Showing papers in "Frontiers in Energy Research in 2023"

A comprehensive review on EV power converter topologies charger types infrastructure and communication techniques

Abstract: The energy transition is a crucial effort from many sectors and levels to create a more integrated, carbon-neutral society. More than 20% of all greenhouse gas emissions are attributed to the transportation sector, predominantly concentrated in metropolitan areas. As a result, various technological hurdles are encountered and overcome. It facilitates the adoption of electric vehicles (EVs) run on renewable energy, making them a practical option in the fight against climate change and the completion of the energy revolution. Recent developments suggest that EVs will replace internal combustion engine (ICE) during the next few months. The EV either gets all of its power from batteries and ultra capacitors or some of it from both. In a plug-in electric vehicle, the battery or ultra-capacitor is charged by an AC supply connected to a grid line. In a hybrid electric vehicle, the ICE charges the battery or ultra-capacitor. Regenerative braking is another way to charge the battery from the traction motor. In a plug-in electric vehicle, the energy from of the battery or ultra-capacitor is put back into the AC grid line. Electronic converters are essential to converting power from the grid line to the traction motor and back again. This paper examines the current state of the electric vehicle market throughout the world and its potential future developments. Power electronics converters (PEC) and energy storage devices significantly impact electric vehicles’ efficiency. Furthermore, general opinions about EVs are soon in this sector, as well as research topics that are still open to industry and University researchers.

Lifecycle-based feasibility indicators for floating solar photovoltaic plants along with implementable energy enhancement strategies and framework-driven assessment approaches leading to advancements in the simulation tool

Abstract: Floating solar photovoltaic (FSPV) systems that allow solar panel installations on water bodies are gaining popularity worldwide as they mainly avoid land-use conflicts created by, and for their superior performance over, ground-mounted photovoltaic installations. Though many studies in the FSPV literature showed how superior FSPVs perform, we still believe there are few potential opportunities for further enhancement in performance. On the other side, the industry’s delivery of FSPV installation service to clients is often questioned, highlighting that FSPV modeling is compromised, leading to false promises on energy performance and feasibility. This might be true given the lack of modeling tools specific to FSPV. With this hypothesis, this review investigates existing modeling approaches by FSPV researchers/industry people practicing and potentially implementable energy performance enhancement strategies leading to the advancement of modeling tools. The review outcome suggested that every FSPV researcher/service provider must carefully design and optimize the FSPV system considering suitable performance enhancement strategies, for instance, replacing conventional solar panels with bifacial ones and integrating various cooling and cleaning methods. Also, while assessing the feasibility, they must follow the lifecycle-based performance indicators that broadly fall under the techno-economic-environmental and social aspects with an appropriate framework-driven assessment approach. Lastly, we have shown a conceptual FSPV project simulation tool consolidating the performance indicators and explored performance enhancement strategies that we believe would help the FSPV community.

China’s green finance and total factor energy efficiency

Abstract: Based on data on 280 prefecture-level cities from 2008 to 2019, this study uses a difference-in-difference (DID) model to first analyze the impact of China’s green finance pilot policy (GFPP) on total factor energy efficiency (TFEE) and then further investigate the mediating and heterogeneous effects of GFPP. Results indicate that first, GFPP effectively improves TFEE, and the robustness tests show that the estimation results are reliable. Second, GFPP mainly improves TFEE by promoting industrial structure optimization and green technology innovation. Last, the role of GFPP in improving TFEE is mainly reflected in cities with high environmental protection enforcement and intellectual property protection. Therefore, China must expand the GFPP scope, further improve the local green finance practice capability, actively guide green fund to support energy technology innovation, accelerate green industrial transformation, and pool social forces to jointly promote green economic development.

Research on the impact of digital transformation on green development of manufacturing enterprises

Abstract: Digital development is changing with each passing day, and the traditional manufacturing industry is gradually shifting from extensive development to intensive development, the most prominent manifestation of which is the digital transformation and development of enterprises. This paper first discusses the internal mechanism of digital transformation promoting the green development of manufacturing enterprises from three aspects: product greening, technology greening and investment greening. Then, based on the panel data of China’s listed manufacturing enterprises from 2011 to 2019, the fixed effect model, two-stage least squares method, mediating effect model and moderating effect model were used to test the relationship between them. The results show that digital transformation can promote the green development of manufacturing enterprises, and this conclusion still holds after a series of robustness tests. The empirical results of the mediating effect model show that digital transformation promotes the green development of manufacturing enterprises through three mediating paths: increasing green product output, technological innovation level and green investment level. Moreover, the promoting effect of digital transformation on the green development of enterprises will be moderated by the heterogeneous effect of environmental uncertainty. Therefore, accelerating the digital development of enterprises and promoting the construction of digital China is conducive to the green development of enterprises, and finally realizing the common development of digitalization and greening.

