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The economic viability of renewable energy generation is vital for sustainability. Ensuring that optimal operation is always achieved, using energy management systems and control algorithms, is essential in this endeavor. Here, a new real-time pricing scheme, the Danish flexible pricing scheme, illustrates how residential PV and battery systems can optimize the electricity bill of households, without changing consumption behavior or providing grid services in exchange. This means that the only addition is PV production, storage, and control. A case study is constructed from Danish household consumption data, irradiance measurements, and recorded spot prices. With the input data, the pricing scheme, and the energy flow, simulation models are computed in MATLAB, thereby validating the algorithmic potential and finding the best strategy for charging and discharging the energy storage unit. Different methods are compared to list the viable options and evaluate them, based on the economic feasibility for the household. Furthermore, a discussion of the system implementation is also included to highlight technical difficulties, co-integration opportunities, short-comings, and advantages present in the case study. In conclusion, it is possible to make renewable energy generation, and storage, viable for a Danish residential household under the new pricing scheme.

Oculomotor nerve palsy can be a strong indicator of an internal carotid artery aneurysm, specifically of the posterior communicating artery, due to the anatomical positioning within the basal cisterns of the subarachnoid space, which allows for nerve compression. This condition can predict an acute subarachnoid haemorrhage, associated with a high mortality rate, often presenting with sudden, severe headaches, with or without deficits. The pathophysiology theories include direct mechanical compression by the aneurysmal sac, arterial pulsations, and nerve tissue oedema from venous obstruction. In cases of unruptured aneurysms, nerve irritation is less common. This palsy occurs in about one-third of cases. Aneurysmal orientation can vary (lateral, superior, inferior, medial, posterior), and due to the nerve’s anatomical position, aneurysms in the postero-lateral-inferior position are more likely to cause nerve damage. Treatment opinions vary from observation to intervention, with options including traditional neurosurgery and endovascular embolization. The latter is increasingly preferred due to its efficacy and lower risk profile. A clinical case involves a 56-year-old female presenting with ptosis, diplopia, and ipsilateral mydriasis. Imaging confirmed a posterior communicating artery aneurysm with a maximum diameter of 5.3 mm and a 3 mm bleb. Endovascular coiling was performed successfully, excluding the aneurysm from circulation without complications. Post-treatment, the patient was discharged in good condition with recommendations for periodic monitoring. In conclusion, while no single best treatment exists for posterior communicating artery aneurysms with oculomotor nerve palsy, early intervention (surgical or endovascular) and patient monitoring are crucial for preventing complications and ensuring quality of life.

The intermittent nature of power generation from photovoltaics (PV) requires reliable energy storage solutions. Using the storage system outdoors exposes it to variable temperatures, affecting both its storage capacity and lifespan. Utilizing and optimizing energy storage considering climatic variations and new storage technologies is still a research gap. Therefore, this paper presents a modified sizing algorithm based on the Golden Section Search method, aimed at optimizing the number of cells in an energy storage unit, with a specific focus on the unique conditions of Denmark. The considered energy storage solutions are Lithium-ion capacitors (LiCs) and Lithium-ion batteries (LiBs), which are tested under different temperatures and C-rates rates. The algorithm aims to maximize the number of autonomy cycles—defined as periods during which the system operates independently of the grid, marked by intervals between two consecutive 0% State of Charge (SoC) occurrences. Testing scenarios include dynamic temperature and dynamic load, constant temperature at 25 °C, and constant load, considering irradiation and temperature effects and cell capacity fading over a decade. A comparative analysis reveals that, on average, the LiC storage is sized at 70–80% of the LiB storage across various scenarios. Notably, under a constant-temperature scenario, the degradation rate accelerates, particularly for LiBs. By leveraging the modified Golden Section Search algorithm, this study provides an efficient approach to the sizing problem, optimizing the number of cells and thus offering a potential solution for energy storage in off-grid PV systems.

