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Paper-based microfluidic devices offer advantages such as low cost and disposability for point-of-care diagnostic applications. However, actuation of fluids on paper can be a challenge in multi-step and complex assays. In this work, a thermally programmable time-delay switch (TPTDS) is presented which operates by causing delays in the fluid path of a microfluidics paper-based analytical device (μPAD) by utilizing screen-printed wax micro-bridges. The time-delay is achieved through an electrical power feedback loop which indirectly adjusts the temperature of each individual micro-bridge, melting the wax into the paper. The melted wax manipulates the fluid flow depending on its penetration depth into the paper channel, which is a function of the applied temperature. To demonstrate functionality of the proposed method, the TPTDS is employed to automate and perform the nitrate assay which requires sequential delivery of reagents. Colorimetric detection is used to quantify the results by utilizing an electronic color sensor. [Display omitted] • Fluid actuation can be a challenge in paper-based microfluidics, limitting its applicability in multi-step assays. • Wax-based micro-bridges placed across paper channels can manipulate fluid motion when melted. • Control over the wax penetration is achieved by an electrical power feedback loop, adjusts the temperature. • Time-delay switches are thermally programmed to automate a nitrate test, requires sequential delivery of the reagents. [ABSTRACT FROM AUTHOR]

The paper is devoted to the development of a device for protecting facilities with turn-to-turn insulation, namely transformers and wind generators in order to increase the reliability of operation of electrical power systems, renewable energy installations and energy storage systems. The paper presents the results of experimental studies of the parameters of a prototype frequency-dependent device, as well as an assessment of the effectiveness of suppression of high-frequency surge voltages by the proposed device. The results of four groups of measurements are shown, which were compared with each other and with the results of previously showed computer simulations. The high efficiency of the proposed device when influencing the slope and amplitude of short high-frequency transients is shown. It is indicated that the parameters proposed earlier in the simulation allow the device to be used effectively. A brief analysis of alternative methods and means of protection is provided, and the prospects for using the device as protective equipment for wind power plants are also considered. [ABSTRACT FROM AUTHOR]

An alkaline electrolyzer is a traditional equipment to generate hydrogen. Improving the yield of electrolyzers has recently captured much attention. In this regard, the utilization of carbon-coated nanomaterial structures is a state-of-art method. On the other hand, considering the current crisis of fossil energies, the exploitation of renewables and green technologies is necessary and unavoidable. Additionally, the design and development of integrated energy systems with two or more output products and the maximum usage of thermal losses to enhance productivity can increase the flexibility and acceptability of the energy system. In this regard, the current paper develops a framework for the performance of a new solar and biomass energies-driven multigeneration system (MGS). The main units installed in MGS are three electric energy generation units based on a gas turbine process, a solid oxide fuel cell unit (SOFCU) and an organic Rankine cycle unit (ORCU), a biomass energy conversion unit to useful thermal energy, a seawater conversion unit into useable freshwater, a unit for converting water and electricity into hydrogen energy and oxygen gas via a nanomaterial-based alkaline electrolyzer, a unit for converting solar energy into useful thermal energy (based on Fresnel collector), and a cooling load generation unit. The planned MGS has a novel structure and layout that has not been considered by researchers in previous papers. The current article is based on presenting a multi-aspect evaluation in order to investigate thermodynamic-conceptual, environmental and exergoeconomic analyzes. The outcomes indicated that the planned MGS can produce about 6.31 MW of electrical power and 0.49 MW of thermal power. Furthermore, MGS is able to produce various products such as potable water (∼0.977 kg/s), cooling load (∼0.16 MW), hydrogen energy (∼1.578 g/s) and sanitary water (∼0.957 kg/s). The overall energy and exergy productivities were measured as 78.13% and 47.72%, respectively. Also, the total investment and unit exergy costs were 47.16 USD per hour and 11.07 USD per GJ, respectively. Further, the content of carbon dioxide released from the planned system was equal to 10.59 kmol per MWh. A parametric study has been also developed to identify influencing parameters. • The nanomaterial-based alkaline electrolyzer is utilized for hydrogen production. • The system is tested by thermodynamic and environmental aspects. • The developed system was capable of generating 1.578 g/s hydrogen energy. • The content of carbon dioxide released from the planned system was less than traditional systems. [ABSTRACT FROM AUTHOR]

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage. • The operation of a multi-carrier energy system is evaluated by stochastic programming. • The risk of different uncertainties is involved using downside risk constraints. • The risk-neutral and risk-averse strategies are developed by adjusting a risk level. • The hydrogen carrier considered as an intermediate to serve multi-energy demands. • The water electrolyze and steam methane reform technologies produce hydrogen gas. [ABSTRACT FROM AUTHOR]

Satellites engaged in observation missions often face severe pointing constraints, which can limit the ability of their solar panels optimize the generation of electrical power during payload operations. Some approaches to maximize power generation involve either using orientable solar panels, or performing orbital maneuvers to point fixed or deployable (non-orientable) solar panels towards the Sun, which can compromise the payload's ability to meet its pointing requirements. In this study, an alternative approach that enables high-consuming remote sensing payloads to operate for extended periods without requiring orientable solar panels is presented. The satellite under consideration is equipped with fixed or deployable (non-orientable) solar panels mounted on up to three different perpendicular faces. The on-board payload is required to remain within a fixed angle from a target direction, which can be either nadir or a specific geographical position. To derive an optimal tracking law, the projected solar array area at each instant is maximized, resulting in optimal electrical power generation. This methodology allows the satellite to maintain its pointing constraints and efficiently operate high-consuming payloads for longer periods. The proposed tracking law is validated using an actual mission scenario, comparing the power generation achieved by the proposed method against other techniques. The approach presented in this paper shows improved performance, enabling satellites equipped with non-orientable solar panels to fulfill their observation requirements while optimizing electrical power generation. This work offers significant benefits for satellite operators, reducing the need for costly orientable solar panels and enhancing the overall efficiency of satellite missions. • Guidance law for optimal power generation while complying with payload pointing constraints. • Simple and easy formulation, suitable for real-time systems. • Methodology validated and compared against other attitude strategies. • Integration and simulation of the guidance law into a control scheme. [ABSTRACT FROM AUTHOR]

The electric power industry is a high-risk industry with frequent accidents. To ensure the life safety of employees and reduce the probability of accidents, it is necessary to utilize certain technical means to identify the main causative factors in electric fatal accident reports. This research proposes an accident causation identification method that combines text categorization and catastrophe association analysis. First, an improved text feature extraction method (TF-IDF-GloVe-LDA) is proposed by fusing term frequency-inverse document frequency (TF-IDF), global vectors for word representation (GloVe), and latent dirichlet allocation (LDA) algorithms. Then, an SVM classifier was used to categorize the 473 accident report texts into five categories, ACCIDENT, PROCESS, CAUSE, PROBLEM, and RESPONSE, based on the extracted text feature vectors. Second, for the 461 CAUSEs categorized from the accident report texts, the TF-IDF algorithm is utilized to mine them to obtain 62 accident causative factors. Using the word cloud map and semantic network, a visualization analysis is performed to reveal the intrinsic connection between causative factors. The human factor analysis and classification system (HFACS) is combined to construct a framework and a Boolean dataset for electric power fatal accident causative factors. Finally, the catastrophe progression method is utilized to improve the FP-growth algorithm and propose a catastrophe association analysis method. It is utilized to mine the Boolean dataset to obtain 28 association rules and construct the accident causal network. The main causative factors were ultimately identified by analyzing the critical causal chains within the network and examining the frequency and sensitivity of each node. The findings of this study indicate that a lack of effective management, supervision, and training serves as the fundamental cause of fatal accidents in the electric power industry when compared to equipment and environmental factors. This paper offered a fresh perspective on identifying primary accident causative factors in non-standardized text and exploring their intricate mechanisms of interaction, which holds significant implications for the pre-control management of accidents in the power industry. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region. [ABSTRACT FROM AUTHOR]