Pore-scale study of microbial hydrogen consumption and wettability alteration during underground hydrogen storage

Abstract: Hydrogen can be a renewable energy carrier and is suggested to store renewable energy and mitigate carbon dioxide emissions. Subsurface storage of hydrogen in salt caverns, deep saline formations, and depleted oil/gas reservoirs would help to overcome imbalances between supply and demand of renewable energy. Hydrogen, however, is one of the most important electron donors for many subsurface microbial processes, including methanogenesis, sulfate reduction, and acetogenesis. These processes cause hydrogen loss and changes of reservoir properties during geological hydrogen storage operations. Here, we report the results of a typical halophilic sulfate-reducing bacterium growing in a microfluidic pore network saturated with hydrogen gas at 35 bar and 37°C. Test duration is 9 days. We observed a significant loss of H2 from microbial consumption after 2 days following injection into a microfluidic device. The consumption rate decreased over time as the microbial activity declined in the pore network. The consumption rate is influenced profoundly by the surface area of H2 bubbles and microbial activity. Microbial growth in the silicon pore network was observed to change the surface wettability from a water-wet to a neutral-wet state. Due to the coupling effect of H2 consumption by microbes and wettability alteration, the number of disconnected H2 bubbles in the pore network increased sharply over time. These results may have significant implications for hydrogen recovery and gas injectivity. First, pore-scale experimental results reveal the impacts of subsurface microbial growth on H2 in storage, which are useful to estimate rapidly the risk of microbial growth during subsurface H2 storage. Second, microvisual experiments provide critical observations of bubble-liquid interfacial area and reaction rate that are essential to the modeling that is needed to make long-term predictions. Third, results help us to improve the selection criteria for future storage sites.

Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction

Abstract: Aiming at the strong non-linear and non-stationary characteristics of power load, a short-term power load forecasting method based on bald eagle search (BES) optimization variational mode decomposition (VMD), convolutional bi-directional long short-term memory (CNN-Bi-LSTM) network and considering error correction is studied to improve the accuracy of load forecasting. Firstly, a decomposition loss evaluation criterion is established, and the VMD optimal decomposition parameters under the evaluation criterion are determined based on BES to improve the decomposition quality of the signal. Then, the original load sequence is decomposed into different modal components, and the corresponding CNN-Bi-LSTM network prediction models are established for each modal component. In addition, considering the influence of various modal components, holiday and meteorological factors on the error, an error correction model considering short-term factors is established to mine the hidden information contained in the error to reduce the inherent error of the model. Finally, the proposed method is applied to a public dataset provided by a public utility in the United States. The results show that this method can better track the changes of load and effectively improve the accuracy of short-term power load forecasting.

Digital economy enables common prosperity: Analysis of mediating and moderating effects based on green finance and environmental pollution

Abstract: This study aims to explore the impact of the digital economy on common prosperity. For this reason, a bidirectional fixed effect model based on panel data of 30 provinces (cities and autonomous regions) in China is empirically tested. The results show that the digital economy can significantly improve the level of common prosperity, and has a positive impact on green and sustainable economic activities such as promoting environmental improvement, coping with climate change and resource conservation and efficient utilization, which is still valid after a series of robustness tests. It also demonstrates the role of green finance as a partial intermediary in the process of shared prosperity and as a negative regulator of environmental pollution. Analysis of regional heterogeneity shows that the enabling effect of the digital economy on common prosperity is more significant in eastern and central provinces, but not significant in western provinces. The results of this study have some reference significance for some countries, where the gap between rich and poor has widened during the epidemic, to narrow the income gap and provide ideas for the parties that made commitments at the Glasgow Climate Summit (COP26) to curb warming and reduce CO2 emissions. That is, continuous improvement of digital infrastructure; emphasis on the intermediary role of green finance and the negative regulating role of local environmental pollution levels; following the relative comparative advantages of regions and formulating differentiated policies for the development of the digital economy, etc.

