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Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Abstract This work demonstrates an innovative Al-air battery made of paper, which can inhibit Al corrosion, simplify battery system and reduce electrolyte storage. One major advantage of it is its minimum cost of fabrication. The corrosion inhibition effect originates from the high porosity and tortuosity of paper, which restricts the diffusive transport of hydroxyl ions to the Al surface. In this manner, low-grade Al can be directly utilized. Targeted for different application fields, three battery subtypes are proposed. The mechanically-rechargeable battery can be used repeatedly by refreshing its Al anode, providing relatively high power for charging portable electronics. The flexible battery employs embedded Al inside paper as anode and oxygen reduction ink as cathode, which is suitable for various single-use and flexible devices. Lastly, a printable battery is proposed by developing a successful Al ink for anode fabrication. Combined with existing printing techniques, this design may lead to a much smarter and more economic battery application in the future. Highlights • A low-cost and green paper-based Al-air battery technology is developed. • It can achieve simplified system, reduced liquid storage and inhibited Al corrosion. • A mechanically-rechargeable battery is designed for portable electronic recharge. • A flexible battery is designed for powering single-use and flexible devices. • A printable battery is also tentatively explored for smarter battery utilization. [ABSTRACT FROM AUTHOR]

Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill. • Pulp and paper mills with electric boilers can supply primary frequency reserves. • Electric boilers can provide up to 50 MW flexibility of 100 MW rating. • An algorithm is developed to optimize the energy & reserve bids of the mill. • Expected cost savings range between 7.4% in summer and 2.3% in winter. [ABSTRACT FROM AUTHOR]

Under the dual pressure of energy consumption and environmental pollution, mankind hopes to develop clean and renewable alternative energy, and the rapid development of fuel cells meets people's demand for energy-efficient power systems. The emergence of portable micro energy systems represented by microfluidic fuel cells, such as paper-based microfluidic fuel cells, has greatly enriched the means of medical detection to better cope with the threat of disease transmission. In this work, the numerical simulation method is innovatively introduced to study the paper-based microfluidic fuel cells. Both transient and steady-state modes are employed to demonstrate the whole operation process of the paper-based microfluidic fuel cell. In addition, the different structural parameters, including electrode spacing, the distance between electrode and inlet, channel thickness, and electrode length, are also investigated their influence mechanisms on cell performance. Results show that the increase of most structural parameters decreases cell output power in different degrees. Even on the premise that increasing channel thickness has a positive impact on the output power, the fuel utilization still shows a downward trend. These conclusions provide theoretical support and reference for future optimization work and accelerate the development of microfluidic fuel cells. • Numerical simulation is employed to study paper-based microfluidic fuel cell. • The effects of structure parameters on cell performance are discussed. • Through fuel concentration distribution to reveal cell performance mechanism. • Fuel utilization is used to evaluate energy efficiency. [ABSTRACT FROM AUTHOR]

• The study promotes a standardized framework for the analysis of complex systems. • Life Cycle Assessment (LCA) and Emergy Accounting (EMA) are integrated for synergy. • Differences and similarities between LCA and EMA add value for deeper understanding. • The proposed framework is based on ex-ante LCA, EMA and ex-post LCA procedures. The analysis of complex systems requires an integrated application of different assessment methods also taking into account different scales and points of view to gain a systemic understanding of the investigated case study. Life Cycle Assessment (LCA) and Emergy Accounting (EMA) are both environmental assessment methods, showing many similarities in the way they are performed, especially with respect to the inventory construction and to the interpretation of results. They also show great differences, the main residing in the different perspectives they give. LCA applies a consumer side perspective, and its space and time scales are set at a boundary capable to include all the process phases in terms of location and durability and their direct impacts on the investigated areas. On the other hand, throughout its donor side perspective, EMA expands the boundaries of the system over the entire biosphere space and time scales. Differences and similarities between LCA and EMA may gain added value by their implementation within a procedural framework which exploits the characteristics of the two methods. The present work proposes a methodological procedure based on the sequential and integrated application of LCA and EMA methods, called LEAF (LCA & EMA Applied Framework). The traditional Amalfi paper production is used as a test case study. The procedure stems include: (i) an ex-ante LCA analysis, to identify the hotspots of the investigated case study; (ii) the assessment of the environmental performance of the system through the development of different EMA-based improvement scenarios built around the chosen hotspots; and (iii) an ex-post LCA application built on each scenario results in order to detect the different environmental burdens. The application of LEAF to the traditional Amalfi paper production shows that the use of a more sustainable energy source is an effective solution (among the set of proposed options) to increase the sustainability of the investigated system. [ABSTRACT FROM AUTHOR]

• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Aerogels, with their exceptional properties such as high insulation performance, lightness, and eco-friendliness, present immense potential for use in the construction industry. This paper reviews the progress in research and application of various types of aerogels in construction, highlighting notable case studies demonstrating energy efficiency gains and sustainability impacts. It delves into the lifecycle of aerogels, discussing environmental implications at each stage. Despite their potential, aerogels face certain limitations including production complexity, brittleness, and high cost which hinder their widespread adoption. The paper further discusses these challenges and highlights the need for continuous research and development efforts to overcome them, with a view to achieving more scalable production, enhanced mechanical properties, and cost reduction. Looking forward, the paper discusses the expansive horizon of aerogel research, emphasizing the potential of smart aerogels, their integration with renewable energy systems, and exploring the possibility of recycling or creating bio-based aerogels to enhance their overall sustainability. While still in early stages, aerogels promise to be a major contributor to the future of construction, leading to more sustainable and efficient built environments. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

Nowadays, grid power loss and power quality deterioration are causing many problems, especially for sensitive uses such as data centers, airports, healthcare, banking, insurance, and telecom. These issues must be resolved through energy continuity coverage and optimization. Dynamic voltage restorers (DVR) are generally used to mitigate network voltage disturbances and maintain a constant voltage value between load terminals for a short and limited duration due to limited energy storage facilities. However, this paper's proposed (DVR) system controlled by the adaptive-network fuzzy inference system (ANFIS) controller can compensate for prolonged power quality disturbances by integrating a hybrid renewable energy system (HRES) managed using classical Proportional Integral (PI) controller power management. This paper proposes a new (DVR) topology coupled with a hybrid renewable energy system (HRES) to exploit free and clean energy, consisting of a solar panel, a PEM fuel cell, and a battery storage device connected through DC-DC converters to a DC transmission, the obtained results from the simulation process of the proposed DVR system in the environment of MATLAB / Simulink showed the ability to eliminate sag that exceeded 0.9 pu in a period of more than 3 min and swells that exceeded 1.2 pu in a period that exceeded one minute. In comparison, the load voltage's total harmonic distortion THDv was reduced from 29% to 5%, and the source current total harmonic distortion THDi from 30.25% to 2.79%. [ABSTRACT FROM AUTHOR]

Flexible dynamic operation of ammonia synthesis is a key enabling factor of green ammonia production in the face of intermittency in renewable energy sources. A nonlinear model predictive control scheme is presented in this paper for the Haber–Bosch ammonia synthesis process with a varying feed flowrate. The considered process consists of three interstage-cooled reactor beds and a flash separator, with a flexible reactor load varying between 50% and 100% of its nominal capacity. The proposed control scheme aims to control the reactor temperatures, separation pressure, and the liquid volume in the flash tank during feed transitions. A simulation study is performed given an assumed 5-hour feed schedule. The results indicate that all controlled variables are maintained in a safe operating range and tracked with small offsets in the nominal case as well as under disturbances in the reaction rate. It takes approximately 10 min for the reactor temperatures and 40 min for the separation pressure to reach steady state after a large step change in the reactor load. In a comparison with a PID control scheme, the investigated NMPC scheme tracks feed temperatures and the separation pressure significantly faster with less oscillations. This study therefore demonstrates the feasibility and effectiveness of the proposed control scheme in enabling flexible operation of the process. [ABSTRACT FROM AUTHOR]

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems. • A hybrid technique for predict fault in standalone PV to improve the performance of PV • The proposed method is the joint execution of ANN and GEO • The major contribution of this work is to improve the performance of PV systems • The use of GEO provides an efficient optimization technique for ANN training • The proposed method is more accurate and effective than existing techniques [ABSTRACT FROM AUTHOR]

Current scientific advances in wastewater treatment increase our ability to acquire clean water; one of these methods is the use of solar energy. Solar irradiation is an important source of renewable energy; thus, extensive efforts have been devoted to the development of light-absorbing particles that can achieve light-to-heat conversion. In this study, novel solar evaporation systems were designed and developed, with magnetic nanoparticles as photo-absorbers. The magnetic nanoparticles were coated with carriers, such as graphene oxide and paper. These hydrophilic carriers could provide large surface areas, low thermal conduction, or microporous surfaces for steam generation. Magnetic nanomaterials can absorb over 95% of sunlight; thus, an evaporation efficiency of up to 75% at only 1 kW/m 2 irradiation, as well as a decreased volume of wastewater were achieved. Furthermore, a reasonable amount of clean water was collected. These nanomaterials could be separated from wastewater by magnetic force, which can significantly reduce material consumption. These novel strategies successfully overcame previous problems, such as the demand for a high amount of sunlight. This study also opens an opportunity for the construction of a solar evaporation system for wastewater treatment with photo-absorbers. [ABSTRACT FROM AUTHOR]

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties. [ABSTRACT FROM AUTHOR]

Meeting global energy demands has been a major challenge due to the shortage of primary energy resources. Adding renewable energy resources to the grid has become increasingly popular. During peak hours of energy demand, the sun's irradiance is strong enough to generate a significant amount of energy. This paper will design a photovoltaic energy generating system that harnesses solar energy using the latest technological advancements available. A system like this one is an ideal solution to addressing rising energy demands and reducing carbon footprint. Renewable energy sources can minimize carbon emissions, operating costs, etc., but their intermittency and uncertainty make their reliability a serious concern. The objective of this paper is to improve the reliability of the power grid through the use of solar photovoltaic (PV), battery energy storage systems (BESS), and distributed generation systems (DG). [ABSTRACT FROM AUTHOR]

