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This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. [ABSTRACT FROM AUTHOR]

The efficient utilization of low-cost carbon feedstocks, such as municipal solid waste (MSW), in biorefineries has become increasingly important for reducing GHG emissions and meeting the growing demand for renewable energy sources. However, MSW as a feedstock presents several challenges, including high moisture content, compositional variability, particle size and shape, density, and ash content. To address these challenges, the potential of mechanical dewatering and high-moisture pelleting processes for densifying MSW fractions, such as paper, cardboard, thin plastic, and thick plastic, into low-cost carbon feedstocks with improved handling and conversion properties were investigated. The effect of these preprocessing technologies on the critical quality attributes (CQAs) of the resulting pellets, including bulk density, durability, and size uniformity, were evaluated. The results showed that with these preprocessing technologies, the paper and cardboard fractions could be pelleted at moisture contents over 40% (w.b.) while achieving >99% durability and >300 kg/m3, while the high moisture plastic fractions were not suitable for pelleting. The thick plastic fraction processed in a screw press was shown to remove up to 30% of the moisture content in a single pass. These findings suggest that these mechanical preprocessing technologies can improve the physical properties of low-cost municipal solid waste fractions for biofuels production. [ABSTRACT FROM AUTHOR]

Floating wind is becoming an essential part of renewable energy, and so highlighting perspectives of developing floating wind platforms is very important. In this paper, we focus on floating wind concepts and projects around the world, which will show the reader what is going on with the projects globally, and will also provide insight into the concepts and their corresponding related aspects. The main aim of this work is to classify floating wind concepts in terms of their number and manufacturing material, and to classify the floating wind projects in terms of their power capacity, their number, character (if they are installed or planned) and the corresponding continents and countries where they are based. We will classify the corresponding additional available data that corresponds to some of these projects, with reference to their costs, wind speeds, water depths, and distances to shore. In addition, the floating wind global situation and its corresponding aspects of relevance will be also covered in detail throughout the paper. [ABSTRACT FROM AUTHOR]

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a methodology for the implementation of edge intelligence for predicting solar panel performances on wireless sensor nodes. The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

The European Union, in pursuit of the goal of reducing emissions by at least 55% by 2030 and achieving climate neutrality by 2050, is deploying different actions, with industry decarbonization as a key strategy. However, increasing electricity demand requires an intensification of energy generation from clean technologies, and the energy system's expansion is hindered by renewable generation's climatic dependencies and the imperative for substantial electrical infrastructure investments. Although the transmission grid is expected to grow, flexibility mechanisms and innovative technologies need to be applied to avoid an overwhelming growth. In this context, this paper presents a thorough assessment, conducted within the FLEXINDUSTRIES project, of the flexibility potential across seven energy-intensive industries (automotive industry, biofuel production, polymer manufacturing, steel manufacturing, paper mills, pharmaceutical industry, and cement production). The methodology followed during the analysis entails reviewing the state-of-the-art existing flexibility mechanisms, industries' energy markets engagement, and technical/operational readiness. The results highlight the feasibility of the proposed actions for enabling energy market flexibility through demand-response programs, quantifying energy opportunities, and pinpointing regulatory and technical barriers. [ABSTRACT FROM AUTHOR]