Solar-powered five-leg inverter-driven quasi-dynamic charging for a slow-moving vehicle

Abstract: Road transport is becoming increasingly electric as it becomes more environmentally friendly. A green transportation system includes solar arrays along the roadside, encouraging the eco-friendly EV charging system. This paper proposes a road-side photovoltaic system to charge the batteries of slow-moving electric vehicles using a five-leg inverter. The five-legged inverter, which utilizes a stand-alone PV system to drive the charging pads, enhances the probability of achieving the sustainability goal. The limitations of the conventional H-bridge inverter, such as its more prominent design and higher number of switches or straightforward design and restricted power level, are addressed by this converter. The proposed 3.3 kW, 85 kHz inverter energizes the four transmitter pads while a receiver pad moves over the transmitter pads and inductively extracts the power. The d.c.-d.c. converter is used to feed the power to the proposed inverter. The P and O-based MPPT algorithm with a tuned PI controller is used to generate the driving pulses of the d.c.-d.c. converter. The signals are generated based on the voltage and current output of the solar panel output. This control algorithm ensures the stability of the system output response. Additionally, the tuned d.c.-d.c. converter achieves maximum efficiency independent of the load resistance. The system maintains constant power transfer profile concerning load resistance variations. The 520*520 mm Double D-pad transmits the power, while the series-series compensation network assists the charging pads in achieving resonance. The developed systems’ nominal charging voltage and current are 144 V, 20 A, with an equivalent battery resistance of 7.2 Ω.

Materials for electrocatalysts in proton exchange membrane fuel cell: A brief review

Abstract: Energy is a requisite factor for technological advancement and the economic development of any society. Currently, global energy demand and supply largely rely on fossil fuels. The use of fossil fuels as a source of energy has caused severe environmental pollution and global warming. To salvage the dire situation, research effort is geared toward the utilization of clean, renewable and sustainable energy sources and the hydrogen energy economy is among the most preferred choices. Hydrogen energy economy, which includes hydrogen production, storage and conversion has gained wide consideration as an ecofriendly future energy solution with a fuel cell as its conversion device. Fuel cells, especially, the proton exchange membrane category, present a promising technology that converts hydrogen directly into electricity with great efficiency and no hazardous emissions. Unfortunately, the current generation of proton exchange membrane fuel cells faces some drawbacks that prevent them from large-scale market adoption. These challenges include the high costs and durability concerns of catalyst materials. The main source of high cost in fuel cells is the platinum catalyst used in the electrodes, particularly at the cathode where the sluggish oxygen reduction reaction kinetics require high loading of precious metals. Many research efforts on proton exchange membrane fuel cells are directed to reduce the device cost by reducing or completely replacing the platinum metal loading using alternative low-cost materials with “platinum-like” catalytic behaviour while maintaining high power performance and durability. Consequently, this review attempts to highlight recent research efforts to replace platinum and carbon support with other cost-effective and durable materials in proton exchange membrane fuel cell electrocatalysts. Overview of promising materials such as alloy-based (binary, ternary, quaternary and high-entropy alloys), single atom and metal-free electrocatalysts were discussed, as the research areas are still in their infancy and have many open questions that need to be answered to gain insight into their intrinsic requirements that will inform the recommendation for outlook in selecting them as electrocatalysts for oxygen reduction reaction in proton exchange membrane fuel cell.

Multi-resolution based PID controller for frequency regulation of a hybrid power system with multiple interconnected systems

Abstract: Automatic generation control (AGC) in modern power systems (PS) is difficult because the output power of many power resources is intermittent, and the load and system parameters vary widely. In this paper, a novel control scheme known as the wavelet based multiresolution proportional integral derivative (MRPID) controller for multiple interconnected hybrid power sources is presented. The discrete wavelet transform (DWT) is used in the proposed wavelet based MRPID controller to split the error between the actual and target responses into different frequency components at several stages. To ensure optimum system performance, the gains of the MRPID controller are fine-tuned using the Fox Optimizer Algorithm (FOA), a new powerful metaheuristic technique. The proposed MRPID controller is evaluated in a three-area hybrid system where each area contains a combination of conventional generation (gas, thermal reheat and hydro) and renewable generation sources (solar, and wind). The proposed controller also accounts for system non-linearities, including boiler dynamics, time delay, dead band, generation rate limitation, system uncertainties, and load changes. In the hybrid system studied, the proposed MRPID is compared with FOA-tuned PID and PI controllers. The proposed MRPID controller tuned with FOA algorithm effectively reducing the peak overshoot of 89.03%, 76.89 and 56.96% and undershoot of 69.52%,66.90 and 94.29% for ∆Ptie12, ∆Ptie23 and ∆Ptie13 respectively as compared to FOA based PI controller.

