Wales Electricity Market Research Articles

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

The creation of strategic pricing models is crucial in the highly competitive centralized energy markets in order to maximize revenues. In order to achieve maximum profit, market participants must act intelligently while bidding on electrical energy purchases or sales. Notwithstanding the many restructuring changes of the Chinese electricity sector, market participants are nonetheless exposed to risks associated with volatility in market prices and uncertainty over demand behavior. The data are collected from New South Wales electricity market. Data-adaptive Gaussian average filtering (DAGAF) is used to pre-process the data during the pre-processing step. Hierarchically Gated Recurrent Neural Networks (HGRNNs) are successfully used to classify both long-term and spot transactions. The neural network's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA), which enhances the HGRNN. The RESMB-HGRNN-LEA proposed is implemented on the Python working platform. To calculate the suggested approach, performance measures including accuracy, precision, sensitivity, computation time, and recall were looked at.In comparison to the existing technique, the proposed RESMB-HGRNN-LEA method yields better results in terms of accuracy (16.65% and 18.85%), sensitivity (16.34% and 12.23%), precision (14.89% and 16.89%), and computing time (82.37% and 94.47%).

A Novel Intelligent Deep Learning-Based Uncertainty-Guided Network Training in Market Price

In this article, we introduce an intelligent probabilistic uncertainty-guided prediction model for energy market price modeling based on the lower–upper bound estimation method. The proposed method borrows the prediction interval (PI) concept to replace the traditional point–point estimation process. Making use of the PI, the forecast error would be captured within the upper and lower bounds of the PI. The proposed method uses the long–short term networks to learn the complex and nonlinear dataset of market prices with high accuracy. Considering the conflicting nature of the prediction indices, a fuzzy min–max solution is introduced to get into a compromised solution. From the optimization point of view, a new method based on the grey wolf algorithm (GWA) is proposed to adjust the model hyperparameters suitably. Moreover, a new modification is introduced to increase the diversity of the GWA and, thus, enhance its search capability. To validate the proposed model, a standard dataset from the New South Wales electricity market is used to assess the high accuracy of the model to capture the prediction error.

Modeling Electricity Price Dynamics Using Flexible Distributions

We consider the wholesale electricity market prices in England and Wales during its complete history, where price-cap regulation and divestment series were introduced at different points in time. We compare the impact of these regulatory reforms on the dynamics of electricity prices. For this purpose, we apply flexible distributions that account for asymmetry, heavy tails, and excess kurtosis usually observed in data or model residuals. The application of skew generalized error distribution is appropriate for our case study. We find that after the second series of divestments, price level and volatility are lower than during price-cap regulation and after the first series of divestments. This finding implies that a sufficient horizontal restructuring through divestment series may be superior to price-cap regulation. The conclusion could be interesting to other countries because the England and Wales electricity market served as the benchmark model for liberalizing energy markets worldwide.

Solar energy penetration and volatility transmission to electricity markets—An Australian perspective

The contribution of solar energy to electricity production has intensified in recent years. In this study, we aim to measure the impact of higher solar penetration on electricity prices across regional and national electricity markets in Australia. Using daily data, we also estimate volatility transmission among the Australian electricity markets. Univariate and multivariate GARCH models are employed to achieve the objectives of the study. The results show that increasing solar penetration decreases electricity prices in the regional electricity markets of Queensland, South Australia and Tasmania. However, the results further reveal that the joint negative effect of solar energy on electricity prices is outweighed by the positive impact of natural gas prices. Finally, the findings show that there are many significant own-volatility and cross-volatility spillovers among the Australian electricity markets. Among the regional electricity markets, the New South Wales electricity market emerged as the most critical market in terms of cross volatility.

A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM

Under the liberalization and deregulation of the power industry, price forecasting has become a cornerstone for market participants' decision-making such as bidding strategies and purchase plans. However, the exclusive nonlinearity dynamics of electricity price is a challenge problem that largely affects forecasting accuracy. To address this task, this paper presents a hybrid forecasting framework for short-term electricity price forecasting by exploiting and mining the important information hidden in the electricity price signal. Moreover, a hybrid feature selection method (HFS) is introduced into the forecasting strategy. To exhibit the dynamical characteristics of electricity price, we primarily perform a singular spectrum analysis (SSA)-based systematic analysis process by using the merit of SSA and analyzing the multiple seasonal patterns of short-term electricity price series, providing a meaningful representation of the hidden patterns and time-varying volatility of electricity price series. Aiming at selecting the key features, the candidate variables are constructed considering the dynamic behavior of price series; further, to capture the optimal features from the candidates, the correlation threshold θ is defined for the adjustable parameters in HFS and optimally determined by the intelligent search algorithm. Additionally, triangulation based on the Pearson, Spearman and Kendall rank correlation coefficient is performed to strengthen the reliability of the proposed method. The proposed hybrid forecasting framework is validated in the New South Wales electricity market, which demonstrates that the developed approach is truly better than the benchmark models used and a reliable and promising tool for short-term electricity price forecasting.

