4E analysis and tri-objective optimization of a landfill plant integrated with power-to-gas and leachate treatment.

Management of waste is essential since waste production has increased drastically. Landfilling is prevalent in controlling and managing wastes, particularly municipal solid wastes. Tackling the environmental problems of landfill is the goal of this work. The outputs of the landfill are biogas and leachate, which are hazardous to the environment. This problem can be solved by using the power-to-gas system and leachate treatment plant. The leachate has the potential to produce biogas, and the CO2 in biogas can be converted to methane in the methanation unit of power to gas. For this, power-to-gas needs the electricity in the electrolyzer, which can be provided from the surplus electricity of available renewables (here solar photovoltaics and wind turbine). Energy, exergy, economic and environmental analyses are applied to the system, and tri-objective optimization by the genetic algorithm is performed to gain optimum results. The obtained exergy efficiency from the given data is 19.03%. Also, the energy efficiency, net electricity generation, methane production rate, total annual cost, and CO2 conversion are 19.51%, 4.24 MW, 176.63 kg/h, €1.8 million, and 82.42%, respectively. In the ideal point of tri-objective optimization, the exergy efficiency, total annual cost, and CO2 conversion become 26.16%, €1.31 million, and 96.57%, respectively.

Environmental emissions influencing solar photovoltaic waste management in Australia: An optimised system network of waste collection facilities.

The Australian urban construction electricity sector has witnessed a transformational effect in the use of small-scale solar photovoltaic (PV) systems in the past decade. Currently, Australia has one of the highest rates of rooftop solar PV users with over 20% of households connected. This will see a rapid growth in the volume of PV waste in the coming years when these PV systems come to their end-of-life or require replacement. The collection and transportation involved in solar PV waste treatment has a significant impact on the environmental sustainability of Australian cities while designing a holistic reverse logistic (RL) network may play an essential role in the reduction of the associated cost and environmental impacts. In this study, the Weibull distribution model is employed to forecast the PV waste in the next three decades in South Australia. The study further estimates the pollutant emission associated with the collection and transportation of the waste for recycling and recovery using hotspot analysis, location allocation modelling and vehicle routing problem. Generation of pollutants - Particulate Matter (PM), Carbon Monoxide (CO), Carbon dioxide (CO2) and Nitrogen Oxides (NOx) associated with transport and energy consumption are estimated through three routing scenarios. Results indicate that, there will be 109,007 tons of PV waste generated in urban and suburban context in South Australia by 2050. Among the three routing scenarios generated, the third scenario with optimised transfer stations and an additional recycling facility showed more than 34% reduction in pollutant emission. Such additional PV waste management facilities require policy support and regulations to effectively manage solar PV waste treatment and logistics.

A Deep Reinforcement Learning-Based MPPT Control for PV Systems under Partial Shading Condition.

On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment pollution. Besides introducing new materials for the solar cells to improve the energy conversion efficiency, the maximum power point tracking (MPPT) algorithms have been developed to ensure the efficient operation of PV systems at the maximum power point (MPP) under various weather conditions. The integration of reinforcement learning and deep learning, named deep reinforcement learning (DRL), is proposed in this paper as a future tool to deal with the optimization control problems. Following the success of deep reinforcement learning (DRL) in several fields, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) are proposed to harvest the MPP in PV systems, especially under a partial shading condition (PSC). Different from the reinforcement learning (RL)-based method, which is only operated with discrete state and action spaces, the methods adopted in this paper are used to deal with continuous state spaces. In this study,DQN solves the problem with discrete action spaces, while DDPG handles the continuous action spaces. The proposed methods are simulated in MATLAB/Simulink for feasibility analysis. Further tests under various input conditions with comparisons to the classical Perturb and observe (P&O) MPPT method are carried out for validation. Based on the simulation results in this study, the performance of the proposed methods is outstanding and efficient, showing its potential for further applications.

Evaluating treatment pathways for managing packaging materials from construction of a solar photovoltaic power station.