State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm

Abstract: Due to excellent power and energy density, low self-discharge and long life, lithium-ion battery plays an important role in many fields. Directed against the complexity of above noises and the strong sensitivity of the common Kalman filter algorithm to noises, the state of charge estimation of lithium-ion battery based on extended Kalman filter algorithm is investigated in this paper. Based on the second-order resistor-capacitance equivalent circuit model, the battery model parameters are identified using the MATLAB/Simulink software. A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the individual battery are tested. The simulation and experimental results of terminal voltage for lithium-ion battery is compared to verify the effectiveness of this method. In addition, the general applicability of state of charge estimation algorithm for the battery pack is explored. The ampere-hour integral method combined with the battery modeling is used to estimate the state of charge of lithium-ion battery. The comparison of extended Kalman filter algorithm between experimental results and simulation estimated results is obtained to verify the accuracy. The extended Kalman filter algorithm proposed in this study not only establishes the theoretical basis for the condition monitoring but also provides the safe guarantee for the engineering application of lithium-ion battery.

Low temperature ensures FeS2 cathode a superior cycling stability in Li7P3S11-based all-solid-state lithium batteries

Abstract: All-solid-state lithium sulfide batteries exhibit great potential as next-generation energy storage devices due to their low cost and high energy density. However, the poor conductivity of the solid electrolytes and the low electronic conductivity of sulfur limit their development. In this work, the highly conductive Li7P3S11 glass-ceramic solid electrolyte with room temperature conductivity of 1.27 mS cm−1 is synthesized and combined with the FeS2 cathode and Li-In anode to fabricate FeS2/Li7P3S11/Li-In all-solid-state Li-S battery. The assembled battery delivers high initial discharge capacities of 620.8, 866.4 mAh g−1, and 364.8 mAh g−1 at 0.1C under room temperature, 60°C and 0°C, respectively. It shows a discharge capacity of 284.8 mAh g−1 with a capacity retention of 52.4% after 80 cycles at room temperature. When the operating temperature rises to 60°C, this battery suffers a fast decay of capacity in 40 cycles. However, this battery sustains a high discharge capacity of 256.6 mAh g−1 with a capacity retention of 87.9% after 100 cycles under 0°C, smaller volume expansion of ASSBs at 0°C keep the solid/solid contact between the electrolyte particles, thus resulting in better electrochemical performances. EIS and in situ pressure characterizations further verify that the differences of electrochemical performances are associated with the volume variations caused by the temperature effects. This work provides a guideline for designing all-solid-state Li-S which is workable in a wide temperature range.

Radical innovation detection in the solar energy domain based on patent analysis

Abstract: Introduction: Detecting radical innovations in the solar energy domain could offer innovation references and support the promotion of solar energy. However, relevant studies in the solar energy domain are lacking, and the related methods need to be improved. Methods: In this paper, a new framework to identify radical innovations in the solar energy domain is proposed by combining a technological convergence study and scientific relation analysis, and the link prediction method is utilized to detect potential radical innovations in this domain. Results: 1) The distributions of both the technological classes and scientific categories are uneven in the solar energy domain. The top 15 technological classes account for nearly 75.46% of all classifications. Fifteen scientific categories are cited by all the patents, and applied physics, multidisciplinary material science, energy and fuels play important roles in this domain. 2) The relationships among technological classes have evolved over time and have mainly focused on neighbouring disciplines. 3) A total of 130 patents containing new convergence relationships and/or closely related to science are identified as radical innovations. Radical innovative topics are related to the subdomains of solar photovoltaic (solar PV), heat storage, heat exchangers, and solar collectors. 4) Five potential radical innovative topics are identified. Automatic plants for producing electric energy, solar energy ecology houses, and so on are considered to have great potential in the future. Discussion: The results are consistent with the authoritative report and previous studies, which verify the viability of our methods. And the findings have important implications for scientists, policy-makers, and investors in this domain.

Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network

Abstract: In recent years, with the rapid development of renewable energy, the penetration rate of renewable energy generation in the active distribution network (ADN) has increased. Because of the instability of renewable energy generation, the operation stability of ADN has decreased. Due to the ability to cut peak load and fill valley load, battery energy storage systems (BESSs) can enhance the stability of the electric system. However, the placement and capacity of BESSs connected to ADN are extremely significant, otherwise, it will lead to a further decline in the stability of ADN. To ensure the effectiveness of the BESSs connected to the grid, this work uses the fuzzy kernel C-means (FKCM) method for scene clustering. Meanwhile, a multi-objective optimization model of BESS configuration is established with the objective of BESS configuration cost, voltage fluctuation, and load fluctuation, and solved by non-dominated sorting genetic algorithm-II (NSGA-II). In this work, the grey target decision method based on the entropy weight method (EWM) is used to obtain the optimal compromise solution from the Pareto non-dominated set. Moreover, the proposed method is tested and verified in the extended IEEE-33 node system and the extended IEEE-69 node system. The results show that the BESSs configuration scheme obtained by NSGA-II can effectively reduce the fluctuation of voltage and load, and improve the stability of ADN operation.

