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A bi-objective distributed flow-shop scheduling problem with multistep electricity pricing and carbon emissions is studied. One objective is to minimise the completion time of production, and the other is to minimise the total cost of multistep electricity pricing and carbon emissions. A mixed integer programming model and a two-stage knowledge based cooperative algorithm with a local reinforcement strategy are proposed for the problem. The extensive numerical experiments show that the two-stage algorithm was effective statistical significantly in generating non-dominated solution sets and Pareto frontiers. Simulations on Electricity Prices are applied to examine different multistep electricity pricing schemes, and management implications were drawn for both government and companies. [ABSTRACT FROM AUTHOR]

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost. [Display omitted] • Techno-economic analysis of hubs facilitating energy transaction is investigated. • Considering fuel cell-based CHP, EVs and shiftable loads for increased flexibility. • Peer-to-peer energy exchange is considered fostering local energy autonomy. • Stochastic programming modeled uncertainties of renewables, demands and prices. • Risk analysis of energy transactions is addressed using conditional value-at-risk. [ABSTRACT FROM AUTHOR]

We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition. [ABSTRACT FROM AUTHOR]

The enhancement of energy networks and cost reduction can be achieved through the implementation of solutions like optimal energy scheduling for residential consumers. This paper proposes day-ahead electrical and thermal energy scheduling for residential sections in energy networks as a bi-criteria optimization approach. The bi-criteria optimization approach is modeled to minimize the operation cost and maximize the reliability of the energy system. Demand management strategies such as demand reduction and demand shifting are considered for residential consumers. The participation of residential consumers in demand management is presented considering electricity prices in the energy markets. The solving bi-criteria optimization approach is done via the weight sum method and LINMAP procedure-based-fuzzy approach. The proposed modeling is implemented in GAMS optimization software for verifying the demand management strategies in optimal energy scheduling in two case studies. [ABSTRACT FROM AUTHOR]

It has been suggested that increasing the use of electric vehicles (EVs) could help reduce global fuel use and greenhouse gas (GHG) emissions, two major contributors to climate change. The purpose of this research is to examine consumer attitudes on EVs and the factors that prevent their mainstream adoption. Unlike conventional survey methods, the researchers used theme analysis to uncover and make sense of the myriad of aspects that went into their purchase. The study also takes into account the often-overlooked part played by emotions. People's openness to express their opinions online makes eWOM a useful source of information for this study on electric automobiles. This technique is useful for identifying any and all gaps (physical and intangible) between a company's offering (EVs) and a customer's expectations. This research also sheds light on the interconnected nature of these obstructions, revealing a potentially crippling snowball effect on adoption rates. In this study, we look at how technology has altered transportation in the United States. Efficiency, sustainability, safety, and accessibility are only few of the topics covered, along with infrastructure, vehicle technology, and mobility services. This study analyzes data and research to provide light on how technology has changed the face of transportation in the United States. This research looked at how electric car market share relates to the availability of government subsidies and other important socioeconomic factors. The findings showed a negative correlation between energy prices and EV adoption, whereas a positive correlation between the market share of electric vehicles in each state and the presence of urban highways and government incentives. Based on the results of the sensitivity analysis, it appears that the cost of power has the greatest impact on the number of people opting to buy electric cars. The analysis of EV adoption found that the price of power and the availability of charging stations in metropolitan areas had the greatest impact. [ABSTRACT FROM AUTHOR]

This article aims to examine the national mechanism applied in Bulgaria to tackle the high electricity prices in the period 2021-2023 and its impact on state-owned enterprises, which are the main source of the compensation funds. The proposed thesis is that with the collection of the revenues of state-owned companies, they are posted in conditions of restriction in terms of development opportunities, which is why the imposed emergency measures should be reviewed in the future. Although the measures themselves are widely discussed, the mechanisms for providing financial support, the financial magnitude of this support, as well as its effect, are yet to be clarified, and the data collected in the material may be useful for further analyses and evaluations. [ABSTRACT FROM AUTHOR]

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]

Biosensing for diagnostics has risen rapidly in popularity over the past decades. With the discovery of new nanomaterials and morphologies, sensitivity is being constantly improved enough for reliable detection of trace biomarkers in human samples, like serum or sweat. This precision has enabled detailed research on the efficacy of biosensors. However, current biosensors suffer from reduced speed of operation. To make better use of this sensitivity, the development of a conductometric biosensor with in-situ use of an Laser Emitting Device (LED) display can provide rapid determination of sample results, steadily pushing biosensors toward more clinical, point-of-care (POC) applications. In this research, a simple LED was used for facile optical determination and visual output of an ultrasensitive bio-signal amplification circuit was made to interface with a B-type Natriuretic Peptide (BNP) biosensor. Tuning circuit gain enables an elegant method for adjustable separation of concentrations into 3 discrete categories: sub-threshold, analog, and saturation regions. These regions corresponded to 0 < [C] < 500 pg/mL (25, 100, 250 pg/mL, LED off), 500 < [C] < 1000 pg/mL (LED varying intensity), and 1000 pg/mL < [C] (LED full intensity). System efficacy was tested using human blood serum samples from University of Pittsburgh Medical Center patients, which were able to be accurately detected and sorted for rapid low cost and power. determination without need for complex digital elements. Additional specificity testing suggests insignificant impact of non-target biomarkers. [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]

