regulated energy markets

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.

Carbohydrate-Based Reprocessable and Healable Covalent Adaptable Biofoams.

Polymeric foams derived from bio-based resources and capable of self-healing and recycling ability are of great demand to fulfill various applications and address environmental concerns related to accumulation of plastic wastes. In this article, a set of polyester-based covalent adaptable biofoams (CABs) synthesized from carbohydrates and other bio-derived precursors under catalyst free conditions to offer a sustainable alternative to conventional toxic isocyanate-based polyurethane foams is reported. The dynamic β-keto carboxylate linkages present in these biofoams impart self-healing ability and recyclability to these samples. These CABs display adequate tensile properties especially compressive strength (≤123MPa) and hysteresis behavior. The CABs swiftly stress relax at 150°C and are reprocessable under similar temperature conditions. These biofoams have displayed potential for use as attachment on solar photovoltaics to augment the output efficiency. These CABs with limited swellability in polar protic solvents and adequate mechanical resilience are suitable for other commodity applications.

Green Hydrogen Driven by Wind and Solar—An Australian Case Study

The energy transition to wind and solar opens up opportunities for green hydrogen as wind and solar generation tend to bring electricity prices down to very low levels. We evaluate whether green hydrogen can integrate well with wind and solar PVs to improve the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during periods of surplus renewable energy. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used system engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, so at present, it would not be viable without subsidy.

Robotics, Artificial Intelligence, and Drones in Solar Photovoltaic Energy Applications—Safe Autonomy Perspective

While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations.

A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition

The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.

Sustainable Economic Growth: Mediation Effect of Decrease of Energy Crises through the Use of Solar PVs

Sustainability is one of the prime issues across the world. Therefore, it is also included in the list of Sustainable Development Goals (SDGs). Therefore, the implications are much higher for developing sides of the world. Therefore, this study has been conducted specifically to check the use of on-roof installation of solar PVs in urban residential areas on the decrease of energy shortfall as well as upon sustainable growth of the country’s economy. Although for the purpose of data collection, the sample has been drawn from the energy sector companies of Karachi in order to reflect vividly upon the sustainable economic growth of the country. The sample size is of 125 respondents based on employees of energy sector. Analysis has been made through SMART-PLS using PLS-SEM findings indicating that the use of on-roof solar PVs is substantial for the decrease in energy shortfall as well as for the sustainable economic growth of the country. In fact, the outcomes of the study also prove the use of mediation through indicating indirect association between use of solar PVs at country’s economic growth.

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.

Office Green Building Modeling Based on EDGE and SketchUp Applications

The importance of the concept of sustainable development and its application in office green buildings, since in the design process, since the modeling of 3D buildings with decisions on design elements applied in the finished building the office building as a one of commercial building type which require a slightly approach to productivity.The purpose of the study is to find out what factors determine office green buildings by using the EDGE and Sketch Up applications. EDGE is a green building simulation and SketchUp is 3D building modelling tool.This research is expected to strengthen the quality of the learning process in the implementation of an efficient design of energy use with the concept of orientation for the realization of green buildings in design. This research uses mix methods, which use of quantitative methods for simulation on SketchUp dan EDGE application, and use of qualitative methods for its advanced stage in an analysis that explains narratively and descriptively. Building modelling factors for SketchUp application are building dimensions, orientations, layout and openings. Design factors for EDGE application are the efficiency of water, electricity and Embodied Energy (EE) use in materials.EDGE application simulations show active and passive building design that high impact intervention on this building are passive cooling and ventilation design, daylighting, lighting control, low energy lighting, high efficiency chillers such as ground source heat pumps and solar PVs on roof which could generate significant energy.

