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Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered. [ABSTRACT FROM AUTHOR]

Dual‐polarization programmable metasurfaces can flexibly manipulate electromagnetic (EM) waves while providing approximately twice the information capacity. Therefore, they hold significant applications in next‐generation communication systems. However, there are three challenges associated with the existing dual‐polarization programmable metasurfaces. This article aims to propose a novel design to address them. First, the design overcomes the challenge of element‐ and polarization‐independent controls, enabling more powerful manipulations of EM waves. Second, by using more energy‐efficient tunable components and reducing their number, the design can be nearly passive (maximum power consumption of 27.7 mW), leading to a significant decrease in the cost and power consumption of the system (at least two orders of magnitude lower than the power consumption of conventional programmable metasurfaces). Third, the design can operate in a broad bandwidth, which is attractive for practical engineering applications. Both the element and array of the metasurface are meticulously designed, and their performance has been carefully studied. The experiments demonstrate that 2D wide‐angle beam scanning can be realized. Moreover, secure communication based on directional information modulation can be implemented by exploiting the metasurface and an efficient discrete optimization algorithm, showing its programmable, multiplexing, broadband, green, and secure features. [ABSTRACT FROM AUTHOR]

Electromagnet coil is mainly composed of enameled wire which wound on the coil skeleton, and the function is to convert the electrical signal into a magnetic signal, which is a very important electric‐magnetic conversion element. When designing the electromagnet coil, usually only the total winding turns and winding inner diameter can be calculated, and the specific design parameters such as the diameter of copper wire, paint layer thickness, and winding turns of each layer can only be selected through experience, indicating that the design method should be improved. In addition, the unreasonable design parameters will seriously affect the power consumption and cost of coil, so it is highly necessary to improve the optimization design method. In order to solve the above problems, an optimization design method for DC electromagnet coil is proposed in this paper. This method simultaneously takes into account both power consumption and manufacturing cost, and can achieve the optimal matching of design parameters. In this paper, the structure of electromagnet coil is introduced, and the theoretical calculation model of resistance is improved first. Then, according to the expected requirements of electromagnet coil for steady current, the optimization design method is proposed and the optimal design parameters are obtained. Finally, the effectiveness of the proposed optimization design method is verified by the test platform of electromagnet coil. The results show that the electromagnet coil designed by the optimization design method fully meets the expected requirements. The relevant research results can provide reference for the optimization design of electromagnet coil, which has a certain engineering practical value. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Abstact: In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]

In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision‐making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto‐regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid‐connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods. [ABSTRACT FROM AUTHOR]

We address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030. [ABSTRACT FROM AUTHOR]

Quantifying the cost‐effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost‐effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single‐season occupancy‐detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1–200) and repeat surveys (2–40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24‐h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25‐ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24‐h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time. [ABSTRACT FROM AUTHOR]

Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network. [ABSTRACT FROM AUTHOR]

A novel bandwidth‐reduced display interface technique using adaptive sub‐color optimization with DCT (AS‐DCT) has been proposed to be able to lower both implementation cost and power consumption with visually lossless image quality for high resolution display products. A DCT conversion is applied to 1‐D line pixel data, and 50% of the redundant data has been compressed adaptively based on both sub‐color priority and the distribution of AC data components. A PSNR of about 45.86dB level was achieved for a given set of test images, and it was implemented using RTL coding in a FPGA to verify image quality and logic size level on an actual display system. Through the proposed technique, it is expected that it can easily reduce display data bandwidth without latency and lower its implementation cost for future display devices, such as 8K/16K resolutions, 240Hz/480Hz refresh rates, immersive AR/VR, Light field displays and beyond. [ABSTRACT FROM AUTHOR]

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work. [ABSTRACT FROM AUTHOR]

A multitubular Fischer–Tropsch (FT) reactor with gas recycle and a cobalt‐based catalyst is simulated by a 2D fixed‐bed model that considers the complex kinetics, all relevant mass and heat transfer aspects, and also the influence of the changing molar flow rate by the FT reaction, of pressure drop, and of formation of steam, which decreases the reaction rate of FT synthesis. Optimal values of gas velocity and single‐tube diameter and length are determined by a technoeconomic analysis. The comparison of the data of the FT reactor modeled in this work with data of commercial reactors indicates that the used model reflects reality well and is reliable. A superficial gas velocity of 0.5 m s−1, a tube diameter of 3 cm, and a length in a range of 12–20 m are appropriate for a high production rate of C2+‐hydrocarbons with minimal operating costs of the FT unit, even for a large range of the electricity price from 0.06 to 0.3 € kWh−1. [ABSTRACT FROM AUTHOR]