Nowadays, the penetration of inverter-based energy resources is continuously increasing in low-voltage distribution grids. Their applications cover traditional renewable energy production and energy storage but also new applications such as charging points for electric vehicles, heat pumps, electrolyzers, etc. The power ratings range from a couple of kW to hundreds of kW. Utilities have, in the last few years, reported more challenges regarding power quality in distribution grids, e.g., high harmonic content, high unbalances, large voltage and frequency excursions, etc. Phase-Lock-Loop (PLL) algorithms are typically used for grid synchronization and decoupled control of power converters connected to the grid. Most of the research within PLLs is mainly focusing on grid voltage angle estimation while the byproducts of the algorithms, e.g., frequency and voltage magnitude, are often overlooked. However, both frequency and voltage magnitude estimations are crucial for grid code compliance. Practical considerations for implementation on microcontroller boards of these algorithms are also missing in most of the cases. The present paper proposes a modified PLL algorithm based on a Synchronous Reference Frame that is suitable for both grid synchronization and frequency monitoring, i.e., the estimation of RMS phase voltages and frequencies in highly distorted distribution grids. It also provides the tuning methodology and practical considerations for implementation on commercial DSP boards. The performance of the proposed approach is assessed through simulation studies and laboratory tests under a wide range of operational conditions, showing that the proposed PLL can estimate the grid frequency, for all considered grid events, with an accuracy of less than ±5 mHz, which is a significant improvement on the current state-of-the-art solutions, having an accuracy of at least ±20 mHz or more.

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.

The modular multilevel converter is considered the state-of-the-art power electronic solution for high-power high-voltage applications that require a DC stage and that operate with fixed frequency at their AC terminals. However, in high-power AC-AC variable-frequency applications, it has been proved that this converter topology presents an unnecessarily large number of components and an intolerably high submodule-capacitor voltage ripple at low frequencies. The modular multilevel matrix converter was proposed as a promising alternative for high-power medium-voltage electrical-machine drives since it presented improved performance at low frequencies and a reduced number of components in comparison to the modular multilevel converter resulting in reduced costs and improved efficiency. Even though, the modular multilevel matrix converter is one of the converter solutions that stands out the most for high-power machine drives, it presents some operational drawbacks such the unstable behavior under unbalanced grid conditions and the extremely complex internal control and modulation. Recently, a new converter topology named modular multilevel series converter was proposed presenting attractive operational characteristics such as the high performance at low frequencies, high performance under unbalanced grid conditions, and straightforward control and modulation, which are important advantages compared to the modular multilevel converter and the modular multilevel matrix converter. Nonetheless, as demonstrated in this paper, the modular multilevel series converter presents large component count and poor efficiency. Thus, in this paper, a new converter solution is proposed, which is an enhanced version of the modular multilevel series converter that preserves the previously mentioned advantageous operational characteristics and that presents an improved efficiency and a considerably reduced number of components in comparison to the other three converter topologies, resulting in reduced cost, size and weight.

This paper presents a new multilevel converter solution with modular structure and hybrid energy-storage integration suitable to drive modern/future high-power medium-voltage wind turbines. The hybrid energy-storage integration means that part of the converter submodules are built with batteries and part of them with conventional capacitors. Since traditional wind turbines are non-dispatchable generators, the integration of an energy storage system could be beneficial in multiple ways as the wind power plant could provide stability support to the grid, improvement of the unit commitment and economic dispatch, and the power plant owner could increase his revenues in the electricity market. The capacitors of the proposed converter are responsible to transfer the power produced by the wind turbine to the grid, and the batteries are only charged/discharged with the mismatch between the power produced by the turbine and the power to be injected into the grid, considering a dispatchable operation where the power injected into the grid is different from the power generated by the turbine. The medium-voltage structure could be an interesting option to overcome problems related to high currents in modern/future high-power wind turbines resulting in more efficient, more compact and lighter solutions. Modular multilevel converters are suitable to handle medium-voltage levels and they allow for a straightforward integration of energy storage systems in a decentralized manner.