In the current model, the unclear and unreasonable method of revenue sharing among wind-solar-storage hybrid energy plants may a lso hinder the effective measurement of energy storage power station costs. This lack of clarity discourages energy storage from effectively collaborating with renewable energy stations for green power trading and spot trading. Therefore, this study proposes an optimal revenue sharing model of wind-solar-storage hybrid energy plant under medium and long-term green power trading market to facilitate the coordinated operation and equitable revenue allocation. Firstly, a method for decomposing transaction volume of green power is introduced by considering the uncertainty of spot market prices and physical delivery characteristics of green power trading. Then, a coordinated scheduling strategy of hybrid renewable energy plant is proposed to maximize revenues generated from both the green power and spot markets. Consequently, a cost-benefit contribution index system is developed to quantify the contribution of energy storage in the wind-solar-storage hybrid power plant. The revenue sharing model based on the minimum cost-remaining savings (MCRS) method can significantly increase overall revenue for renewable energy plants by reducing deviation penalties. It also enhances the operating revenue of energy storage power stations by considering the contributions of both energy storage and renewable energy plant in the green power market. The superiority of the proposed cooperation revenue sharing model for profitability enhancement of energy storage is validated through comparative case studies. [ABSTRACT FROM AUTHOR]

Under the background of cap-and-trade mechanisms, this article constructs a game model of the electricity supply chain, which is dominated by electricity generators and followed by electricity sellers, taking into account the situation of electricity generators investing in renewable energy and energy storage under the grandfathering mechanism (GM) and benchmarking mechanism (BM). By comparing the equilibrium solutions in different cases, the research finds that (1) compared with the grandfathering mechanism, the benchmarking mechanism has more investment in renewable energy and higher energy storage quality; (2) in the consumer market, compared with GM, the electricity price and the electricity demand are higher under BM; (3) an increase in the renewable energy preference coefficient or carbon price will lead to an increase in the electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand. Further, an increase in the energy storage cost coefficient or renewable energy investment cost coefficient will result in lower electricity wholesale price, renewable energy investment, energy storage quality, electricity price, and electricity demand; and (4) the profit of the generator under GM is higher than that under BM when the total carbon quota is larger, while the profit of the generator under BM is higher than that under GM when the unit carbon quota is larger. [ABSTRACT FROM AUTHOR]

To optimize energy structure and efficiently utilize renewable energy sources, it is necessary to establish a new electrical power–gas mutual transformation virtual power plant that has low-carbon benefits. To promote the economic and low-carbon operation of a virtual power plant and reduce uncertainty regarding the use of new energy, a multi-timescale (day-ahead to intraday) optimal scheduling model is proposed. First, a basic model of a new interconnected power–gas virtual power plant (power-to-gas demand response virtual power plant, PD-VPP) was established with P2G and comprehensive demand response as the main body. Second, in response to the high volatility of new energy, a day-ahead to intraday multi-timescale collaborative operation optimization model is proposed. In the day-ahead optimization period, the next day's internal electricity price is formulated, and the price-based demand response load is regulated in advance so as to ensure profit maximization for the virtual power plant. Based on the results of day-ahead modeling, intraday optimization was performed on the output of each distributed unit, considering the cost of the carbon emission reductions to achieve low-carbon economic dispatch with minimal operating costs. Finally, several operation scenarios are established for a simulation case analysis. The validity of the proposed model was verified via comparison. [ABSTRACT FROM AUTHOR]

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks. [ABSTRACT FROM AUTHOR]

Solar tower with thermal energy storage (ST-TES) represents a promising technology for large-scale exploitation of solar irradiation for electricity generation. A ST-TES has the potential to extend electricity generation to more favorable conditions, such as high electricity prices. The size of TES, however, constrains the flexibility of dispatching, especially when there is significant uncertainty in forecasts of solar irradiation and electricity prices. This study explores the impact of TES size when the plant uses model-predictive control (MPC) for dispatch planning. The performance of MPC is benchmarked against one perfect knowledge (PK) and two day-ahead strategies. The optimal achievable profit for each TES size is determined using the PK strategy. An analysis is conducted to evaluate the relative profit losses for all the other simulated strategies compared to the PK strategy. A case study is conducted for a hypothetical 115 MWe ST-TES in South Australia. For January and August, 100 tests are performed for each dispatch policy, with the TES size varying from 6 to 14 h. The revenue evaluation is conducted with both fixed and wholesale spot prices. The analysis shows that MPC-aided dispatching enables the adoption of a smaller TES compared to day-ahead policies while maintaining the same expected profit. The resulting TES size reduction from 14 to 10 h translates into approximately up to $45.4 million in capital cost savings. The findings of this study can inform the ST-TES plant's design procedures and facilitate negotiations for electricity sales contracts. [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]