The transition process from fossil fuels to environmentally friendly renewable energy sources carries the risk of creating new environmental damages. Photovoltaic technology represents one of the alternatives with the least risk of harmful environmental impact. However, this technology has two important drawbacks: the significant land occupation for the installation of PV systems and the uncontrollability of production. By constructing floating photovoltaic plants on hydroelectric reservoirs, both of these problems can be reduced to an acceptable level. Some artificial reservoirs, originally built for hydroelectric power plants, have acquired a significant secondary function as recreational areas and fish breeding sites. Therefore, there is justified resistance from the local community to change the existing appearance and purpose of such reservoirs. This paper proposes a completely new concept of integrating the interests of the local community into such objects. In addition to preserving existing uses, the concept also offers new features. This can make the entire system environmentally friendly and sustainable. This paper details the technology behind the construction of floating photovoltaic power plants on artificial reservoirs and emphasizes their various advantages. These benefits include the non-utilization of cultivable land, the ease of assembly and construction, integration into existing power grids, and the potential to address electricity storage issues. For instance, Buško Lake, covering an area of 55.8 km2, may host 2.93 km2 of installed floating photovoltaic (FPV) facilities, enabling a total installed capacity of 240 MW. With an average of 5.5 h of daily sunshine, this totals 2007 annual hours, equivalent to a 55 MW thermal power plant. An analysis showed that, with losses of 18.2%, the average annual production stands at 302 G W h , translating to an annual production value of 18 million € at 60 €/MWh. The integration of this production into an existing hydroelectric power plant featuring an artificial reservoir might boost its output by 91%. The available transmission line capacity of 237 MW is shared between the hydroelectric power plant (HPP) and FPV; hence during the FPV maximum power generation time, the HPP halts its production. HPP Orlovac operates a small number of hours annually at full capacity (1489 h); therefore in combination with the FPV, this number can be increased to 2852 h. This integration maintains the lake's functions in tourism and fishing while expanding its capabilities without environmental harm. [ABSTRACT FROM AUTHOR]

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade. [ABSTRACT FROM AUTHOR]

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

The article discusses the process of biomass pyrolysis and the importance of kinetic modeling in understanding and optimizing this process. Pyrolysis is a thermal conversion method that can produce solid and liquid biofuels, as well as raw materials for bio-based products. The distribution and quality of the products depend on the properties of the biomass and the thermal conditions used. However, there are challenges in accurately determining the kinetics of pyrolysis reactions due to factors such as the variability of biomass composition and the presence of catalysts. The article also provides an overview of nine research papers published in a special issue of the journal Processes, which cover topics such as the devolatilization behavior of different biomass feedstocks, the effects of torrefaction pretreatment, and the influence of catalysts on conversion dynamics. [Extracted from the article]

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter's operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter's application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter's efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter's performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter's ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter's potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid. [ABSTRACT FROM AUTHOR]

In recent years, the interest in multifunctional façade (MF) technology has increased significantly. Recent advances in the integration of active and passive technologies have led to a new concept of building skins with highly flexible and decentralized control. Such an approach is considered capable of tackling environmental challenges and enhancing indoor environmental quality (IEQ). Integrated HVAC systems, dynamic blinds, and renewable energy systems can drastically increase façade responsiveness and efficiency. Although the technical feasibility of active and MF technology has already been demonstrated, market applications are still limited. The goal of this paper is to define the state of the art of MFs and clarify how the integration of IoT technologies, supported by AI, can increase market interest by fully exploiting the value of these systems. Indeed, recent advances in the IoT and data analysis tools are opening up attractive scenarios in optimization process. Starting with an overview of the most interesting EU-funded projects, this paper presents a MF case study in which IoT infrastructures are fully integrated. The prototype, realized within the MEZeroE Horizon project, stimulates a debate on future trajectories (and gaps) for the marketability of MF. [ABSTRACT FROM AUTHOR]

Today's energy challenges are multifaceted. Over the past 30–40 years, energy issues have been discussed and published on an extensive scale. The green transition involves concrete actions related to increasing energy efficiency, replacing fossil fuels with alternative fuels, producing energy using renewable resources, creating various means of transport that use electric motors, identifying technical solutions that generate an increased energy yield in the case of buildings, and waste reduction, reuse and recycling. In order to attain a climate-neutral environment, it is mandatory to impose regulations, measures and actions to help decarbonize the energy sector. The analysis of published articles on these issues is the subject of this large and information-dense review. Concretely, the transition to climate neutrality will generate obvious advantages at an economic, social and technological level, for example, the opportunity for economic growth, new business models and new markets, and the generation of new jobs or technological development. At the same time, this paper underscores the need for a multifaceted approach, integrating technological innovation, policy intervention and global cooperation for an effective energy transformation. The review suggests future issues and research directions, focusing on viable strategies for energy transition and its socio-economic environmental impacts. [ABSTRACT FROM AUTHOR]