Wind power generation has the problem of wind resource waste. Wind‐hydrogen coupled can enhance wind power's utilization and revenue. Currently, there is no discussion of the impact of multiple factors on the system configuration. In this study, take the annual profit of the wind‐hydrogen coupled power generation systems (WHCPGS) as the objective function, and construct the multi‐factor capacity configuration model. Using particle swarm optimization (PSO) to solve the model, and analyzes the impact of multi‐factors on the system's annual profit and revenue mode. The results show that with 30 wind turbines, the coupled system annual profit increases by 43% compared to the alone system. The higher investment does not make the system more rewarding. The configuration results are the same for investments of 0.8 billion ¥ and 1 billion ¥. The price at 0.20 ¥ kW−1 h−1, the coupled system has the optimal annual profit. Fuel cells' (FCs) electricity price and hydrogen transportation distance are crucial factors influencing the mode of the system profit. This study has specific reference significance for the practical application of capacity configuration of the system. [ABSTRACT FROM AUTHOR]

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

As electric buses become increasingly popular, it is imperative to optimize the schedules of electric buses with explicit consideration of their charging requirements. Unfortunately, existing studies failed to properly model the impacts of essential operating factors, including the time‐of‐use (TOU) electricity price, partial charging, and limited chargers. Our paper proposes a mixed‐integer nonlinear program to minimize the total operating cost of an electric bus fleet for fulfilling a group of timetabled trips, considering the above realistic features simultaneously. To effectively solve the problem of global optimality, we convert this model to an equivalent set partitioning model and develop a specialized branch‐and‐price approach subsequently. Our approach's computational efficiency is verified via extensive numerical experiments. The model is applied to a real‐world case study in Nanjing, China. Results show that incorporating the TOU electricity price into the electric bus scheduling problem can produce a sizeable cost saving of up to 22%. Managerial insights unveiled by the numerical results are discussed. These insights inform the practitioners of how the operations of electric buses can be both cost‐effective and grid‐friendly. [ABSTRACT FROM AUTHOR]

Vehicle electrification can significantly decarbonize the transportation sector. Widespread adoption of electric vehicles (EVs) depends on their cost relative to conventional alternatives. Here we compare the total cost of ownership (TCO) of gasoline, hybrid, and electric vehicles. First, we review previous TCO studies, showing that the components (e.g., purchase price, financing, taxes, fees, insurance, refueling, maintenance, repair, and home charging equipment for EVs), parameters (e.g., vehicle miles traveled, discount rate, and lifetime), and methods differ greatly. Then, we develop a comprehensive TCO model comparing across five vehicle classes, three powertrains, and three EV ranges. Using 14 cities in the United States and multiple charging scenarios, we investigate TCO variability based on location and use pattern. We include adjustments for local gasoline prices, electricity rate plans, home charging access, and the impact of local temperatures and drive cycles on fuel economy, among other factors. We show that for a 300‐mile range midsize electric SUV, TCO varies by $52,000, or nearly 40%, across locations. Home charging access reduces the lifetime cost by approximately $10,000 on average, and up to $26,000. EVs are more competitive in cities with high gasoline prices, low electricity prices, moderate climates, and direct purchase incentives, and for users with home charging access, time‐of‐use electricity pricing, and high annual mileage. In general, we find that small and low‐range EVs are less expensive than gasoline vehicles. Larger, long‐range EVs are currently more expensive than their gasoline counterparts. And midsize EVs can reach cost parity in some cities if incentives are applied. [ABSTRACT FROM AUTHOR]

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary: With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points: A robust pump scheduling approach toward real‐time electricity price market is developedDeveloping a posteriori random forest algorithm to predict the probability density function of Real‐time electricity priceOptimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting [ABSTRACT FROM AUTHOR]

Multi‐user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth‐generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1‐bit digital‐to‐analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1‐bit DACs on the system envisaged for a Rayleigh‐type fading channel. Compact‐form expressions are derived for the symbol error rate. Afterwards, an efficient end‐to‐end deep learning technique to compensate for the joint effect of 1‐bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU‐mMIMO system in the presence of hardware imperfections using the considered end‐to‐end compensation technique. [ABSTRACT FROM AUTHOR]

In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors. [ABSTRACT FROM AUTHOR]

Time‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand. [ABSTRACT FROM AUTHOR]