Electric power marketing system to electric power group provides the convenience, makes the power organization economic gains and also solve the many problems, but with the deepening of the application system, electric power group met with a lot of network attacks, shows that the system is under a state of risk, if attacked, the key information of power marketing may be leaked, resulting in economic and other losses. Therefore, in order to better protect the electric power marketing system, this paper has carried on the research. This paper introduces the current situation of network security protection of electric power marketing system, and then puts forward the protection technology scheme. The research shows that the technical scheme in this paper has better protection intensity and can protect the safety of electric power marketing system more effectively. [ABSTRACT FROM AUTHOR]

In the contemporary global discourse on environmental and developmental issues, the dual challenges of sustainable green energy and water supply stand paramount. These elements are vitally intertwined with the socio-economic vitality of the world. Notably, regions plagued by freshwater scarcity are increasingly turning to desalination which consists of a reliable and unconventional water source. The intersection of renewable energy with desalination and water purification processes presents a compelling synergy. This approach is particularly pertinent in areas where freshwater shortages coexist with abundant solar energy availability. Additionally, these technologies boast the advantage of low operational and maintenance costs. This paper delves into the design, optimization and financial analysis of a novel, standalone hybrid energy system, integrating photovoltaic and fuel cell technologies, for an agriculture farm situated in Kairouan, Tunisia. Unlike conventional systems, this model foregoes battery storage in favour of hydrogen storage, generated through water electrolysis powered by solar energy. This system harnesses solar energy for direct electrical power generation and hydrogen gas production. A segment of the generated electricity is allocated to electrolysis for green hydrogen production. In times of solar unavailability, the stored hydrogen is reconverted to electricity via a fuel cell. An intriguing aspect of this system is the utilization of saline well water, which, due to its high salt content, necessitates purification through desalination to prevent damage to the electrolysis unit and to render it suitable for agricultural irrigation. [Display omitted] • The production of green hydrogen, water and electricity for agriculture farm in Tunisia is analyzed. • The Levelized cost of green hydrogen produced in the PV farm is 2.25 €/kg. • A PV hydrogen Farm station with a capacity of 140 kW produces 1991 kg of green hydrogen and 235 MW h of electricity yearly. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

In recent times, various research studies have proven that the marine currents turbine (MCT) resource has enormous potential for electric power. For the proper exploitation of this resource, it is necessary to understand the hydrodynamics of the marine current turbine. In this paper a modeling was done in order to design a horizontal axis MCT, in an optimal way. In the first step, we evaluated the energy potential of the northern region of Morocco Ksar Srir. In the second step, we described the different building blocks of the conversion chain of a marine current turbine system. Finally, we present the simulation results obtained on Matlab/Simulink. The multi-physical and the hydrodynamic modeling were tested on the P66 marine current turbine of Guinard energy of 3,5 kW. [ABSTRACT FROM AUTHOR]

The increasing demand for green hydrogen and power production necessitates the development of novel and upgraded power generation technologies. The solid oxide fuel cell combined heat and power (SOFC–CHP) system is a promising solution due to its high efficiency, clean operation, and fuel flexibility. In practical terms, SOFCs can operate using a variety of fuels such as alcoholic fuel, hydrogen, ammonia, etc. Recently, methanol and ammonia have become noteworthy alternative fuel options as it possesses advantages such as low transportation cost and high hydrogen storage capacity. In this paper, we have established two SOFC–CHP models fueled by methanol or ammonia integrated with the steam Rankine cycle. A comprehensive thermodynamic model is proposed for energy and exergy analyses. Sensitivity analysis is performed to study the system's characteristics. The results show that the SOFC–CHP system fueled by ammonia exhibits superior performance, the maximum electrical power, efficiency, and exergy efficiency is 430W, 7% and 10% greater than the methanol fueled system. The operating temperature is about 30K higher in methanol supplied system. The largest thermal power difference can reach 0.65 kW. Overall, the ammonia fueled SOFC–CHP system shows greater potential than that fueled by methanol. [Display omitted] • Using ammonia as fuel can improve energy and exergy efficiency by 7% and 10%. • Methanol fueled system requires almost half fuel than that supplied by ammonia. • The operating temperature is about 30K higher in methanol supplied system. • The largest thermal power difference can reach 0.65 kW. • The exergy destruction of preheat process accounts for the majority of both systems. [ABSTRACT FROM AUTHOR]

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Mo del Ca libration, and Val idation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector's heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous. This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression. The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results. • Experimental investigation of a scroll compressor. • Model calibration and validation using full factorial design and optimal experimental design. • Comparison of automated model development by Optimal Experimental Design and Machine Learning. • Test time reduction up to 80 % while still keeping reasonable accuracy. [ABSTRACT FROM AUTHOR]

The evolution of district heating systems is characterized by a trend towards lower supply temperatures and higher energy efficiencies. The most recent advancement in this field is the fifth-generation district heating and cooling systems (5GDHCS). Current literature identifies three key features of 5GDHCS: operating at near-ambient temperatures, the use of distributed booster heat pumps at end-users, and the capability to provide both heating and cooling services. However, there is ongoing debate on whether 5GDHCS represents a significant technological leap beyond its predecessor, the fourth-generation district heating system (4GDHS). To resolve fundamental questions surrounding the 5GDHCS concept and to highlight its distinctive features, this paper conducts a detailed comparison between 5GDHCS and 4GDHS, examining their functional, technological, and operational differences. Based on this, limitations in existing definitions of 5GDHCS are identified, and a refined definition better characterizing its unique attributes and advancement is suggested. Additionally, considering the 5GDHCS-related studies are growing fast, with over 30 works published each year since 2021, this paper tries to complement previous reviews with an updated perspective on its latest research progress and practical deployment, through a comprehensive state-of-the-art analysis. Results show that while 5GDHCS is a promising technology to decarbonize domestic heating and to promote sector coupling between electric power and district heating systems, it is still in the early stage of development, marked by substantial research gaps in technical, policy, regulatory, and market aspects. Analysis on an exhaustive collection of real-life cases also indicates a long way ahead to achieve its widespread commercialization. • Diverged opinions on the term of "fifth-generation" are summarized and discussed. • A comprehensive comparison between 5GDHCS and 4GDHS is provided. • Distinctive features of 5GDHCS are analyzed and a refined definition is suggested. • An updated review on the research progress and practical development of 5GDHCS. • A collection and analysis of research projects and real-life systems are presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper demonstrates the Kartini Reactor's educational utility. • A case study was conducted by investigating reactor criticality. • The study achieved reactor criticality by inserting 5 fuel elements, totaling 2524.57 g. • The critical mass determination was refined by plotting the inverse of counts against the total mass of U-235. • This approach utilized both polynomial and linear trend lines to analyze the data. Research reactors serve critical roles across multiple domains—from physics to medicine—while distinctly not being used for electrical power generation. The Kartini Research Reactor located in Indonesia exemplifies the integration of research reactors in education and scientific research, particularly highlighting its educational applications through the Internet Reactor Laboratory (IRL). Established in 1978 and operational since 1979, the Training Research and Isotope Production by General Atomic (TRIGA) Mark II reactor is instrumental in advancing nuclear science with facilities like the Lazy Suzan/Rotate Rack and Beam Port. The IRL significantly enhances its educational process, enabling remote access to reactor physics experiments, thus facilitating global university participation in nuclear engineering education. This capability allows for the real-time visualization of reactor operations and detailed presentation of operational data and experiment results, enriching the learning experience. This paper demonstrates the Kartini Reactor's educational utility through a case study investigating reactor criticality and determining the critical fuel mass under safe operational parameters. Employing telecommunication hardware and software, the study achieved reactor criticality by inserting 5 fuel elements, totaling 2524.57 g. The critical mass determination was further refined by plotting the inverse of counts against the total mass of U-235. This approach utilized both polynomial and linear trend lines to analyze the data. While the polynomial analysis provided upper and lower thresholds for critical mass, the linear regression offered a precise critical mass value of 2428.62 g, showcasing a deviation of only 4% from the experimental findings. [ABSTRACT FROM AUTHOR]