Instead of storing the energy produced by photovoltaic panels in batteries for later use to power electric loads, green hydrogen can also be produced and used in transportation, heating, and as a natural gas alternative. Green hydrogen is produced in a process called electrolysis. Generally, the electrolyser can generate hydrogen from a fluctuating power supply such as renewables. However, due to the startup time of the electrolyser and electrolyser degradation accelerated by multiple shutdowns, an idle mode is required. When in idle mode, the electrolyser uses 10% of the rated electrolyser load. An energy management system (EMS) shall be applied, where a storage technology such as a lithium-ion capacitor or lithium-ion battery is used. This paper uses a state-machine EMS of PV microgrid for green hydrogen production and energy storage to manage the hydrogen production during the morning from solar power and in the night using the stored energy in the energy storage, which is sized for different scenarios using a lithium-ion capacitor and lithium-ion battery. The mission profile and life expectancy of the lithium-ion capacitor and lithium-ion battery are evaluated considering the system’s local irradiance and temperature conditions in the Australian climate. A tradeoff between storage size and cutoffs of hydrogen production as variables of the cost function is evaluated for different scenarios. The lithium-ion capacitor and lithium-ion battery are compared for each tested scenario for an optimum lifetime. It was found that a lithium-ion battery on average is 140% oversized compared to a lithium-ion capacitor, but a lithium-ion capacitor has a smaller remaining capacity of 80.2% after ten years of operation due to its higher calendar aging, while LiB has 86%. It was also noticed that LiB is more affected by cycling aging while LiC is affected by calendar aging. However, the average internal resistance after 10 years for the lithium-ion capacitor is 264% of the initial internal resistance, while for lithium-ion battery is 346%, making lithium-ion capacitor a better candidate for energy storage if it is used for grid regulation, as it requires maintaining a lower internal resistance over the lifetime of the storage.

Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The proposed approach trains the shallow classifier in approximately 13 s with 95.5% testing accuracy. The approach is validated by manually extracting thermograph features and through the transfer of learned deep neural network approaches in terms of accuracy and speed. The proposed method is also compared with other existing methods.

PV and battery systems have been widely deployed in residential applications due to increasing environmental concerns and fossil energy prices. Energy management strategies play an important role in reducing energy bills and maximize profits. This paper first reviews the state of energy management problems, including commonly used objectives, constraints, and solutions for PV and battery applications. Then, a comparative study of different energy management strategies is conducted based on a real applied product and household profile. Moreover, results are discussed, and suggestions are given for different scenarios. Finally, conclusions and insights into future directions are also provided.

In order to provide Internet access to rural areas and places without a reliable economic electricity grid, self-sustainable drone-based cellular networks have recently been presented. However, the difficulties of power consumption and mission planning lead to the challenge of optimal sizing of the power supply for future cellular telecommunication networks. In order to deal with this challenge, this paper presents an optimal approach for sizing the photovoltaic (PV)-battery power supply for drone-based cellular networks in remote areas. The main objective of the suggested approach is to minimize the total cost, including the capital and operational expenditures. The suggested framework is applied to an off-grid cellular telecommunication network with drone-based base stations that are powered by PV-battery systems-based recharging sites in a rural location. The PV-battery system is optimally designed for three recharging sites with three different power consumption profiles with different peak and cumulative loads. Results show that the optimal design of the PV-battery system is dependent on geographical data, solar irradiation, and ambient temperature, which affect the output power of the PV system, as well as the power consumption profile, which affects the required number of PV panels and battery capacity.

