Electricity Price
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Significance The cost of gas-fired generation sets the electricity price in much of Europe today. Falling indigenous production has left Europe reliant on gas imports and exposed it to global liquefied natural gas (LNG) prices set by fast-recovering China. This has left retail-only electricity suppliers vulnerable and increases the risk that falling disposable incomes will undermine post-pandemic recovery. Impacts EU carbon allowance prices will stay strong. Higher energy prices will stoke inflation amid a fragile recovery, posing a dilemma for central banks. Rising gas prices have had ancillary but potentially alarming impacts as some fertiliser and CO2 producers have shut in production.


Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6104
Author(s):  
Alireza Pourdaryaei ◽  
Mohammad Mohammadi ◽  
Mazaher Karimi ◽  
Hazlie Mokhlis ◽  
Hazlee A. Illias ◽  
...  

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.


2021 ◽  
Author(s):  
Xavier Labandeira ◽  
José M Labeaga ◽  
Jordi J Teixidó

Abstract The global energy mix and cost structure of the power industry are experiencing a redefinition. Many countries are revamping electricity-pricing systems to guarantee fixed-cost recovery, often by raising the fixed charge of two-part tariff schemes. However, a key assumption of two-part tariff schemes and associated fixed cost recoveries is that consumers discriminate fixed from marginal costs. We conduct a quasi-experiment with data from a major electricity price reform recently implemented in Spain and find robust evidence indicating that consumers fail to distinguish between fixed and marginal costs. As a result, policymakers are not achieving the goal of cost recovery


2021 ◽  
Vol 9 ◽  
Author(s):  
Jing Wang ◽  
Hong Li

Being affected by a variety of factors, power-generation structure plays an essential role in a high-quality and sustainable development. The focus of this paper is to evaluate the influence of electricity price on it. First, we provide a microeconomic framework to understand the impact mechanism. We discuss two effects through which price level can affect power generation, and then the power-generation structure. After that, an empirical test is conducted using provincial panel data, and the results of it are robust. We also test the above-mentioned mechanism empirically. There are two main conclusions. First, the electricity price has a positive effect on the share of thermal power in electricity generation. Second, the mechanism test shows that an increase of electricity price can not only improve efficiency of power plants but also propel firms to invest in more renewable energy plants.


Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5858
Author(s):  
Mahmood Hosseini Imani ◽  
Ettore Bompard ◽  
Pietro Colella ◽  
Tao Huang

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.


2021 ◽  
Author(s):  
Salman Sadiq Shuvo ◽  
Yasin Yilmaz

Aras activity dataset<div>NYISO dynamic electricity price</div><div>A2C implementation in Python</div><div><br></div><div>Article under review in IEEE Transactions on Smart Grid </div>


2021 ◽  
Author(s):  
Salman Sadiq Shuvo ◽  
Yasin Yilmaz

Aras activity dataset<div>NYISO dynamic electricity price</div><div>A2C implementation in Python</div><div><br></div><div>Article under review in IEEE Transactions on Smart Grid </div>


Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2214
Author(s):  
Rahmad Syah ◽  
Afshin Davarpanah ◽  
Marischa Elveny ◽  
Ashish Kumar Karmaker ◽  
Mahyuddin Nasution ◽  
...  

This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.


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