scholarly journals Research on the Hybrid Recommendation Method of Retail Electricity Price Package Based on Power User Characteristics and Multi-Attribute Utility in China

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2693
Author(s):  
Yongxiu He ◽  
Meiyan Wang ◽  
Jinxiong Yu ◽  
Qing He ◽  
Huijun Sun ◽  
...  
Keyword(s):  
Electricity Market ◽  
Utility Theory ◽  
Measurement Method ◽  
Electricity Price ◽  
Utility Model ◽  
User Characteristics ◽  
Market Competitiveness ◽  
Multi Attribute Utility Theory ◽  
The Impact ◽  
Hybrid Recommendation

With the deregulation of the retail electricity market and the increase of the types of electricity price packages, electricity retail companies provide the recommended service of price packages for users, so as to improve the market competitiveness and user stickiness of enterprises. The existing research does not fully consider the impact of user characteristics and package attributes on recommendation results. This paper proposes a hybrid recommendation method of retail electricity price package based on the characteristics of power users and the multi-attribute utility of price package. Firstly, the hierarchical model of hybrid characteristics of power users in retail electricity market is constructed based on the tree structure, and all characteristics are analyzed quantitatively by proximity measurement method. Then, based on the multi-attribute utility theory, the utility model of retail electricity price package to users is constructed. Secondly, the accurate recommendation of the package is realized according to the characteristics of power users and the multi-attribute utility of price package. Finally, the rationality of the hybrid recommendation method of the retail electricity price package is verified by empirical analysis. This study provides valuable support for user to choose the retail electricity price package and improve the competitiveness of power retail companies.

scholarly journals PENERAPAN METODE MULTI ATTRIBUTE UTILITY THEORY (MAUT) DALAM PEMETAAN TINGKAT DAMPAK BENCANA BANJIR DI KABUPATEN BANTUL

Telematika ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 26
Author(s):  
Afif Irfan Abdurrahman ◽  
Bambang Yuwono ◽  
Yuli Fauziah
Keyword(s):  
Utility Theory ◽  
Matrix Multiplication ◽  
Flood Disaster ◽  
Accuracy Rate ◽  
Affected Area ◽  
A Value ◽  
Multi Attribute Utility Theory ◽  
The Matrix ◽  
The Difference ◽  
The Impact

Flood disaster is a dangerous disaster, an event that occurs due to overflow of water resulting in submerged land is called a flood disaster. Almost every year Bantul Regency is affected by floods due to high rainfall. The flood disaster that struck in Bantul Regency made the Bantul District Disaster Management Agency (BPBD) difficult to handle so that it needed a mapping of the level of the impact of the flood disaster to minimize the occurrence of floods and provide information to the public.This study will create a system to map the level of impact of floods in Bantul Regency with a decision support method namely Multi Attribute Utility Theory (MAUT). The MAUT method stage in determining the level of impact of flood disasters through the process of normalization and matrix multiplication. The method helps in determining the areas affected by floods, by managing the Indonesian Disaster Information Data (DIBI). The data managed is data on criteria for the death toll, lost victims, damage to houses, damage to public facilities, and damage to roads. Each criteria data has a value that can be used to determine the level of impact of a flood disaster. The stages for determining the level of impact of a disaster require a weighting calculation process. The results of the weighting process display the scoring value which has a value of 1 = low, 2 = moderate, 3 = high. To assist in determining the affected areas using the matrix normalization and multiplication process the process is the application of the Multi Attribute Utility Theory (MAUT) method.This study resulted in a mapping of the level of impact displayed on google maps. The map view shows the affected area points and the level of impact of the flood disaster in Bantul Regency. The mapping produced from the DIBI data in 2017 produced the highest affected area in the Imogiri sub-district. The results of testing the data can be concluded that the results of this study have an accuracy rate of 95% when compared with the results of the mapping previously carried out by BPBD Bantul Regency. The difference in the level of accuracy is because the criteria data used are not the same as the criteria data used by BPBD in Bantul Regency so that the accuracy rate is 95%.

scholarly journals Evaluation of the Administrative Phase-Out of Coal Power Plants on the Italian Electricity Market

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4596
Author(s):  
Michele Fiorelli ◽  
Dogan Keles ◽  
Francesco Montana ◽  
Giovanni Lorenzo Restifo ◽  
Eleonora Riva Sanseverino ◽  
...  
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Small Variation ◽  
Electricity Price ◽  
Spot Price ◽  
Price Variation ◽  
Coal Power Plants ◽  
Coal Power ◽  
Phase Out ◽  
The Impact