Prediction of coal seam gas content based on the correlation between gas basic parameters and coal quality indexes

Abstract: The measurement of gas content in coal seam by means of indirect method involves heavy workload, long period, high cost and complicated operation and a proneness of negative values in the process of measuring gas absorption constant. To address these problems, the gas basic parameters and coal quality indexes of 90 coal samples from 90 coal mines in 13 provinces of China are determined experimentally in this paper. The intrinsic relationship between gas adsorption constant a and atmospheric adsorption capacity Q 0 -initial velocity index of gas emission Δ p , gas adsorption constant b and volatile V daf - apparent density ARD is analyzed, and a prediction model of coal seam gas content based on gas basic parameters and coal quality index is established. The results show that the effect of Q 0 - Δ p correlation on a is mainly caused by the change of specific surface area and gas pressure of coal, while the effect of V daf -ARD correlation on b is mainly caused by the change of pore volume of coal. By comparing the predicated value from the prediction model of coal seam gas content with the measured value, it is found that the average absolute error rate of predicted value is 8.15%. This method is proven to be effective and feasible in routine gas content predictions, and can provide a reference for coal seam gas content prediction in China.

Differential voltage analysis for battery manufacturing process control

Abstract: Voltage-based battery metrics are ubiquitous and essential in battery manufacturing diagnostics. They enable electrochemical “fingerprinting” of batteries at the end of the manufacturing line and are naturally scalable, since voltage data is already collected as part of the formation process which is the last step in battery manufacturing. Yet, despite their prevalence, interpretations of voltage-based metrics are often ambiguous and require expert judgment. In this work, we present a method for collecting and analyzing full cell near-equilibrium voltage curves for end-of-line manufacturing process control. The method builds on existing literature on differential voltage analysis (DVA or dV/dQ) by expanding the method formalism through the lens of reproducibility, interpretability, and automation. Our model revisions introduce several new derived metrics relevant to manufacturing process control, including lithium consumed during formation and the practical negative-to-positive ratio, which complement standard metrics such as positive and negative electrode capacities. To facilitate method reproducibility, we reformulate the model to account for the “inaccessible lithium problem” which quantifies the numerical differences between modeled versus true values for electrode capacities and stoichiometries. We finally outline key data collection considerations, including C-rate and charging direction for both full cell and half cell datasets, which may impact method reproducibility. This work highlights the opportunities for leveraging voltage-based electrochemical metrics for online battery manufacturing process control.

A review of renewable off-grid mini-grids in Sub-Saharan Africa

Abstract: Sub-Saharan Africa (SSA) is home to 75% of the world’s unelectrified population, and approximately 500 million of these live in rural areas. Off-grid mini-grids are being deployed on a large scale to address the region’s electrification inequalities. This study aims to provide a comprehensive review of the research on the off-grid renewable mini-grids in SSA. The study covers the current status of the level of deployment of off-grid mini-grids. It also reviews multi-criteria decision-making models for optimizing engineering, economics, and management interests in mini-grid siting and design in SSA. The statuses of financing, policy, and tariffs for mini-grids in SSA are also studied. Finally, the current status of energy justice research in respect of mini-grids in SSA is reviewed. The study shows the important role of decentralized renewable technologies in the electrification of SSA’s rural population. Within a decade since 2010, the rural electrification rate of SSA has increased from 17% to 28%, and 11 million mini-grid connections are currently operational. Despite these gains, the literature points to several injustices related to the present model by which SSA’s renewable mini-grids are funded, deployed, and operated. Hence, several recommendations are provided for the effective application of the energy justice framework (EJF) for just and equitable mini-grids in SSA.

A new test method for simulating wear failure of hydraulic pump slipper pair under high-speed and high-pressure conditions

Abstract: In practical engineering, it is very difficult to obtain data on the slipper wear of hydraulic pumps, especially under high-speed, high-pressure conditions, which limits the development of fault diagnosis technology for hydraulic pumps. At present, a test method that can accurately simulate the operating state of the slipper pair under high-speed and high-pressure conditions does not exist. The reliable load-bearing design of the slipper pair is difficult to carry out effectivetest verification, which limits the development of high-speed and high-pressure piston pumps. Therefore, an experimental design method was proposed to directly simulate the high-speed, high-pressure friction state of the slipper pair based on the change law of reprinting residual pressing force.