The orderly charge and discharge scheduling of Electric Vehicles (EVs) plays an increasingly important role in safe operation of the distribution network. Currently, there exists the problem that some charging station agents do not pay attention to the difference between the charging information of EVs and the charging demand of users, and treat the charge and discharge scheduling of EVs undifferentiated, which results in users' dissatisfaction. This paper presents a three-stage optimization of charging scheduling of EVs, which is based on the electricity price to guide charging selection of users and the continuous charging and discharging of EVs while scheduling. In first stage, the agents provide different charging modes for users to choose from, a charging selection model for users based on EV charging information and the time-of-use (TOU) electricity price is established. The arrival and departure time of EVs, state-of-charge (SOC) and TOU electricity price are all factors that affect users' charging selection. Differential group dispatching of EVs with different charging modes to realize the requirements of the power grid and users. In second stage, based on the charging selection of users and the charging information of EVs, the charging load in different periods can be predicted, which can guide the power purchase for charging agents. The third stage is the integrated charging scheduling of EVs. EVs dispatching is based on the priority, which takes the charge–discharge switching times of EVs into account. The model takes the maximum income of the charging agents as the objective and takes the requirements of the power grid and users as the constraints. Modified adaptive step size fruit fly optimization algorithm (MASSFOA) is utilized to optimize the TOU electricity price, and the optimized TOU electricity price is used to guide EV user's charging selection. The results of simulation analysis show that the proposed strategy can reduce the peak-to-valley difference of the load curve, improve the continuous charging and discharging of EVs, and increase the income of charging station agents. [ABSTRACT FROM AUTHOR]

Taking the key components of PV power generation system as the research object, based on the traditional opportunity maintenance, an opportunity maintenance strategy of PV power generation system considering the structural relevance is proposed to solve the problem of the high maintenance cost of PV power generation system due to unreasonable opportunity maintenance grouping. Firstly, the reliability model of key components of the PV power generation system is established, and the preventive maintenance time of each key component is given with reliability as a constraint. Then, the degree of impact on other components during preventive maintenance is used as an index for judging the relevance of the component structure, and the degree of relevance determines the size of the opportunity threshold and uses it as a decision variable to find the optimal opportunity maintenance threshold under the relevance of each component by bacterial foraging optimization algorithm to minimize the average maintenance cost. Finally, the proposed model is compared and analyzed with a PV power generation system without considering the opportunity maintenance model and the traditional opportunity maintenance model, and the results show that the average maintenance cost of the proposed method is reduced by 26.7 and 4.9%, respectively, compared with the above two methods, indicating that the proposed method can effectively reduce the maintenance cost while ensuring the reliable operation of the PV power plant. [ABSTRACT FROM AUTHOR]

The energy market operates in a highly deregulated and competitive environment, where electricity price plays a crucial role. Forecasting electricity prices presents a significant challenge due to the influence of complex factors such as weather patterns, fuel costs, and the advancement of renewable energy technologies. This study focuses on monthly electricity prices in four neighboring European countries: Bulgaria, Greece, Hungary, and Romania, which share similar weather conditions and economic characteristics. The research investigates the efficacy of four forecasting methods: Grey Verhulst Model (GVM), Nonlinear Regression, Feedforward Neural Network, and Support Vector Regression (SVR). These methods are applied to both short-term (1 month-ahead) and long-term (up to 7 months) electricity price forecasting in the aforementioned countries. The findings reveal that GVM proves suitable for short-term predictions. However, when it comes to long-term forecasting, SVR accurately captures the trends and turning points in electricity prices, albeit with unsatisfactory error rates. To address this issue, a modified version of SVR, referred to as Modified SVR (MSVR), is proposed to mitigate the errors. The results demonstrate that MSVR is an effective approach for long-term electricity price prediction. [ABSTRACT FROM AUTHOR]