• Proposes an improved YOLOv5s model which combines the GIoU, Mish, and CBAM modules. • The improved algorithm can effectively detect different-size insulator defects under different environmental backgrounds. • The AP value in detecting insulator defects can be improved by 5 %. As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects. [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]

The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties. • Comprehensive study on enhanced voltage sag source localization assisted by ML. • Proposing three new time sample-based feature forms, effective for ML methods. • Identifying the most influential feature form (form 4) utilizing k-means clustering. • Developing diverse supervised models, including a designed one-dimensional CNN. • CNN-Feature form 4 achieved 99.37% accuracy with an acceptable prediction speed. [ABSTRACT FROM AUTHOR]

Growing demand for electricity due to economic development contributes to increased greenhouse gas production, especially CO 2. However, emissions can be limited by enhancing the efficiency of primary energy conversion, such as integrating geothermal energy into coal-fired power plants. Therefore, this paper proposes replacing conventional feed-water heaters with geothermal preheaters to create a hybridized system. This study was based on a numerical model validated at a selected Polish power unit. The model was subsequently calibrated for off-design conditions to facilitate partial load analysis. The obtained characteristics outperformed those of the non-hybrid unit, generating over 18 MW of electric power output. Such an improvement could potentially boost the unit's net efficiency by more than 2.6 %. This enhancement is significant as power units typically operate under part load for approximately 90 % of the time, hence the need to evaluate the performance characteristics of hybridized units in those states. Furthermore, the research outlines the potential decrease in the plant's CO 2 emission factor, with reductions reaching up to 6.5 % under off-design conditions. Based on a gap analysis of the existing literature, this paper's comprehensive partial load evaluation serves as a new addition to research on hybridized systems. • An in-depth analysis of feed-water heaters operation is performed. • Turbine performance calculations for both design and off-design states is validated. • The comprehensive study of hybridized systems integrates three major factors. • Hybrid geothermal-fossil power cycle is vital for specific CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of electrical power output (P e) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy P e. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R 2), mean absolute error (M A E), mean square error (M S E), root mean square error (R M S E), mean absolute percentage error (M A P E), Kling–Gupta Efficiency (K G E), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE Y V E − 2 achieves the highest (P e) accuracy, including R 2 = 0. 9368 , M A E = 3. 3378 , M S E = 18. 4800 , R M S E = 4. 2985 , M A P E = 0. 7354 % , and K G E = 0. 9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating P e in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology. • GP symbolic expressions can estimate electrical power output of CCPP. • Symbolic expressions with AT-V, AT-AP, AT-RH, AT-V-AP, AT-V-RH, AT-AP-RH, AT-V-AP-RH as input variables are most accurate. • B-A analysis, R2, MAE, MSE, RMSE, MAPE, and KGE metrics used as filter of large number of expressions. • Lower estimation errors are achieved with averaging ensemble. [ABSTRACT FROM AUTHOR]

The paper discusses issues involved in mission control of the International Space Station (ISS), in particular, specific aspects of monitoring and evaluating the status of Power Supply Systems (PSS) and solar arrays (SA) as their components. Power budget simulations use as their initial computational parameter the current SA performance, which is determined on the basis of a special performance evaluation mode. The obtained data make it possible to take into account the spaceflight factors resulting in performance degradation. Besides that, when using the SA performance evaluation mode as a method for periodically checking the SA status, the more accurate determination of the actual SA performance involves taking into account the shadowing of the photovoltaic cells, the effect of the light reflected off the Earth surface, and current generation resulting from illumination of both the front and the back surfaces of the SA. The paper presents the results of space experiment Albedo carried out onboard the Russian Segment (RS) of the ISS, which included development of techniques for taking into account the above factors when simulating the PSS power budget for the Service Module (SM) Zvezda. Developed as a result of carrying out the space experiment was a procedure for determining SA performance and simulating the power input from the ISS RS SM SA taking into account the Earth albedo; recommendations were made for the orbital spacecraft PSS control modes taking into account the possibility of power generation from the Earth outgoing light flux. Based on the results of testing the developed computational schemes for evaluating the SA performance and simulations of incoming power that take into account the Earth albedo, substantiated values were obtained for the proposed reference parameter for evaluating the ISS RS SM SA. The developed procedure provides the most accurate performance evaluation for both front and back surfaces of the SA. Computational schemes for predicting incoming power proposed within the framework of the procedure make it possible to give highly accurate predictions of incoming power using precise simulations of light flux coming from Earth, as well as to determine the contribution of variable components into the SA performance determined during a periodic SA performance estimate. • Solar arrays current as the sum of the front and back solar arrays sides components. • Solar arrays current as the sum of Sun and emissions coming from Earth components. • Prediction models of incoming electric power characterized by Earth albedo accounting features. • Current generation coefficients are found by function minimizing by Newton-Gauss method. [ABSTRACT FROM AUTHOR]

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China's renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China's renewable energy industry. [ABSTRACT FROM AUTHOR]

Efficient treatment of antibiotic residue, containing a range of persistent organic compounds, is essential to ensure the protection of both the environment and human health. Supercritical water gasification technology could achieve the clean and efficient energy conversion of antibiotic residues. In this paper, a self-thermal supercritical water gasification system for antibiotic residues is developed to convert antibiotic residues to high-purity H 2 and electrical power. The system analysis results demonstrate that increasing the antibiotic residue slurry concentration, elevating the gasification reactor temperature, and optimizing the ratio of water-to-dry antibiotic residues effectively enhance system efficiency and H 2 yield within specific ranges. The H 2 yield and net electrical power at optimal operating conditions are 634.52 kg/h and 2.3 MW with 10 t/h dry antibiotic residue feeding. Meanwhile, the energy efficiency, exergy efficiency, and cold gas efficiency of the system are 61.32%, 63.05%, and 53.96%, respectively. The life cycle assessment analysis shows that the global warming potential of the system is 20.77 kg CO 2 -eq/kg H 2 with the above operating conditions. Taking into account the emission reduction impact of the system on thermal power generation, the inclusion of CO 2 capture and storage units can potentially lead to a negative global warming potential for the system. [ABSTRACT FROM AUTHOR]