Real-time Volt-Var control of grid forming converters in DER-enriched distribution network

Abstract: The growing installation of distributed energy resources (DERs) in a distribution network (DN) poses substantial issues related to voltage regulation. Due to constrained switching operation and slower response time, traditional voltage regulation devices cannot handle current voltage-related challenges. One alternative to solve these problems is to use smart converters to control the reactive power to regulate the voltage. Volt-Var control (VVC) is one of the simplest approaches for controlling the reactive power from smart converters. Among several converters, grid forming converters (GFCs) are more suitable in DER-enriched distribution networks. Since DER-enriched distribution networks have a higher fluctuation in voltage profile, real-time control is advantageous. Therefore, this work presents an advanced real-time reactive power control for handling voltage violations in a DN using GFC. The uniqueness of this method is that it controls the voltage magnitude of affected nodes by dispatching reactive power from smart converters in real-time. By running cyber-physical co-simulation (CPCS) between the Typhoon HIL 604 and OpenDSS, the Volt-Var control can be done in real time. The grid-forming converter is modelled in Typhoon HIL 604, which acts as a physical layer of the proposed cyber-physical system for real-time VVC. A CIGRE medium voltage distribution network is designed in OpenDSS and serves as one of the parts of the cyber layer. The CPCS between Typhoon HIL and OpenDSS and the control algorithm are both done by a programme written in Python. The execution of the control algorithm is performed in real time using the Supervisory Control and Data Acquisition (SCADA) developed in this study. The real-time simulation shows that the proposed real-time VVC is capable of handling voltage violations in real time in DER-enriched distribution networks.

Carbonaceous electrode materials for supercapacitor: Preparation and surface functionalization

Abstract: Supercapacitors became more and more important recently in the area of energy storage and conversion. Their large power deliveries abilities, high stability and environmental friendliness characteristics draw tremendous attention in high-power applications such as public transit networks. Carbonaceous materials with unique surface and electrochemical properties were widely used in supercapacitors as electrode materials. This review focuses on the developments in supercapacitor electrodes made from carbonaceous materials recently, their working principle and evaluation parameters were summarized briefly. The preparation methods and electrochemical properties of different carbonaceous materials were compared and classified. It was found that the surface situation (e.g., porous structure, hydrophilic) of carbonaceous materials strongly affect the electrochemical performances of supercapacitor. So far, active carbons would be the most applicable carbonaceous electrode materials owing to their good chemical stability and conductivity, extensive accessibility inexpensiveness. But their energy densities still fall behind practical demands. Both theoretical calculations and experimental studies show that surface modification and doping of carbonaceous materials can not only optimize their pore size, structure, conductivity and surface properties, but also can introduce extra pseudocapacitance into these materials. Considering global environmental pollution and energy shortage problems nowadays, we sincerely suggested that future work should focus on domestic, medical and industrial wastes residues derived carbonaceous materials and scaled production process such as reactors and exhaust gas treatment.

Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

Abstract: Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is proposed for boosting the accuracy of the prediction accuracy of wind speed. The optimization is performed in terms of a new optimization algorithm based on dipper-throated optimization (DTO) and genetic algorithm (GA), which is referred to as (GADTO). The proposed optimization algorithm is used to optimize the bidrectional long short-term memory (BiLSTM) forecasting model parameters. To verify the effectiveness of the proposed methodology, a benchmark dataset freely available on Kaggle is employed in the conducted experiments. The dataset is first preprocessed to be prepared for further processing. In addition, feature selection is applied to select the significant features in the dataset using the binary version of the proposed GADTO algorithm. The selected features are utilized to learn the optimization algorithm to select the best configuration of the BiLSTM forecasting model. The optimized BiLSTM is used to predict the future values of the wind speed, and the resulting predictions are analyzed using a set of evaluation criteria. Moreover, a statistical test is performed to study the statistical difference of the proposed approach compared to other approaches in terms of the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with an average root mean square error (RMSE) of 0.00046, which outperforms the performance of the other recent methods.

An electrical vehicle-assisted demand response management system: A reinforcement learning method

Abstract: With the continuous progress of urbanization, determining the charging and discharging strategy for randomly parked electric vehicles to help the peak load shifting without affecting users’ travel is a key problem. This paper design a reinforcement learning-based method for the electric vehicle-assisted demand response management system. Specifically, we formalize the charging and discharging sequential decision problem of the parking lot into the Markov process, in which the state space is composed of the state of parking spaces, electric vehicles, and the total load. The charging and discharging decision of each parking space acts as the action space. The reward comprises the penalty term that guarantees the user’s travel and the sliding average value of the load representing peak load shifting. After that, we use a Deep Q-Network (DQN)-based reinforcement learning architecture to solve this problem. Finally, we conduct a comprehensive evaluation with real-world power usage data. The results show that our proposed method will reduce the peak load by 10% without affecting the travel plan of all electric vehicles. Compared with random charging and discharging scenarios, we have better performance in terms of state-of-charge (SoC) achievement rate and peak load shifting effect.