This article focuses on Energy Management Problem (EMP) in Muti‐microgrid systems. Each microgrid (MG) has four basic equipments including Renewable Generator (RG), Fuel Generator (FG), Storage Device (SD) and Smart Load (SL). In consideration of equipment capacity, confidence interval for forecasts of RG output, supply–demand balance and other factors, an optimization model with the objective of minimizing operation cost was established. Through Lagrange dual problem and variable substitution, we transform the centralized problem into an equivalent distributed form. Then, a completely distributed algorithm based on Alternating Direction Method of Multipliers (ADMM) and Average Consensus (AC) is proposed to attain a global optimal schedule plan. The algorithm overcomes the defect that each microgrid needs to know the total must‐run load in advance. At the same time, the electricity clearing price is related to the objective function through Lagrange dual variables, which can be obtained while the optimal plan is determined. Finally, the simulation verifies the convergence of the algorithm, and the calculated optimal cost is the same as that of the centralized method, ensuring its effectiveness. Besides, the algorithm also has good performance in plug‐and‐play scenarios. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Under the new power system, a high proportion of new energy is widely connected to the power grid, and it is necessary to increase investment in centralized and distributed energy storage, flexible resource regulation, and transmission and distribution grids, resulting in an increase in power system costs. The current electricity capacity price does not reflect the economic value of the added system adequately, and flexible capacity is needed to ensure the safety of the power grid under the new power system. In the context of the transformation of the new power system and investment function, it is increasingly difficult to recover the costs according to the traditional electricity price. It is necessary to establish a coordinated development mechanism for the electricity capacity price and the electricity price that recovers the fixed costs according to the investment function to ensure a reasonable return on investment in flexible resources and to provide effective investment signals. Therefore, this work first studied and proposed a mechanism for the formation of capacity and electricity prices based on the proportion of allowed income and fixed costs at different voltage levels and calculates the level of electricity capacity (demand) prices and electricity consumption prices. Then, using the BARY curve, genetic algorithm, and clustering of user load rates, a collaborative mechanism between the electricity capacity price and the electricity price was designed. With the goal of optimizing the electricity capacity price and considering constraints such as the flexibility and reliability of the new power system, the ratio of the capacity cost allocated to the electricity capacity price by voltage level users to the capacity cost allocated to the electricity price was calculated, and the optimal combination of electricity capacity price and electricity price for different load‐rate intervals was determined. Finally, the effectiveness of the model was verified through numerical simulations. On the basis of the investment characteristics of the new power system, suggestions for a capacity pricing mechanism under the new power system were proposed from the perspectives of transfer capacity function, transfer electricity function, grid reliability, and environmental and social benefits. [ABSTRACT FROM AUTHOR]

Electricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets. This article is categorized under:Energy and Power Systems > Energy ManagementEnergy and Power Systems > Distributed GenerationEmerging Technologies > Digitalization [ABSTRACT FROM AUTHOR]

In this paper, I consider a doubly stochastic Poisson process with intensity λt=qXt$$ {\lambda}_t=q\left({X}_t\right) $$ where X$$ X $$ is a continuous Itô semi‐martingale. Both processes are observed continuously over a fixed period 0,1$$ \left[0,1\right] $$. I propose a local polynomial estimator for the function q$$ q $$ on a given interval. Next, I propose a method to select the bandwidth in a nonasymptotic framework that leads to an oracle inequality. Considering the asymptotic n$$ n $$, and q=nq˜$$ q=n\tilde{q} $$, the accuracy of the proposed estimator over the Hölder class of order β$$ \beta $$ is n−β2β+1$$ {n}^{\frac{-\beta }{2\beta +1}} $$ if the degree of the chosen polynomial is greater than ⌊β⌋$$ \left\lfloor \beta \right\rfloor $$ and it is optimal in the minimax setting. I apply those results to data on French temperature and electricity spot prices from which I infer the intensity of electricity spot spikes as a function of the temperature. [ABSTRACT FROM AUTHOR]

Electrolysis‐based hydrogen production can play a significant role in industrial decarbonization, and its economic competitiveness can be promoted by designing demand response operating schemes. Nevertheless, the scale of industrial supply plants may be significantly large (on the order of gigawatts), meaning that electricity prices cannot be treated as an input for scheduling problems, that is, the "price taker" approach. This article presents a framework for the optimization of a large‐scale, electricity‐powered hydrogen production facility considering its integration with the power grid. Using a computational case study, we present an iterative scheme for integrating the process model with a model for power grid optimization and capacity expansion, taking the popular GenX model as an example. [ABSTRACT FROM AUTHOR]

This paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities. [ABSTRACT FROM AUTHOR]

The iron and steel industry, which is a highly energy‐intensive sector, seeks to increase energy efficiency and reduce operating costs by optimizing the distributions of byproduct gases, steam, and electricity throughout the production process. The operating costs of an iron and steel plant typically include the purchase cost of liquefied natural gas fuel, electricity, and penalties for byproduct gas holders. Electricity tariffs have a significant impact on energy costs through two factors: the energy charge, which is related to energy consumption varying with time, and the demand charge, which is proportional to the peak power. In this study, the characteristics of electricity tariffs and multi‐energy systems, specifically for iron and steel plants, are considered. In the proposed method, electric arc furnaces are employed as controllable units to flexibly manage the power load. The optimization model utilized in the plant adopts a mixed‐integer linear programming approach to minimize the overall operating costs. Several case studies were conducted to verify the effectiveness of the proposed methods, and the total costs associated with different cases were compared. [ABSTRACT FROM AUTHOR]

Power system can utilize the flexibility provided by energy‐intensive enterprises (EIEs) for large‐scale renewable energy penetration. However, system operators cannot obtain the manufacture scheme of EIEs due to business privacy protection. Furthermore, highly regulated electricity prices further restrict the flexible utilization of EIEs. To overcome these issues, a coordinated dispatch framework between power system and EIEs under the intrinsic time of use (TOU) prices is designed in this paper, which requires only several boundary information for interaction. Firstly, the mathematical formulation of EIEs is given based on the inherent features analysis of typical production equipment and the optimal operation scheme of EIEs can be obtained. Then, considering uncertainties of both source and loads, the purchasing energy of EIEs from the network can be adjusted through the designed framework to track renewable energy dynamics and the clear equivalence class method is used to solve fuzzy chance constrained programming. Next, the improved Asymmetric Nash Bargaining (ANB) method is adopted to carry out the distribution of benefits. Finally, a modified IEEE‐30 test system is given to verify the feasibility of the proposed method, which can effectively improve the regional consumption level of renewable energy and ensure interests of all sides. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence-to-sequence (seq2seq) long short-term memory (LSTM) neural networks for day-ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model. [ABSTRACT FROM AUTHOR]

With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis. [ABSTRACT FROM AUTHOR]

Summary: Power solutions contribute a remarkable role in all the sectors. In order to develop the sustainable and affodable power solution, the bettered power quality (PQ) based modified bridgeless switched inductor (MBSL) Sepic power factor correction (PFC) converter is introduced in this article. An added feature to an already existing traditional Sepic converter resulted in switched inductor (SL) network that reasonably improves the step‐down voltage gain with low current stress across the output inductors and diodes which restricts the utilization of transformer that ultimately reduces the overall cost of power solution. This introduced structure acts as a good choice of low‐cost power conversion solution in the perspective of improved inline PQ indices, high efficiency and good reliability. Furthermore, the discontinuous conduction mode (DCM) functioning of SL network reveals the innate zero current switching (ZCS) capability with enhanced efficiency. Besides, the effective functionality of the one‐stage PFC converter is tested on hardware prototype of 250 W/48 V, and it also adopts an appropriate control approach to stabilize the output voltage. This study also exposes small signal modeling to prove the stability of the power solution. In addition, the test outcomes reveal that the preferred PFC procure sinusoidal current from the AC mains that confirms the good enshaping attribute and low current harmonic distortions within the stipulated IEC 61000‐3‐2 standard guidelines for both standstill and fluctuating conditions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a crucial technology in the healthcare industry thanks to recent developments in automated data collection. Current statistics of the healthcare sector show that it is expanding at a remarkable rate. However, the performance of the healthcare system is impacted by many challenges, such as security, privacy, latency, scalability, and heterogeneity that come with advancement. Fog computing and software‐defined networks (SDN) are critical enabling technologies that help to improve IoT‐based healthcare systems by ensuring timely and reliable data provisioning for use in healthcare applications. SDN solves interoperability, device management, and network management issues and automates patient health monitoring without human interaction. SDN and fog computing in IoT‐based healthcare can optimize device communication and computing power at a low cost. SDN‐enabled fog computing has lower latency and better security than cloud‐based IoT healthcare architecture. The researchers have not entirely discovered SDN‐enabled fog computing to the best of our knowledge. This paper covers healthcare IoT technicalities and related technologies. We examined ways to merge SDN, fog computing, and IoT to improve healthcare solutions. Finally, we concluded the paper with research issues and future healthcare system improvements. [ABSTRACT FROM AUTHOR]