Examining the Risk of Summertime Overheating in UK Social Housing Dwellings Retrofitted with Heat Pumps

The UK government has announced its ten-point plan to annually install 600,000 low-carbon heat pumps by 2028. However, there is a lack of evidence showing potential overheating risk in dwellings retrofitted with heat pumps. This paper examines the prevalence and magnitude of summertime overheating across 24 naturally ventilated social housing dwellings retrofitted with ground source heat pumps (GSHPs). The dwellings are located in a socially deprived area in Oxford (UK). The empirical study included longitudinal monitoring of indoor temperatures in the living rooms and bedrooms during the non-heating seasons of 2021 and 2022 (May–September), which included a record-breaking heatwave in July 2022. Indoor temperature and CO2 levels in bedrooms were monitored across a subset of six dwellings alongside the monitoring of window opening state in three bedrooms to understand the effect of natural ventilation in removing excess heat. About 136 thermal comfort surveys were conducted to ascertain the subjective responses of residents. Overheating risk assessment was carried out using CIBSE static and adaptive methods, which revealed that summertime overheating was prevalent across half of the dwellings in the non-heating season of 2022, as compared to 17% overheated dwellings in 2021. Bungalows with upgraded cavity wall insulation and top floor flats facing south and south-west had a propensity to overheat. The variation in indoor temperature and CO2 levels across a small sample also indicated the relationship between overheating and residents’ behaviour. Given that the majority of the dwellings were occupied by retired elderly people with low incomes who are vulnerable to heat and cannot afford active forms of cooling, it is vital to deploy passive design measures, such as appropriate shading devices that are suitable for a heating-dominated climate and enhanced ventilation, as part of home energy retrofits. Implementing reversible heat pumps coupled with solar PVs can provide cooling during heatwaves while delivering low-carbon heat in the winter.

Is hydrophobic coating on glass equally efficient in reducing % soiling loss of solar PVs in clean and polluted environments?

Hydrophobic-coated glass (CS) on PV surfaces is known to reduce particulate matter (PM) accumulation. However, there is no comparative study on the % reduction in soiling loss (% RSL) on CS with respect to uncoated glass (UCS) for clean and polluted environments (higher atmospheric PM). The present field study (∼210 km2 in Gujarat, Western India) focuses on the evaluation of particle-size distribution of PM on CS and UCS, quantification of size-resolved soiling due to PM accumulation on CS and UCS, and comparison of % RSL of CS in cleaner and polluted environments. The findings show that 3 to 10 µm particles contribute to ∼21 % and ∼12 % of the total mass accumulated on CS and UCS, respectively. The observations are also validated using the theoretical adhesion model and critical rebounding velocity. Moreover, this particle-size fraction contributes to ∼50 % of the total soiling loss for CS against only ∼27 % soiling loss for UCS. Therefore, CS surfaces under polluted environments are expected to have a higher % of finer particle-size fraction in accumulated PM, which contributes significantly to the total soiling loss of CS and reduces the difference in % soiling loss between CS and UCS. Although CS reduces PM accumulation in both clean and polluted regions, the % RSL using CS is comparatively less in a polluted environment. The observations from the present study provide greater insight into the soiling aspect of solar PVs and have significant economic implications in deploying PV surfaces with hydrophobic coated glass.

Impact of Electric Vehicle Charging Infrastructure on the Electric Load Profile of Power System: The Case of Latvia

The number of electric vehicles (EVs) is increasing rapidly, and charging infrastructure must keep up with that pace. With increasing charging load, a power system must adapt to the increasing power demand. The research question of this study is: what impact of EV charging on electric load profile is depending on the number of EVs and the mix of charging units (slow, medium, fast)? How much of the total power demand of EVs can be supplied from renewables, i.e., wind and solar power, considering the installed capacities of these technologies, and the match between power production and consumption. Energy system modeling on an hourly basis for different scenarios of the mix of charging units, number of EVs, and installed capacities of wind power plants and solar PVs was used as the method. EnergyPLAN software was used as the modeling tool. The results show the total power demand, peak load, and share of EV charging power demand that can be covered by renewable power technologies for Latvia’s power system in the year 2050. The results are obtained for scenarios of different combinations of EV charging units.