This document discusses the challenges of achieving high-power quality in modern power grids with a large share of distributed power production, particularly due to the integration of renewable energy sources (RES). The variable nature of RES can lead to disturbances and power quality degradation. The document highlights the importance of voltage source converters and control systems in enabling the flexible operation of RES. It also mentions the concept of microgrids, which are closely connected to power quality and other factors such as the Internet of Things and cyber security. The document includes eight research papers covering various topics related to power quality analysis and control of renewable energy sources in power grids. These topics include power factor correction, supercapacitor parameters identification, pulse width modulation, disturbance analysis, vibration analysis of motors, control of converters, phasor measurement units, and optimization of controller parameters. The document concludes by expressing gratitude to the authors, staff, and reviewers involved in the research papers. [Extracted from the article]

System strength is both supplied and demanded in a power system during normal operations and in the presence of disturbances. This is characterised by stable voltage and frequency, supporting renewable generation such as wind and solar. Because the retirement of synchronous generators reduces system strength supply, and the connection of new inverter-based resource (IBR) generators increases demand, there is an urgent need for new sources of system strength. This paper provides an overview of the challenges brought about by grid modernisation. It highlights tangible solutions provided by synchronous condensers (SCs) to bolster grid strength, stability, and reliability while accommodating the rising influx of renewable energy sources (RESs). Furthermore, this paper examines the role of SCs in improving weak grids, voltage control, power quality, short-circuit levels, and inertia management. It introduces the role of innovative hybrid synchronous condenser (HSC) systems to boost grid reliability and resilience. It also elaborates on the optimisation strategies for SC sizing, placement, and control and outlines economic aspects of their deployment. The review also highlights future directions and challenges in SC technology, emphasising the need for ongoing research and development to enhance system design and operation. [ABSTRACT FROM AUTHOR]

Accelerating the transition to a cleaner global energy system is essential for tackling the climate crisis, and green hydrogen energy systems hold significant promise for integrating renewable energy sources. This paper offers a thorough evaluation of green hydrogen's potential as a groundbreaking alternative to achieve near-zero greenhouse gas (GHG) emissions within a renewable energy framework. The paper explores current technological options and assesses the industry's present status alongside future challenges. It also includes an economic analysis to gauge the feasibility of integrating green hydrogen, providing a critical review of the current and future expectations for the levelized cost of hydrogen (LCOH). Depending on the geographic location and the technology employed, the LCOH for green hydrogen can range from as low as EUR 1.12/kg to as high as EUR 16.06/kg. Nonetheless, the findings suggest that green hydrogen could play a crucial role in reducing GHG emissions, particularly in hard-to-decarbonize sectors. A target LCOH of approximately EUR 1/kg by 2050 seems attainable, in some geographies. However, there are still significant hurdles to overcome before green hydrogen can become a cost-competitive alternative. Key challenges include the need for further technological advancements and the establishment of hydrogen policies to achieve cost reductions in electrolyzers, which are vital for green hydrogen production. [ABSTRACT FROM AUTHOR]

The integration of Distributed Energy Resources utilizing Renewable Energy Sources, Energy Storage Systems, and Information and Communication Technologies is transforming traditional energy systems into adaptable, flexible, and sustainable systems, with the Smart Grid concept playing a pivotal role. This paper surveys intelligent techniques and methods applied in various markets and applications, particularly focusing on their potential adaptation for negotiation processes in Smart Grid contexts. The negotiation mechanisms, crucial for prosumers who engage in real-time transactions, are analyzed with a focus on fuzzy logic tools, specifically q-Rung Orthopair Fuzzy Sets. These tools are evaluated for their capability to handle negotiation tasks and Multi-Criteria Decision-Making problems. The paper proposes a negotiation schema for healthcare buildings, especially hospitals, given their significant environmental impact, providing insight for future research. [ABSTRACT FROM AUTHOR]