High frequency systems (HF) are still used in many communication applications. Data can be sent for hundreds of kilometers at a low power cost with only one drawback, low bitrate. To increase the bitrate, several methods were reported using the single‐Tone modulation with 24 kHz bandwidth, but still not practical in real‐time applications because, the 24 kHz is unavailable due to the instability of the ionosphere layer. In this work, we propose a structured method built on the Non‐Orthogonal Multiple Access technique (NOMA) to increase the bit rate per HF narrowband channel. To increase the data rate, two parts of the data are divided and sent to one user over two different power levels. We use the NOMA technique on the receiver's side to recognize the two parts of data. The results show a significant improvement (up to 45%) using NOMA in narrowband 3 kHz channels. The bit rate at the receiver has increased up to 200 Kb/s concerning, while the maximum bit rate recorded by conventional methods used in previous studies is 138 Kb/s. The system is built on the SDR platform and tested in multiple channels, weather, and power conditions in real‐time experiments to validate its reliability. [ABSTRACT FROM AUTHOR]

Filter operation of a pilot‐scale baghouse filter was evaluated under energy and particle emission criteria. Evaluation of the required total power for filter operation takes into account the fan power as well as the consumption of pressurized air. Filter face velocity, raw‐gas concentration, and tank pressure for regeneration were varied for several cycle time settings to identify the minimum power. Cycle times shorter than at minimum power are not feasible due to increased dust emissions and no additional energetic benefit. Cycle times longer than at minimum power may lower the dust emissions at the cost of increased power consumption. Lowering filter face velocity can greatly lower the power consumption of baghouse filters, having implications on filter layout. [ABSTRACT FROM AUTHOR]

Induction motors, particularly single‐phase induction motors (SPIMs), represent the major consumption of power in the household sector. SPIMs that fail during their life cycle represent the best opportunity to improve their efficiency by replacing or repairing them. Although replacing faulty motors with new and efficient ones results in significant energy savings, this replacement is uneconomical due to its high initial costs. Additionally, this option requires extensive energy and materials to manufacture, resulting in a considerable environmental impact. This article aims to introduce a three‐phase rewinding of a failed SPIM using a rewinding algorithm that significantly improves the machine's efficiency and performance in an economically viable way. The 0.75 HP single‐phase capacitor‐start induction motor has been rewound to convert it into a three‐phase motor with the required 110 V line voltage and 50 Hz frequency. The economic analysis implies that the rewound machine's life‐cycle cost is 6% and 18% lower than the purchased SPIM IE1 and existing SPIM by below efficiency class respectively. The rewound motor achieves a payback period of 1–1.6 years for different motor loading levels, based on the world average electricity price, indicating an economically viable solution. [ABSTRACT FROM AUTHOR]

Summary: A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand‐side management, and prosumers handling. The current state‐of‐the‐art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)‐based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences. [ABSTRACT FROM AUTHOR]

We propose a nonlinear prediction (NOP) method for functional time series. Conventional methods for functional time series are mainly based on functional principal component analysis or functional regression models. These approaches rely on the stationary or linear assumption of the functional time series. However, real data sets are often nonstationary, and the temporal dependence between trajectories cannot be captured by linear models. Conventional methods are also hard to analyze multivariate functional time series. To tackle these challenges, the NOP method employs a nonlinear mapping for functional data that can be directly applied to multivariate functions without any preprocessing step. The NOP method constructs feature space with forecast information, hence it provides a better ground for predicting future trajectories. The NOP method avoids calculating covariance functions and enables online estimation and prediction. We examine the finite sample performance of the NOP method with simulation studies that consider linear, nonlinear and nonstationary functional time series. The NOP method shows superior prediction performances in comparison with the conventional methods. Three real applications demonstrate the advantages of the NOP method model in predicting air quality, electricity price and mortality rate. [ABSTRACT FROM AUTHOR]