The reuse of end-of-life packaging materials (EOLPM) on site represents, particularly for remote sites, an important contribution to sustainable business practice because it provides a higher value end use when used to develop on-site mulch to enable soil improvement, thereby reducing transport emissions (in relation to the least preferred option of off-site disposal to landfill), lowering costs and offering employment to local contractors. The objective of the study was to demonstrate a local application of the circular economy for EOLPM to a utility-scale solar electricity (USSE) construction site. Although the principles of the circular economy could not be applied fully at the site, it was possible to demonstrate that EOLPM can be reused on site for a higher value than off-site disposal would give. Given the common occurrence of these materials in the rapidly growing renewable energy sector, this represents an important step forward for the sector internationally. The study is the first of its type reported, and the methods used for characterization of the EOLPM included a range of organic and inorganic chemical analyses and phytotoxicity testing, which were followed by an environmental and financial cost-benefit analysis. The selected option of on-site reuse of the materials as a mulch had a global warming potential of 58 t CO2e compared with the business as usual option (transport to landfill) of 3145 t CO2e. The results also demonstrated the broader potential for using EOLPM from USSE sites for soil improvement at remote locations rather than transporting these materials off site for disposal or reuse.

Adoption of residential rooftop solar PV systems in South Africa: A scoping review of barriers.

Global sustainability challenges such as climate change are linked to carbon emissions from fossil fuel powered energy needed for commercial and household consumption. South Africa is highly depended on coal for energy production hence the transition to renewable energy sources such as solar PV is seen as a pathway towards emissions reduction and a sustainable future. Yet, despite the huge potential for solar PV technologies adoption remains very low. This scoping review examines the barriers to household solar PV adoption in South Africa to advance our understanding beyond case study level studies. We analysed all published literature on household solar PV in South Africa as a basis for finding themes, gaps, and trends on solar PV research. Review results show that key barriers can be grouped into financial, personal, institutional, technical and societal barriers, however there were no studies on barriers across an income gradient, a glaring omission given debates on just transitions. Given the complexity of the barriers ranging from personal, societal, to technical barriers, it is not reasonable to expect the government to facilitate transition to solar PV alone. Rather, collective approaches are needed to create enabling conditions for solar PV adoption such as the financial means and information availability. The private sector has a key role to play either in supporting state-initiated programmes or creating the means for solar PV adoption such as power purchase agreements. That said, the state remains a central player in facilitating an enabling economic and political environment to leverage responsiveness from other actors. Without an integrated approach to addressing barriers to solar PV adoption, solar adoption will remain a source of energy for the economically privileged, and the imperative to just transition to renewable energy a pipe dream, in a country characterised by large inequalities among households.

Prosumer solar power and energy storage forecasting in countries with limited data: The case of Thailand.

Forecasts of distributed energy resource deployment are becoming increasingly important in electric power purchase plans and difficult for countries with limited data. This study utilizes the Customer Adoption Model to forecast the deployment of behind-the-meter distributed solar photovoltaics and battery energy storage systems until the year 2050 and Thailand is used as a case study of the countries with limited data. Comparing methods and results from this study with those used in past studies shows that methodological choices can produce diverging results that shape investment plans and the estimated cost of power supplies. Several input variables of the Customer Adoption Model are discussed that will require continuous refinements as more data become available. The results show that pairing solar systems with batteries could in principle accelerate solar deployment and carbon emissions reduction but the high cost of batteries lengthens the payback period, raising questions about forecasting methodologies that rely mainly on the payback period. The methodological contribution points to a "chicken-and-egg" problem between forecasting and policy uncertainties: accurate forecasting depends on policy certainty, but getting policy right depends on accurate forecasting. Integrated scenario construction and the determination of a specific timeframe for achieving the adoption goal can help policymakers understand the impacts of different policy designs on distributed energy resource deployment and overcome this problem.

Potential data for feasibility assessment and deployment of a 6.7 MW floating solar PV plant in Hatirjheel Lake, Dhaka, Bangladesh.

Floating solar photovoltaic has emerged as a highly sustainable and environmentally friendly solution worldwide from the various clean energy generation technologies. However, the installation of floating solar differs from rooftop or ground-mounted solar due to the significant consideration of the availability of water bodies and suitable climatic conditions. Therefore, conducting a feasibility analysis of the suitable climate is essential for installing a floating solar plant on water bodies. These data are evaluated for the viability of installing a 6.7 MW floating solar power plant on Hatirjheel Lake in Dhaka, Bangladesh. The feasibility analysis incorporated various climatic data, such as temperature, humidity, rainfall, sunshine hours, solar radiation, and windspeed, obtained from Meteonorm 8.1 software and the archive of the Bangladesh Meteorological Department. Besides, this study gathered and analyzed the energy demands of the local grid substation operated by Dhaka Power Distribution Company, to determine the appropriate capacity and architecture of the power plant. The power plant design was conducted using the PVsyst 7.3 software, which determined the necessary equipment quantities, DC energy generation capacity, and the energy injected into the grid in MWh. The study also calculated the Levelized Cost of Energy per kilowatt-hour and the payback period for the system, which indicates the economic viability of installing the system. Furthermore, the acquired dataset possesses significant potential and can be utilized for the establishment of all sorts of solar power plants, including floating solar plants, in any location or body of water within the Dhaka Metropolitan area.