Current source inverters (CSIs) have been widely used for renewable energy sources integration with the utility grid. However, traditional CSIs provide only voltage-boost power conversion, which can be considered a limitation for grid-interfacing applications when consistent tracking of a widely varying input voltage is necessary. Hence, an additional front-end converter is usually utilized in an attempt to step down the input source voltage. Nevertheless, adding the dc-dc bucking stage would reduce the system reliability and its overall efficiency, increase the converter cost, and complicate the control scheme. In order to address the aforementioned shortcomings, this paper proposes a novel three-phase single-stage inverter, suitable for low-power applications, called split-source current-type inverter (SSCTI). The proposed converter can achieve high current boosting capability with lower component count and high-quality output current in comparison with other single-stage topologies. In addition, it utilizes the conventional modulation scheme of the CSI, while maintaining low current stresses on the inverter switches. Comprehensive analysis, modelling, and closed-loop control scheme of the proposed converter are developed. Also, a comparison between the proposed topology and several single-stage topologies is carried out. Moreover, an experimental prototype is built to validate the proposed converter analysis under steady-state operation and different transient scenarios. [ABSTRACT FROM AUTHOR]

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems. [ABSTRACT FROM AUTHOR]

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε -based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems. • In a RESs integrated network, the OPF problem is assessed considering FACTS devices. • The placement and sizing of such devices are augmented, considering uncertainties. • Stochastic PV and wind power and uncertain load demand are included in OPF. • Formulation of multi-objective flow direction algorithm and constraint handling mechanism for the said problem. • Performance comparison among various well-known algorithms. [ABSTRACT FROM AUTHOR]

With the large-scale integration of renewable energy systems, the power inertia and voltage support of the grid are low. Using the inverter with temporary storage capabilities cope with shortages and power fluctuations. This paper presents a new sensorless control of a single-phase grid-connected PV system based on a multilevel flying capacitor inverter MFCI) able to smooth out the power produced. The designed controller involves two cascade loops, an outer loop control extracts the maximum power despite climate change, and an inner loop consists of a model predictive control based on the Lyapunov theory for power factor correction and flying capacitor voltage balancing. Furthermore, an interconnected Kalman-like high-gain observer is combined with the predictive control algorithm to estimate the flying capacitor voltages accurately based on the actual grid current. The stability study of the suggested observer in the closed loop has been described. An algorithm that generates reference signals to manage power flows is also developed. The performance of the proposed controller has been verified on the MATLAB/Simulink platform, and the simulation results confirm the effectiveness of the proposed controller over different grid operating modes. • the proposed PV system is based on MFC inverter able to smooth out the grid-injected power. • The designed controller combines a Lyapunov-based model predictive control with a Kalman-like high-gain observer. • The PV conversion system optimizes energy extraction, injecting it smoothly into the grid. • The inverter, with temporary storage, copes with power fluctuations without external energy storage during grid faults. • The closed-loop stability of the controller is rigorously proved during the normal mode and grid fault conditions. [ABSTRACT FROM AUTHOR]

This article presents a state-of-the-art review of the status, development, and prospects of DC-based microgrids. In recent years, researchers' focus has shifted to DC-based microgrids as a better and more feasible solution for meeting local loads at the consumer level while complementing a given power system's reliability, stability, and controllability. DC microgrid has an advantage in terms of compatibility with renewable energy systems (RESs), energy storage, modern electrical appliances, high efficiency, and reliability. However, the integration of different distributed generations has complicated the control of bus voltage and current. Therefore, several efforts have been made in the research community to further explore efficient control techniques for a reliable and stable DC microgrid. In spite of the numerous review papers published on DC microgrid control, so far, not any has given sufficient emphasis on the power flow analysis methods used in various DC microgrid topologies and the hybrid control topologies discussed in this review. In general, this paper presents a meticulous explanation of DC microgrid architecture; power flow analysis; control strategies with comparative analysis; challenges with recommendations; as well as classical and intelligent-based energy management strategy (EMS). Finally, suggestion for further research is presented. This review paper will go a long way in helping readers to understand the present state of development on DC microgrid control. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns, scarcity of fossil fuel resources, and uncontrolled demand growth have led to upgrading and restructuring existing energy systems. Sustainable multi-energy systems (MESs) will dominate energy production in the future. Integrated management systems must be provided to plan and control the sustainable MESs for optimal operation. Energy hubs (EHs) represent one of the most promising concepts for optimal management systems in sustainable MESs. They can play a great role in moving towards sustainable MES models. This paper provides a comprehensive review of the concepts of EHs and their applications, also benefits gained from the integration of different energies. In addition, the paper discusses the effect of other related options like renewable energy resources, MESs, demand-side management, and energy storage systems. Finally, the paper presents the most common research areas, methods of solving the EHs' associated problems, presents real applications of EHs and explores the future challenges that face the EHs. [ABSTRACT FROM AUTHOR]