The Impact of Behavioral and Structural Remedies on Electricity Prices: The Case of the England and Wales Electricity Market

During the liberalization process the UK regulatory authority introduced a behavioral remedy (through price-cap regulation) and structural remedy (through divestment series) in order to mitigate an exercise of market power and lower the influence of incumbent producers on wholesale electricity prices. We study the impact of these remedies on the dynamics of the wholesale electricity price during the peak-demand period over trading days. An extended autoregressive and autoregressive conditional heteroscedasticity (AR–ARCH) model with a novel skew generalized error distribution is used. This distribution allows one to capture the features of asymmetry, excess kurtosis, and heavy tails. The model is extended to include individual incumbent producers’ market shares and other explanatory variables reflecting seasonal patterns and regulatory regimes. We find that the structural remedy was more successful than the behavioral remedy because the effect of market share of the previously larger incumbent producer on the wholesale price is statistically insignificant. Moreover, after the second series of divestments, price volatility reduced.

Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets

We use high-frequency spot prices from the Australian New South Wales (NSW) electricity market to calculate the non-parametric realized volatility as well as identify price jumps. We show that the residuals of the heterogeneous autoregressive (HAR) models of realized volatility still exhibit volatility clustering. Therefore, we extend the HAR models by characterizing such time-varying volatility of realized volatility through three GARCH-type models: the GARCH model, the long-memory FIGARCH model, and the asymmetric EGARCH model. Furthermore, we augment the above HAR-GARCH-type models to capture the inverse leverage effect and to exploit the errors in realized volatility estimators. The resulting models are referred to as the HARQ-L-GARCH-type models. They each have better in-sample fit than the corresponding HAR-GARCH-type models, whose in-sample fit are much better than the benchmark HAR models. More importantly, Diebold-Mariano tests on out-of-sample forecasts reinforce our extensions, as the forecast accuracy of the HAR-GARCH-type models significantly outperforms that of the benchmark HAR models under six conventional criteria, and the forecast accuracy of the HARQ-L-GARCH-type models is even higher. Finally, the model confidence set tests indicate that, 1) modeling the residual variance with the GARCH structure and the FIGARCH structure can more effectively improve the out-of-sample forecasting performance of the HAR models. 2) Incorporating jumps in the HAR structure improves the out-of-sample forecasting performance. 3) The HARQ-L-CJ-GARCH model is superior for predicting volatility in the NSW electricity market.

Short Term Price Forecasting Using Adaptive Generalized Neuron Model

This article described how in the competitive deregulated electricity market forecasting has become one of the essential planning tool that assists the planners in preparing the power systems for future demands. The commercial success of the market players depends on their competitive bidding strategy which is suffuicient enough to meet the regulatory requirements and minimize the cost. Artificial neural networks due to their capability of non-linear mapping finds extensive application in the field of price forecasting. Although, they are extensively used as forecasting model, they have certain limitations which are detrimental to system performance. The training time of the artificial neural network is affected by the complexity of the system, and moreover, they require a large amount of data for complex problems. The worl presented in this article deals with the application of the generalized neuron model for forecasting the electricity price. The generalized neuron model overcomes the limitation of the conventional ANN. The electricity price of the New South Wales electricity market is forecast to test the performance of the proposed model. The free parameters of the proposed model are trained using fuzzy tuned genetic algorithms to increase efficacy of the model.

Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models

We apply the non-parametric realized volatility technique and the associated jump detection test to measure volatility and jumps in electricity prices. Then, we propose a group of logistic smooth transition heterogeneous autoregressive (LSTHAR) models of realized volatility. The models can simultaneously approximate long memory behavior and describe sign and size asymmetries. They differ in the underlying heterogeneous autoregressive structure and the transition variable specification. The out-of-sample forecast accuracy of the LSTHAR models is evaluated through the Diebold–Mariano test and the superior predictive ability test, in terms of the mean square error and the mean absolute error. Using high-frequency prices from the Australian New South Wales (NSW) electricity market as empirical data, we draw the following conclusions. 1) Introducing the logistic smooth transition structure with appropriate transition variable specification to the heterogeneous autoregressive models improves volatility forecasts. 2) Overall, the LSTHAR model that uses the sum of Beta function weighted past returns as the transition variable and includes past daily jumps as a predictor is the superior model for predicting volatility in the NSW market. This model significantly outperforms the others.