In this research paper, a new research path was opened with a new scientific view and a new philosophy for the goals of crude oil production through the development of important joints in the production process of crude oil where a horizontal three-phase separator was developed to produce green electrical energy without the need for combustion processes by creating a design for rotating parts capable of converting the momentum of the flow into electrical energy, the first part is the rotating diverter, which collides with the input mixture, which leads to a reduction in the momentum of the flow and an initial separation of gas from liquid, the collision results in the rotation of the wheel on which the concave bowls are installed, and thus generates electrical power by the dynamo connected to it. The second part of the separator that has been developed is to add a cone-shaped rotating external mist extractor with helical fin to perform two functions at the same time separating suspended liquid particles from the gas and generating clean electric power by the dynamo connected to it when it rotates under the influence of gas pressure. The efficiency of the developed separator parts was tested in performing their traditional function to separate the different phases and it was found that the developed separator is more efficient in addition to its ability to generate clean energy. [Display omitted] • A new vision for the purpose of investing in hydrocarbon fuels and develop production equipment. • The study of generating green electric power from crude oil and natural gas without combustion. • The rotating parts in the upgraded separator to make it more efficient in separating the three phases gas,oil,water. [ABSTRACT FROM AUTHOR]

The vast majority of Earth-orbiting satellites carry an electrical power subsystem (EPS) which main components are solar panels and secondary batteries. During eclipse periods, satellites are powered only by rechargeable batteries which have a large energy density but a limited power density. This fact limits the power capabilities of small satellites during eclipse periods. Due to the large power density of Supercapacitors (SCs), ground and on-orbit tests have been conducted to verify their applicability on satellite EPSs. At the moment, no studies are underway on the issue of sizing SCs for eclipse operations. Hybrid configuration could reduce the mass and volume of EPS or maintain those reference values but increasing peak power capabilities. This paper deals with this issue. On the one hand, novel analytical expressions of a variable capacitance SCs are derived, including time–voltage dependence in constant power applications. Also an equivalent formulation for a SCs bank (SCsB) is derived. On the other hand, a SCsB sizing procedure is presented, considering the energy and power requirements of a particular space mission as an input. Finally, a simple mission is analysed, the results showing an improvement on the hybrid EPS design with respect to the traditional, being the mass reduction of 36%. • Implementation of a simple but accurate supercapacitor (SC) model for spacecraft applications. • Innovative analytical expressions of the performance of a SC in constant power applications. • Novel determination of the electrical parameters of a SCs bank, treated as an equivalent SC model. • Estimation of key variables in the operation of hybrid storage systems for space applications. • Preliminary design of a SCs bank devoted to supply high power during eclipse times. [ABSTRACT FROM AUTHOR]

• Electrical Power Systems Research. • This paper proposes a joint planning scheme for soft open points and energy storage to address the issue of unbalanced supply and demand in distribution networks, aiming to enhance the system's economic efficiency and stability. • This paper evaluates the proposed scheme in comparison with other literature under different electricity price conditions. The results indicate that as the electricity price increases, the economic growth rate of the proposed scheme in this paper is lower than that of the schemes in other literature, further confirming the advantages of this scheme. • This paper takes full account of the uncertainty of distributed power sources, adopts a two-stage robust model, and provides reasonable values for robust control parameters based on the planning results. With the increasing penetration rate of renewable energy, the uncertainty of its output has exacerbated power imbalances among different distribution networks. In this paper, we propose collaborative planning of soft open points and energy storage systems to balance a distribution network with source-load imbalance, aiming to improve the economy and reliability of the distribution network. Firstly, considering that distributed generation has uncertainty, the K-means is used to construct a typical day scenario, on the basis of which a collaborative planning model is established with the objective function of minimizing the annual integrated cost. Then, the original nonlinear nonconvex model is transformed into a mixed-integer second-order conical planning model by Big-M and second-order conical relaxation, and a two-stage robust optimization is used to seek the planning solution with the lowest operational cost under the worst-case scenarios, aiming to enhance the robustness of the planning model. Finally, the feasibility of the proposed planning model is verified on two source-load unbalanced IEEE 33-node active distribution network arithmetic cases. [ABSTRACT FROM AUTHOR]

• Challenges that mass amounts of electric vehicles (EVs) impose to power system operations. • Opportunities of intelligently managing the flexibility of smart charging of EVs are discussed. • Hybrid DERMS concept is proposed as a key tool for safe integration and proper management of emerging distribution grids with high amounts of EVs. • A real-life case study of integrating two large-scale EV charging stations into an existing feeder is presented. Global initiatives are actively progressing to integrate large numbers of electric vehicles (EVs) as part of efforts to electrify and decarbonize the transportation sector. This approach plays a crucial role in addressing global greenhouse emissions and pollution problems. However, mass integration of EVs imposes unprecedented challenges to electrical power systems, especially to their distribution grids. These challenges range from planning issues, where the traditional panning procedures are not adequate for integration of EVs and their charging stations, to operational challenges, where a huge number of dispersed EVs can cause peak load increase, voltage violations, congestions, feeder overloads, etc. Hence, despite the significant importance of maximizing the deployment of EVs, their stochastic integration can give rise to critical challenges in electric power systems, thereby impeding their subsequent expansion. Therefore, in order to properly integrate mass amounts of EVs, without causing technical violations in the electrical power system, intelligent software tools are required, to aid distribution system operators and engineers in the transition to a new era of electrified transportation. In this paper, we argue that novel software solutions called Distributed Energy Resource Management Systems (DERMSs) are a key solution for enabling a safe integration of mass amounts of EVs into emerging distribution grids. Specifically, we introduce a novel Hybrid DERMS framework, as an integration of multiple hierarchical levels of DER management solutions, and propose this solution as a suitable instrument for distribution system operators and grid engineers to safely integrate and manage high volumes of EVs within the grid infrastructure. The hypothesis is validated through a real-life use case for integration and management of high amounts of EVs into an existing distribution grid, using an industrial DERMS solution. Specifically, an in-depth analysis of adding two large charging stations, boasting capacities of 5 MW each, has been performed through all stages of integration, from the planning stage, up to using their flexible output to manage technical constraint violations in real time. The influence of these EV charging stations on both the feeder where they are planned to be integrated, as well as on the entire supply substation, have been analyzed in detail. The resultant findings, especially on successfully managing overloads, peak load increases, as well as voltage violations through intelligent use of aggregated EVs through DERMS, have proven to be notably encouraging, further substantiating the paper's hypothesis. [ABSTRACT FROM AUTHOR]