The penetration of solar energy in the modern power system is still increasing with a fast growth rate after long development due to reduced environmental impact and ever-decreasing photovoltaic panel cost. Meanwhile, distribution networks have to deal with a huge amount and frequent fluctuations of power due to the intermittent nature of solar energy, which influences the grid stability and could cause a voltage rise in the low-voltage grid. In order to reduce these fluctuations and ensure a stable and reliable power supply, energy storage systems are introduced, as they can absorb or release energy on demand, which provides more control flexibility for PV systems. At present, storage technologies are still under development and integrated in renewable applications, especially in smart grids, where lowering the cost and enhancing the reliability are the main tasks. This study reviews and discusses several active power control strategies for hybrid PV and energy storage systems that deliver ancillary services for grid support. The technological advancements and developments of energy storage systems in grid-tied PV applications are also reviewed.

Partial shading affects the energy harvested from photovoltaic (PV) modules, leading to a mismatch in PV systems and causing energy losses. For this purpose, differential power processing (DPP) converters are the emerging power electronic-based topologies used to address the mismatch issues. Normally, PV modules are connected in series and DPP converters are used to extract the power from these PV modules by only processing the fraction of power called mismatched power. In this work, a switched-capacitor-inductor (SCL)-based DPP converter is presented, which mitigates the non-ideal conditions in solar PV systems. A proposed SCL-based DPP technique utilizes a simple control strategy to extract the maximum power from the partially shaded PV modules by only processing a fraction of the power. Furthermore, an operational principle and loss analysis for the proposed converter is presented. The proposed topology is examined and compared with the traditional bypass diode technique through simulations and experimental tests. The efficiency of the proposed DPP is validated by the experiment and simulation. The results demonstrate the performance in terms of higher energy yield without bypassing the low-producing PV module by using a simple control. The results indicate that achieved efficiency is higher than 98% under severe mismatch (higher than 50%).

Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in a self-sufficient and cost-effective manner. This paper presents an optimal method for designing a photovoltaic (PV)-battery system to supply base stations in cellular networks. A systematic approach is proposed for determining the power rating of the photovoltaic generator and battery capacity from a technical and economical point of view in order to minimize investment cost as well as operational expenditure, while the power autonomy of the PV-battery system is maximized in a multi-objective optimization framework. The proposed method is applied to optimally size a photovoltaic-battery system for three cases with different availability of solar power to investigate the effect of environmental conditions. Problem-solving using the proposed approach leads to a set of solutions at different costs versus different levels of power autonomy. According to the importance of each criterion and the preference of decision-makers, one of the achieved solutions can be selected for the implementation of the photovoltaic-battery system to supply base stations in cellular networks.

The modular multilevel converter is capable to reach high-voltage levels with high flexibility, high reliability, and high power quality as it became the standard solution for high-power high-voltage applications that operate with fixed frequency. However, in machine-drive applications, the modular multilevel converter shows critical problems since an extremely high submodule-capacitor voltage ripple occurs in the machine start-up and at low-speed operation, which can damage the converter. Recently, a new converter solution named modular multilevel series converter was proposed as a promising alternative for high-power machine-drive applications since it presented many important structural and operational advantages in relation to the modular multilevel converter such as the reduced number of submodule capacitors and the low submodule-capacitor voltage ripple at low frequencies. Even though the modular multilevel series converter presented a reduced number of capacitors, the size of these capacitors was not analyzed. This paper presents a detailed comparison analysis of the performance of the modular multilevel converter and the modular multilevel series converter at variable-frequency operation, which is based on the proposed analytical description of the submodule-capacitor voltage ripple in such topologies. This analysis concludes that the new modular multilevel series converter can be designed with smaller capacitors in comparison to the modular multilevel converter if these converters are used to drive electrical machines that operate within a range of low-frequency values. In other words, the modular multilevel series converter experiences extremely low submodule-capacitor voltage ripple at very low frequencies, which means that this converter solution presents high performance in the electrical machine start-up and at low-speed operation.

