Hasil untuk "Electricity"

Tao Wang, Hongbo Zhang, Dawei Yin et al.

In this paper, a novel 3D-printed high-gain wideband antenna composed of split-ring resonators, a bowl-shaped reflector, and a circular-fractal, multilayered, stacked microstrip antenna is presented. The cambered internal surface of the bowl-shaped structure is coated with sliver conductive adhesive. The microstrip antenna and split-ring resonators are installed inside and on top of the bowl-shaped structure, respectively. High gain is achieved due to the split-ring resonators and the curved reflective surface formed inside the bowl-shaped structure. At the same time, a high bandwidth is realized owing to the split-ring resonators and the microstrip antenna’s multilayered, stacked, and fractal structure. The proposed antenna is fabricated and measured. Operating within the frequency range of 5.63–6.78 GHz (reflection coefficient ≤ −10 dB), the antenna achieved a gain between 10.9 dBi and 14.6 dBi, with a peak gain of 14.6 dBi at 5.7 GHz. In addition, the antenna offers other significant advantages–low cost, low cross-polarization, and easy fabrication.

Epiphane Zingbe, Damgou Mani Kongnine, Bienvenu M. Agbomahena et al.

In a plant microbial fuel cell (P-MFC), the plant provides the fuel in the form of exudates secreted by the roots, which are oxidised by electroactive bacteria. The immature plant is hampered by low energy yields. Several factors may explain this situation, including the low open-circuit voltage of the plant cell. This is a function of the development of the biofilm formed by the electroactive bacteria on the surface of the anode, in relation to the availability of the exudates produced by the roots. In order to exploit the fertilising role of biochars, a plant cell was developed from <i>C. citratus</i> and grown in a medium to which 5% by mass of coconut shell biochar had been added. Its effect was studied as well as the distance between the electrodes. The potential of Cymbopogon citratus was also evaluated. Three samples without biochar, with inter-electrode distances of 2, 5 and 7 cm, respectively, identified as SCS2, SCS5 and SCS7, and three with the addition of 5 % biochar, with the same inter-electrode distance values, identified as S2, S5 and S7, were prepared. Open-circuit voltage (OCV) measurements were taken at 6 a.m., 1 p.m. and 8 p.m. The results showed that all the samples had high open-circuit voltage values at 1 p.m. Samples containing 5% biochar had open-circuit voltages increased by 16 %, 8.94% and 5.78%, respectively, for inter-electrode distances of 2, 5 and 7 cm compared with those containing no biochar. Furthermore, the highest open-circuit voltage values were obtained for all samples with <i>C. citratus</i> at an inter-electrode distance of 5 cm. The maximum power output of the PMFC with <i>C. citratus</i> in this study was 75.8 mW/m², which is much higher than the power output of PMFCs in recent studies.

Paul Ghelasi, Florian Ziel

Electricity price forecasting approaches generally fall into two categories: data-driven models, which learn from historical patterns, or fundamental models, which simulate market mechanisms. We propose a novel and highly efficient data-driven merit order model that integrates both paradigms. The model embeds the classical expert-based merit order as a nested special case, allowing all key parameters, such as plant efficiencies, bidding behavior, and available capacities, to be estimated directly from historical data, rather than assumed. We further enhance the model with critical embedded extensions such as hydro power, cross-border flows and corrections for underreported capacities, which considerably improve forecasting accuracy. Applied to the German day-ahead market, our model outperforms both classic fundamental and state-of-the-art machine learning models. It retains the interpretability of fundamental models, offering insights into marginal technologies, fuel switches, and dispatch patterns, elements which are typically inaccessible to black-box machine learning approaches. This transparency and high computational efficiency make it a promising new direction for electricity price modeling.

Mohammadreza Daneshvar, Behnam Mohammadi-ivatloo, Somayeh Asadi et al.

The considerable development of the electricity market subjects in recent years has provided a complex and more competitive environment for the participants. Each participant in this environment adopts a special strategy to maximize its profit or minimize its energy costs considering the significant constraints. In this paper, a short term optimal scheduling of thermal units, hydropower units, wind turbines, and pumped storage units has been proposed based on the energy market guidelines. The main objective of this research is to minimize the thermal energy production costs considering the uncertainty parameters along with the maximum utilization of clean energy production in the system. In order to evaluate the research goals, IEEE 5-bus standard test system is selected as the case study, which is equipped with both conventional and clean energy resources. In addition, probabilistic behaviors related to energy demand and wind production have been considered. Results proved the effectiveness of this model in minimizing the energy cost of thermal units.

Babar Sattar Khan, Babar Sattar Khan, Affaq Qamar et al.