The impact of solar energy investment in multilateral development banks on technological innovation: Evidence from a multi-period DID method

Abstract: Solar energy technology innovation plays a crucial role in achieving green and sustainable development and a low-carbon economy. The literature focuses on the economic and environmental effects of solar energy but ignores the role of solar energy investment in multilateral development banks (MDBs) on technological innovation. Using the panel data of 37 countries, including OECD countries and China, from 2006 to 2019, we adopt a multi-period DID model to empirically analyze the impact of solar energy investment in MDBs on technological innovation. The results show that solar energy investment in MDBs can significantly promote technological innovation, with the conclusion still being valid after conducting a series of robustness tests. The heterogeneity results indicate that the promoting effect of solar energy investment in MDBs on technological innovation is more significant in regions with higher human capital and higher innovation ability. The findings of this paper can be a useful addition to the literature on solar energy and technological innovation and serve as a useful reference for countries around the world as they accelerate solar energy investment and promote technological innovation.

Integration of solar based charging station in power distribution network and charging scheduling of EVs

Abstract: The rapid development of electric vehicles (EVs) such as Easy Bike, Auto-Rickshaw, and Electric Bike is a major contributor to global energy concerns. Although electric vehicles are bringing a new dimension to the transportation sector, with advantages such as being the cheapest method of transportation and emitting fewer greenhouse gases (GHGs), the massive amounts of energy required to charge the electric vehicles is a challenging issue. Pakistan is also moving toward the use of electric vehicles however the absence of charging facilities in Pakistan slows down the charging process and increases the prices for electric vehicle users. Finding the requisite charging without threatening the current power infrastructure is one of the most challenging tasks of the present era. Renewable energy-based charging is required to fulfill the charging demand of electric vehicles. To find the best configuration to meet the necessary daily charging demand, this proposed work undertakes a techno-economic assessment for a novel renewables-based grid-tied charging station. The technical, economic and environmental impacts of Solar based grid-tied charging stations are taken into account. Moreover, the results are justified by considering the losses and building the system model. The suggested strategy decreases energy costs from $.200/kWh to $.016/kWh while reducing grid load by 254,030 kWh/yr. Furthermore, the system completes 7.7 charging sessions every day, using 13% of the electricity generated. The remaining 87% of the electricity is sold back to the grid, which generates significant revenue.

Demand-driven NEV supplier selection: An integrated method based on ontology–QFD–CBR

Abstract: With the rapid development of new energy vehicles (NEVs), the market competition in the NEV industry is becoming increasingly fierce. Selecting the right supplier has become a critical aspect for NEV manufacturers. Therefore, based on the user’s demand information, selecting a suitable NEV supplier to support the NEV manufacturer’s management decision is a noteworthy research problem. The purpose of this study is to develop an integrated method for demand-driven NEV supplier selection based on ontology–quality function deployment (QFD)–case-based reasoning (CBR). The method is composed of three parts: 1) construction of domain ontology of NEV component supplier selection criteria based on text information mining; 2) extraction of demand attributes and determination of their weight based on latent Dirichlet allocation (LDA) and Kano model, as well as determination of expected attributes and their weights based on QFD; and 3) selection of an NEV component supplier based on CBR. To illustrate the use of the proposed method, an empirical study on the supplier selection of the XP NEV manufacturer is given. This method is helpful in selecting the most suitable component supplier for NEV manufacturers and relevant decision-makers.

Editorial: Smart grids and EnergyTech as a way for sustainable and environmental development of energy economy

