Hasil untuk "Electricity"

S. Borenstein, J. Bushnell, F. Wolak

F. Nogales, J. Contreras, A. Conejo et al.

A. Shiu, Pun-Lee Lam

A. Faruqui, Sanem Sergici

H. Lund, Anders N. Andersen, P. A. Østergaard et al.

D. Lovley

P. Joskow

Niklas Rotering, Marija D. Ilic

Sanya Carley

S. Borenstein

Ahmed S. Alahmed, Audun Botterud, Saurabh Amin et al.

We develop a mathematical framework for the optimal scheduling of flexible water desalination plants (WDPs) as hybrid generator-load resources. WDPs integrate thermal generation, membrane-based controllable loads, and renewable energy sources, offering unique operational flexibility for power system operations. They can simultaneously participate in two markets: selling desalinated water to a water utility, and bidirectionally transacting electricity with the grid based on their net electricity demand. We formulate the scheduling decision problem of a profit-maximizing WDP, capturing operational, technological, and market-based coupling between water and electricity flows. The threshold-based structure we derive provides computationally tractable coordination suitable for large-scale deployment, offering operational insights into how thermal generation and membrane-based loads complementarily provide continuous bidirectional flexibility. The thresholds are analytically characterized in closed form as explicit functions of technology and tariff parameters. We examine how small changes in the exogenous tariff and technology parameters affect the WDP's profit. Extensive simulations illustrate the optimal WDP's operation, profit, and water-electricity exchange, demonstrating significant improvements relative to benchmark algorithms.

Nazzla Rauzatul, Jaya Indra, Panggabean Donwill et al.

This study aims to determine the potential locations for alternative energy sources from waves and currents. Located in the Indian Ocean, the hydrodynamics potential of Bengkulu waters is quite high. In this study, we used data obtained from the OSCAR satellite series and bathymetric data obtained from Dishidrosal. The data series for currents are 5-year from 2019 to 2023 and were analyzed to classify the distribution values of ocean currents and bathymetry to generate the seabed topography profile. The method used in this study employ Inverse Distance Weighting and Fuzzy Logic. The sea surface current velocity is represented by the distribution of the average current speed (cm/s), which is divided into three classes slow (3.08–3.50), medium (3.5-7.84), and fast (7.84 – 12.65). The fuzzy analysis results show the estimation of suitable sites using defuzzification results at approximately 12 m. The classes for sea depth (m) were shallow (0.13-5.0), medium (5.0-20), and deep (20-315.35). The potential location is in the northern part of the province, specifically in North Bengkulu, Central Bengkulu, Bengkulu City, Seluma, and South Bengkulu, which topographically allows energy accumulation. These three districts can be designated as locations for the development of alternative electrical energy using ocean waves and currents.

You-Seok Yeoh, Kyeong-Sik Min

This paper proposes a high-gain slotted waveguide array antenna design for Ku-band wave-monitoring radar systems. The antenna structure features a two-layer design that integrates the feeding and radiating sections. A grid cavity is stationed on top of the radiating section to suppress the first sidelobes and increase antenna gain. Subsequently, the antenna combined with the grid cavity is designed and fabricated, and its performance is analyzed. The measurement results show a frequency bandwidth of more than 2.8% based on the −10 dB reflection coefficients. The implementation of the grid cavity improves the first sidelobe level by approximately 2 dB. The measurement results also indicate that the proposed antenna achieves a gain of approximately 30.5 dBi—an improvement of approximately 2 dB over that of a conventional slotted waveguide array antenna without a grid cavity. Based on these results, the proposed antenna can be expected to significantly contribute to the development of Ku-band wave-monitoring radar systems for coastal erosion prevention.

Ruoqing Yin, Liz Varga

As renewable energy penetration and extreme weather events increase, accurately predicting power system behavior is essential for reducing risks and enabling timely interventions. This study presents a physics-informed learning approach to forecast transient voltage angles in power systems with integrated wind energy under gusty wind conditions. We developed a simulation framework that generates wind power profiles with significant gust-induced variations over a one-minute period. We evaluated the effectiveness of physics-informed neural networks (PINNs) by integrating them with LSTM (long short-term memory) and GRU (gated recurrent unit) architectures and compared their performance to standard LSTM and GRU models trained using only mean squared error (MSE) loss. The models were tested under three wind energy penetration scenarios—20%, 40%, and 60%. Results show that the predictive accuracy of PINN-based models improves as wind penetration increases, and the best-performing model varies depending on the penetration level. Overall, this study highlights the value of physics-informed learning for dynamic prediction under extreme weather conditions and provides practical guidance for selecting appropriate models based on renewable energy integration levels.

Antoine Pesenti, Aidan OSullivan

Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models.