The modern power generation systems are increasing their reliance on high penetrations of distributed energy resources (DERs). However, the optimal dispatching mechanisms mainly rely on central controls which receive the load demand information from the electricity utility providers and allocate the electricity production targets to participating generating units. The lack of transparency and control over the DER fuel inputs makes the physical power purchase agreements (PPAs) a cumbersome task. This research work proposes an innovative fractal moth flame optimization (FMFO) approach to tackle the problem of integrated load dispatch (ILD). The proposed methodology provides a mechanism to integrate the information of the proposed optimizer, i.e., FMFO into the smart contracts enabled by the blockchain technology. This problem entails the allocation of loads to power-generating units in a manner that minimizes the total generation cost in a decentralized manner. To improve the efficiency of dispatch operations in the presence of a substantial integration of wind energy, this study proposes a novel framework based on the principles of fractal heritage, drawing inspiration from the classical MFO method. To assess the effectiveness and adaptability of the algorithm suggested, various non-convex scenarios in the context of optimization for ILD are considered. These scenarios incorporate valve-point loading effects (VPLEs), capacity limitations, power plants with multiple fuel options, and the presence of stochastic wind (SW) power uncertainty, following a Weibull distribution. The findings demonstrate exceptional performance in terms of minimizing fuel generation costs compared to traditional algorithms.

Zhiwei SONG, Xinbo HUANG, Chao JI et al.

The transmission lines are complex in distribution and it is difficult to effectively detect their faults. Among them, the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments. Aiming at the problem that the transmission line connection fitting components are varied in scale and have poor accuracy in detecting their corrosion faults, a detection method is proposed for transmission line connection fittings and their corrosion faults based on dual attention embedding reconstruction and Swin Transformer, i.e., PCSA-YOLOv7 Former. The experimental results show that the proposed method is superior to 12 existing state-of-the-art object detection algorithms in comprehensive detection performance of the constructed TLCF dataset, with the mAP0.5 of the test set reaching 94.9 %. Compared with the baseline model YOLOv7, the proposed method improves the indexes F1 and mAP0.5 by 2.6 percentage points and 2.2 percentage points, respectively, indicating that the proposed method can more comprehensively understand the multi-scale semantic information in the images of transmission line connection fittings and learn their subtle details that are difficult to distinguish.

Shuyang Zhu, Ziqing Zhu, Linghua Zhu et al.

The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactions. Quantum computing offers a promising solution to resolve these uncertainties, and this paper introduces the Quantum Multi-Agent Deep Q-Network (Q-MADQN) method, which integrates variational quantum circuits into the traditional MARL framework. The main contributions of the paper are: identifying the correspondence between market uncertainties and quantum properties, proposing the Q-MADQN algorithm for simulating electricity market bidding, and demonstrating that Q-MADQN allows for a more thorough exploration and simulates more potential bidding strategies of profit-oriented GENCOs, compared to conventional methods, without compromising computational efficiency. The proposed method is illustrated on IEEE 30-bus test network, confirming that it offers a more accurate model for simulating complex market dynamics.

Saud Alghumayjan, Ming Yi, Bolun Xu

This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and uncertainty of real-time electricity prices, which create substantial risks of financial losses for energy storage participants relying on future price forecasts to plan their operations. The framework comprises a two-layer prediction model to quantify real-time price uncertainty confidence intervals with high coverage. The framework is distribution-free and can work with any underlying point prediction model. We evaluate the quantification effectiveness through storage price arbitrage application by managing the risk of participating in the real-time market. We design a risk-averse policy for profit-maximization of energy storage arbitrage to find the safest storage schedule with very minimal losses. Using historical data from New York State and synthetic price predictions, our evaluations demonstrate that this framework can achieve good profit margins with less than $35\%$ purchases.

Shima Amirinalloo, Zahra Atlasbaf

Abstract In this article, the authors propose a super wide band CPW‐fed fractal monopole antenna suitable for phased array antennas with a shaped beam radiation pattern, operating in the frequency ranges below and above 1 GHz at the same time. The antenna has an octagonal radiator with fractals of the same shape added to its edges and sides. Also, the symmetrical elliptical ground planes have reduced the antenna size and, consequently, the mutual coupling in the antenna array. Despite the super‐wide frequency range, the antenna's radiation pattern is relatively steady in the E‐plane, and the negligible ripple on the radiation pattern makes it suitable for shaped beam radiation patterns in phased array design. The single‐element antenna with an overall size of 0.24λ × 0.38λ at 500 MHz is printed on an FR‐4 substrate. The outcomes of the simulation and measurement are well agreed in the frequency band of [500 MHz to 5.5 GHz] with an impedance bandwidth (|S11|< −10dB) of 166.67% and VSWR< 2. Also, the fidelity factor of the antenna is studied to investigate the SWB performance.

