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.

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.

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.

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.

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.

A review on life cycle environmental impacts of emerging solar cells

The development of solar technologies requires increased efficiency in converting solar radiation to energy, as well as innovative materials and structure to go beyond the conventional power conversion ratio. In line with these innovations, there are concerns about greenhouse gas emissions of the solar cells, materials for the solar technologies and other relevant environmental impacts of the manufacturing processes. This review is conducted on life cycle assessments of solar cells, considering the climate change and natural resource shortage context. It is identified that the majority of existing life cycle assessments on solar cells take into account four typical environmental impacts: energy consumption, greenhouse gas emissions, material depletion, and toxicity. Though the diverse methodological aspects make it difficult to directly compare these environmental impacts among various types of solar cells, the obtained results hinder that emerging solar cells such as perovskite solar cells or tandem solar cells are likely to have better environmental profiles than conventional silicon based and thin film solar cells, in terms of energy consumption, greenhouse gas emissions and material consumption. However, the emerging solar cells may utilize toxic materials in which their eco-toxicity and human toxicity should be further considered during the design of the technologies. Moreover, it is identified that the energy and environmental hotspot lies in the manufacturing process, regardless of impact indicators and types of solar cells.

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.

An improved numerical model to predict the operating temperature and efficiency of solar photovoltaic systems

Solar photovoltaic (PV) technology has a huge potential for producing renewable energy and reducing greenhouse gas emissions. An increase in the PV cell temperature in real operating conditions reduces the actual output of a solar PV system. A 1D transient multi-layered model, based on the fundamentals of the finite difference method, has been developed to predict the operating cell temperature. Since a PV system operates in stochastic wind conditions and is not subjected to any predefined thermal boundary condition, several expressions of convection coefficient have been scientifically analyzed to determine the most suitable expression. The novel calculation approach assumes explicit radiation terms and implicit convection terms to linearize the equations and get rid of any iterative process. Comparison with experimental results shows that the convection coefficient derived from boundary layer theory corresponding to uniform heat flux predicts the cell temperature with the best accuracy showing a mean error of only [Formula: see text] and [Formula: see text]. Splitting the heat source across different solar PV layers produces a maximum change of [Formula: see text] only and can be avoided due to the involved complexity. The study proposes a new piece-wise function for PV efficiency in terms of cell temperature and irradiation. This novel function predicts PV efficiency on a sunny and a cloudy day with [Formula: see text] and [Formula: see text] mean errors, respectively, which are considerably lower than errors obtained using other popular functions in the literature. The model helps in predicting actual output from a PV system more accurately which should enable taking more informed decisions regarding the location of installation, PV technology, and the need for a cooling method.

Power enhanced solar PV array configuration based on calcudoku puzzle pattern for partial shaded PV system

The power output of solar photovoltaic systems can be affected by environmental factors, such as partial shading. This can lead to a decrease in the power conversion rate of the system. Although existing solutions for this issue are cost-effective and efficient, new solutions could further improve the system's performance by increasing consistency, power generation, and reducing mismatch loss and costs. To address this, a new method for configuring PV arrays was proposed using the calcudoku puzzle pattern. The performance of this new array configuration was evaluated in MATLAB/Simulink® for a 9 × 9 PV array and compared to conventional methods like Series-parallel, Total Cross Tied (TCT), and Sudoku array configurations. The performance was evaluated under eight different shading patterns based on power conversion rate and mismatch losses between the PV rows. The proposed array configuration resulted in 3.9%-13.3% of mismatch losses across the different shading patterns, while other configurations had a minimum of 13.8% to a maximum of 51.9% of mismatch losses. This reduction in mismatch losses directly improved the power conversion rate of the PV array.

How does satisfaction of solar PV users enhance their trust in the power grid? - Evidence from PPAPs in rural China

BACKGROUND

Photovoltaic Poverty Alleviation Projects (PPAPs) have been implemented in Chinese rural areas since 2014. As a new energy policy, PPAPs have played an important role in alleviating rural poverty. However, the adoption of solar PV faces multiple barriers from the perspective of beneficiaries. Therefore, this study aims to discuss and analyze factors affecting beneficiaries' satisfaction and their trust in State Grid, promoting the adoption of solar PV.

