Search Results (44)

Many electric vehicle (EV) batteries will retire in the next 5–10 years around the globe. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. The second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESSs) for the electric grid, which will also create a robust circular economy for EV batteries. This article proposes a two-layered energy management algorithm (monthly layer and daily layer) for demand charge reduction for an industrial consumer using photovoltaic (PV) panels and BESSs made of retired EV batteries. In the proposed algorithm, the monthly layer (ML) calculates the optimal dispatch for the whole month and feeds the output to the daily layer (DL), which optimizes the BESS dispatch, BESSs’ degradation, and energy imported/exported from/to the grid. The effectiveness of the proposed algorithm is tested as a case study of an industrial load using a real-world demand charge and Real-Time Pricing (RTP) tariff. Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer. Full article

The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, energy management, and control. This research presents a methodology and realization of a set of 11 BESS models based on different machine learning methods. The performance of the proposed models is tested using real-life BESS data, after which a comparative evaluation is presented. Based on the results achieved, a valuable discussion and conclusions about the models’ performance are made. This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. A Bayesian hyperparameter search is proposed and implemented for all of the included models. Among the main objectives of this study is to propose hyperparameter optimization for the included models, research the optimal training period for the available data, and find the best model from the ones included in the study. Additional objectives are to compare the test results of heterogeneous and homogeneous ensembles, and grid search vs. Bayesian hyperparameter optimizations. Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R² of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. CatBoost provides the best results, with an nRMSE of 0.662% for a 30-day training period, and offers competitive results for R²—0.772. This study underscores the significance of model selection and training period optimization for improving battery performance forecasting in energy management systems. The trained models or pipelines in this study could potentially serve as a foundation for transfer learning in future studies. Full article

This study includes a simulation of two variants of a 1 MW photovoltaic farm, differing in the types of photovoltaic modules used in the PVSyst program. The first uses monofacial modules, and the second uses bifacial. The studies showed an 8% increase in the energy obtained in the variant with bifacial modules, under the assumed simulation conditions. In the next stage, an environmental analysis was carried out using the Life Cycle Assessment (LCA) method with a “gate-to-gate” approach for the mass packaging process in three different variants, differing in the source of energy powering the machines in the SimaPro program. In the first variant, electricity from the national energy mix was used. In the second, in addition to energy from the same mix, natural gas was additionally used in the shrinking stage of the film. In the third variant, energy obtained from a previously designed photovoltaic farm was considered. The results showed an about 80% reduction in the carbon footprint of the tested process in the case of changing the energy source to energy from a PV installation. Full article

The aim of this study was the hydrothermal leaching of silver from waste monocrystalline silicon (m-Si) and polycrystalline silicon (p-Si) photovoltaic panel (PV) cells using organic acids, namely oxalic acid (OA) and citric acid (CA). Before leaching, two different pretreatment procedures were applied. First, the fluoropolymer backsheet was manually removed from the panel pieces and, then, the samples were subjected to high-temperature heating for the thermal degradation of the ethylene vinyl acetate (EVA) polymer. When removal by hand was not feasible, the second pretreatment procedure was followed by toluene immersion to remove the EVA and backsheet and separate the cells, glass, and films. After pretreatment, 4 M HCl leaching was applied to remove the aluminum layer from the cells. The remaining cells were subjected to hydrothermal leaching with organic acids to extract the silver. Several hydrothermal parameters were investigated, such as acid concentration (1-1.5-2 M), processing time (60-105-150 min), and temperature (150-180-210 °C), while the liquid-to-solid (L/S) ratio was fixed at 30 mL: 1 g, based on preliminary tests. Response surface methodology (RSM) was applied to optimize the hydrothermal leaching parameters. The optimized parameters were 210 °C, 95 min, 2 M CA or 210 °C, 60 min, 1 M OA. OA was more effective in Ag leaching than CA. The results were compared to HNO₃ leaching. The green leaching of silver from end-of-life PV panels with organic acids is an environmentally beneficial route. Full article

Due to global changes, interest in solar energy is increasing day by day. The share of solar energy in energy production is constantly increasing, replacing limited resources such as oil and gas, due to the fact that its source is inexhaustible and free and it does not emit CO₂. The increasing prevalence of photovoltaic (PV) technology has brought about the problem of disposing of end-of-life panels in an environmentally friendly manner. In this study, a two-stage system model was developed to estimate Türkiye’s PV panel waste amount up to 2050. First, a new Artificial Neural Network (ANN) model was developed to estimate Türkiye’s total PV panel installed power in the coming years. The performance of the ANN model was compared with PV panel installed power estimation data obtained using multiple regression analysis. In the second stage, a mathematical model was created to estimate the amount of PV module waste. In the waste potential estimations for both methods, end-of-life and early failure scenarios due to various reasons were taken into account. As a result of the study, it was found that Türkiye’s total waste potential aligns with the future projection data published by the International Energy Agency (IEA) and the International Renewable Energy Agency (IRENA). Full article