This paper presents the design and performance of an energy subsystem (ES) dedicated to hybrid energy harvesters (HEHs): wave energy converters (WECs) combined with photovoltaic panels (PVPs). The considered ES is intended for compact HEHs powering autonomous end-node devices in distributed IoT networks. The designed ES was tested experimentally and evaluated in relation to the mobile and wireless distributed communication use case. The numerical evaluation was based on the balance of the harvested energy versus the energy consumed in the considered use case. The evaluation results proved that the ES ensured energy surplus over the considered IoT node consumption. It confirmed the proposed solution as convenient to the compact HEHs applied for sustainable IoT devices to power them with renewable energy harvested from light and sea waves. It was found that the proposed ES can provide the energy autonomy of the IoT end node and increase its reliability through a hybrid energy-harvesting approach. [ABSTRACT FROM AUTHOR]

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

The electrification of society, increasing renewable energy sources and mobility charging lead to new loading patterns for power transformers. Dynamic load conditions induce enhanced mechanical stress on the transformers' windings, potentially causing degradation of the solid insulation over time and compromising the transformer's short-circuit withstand capability. Thermal expansion of the windings, caused by losses in the copper conductors, occurring as the transformer is loaded, increases the stress. Conversely, magnetic losses in the core and tie plate expansion contribute to a reduction in stress. This paper presents the effect of step changes in core losses and copper losses by on-line measurements of the clamping pressure, to better understand the mechanical stresses acting upon the solid insulation cellulose materials. Energisation is found to decrease the clamping pressure following warming up of the transformer, and loading the transformer increased the pressure as the windings increased in temperature. The converse effect was found when unloading and de-energising. The on-line monitoring system provides a new and important step towards ensuring the short-circuit performance of power transformers. [ABSTRACT FROM AUTHOR]

The increasingly extensive use of non-linear loads, mostly including static power converters, in large industry, commercial, and domestic applications, as well as the spread of renewable energy sources in distribution-generated units, make the use of the most efficient power quality improvement systems a current concern. The use of active power filters proved to be the most advanced solution with the best compensation performance for harmonics, reactive power, and load unbalance. Thus, issues related to improving the power quality through active power filters are very topical and addressed by many researchers. This paper presents a topical review on the shunt active power filters in three-phase, three-wire systems. The power circuit and configurations of shunt active filtering systems are considered, including the multilevel topologies and use of advanced power semiconductor devices with lower switching losses and higher switching frequencies. Several compensation strategies, reference current generation methods, current control techniques, and DC-voltage control are pointed out and discussed. The direct power control method is also discussed. New advanced control methods that have better performance than conventional ones and gained attention in the recent literature are highlighted. The current state of renewable energy sources integration with shunt active power filters is analyzed. Concerns regarding the optimum placement and sizing of the active power filters in a given power network to reduce the investment costs are also presented. Trends and future developments are discussed at the end of this paper. For a rigorous substantiation, more than 250 publications on this topic, most of them very recent, constitute the basis of bibliographic references and can assist readers who are interested to explore the subject in greater detail. [ABSTRACT FROM AUTHOR]