Kwang-Ho Jang, Jong-Hyun Kim, Geun-Ju Kim et al.

This study showed the possibility of using a sub-terahertz (THz) traveling-wave tube (TWT) via measuring the transmission characteristics and TWT performance of the circuit by applying X-ray LIGA, a micro-fabrication process, to the interaction circuit. The applied circuit type, an E-bend folded waveguide, is a simple structure most suitable for lithography. A total of three applied frequencies were used the W-band, G-band, and 850 GHz. Among the manufactured circuits, the W-band circuit was applied to the TWT, one of the vacuum electronics devices (VEDs). This was done to prove the manufacturing accuracy of the circuit by comparing the nonlinear characteristics of the circuit with the prediction results. Through such testing, the small signal gain was measured as 13 ± 2 dB under the conditions of 13.96-kV and 24.2-mA electron beam energy. The frequency bandwidth was extremely wide, about 9 GHz, and showed similar characteristics to the simulation predictions. The maximum output of the device was obtained up to 1 W or more at 87.12 GHz by slightly increasing the beam current. These characteristic achievements showed the suitability of the TWT for very small circuits fabricated using the X-ray LIGA process, further suggesting the applicability of other sub-THz bands.

Salisu R. Isihak

Achieving universal electricity access by 2030 is one of the energy-related development targets in Nigeria and the electricity access rate is estimated at 62%, with urban being 91% and rural 30%. In line with the set SDG7 target of achieving universal electricity access by 2030, this study examines the least-cost options for providing electricity access to thousands of unelectrified communities in Nigeria using the open source spatial electrification toolkit (OnSSET). The study focuses on the coverage area of one of the distribution companies, i.e. Kaduna Electricity Distribution Company (KEDCo) which covers Kaduna, Kebbi, Sokoto, and Zamfara States in the North-Western part of Nigeria. Spatial data covering different areas such as population, digital elevation, energy resource availability, coverage of the distribution lines, among otherswere obtained from different sources and used in the OnSSET model. The result shows that by 2030, mini-grid PV will be the least-cost technology option for 58.82% of the unelectrified communities, followed by grid-extension (20.87%), standalone PV (20.15%), and mini-grid hydro (0.17%). Further, by 2025, the total number of settlements to be electrified will be 18,182. The number of settlements electrified by 2030 will be 22,727 with an estimated population of 15.4 million. To the achieve universal electrification, a total investment of US$4.97 billion is required by 2025 with an additional US$2.88 billion by 2030. The study recommends that the state and local governments should play a key roles in providing enabling environment for community-led off-grid projects since most of the settlements require electrification projects that are less than 20 kW.

José Rubio-León, José Rubio-Cienfuegos, Cristian Vidal-Silva et al.

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.

Frank Kofi Owusu, Peter Amoako-Yirenkyi, Nana Kena Frempong et al.

In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the SUTSE model to simulate the variances to predict the next day's peak and electricity demand. Relying on the study results, the running ergodic mean showed the convergence of the MCMC process. Before forecasting the peak and short-term electricity demand, a week's prediction from the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about 2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC. Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for Peak demand and daily energy respectively.

Hui WANG, Yi CAO, Ning LUO et al.

This paper proposes a three-terminals soft open point (SOP) operation control mode switching technology under the multi-feeder faults. Firstly, combined with the inner and outer loop structures of control mode of three-terminals SOP, a control mode switching strategy with improved control logic is proposed. Secondly, in order to realize the smooth transition between SOP working modes under multiple feeder faults, a control mode switching process suitable for three-terminals SOP under multiple feeder faults is proposed. Then, by adopting the phase angle pre-synchronization strategy, the smoothness of the phase angle when the power-loss feeder is connected to the grid is ensured. Thirdly, the phase angle pre-synchronization strategy is adopted to ensure the smoothness of the phase angle when the power-loss feeder is connected to the grid. Finally, A power distribution system model with three-terminals SOP is built for simulation. The simulation results show that the proposed operation control mode switching technology can reduce the maximum voltage fluctuation on the DC side, and the voltage and phase angle of each port can transition smoothly.

Alejandro Martín-Crespo, Enrique Baeyens, Sergio Saludes-Rodil et al.

The aggregate demand flexibility of a set of thermostatically controlled residential loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced. This probability function is used to predict the demand flexibility using a new experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection algorithm (MC&ESB). As a result, the maximum flexibility power that a VB can provide with a certain guaranteed probability is obtained. The performance and validity of the proposed method are demonstrated in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBM).