High‐frequency systems (HF) are still used in many communication applications, especially at long distances, due to their robustness. Data can be sent for hundreds of kilometres at a low power cost with only one drawback, low bitrate. To increase the bitrate, several methods were reported using the single‐tone modulation with 24 kHz bandwidth, but still not practical in real‐time applications because, the 24 kHz is unavailable due to the instability of the ionosphere layer. Here, a structured method built on the non‐orthogonal multiple access technique (NOMA) is proposed to increase the bit rate per HF narrowband channel. To increase the data rate, two parts of the data are divided and sent to one user over two different power levels. Since the ionosphere conditions prohibit the use of single‐tone channels most of the time, spread narrowband channels are used instead. The NOMA technique is used on the receiver's side to recognize the two parts of data. The results show a significant improvement (up to 45%) using NOMA in narrowband 3 kHz channels. The bit rate at the receiver has increased up to 200 kb/s concerning, while the maximum bit rate recorded by conventional methods used in previous studies is 138 kb/s. Combined with multichannel algorithms such as HF XL, the total increase in bit rate can be considerable. The system is built on the SDR platform and tested in multiple channels, weather, and power conditions in real‐time experiments to validate its reliability. [ABSTRACT FROM AUTHOR]

Since 1989, Russia's "first business newspaper," Kommersant, worked to cultivate a new kind of collective post‐socialist subject, a "vanguard group" of "New Russians." How did Kommersant imagine this subject? Who were its antagonists and why, and how would the New Russian triumph over them? This article examines the 1992–93 issues of Kommersant Daily, its glossy monthly supplement Domovoi, and Kommersant's recurring cartoon character of the post‐Soviet everyman "Petrovich," drawn by Andrei Bil'zho. Through these examples, I show how the Kommersant team convinced its audience to feel elite superiority over other newly rich Russians, and to feel having morally overcome the brutish egalitarian past shared by the "vanguard class" with its former late Soviet peers among the educated, urbanized masses. I argue that both objectives were achieved through didactic, disciplinary means, integrated both into the messaging of Kommersant and into its style and editorial policies. Moreover, by analyzing Bil'zho's Petrovich, I conclude that playful, ironic "reflective nostalgia" (Svetlana Boym's term) vis‐à‐vis the Soviet past was one such disciplinary tool which helped convince New Russians to maintain power at all costs, and thereby prevent the bloody return of neo‐Soviet socialism, which their Petrovich‐like neighbors supposedly desired. [ABSTRACT FROM AUTHOR]

Dry cooling, where forced air is the heat transfer medium, is a preferable cooling method in arid locations lacking readily available process water. However, such locations often experience high ambient temperatures that limit the effectiveness of air cooling. The objective of this study is to quantify the economic and energetic benefits of heat transfer intensification via the implementation of aeroelastically fluttering reeds to the air‐cooled condenser of a methanol distillation column. Condenser size and performance, regarding recovered methanol and required fan power, is evaluated across condenser operating temperatures (Tcond) from 60 to 62°C and heat transfer coefficients (U) Ubase–2Ubase for a range of inlet air temperatures based on ambient temperature data from Yuma, Arizona. Under typical design sizing, condenser capital cost was reduced by 6%–35% (1.3Ubase–2Ubase) and nominal methanol recovery was increased from 0.26% to 0.38% (Tcond = 62–60°C). At optimized condenser size, all enhanced U and Tcond pairs increase methanol recovery and reduce fan power costs compared to the optimal Ubase reference. Overall, using enhanced heat transfer to maintain condenser temperature under a wider range of inlet conditions, rather than to reduce operation temperature, produces more favorable performance. Methanol price is not a determining factor in which pairs are profitable. Analysis was repeated for a global warming scenario, revealing more valuable improvements under elevated temperatures. Energy savings from condenser improvement to a methanol production system are not significant with respect to an optimized conventional system. Unit economic and energetic incentives suggest implementation of fluttering reeds may be justified in other applications. [ABSTRACT FROM AUTHOR]

This paper examines the impact of a change in the policy regime from flat rate to free farm electricity pricing, introduced in Punjab, India in February 1997 using a difference‐in‐differences framework. Based on village‐level data from the second and the third rounds of the Minor Irrigation Census, the study finds a differential increase in the number of electric‐operated tubewells and horsepower load of pumps in Punjab as compared to an agriculturally‐similar and neighboring state, Haryana, which is taken as the control group. Through these channels, the study finds that the average groundwater depth increased by 1.9 meters more in Punjab as compared to Haryana, which is 22 percent of the baseline average groundwater depth in Punjab. Nationally‐representative well‐level data on groundwater depths from the Central Ground Water Board shows impact heterogeneity with sharper effect on groundwater depth for wells that are lying closer to the cut‐off of about 10 meters where a technological shift from centrifugal to submersible pumps is required to maintain access to groundwater pumping. [ABSTRACT FROM AUTHOR]

