scholarly journals Price Setters and Price Takers in the EU Electricity Market, a Comparative Analysis of Household Consumer Prices

2017 ◽  
Vol 6 (1) ◽  
pp. 174
Author(s):  
Aranit Shkurti ◽  
Macit Koc
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Time Series Data ◽  
Weather Conditions ◽  
Series Data ◽  
Important Indicator ◽  
Spot Prices ◽  
Consumer Prices ◽  
Yearly Average ◽  
The Eu

The article is concerned with the analysis of the electric power prices at the European spot exchanges, taking in consideration 27 Countries of the Union (excluding UK). The time series data are considering the half yearly average of the countries, as reported by the Eurostat database. The article examines the way spot prices are influenced by power exchanges, based on the overall installed power of more healthier economies. In recent years a growing capacity from renewable sources is pouring in the system, anyway the implementation of renewable energies do not guarantee constant supply to the network as they depend on weather conditions and therefore must still have recourse to conventional generation types - such as gas and coal - which generally have higher operating costs than renewable. An increasing number of Member States have adjusted mechanisms to promote investment in power plants or provided incentives to keep them standing. These public measures may be justified in certain situations but according to recent guidelines, the European Commission has established that the adjustment mechanisms can be in contrast with the legislation on state aid. The identification of these discrepancies is studied in this article through the key characteristics of the price differential for the EU spot markets. The inflation generated from the price adjustments within the EU members can be considered an important indicator of market inefficiency.Key words: electricity spot exchanges, subsidies, price setter, price taker, household consumers.

scholarly journals Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices

2020 ◽  
Author(s):  
Jacopo Torriti
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Electricity Demand ◽  
Market Prices ◽  
Vast Number ◽  
Spot Prices ◽  
Time Use Survey ◽  
Wholesale Electricity Market ◽  
The Uk ◽  
Flexibility Index

AbstractDuring peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility.

scholarly journals PENGARUH INFLASI, SUKU BUNGA, DAN NILAI TUKAR TERHADAP INDEKS HARGA SAHAM GABUNGAN PERIODE 2016 – 2018

2021 ◽  
Vol 6 (1) ◽  
pp. 50-59
Author(s):  
Irine Melyani ◽  
Martha Ayerza Esra
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Price ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Future Research ◽  
Time Range ◽  
Important Indicator ◽  
The Interest Rate

The movement of stock price index is the important indicator for investors to determine whether the investor would sell, buy, or hold shares. The movement of CSPI is affected by several factor like macroeconomy. The purpose of this study was to determine the effect of inflation, interest rate, and exchange rate against CSPI. Theoretically, the effect of inflation, interest rate, and exchange rate is based on efficient market hyphothesis and signalling theory which inflation, interest rate and exchange rate provide signal to investor which affect their decision that cause change to CSPI. The type of data used in this study is secondary data with quantitative approach. The sampling is based on time series data from 2016-2018 using purposive sampling methodso that 36 samples are obtained. This research uses multiple uses multiple regression analysis method using SPSS 2.2. The results of this study indicate that during the period 2016-2018 inflation does not affect CSPI, the interest rate have negative affect on CSPI and exchange rate have positive affect on CSPI. Future research is expected to add another independent variable and extend the time range of the research to obtain ore accurate and comprehensive results. Keywords: Inflation, Interest Rate, Exchange Rate, Composite Stock Price Indonesia

scholarly journals Explainable Deep Neural Networks for Multivariate Time Series Predictions

2019 ◽  
Author(s):  
Roy Assaf ◽  
Anika Schumann
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Network Architecture ◽  
Power Plants ◽  
Deep Neural Networks ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Average Energy ◽  
Series Data ◽  
Time Interval

We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions. This is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial. We design a two stage convolutional neural network architecture which uses particular kernel sizes. This allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features. These are then used for explaining which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction. We demonstrate our approach for predicting the average energy production of photovoltaic power plants and for explaining these predictions.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals Encompassing Electricity Market Predictions and Weather Forecasts in Power Plan O&M Strategies

2017 ◽  
Author(s):  
Linda Ponta ◽  
Luca Oneto ◽  
Davide Anguita ◽  
Silvano Cincotti
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Power Plants ◽  
Weather Condition ◽  
Weather Conditions ◽  
Fixed Time ◽  
Important Variable ◽  
Electricity Price ◽  
Maintenance Schedule ◽  
Wind Power Plants

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

scholarly journals Land Use Land Cover Change and its Pathways in Sidin VDC, Panchthar District, Nepal