Anthony D Stephens, David R Walwyn

The use of wind and solar generation is fundamental to the decarbonisation of the United Kingdom electricity system. However, the optimal level of renewable energy as a proportion of total demand is still being debated. In this paper, several models, whose aims are to predict the efficiency of future system configurations, are explained. The models use historic records from the Gridwatch website for the year 2017, which are then scaled accordingly. The model predictions are first demonstrated for the 2035 Scenario as proposed by the National Grid in FES 2022. The analysis reveals that at least one third of the available wind and solar generation will exceed the ability of the electricity system to use it and will have to be shed. By defining an efficiency measure, the Marginal Decarbonisation Efficiency, which quantifies the incremental extent to which wind generation can decarbonise the electricity system, it is shown that the 2035 Scenario will have a low efficiency. Moreover, it will require the use of combined cycle gas turbines, which is at variants with the predictions of the National Grid steady state model. The paper also describes the derivation of a Generic Model, which allows the level of wind energy and dispatchable generation for all system configurations likely to be encountered in future decades, to be calculated without the use of computer models.

Monika Figiel-Kroczyńska, Ireneusz Ochmian, Marcelina Krupa-Małkiewiecz et al.

It is important to use light that has a positive effect on plants. For plant growers, achieving the lowest possible cost of shrub production is crucial. We investigated the influence of light (white and violet LEDs as well as fluorescent white and red light) on the rooting and growth of blueberry cuttings (V. corymbosum L.) 'Aurora' and 'Huron'. Blueberry cuttings (4 cm tall) were planted into boxes with peat, which were placed in a phytotron at 22 °C and illuminated for 16 hours a day. The plants died under the red fluorescent light source and, therefore, we discontinued its use. The other three light sources had a positive effect on plant growth and development. The light source had little effect on the content of macroelements in the leaves. Plants grown under white fluorescent and white LED light did not significantly differ in the height (22.0-25.8 cm), proline (4.67-7.23 μmol g-1), and polyphenol content (4987-5212 mg 100 g-1). In both cultivars, the violet LED light reduced plant growth and increased the content of polyphenols (6,448 mg 100 g-1) and proline (8.11-9.06 μmol g-1) in the leaves, which may indicate abiotic stress.  During the rooting of highbush blueberry cuttings, it is advisable to use white LED light. It has a positive economic impact on crop production due to low electricity consumption and it benefits the environment by eliminating mercury. The plant quality is similar to that of fluorescent white light.

Wei LI, Tingfang YANG, Lei ZHANG et al.

In order to reduce the workload of the operation and maintenance of distribution line arresters, this paper designs a frequency-dividing disconnector using double sphere gaps based on the inverse time principle, which can reliably shunt high and low frequency current. The disconnector can be separated automatically and reliably while the insulation of the arrester is damaged, and the fault section can be found. The disconnector realizes the mechanism of frequency division of leakage current by using inductance and large sphere gaps in parallel. Under the impulse or high frequency, the leakage currents flow through the big sphere gap and the disconnector would not act. While under the power or low frequency, the leakage currents flow through the inductor. When the current takes value from 40 mA to 0.5 A, resistance heating action would be utilized. When the current is larger than 0.5 A, the small sphere gap breakdown action would be adopted, which avoids the mal-operation of disconnector due to burned out resistance. In these two cases, the greater the leakage current, the faster the disconnector operates. Through the ATP model simulation, the parameters of the inductance and the operating characteristics of the large gap can be determined. The numerical simulation and experimental results show that the disconnector can operate reliably when the insulation of the arrester is damaged.

Haoran Zhao, Sen Guo

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

Yanbiao Feng, Jue Yang, Zuomin Dong

Electrified vehicles (xEV), especially the battery electric vehicle (BEV), are burgeoning and growing fast in China, aimed at building a sustainable, carbon-neutral future. This work presents an overview and quantitative comparison of the carbon-neutral vehicles fuel options based on the well-to-wheel (WTW) analysis. A more intuitionistic figure demonstrates the fuel options for greenhouse gas (GHG) emissions and describes the sustainability. Electricity and hydrogen shift the tailpipe emissions to the upstream process, forming larger WTW emissions from a fuel cycle view. The electricity WTW GHG emission reaches as much as twice that of gasoline. However, the high efficiency of the electric drive system improves the WTW emission performance from a vehicle view, making the lowest WTW emission of BEV. The fuel options’ technical and environmental perspectives are presented. Finally, long-term carbon-neutral vehicle development is discussed.