The electric power sector is experiencing a rapid shift towards renewable energy resources. To accommodate this transition, the transmission system requires significant upgrades. Although enhancing grid capacity through transmission network expansion is always a solution, it can be very costly and requires a protracted permitting process. Grid-enhancing technologies (GETs) encompass a broad range of hardware and software tools that enable reconfiguration of the transmission grid and adjustment of its parameters. The proliferation of such technologies enhances transfer capability over the current transmission network, thus reducing the need for grid expansion. This paper offers a comprehensive review of grid-enhancing technologies. The paper discusses definitions of transmission flexibility and presents methods that are developed to quantify grid flexibility. The paper offers a comprehensive review of an extensive range of grid-enhancing technologies, including both principles of operation and state-of-the-art developments. Environmental impacts of grid-enhancing technologies, including renewable energy curtailment and carbon emission reduction, are also discussed. In addition to technical aspects of grid enhancing technologies, the paper also reviews the literature on the incentive challenges facing GETs adoption and efficient. Overall, the paper offers a comprehensive review of the advancements in grid-enhancing technologies, discusses the remaining critical challenges, and identifies directions for future research. • Grid-enhancing technologies (GETs) are necessary for the future grid. • GETs can enhance the transfer capability up to 50% over the existing grid. • While some GETs rely on mature technology, industry adoption remains rather limited. • GETs integration within energy management systems faces modeling challenges. • Current market and regulatory structures provide little to no incentive for adoption and efficient operation of GETs. [Display omitted] [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

Life Cycle Climate Performance (LCCP) is a widely accepted metric to evaluate the carbon footprint of air conditioning (AC) systems "from cradle to grave." This paper: (1) reviews the invention and evolution of LCCP, including a comprehensive timeline and bibliography; (2) documents the successful application of LCCP in the replacement of HFC-410A with HFC-32 in room air conditioners; (3) compares the conceptual frameworks and the operational approaches; and 4) reflects on the drawbacks of current LCCP research and points out possible future work. The major policy-relevant findings are: 1. The indirect emissions caused by energy consumption is 70 to 80 percent of the LCCP of AC systems in most countries but will decline in importance as electric power supply shifts rapidly from fossil fuel to renewable energy sources, which have near-zero carbon intensity; 2. The embodied greenhouse gas (GHG) emissions in refrigerant manufacture are, in most cases, negligible but the physical and chemical properties are crucial for system optimization for low carbon footprint; 3. The LCCP metric can be used for multiple purposes such as refrigerant selection and AC system architecture optimization; and 4. Data limitations in material manufacturing and the carbon intensity of electric power are the most significant challenges. Finally, this paper describes a variety of methods to fill in data gaps, including the correction factor method , the data-driven method , and the database searching method. The next-generation LCCP will be an enhanced evaluation process considering local climate, heat islands, and local power supply characteristics. [ABSTRACT FROM AUTHOR]

Nowadays, there is extensive penetration of renewable energy sources (RESs) in microgrids such as solar power stations (SPS) and wind power stations (WPS). The RESs are power electronic converter-dominated systems that have zero inertia making the microgrid to have very low inertia. Low inertia microgrid has a high rate of change of frequency (RoCoF), and the frequency response is highly volatile. To cope with this issue virtual inertia and damping are emulated into the microgrid. Virtual inertia and damping, i.e., converter with short-term energy storage device (ESD), which delivers and absorbs electrical power depending on the frequency response of microgrid and minimizes the power variation between power generation and power consumption. In this paper virtual inertia and damping are emulated based on a novel two-degree of freedom PID (2DOFPID) controller optimized with African vultures optimization algorithm (AVOA) technique. The meta-heuristic technique, AVOA, tunes the gains of the 2DOFPID controller and also the inertia and damping gain of the virtual inertia and damping control (VIADC) loop. AVOA comes out to be superior to other optimization techniques when compared in terms of convergence rate and quality. The performance of the proposed controller is compared to other conventional control methodology that has demonstrated its better performance. The dynamic response of such a proposed methodology in a microgrid model is verified in an OPAL-RT real-time environmental simulator, i.e., OP4510. • The Renewable energy sources are mostly converter-based sources which cause the microgrid to have low Inertia. • Low inertia microgrid experiences a high rate of change of frequency. • Enhance the microgrid inertia to emulate virtual inertia and damping in the microgrid. • Estimate the inertia and damping gain of the virtual synchronous generator and tune by the two degrees of freedom order controller using different metaheuristic optimization. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between climate risk and climate policy uncertainty, and CO2 emissions in the US over the 2000–2022 period using a structural Factor- Augmented Vector AutoRegression (FAVAR) model with a two-step principal component analysis based on monthly observations. We employ a very recent measure to proxy for uncertainty regarding climate policy based on the Climate Policy Uncertainty Index (CPU) of Gavriilidis (2021), while Climate Risk is proxied by financial cost of natural disasters and number of deaths. We use different variables for CO2 emissions, based on total and sectoral emission (commercial, electric power, residential sector, transportation, and industrial sector). The results indicate that a significant percentage of the variance of CO2 emissions in the US, is explained by Natural Disasters Cost, which also seem to account for a significant percentage of the US Total Renewable Energy Consumption variance. Shocks to disaster costs seem to decrease all type of emissions significantly and also increase renewable energy use significantly. Natural disasters increase political disagreement among U.S. politicians, as well as, the climate policy uncertainty, highlighting the need for efficient policymaking and regulations. In further results, we find that an increase in Partisan Conflict decreases emissions and explains a significant amount of renewable energy variance. [ABSTRACT FROM AUTHOR]

A mixed-signal four-channel biphasic current-mode functional electrical stimulator chip with a novel charge compensation circuit is presented in this paper. This chip can induce programmable biphasic current ranging from 10 μ A to 2.56 mA for each channel. However, a significant problem with functional electrical stimulation is charge balancing (CB) due to anodic and cathodic current mismatch for process variation. Therefore, a novel compensation module including both passive and active charge compensation schemes are adopted in this chip. The charge compensation module autonomously adjusts the supply and compensation current to achieve fast compensation and low-power consumption simultaneously. The proposed biphasic stimulator is designed and fabricated in a 0.18 μ m CMOS process with a die area of 2.27 mm × 1.99 mm. The measurement results from a test bench demonstrate the proposed circuit functionalities, and the standby power consumption of this chip is 193.7 μ W. Besides, animal experiments on mice for seizure suppression with deep brain stimulation were performed to verify the efficacy and safety of the developed stimulation chip. • A biphasic current-mode electrical stimulator with charge compensation is presented. • A novel module including both passive and active charge compensation schemes is adopted in this chip. • The proposed biphasic stimulator is designed and fabricated in a 0.18 μ m CMOS process. • Fast residual charge compensation and low power consumption module implemented. • Animal experiments were performed to verify the efficacy and safety of the developed stimulator. [ABSTRACT FROM AUTHOR]

With the rising usage of fossil fuels, there is an urgent need to develop new technologies specifically based on renewable energy sources to power the vehicles running on fuel. A fuel cell is an electrochemical cell that is used to convert the chemical energy of a fuel directly to electric power. Fuels cells possess advantages such as smaller size, high efficiency, silent operation, etc. However, there can be significant variations in the size and power output of the fuel cells depending upon the application. The focus of this paper is to estimate the performance of an integrated system comprising of Polymer Exchange Membrane Fuel cell (PEMFC) and vapour adsorption refrigeration system to produce electric output and cooling effect simultaneously. The adsorption system in this study is based on activated carbon and methanol combination. The effect of operating parameters such as the operating temperature, current density and evaporator temperature on the energy and exergy efficiency of the system is presented. The study shows a remarkable improvement in the performance of the integrated system compared to PEMFC alone. The results show that the system energy and exergy efficiency decrease as the current density value increases. Maximum system energy and exergy efficiency of 63.01% and 29.88% are achieved. In addition, a maximum energy efficiency of 65.39% was reported at an evaporator temperature of 5 °C and a current density of 0.8 A/cm2. • Thermal integration of PEMFC with vapour adsorption refrigeration cycle is studied. • Energy and exergy efficiency of PEMFC with and without VADRC is analyzed. • The influence of key operating parameters on system performance is investigated. • A maximum system efficiency of 66.83% is achieved after incorporating VADRC. [ABSTRACT FROM AUTHOR]