This paper shows an impact assessment of second-life batteries (SLBs) and local photovoltaics (PV) for decarbonizing enterprises through system digitalization and energy management. SLBs from electric vehicles offer a cost-effective and environmentally sustainable energy storage solution for enterprises. These systems can significantly reduce fossil fuel dependence coupled with local PV installations. This paper proposes a methodology for developing the complete digital twin of an enterprise in combination with an optimization algorithm for energy management. This methodology can be applied to a wide range of enterprises across different sectors, both industrial and non-industrial, with diverse consumption patterns. A sensitivity analysis has been carried out to evaluate the potential of this methodology for enterprises in different contexts, where different battery sizes, PV installations, consumption types, and environmental prioritization policies are encountered. Findings indicate that combining SLBs and PV installation, supported by digital energy management, can substantially reduce carbon footprints and operational costs. Full article

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder. Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

The growing demand for high-power DC fast-charging (DCFC) stations for electric vehicles (EVs) is expected to lead to increased peak power demand and a reduction in grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities, utilities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid, employing low-power interconnection rules and distributed energy resources (DERs). The system uses automotive second-life batteries (SLBs) and photovoltaic (PV) systems as energy buffer and local energy resources to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a design space exploration (DSE) and exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without losing generality, this study considers a 150 kW DCFC station with a range of DER sizes, grid interconnection specifications and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various realistic scenarios and strategies are defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance over a ten-year period for different scenarios is analyzed and compared using a multitude of metrics. The results of the analysis show that the the integration of DERs in DCFC stations has a positive impact on the economic value of the investment when compared to traditional installations. Full article

The potential of renewable energy sources to lower greenhouse gas emissions and lessen our reliance on fossil fuels has accelerated their integration globally, and especially that of solar photovoltaic (PV) systems. Malaysia has shown great progress in the adoption of photovoltaic systems thanks to its plentiful solar resources. On the other hand, energy storage systems (ESSs) are becoming more and more necessary in order to guarantee grid stability and fully realize the benefits of PV systems. This study attempts to assess the current condition of PV installations in Malaysia with an emphasis on their economic feasibility, regulatory compliance, technological capabilities, and compatibility with various energy storage technologies. Malaysian photovoltaic (PV) systems’ readiness to integrate energy storage systems (ESSs) using second-life electric vehicle batteries (SLEVBs) is examined in this article. Integrating PV systems with SLEVBs in residential ESSs shows economic viability, with a 15-year payback and 25% return on investment (ROI). Therefore, for every 1 MW of installed PV capacity, with ESS integration it is estimated to reduce approximately 3504 metric tons of CO₂ emissions annually in Malaysia. The homeowner benefits from large electricity bill savings, net metering revenue, and various incentives or financing alternatives that make the project financially attractive despite the extended payback time. Energy storage solutions are needed to improve grid stability, energy usage, and solar power generation in Malaysia as renewable energy adoption increases. Reusing retired EV batteries for stationary storage could solve environmental and economic issues. This study examines the feasibility, regulatory frameworks, and economic viability of combining second-life EV batteries with PV installations in Malaysia. Full article

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction. Full article

In this paper, we explore the integration of building information modeling (BIM) technology to assess carbon emissions, emphasizing the unique contributions to smart and sustainable approaches in prefabricated buildings and focusing on the application of digital construction strategies facilitated by BIM to evaluate carbon emissions in green prefabricated buildings, with a detailed case study on C-House at Southeast University, Nanjing, China. The research methodology involved creating a BIM model of C-House in Rhino and collecting data from the operationalization phase. This research work delves into analyzing the structural components, on-site assembling process, and evaluation of carbon emissions by using a BIM-based assessment, as well as the energy load and consumption of prefabricated components, including sustainable PV panels, to enhance building efficiency and sustainability. The findings uncover the life cycle of C-House, which spans seven stages, compared with the five stages of conventional builds. Currently in its third cycle, C-House exhibits significant reductions of 70.57% in carbon emissions during the second cycle and 43.53% in the first one. This highlights the pattern showing that the prolonged reuse of prefabricated buildings leads to decreasing emissions over time. Such results underscore the potential carbon emission reductions and environmental advantages of reusing green prefabricated buildings. Furthermore, this study provides insights into the entire life cycle of the building, from inception to occupation and post-phase performance evaluation. By employing BIM for modeling, simulation, and analysis, we offer practical insights into the application of smart technologies for sustainable construction practices, significantly contributing to the advancement of green and digital construction technologies. Full article