Although decarbonisation is one of the most important macro-trends of this century, electricity generation from coal power plants is still broadly common. The main goal of this study is to evaluate the impact of a premature coal power plants phase-out on the Italian day-ahead electricity market. For this purpose, two electricity price forecasts, related to different scenarios between 2019 and 2030, and two different hypotheses for the creation of electricity spot price, were compared. The results from the different scenarios show that coal power plants phase-out determines a small variation in electricity price when bid-up is not considered; instead, when operators’ bid-up is included in the study, the price variation becomes relevant.

scholarly journals Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5858
Author(s):  
Mahmood Hosseini Imani ◽  
Ettore Bompard ◽  
Pietro Colella ◽  
Tao Huang
Keyword(s):  
Regression Model ◽  
Electricity Market ◽  
Multivariate Regression ◽  
Order Effect ◽  
Electricity Price ◽  
Future Market ◽  
Multivariate Regression Model ◽  
Price Demand ◽  
The Impact ◽  
Solar Generation

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

scholarly journals Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2347
Author(s):  
Heike Scheben ◽  
Nikolai Klempp ◽  
Kai Hufendiek
Keyword(s):  
Renewable Energy ◽  
Electricity Market ◽  
Renewable Energies ◽  
Electricity Price ◽  
Water Inflow ◽  
Programming Approach ◽  
Strong Impact ◽  
Linear Optimisation ◽  
The Impact ◽  
Dual Dynamic Programming

Renewable energy shares in electricity markets are increasing and therefore also require an increase in flexibility options. Conventional electricity price modelling with optimisation models in thermally dominated markets is not appropriate in markets with high shares of renewable energies and storages because price structures are not adequately represented. Previous research has already identified the impact of uncertainty in renewable energy feed-in on investment and dispatch decisions. However, we are not aware of any work that investigates the influence of uncertainties on price structures by means of optimisation models. Appropriate modelling of electricity price structures is important for investment and policy decisions. We have investigated the influence of uncertainty concerning water inflow by applying a second stage stochastic dual dynamic programming approach in a linear optimisation model using Norway as an example. We found that the influence of uncertainty concerning water inflow combined with high shares of storages has a strong impact on the electricity price structures. The identified structures are highly influenced by seasonal water inflow, electricity demand, wind, and export profiles. Additionally, they are reinforced by seasonal primary energy source prices and import prices. Incorporating uncertainties in linear optimisation models improves the price modelling and provides, to a large extent, an explanation for the seasonal patterns of Norwegian electricity market prices. The paper explains the basic pricing mechanisms in markets with high shares of storages and renewable energies which are subject to uncertainty. To identify these fundamental mechanisms, we focused on uncertainty regarding water inflow, but the basic results hold true for uncertainties regarding other renewable energies as well.

scholarly journals Valoración de bienes ambientales complejos: Una alternativa multicriterio

2011 ◽  
Vol 7 (14) ◽  
pp. 83 ◽  
Author(s):  
José A. Gómez-Limón ◽  
Jesús Barreiro-Hurlé
Keyword(s):  
Utility Theory ◽  
Methodological Approach ◽  
Economic Valuation ◽  
Individual Utility ◽  
Valuation Function ◽  
Environmental Goods ◽  
Multi Attribute Utility Theory ◽  
Individual Utility Function ◽  
Environmental Good ◽  
The Impact

Economic valuation of complex environmental goods has several issues still opened to debate. This paper focuses on two of these aspects; linearity of attributes in the valuation function and individual utility function heterogeneity. A methodological approach based on Multi-attribute Utility Theory is proposed which allows to contrast the impact of these concerns on environmental good valuation. We apply the proposed methodology to value a protected natural in the province of Granada (Spain). From the results obtained we can conclude that attribute non-linearity and individual utility function’s heterogeneity are relevant aspects to be taken into account in environmental valuation.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 1083-1103 ◽  
Author(s):  
George P. Papaioannou ◽  
Christos Dikaiakos ◽  
Athanasios S. Dagoumas ◽  
Anargyros Dramountanis ◽  
Panagiotis G. Papaioannou
Keyword(s):  
Electricity Market ◽  
Garch Model ◽  
Wholesale Electricity Market ◽  
The Impact ◽  
Regulatory Reforms

scholarly journals A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks

2021 ◽  
Vol 11 (3) ◽  
pp. 965
Author(s):  
Irina Stipanovic ◽  
Zaharah Allah Bukhsh ◽  
Cormac Reale ◽  
Kenneth Gavin
Keyword(s):  
Utility Theory ◽  
Structural Reliability ◽  
Structural Performance ◽  
Maintenance Planning ◽  
Relative Importance ◽  
User Cost ◽  
Multi Attribute Utility Theory ◽  
Network Maintenance ◽  
Decision Making Model ◽  
Do So

Aged earthworks constitute a major proportion of European rail infrastructures, the replacement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi-attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization.

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