Eden Hartman, Dinesh Kumar Baghel, Erel Segal-Halevi

In many parts of the world - particularly in developing countries - the demand for electricity exceeds the available supply. In such cases, it is impossible to provide electricity to all households simultaneously. This raises a fundamental question: how should electricity be allocated fairly? In this paper, we explore this question through the lens of egalitarianism - a principle that emphasizes equality by prioritizing the welfare of the worst-off households. One natural rule that aligns with this principle is to maximize the egalitarian welfare - the smallest utility across all households. We show that computing such an allocation is NP-hard, even under strong simplifying assumptions. Leximin is a stronger fairness notion that generalizes the egalitarian welfare: it also requires to maximize the smallest utility, but then, subject to that, the second-smallest, then the third, and so on. The hardness results extends directly to leximin as well. Despite this, we present a Fully Polynomial-Time Approximation Scheme (FPTAS) for leximin in the special case where the network connectivity graph is a tree. This means that we can efficiently approximate leximin - and, in particular, the egalitarian welfare - to any desired level of accuracy.

Nugraha Anggara Trisna, Ahmad Putra Zindhu Maulana, Santoso Mardi et al.

Electrical energy is one of the most important basic human needs and is used in everyday life in various activities. Over time, the need for electricity will increase due to the increase and development of both the population, the amount of in-vestment and technological developments. The use of coal as the main fuel for power plants is also running low and its existence is not renewable. One solution to deal with these problems is the utilization of renewable energy. One type of re-newable energy that is environmentally friendly in order to meet the needs of electrical energy is solar energy. The existence of solar energy which is very abundant is one solution to reducing fossil fuels, which are currently running low. In PPNS Baruna 01 Crewboat the DC power source used comes from Solar Panels with a total of two pieces and each power is 300WP. As we know solar energy is fluctuating (up and down), therefore a DC-DC converter is needed so that the resulting voltage is stable. The DC-DC converter used in this research is a buck-boost converter. This buck-boost converter is designed with a set point of 14.4V which is then used for battery charging. The components used are 22.5μH inductor, 2275μF capacitor, resistor, diode and mosfet. The simulation results of the buck-boost converter in PSIM software show that the converter is able to work in two modes with an output voltage in accordance with the set point. When buck mode the input voltage is 24V to 14.4V and when boost mode the input voltage is 12V to 14.4V. From the simulation results, the buck-boost converter can be realized on the PLTS in the PPNS Baruna 01 Crewboat.

Vicente León-Martínez, Clara Andrada-Monrós, Elisa Peñalvo-López et al.

The main objective is to propose a calculation method for assessing the benefits of individual domestic prosumers in self-consumption and economic savings when managing their own energy resources. The paper applies the demand-side management concept in the residential sector from the individual domestic perspective so that customers can understand the value of their own sustainable energy resources, conducting self-generation and demand management. The novelty lies in allowing the prosumer to manage their own energy resources to their benefit at a reasonable cost, instead of participating in automated large residential demand-side-management programmes that respond to the means of the grid system operator or other energy service companies, such as aggregators. A methodology for calculating the self-consumption rate and the economic benefit for the consumer is proposed, including three different cases: consumer demand is higher than self-generation, and consumer demand is equal to self-generation, and consumer demand is lower than self-generation. The methodology is validated with actual data from a household in Valencia (Spain) during a complete year, obtaining an average reduction in the annual electricity bill of 70% and a demand coverage with the self-renewable system reaching values of 80% throughout the year. The significance of this methodology goes beyond the economic revenue of the individual consumer; it also aims to guide consumers towards efficient practices in the use of their available energy resources and raise awareness on their energy behaviour.

Luis C. A. Gutiérrez-Negrín

Abstract Only 32 countries in the world have geothermal power plants in operation, with a combined capacity of 16,318 MW installed in 198 geothermal fields with 673 individual power units. Almost 37% of those units are of flash type with a combined capacity of 8598 MW (52.7% of total), followed by binary ORC type units with 25.1% of the installed capacity. The select list of geothermal power countries continues to be headed by the US, followed by Indonesia, the Philippines and Türkiye, and generated 96,552 GWh of electricity, at an average annual capacity factor of 67.5%, which represented 0.34% of the worldwide electric generation. Electricity from geothermal origin represented more than 10% of the total generated in at least seven countries, headed by Kenya, Iceland, and El Salvador. Practically, all geothermal fields in operation are harnessing resources from hydrothermal, conventional reservoirs, through an estimate of 3700 production wells at an annual average production of almost 3 MWh per well. Things could be similar in the next few years if the current trend continues, but all can change due to the world urgency to maintain global warming below the 1.5 °C threshold in the following years.

Andrei Renatovich Batyrov

There are several approaches to modeling and forecasting time series as applied to prices of commodities and financial assets. One of the approaches is to model the price as a non-stationary time series process with heteroscedastic volatility (variance of price). The goal of the research is to generate probabilistic forecasts of day-ahead electricity prices in a spot marker employing stochastic volatility models. A typical stochastic volatility model - that treats the volatility as a latent stochastic process in discrete time - is explored first. Then the research focuses on enriching the baseline model by introducing several exogenous regressors. A better fitting model - as compared to the baseline model - is derived as a result of the research. Out-of-sample forecasts confirm the applicability and robustness of the enriched model. This model may be used in financial derivative instruments for hedging the risk associated with electricity trading. Keywords: Electricity spot prices forecasting, Stochastic volatility, Exogenous regressors, Autoregression, Bayesian inference, Stan