The collection of sawmill residues is an important logistic activity for the pulp and paper industry, which uses the biomass as a source of energy. We study a vehicle routing problem for a network composed of a single depot and several sawmills. The sawmills serve as potential suppliers of biomass residues to the depot, which in turn processes and distributes the residues to the pulp and paper mills. This problem consists of identifying the best daily routing schedule for a fixed number of trucks. The objective is to maximize the ratio of energy returned on energy invested, while satisfying a minimum daily amount of dried biomass residues. There are several random components in the problem, including the availability and moisture content of the biomass residues. We use a combination of scenario analysis and heuristics to solve this stochastic vehicle routing problem. A performance comparison of the proposed method reveals an estimated daily energy savings of 6 GJ over the benchmark method. [ABSTRACT FROM AUTHOR]

This paper deals with a dynamical analysis of a supercapacitor based multilevel inverter. In Fact, applications for multi-level inverters based on supercapacitors include uninterruptible power supplies (UPS), grid-tied renewable energy systems, electric vehicles, and other power conversion systems where high-power quality and rapid energy transfer are important. These systems are the subject of ongoing research and development in the field of power electronics and energy storage, as they have the potential to enhance the performance and efficiency of various electrical and electronic applications. However, If the input voltage sources are fixed, this can limit the flexibility of the inverter, especially in terms of regulating the output voltage and adapting to load variations. This study is focused on the dynamic analyses which is crucial for designing, optimizing, and verifying the performance of supercapacitor-based multi-level inverters in various applications. Hence, the behaviour of the system was studied on a resistive load, material calculations showing the functioning of the circuit as well as simulations were performed for the positive phase of the sinusoid at the terminals of the charge. [ABSTRACT FROM AUTHOR]

The energy demand has increased rapidly worldwide due to the depletion of fossil fuels. Recently, the rate of energy consumption has become unsustainable, providing a strong incentive for many countries to pursue renewable energy sources. By 2030, Oman's national energy strategy aims to generate 20% of its electricity from renewable sources. In the same context, Oman has the target to join the front rank of countries that aim to capitalize on their renewable resources to produce green hydrogen for export to the global market and domestic utilization. This study investigates the prospects for creating green hydrogen using solar energy in Oman's Dhofar region. The key obstacles to solar-to-hydrogen generation are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy is a renewable source which provides clean energy in power systems. Because of low efficiency of energy conversion in PV systems, harvesting maximum power from these systems is important. Many papers have been focused on this challenge to obtain the maximum power from PV array. Sliding mode control is an efficient control method to achieve maximum power. Although, due to non-linear curve of P–V and I–V of PV array in one side and uncertain PV generated power due to climate and temperature conditions, different evolutionary algorithms must be applied for accurate maximum power point tracking. Perturbation and observation (P&O) is a well-known method in this field but it has some defects in case of shadow-condition multiple maxima. In this paper, considering analysis of two sliding mode control structures, modified particle swarm optimization (PSO) algorithm has been used in combination with modified P&O method called modified PSO-modified P&O (MPSO-MP&O) to compensate the problem of P&O tracking in shadow conditions. The proposed method selects the global maximum point by searching all maximum points in many iterations. Also, the achievement stability of the maximum point is interesting in different temperature conditions and sudden changes of physical parameters of PV system. Simulation results show the efficiency of the proposed method performance in obtaining the maximum point faster with more PV power compared to other efficient methods in this field. • Using low-order and high-order sliding mode control for control structure of MPP. • Using combination of modified PSO and modified P&O (MPSO-MP&O) for obtaining MPP. • Optimal point stability analysis using Lyapunov functions. • Analysis of thermal effect and sudden change of the effective parameters on stability of MPP. [ABSTRACT FROM AUTHOR]

Buildings have significantly contributed to high energy demand (30.3%) and GHG emissions (26.1%) worldwide. Consequently, many developed countries have set carbon-neutral targets for 2050, mandating that all new constructions thereafter be designed as net-zero emission buildings (NZEBs). To achieve this goal, one of the most practical sources of renewable energy, solar power, is employed in this study to estimate the economic feasibility of implementing NZEBs in the non-residential sector of the United States. While past studies have typically estimated the economic payback period of solar power technology by assuming fixed values for "PV energy conversion rates (%)" and "investment costs ($USD)", this paper carefully investigated the economic feasibility of "solar-based NZEBs" by precisely tracking these dynamic future changes in PV panels. These techno-economic variables were carefully predicted using a statistical technique known as the five-parameter logistic (5 PL) function. The results show that Site 4 is the only solar region suitable for implementing "PV-integrated NZEBs", reaching a payback period of 8.44 years in 2040 (Scenario 1). Furthermore, with small technological improvements in PV energy efficiency (%), buildings located in Site 3 and Site 4 can meet the net-zero emission target with payback periods of less than 10 years in 2034 (Scenario 2). Finally, starting in 2044, the "PV-integrated system" will have a payback period of approximately 7 years in most study locations, even without federal support for the solar investment tax credit (SITC). Accordingly, federal support for solar power generation will be more effective if the SITC rate is gradually reduced by 2.5% annually starting in 2033. In conclusion, this paper suggests the economic feasibility of implementing solar-based NZEBs in the United States non-residential sector by considering the synergetic effect of technological improvement and cost reduction in PV-integrated systems. • This study predicted the cost and efficiency of PV panels to estimate the economic feasibility of solar-based NZEBs. • The cost reduction and technical advancement in PV panels can remarkably improve the economics of solar power. • The 5 PL function is systematically modeled to predict the total investment cost and efficiency of PV panels. • In most study locations, solar-based NZEBs are projected to achieve a 7-year payback period beginning in 2044. • The federal support for the SITC rate (%) is recommended to be gradually lowered starting in 2033. [ABSTRACT FROM AUTHOR]