Life cycle inventories (LCIs) and life cycle assessments (LCAs) of photovoltaic (PV) modules and their components focus on the operations of PV factories, but the factories and industrial site product and construction stages are either not or only partially tackled. This work contributes through the bottom-up, model-based generation of LCIs and LCAs for setting up a vertically integrated 5 GWp/a PV industrial site, including the manufacturing of silicon ingots, wafers, solar cells, and PV modules, on a 50 ha greenfield location. Two comparative LCAs are performed. The first compares the annualized environmental impacts of the developed LCI sets with four existing inventories in the Ecoinvent v3.8 database. The second comparative LCA explores the environmental impact differences concerning the industrial site when using different building systems for the factories. Here, the reference system with a steel structure is compared with two alternative building systems: precast concrete and structural timber. The results show that the wafer, cell, and module factories’ annualized environmental impacts with the Ecoinvent LCIs are strongly overestimated. For the ingot factory, the opposite result is identified. The impacts of all four factories show reductions of between 11.7% and 94.3% for 14 of the 15 impact categories. High mean environmental impact shares of 79.0%, 78.2% and 79.2% for the steel, precast concrete and timber structural building systems, respectively, are generated at the product stage. The process and facilities equipment generates 54.2%, 54.4% and 58.2% of the total product and construction stages’ mean environmental impact shares. The proposed alternative timber building system reduces the environmental impacts in 14 of the 15 evaluated categories, with reductions ranging from 1.1% to 12.4%. Full article

Solar-based home PV systems are the most amazing eco-friendly energy innovations in the world, which are not only climate-friendly but also cost-effective solutions. The tropical environment of Malaysia makes it difficult to adopt photovoltaic (PV) systems because of the protracted rainy monsoon season, which makes PV systems useless without backup batteries. Large quantities of lithium-ion battery (LIB) trash are being produced by the electric vehicle (EV) sector. A total of 75% of the highest capacity levels have been discarded. By 2035, it is predicted that the wasted LIBs held as a result of expensive recycling and difficult material separation would carry up to 1200 GWh. An economical and sustainable option is offered by our study, which prototypes a replicated LIB pack that is incorporated into a PV home system. This study investigates the transformational power of second-life electric vehicle batteries (SLEVBs) when incorporated into home photovoltaic (PV) systems. The concept entails reusing existing electric vehicle batteries for stationary applications, offering a unique approach to extending the life of these batteries while meeting the growing need for sustainable domestic energy storage. The study looks at the technological feasibility, economic viability, and environmental effect of introducing SLEVBs into household PV systems, giving vital insight into their role in revolutionizing energy storage techniques and promoting sustainability. In comparison to the Lead–Acid Battery (LAB) system, the SLEVB system has a cheaper total cost of ownership, with savings of 12.62% compared with new LABs. A CO₂ emission reduction of at least 20% is achieved by using the SLEVB system compared with LABs. Electricity can be provided in houses in rural areas where there is no electricity. As a result, the security and superiority of the life of rural residents will improve. It is anticipated that the suggested strategy will lower EV pricing, enabling EV adoption for M40 and B40 groups. Consequently, the Malaysian and worldwide EV business will remain viable. Full article

Battery energy storage systems (BESSs) can overwhelm some of the environmental challenges of a low-carbon power sector through self-consumption with standalone photovoltaic (PV) systems. This solution can be adapted for different applications such as residential, commercial, and industrial uses. Furthermore, the option to employ second-life batteries derived from electric vehicles represents a promising opportunity for preserving the environment and improving the circular economy (CE) development. Nowadays, the industrial sector is progressively applying CE principles in their business strategies, and focusing on the potential positive consequences of CE eco-innovations on climate change mitigation. With the aim to promote the transition to an open-loop circular economy for automotive batteries, this study assesses and quantifies the potential environmental benefits resulting from the integration of a second-life battery-based BESS (SL-BESS) connected to an industrial machine. For this purpose, various scenarios involving the use of BESS, SL-BESS, and a standalone PV system are compared with a base case, where the machine is entirely powered by electricity from the grid. The examination of life cycle stages follows the life cycle assessment (LCA) cradle-to-grave methodology as outlined in ISO 14040:2006 and ISO 14044:2006/Amd 1:2017. Simapro^® 9 is utilized as the software platform. Results demonstrate that the combination of the SL-BESS with a standalone photovoltaic (PV) system represents the optimal solution in terms of global warming potential (GWP) reduction, with a saving of up to −74.8%. However, manufacturing and end-of-life stages of PV and batteries contribute to abiotic depletion and human toxicity, resulting from the use of chemicals and the extraction of resources essential for their manufacture. Indeed, when BESS is made of new batteries, it demonstrates the most significant impacts in terms of AD at 1.22 × 10⁻¹ kg Sb eq and human toxicity (HT) at 3.87 × 10³ kg 1,4-DB eq, primarily attributable to the manufacturing stages of both BESS and PV systems. The findings represent a significant breakthrough, highlighting the substantial capacity of incorporating SL-BESS alongside renewable energy sources to mitigate GWP resulting from industrial applications, and the criticality of repurposing decommissioned batteries from the automotive industry for secondary use. Full article