The electric power industry is related to the national livelihood, and regional economic development is closely related to the pillar industry. At present, China's economy continues to develop steadily, and the promotion of domestic industrialization has also increased the demand for electricity. In recent years, the state power industry has made great progress, in order to further adapt to the needs of domestic and foreign market competition, the State Grid has also carried out a systematic reform of its own management system. In the reform process, strengthening the construction of performance appraisal system is a very important change. According to statistics, as of June 2020, the number of employees of State Grid has reached more than 900,000. As the most basic business unit of the state grid, the team is the cell of the state grid. It is the basis for the completion of various production management work and plays an important role in the safe production and stable output of electric power enterprises. Based on the relevant indicators of the work of the team, this paper analyzes the performance of the team through the production cost performance model, and provides suggestions for the optimization of the team for power grid enterprises [ABSTRACT FROM AUTHOR]

• Blackouts bring major economic and societal repercussions. • Efficient black-start strategy is critical to address increasing risk of blackouts. • Variable speed hydro can provide a fast response and is used during black start. • Meet black start needs with renewables and cutting diesel use. • RES-BESS boosts power and fortifies an integrated renewable grid. A blackout refers to a total power outage that rapidly plunges the electrical power system into a severe crisis, posing significant challenges for subsequent restoration efforts. Blackouts can occur due to a range of factors, such as delays in clearing faults, generator malfunctions, equipment failures, and human errors. After a blackout occurs, all possible measures are taken to achieve the fastest power system restoration. To ensure preparedness for such emergencies, scheduled black start exercises are conducted using units capable of initiating power restoration from scratch, allowing authorities to assess their readiness in catastrophic scenarios. Numerous similar exercises have been conducted regularly generating to highlight the complexities of power system restoration of the grid with the black starts of hydro generating units. This has aided plant operators in improving restoration methods and finding additional remedial steps to enhance existing processes to achieve an effective black start exercise. This paper overviews the multiple mock black start exercises applied to hydroelectric generating units and discusses several blackouts worldwide. In addition, Tehri Hydro Power Plant is considered for the case study. Tehri HPP is linked to the 400 kV EHV grid, which was later upgraded to 765 kV transmission lines. Further, this paper observes and discusses various challenges encountered during mock black start exercises. Possible remedial actions are suggested to address these challenges. [Display omitted] [ABSTRACT FROM AUTHOR]

Most of the leading countries have already started mass scale electrical power production by renewable energy sources (RESs) to meet global net-zero emissions target. Harvesting RESs for electrical power generation has significant negative environmental impact which must be addressed before their full-fledged implementation. Crucial challenge of the scientist, as considered in this paper, is to supply electrical power with increasing future demand avoiding negative environmental impact as much as possible. In this context, correspondence of the adverse environmental impacts for utilizing RESs for the leading countries are focused on this paper as they are major stakeholders in this regard. State of the art, present status, prospects of various RES based power plants of the leading countries, forecasted capacity of installation, global potential, emission, required annual growth rate of utilizing RESs to meet the global net-zero emissions target, their environmental impacts considering comparative analysis of RES practices, and different government plans are discussed. It would be supportive to find out the possible sustainable solution to meet the future electricity demand with minimizing adverse environmental impact. [Display omitted] • Prospects of harvesting different RESs for electrical power generation are pointed out. • Analysis of environmental impacts for utilizing RESs is performed. • Forecast with time series analysis is illustrated to find the gap between the current progress and target. • Correspondence among the current installation, prospect, challenge, target, and forecast are depicted. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. The power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange. • This paper proposes a cascaded structure energy management system (EMS) for nanogrid to balance power for both forecasted demand and forecast errors including high instantaneous demand. • The proposed EMS comprises a power balancing optimization process for forecasted demand and reference power modulation strategy for the forecast errors. The power balancing optimization utilizes nanogrid sources, such as PV, fuel cell, and battery, to meet forecasted demand as well as a supercapacitor charging process for addressing low energy density characteristics. • The proposed reference power modulation strategy is utilized to allocate power from the HESS comprising a battery and supercapacitor, in order to compensate forecast errors. In addition, this proposed strategy is performed considering battery and supercapacitor constraints such as their power changing rate, total power limitation. [ABSTRACT FROM AUTHOR]

• This paper presents a review on the state-of-the-art electric vehicle charging technologies i.e., fast, super-fast, and ultra-super-fast charging stations that are under development. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. • EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper has highlighted these issues and their impacts to power grids. • This paper investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. • The charging stations are modeled for three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and Chargepoint Express 20), with separate CC-CV charging modes of the DC fast chargers incorporated. To quantify the impacts, case studies are conducted on the simulation platform for two different scenarios. • The impact on the electric power grid is evaluated in terms of voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) experienced by the corresponding transformer connected to the charging stations. • The results have revealed that random and fluctuating EV fast-charging loads pose much more intense impacts on the power systems than the normal EV slow charging loads. The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to evaluate their impact on the grid, using factors such as voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) for the transformer that was connected to the charging stations. The study results indicated that the power systems were affected more significantly by random and fluctuating EV fast-charging loads, compared to normal EV slow-charging loads. [ABSTRACT FROM AUTHOR]

CubeSats have become in the last decade ideal platforms to demonstrate new technology or to facilitate access to space due to the reduction in development time and costs. However, one of the main problems is the short lifetime of their missions. In many cases, it is due to the lack of correct thermal design and thermal analysis or even no thermal analysis at all. Specifically, the solar panel thermal design affects the global satellite behavior as the electric power supplied by the solar cells depends on their temperature. Typically, the solar panel thermal design is reduced to use its backside as a radiator (if they are deployable). This paper presents a new thermal solar panel assembly using an intermediate layer of PGS (Pyrolitic Graphite Sheet), which at room temperature has a thermal conductivity 3.8 times greater than pure copper, between the solar cells and the solar panel support, in order to homogenize the solar cells temperature. The new design is compared to the standard design developing the GMM (Geometrical Mathematical Model) and TMM (Thermal Mathematical Model) of a CubeSat 6U located in LEO using ESATAN-TMS. The proposed design gets a reduction of up to 5 °C in the temperature variation of the solar cells. [ABSTRACT FROM AUTHOR]