Key parameters of analog‐to‐digital converters (ADCs) are their sampling rate and dynamic range. Power consumption and cost of an ADC are directly proportional to the sampling rate; hence, it is desirable to keep it as low as possible. The dynamic range of an ADC also plays an important role, and ideally, it should be greater than the signal's; otherwise, the signal will be clipped. To avoid clipping, modulo folding can be used before sampling, followed by an unfolding algorithm to recover the true signal. Here, the authors present a modulo hardware prototype that can be used before sampling to avoid clipping. The authors' modulo hardware operates prior to the sampling mechanism and can fold higher frequency signals compared to existing hardware. The authors present a detailed design of the hardware and also address key issues that arise during implementation. In terms of applications, the authors show the reconstruction of finite‐rate‐of‐innovation signals, which are beyond the dynamic range of the ADC. The authors' system operates at six times below the Nyquist rate of the signal and can accommodate eight times larger signals than the ADC's dynamic range. [ABSTRACT FROM AUTHOR]

As an important form of renewable energy utilization, microgrid (MG) is considered to be the main bearing form of distributed generation in the future. One of the most concerning issues in the operation of MG is how to realize its economic dispatch (ED). Nowadays, distributed algorithms have been increasingly used to solve the economic dispatch problem of MG. However, the MG based on the distributed optimization architecture must bear higher cyber‐attack risks. To address this issue, this paper investigates the distributed robust ED problem of MG. Firstly, a multi‐objective dispatch model of MG using a linear weighted sum (LWS) algorithm is developed, which considers the environmental and economic costs. On this basis, an event‐triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which has strong robustness against various colluding attacks and non‐colluding attacks, and can eliminate incorrect measurement of incremental cost and power generation data. Finally, the effectiveness of the proposed distributed control strategy is demonstrated through case studies in this paper. [ABSTRACT FROM AUTHOR]

Fault indicators (FI) and remotely controlled switches (RCS) can reduce the power outage time in distribution network (DN) by finding and isolating the faulty area. Therefore, they play an important role in reducing the power outage cost and increasing the reliability of the DN. In DN, there may be high‐priority loads. One of the goals of improving and updating the DN is to increase the reliability of all network buses, especially buses feeding such loads. Here, the placement of FI and RCS, as the distribution system automation devices, is designed and modelled to increase the reliability of the buses supplying high‐priority loads. For the placement problem, two goals of cost and reliability have been considered. But the problem is not defined as a multi‐objective optimization. Rather, it is modelled as a one‐objective optimization (i.e. network cost) and constrained to a certain level of reliability in predetermined locations and buses. The IEEE‐33bus network is used as the sample network and different scenarios are simulated. The number of FI and RCS and the optimization cost of simulated scenarios are compared to evaluate the efficiency of the proposed model. The obtained results show the adequacy of the modelling. [ABSTRACT FROM AUTHOR]

Unprecedented increase in atmospheric CO2 levels calls for efficient, sustainable, and cost‐effective technologies for CO2 removal, including both capture and conversion approaches. Current CO2 abatement is largely based on energy‐intensive thermal processes with a high degree of inflexibility. In this Perspective, it is argued that future CO2 technologies will follow the general societal trend towards electrified systems. This transition is largely promoted by decreasing electricity prices, continuous expansion of renewable energy infrastructure, and breakthroughs in carbon electrotechnologies, such as electrochemically modulated amine regeneration, redox‐active quinones and other species, and microbial electrosynthesis. In addition, new initiatives make electrochemical carbon capture an integrated part of Power‐to‐X applications, for example, by linking it to H2 production. Selected electrochemical technologies crucial for a future sustainable society are reviewed. However, significant further development of these technologies within the next decade is needed, to meet the ambitious climate goals. [ABSTRACT FROM AUTHOR]

Despite the growing literature on the causal association between electricity supply and economic growth, little is known about the causal linkage between electricity transmission and distributional losses and economic growth, particularly in Ghana. The paper studies the causal association between electricity transmission, distributional losses and economic growth in Ghana. Further, we interacted ETDL with electricity price to examine their combined impact on Ghana's gross domestic product. We utilised time series data from the period 1971 to 2014. Given that our data span is relatively shorter, we utilised the Toda and Yamamoto and Autoregressive distributed lag model (ARDL) methods. This is so because these time series approaches can produce efficient, reliable and eliminate spurious regression estimates even if the sample size is relatively short. Our ARDL estimates suggest that ETDL exert an adverse impact on economic growth while the Toda and Yamamoto estimates suggest that economic growth has a strong influence on ETDL and not the other way round, supporting the growth‐led energy hypothesis. Also, the interactive effect of ETDL and electricity prices on economic growth was negative and statistically significant. [ABSTRACT FROM AUTHOR]