Use of Synthetic Fuels Derived from Green Hydrogen and CO2 in Heavy-duty and Long-range Transport: the Case of Latvia

Decarbonization of the transport sector may be more challenging than it is for the power supply and heating sectors. Green hydrogen, i.e., produced from renewable energy sources, combined with CO2 captured from flue gases or air can be used to produce synthetic fuels, e.g., dimethyl ether (DME), ammonia, and jet fuel. These synthetic fuels can be used in heavy-duty and long-range transport, i.e., trucks, ships, and airplanes. The research question of this study is: how much green hydrogen and CO2 is needed to replace fossil fuel in the mentioned transport sectors with synthetic fuels? How much of the power demand for production of the synthetic fuels can be supplied from renewables, i.e., wind and solar power, considering the installed capacities of these technologies, and the excess power that can be used for the hydrolysis process. The case of Latvia for the year 2050 is used for the simulation of scenarios with various mixes of renewable power production. The simulation is done on an hourly basis for the whole year, using EnergyPLAN software as the modeling tool. The results show the total hydrogen and CO2 demand, the total power demand for hydrolysis of green hydrogen, and the share of the demand that can be covered by renewable power technologies. The results also include the costs of synthetic fuel supply for the considered transport sector. The results are obtained for scenarios of different combinations of installed capacities of wind power plants and solar PVs.

The menace and mitigation of air pollution in the built environment: A review

Air pollution has become one of the leading causes of death globally. This paper investigates the causes as well as sources of air pollution in the indoor and outdoor environment. It identifies anthropogenic activities related such as transportation and other activities involving the combustion of fossil fuel as the major causes of air pollution. The several and diverse health implications of air pollution were highlighted. The dangerous nature of carbon monoxide was x-rayed and the common indoor and outdoor sources were discussed. The risks of carbon monoxide poisoning from generator fumes and vehicle exhaust were clearly enumerated. Particulate matter (PM10, PM2.5 and ultra fine PM) were clearly shown to be the major casue of chronic and fatal respiratory and cardiovascular diseases. Indoor air quality, which is usually overlooked was brought to the fore and shown to be worse than outdoor air quaility in some cases, especially where ventilation is poor. The inextricable link between air pollution and climate change was also established. Mitigative measure such as policies/acts, replacement of fossil fuel with solar PVs, replacement of conventional vehicles with electric vehicles and establishment of urban forests were discussed. Frequent and evenly distributed air quality monitoring, especially using android-enabled portable devices was broached. Besides, policies and other global approaches, individuals must adopt personal protective measures to protect themselves from the menace of air pollution.

Sustainable energy and environmental management practices in the recreation and tourism industries in Nsukka metropolis of Enugu State

Sustainable energy and good environmental management practices are potent factors that can be used to achieve clean energy and sustainable economic growth. This is a global problem that needs immediate action and intervention to reduce the adverse impact of climate change to humanity. This study investigated the sustainable energy and environmental management practices in the recreation and tourism industries in Nsukka metropolis of Enugu State. The descriptive research design was adopted for the study. The population of the study consisted of 209 recreation and tourism service providers in Nsukka Metropolis. Instrument for data collection was a structured questionnaire which was validated by three experts from the University of Nigeria, Nsukka. The instrument has a reliability coefficient of 0.81. Frequency and percentages were used to determine level of sustainable energy and environmental management practices, while phi coefficient was used to determine relationship between sustainable energy and environmental management practices. Findings showed that recreation and tourism possess very low level of knowledge of sustainable energy use. They however showed positive attitude towards sustainable energy through the adoption of energy saving devices like energy bulbs, solar PVs etc. There is strong positive relationship between knowledge of sustainable energy and environmental management practices. The researchers recommended that government should improve on its effort to create awareness of sustainable energy use and environmental management practices among the populace if the problem of climate change is to be abated.

Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization

Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.