Hail has a significant impact on the output of photovoltaic (PV) modules. Hence, this paper aims to give complete understanding of hail impacts on PV modules performance analytically and experimentally. The investigation was carried out followed the guidelines as prescribed by the IEC 61215–2:2016/IS 14286: 2019 standard on three PV modules of different thicknesses (2.8 mm, 3 mm, 4 mm) of the front glass. Hail size has been varied from 25 mm to 55 mm, the variation in weight of the ice ball is 7.5 gm to 80 gm, and the variation in speed of the ice ball is from 23 m/s to 34 m/s. After each round of the hail test, the samples are tested for Standard Test Condition (STC), Insulation Test (IT) and Wet Leakage Current Test (WLCT). In addition to measuring electric power output, an electroluminescence (EL) inspection of the cracks of the PV module has been performed. The scientific novelty is the optimization of the PV module based on experimental data under hail tests. Results show that the there is a continuous irreversible effect of the excitation force on the PV modules in the event of hail, and it can reduce the power output. Parametric analysis reveals that the glass thickness of 4 mm is sufficient to withstand severe damage; sample 3 only loses 1.1% of its power output compared to the initial value. However, samples 1 and 2 lose 21.8% and 11.74%, respectively. The insulating resistance of the samples appears to be decreasing with each round of the testing. The % reduction in wet leakage current resistance with respect to the initial value for samples 1, 2 and 3 are 55.25%, 46.81% and 27.23%, respectively. Lesser decrement in wet leakage current resistance indicates that sample 3 is more reliable for the hail-prone area. According to the findings, PV modules with a front glass thickness of 3.2 mm are exemplary when hit by hail up to 35 mm in diameter at a velocity of 27 m/s. However, in hail-prone areas, installers should choose PV modules with a front glass thickness of 4 mm or higher to minimize or eliminate hail damage. • Hail tests on photovoltaic (PV) modules should be beyond the conventional testing. • Power reduction of 21.47% is observed in glass to backsheet PV modules under hail. • PV modules with front glass thickness of 4 mm can withstand severe hail damage. • Use low wet-leakge current resistance modules for high hail-prone regions. • PV modules with glass to backsheet design are suitable for high hail-prone regions. [ABSTRACT FROM AUTHOR]

A ferroelectret is a flexible cellular polymer structure that generates electrical power under mechanical force. It behaves like a piezoelectric material in that a voltage is generated when the ferroelectret deforms. Its electrical output significantly depends on the surface charge density of the material used. Previous ferroelectrets fabricated from polydimethylsiloxane (PDMS) benefit from the soft, stretchable mechanical properties of the PDMS but the ability of this material to trap charge at its surface is poor. This paper presents approaches to enhance the surface charge density and stability of a PDMS based ferroelectret device by adding Polytetrafluoroethylene (PTFE) to the PDMS. This PDMS/PTFE composite has been evaluated when used to fabricate the ferroelectret device and when used as an additional coating the internal void surfaces of an otherwise plain PDMS ferroelectret. As the proportion of PTFE increases, the surface charge density on the void surfaces increases resulting in a corresponding increase in the piezoelectric properties of the ferroelectret. A weight ratio of PTFE powder and PDMS of 1:3 was found to achieve improved piezoelectric performance with an initial effective piezoelectric coefficient, d 33 e , of around 700 pC/N which decayed initially and reached a steady state value of around 365 pC/N measured after 4 months after poling. This presents a considerable improvement in performance when compared with the effective d 33 e of pure PDMS which has an initial value of around 110 pC/N and drops to below 8 pC/N after 4 months. The energy harvester potential of the ferroelectret was explored by cyclically compressing the PDMS/PTFE composite layer ferroelectret structure with a force of 500 N applied at 1 Hz. The output of the ferroelectret was found to charge a 10 μF capacitor to 0.12 V after 40 s. Maximum output power occurs at a load resistance of 15 MΩ, with a peak power of 4 μW. A fabricated PDMS/PTFE composite layer ferroelectret sample (weight ratio of PTFE powder and PDMS of 1:3) was also tested as a pressure sensor range from 0 to 500 N and achieved a sensitivity of 9.9 mV/N. The nonlinearity error of the proposed sensor was 1.7%. [Display omitted] • This paper explores the addition of PTFE particles to enhance the surface charge density in PDMS ferroelectrets. • Different approaches to adding the PTFE particles to the ferroelectret structure are evaluated. • Adding a PDMS/PTFE blend as a layer within the ferroelectret device is the preferred fabrication approach. • The PDMS/PTFE composite layer ferroelectret device shows good sensitivity to mechanical strain. • A PTFE/PDMS weight ratio of around 1:3 was found to achieve the highest effective piezoelectric coefficient d 33e values. [ABSTRACT FROM AUTHOR]

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based traversability analysis method that combines geometric features with a pyramid-polar space representation based on SVM classifiers. In particular, we show that by fusing geometric features with information stemming from coarser pyramid levels that account for a broader space portion, as well as integrating important implementation details, allows for a noticeable boost in performance and reliability. The main goal of this work is to demonstrate that traversability analysis is possible with effective results and in real-time even on cheaper hardware than expensive GPUs, e.g. CPU-only PCs. The proposed approach has been compared with state-of-the-art deep learning approaches on publicly available datasets of outdoor driving scenarios, running such algorithms both on GPU and CPU to compare runtimes. Our method can be fully executed on CPU and achieves results close to the best-in-class methods, runs faster, and requires fewer and less expensive hardware resources, consuming less than 30% electrical power with respect to deep learning models on embedded processing units. We release with this paper the open-source implementation of our method. • Real-time geometrical approach for traversability analysis with SVMs on CPU. • Feature fusion module on a pyramidal polar grid space representation. • Comparison with SOTA semantic segmentation methods on publicly available datasets. • In-depth analysis of performance and power consumption on an embedded device. • Open-source implementation of the proposed method is released for public usage. [ABSTRACT FROM AUTHOR]

• CVR determination in (non)-sinusoidal conditions for low-cost digitizers. • Measurement uncertainty analysis using Monte Carlo method for CVR. • Low-cost paradigm related to improvement of metrology in underdeveloped countries. The paradigm of low-cost metrology implies the application of low-cost measuring devices in order to achieve satisfactory precision of the relevant measuring quantities. This is particularly important in underdeveloped countries where it is necessary to improve their metrology capabilities to ensure satisfactory standards for industry requirements. Therefore, the previously published conference paper is extended, and the performance of low-cost sampling devices is investigated for the purpose of high-precision electricity measurement in relevant national metrology institutes or measurement laboratories. The performance of the DMM 3000 device and the DAQ970A digitizers compared with high-resolution (24-bit) ADC-based sampling devices such as the NI PXI 5922 and NI PXI 4461. One of the main contributions of this article is the characterization of the channels of the low-cost digitizers in sinusoidal and non-sinusoidal regimes along with the uncertainty analysis using Monte Carlo method which has been carried out as well. [ABSTRACT FROM AUTHOR]