Abstract: Sustainable and environmental development of the energy economy remains a strategic priority of today’s economy, shaped by the United Nations’ environmental initiatives. This priority, indicative of a progressive society and with the enormous power of uniting the world community to overcome common problems, is underpinned by a series of Sustainable Development Goals (SDGs). Sustainable and environmental development of the energy economy ensures universal energy security (SDG 7 “affordable and clean energy”) and environmental friendliness (SDG 13 “climate action”), achieved through the economic extraction of fossil fuel energy to preserve the heritage for future generations (Popkova et al., 2021; Popkova and Sergi, 2021). Other priorities are environmentally safe transportation to care for ecosystems (SDG 14 “life below water” and SDG 15 “life on land”), responsible consumption and production, and the development of clean energy (as an energy system) (SDG 12 “responsible production and consumption”) (Isiksal and Assi, 2022; Sun et al., 2022; Tang et al., 2022; Wang et al., 2022). Smart grids and EnergyTech have enabled sustainable and environmental development, unlocking the potential of industrialization 4.0, digital innovation, and smart infrastructure (SDG 9 “industry, innovation and infrastructure”). Smart Grids optimize the production and distribution of energy in utility systems through advanced metering (smart meters) and automated Big Data Research Topic through ubiquitous computing (UC) and the Internet of Things (IoT) and their analytics by artificial intelligence (AI) (Qin et al., 2022). EnergyTech is a high-tech fuel and energy complex that uses breakthrough technologies, including robots and blockchain, to balance energy markets and ensure responsible environmental management (Li et al., 2022; Mahboob Ul Hassan et al., 2022). OPEN ACCESS

Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode

Abstract: To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability.

An adaptive forecasting method for the aggregated load with pattern matching

Abstract: Electrical load forecasting plays a vital role in the operation of power system. In this paper, a novel adaptive short-term load forecasting method for the aggregated load is built. The proposed method consists of two stages: load forecast model preparation stage and adaptive load forecast model selection stage. In the first stage, based on historical load data of all consumers, the typical monthly load patterns are firstly identified in an optimal fashion with the aid of the cosine similarity. Then, for each identified monthly load pattern, a stacking ensemble learning method is proposed to train the load forecasting model. In the second stage, according to the similarity between individual load data of the latest month and the identified monthly load pattern, all the consumers are firstly classified into different groups where each group corresponds to a particular load pattern. Then, for each group, the corresponding trained load forecasting model is employed for short-term load forecast and the final forecast of the aggregated load is calculated as a simple aggregation of the produced load forecast for each group of consumers. Case studies conducted on open dataset show that, compared with the single forecasting model, the proposed adaptive load forecasting method can effectively improve the load forecasting accuracy.

Voltage monitoring based on ANN-aided nonlinear stability analysis for DC microgrids

Abstract: Due to the low inertia of the DC microgrid, the DC bus voltage is prone to drop or oscillate under disturbance. It is also challenging to supervise the stability of a DC microgrid since it is a highly nonlinear dynamic system with high dimensionality and randomness. To tackle this problem, this paper proposes a new method using ANN-aided nonlinear dynamic stability analysis for monitoring the DC bus voltage, which is combined with two steps. The first step is to establish six corresponding nonlinear accurate discrete iterative models of six switching modes of the PV-battery-load-based DC microgrid system, based on the Poincaré map theory, in order to judge the stability quantitatively with a promoted stability margin index. The second step is to use artificial neural networks (ANNs) to forecast the operating mode of the system when random changes occur in environmental circumstances and load power; this will aid the first step in being efficient and adaptable while determining stability cases. And the employed ANNs are trained with the datasets, including the circuit data, ambient temperature, irradiance, and load power, which are generated by MATLAB/Simulink simulation. Theoretical and simulation analyses are carried out under different operating conditions to validate the proposed method’s efficacy in judging the DC microgrid’s destabilizing oscillation and stable running.

Optimal control of renewable energy communities with controllable assets

Abstract: Introduction: The control of Renewable Energy Communities (REC) with controllable assets (e.g., batteries) can be formalised as an optimal control problem. This paper proposes a generic formulation for such a problem whereby the electricity generated by the community members is redistributed using repartition keys. These keys represent the fraction of the surplus of local electricity production (i.e., electricity generated within the community but not consumed by any community member) to be allocated to each community member. This formalisation enables us to jointly optimise the controllable assets and the repartition keys, minimising the combined total value of the electricity bills of the members. Methods: To perform this optimisation, we propose two algorithms aimed at solving an optimal open-loop control problem in a receding horizon fashion. Moreover, we also propose another approximated algorithm which only optimises the controllable assets (as opposed to optimising both controllable assets and repartition keys). We test these algorithms on Renewable Energy Communities control problems constructed from synthetic data, inspired from a real-life case of REC. Results: Our results show that the combined total value of the electricity bills of the members is greatly reduced when simultaneously optimising the controllable assets and the repartition keys (i.e., the first two algorithms proposed). Discussion: These findings strongly advocate the need for algorithms that adopt a more holistic standpoint when it comes to controlling energy systems such as renewable energy communities, co-optimising or jointly optimising them from both a traditional (very granular) control standpoint and a larger economic perspective.