The wind power market is expanding quickly and the cost of wind power equipment is decreasing, making wind power technology a key player in the world's energy transition. Assessing wind potential and selecting the right wind turbine site are crucial parameters for developing a wind farm. Vietnam focuses on onshore and nearshore wind power projects due to its promising wind power potential and supportive policies. However, Vietnam has diverse climate characteristics and wind patterns. Therefore, initial basic research is necessary to evaluate the feasibility of investing in wind power projects. This study examines the technical feasibility of a typical wind power project in the Southeast region of Vietnam using Wind Atlas Analysis and Application Program (WAsP) software. The results indicate that the wind turbine's type and installation location significantly affect wind power plants' efficiency. The total power output of the wind power project after factoring with losses at a rate of 17%, is 304,149 MWh. [ABSTRACT FROM AUTHOR]

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics. [ABSTRACT FROM AUTHOR]

In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market. [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]

The utilization of the district heating system (DHS) thermal inertia is greatly influenced by heating regulation modes and time steps, however, which has been rarely analysed in almost all integrated heat and power dispatch system (IHPD) models, for the purpose of wind power integration. In this study, an IHPD model considering the thermal inertia of the DHS was established, in which a data interaction problem amongst the power sources, heat sources and district heating networks was solved. Thereafter, four most commonly used heating operation modes and different heating regulation time steps were applied to the IHPD model, respectively, to analyse and compare their effects on the IHPD system under different operation conditions, mainly considering the operation cost and the wind power integration. Results of the case studies showed that the variable flow and variable supply temperature mode produced the lowest operation cost of the IHPD system during a whole heating season. Consequently, this mode is recommended to be applied in the optimization operation of the IHPD system with the wind power integration. Meanwhile, a heating regulation time step within 3 h could make full use of the DHS thermal inertia, thus to improve the operational flexibility of the IHPD system. [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]

This study examines the impact of the growing elderly population and single-person households on residential electricity demand in China. Using a dynamic panel model, this study investigates a balanced panel dataset encompassing 30 provinces in China from 2006 to 2016. The results indicate that households in China show limited responsiveness to changes in electricity prices and household income in the short and long run. Regarding demographic transitions, the results reveal that the presence of working-age individuals, elderly individuals, and single-person households contributes to an increase in electricity demand. Furthermore, the results highlight the significance of higher education in reducing electricity demand, suggesting the need for educational programmes that promote energy-saving behaviour and environment-friendly attitudes. These findings offer valuable insights for policy makers and stakeholders aiming to develop effective strategies for managing residential electricity demand in light of changing demographics. [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]

Due to the fast response characteristics of battery storage, many renewable energy power stations equip battery storage to participate in auxiliary frequency regulation services of the grid, especially primary frequency regulation (PFR). In order to make full use of the battery capacity and improve the overall revenue of the renewable energy station, a two-level optimal scheduling strategy for battery storage is proposed to provide primary frequency regulation and simultaneously arbitrage, according to the peak-valley electricity price. The energy storage output is composed of the droop-based primary frequency regulation output and the economic output, according to the electricity price. First, day-ahead optimization defines the economic output profile and an appropriate droop coefficient, considering regulation needs, with the goal of maximizing the overall return. The scheduling result is then adjusted for hour-ahead optimization based on the updated regulation information to ensure more durable and reliable performance. Simulation results show that the proposed scheduling strategy can fully utilize the battery capacity, realize peak-valley arbitrage while assuming the obligation of primary frequency regulation of the renewable energy power station, and then improve the overall income of the power station. [ABSTRACT FROM AUTHOR]

This study examined the price risk of the Belize–Mexico interconnection using ARMA-ARCH models to assess electricity pricing volatility and autoregression to determine the influence of conditional volatilities and import consumption. The findings revealed that the volatility of both markets, especially spot price, showed rare high-impact shocks and prolonged periods of volatile clusters. Volatile pricing tendencies, and forward pricing to a lesser extent, had positive effects on premiums. Premiums were largely dependent on pricing properties and system conditions of the supplier's (Mexico) side, with negligible influence from the buyer's (Belize) side. However, significant effects were found during certain hours with the buyer's loading and transmission loss patterns. Practical implications revealed the possibility of occasional losses caused mostly by information delays and misalignment of risk factors and premiums, which could affect Belize's power cost affordability and scheduling of local generation. Further research can broaden this current scope to enhance planning on local power supply by integrating current findings on the importation market behavior with the buyer's local power expansion plan. [ABSTRACT FROM AUTHOR]