Combined cycle gas turbine power plants (CCGTs) are a combined cycle consisting of a gas turbine and a steam turbine to generate electricity. Sometimes CCGTs incorporate cogeneration to produce both heat and power. After the gas fuel combustion in the gas turbine combustion chamber, the flue gases are passed through the Heat Recovery Steam Generator (HRSG) to extract heat and generate additional electrical power using the steam turbine. The CCGT technology is recognized for its highest efficiency of 58% in electricity production among the power production technology. A highly efficient CCGT power plant with 60% efficiency produces even 2.5 times less CO 2 than a modern coal power plant with an efficiency of 45% because of the use of gas fuel and electrical efficiency. The CO 2 emission can be reduced further when the post-combustion CO 2 capture methods are applied. To perform CO 2 capture and to reduce the CO 2 emission from power plants, the CO 2 mass fraction in flue gas is the crucial parameter for the operation of the Post-combustion Carbon Capture and Storage (PCCS) technology. The PCCS technology uses solvents, which is the aqueous solution of amine that reacts with flue gas and absorbs the CO 2 , which is treated and separated later. The paper presents the results of the energy analysis of a post-combustion carbon capture process when integrated with a combined cycle gas power plant. The study considered two different gas fuels such as methane and syngas. Syngas composition was determined from the sewage sludge gasification process and can be treated as zero-emission CO 2 gas fuel. When the flue gas produced from the syngas is used in PCCS at more than 50% of load conditions, the negative CO 2 emission level in large-scale CCGT power plants can be reached. The paper presents the results of mass and energy balance analysis of HRSG of a CCGT integrated with PCCS to perform CO 2 capture. The analysis of HRSG using flue gases from methane and syngas is performed by calculating the enthalpy of flue gas, rate of heat exchange, and temperature distribution of each component of HRSG. The comparison of the result shows slight differences due to the different composition and flow rates of flue gases. The flue gas at the outlet of two HRSG is used in the PCCS at different load conditions. An aqueous solution of 30 wt% Monoethanolamine (MEA) with rich loading of 0.5 mol-CO 2 /mol-MEA and a mixture of 16 wt% 2-amino-2-methyl-1-propanol (AMP) – 14 wt% Piperazine (PZ) with rich loading of 0.62 and 0.86 mol-CO 2 /mol-amine respectively are used in the PCCS. Due to the reboiler duty of amines, the steam consumed by the PCCS for AMP-PZ regeneration is less compared to MEA. Depending upon the flue gas and solvent used in the PCCS, the power consumed by the PCCS from CCGT power plant is measured from 9.6% to 11.6%. For the given operating condition of the CCGT and PCCS at more than 50% load condition, the negative CO 2 emission level can be achieved. • Analysis of the post-combustion carbon capture process with CCGT was performed. • Comparison of PCCS performances using MEA and AMP-PZ mixture solvent was performed. • Achieving negative CO 2 emission level in CCGT using PCCS are presented. • Flue gas from methane and syngas from sewage sludge were used in the PCCS analysis. • CO 2 emission in flue gases from methane and syngas fuel was calculated. [ABSTRACT FROM AUTHOR]

Drones and UAVs are gaining worldwide popularity and are now considered a popular choice for both military and commercial applications. Icing poses tremendous safety and performance concerns for the operation of all types of drones. This paper is part of a project that studies icing effects on the hovering 0.66 m diameter Bell APT70 drone rotor and the development of potential ice protection systems. The objective of this paper is to investigate the use of electrothermal heaters with severe icing conditions and to characterize the energy efficiency of anti-icing or de-icing regimes. Rotor speeds of 3880, 4440 and 4950 RPM were used while two air temperatures, −5 and −12 °C, were also tested. The heat transfer profile on the blade was studied using dry run tests where the rotor operated with heating but without water spray. Anti-icing tests with variable heating power were tested to determine the location of the initial ice accumulation as well as the minimum heating power requirement to protect the leading edge. Moreover, anti-icing tests with constant heating rates were done and compared to previous tests of unprotected blades, under similar condition. On the other hand, de-icing tests with various heating cycles were also carried out. Results indicate that although de-icing requires significantly less heating energy compared to anti-icing, there is clear trade-off on rotor stable aerodynamic output, electrical power consumption and vibration levels, depending on the adopted de-icing mode. Future work is about investigating the rise of secondary effects, such as shed ice impact due to de-icing. Work is also planned for a hybrid ice protection system that combines electro-thermal heaters with icophobic or superhydrophobic coatings. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000. [ABSTRACT FROM AUTHOR]

• Compared with the traditional flexible interconnection method, the EDC-DN power supply architecture proposed in this paper does not simply use MTDC to achieve feeder interconnection but first optimizes and adds dynamic reactive power compensation devices based on voltage source converters in the context of a high percentage of DG access to the grid, and then the dynamic reactive power compensation equipment between different feeders is interconnected on the DC side by adding additional lines, thus weakening the voltage fluctuation of the grid when DG is connected while achieving closed-loop flexible interconnection between multiple feeders. This can achieve closed-loop flexible interconnection between multiple feeders while weakening the voltage fluctuation of the grid when DG is connected, provide a flexible active power adjustment path for high proportional DG access to DN, and realize power mutual aid between different feeders and network-level bearing of DG. • For the issue of power supply recovery after EDC-DN fault, a comprehensive analysis was conducted on the impact of different fault locations on the power supply recovery process. The collaborative plan between EDC and DAS was first proposed, clarifying the information interaction content between the distribution network automation central station, feeder sub-station, and EDC sub-station during the power supply recovery process. An EDC-DN two-stage power supply restoration architecture considering EDC continuous regulation, combinatorial optimization of interconnection switch and sectionalizing switch is proposed and designed, which fills the technical gap in the integration of EDC and DAS systems. • This paper demonstrates the real-time dynamics of EDC in the EDC-DN power supply restoration process through simulation. The restoration process includes a fast restoration phase based on EDC continuous regulation of power supply, a non-fault section transfer process based on mechanical switch action, and an economically optimal operation phase using an optimal combination of switches to reconfigure the distribution network topology. The current power supply restoration strategy of SOP focuses on the final restoration state of EDC continuous regulation and switch combination rather than on the whole process of power supply restoration. This paper considers the real-time dynamics of the entire EDC-DN power supply restoration process. It fills the technical gap in the current SOP power supply restoration without considering the intermediate process. The flexible interconnection device (FID) enables multiple distribution feeders in closed-loop mode to support a higher proportion of distributed generation (DG) and large-scale new loads connected to the distribution network (DN). Moreover, the closed-loop operation mode based on FIDs provides an effective technology roadmap for high-quality and highly reliable power users, especially the quick restoration of power supply after the failure of DN. This paper proposes a new supply shape in which the multi-terminal DC links are flexibly embedded in the AC DN (EDC-DN) to realize closed-loop operation, achieving lower investment costs than the existing mode that replaces tie switches with soft open points (SOPs). Meanwhile, a two-stage power supply quick recovery control technology is proposed for the EDC-DN, including the fast power supply recovery stage based on EDC and the optimizing restoration stage based on network reconfiguration. Firstly, the role of EDC in the process of power supply restoration of three different faults on AC feeder is analyzed. Secondly, a detailed power supply recovery process is proposed, which is based on a distribution automation system (DAS) and the real-time control capability of EDC. Thirdly, the power supply restoration second stage is implemented, which is based on network reconfiguration with switch combination and EDC continuous variable optimization. Finally, a 58-node DN with an EDC system was constructed using PSCAD/MTDC simulation software to verify the effectiveness of the two-stage strategy for EDC-DN power restoration. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• The optimization problem of EPS and flexibility-enhanced DHSis formulated. • The DHS is retrofitted with BCT and uses a variable mass flow operation strategy. • A conditional-DRO is employed to hedge against the uncertainty of renewable energy. • Propose adouble-layer distributed algorithm to achieve the distributed solution. In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results. [ABSTRACT FROM AUTHOR]