Life Cycle Assessment of Photovoltaic electricity production in Italy: Current scenario and future developments.

This study presents a Life Cycle Assessment (LCA) of photovoltaic (PV) electricity production in Italy based on the composition of the current and future Italian PV scenario. Using detailed and site-specific data, the actual composition of the Italian mix of PV technologies at the end of 2022 and those expected for 2030 were defined. A new LCA modelling of the most relevant PV technologies was carried out using updated and reliable inventory data. The impact assessment was performed adopting the most relevant impact categories of Environmental Footprint Method v. 3.1. The environmental profiles of the two Italian PV scenarios (PV Scenario_2021 and PV Scenario_2030) analysed in this study were compared with that of the PV scenario achievable using unaltered Ecoinvent v 3.9.1 datasets specific to Italian. The obtained results highlighted that the use of Ecoinvent datasets and hypothesis entails a significant overestimation of the environmental impacts of photovoltaic electricity production in Italy; showing higher impacts ranging from 70 % to 30 % (depending on the impact category considered) and the main key factors affecting the results were investigated. However, the wide impacts gaps pointed out the importance of conducting representative LCA studies of the fast-growing and evolving PV context of the countries, to provide reliable impact results to policy makers and to other researchers and who need to include the PV electricity generation in their studies. Furthermore, the environmental performance analysis of the two Italian PV scenarios highlighted the higher sustainability of the PV electricity production in the next years (PV Scenario_2030) for all considered impact categories (except for land use). This improvement can be primarily attributed to the higher annual energy yield and the greater utilization of high-efficiency PV technologies, along with the expansion of ground-mounted PV plants.

Development and application of an urban solar photovoltaic opportunity mapping tool.

A Geospatial Opportunity Mapping (GOMap) tool was created to identify policy unconstrained land in urban cities that is technically feasible for the deployment of solar photovoltaic power stations; and identify buildings with north- or south-facing orientation for the installation of building integrated PV (BIPV). Collaboration with a local Governing authority and a local electricity provider enabled the process to elicit comprehensive policy and technical aspect information respectively that would impact the site selection process. Five policy and four technical aspects are comprised of a total of 36 individual factors displayable by GOMap on a high-resolution city grid with a scoring system implemented to distinguish between factors that encourage or inhibits solar PV deployment. Weightings can be applied, and different scenarios explored including alternative policy changes and infrastructure upgrades. GOMap generates opportunity maps in the form of available land estimates which can be extrapolated by an in-built solar PV model to quantify annual energy generation based on local weather data, array spacing, panel type and array tilt angle. Three scenarios were devised to identify unconstrained land for solar PV deployment with varying levels of policy and technical factor relaxation, and a fourth scenario to identify dwellings for potential BIPV. These scenarios aim to tackle Glasgow City's growing energy demand and fuel poverty issue, the latter of which can supply energy to dwellings categorised as 'hard-to-heat' once heating is electrified due to the Scottish Government's Energy Strategy commitment.

Energy based techno-economic and environmental feasibility study on PV/T and PV/T heat pump system with phase change material-a numerical comparative study.

A sustainable, affordable, and eco-friendly solution has been proposed to address water heating, electricity generation, space cooling, and photovoltaic (PV) cooling requirements in scorching climates. The photovoltaic thermal system (PV/T) and the direct expansion PV/T heat pump (PV/T DXHP) were numerically studied using MATLAB. A butterfly serpentine flow collector (BSFC) and phase change material (PCM) were assimilated in the PV system and MATLAB model was developed to evaluate the economic and enviroeconomic performance of the PV/T water system (PV/T-W), PV/T PCM water system (PV/T PCM-W), the PV/T DXHP system, and the PV/T PCM heat pump system (PV/T-PCM-DXHP). In this study, annual energy production, socioeconomic factors, enviro-economic indicators, and environmental characteristics are assessed and compared. Also, an economic, environmental, and enviro-economic analysis was conducted to assess the commercial viability of the suggested system. The PV/T PCM-DXHP demonstrated the highest electrical performance of 53.69%, which is comparatively higher than the other three configurations. The discounted levelized cost of energy (DLCOE) and payback period (DPP) of the PV/T PCM-DXHP were ₹2.87 per kW-h and 3-4 years, respectively, resulting in a total savings of ₹67,7403 over its lifetime. Furthermore, installing this system mitigated 280.72 tonnes of CO2 emissions and saved the mitigation cost by ₹329,700 throughout its operational lifecycle.