Accurate net load forecasting contributes to increasing the integration of renewable energy sources and reducing the operating cost of the power grid. In recent years, deep learning models have been applied in net load forecasting. However, there is still a large scope for them to improve the extraction of complex multi-scale temporal features in the net load. In addition, most researches do not establish explicit mechanisms to consider the impact of relevant factors on net load forecasting, such as renewable energy outputs and load demand. Therefore, in this paper, we propose a multivariate Transformer-based model, named TCAMS-Trans, to achieve direct net load forecasting. First, TCAMS-Trans exploits multi-scale patch-based variable-independent embedding to model potential multi-scale patterns in the load data. Subsequently, TCAMS-Trans designs a new hierarchical encoder–decoder pair configured with temporal-channel attention block (TCA block) to further extract multi-scale features and capture the connections between the net load and the relevant factors. The temporal attention in TCA block is used to mine complex temporal dependency along the time dimension, and the channel attention is used to capture the inter-variable dependency along the variable dimension. Experiments conducted on three publicly available datasets show that TCAMS-Trans has higher prediction accuracy than the current state-of-the-art models at different prediction lengths. The model proposed in this paper provides a new powerful solution for net load forecasting. [ABSTRACT FROM AUTHOR]

• Classified sales channels for different quality renewable energy. • Dynamic assessment indicators for the renewable energy's power deviation. • Power curve optimization strategy considers the self-owned battery. • Accurately and timely transmit the peak shaving pressure. • Guarantee the independent decision-making power of new energy stations. By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• The Bergeron model error resulting from parameter deviation is discussed, followed by a line parameter adjustment strategy to mitigate its impact. • The analysis of the coupling mechanism between the differential current and the fault current is followed by a proposed modified method to eliminate the 0-mode line parameter error. • A novel time-domain current differential protection scheme with high sensitivity is proposed in this paper. The influence of line parameters is addressed by adjusting α - and β -mode parameters and eliminating the 0 -mode parameter from the criterion. Integrating large-scale renewable energy sources in modern power systems has altered fault characteristics, resulting in the performance degradation of conventional current differential protection. This paper proposes a novel high-sensitivity time-domain current differential protection scheme. The proposed scheme utilizes the Bergeron model to eliminate the impact of the distributed capacitive current. Furthermore, the Bergeron model error is analyzed theoretically during normal operation, and an algorithm is designed to identify and adjust the α- and β-mode line parameters. Additionally, novel internal fault criteria are presented to mitigate the negative impact of the 0-mode line parameter error. Compared to traditional frequency-domain approaches, the proposed method can more accurately and rapidly distinguish internal and external faults, while reducing the dependency on precise line parameters. Eventually, a VSC-HVDC sending-end grid model consisting of 100% renewable power generations is established using PSCAD/EMTDC software. Simulation results confirm that the proposed protection scheme exhibits exceptional performance in terms of operating speed, sensitivity, and tolerance to fault resistance and noise. [ABSTRACT FROM AUTHOR]

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability. [Display omitted] • Focusing on industrial-building energy conjugation issues • Proposing a low-carbon energy system planning framework for industrial parks • Establishing a networked matching mechanism for cold and hot flow streams • Short system recovery cycle, good economic performance • High contribution rates of waste heat and renewable energy, evident low-carbon effect [ABSTRACT FROM AUTHOR]

As the renewable energy consumption responsibility weight system is gradually implemented in various provinces, there is a closer intersection between the excess consumption and green certificates trading. Existing trading platforms can only meet the needs of green certificates trading. This single-platform solution is no longer sufficient to meet the trading requirements under this system. Consequently, this paper proposes a excess consumption and green certificates trading platform based on blockchain technology. This platform utilizes the decentralized, open, and anonymous characteristics of blockchain technology and rely on its smart contract capabilities to simultaneously meet the comprehensive trading needs of surplus consumption and green certificates trading through combining user-initiated transactions with platform-intelligent recommendations. It adapts to the complex excess consumption and green certificates trading market that has emerged following the issuance of the renewable energy consumption responsibility weighting policy. To conduct simulated transactions, 16 groups of buyers simulate a total of 215,792 excess consumption vouchers and 143,861 green certificates in this paper. The transaction results all meet the expected transaction scenarios under the conditions of smart recommendation, demonstrating the platform's transaction feasibility. [Display omitted] • Design a blockchain-based smart contract solution for volume consumption trading and green certificate trading. • Build a database containing 215,792 excess consumption vouchers and 143,861 green power certificates. • Simulate the purchase of 160 buyers by establish a transaction platform. • Analyzed the application direction of the data produced by the trading platform [ABSTRACT FROM AUTHOR]