Smart grids are a cornerstone of the transition to a decentralised, low-carbon energy system, which offer significant benefits, including increased reliability, improved energy efficiency, and seamless integration of renewable energy sources. However, ensuring the security and resilience of smart grids is paramount. Cyber attacks, physical disruptions, and other unforeseen threats pose a significant risk to the stability and functionality of the grid. This paper identifies the research gaps and technical hurdles that hinder the development of a robust and secure smart grid infrastructure. This paper addresses the critical gaps in smart grid security research, outlining the technical challenges and promising avenues for exploration by both the industry and academia. A novel framework designed to enhance the reliability and security of smart grids was proposed against cyber attacks, considering the interconnectedness of the physical and cyber components. The paper further explores future research trends and identifies the key open issues in the ongoing effort to strengthen the security and resilience of smart grids. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy sources and power electronic devices accessed in the distribution network, the harmonic pollution problem has become increasingly serious. The traditional centralized harmonic mitigation strategy has difficulty in effectively dealing with these scattered and random harmonics. Therefore, a distributed harmonic mitigation strategy based on the dynamic points incentive of blockchain communities is proposed in this paper. Firstly, a comprehensive voltage sensitivity partitioning method with harmonic weight differentiation is proposed to realize the reasonable partitioning of each control node and controlled node in the distribution network concerning variability in harmonic components and their distribution. Then, a harmonic mitigation strategy based on the dynamic integral excitation of self-learning algorithms is constructed to promote self-organized optimization and active distributed coordinated control of mitigation devices. The strategy ensures that the total harmonic voltage distortion rate of each node meets the requirements by adjusting the partitioned collaboration to realize optimal harmonic mitigation. By setting optimized partitions in different scenarios and conducting simulation verification, the results demonstrate the effectiveness of the strategy in this paper. It stimulates synergy between devices through a dynamic incentive mechanism and significantly reduces the total harmonic voltage distortion rate across various test scenarios, reflecting the adaptability of the harmonic mitigation method presented. [ABSTRACT FROM AUTHOR]

This paper investigates whether renewable energy influences economic growth and happiness. Using panel data from 25 European Union countries for the period 2012–2022, this study employs a panel model for estimation with fixed and random effects, and robust HAC standard errors. According to the research results, in general, the growing share of renewable energy in the energy mix has a positive impact on economic growth and the happiness of citizens. However, detailed research has shown that this effect depends on the type of energy; a significant positive impact was recorded only in solar share energy, wind share energy and economic growth. However, almost all types of renewable energy were included, i.e., biofuel, hydro, solar and other renewable share energy, and all had a significantly positive impact on the level of happiness. The exception was wind share energy, which showed a significant negative impact. The research findings of this paper provide empirical support for promoting renewable energy, which is positive both for economies and the happiness of citizens. It is one of the main aspects of sustainable economic growth. [ABSTRACT FROM AUTHOR]

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs. [ABSTRACT FROM AUTHOR]

One of the United Nations' 17 Sustainable Development Goals is to "Ensure access to affordable, sustainable, and clean energy for all." Additionally, the growing concerns about climate change and energy security have heightened the importance of exploring alternative energy sources to replace fossil fuels. The utilisation of agricultural waste for bioenergy production has acquired significant attention due to its potential to mitigate environmental impacts and provide renewable energy sources. However, the major obstacle to producing bioenergy is managing the supply chain while considering economic, environmental, and social factors in an optimal way. This paper presents a comprehensive overview of the literature on the management of agriculture waste supply chains, specifically related to the use of modelling and optimisation techniques for planning. The first section describes different stages of the supply chain and various technologies for converting biomass to bioenergy. This is followed by a synopsis of the literature reviewed based on decision levels, objective functions, modelling methodologies, and optimisation approaches. Finally, the review highlights limitations and gaps in current research and the areas with potential for further exploration. [ABSTRACT FROM AUTHOR]

As the demand for sustainable energy solutions grows, there is a critical requirement for continuous innovation to optimize the performance and safety of renewable energy systems (RESs). Closed-loop digital twins (CLDTs)—synchronized virtual replicas embedded with real-time data and control loops to mirror the behavior of physical systems—have emerged as a promising tool for achieving this goal. This paper presents a systematic literature review on the application of digital twin (DT) technology in the context of RESs with an emphasis on the impact of DTs on the efficiency, performance, and safety assurance of RESs. It explores the concept of CLDTs, highlighting their key functionalities and potential benefits for various renewable energy technologies. However, their effective implementation requires a structured approach to integrate observation, orientation, decision, and action (OODA) processes. This study presents a novel OODA framework specifically designed for CLDTs to systematically identify and manage their key components. These components include real-time monitoring, decision-making, and actuation. The comparison is carried out against the capabilities of DT utilizing the OODA framework. By analyzing the current literature, this review explores how DT empowers RESs with enhanced efficiency, reduced risks, and improved safety assurance. [ABSTRACT FROM AUTHOR]