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons.

This study uses operational data from a 180 kWp grid-connected solar PV system to train and compare the performance of single and hybrid machine learning models in predicting solar PV production a day-ahead, a week-ahead, two weeks ahead and one month-ahead. The study also analyses the trend in solar PV production and the effect of temperature on solar PV production. The performance of the models is evaluated using R2 score, mean absolute error and root mean square error. The findings revealed the best-performing model for the day ahead forecast to be Artificial Neural Network. Random Forest gave the best performance for the two-week and a month-ahead forecast, while a hybrid model composed of XGBoost and Random Forest gave the best performance for the week-ahead prediction. The study also observed a downward trend in solar PV production, with an average monthly decline of 244.37 kWh. Further, it was observed that an increase in the module temperature and ambient temperature beyond 47 °C and 25 °C resulted in a decline in solar PV production. The study shows that machine learning models perform differently under different time horizons. Therefore, selecting suitable machine learning models for solar PV forecasts for varying time horizons is extremely necessary.

Techno-economic data and assumptions for open energy modelling of decarbonisation pathways in the Philippines.

This article introduces an openly accessible dataset aimed at supporting energy system modelling of decarbonisation pathways in the Philippines. The dataset was compiled through an extensive literature review, incorporating information from various sources such as the Philippines Department of Energy, academic publications, and international organisations. To ensure compatibility with OSeMOSYS modelling requirements, the data underwent processing and standardisation. It includes power plant data covering existing capacity from classified by grid, off-grid, and planned additions, as well as historical generation data. Additionally, the dataset provides historical and projected electricity demand from 2015 to 2050 segmented by sectors. It also offers technical potential estimates for fossil fuels and renewable energy sources, along with key techno-economic parameters for emerging technologies like floating solar PV, in-stream tidal, and offshore wind. The dataset is freely available on Zenodo, empowering researchers, policymakers, and private-sector actors to conduct independent energy modelling and analyses aligned with the U4RIA framework principles. Its open access encourages collaboration and facilitates informed decision-making to advance a sustainable energy future not only for the Philippines but also for broader global contexts.

Dataset on the optimization of a photovoltaic solar water pumping system in terms of pumping performance in remote areas of Phayao province using response surface methodology.

The Response Surface Methodology (RSM) was employed to examine the impact of the pumping system in a photovoltaic solar water pumping system, while operating under ideal conditions. The input parameters for optimizing the pump performance of the PV water pump include three parameters: Solar irradiance (550-950, W/m2), temperature (30-45, °C), and voltage (420-540, V). The experimental values of PV water pump efficiency showed that the efficiency of PV water pumps was in the range of 55.24-80.80% of the experiment. At a solar irradiance of 750 W/m2, a voltage of 480 V and a temperature of 37.5 °C shows the maximum efficiency of the solar PV water pump systems was 80.80% under optimal conditions. This work demonstrates the potential of solar water pumps as a reliable, cost-effective, and environmentally friendly solution to support agriculture in remote areas. In addition, the costs and economic parameters of solar photovoltaic water pumps and conventional systems were compared by the social return on investment (SROI) evaluation. This indicates that sales are profitable or create social value that benefits society and local stakeholders in remote areas. This work demonstrates the potential of solar water pumps as a reliable, cost-effective, and environmentally friendly solution to support agriculture in remote areas.

A novel semi-quadratic buck-boost structures with continuous input current for PV application.