We study the importance of time‐varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well‐known time series models in a large dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non‐Gaussian error terms in stochastic volatility. We find that using regressors as fuel prices, forecasted demand and forecasted renewable energy is essential to properly capture the volatility of these prices. Moreover, we show that the time‐varying volatility models outperform the constant volatility models in both the in‐sample model‐fit and the out‐of‐sample forecasting performance. [ABSTRACT FROM AUTHOR]

Summary: Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two approaches to constructing deep time series probabilistic models based on a variant of RNN called an echo state network (ESN). The first is where the output layer of the ESN has stochastic disturbances and a Bayesian prior for regularization. The second employs the implicit copula of an ESN with Gaussian disturbances, which is a Gaussian copula process on the feature space. Combining this copula process with a nonparametrically estimated marginal distribution produces a distributional time series model. The resulting probabilistic forecasts are deep functions of the feature vector and marginally calibrated. In both approaches, Markov chain Monte Carlo methods are used to estimate the models and compute forecasts. The proposed models are suitable for the complex task of forecasting intraday electricity prices. Using data from the Australian market, we show that our deep time series models provide accurate short‐term probabilistic price forecasts, with the copula model dominating. Moreover, the models provide a flexible framework for incorporating probabilistic forecasts of electricity demand, which increases upper tail forecast accuracy from the copula model significantly. [ABSTRACT FROM AUTHOR]

A new three‐phase two‐switch voltage clamping buck Power Factor Correction (PFC) is presents in this paper. The principle and equations of operations are described in detail. Based on the volt‐second balance principle of input inductance in the new converter, this paper gives the design method of the circuit's key components, with voltage and current stresses analyzed. On the basis of the theory above, a prototype with parameters of a 5 kW is designed. Problems of extremely high switch peak voltage stress in the conventional one are solved. The manufacturing cost of high power three phase rectifier has been significantly decreased. A 5 kW experimental prototype with unity power factor, 4.4% input line current THD, 200 Vdc output, and system efficiency of 96% at full load under 380 Vrms input line‐to‐line voltage has been successfully implemented. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Electricity production in highly hydrological‐dependent systems is determined by different weather phenomena, which strongly impact spot prices. To account for such stylized facts, we propose a stochastic process with a mean reversion and switching regime component to represent the dynamics of the spot price. The short‐term movements are represented by semi‐nonparametric (SNP) distributions, in contrast to previous studies that traditionally assume Gaussian processes. We consider the Colombian electricity market as a study case, in which 68% of its electrical generation comes from water resources, and the El Niño phenomenon represents a critical source of risk for maintaining long‐term supply, sustainability of investments, and efficiency of prices. We show that under scarcity seasons, the spot price mean, variance, and some superior‐order moments of electricity price distribution increase, as does the risk level of the system. In particular, the switching regime model with SNP distributions for the random components outperforms traditional models, leading to accurate estimates and simulations, thus being a helpful tool for resource planning, risk management, and policy‐makers for electricity markets with high climatic dependency. [ABSTRACT FROM AUTHOR]

As groundwater levels steadily decline in India, authorities are concerned about reducing extraction for irrigation purposes without jeopardizing food security. Very low or zero prices for electricity and water in agriculture is partly responsible for overextraction, but charging higher prices is politically not feasible. In this study, we describe the results of a pilot scheme implemented in Punjab, India, where farmers who enrolled were allocated a monthly entitlement of electricity units and compensated for unused electricity. Eight hours of uninterrupted daytime electricity supply were also provided under the scheme instead of the usual mix of daytime and night‐time supply. Analyzing data from a cross‐sectional farm household survey and instrumenting for enrollment, we find that self‐reported hours of irrigation for enrolled farmers were significantly lower than for non‐enrolled ones, with no impact on rice yields. We also find a reduction in monthly electricity consumption at electricity‐feeder level due to the pilot scheme using the synthetic control method. Our results suggest that the combination of daytime electricity provision and cash incentives for unused electricity has the potential to incentivize farmers to reduce electricity consumption and irrigation hours by at least 7.5% and up to 30% without impacting paddy yields. [ABSTRACT FROM AUTHOR]