This work considers the issue of modeling and forecasting electricity prices within the functional time series approach. As this is often performed by estimating and predicting the different components of the price dynamics, we study whether jointly modeling the components, able to account for their inter-relations, could improve prediction with respect to a separate instance of modeling. To investigate this issue, we consider and compare the predictive performance of four different predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive (SS-SAR) and Smoothing Splines-Functional Autoregressive (SS-FAR) are based on separate modeling while the third one is derived from a single-step procedure that jointly estimates all the components by suitably including exogenous variables. It is called Functional Autoregressive with eXogenous variables (FARX) model. The fourth one is a combination of the SS-FAR and FARX predictors. The predictive performances of the models are tested using electricity price data from the northern zone of the Italian electricity market (IPEX), both in terms of forecasting error indicators (MAE, MAPE and RMSE) and by means of the Diebold and Mariano test. The results point out that jointly estimating the components leads to significantly more accurate predictions than using a separate instance of modeling. In particular, the MAE, MAPE, and RMSE values for the best predictor, based on the FARX( 3 , 0 , 4 ) model, are 4.25, 9.28, and 5.38, respectively. The percentage error reduction is about 20% with respect to SS-SAR (3 , 1) and about 10% with respect to SS-FAR(5). Finally, this study suggests that the forecasting errors are generally higher on Sunday and Monday, from hours 3 to 6 in the morning and 14 to 15 in the afternoon, and in June and December. On the other hand, prices are relatively lower on Wednesday, Thursday, and Friday, from hour 20 to 1 a.m., and in January and February. [ABSTRACT FROM AUTHOR]

Learning the dynamics of power prices in a given market is important for a number of players (e.g., producers, consumers, and policy makers) at both macro- and microeconomic levels. This paper analyzes the recent behavior of spot prices in eight Western European countries. The sample period coincides with the COVID-19 pandemic for the most part: it starts in April 2020 and runs until May 2023; it includes the start of the Russia–Ukraine war. We introduce a new model for the hourly spot price of electricity. The deterministic component includes yearly, weekly, and daily seasonalities; the stochastic component accounts for volatility, mean reversion, and discrete jumps. We estimate the model with publicly available hourly data. Regarding the development of the internal market for electricity, we find that core mainland power markets now move closer in step with one another than before, but the integration process of the Iberian Peninsula seems to have kicked into reverse. As for the dynamics of power prices, in the last part of the sample period the speed of reversion falls everywhere, and price volatility increases noticeably; the expected number of jumps per hour decreases, but their average size turns to positive and they become more volatile. [ABSTRACT FROM AUTHOR]

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors. [ABSTRACT FROM AUTHOR]

This document calculates targeting performance indicators of electricity subsidies in Mexico, Panama, the Dominican Republic, and Uruguay. To do this, beneficiary and benefit incidence indicators are calculated using official household survey data and administrative records on electricity tariffs. In addition, this paper calculates inclusion and exclusion errors in the targeting of electricity subsidies. The results show that the share of poor households that receive the subsidy is very large in Mexico, Panama, and the Dominican Republic. However, the distribution of the subsidy is regressive in absolute terms, which implies that most of every dollar spent on keeping electricity prices low will benefit the wealthiest households. This document also finds large inclusion errors suggesting that an important share of the non-poor is also benefiting from the subsidy. These results shed light on the evaluation of electricity subsidies in the region to improve targeting and optimisation of resources. [ABSTRACT FROM AUTHOR]

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, thus leading to increased economic and social benefits. Currently, some simple AI and hybrid models have issues to deal with and struggle with multivariate dependencies, long-term dependencies, and nonlinear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while lasso regression serves as the metalearner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in the forecasting accuracy, with a substantial reduction in the mean absolute percentage error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, thus suggesting potential operational efficiency benefits for the power industry. [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]

The cost reflectivity of electricity distribution network tariffs has been debated in several countries, and various ways to enhance it have been investigated in recent years. However, the recent academic literature regarding the approach based on cost causation has a clear gap because no case studies show how distribution network tariffs can be determined in practice for large customer groups. This article offers a calculation methodology to determine distribution network tariffs based on cost causation along with a case study where unit prices are determined for the tariff structures still widely used today using the data for two separate network areas being operated by two Finnish distribution system operators (DSOs) in an unbundled electricity market environment. The results of the case study show that the total differences between the target and the realized turnovers in both investigated networks are smaller than 1%, which means almost a full cost recovery. In addition to traditional tariff structures, the proposed calculation methodology can also be modified to design and determine other pricing schemes. The need for systematic calculation processes is growing to improve the cost reflectivity of present tariffs and adapt to the needs of the evolving operating environment, novel tariff structures, and new emerging customer groups. [ABSTRACT FROM AUTHOR]