Renewable Energy Sources (RESs) such as hydro, wind, and solar are merging as preferred alternatives to fossil fuels. Among these RESs, solar energy is the most ideal solution; it is gaining extensive interest around the globe. However, due to solar energy's intermittent nature and sensitivity to environmental parameters (e.g., irradiance, dust, temperature, aging and humidity), real-time solar plant monitoring is imperative. This paper's contribution is to compare and analyze current IoT trends and propose future research directions. As a result, this will be instrumental in the development of low-cost, real-time, scalable, reliable, and power-optimized solar plant monitoring systems. In this work, a comparative analysis has been performed on proposed solutions using the existing literature. This comparative analysis has been conducted considering five aspects: computer boards, sensors, communication, servers, and architectural paradigms. IoT architectural paradigms employed have been summarized and discussed with respect to communication, application layers, and storage capabilities. To facilitate enhanced IoT-based solar monitoring, an edge computing paradigm has been proposed. Suggestions are presented for the fabrication of edge devices and nodes using optimum compute boards, sensors, and communication modules. Different cloud platforms have been explored, and it was concluded that the public cloud platform Amazon Web Services is the ideal solution. Artificial intelligence-based techniques, methods, and outcomes are presented, which can help in the monitoring, analysis, and management of solar PV systems. As an outcome, this paper can be used to help researchers and academics develop low-cost, real-time, effective, scalable, and reliable solar monitoring systems. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting. [ABSTRACT FROM AUTHOR]

Featured Application: The paper investigates the efficacy of grounding systems in power distribution networks during the transition from overhead to underground network configurations. Through scenario analysis and comparative evaluations, it aims to provide insights for optimal design methodologies, emphasizing safety and techno-economic criteria. Factors such as soil structure, fault current magnitude, and fault clearing times are examined to standardize the design of grounding systems in distribution networks. Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure. [ABSTRACT FROM AUTHOR]

The expansion of renewable electricity storage technologies, including green hydrogen storage, is spurred by the need to address the high costs associated with hydrogen storage and the imperative to increase storage capacity. The initial section of the paper examines the intricacies of storing electricity generated from renewable sources, particularly during peak periods, through green hydrogen. Two primary challenges arise: firstly, the complexity inherent in the storage technology and its adaptation for electricity reproduction; and secondly, the cost implications throughout the technological chain, resulting in a significant increase in the price of the reproduced energy. Electric energy storage emerges as a pivotal solution to accommodate the growing proportion of renewable energy within contemporary energy systems, which were previously characterized by high stability. During the transition to renewable-based energy systems, optimizing energy storage technology to manage power fluctuations is crucial, considering both initial capital investment and ongoing operational expenses. The economic analysis primarily focuses on scenarios where electricity generated from renewable sources is integrated into existing power grids. The subsequent part of this paper explores the possibility of localizing excess electricity storage within a specific system, illustrated by domestic hot water. [ABSTRACT FROM AUTHOR]

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship's carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO2 emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships. [ABSTRACT FROM AUTHOR]

A large number of research stations have been established to provide members of Antarctic expeditions with logistical support. A previous study confirmed that the wind and solar energy resources of the Chinese Zhongshan Station, a coastal station located in an area of Lassmann Hills in East Antarctica, are highly synergetic and complementary. Considering the demand for a renewable energy power supply in Zhongshan Station, this paper introduces a hybrid energy system with wind–solar–diesel–battery co-generation used as a power supply scheme. Based on the 2015 climate data for Zhongshan Station, the wind–solar resources, conventional energy system, and annual diesel consumption of the station area were analyzed. The annual electrical load demand of each building in the station area was quantitatively expounded. Compared with the original power supply system, and combined with the analysis results of the thermal load and electrical load demands of the station, an objective function based on the requirements of economy, reliability, and environmental protection was presented. According to the constraint conditions of the heat energy and electrical energy load balance in the station area, a multi-objective scheduling strategy for the system was designed. Finally, the effects of this scheduling strategy were analyzed under three different application scenarios. The results indicated that the annual load demand was significantly lower than before the scheduling, and that a 50% reduction in diesel consumption could be achieved, demonstrating that the multi-objective scheduling strategy proposed in this paper could achieve optimal energy scheduling and management of the renewable hybrid energy system. [ABSTRACT FROM AUTHOR]