A Distributed Dual-Optimization Framework for Ancillary-Service Coordination Between MV Microgrids and LV Distribution Networks

This article aims to develop an efficient distributed real-time strategy for internal device scheduling and ancillary service coordination in the interconnected system of a medium voltage microgrid and a low voltage distribution network (LVDN). A distributed agent-based dual engine is proposed for coordinated ancillary service optimization, thereby reducing communication and computational burden. The strategy enables the microgrid operator and the LVDN aggregator, which are selfish entities, to perform ancillary service power exchanges by coordinating for both the active/reactive power as well as the compensation rates. The proposed strategy is numerically validated on a 30-node microgrid-LVDN test system consisting of solar PVs, conventional and renewable distributed generators, loads, community battery, and electric vehicles (EVs). This is shown to be beneficial than simply tracking a power profile by the LVDN or considering fixed compensation rates. Microgrid reduces its total operating cost significantly by approximately 31.78%, while maintaining the voltage and state of charges of EVs and community battery within the operation limit. Further, ancillary service coordination for residential and commercial LVDNs with a car park is analyzed, and the study is extended to consider fast charging of EVs with other related constraints.

Quantifying the accuracy of optical transmission loss techniques and identifying the best wavelengths for estimating soiling in a field study

The optical transmittance loss (OTL) of glass coupons is frequently used to estimate the soiling losses of solar PVs. The soiling loss estimation methodologies (SLEMs) and selection range of wavelengths for OTL evaluation vary significantly, influencing the accuracy of soiling loss estimation. To our knowledge, no studies have quantified the accuracy of SLEMs (requiring hemispherical transmittances of glass coupons) with a standard soiling reference station in the field. Moreover, the data regarding the best single wavelengths for estimating the soiling loss with minimum error is significantly lacking in the literature. The present study focuses on quantifying the accuracy of SLEMs and single wavelengths by using multiple glass samples over a 150-day sampling period. The results showed that SLEMs that consider either the spectral irradiance of the solar spectrum or both the spectral irradiance of the solar spectrum and the spectral response of the polycrystalline PV material provided the best soiling estimates (no statistically significant difference between SLEMs: two-tailed p-values > 0.05). Moreover, the Ultraviolet (RMSE: 7.89 ± 6.39) and the Visible (RMSE: 1.49 ± 0.47) regions have low accuracy in estimating actual soiling losses. The results also show that the best single wavelengths for estimating the soiling loss are 760 to 850 nm (for polycrystalline), significantly different from the previous studies (Spain-600 nm and Golden Colorado-700 nm). Overall, the present study has implications for independent monochromatic optical soiling technologies. The use of multiple glass samples in the study provides reasonable statistical confidence in the correctness of the results.

An e-BAS Optimized d-q Controller for Grid Voltage Stability of Single Stage Solar PVS with PQ Disputes

An e-BAS Optimized d-q Controller for Grid Voltage Stability of Single Stage Solar PVS with PQ Disputes

Life-Cycle Assessment Study for Bio-Hydrogen Gas Production from Sewage Treatment Plants Using Solar PVs

Currently, there is a global challenge of water scarcity due to climate change, rising temperatures, and other factors. One way to address this growing global challenge is by implementing technology to treat polluted water by reusing it in areas such as irrigation, cooling, and energy production, based on bio-hydrogen gas. Hydrogen gas can be produced by several methods, including dark fermentation. In this study, hydrogen gas was produced by 1L of sludge and Treated Effluent (TE) with several methods, using a reactor with a volume of 0.96 H2 L/L media. The Life-Cycle Impact Assessment (LCIA) process was used to study resource depletion, the ecosystem, and human impacts, and efforts were made to reduce the negative impacts by implementing several solutions. In this study, OpenLCA software was used as a tool for calculating the impacts, along with the ecoinvent database. Further analysis was carried out by comparing the LCIA with and without the use of solar energy. The results show that implementing hydrogen gas production with a solar energy system will help to obtain the best solution and reduce the carbon footprint, with 1.12 × 104 kg CO2 equivalent and a water depletion of 2.83 × 104 m3.