In the context of power system decarbonization, the demand-side strategy for increasing the share of renewable energy is studied for two constrained energy systems. This strategy, which is currently widely suggested in policies on the energy transition, would impact consumer behavior. Despite the importance of studying the latter, the focus here is on decisions regarding the type, location, and timeframe of implementing the related measures. As such, solutions must be assessed in terms of cost and feasibility, technological learning, and by considering geographical and environmental constraints. Based on techno-economic optimization, in this paper we analyze the evolution of the power system and elaborate plausible long-term trajectories in the energy systems of two European islands. The case studies, Procida in Italy and Hinnøya in Norway, are both electrically connected to the mainland by submarine cables and present issues in their power systems, which are here understood as relatively isolated power systems. Renewable energy integration is encouraged by legislative measures in Italy. Although not modeled here, they serve as a backbone for the assumptions of increasing these investments. For Procida, rooftop photovoltaics (PV) coupled with energy storage are integrated in the residential, public, and tertiary sectors. A price-based strategy is also applied reflecting the Italian electricity tariff structure. At a certain price difference between peak and off-peak, the electricity supply mix changes, favoring storage technologies and hence decreasing imports by up to 10% during peak times in the year 2050. In Norway, renewable energy resources are abundant. The analysis for Hinnøya showcases possible cross-sectoral flexibilities through electrification, leading to decarbonization. By fine-tuning electric vehicle charging tactics and leveraging Norway's electricity pricing model, excess electricity demand peaks can be averted. The conclusions of this double-prospective study provide a comparative analysis that presents the lessons learnt and makes replicability recommendations for other territories. [ABSTRACT FROM AUTHOR]

This work quantifies the techno-economic performance of AC and DC residential building distribution. Two methods, utilising software and hardware configurations, are showcased to improve DC distribution: (i) a novel rule-based battery dual-objective operation (DOO) and (ii) a modular Master/Slave design of the grid-tied converter (GC). Both methods use the GC's load-dependent efficiency characteristic, eliminating partial-load operation and enhancing energy efficiency. The work uses measured annual PV and load data to evaluate the performance of the methods compared to AC and DC references. The techno-economic analysis includes the annual net electricity bill and monetised battery degradation. The results show that the DOO eliminates GC partial-load operation at the cost of increased battery usage, resulting in marginal net savings. In contrast, the modular converter design significantly reduces losses: −157 kWh/a (−31%) and −121 kWh/a (−26%), respectively, relative to the DC and AC references. For a parametric sweep of electricity price and discount rate, the Lifetime Operating Cost (LOC) comparison shows savings from DC of up to USD 575 compared to AC. [ABSTRACT FROM AUTHOR]

This work presents an economic analysis of the use of electricity storage in PV installations, based on previously adopted assumptions, i.e., the type and location of the tested facility and comparative variants, divided into the share of the storage in the installation, and the billing system. The work takes into account the share of the energy shield and assumes a consumption limit of 2000 kWh. The cost of building the installation is based on July 2023 prices. The work assumes potential directions of changes in electricity prices, based on which the degree of investment profitability for a given price situation is determined. Depending on the adopted change in the direction of electricity prices, with a low price increase rate, for installations in the new billing system (net-billing), the optimal choice is an installation without energy storage with a power exceeding the actual energy demand. Assuming a high increase in electricity prices, the optimal choice is an installation with energy storage with an installation capacity exceeding the actual demand. For installations billed using the net-metering system, the optimal choice is an installation without storage with an appropriately selected installation power. This article shows how much you can gain after installing a PV installation and not only what costs must be incurred to complete the investment. Profit analysis will enable a more complete assessment of the profitability of investing in PV panels (with or without energy storage). It describes the verification of the profitability of a PV installation for a standard user depending on various types of settlements with the electricity supplier and the lack or installation of an energy-storage facility. [ABSTRACT FROM AUTHOR]

This article introduces a novel optimization approach known as fractional order whale optimization algorithm (FWOA). The proposed optimizer incorporates the idea of fractional calculus (FC) into the mathematical structure of the conventional whale optimization algorithm (WOA). To validate the efficiency of the proposed FWOA, it is applied to address the challenges associated with the economic load dispatch (ELD) problem, which is a nonconvex, nonlinear, and non-smooth optimization problem. The objectives associated with ELD such as fuel cost and wind power generation cost minimization are achieved by taking into consideration different practical constraints like valve point loading effect (VPLE), transmission line losses, generator constraints, and stochastically variation of renewable energy sources (RES) integration. RES, particularly wind energy, has garnered more attention in recent times due to a range of environmental and economic factors. Stochastic wind (SW) power is also included in the ELD problem formulation. The incomplete gamma function (IGF) quantifies the influence of wind power. To assess its efficacy, the suggested approach is applied to a range of power systems including 3 generating units, 13 generating units and 40 generating units, consisting of 37 thermal units and 3 wind power units. To further strengthen the performance of the optimizer, the FWOA is hybridized with the interior point algorithm (IPA) to further refine the outcomes of the FWOA. The FWOA and IPA are used to address the problem of ELD while including the unpredictable nature of wind power. The simulation results of the suggested technique are compared with the most advanced heuristic optimization methods available, and it has been observed that the proposed optimizer obtained a superior and refined solution when compared to other state of the art optimization techniques. Furthermore, the efficacy of the suggested strategy in enhancing the solution of the ELD issue is validated through statistical analysis in terms of minimum fitness value. [ABSTRACT FROM AUTHOR]