This paper recommends new design for non-isolated semi-quadratic buck/boost converter with two similar structure that includes the following features: (a) the continuous input current has made it reasonable for PV solar applications and reduced the value of the capacitors in the input filter reducing the input ripple as well as EMI problems; (b) the topology is simple, and consists of a few numbers of components; (c) the semiconductor-based components have lower current/voltage stresses in comparison with the recently recommended designs; (d) semi-quadratic voltage gain is D (2 - D) / (1 - D)2; (e) 94.6 percent from the theoretical relations and 91.8 percent from the experimental for the output power of 72W, the duty of 54.2 percent, and output voltage of 72 V are the efficiency values in boost mode; (f) 89.3 percent from the theoretical relations and 87.2 percent from the experimental for the output power of 15W, the duty of 25.8 percent, and output voltage of 15 V are the efficiency values in buck mode. One structure is the continuous output current and negative output polarity, and other structure is positive output polarity. The recommended topologies have been studied in both ideal and non-ideal modes. The continuous current mode (CCM) is the suggested mode for the proposed converters. Moreover, the requirements of CCM have been discussed. The various kinds of comparisons have been held for voltage gain, efficiency, and structural details, and the advantages of the suggested design have been presented. A small-signal analysis has been completed, and the suitable compensator has been planned. Finally, PLECS simulation results have been associated with the design considerations.

Assessing and comparing a DDPG model and GA optimization for a heat and power virtual power plant operating in a power purchase agreement scheme.

This paper proposes a deep deterministic policy gradient (DDPG) model for the operation management of a solar power-based virtual power plant (VPP) having a PPA with the grid and supplying power and thermal energy to consumers. The VPP serves to balance the solar power intermittency, cover the demand whenever solar power is absent, and ensure an efficient supply of energy. The literature in this field has introduced optimization algorithms to determine the power plant's output power or heat on a rolling-horizon basis. Using the function approximation category, which involves reinforcement learning with neural networks, to solve the simultaneous thermal and power operation management of VPPs is still not well developed. The challenges imposed in this model are sourced from the non-linearity of the CCHP, the power and thermal balance constraints, and the consideration of continuous variables rather than discrete ones. A case study is simulated in Egypt to assess and compare the models. Compared to the genetic algorithm optimization, the proposed DDPG model achieved 3% more profit, 12% higher carbon dioxide (CO2) emissions, and 9% lower natural gas consumption. The DDPG solution was 57% faster than the GA. The results of the DDPG model proved that machine learning methods could outperform optimization in terms of optimality achievement and speed of solution. The DDPG improved the operation of energy storage units and was able to recognize the supply-demand operational pattern, ensuring the scalability of the VPP to cope with different energy demand levels.

Hybrid off-grid energy systems optimal sizing with integrated hydrogen storage based on deterministic balance approach.

The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods. Comparative analysis with HOMER Pro demonstrates a strong alignment of results, with deviations limited to a 5% margin, confirming the precision of our method in sizing determinations. Utilizing solar and wind data, our research includes a case study of Cairo International Airport, applying the DBM to actual energy demands.

Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using African vulture optimization algorithm.

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of African Vulture Optimization Algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (all sources), with significant reduction of 5.25% and 33.09% in cost (323318.21$/day) and emission (of 2433.95 Tons/day) respectively compared to the closest competitive method.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration.

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Real time implementation of scaled droop control in hybrid microgrid with hydrogen storage for regulation of voltage and frequency.

The incorporation of renewable energy resources (RERs) into smart city through hybrid microgrid (HMG) offers a sustainable solution for clean energy. The HMG architecture also involves linking the AC-microgrid and DC-microgrid through bidirectional interconnection converters (ICC). This HMG combines AC sources like wind-DFIG with DC sources such as solar PV and solid oxide fuel cell (SOFC), supported by battery energy storage systems (BESS) and hydrogen storage units (HSU). The HSU can generate and store hydrogen during RER surplus. This stored hydrogen can be further employed for production of electrical power along with numerous other applications. The HSU is emerged as a competent tool which can be utilised alone/in combination with BESS to enhance the system reliability. Harvesting power from clean and green sources requires its optimal operation and control while feeding to the existing grid. The existing strategies of controlling ICC are complex and not efficient; hence, a novel intelligent scaled droop control structure (SDCS) is proposed, utilizing frequency, DC voltage, and active power. The SDCS regulate voltage and frequency in both islanded mode (IM) and grid connected mode (GCM) of HMG. Experimental validation demonstrates its simplicity and effectiveness, making it suitable for smart city environments, ensuring uninterrupted power for critical loads with improved air quality.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system.

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I-V and P-V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.