Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.

三色膏联合PRICE疗法治疗急性踝关节Ⅰ和Ⅱ度扭伤的临床效果

目的探讨三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤的临床效果。方法选取2022年2月至2023年5月上海市嘉定区中医医院骨伤科收治的52例Ⅰ、Ⅱ度急性踝关节扭伤患者作为研究对象,其中急诊患者35例,门诊患者17例,采用随机数字表法将其分为对照组和观察组,每组均失访1例,两组各25例纳入统计。对照组采用PRICE疗法治疗,观察组采用三色膏联合PRICE疗法,两组患者均治疗1周,治疗结束后,比较两组患者治疗前和治疗后第1、3周的视觉模拟评分法(VAS)评分及Mazur功能评分。结果52例患者中50例得到随访,随访时间不短于4周。治疗1、3周后,两组患者的VAS评分低于本组治疗前,Mazur功能评分高于本组治疗前,观察组的VAS评分低于对照组,Mazur功能评分高于对照组,差异有统计学意义(P<0.05)。结论三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤可以获得更好的疗效,具有重要的临床价值和推广意义。

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Economic Resilience in Bangladesh: Analyzing Household Well-Being amidst Price Hikes through ANOVA and Paired Sample t-Test Insights

This study delves into the multifaceted impact of price hikes on the standard of living in Bangladesh, with a specific focus on distinct socioeconomic segments. Amidst Bangladesh’s economic growth, the challenges of rising inflation and increased living costs have become pressing concerns. Employing a mixed-methods approach combines quantitative data from a structured survey with qualitative insights from in-depth interviews and focused group discussions to analyze the repercussions of price hikes. Stratified random sampling ensures representation across affluent, middle-class, and economically disadvantaged groups. Utilizing data [1] from 2020 to November 2023 on the yearly change in retail prices of essential commodities, analysis reveals significant demographic shifts, occupational changes, and altered asset ownership patterns among households. The vulnerable population, including daily wage laborers and low-income individuals, is disproportionately affected by adjustments in consumption, income generation, and living arrangements. Statistical analyses, including One-Way ANOVA and Paired Sample t-tests, illuminate significant mean differences in strategies employed during price hikes. Despite challenges, the prioritization of education remains evident, emphasizing its resilience in the face of economic hardships. The result shows that price hikes, especially in essential items, lead to substantial adjustments in living costs, with items like onions, garlic, and ginger experiencing significant increases of 275%, 108%, and 483%, respectively.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Spatiotemporal Evolution and Linkage Analysis of Mutton Price in Xinjiang

The mutton industry plays a pivotal role in the animal husbandry industry of Xinjiang Uygur Autonomous Region.To better monitor the volatility movement and risk warning of mutton price,the fluctuation characteristics of mutton prices and the future trend of prices are analyzed systematically.On the one hand,the Hodrick Prescott(HP)filter method is used to analyze the long-term trends and cyclical characteristics of mutton prices in Xinjiang and explored the spatial evolution characteristics of mutton price fluctuations in various regions of Xinjiang.On the other hand,the Threshold Auto-Regressive(TAR)model is used to analyze the linkage relationship between mutton prices and the prices of other livestock products.The empirical results show that 1)the overall volatility of mutton price in Xinjiang is high,showing a trend of rising first,then falling and then rising from the temporal perspective.2)At the regional level,the price in the south is higher than that of the north,showing a decreasing trend from south to north on the whole from the spatial viewpoint.3)From the linkage relationship perspective,mutton and beef are complementary in the short term,but they are substitutes each other in the long term.This paper explores the characteristics of mutton price fluctuations in Xinjiang from the single time series of mutton prices and the linkage with other livestock products,which provides a reliable basis for the monitoring and risk warning of mutton price fluctuation.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

From Finnish Assortment Pricing to Market Economy Using Prices for Sawn Wood and Chips in Reference Bucking

Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.

Comparative Analysis of Machine Learning Models for Stock Price Prediction: Leveraging LSTM for Real-Time Forecasting

The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.

The Prediction for the Consumer Price Index of Residents in Perspective of Time Series Method in Case of Chongqing

The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

Fuzzy Inventory Model under Selling Price Dependent Demand and Variable Deterioration with Fully Backlogged Shortages

The objective is to develop a model considering demand dependent on selling price and deterioration occurs after a certain period of time, which follows two-parameter Weibull distribution. Shortages are allowed and fully backlogged. Fuzzy optimal solution is obtained by considering hexagonal fuzzy numbers and for defuzzification Graded Mean Integration Representation Method. A numerical example is provided for the illustration of crisp and fuzzy, both models. To observe the effect of changes in parameters, sensitivity analysis is carried out.

Does US Dollar Supply Trigger House Price Fluctuations?An International Comparison

Cyclical fluctuations in asset prices are closely related to the overall stability of the macroeconomy and have thus become a significant topic in academic and professional exploration.Among these fluctuations,changes in the supply of the U.S.dollar,the global reserve,and primary settlement currency have a broad and far-reaching impact on international financial markets.Since COVID-19,asset prices across various countries have shown differing trends due to fluctuations in the U.S.dollar supply.Housing prices,a key focus in asset pricing research,have been affected to varying degrees.This thesis utilizes seven house price indices from representative cities worldwide from 2020 to 2023 to compare the differential impacts across various countries using an empirical approach.The results indicate that the impact of the U.S.dollar money supply(M2)diminishes in a gradient from the U.S.to East Asia,and then to Europe.

The Effects of Infrastructure Projects on House Prices and Rents:Evidence from the HS2 Extension Cancellation in the UK

This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.

基于Morgenstern-price法的粘土心墙土石坝坡面稳定计算分析

土石坝的坝坡面是否稳定一直是土石坝结构设计研究的重点问题,尤其是在渗流场的影响作用下尤为显著,研究以某水库工程坝体坡面在不同使用工况下的的安全稳定计算为例,通过软件建立坝体的模型,基于不同运行条件下的渗流数据,选用内嵌于计算软件Geostudio中的LOPE/W计算模块中的极限平衡算法(Morgenstern-Price)进行计算,计算结果满足规范要求,说明大坝坡面安全稳定。由于渗流场的引入,能更好的反映工程中的真实运行状况,为工程的建设提供建设依据和数据支持。

Scheduling optimization problem considering time-of-use tariffs and piece-rate machine maintenance in EAF steelmaking

An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and the total electricity cost.This problem is proved to be an NP-hard problem,and an effective solution algorithm,longest processing time-genetic(LPT-gene)algorithm,is proposed.The impacts of varied processing energy consumption and electricity price on the optimal schedules are analyzed.The integrated influence of the different weight values and the variation between the peak price and the trough price on the optimal solution is studied.Computational experiments illustrate that considering the energy consumption costs in production has little influence on makespan;the computational performance of the proposed longest processing time-genetic algorithm is better than the genetic algorithm(GA)in the issue to be studied;considerable reductions in the energy consumption costs can be achieved by avoiding producing during high-energy price periods and reducing the machining energy consumption difference.The results can be a guidance for managers to improve productivity and to save energy costs under the time-of-use tariffs.

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.