The use of power converters to regulate voltage and power in electrical power systems has been attracting attention due to the increasing number of research and applications on renewable energy. The integration of these switched devices may cause instability and undesired oscillations depending on the operation conditions, such as when they feed constant power loads (CPLs). Therefore, this paper proposes an analysis of the effect of the power regulation in a multi-stage converter system made by buck-buck cascaded converters, in which the first converter operates as a voltage regulator and the second operates as a power regulator. To support that analysis, the region of attraction (ROA) is estimated based on convex optimization with linear matrix inequalities (LMIs) constraints considering the effect of the power variation and the voltage regulator dynamics. These regions are validated through tests in a simulated environment, showing that the trajectories initiated within those regions remain confined in those despite the CPL power variation disturbance. Finally, a case study for the experimental environment is presented. The power variation test is carried out and shows the efficacy of the stability analysis approach ratifying the simulation results. • In this paper, an LMI-based stability analysis for the microgrid operation point is presented considering the CPL effects. • The region of attraction (ROA) of a microgrid with CPL is estimated to ensure the operational stability conditions. • Analysis of the impact of the voltage regulation speed on the stability of the microgrid with a CPL whose dynamics are previously known. [ABSTRACT FROM AUTHOR]

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as "Recipients" or "Providers", is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes "SecFlex market organization–trading volume collation–response schedule optimization". At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market. • A secondary flexibility market mechanism with high share of BTM RERS. • A comprehensive process including market, technical, and control aspects. • A trading volume collation method that supports the secondary flexibility market. • Discussion and control analysis of different behavior patterns of market players. [ABSTRACT FROM AUTHOR]

A push-pull forward LLC resonant converter suitable for renewable energy power generation systems is proposed in this paper. The converter introduces the LLC resonant tank into the traditional push-pull forward converter and adopts pulse frequency modulation (PFM) control, which solves the problem of voltage spike of switches of the conventional push-pull converter, realizes the zero-voltage switching (ZVS) of the switches and has high conversion efficiency. In addition, the rectifier part of the structure adopts voltage doubling rectifier technology, which is beneficial to reduce the turn ratio and optimize the leakage inductance of the transformer. In this paper, the working principle of the converter is described in detail, and its gain characteristics and soft switching conditions are analyzed. Finally, an experimental prototype of 1 kW is built, and the output voltage of 380 V is achieved when the rated input voltage is 48–72 V, and its efficiency is up to 96.1 %. [ABSTRACT FROM AUTHOR]

The transmission of offshore wind power through High Voltage Direct Current (HVDC) systems is an important way to utilization of offshore renewable energy. However, onshore AC grid faults can make it difficult to transmit the energy from offshore wind farms, resulting in HVDC system overvoltages. Existing solutions to this problem mainly include adding DC or AC energy-dissipation devices, adding communication systems, and improving the fault ride-through(FRT) strategy of wind turbines, which have issues with economics or reliability. Based on this, this paper proposes a device-free, communication-free FRT strategy, and the fault ride-through strategy of wind turbines does not need improvement. The exchange of active power between the AC side and DC side of the onshore MMC is calculated under grid-following and grid-forming control strategy. The reference value of DC voltage and offshore MMC capacitance energy are proposed at different control stages. Especially, the dynamically calculated offshore AC voltage reference values have been theoretically provided. This approach ensures that the HVDC system avoids over-voltage and effectively stores more energy. The effectiveness and correctness of the proposed scheme were verified through simulation on the RTDS platFform. This paper is of great significance to promote the development of offshore renewable energy and ensure the safe operation of offshore wind HVDC systems. [ABSTRACT FROM AUTHOR]

Large-scale concentrated Variable Renewable Energy (VRE) sources pose a challenge to the balance and security of electricity systems. Studies have shown that the demand side may offer a greater responsiveness based on the shiftability of loads. However, there is currently no known literature on the demand response (DR) of large-scale shiftable loads in the residential sector. Firstly, there is no sufficiently large database of residential appliance-level load. Secondly, scheduling a large number of small residential shiftable loads simultaneously is a major challenge. Ultimately, the DR of large-scale residential buildings encounters the challenge of ensuring sufficient backup shiftable load to effectively cope with real-time VRE fluctuations during the response period. To address these issues, this paper proposes an appliance-level load data generation method based on diffusion neural networks. Meanwhile, a clustering method based on Generalized End to End (GE2E) loss neural networks is proposed to solve the complexity obstacles in simultaneous load swarm scheduling. Thirdly, a two-stage scheduling approach is proposed to address the challenges in the large-scale DR period through constructing day-ahead and real-time response models. The simulation results show that large-scale residential shiftable loads have a strong response capability to VRE fluctuations, and their response cost is lower than the conventional fuel-fired-power-plant-based-response within a certain user preference cost range. In practice, the clustering and scheduling methods proposed in this paper can be applied after the non-intrusive load monitoring (NILM) method is deployed to decompose the load data. • This paper addresses the research gap regarding large-scale residential load response to fluctuations in concentrated variable renewable energy sources. • A novel data generation model is proposed to address the issue of insufficient data scale in existing databases. • A novel load clustering algorithm is proposed to address the curse of dimensionality in simultaneous load swarm scheduling. • A novel two-stage response model is proposed is proposed to minimize the response cost and user preference loss. • When the unit user preference cost is lower than a certain boundary, the proposed response method is more cost-effective than conventional methods. [ABSTRACT FROM AUTHOR]