In transformerless grid-connected photovoltaic (PV) systems, leakage currents should be properly addressed. The voltage fluctuations between the neutral point of the grid and the PV array, i.e., common-mode voltage (CMV), will affect the value of the leakage currents. Therefore, the leakage currents can be attenuated through proper control of the CMV. The CMV depends on the converter topology and the modulation strategy. For the quasi-Z-source inverter (qZSI), the amplitude of the high-frequency components in the CMV increases due to the extra shoot-through (ST) state. The CMV reduction strategies for the conventional voltage source inverter (VSI) should be modified when applied to the qZSI. In this paper, an input-split-inductor qZSI is introduced to reduce the CMV, in which all the CMV reduction strategies for the VSI can be used directly with appropriate ST state insertion. Moreover, the proposed method can be extended to impedance source converters with a similar structure. Simulations and experimental tests demonstrate the effectiveness of the proposed strategy for the qZSI in terms of CMV reduction.

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.

Due to its scalability, reliability, high power quality and flexibility, the modular multilevel converter is the standard solution for high-power high-voltage applications in which an AC–DC–AC connection is required such as high-voltage direct-current transmission systems. However, this converter presents some undesired features from both structural and operational perspectives. For example, it presents a high number of components, which results in high costs, size, weight and conduction losses. Moreover, the modular multilevel converter presents problems dealing with DC-side faults, with unbalanced grid conditions, and many internal control loops are required for its proper operation. In variable-frequency operation, the modular multilevel converter presents some serious limitations. The most critical are the high-voltage ripples, in the submodule capacitors, at low frequencies. Thus, many different AC–AC converter solutions, with modular multilevel structure, have been proposed as alternatives for high-power machine-drive applications such as offshore wind turbines, pumped-hydro-storage systems and industrial motor drives. These converters present their own drawbacks mostly related to control complexity, operational limitations, size and weight. This paper introduces an entirely new medium-voltage AC–AC modular multilevel converter solution with many operational and structural advantages in comparison to the modular multilevel converter and other alternative topologies. The proposed converter presents high performance at low frequencies, regarding the amplitude of the voltage ripples in the submodule capacitors, which could make it very suitable for machine-drive applications. In this paper, an analytical description of the voltage ripples in the submodule capacitors is proposed, which proves the high performance of the converter under low-frequency operation. Moreover, the proposed converter presents high performance under unbalanced grid conditions. This important feature is demonstrated through simulation results. The converter solution introduced in this paper has a simple structure, with decoupled phases, which leads to the absence of undesired circulating currents and to a straightforward control, with very few internal control loops for its proper operation, and with simple modulation. Since the converter phases are decoupled, no arm inductors are required, which contributes to the weight and size reduction of the topology. In this paper, a detailed comparison analysis with the modular multilevel converter is presented based on number of components, conduction and switching losses. This analysis concludes that the proposed converter solution presents a reduction in costs and an expressive reduction in size and weight, in comparison to the modular multilevel converter. Thus, it should be a promising solution for high-power machine-drive applications that require compactness and lightness such as offshore wind turbines. In this paper, simulation results are presented explaining the behavior of the proposed converter, proving that it is capable of synthesizing a high-power-quality load voltage, with variable frequency, while exchanging power with the grid. Thus, this topology could be used to control the machine speed in a machine-drive application. Finally, experimental results are provided to validate the topology.

Photovoltaic (PV) energy has been competitive in power generation as an alternative to fossil energy resources over the past decades. The installation of grid-connected solar energy systems is expected to increase rapidly with the fast development of the power electronics technology. As the key to the interface of the PV energy and the grid, power converters should be reliable, efficient and comply with the grid requirements. Considering the nature of PV energy, the power conversion should be flexible (e.g., high step-up DC-DC conversion and harmonic-free DC-AC conversion). Accordingly, many power electronic converters have been reported in literature. Compared with isolated inverters, transformerless inverters show great advantages. This paper thus presents an overview of the transformerless step-up single-phase inverters for PV applications based on the dc-link configurations. Grid-connected PV inverters are classified as constant dc-link voltage structures, pseudo-dc-link voltage structures, pulsating dc-link voltage structures and integrated dc-link voltage structures. The discussion on the composition of different dc-link structures is presented, which provides guidance to select appropriate transformerless inverter topologies for PV applications.