2018 ◽  
Vol 11 ◽  
pp. 77-94 ◽  
Author(s):  
Prem Sagar Chapagain ◽  
Mohan Kumar Rai ◽  
Basanta Paudel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Time Series Data ◽  
Household Survey ◽  
Human Interaction ◽  
Series Data ◽  
Land Use Land Cover ◽  
Important Indicator

Land use/land cover situation is an important indicator of human interaction with environment. It reflects both environmental situation and the livelihood strategies of the people in space over time. This paper has attempted to study the land use/ land cover change of Sidin VDC, in the Koshi River basin in Nepal, based on maps and Remote sensing imageries (RS) data and household survey using structured questionnaires, focus group discussion and key informant interview. The study has focused on analysis the trend and pathways of land use change by dividing the study area into three elevation zones – upper, middle and lower. The time series data analysis from 1994-2004-2014 show major changes in forest and agricultural land. The dominant pathways of change is from forest to agriculture and forest to shrub during 1994-2004 and agriculture to forest during 2004-2014. The development of community forest, labor migration and labor shortage are found the major causes of land use change.The Geographical Journal of NepalVol. 11: 77-94, 2018

Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

2021 ◽  
Author(s):  
Kazuhiko Komatsu ◽  
Hironori Miyazawa ◽  
Cheng Yiran ◽  
Masayuki Sato ◽  
Takashi Furusawa ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plants ◽  
Time Series Data ◽  
Thermal Power ◽  
Turbine Blades ◽  
Series Data ◽  
Thermal Power Plants ◽  
Steam Turbines ◽  
Pressure Steam ◽  
The Status

Abstract The periodic maintenance, repair, and overhaul (MRO) of turbine blades in thermal power plants are essential to maintain a stable power supply. During MRO, older and less-efficient power plants are put into operation, which results in wastage of additional fuels. Such a situation forces thermal power plants to work under off-design conditions. Moreover, such an operation accelerates blade deterioration, which may lead to sudden failure. Therefore, a method for avoiding unexpected failures needs to be developed. To detect the signs of machinery failures, the analysis of time-series data is required. However, data for various blade conditions must be collected from actual operating steam turbines. Further, obtaining abnormal or failure data is difficult. Thus, this paper proposes a classification approach to analyze big time-series data alternatively collected from numerical results. The time-series data from various normal and abnormal cases of actual intermediate-pressure steam-turbine operation were obtained through numerical simulation. Thereafter, useful features were extracted and classified using K-means clustering to judge whether the turbine is operating normally or abnormally. The experimental results indicate that the status of the blade can be appropriately classified. By checking data from real turbine blades using our classification results, the status of these blades can be estimated. Thus, this approach can help decide on the appropriate timing for MRO.

Detection of Machinery Failure Signs From Big Time-Series Data Obtained by Flow Simulation of Intermediate-Pressure Steam Turbines

2021 ◽  
Author(s):  
Kazuhiko Komatsu ◽  
Hironori Miyazawa ◽  
Cheng Yiran ◽  
Masayuki Sato ◽  
Takashi Furusawa ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plants ◽  
Time Series Data ◽  
Thermal Power ◽  
Turbine Blades ◽  
Series Data ◽  
Thermal Power Plants ◽  
Steam Turbines ◽  
Pressure Steam ◽  
The Status

Abstract The periodic maintenance, repair, and overhaul (MRO) of turbine blades in thermal power plants are essential to maintain a stable power supply. During MRO, older and less-efficient power plants are put into operation, which results in wastage of additional fuels. Such a situation forces thermal power plants to work under off-design conditions. Moreover, such an operation accelerates blade deterioration, which may lead to sudden failure. Therefore, a method for avoiding unexpected failures needs to be developed. To detect the signs of machinery failures, the analysis of time-series data is required. However, data for various blade conditions must be collected from actual operating steam turbines. Further, obtaining abnormal or failure data is difficult. Thus, this paper proposes a classification approach to analyze big time-series data alternatively collected from numerical results. The time-series data from various normal and abnormal cases of actual intermediate-pressure steam-turbine operation were obtained through numerical simulation. Thereafter, useful features were extracted and classified using K-means clustering to judge whether the turbine is operating normally or abnormally. The experimental results indicate that the status of the blade can be appropriately classified. By checking data from real turbine blades using our classification results, the status of these blades can be estimated. Thus, this approach can help decide on the appropriate timing for MRO.

scholarly journals Construction of Complex Network with Multiple Time Series Relevance

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 202
Author(s):  
Zongwen Huang ◽  
Lingyu Xu ◽  
Lei Wang ◽  
Gaowei Zhang ◽  
Yaya Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Weather Conditions ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Optimal Network ◽  
Multiple Variables ◽  
Threshold Network

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

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