The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods. [ABSTRACT FROM AUTHOR]

• Mechanisms to encourage clean energy in Spain from 2005 to the present are analyzed. • FiT and FiP mechanisms can be effective in promoting new renewable projects. • Experience suggests that excessive support can jeopardize the entire market. • A transitional retribution scheme has proven to be effective if correctly supervised. This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a "total market" for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System. [ABSTRACT FROM AUTHOR]

• Introducing cryptocurrency mining as a novel virtual energy storage system. • Offering a structure to store excessive renewable energy in cryptocurrency units. • Proposing an opportunity to generate more electricity from renewable sources. • Modeling cryptocurrency storage in the energy management studies of the microgrids. • Studying the profitability of cryptocurrency mining in the microgrids. Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM. [ABSTRACT FROM AUTHOR]

Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration. [Display omitted] [ABSTRACT FROM AUTHOR]

The application of virtual inertia (VI) has become a novel solution and new orientation for the frequency regulation of the high variable renewable energy (VRE) penetrated low inertia systems. How to provide a frequency regulation strategy for the system with VI is a key problem. Firstly, the power system is a multi inputs multi outputs (MIMO) system, in which the coupling characteristics of VI control and the primary and secondary frequency controls can't be ignored. Secondly, the system inertia constant is time-varying and the estimation of it is crucial for gaining a satisfying regulation performance. This paper intends to provide a frequency regulation strategy for this problem. In this work, an Elman neural network (ENN) based inertia estimation with a deadband method is proposed for the identification of the system's instant inertia. And the Laguerre function-based rate of change of frequency (ROCOF) constrained MIMO-MPC is constructed to provide the collaboration strategy. Then, the control parameters are tuned by the gravitational search algorithm (GSA) considering both the system performance indexes and the operation burden indexes. The proposed strategy is testified based on a three-area power system. The results prove not only the robustness of the controller but also the effectiveness of the ROCOF constraint, the operation burden index, and the instant inertia estimation method. • A MIMO-MPC controller is proposed to provide the coordination strategy of the inertia, primary and secondary controllers. • The value of ROCOF can be restrained by the MIMO-MPC's built-in ability for state variable constraining. • An ENN based inertia estimation with a deadband method is proposed and is planted in the MIMO-MPC controller. • The ESS in it can be guaranteed with less regulation movements and smaller maintenance costs. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources in Medium-/High-voltage DC grids has called up continuous research to develop DC-DC conversion systems. This paper presents a hybrid modular multilevel DC-DC converter topology which successfully overcomes the issue of capacitors voltage imbalance during DC-DC conversion. In the proposed topology, single MMC leg is employed with half-bridge submodules in the upper arm and full-bridge submodules in the lower arm along with soft-switched high-voltage valves. The proposed hybrid modular multilevel converter has inherited arm energy equalization with the help of the parallel connectivity of involved arms' capacitors. During the equalization period, the series-connected upper arm capacitors are connected in parallel to the series-connected lower arm capacitors to transfer the energy between the involved arms which results in operating with balanced capacitors voltages. The proposed modular DC-DC converter is a bidirectional converter which allows power flow from high-voltage side to low-voltage side and vice versa, i.e., it can be operated successfully as a DC transformer. The operational concept of the proposed configuration, control strategy, and parameters design are presented. Simulation results are presented along with experimental results of a scaled-down prototype to validate the proposed approach. Simulation and experimental results show the viability of the suggested approach. [ABSTRACT FROM AUTHOR]

In this paper, a terminal sliding mode backstepping controller (TSMBC) has been proposed for various components of a hybrid AC/DC microgrid (HADMG) to enhance its dynamic stability. The proposed control technique is employed to generate switching control signals for converters, which serve as the primary interface between the DC bus and the AC bus in a hybrid microgrid. Additionally, this technique facilitates the interface of PMSG-based wind generators, solar photovoltaic generators, and battery energy storage systems with the DC bus. Through the implementation of the composite control technique, the global stability of the microgrid is ensured by driving all the states of the HADMG associated with various components to converge towards their intended values. Afterward, the Lyapunov control theory has been used to analyze the converter and inverter's large-signal stability while ensuring the robustness of the proposed robust composite controller. Finally, an extensive simulation study was conducted on a hybrid microgrid to verify the efficacy of the designed controller in maintaining power balance amidst variations in the system's operational regimes. Moreover, the effectiveness of the controller's practical implementation is confirmed by real-time processor-in-the-loop analysis. Simulation results clearly show that the proposed TSMBC improves the overall dynamic performance of the hybrid microgrid with less overshoot (0%) and settling time (110 ms) in DC bus voltage when compared to the existing sliding mode controller. • Enhance the dynamic and transient stability of an AC/DC hybrid microgrid (ADHMG). • A composite terminal sliding mode backstepping controller (TSMBC) is used to obtain the switching control signals for the converters and inverters. • Maintain the power balance with changes in the system's operating point. • It provides superior performance as compared to the existing control approach in terms of overshoot and settling time. [ABSTRACT FROM AUTHOR]