In recent years, internet of underwater things (IoUT) has become a hot topic of research and attracted many researchers to use them for applications such as underwater environment monitoring and exploration, monitoring geological and biological changes, defense, disaster monitoring and prevention. Smart underwater objects in IoUT are generally equipped with high definition cameras for capturing underwater images. Storage and transmission of high-quality images captured by these smart objects is a challenging task due to huge volumes of data generated. Compressive sensing (CS) based data sampling and acquisition strategy provides promising ways to compress image at low computational cost and power. However, the samples are chosen randomly compromising reconstructed image's visual quality. To select signal samples with more importance, Adaptive Block Compressive Sensing (ABCS) can be utilized. In this research paper, we implement two-modified fast haar wavelet transform (MFHWT) coefficient thresholding based ABCS strategies for compression of underwater surveillance images in IoUT. The novelty lies in the fact that the proposed strategies choose only 5–20% of samples/measurements to achieve a rise in PSNR of about 3-5dB with respect to conventional techniques. The results show that the proposed strategies have NCC and SSIM values closer to 1 and NAE values closer to 0. Also, around 70% memory space can be saved. [ABSTRACT FROM AUTHOR]

This paper investigates the obstacles hindering the deployment of energy storage (ES) in distributed photovoltaic (DPV) systems by constructing a tripartite evolutionary game model involving energy storage investors (ESIs), distributed photovoltaic plants (DPPs), and energy consumers (ECs). Utilizing system dynamics (SD), this study systematically analyzes how parameter changes impact the deployment of DPV-ES systems. The findings reveal several critical impediments: (1) Lack of Incentives for DPPs. Under basic parameters, DPPs show no incentive to adopt positive deployment strategies due to high costs without corresponding returns. (2) Parameter Influence. Factors such as weak electricity supply capacity for ECs, high accident risk charges, and land rent may hinder ESIs or DPPs from adopting positive deployment strategies. However, stakeholders' initial willingness, fines for ECs, profit distribution ratios, and annual investment costs of ES systems do not alter participant strategies. (3) Cooperative Relationships and Economic Factors. Higher unit electricity prices and accident risk charges associated with DPV-ES plants increase electricity costs for ECs, potentially hindering the deployment of ES systems and leading to a circular game among stakeholders without ESS deployment. This study provides valuable insights for government and industry stakeholders to identify and eliminate barriers to the integrated development of DPV and ES systems, thereby promoting more effective deployment strategies. [ABSTRACT FROM AUTHOR]

Residential energy storage systems offer significant potential for price arbitrage by capitalizing on fluctuations in electricity prices throughout the day. This study investigates the potential revenue from optimal battery scheduling for residential storage in different north-western European electricity price zones during 2023. Using Nord Pool day-ahead prices, we applied an optimization model to determine the revenue for two types of batteries: 5 kW/10 kWh and 10 kW/10 kWh. The analysis considered battery capacity, charging and discharging efficiency, and maximum charge/discharge rates. Our results show notable variations in potential revenue across different regions, with the Baltic states demonstrating the highest revenue potential. The findings indicate that while 10 kW batteries can generate higher total revenue, 5 kW batteries are more efficient in terms of revenue per cycle. These regional disparities underscore the need for targeted incentives and policies to enhance the economic viability of residential energy storage. The research results provide valuable insights into optimizing residential battery storage for price arbitrage, offering guidance for consumers, policymakers, and energy providers to maximize economic benefits in various electricity markets. [ABSTRACT FROM AUTHOR]

Harvesting Solar Power Foments Prices in a Vicious Cycle: Breaking the Cycle with Price Mechanisms An important paper in this issue quantifies and explores how residential solar power adoption affects the price of electricity and the profit of a utility firm. It reveals the mechanism behind what is known as the "utility (death) spiral." As more residences adopt solar power generation, the price of electricity goes up, whereas the profit of utility firm comes down. The study finds the equilibrium of a game played between consumers deciding to adopt residential solar power and the utility deciding on prices in a regulated market. The equilibrium, involving a potential bankruptcy for the utility firm and a high price for a nonadopter consumer, is desirable for neither. To avoid this, it suggests either decreasing the buyback price that the utility pays for the excess power of an adopter or introducing a subscription for adopters. These suggestions can inform pricing policies in the power market. Distributed solar power generation is growing but not necessarily benefiting the utility firms. Reducing the demand, it hinders the coverage of utility costs with reasonable retail electricity prices. Utilities raise prices, unintentionally reducing both demand and affordability of electricity, and are said to be caught in a utility (death) spiral. The reduced affordability adversely affects consumers who cannot invest in solar generation. Environmentally desirable solar power paradoxically can be socially undesirable. Market regulators are challenged to keep prices low within the current pricing mechanisms. We provide a profit maximization formulation for a regulated utility and reveal the interaction between optimal price increases and growing solar power adoption. Iterating with this interaction, we study the utility death spiral for myopic and forward-looking consumers. We consider new pricing mechanisms with a buyback price and a subscription fee paid only by solar power-generating consumers. The fee mitigates the optimal retail price increase by allowing for the coverage of fixed costs in part. We find appropriate values for the buyback price and subscription fee to, respectively, slow or stop the utility spiral. These mechanisms are important not only for the utility and its regulator but also for all electricity consumers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0756. [ABSTRACT FROM AUTHOR]