METHODS

Based on the integrated American Customer Satisfaction Index (ACSI) and Unified Theory of Acception and Use of Technology (UTAUT) model, this study used the Structural Equation Model (SEM) to reveal how the beneficiaries' satisfaction enhance their trust in State Grid. The data were obtained from a survey of 928 PPAPs' beneficiaries by stratified and random sampling in Chinese rural areas.

RESULTS

The results confirm that environmental perception in this study has positive impact on beneficiaries' satisfaction. In addition, perceived quality also has a positive effect on beneficiaries' satisfaction and trust in State Grid; however, social influence has a negative impact on beneficiaries' satisfaction; behavior expectation can directly promote beneficiaries' satisfaction while indirectly propel their trust in State Grid.

CONCLUSIONS

This study constructs an integrated customer satisfaction model from the perspective of beneficiaries and proposes relevant measures to promote the adoption of solar PV that can be applied to poverty reduction in other developing countries worldwide.

Reconfigurable solar photovoltaic systems: A review

Even though solar power generation has become an emerging trend in the world, its penetration into the utility grid as a distributed generation source is not a satisfactory measure due to the inherent issues related to solar photovoltaic systems (SPVSs). In addressing these issues, microgrids have been identified as suitable integrating platforms for distributed, clean energy resources such as SPV. Different SPV and microgrid architectures are available for different applications depending on the resource availability and controllability. Reconfigurability is a concept that makes a system adaptable to two or more different environments by effectively utilizing the available resources. The review explains the applications of reconfigurable approaches on solar PV systems such as reconfigurable PV arrays, power conditioning unit (DC/DC converter, DC/AC inverter), microgrid controller and topology of distribution network with relevant studies. An analysis is also presented considering the unique features of reconfigurable systems in comparison to the static systems.

Dataset from the zero-energy log house project

In this data article, we present a supplementary dataset from a zero-energy log house project in southern Finland, presented in detail in [1]. This article consists of comprehensive energy-related data collected in practice from several sources from the house during the period of 2017-2019. The data include solar PV production data of two separate systems, in south and east-west directions. The solar PV data are presented on a different time scale to demonstrate the operation of two installations in different seasonal conditions. Simulated results are also included. The electrical energy consumption is distributed between the consumer, ventilation, the ground source heat pump for space heating and the domestic hot water energy at the monthly level. The realized electrical energy prices, self-sufficiency rates, and costs are also presented at the monthly level. The heat production of the ground source heat pump is estimated according to the service hour data and the performance data given by the manufacturer. The data can be applied in new and building-under-renovation projects.

The impact of air pollutant deposition on solar energy system efficiency: An approach to estimate PV soiling effects with the Community Multiscale Air Quality (CMAQ) model

Deposition and accumulation of aerosol particles on photovoltaics (PV) panels, which is commonly referred to as "soiling of PV panels," impacts the performance of the PV energy system. It is desirable to estimate the soiling effect at different locations and times for modeling the PV system performance and devising cost-effective mitigation. This study presents an approach to estimate the soiling effect by utilizing particulate matter (PM) dry deposition estimates from air quality model simulations. The Community Multiscale Air Quality (CMAQ) modeling system used in this study was developed by the U.S. Environmental Protection Agency (U.S. EPA) for air quality assessments, rule-making, and research. Three deposition estimates based on different surface roughness length parameters assumed in CMAQ were used to illustrate the soling effect in different land-use types. The results were analyzed for three locations in the U.S. for year 2011. One urban and one suburban location in Colorado were selected because there have been field measurements of particle deposition on solar panels and analysis on the consequent soiling effect performed at these locations. The third location is a coastal city in Texas, the City of Brownsville. These three locations have distinct ambient environments. CMAQ underestimates particle deposition by 40% to 80% when compared to the field measurements at the two sites in Colorado due to the underestimations in both the ambient PM₁₀ concentration and deposition velocity. The estimated panel transmittance sensitivity due to the deposited particles is higher than the sensitivity obtained from the measurements in Colorado. The final soiling effect, which is transmittance loss, is estimated as 3.17 ± 4.20% for the Texas site, 0.45 ± 0.33%, and 0.31 ± 0.25% for the Colorado sites. Although the numbers are lower compared to the measurements in Colorado, the results are comparable with the soiling effects observed in U.S.