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system's ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) adoption grows steadily on a global scale, yet there is no consistent experience for EV drivers to charge their vehicles, which hinders the important EV mass market adoption. The Open Charge Point Protocol (OCPP) is the solution to this challenge, as it provides standardization and open communication between EV infrastructure components. The interplay of the OCPP with open cross-functional communication standards boosters driver experience on the one hand, while the charging station itself is integrated into a renewable energy ecosystem. This paper presents a deep dive into the combination of the OCPP with the OpenADR protocol, the Open Smart Charging Protocol (OSCP), the ISO 15118, and eRoaming protocols to explore possibilities and limitations. Furthermore, we suggest LoRa communication as an alternative to IP-based communication for deep-in building applications. Hence, this paper reveals the next important steps towards a successful EV mass market transition powered by user-friendliness and green energy. [ABSTRACT FROM AUTHOR]

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m. [ABSTRACT FROM AUTHOR]

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system's short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization. [ABSTRACT FROM AUTHOR]

In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method in this paper can be utilized to assess the enhanced accommodation capability of renewable energy sources following the coupling integration with retrofitted thermal power units. Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs' downward regulation capabilities by 32%. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

Recent technological advances have renewed the research interest in current-source inverters (CSIs). Nonetheless, CSI research still falls behind its voltage-source counterpart with regards to topologies, modulation, and control. Acknowledging the above, this paper presents a novel single-phase five-level CSI topology. The proposed circuit utilises eight switches and two inductors for the generation of five distinct output levels while maintaining low output voltage THD and d v / d t . Furthermore, by offsetting the inductor currents from a binary 1:2 to a trinary 1:3 ratio, the proposed inverter can generate seven current levels at its output. The inverter offers built-in short-circuit protection and can boost a low input DC voltage to a higher peak AC output voltage. These merits, alongside an electrolytic-capacitor-free design, simple current balancing mechanism, and fault-tolerant characteristics, make it a promising candidate for PV module-integrated inverter (MII) systems. The current topology utilises two inductors but is fully functional with single-inductor operation. The paper provides a functional analysis of the inverter topology alongside the inverter switching states and corresponding conduction paths. A detailed analysis of the inductor current dynamics as well as a current-balancing algorithm for dual- and single-inductor operations are given. The theoretical analysis of the proposed circuit and its functional operation are verified using simulations and experimental results carried out on a laboratory prototype. [ABSTRACT FROM AUTHOR]

Microgrids are small power systems, often equipped with renewable energy sources, that are alternatives or supplementary to utility grids. Many studies have been conducted on the design and implementation of microgrids and their interconnects to utility grids, and investigations have been extended to the use of Internet of Things technology (IoT) to monitor and operate such power grids. However, the broad applications of the IoT technology itself also call for a green energy solution. This paper investigates how to power local IoT applications via an integration of a microgrid and the utility grid. Together, we call such a system an IoT microgrid. The goal of an IoT microgrid is to maintain the availability of IoT applications while saving energy costs, and this is achieved by sustaining IoT applications via local renewable energy from a microgrid and by mitigating the intermittent power supply using the utility grid. This paper characterizes the IoT microgrid and proposes a configurable cyber-physical testbed for its design and validation. The testbed incorporates the hardware-in-the-loop (HIL) approach, where real-time simulation is integrated with physical elements for quick prototyping of those components in an IoT microgrid. The paper concludes with an example implementation of the proposed testbed, which demonstrates its use for validating both an IoT microgrid and the IoT application it sustains. [ABSTRACT FROM AUTHOR]