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids. • Use diesel generation exhaust for district heating in remote communities. • Use heat pump demand response for frequency control in remote microgrids. • Facilitate renewable energy integration using district heating networks in isolated microgrids. • Compare district heating networks with electric thermal storage units for thermal supply and frequency control in remote microgrids. [ABSTRACT FROM AUTHOR]

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system. • The overall levelized cost model of large-scale mobile energy storage system is established. • Tech-economic performance of fixed and mobile energy storage system is compared. • The proposed method can improve system economics and renewable shares. [ABSTRACT FROM AUTHOR]

Biomass is a renewable and clean energy source, offering the potential to reduce fossil fuel consumption and greenhouse gas emissions when it is used as a biofuel. However, the disadvantages of raw biomass such as poor grindability, high moisture content, and low calorific value hinder its widespread industrial combustion. Thermochemical conversion techniques are widely used to convert biomass to biochar, which serves not only as an adsorbent and soil amendment but also as solid fuels alternative to fossil energy. The upgraded biochar exhibits better physicochemical properties and improved combustion performance. This paper focuses on the properties and combustion behavior of biochar as a solid fuel, reviewing common production techniques of biochar and their effects on yields and properties of biochar. It also examines the combustion characteristics and kinetics of biochar in single- and co-combustion scenarios with other fuels, such as coal, sludge, and petroleum coke, analyzing the influencing factors of combustion behavior and kinetic parameters. This study further investigates the emission characteristics during the combustion of biochar, including gaseous pollutants (such as CO, NO x , SO x , and HCl), ash-related issues, and particulate matter (PM) emissions. Additionally, a new fuel type named "bioslurry", which is developed with biochar as a main material, is introduced. Furthermore, this paper discusses the techno-economic and environmental aspects of biochar application, exploring the feasibility of its industrial production and utilization. • The thermochemical pretreatment methods of biomass to produce biochar are summarized. • Combustion behavior and kinetics during single- and co-firing of biochar are reviewed. • The pollutant emission characteristics during biochar combustion are investigated. • The techno-economic and environmental evaluation during biochar combustion are given. [ABSTRACT FROM AUTHOR]

Having no native energy resources of fossil fuels, with poor wind resource and scarcity of land, the Solar Photovoltaic (PV) roadmap identified solar electricity as the most feasible source of renewable energy for Singapore's energy mix and supply. Moving towards net-zero emissions and to combat climate change, the Singapore government is aiming to achieve 2-Gigawatt-peak (GWp) of solar electricity target by 2030. Accordingly, the share of solar energy in the national grid is targeted to be between ∼2–6% in 2030 and ∼ 3.5–8% in 2040, and carbon emission savings to be ∼0.5–1.4 and ∼ 0.8–2.1 million tonnes per annum in 2030 and 2040 respectively. Although these ambitious targets align with the government's plans for mitigating emissions, Singapore faces great challenge in terms of land availability to install ground-mounted solar PV panels. In this paper, a system dynamics model is developed to study- to what extent can Singapore achieve the targeted solar electricity goals by 2030 or even beyond based on Green Plan 2030, what policies can be identified to achieve these targets, and how much carbon savings can be achieved through Solar electricity deployment. Accordingly, this paper presents systems thinking and system dynamics (ST&SD) methodology to model the growth of Singapore's solar capacity, carbon emission savings and share of electricity demand met by solar electricity while focusing on key complex factors such as area utilisation, subsidies, PV panel efficiency etc. Results of our model simulations and projections, based on the key data and assumptions, and policy scenarios show that Singapore's solar capacity can be accelerated by the implementation of the proposed policies to reach 2GWp goal towards 2030 or even slightly ahead of this timeline. However, should the government revise its solar capacity targets higher for the years past 2030 i.e. to achieve 8% share of total electricity generation, perhaps by 2040, policies such as an increased area utilisation, subsidies and higher panel efficiency need to be introduced. Our model simulations incorporating and evaluating these policy scenarios yielded the results aligning with the projections mentioned above. The results and insights presented in this paper offer useful recommendations to the researchers and policy makers in the field of solar electricity system in Singapore, and to study further for better policy making. [Display omitted] • System dynamics modelling of the solar electricity targets of Singapore's Green Plan 2030 and beyond. • Evaluation of government's initiatives and policies towards 2-GWp Solar capacity goals. • Projection of carbon emissions savings by 2030 and beyond due to Solar deployment • Policy rrecommendations for the post-2030 solar electricity targets (8% share) • Estimating the % share of Solar energy mix to meet electricity demand. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]