A growing proportion of forested landscapes are interspersed with human infrastructure, such as utility lines and roads, increasing the potential for tree-failure consequences due to storms and other causes. Utilities and other institutions have strong incentives to reduce such interactions and allocate substantial resources to risk reduction, but trees and forests in densely populated landscapes also provide significant amenities to society. We present a generalized framework for "Stormwise" forest management, focused on resistance of roadside forests to storms, based on elements of arboricultural and silvicultural practice and tree biomechanics and architecture. We detail results of a multidisciplinary research program focused on management outcomes, opportunities and barriers to implementation, and allocation of investment based on physical and social landscape characteristics. We discuss initial findings, the potential for widespread adoption of resilience-focused management in roadside and infrastructure-adjacent forests, and the importance of such work, considering a changing climate. Study Implications: The research and implementation program we detail here illustrates the potential for "Stormwise" forest management to reduce storm-damage recovery costs, result in fewer and shorter-duration power and transportation interruptions, and allow for low-investment ground-based management in future forest entries. We illustrate a multifaceted, interdisciplinary research program that links the geospatial, social, and biophysical components of understanding forest infrastructure systems. We illustrate how implementation of Stormwise management has the potential to benefit stakeholders such as residents and utilities, offset the substantial economic costs of tree-related power outages, and reduce societal disruptions associated with interactions between trees and infrastructure during storms. [ABSTRACT FROM AUTHOR]

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A "sandwich" capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays' benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. [ABSTRACT FROM AUTHOR]

Nanosheet transistors are poised to become the preferred choice for the next generation of smaller-sized devices in the future. To address the future demand for high-performance and low-power computing applications, this study proposes a nanosheet structure with a vertically stacked design, featuring a high ION/IOFF ratio. This Nanosheet design is combined with an induced tunnel field-effect transistor. By utilizing SiGe with a carrier mobility three times that of Si and employing a line tunneling mechanism, the research successfully achieves superior Band to Band characteristics, resulting in improved switching behavior and a lower Subthreshold Swing (SS). Comparative studies were conducted on three TFET types: Nanosheet PIN TFET, Nanosheet Schottky iTFET, and Fin iTFET. Results show that the Nanosheet PIN TFET has a higher ION/IOFF ratio but poorer SSavg values at 47.63 mV/dec compared to the others. However, with a SiGe Body thickness of 3 nm, both Nanosheet iTFET and Fin iTFET exhibit higher ION/IOFF ratios and superior SSavg values at 17.64 mV/dec. These findings suggest the potential of Nanosheet iTFET and Fin iTFET for low-power, lower thermal budgets, and fast-switching applications. [ABSTRACT FROM AUTHOR]

This research article scrutinises the connotation among bond prices and electricity prices (EP) across different threshold regions in India using threshold regression analysis. We resolve to investigate whether or not there exists a non-linear association between bond prices and EP and if this relationship differs across different threshold regions. The data covers a period of five years from 2018 to 2022, and the analysis includes three different threshold regions based on different threshold bond prices. The results show that there exists significant non-linear association between bond prices and EP, and this relationship differs across the three threshold regions. We surmise presence of an optimality between bond prices (interest rates) and EP, beyond which the EP increase. We conclude that as interest rates increase, the cost of financing for electricity companies also increases, which may lead to higher EP. However, there may be a certain threshold beyond which the cost of financing becomes too high, leading to a decrease in investment and ultimately a decrease in electricity supply, which in turn could lead to higher EP. [ABSTRACT FROM AUTHOR]

We combine panel household data with the introduction of an increasing block tariffs (IBT) for residential electricity in three experimental regions of Russia to analyze the relationship between the IBT and the propensity of households to purchase dirty fuels. Using a difference-in-differences empirical specification, we find that the propensity to purchase dirty fuels has increased in the regions where the IBT schemes were introduced. The size of the increase varies from 3.8 percentage points for the full sample of households to 13.4 percentage points when restricting the sample to households that do not have access to district heating networks. [ABSTRACT FROM AUTHOR]