Do producers apply a capacity cutting strategy to increase prices? The case of the England and Wales electricity market

Promoting competition among electricity producers is primarily targeted at ensuring fair electricity prices for consumers. Producers could, however, withhold part of production facilities (i.e., apply a capacity cutting strategy) and thereby push more expensive production facilities to satisfy demand for electricity. This behavior could lead to a higher price determined through a uniform price auction. Using the case of the England and Wales wholesale electricity market we empirically analyze whether producers indeed did apply a capacity cutting strategy. For this purpose we examine the bidding behavior of producers during high- and low-demand trading periods within a trading day. We find statistical evidence for the presence of capacity cutting by several producers, which is consistent with the regulatory authority's reports.

Can Producers Apply a Capacity Cutting Strategy to Increase Prices? The Case of the England and Wales Electricity Market

Promoting competition among electricity producers is primarily targeted at ensuring low electricity prices for consumers. Producers could, however, withhold part of production facilities (i.e., apply a capacity cutting strategy) and thereby push more expensive production facilities to satisfy demand for electricity. This behavior could eventually lead to a higher price determined through a uniform price auction. In this paper, using the case of the England and Wales wholesale electricity market, we empirically examine whether producers can indeed apply a capacity cutting strategy. We analyze the bidding behavior of producers during high- and low-demand trading periods across trading days and find direct and indirect evidence for producers' successful manipulation of capacity bids targeted at increasing a wholesale electricity price. We also examine whether the regulatory reforms to improve competition were successful at mitigating the extent of strategic capacity manipulation.

An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting

Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR) for small sample and balanced data, this paper presents an adaptive fuzzy combination model based on the self-organizing map (SOM), the SVR and the fuzzy inference method. The adaptive fuzzy combination model can effectively count for electric load forecasting with good accuracy and interpretability at the same time. The key idea behind the combination is to build a human-understandable knowledge base by constructing a fuzzy membership function for each homogeneous sub-population. The comparison of different mathematical models and the effectiveness of the presented model are shown by the real data of New South Wales electricity market. The obtained results confirm the validity of the developed model.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Designing electricity auctions

Motivated by the new auction format in the England and Wales electricity market, as well as the recent debate in California, we characterize bidding behavior and market outcomes in uniform and discriminatory electricity auctions. Uniform auctions result in higher average prices than discriminatory auctions, but the ranking in terms of productive efficiency is ambiguous. The comparative effects of other market design features, such as the number of steps in suppliers' bid functions, the duration of bids, and the elasticity of demand are analyzed. We also consider the relationship between market structure and market performance in the two auction formats.

Theory and Application of Linear Supply Function Equilibrium in Electricity Markets

We consider a supply function equilibrium (SFE) model of interaction in an electricity market. We assume a linear demand function and consider a competitive fringe and several strategic players having capacity limits and affine marginal costs. The choice of SFE over Cournot equilibrium and other models and the choice of affine marginal costs is reviewed in the context of the existing literature. We assume that bid rules allow affine or piecewise affine non-decreasing supply functions by firms and extend results of Green and Rudkevitch concerning the linear SFE solution. An incentive compatibility result is proved. We also find that a piecewise affine SFE can be found easily for the case where there are non-negativity limits on generation. Upper capacity limits, however, pose problems and we propose an ad hoc approach. We apply the analysis to the England and Wales electricity market, considering the 1996 and 1999 divestitures. The piecewise affine SFE solutions generally provide better matches to the empirical data than previous analysis.

Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation

We use agent-based simulation in a coordination game to analyse the possibility of market power abuse in a competitive electricity market. The context of this was a real application to the England and Wales electricity market as part of a Competition Commission Inquiry into whether two particular generators could profitably influence wholesale prices. The research contributions of this paper are both in the areas of market power and market design policy issues for electricity markets, and in the methodological use of large industry-wide evolutionary simulation models.

Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market

The question of whether the uniform price or discriminatory auction format is the better multi-unit auction mechanism is addressed in the context of the 1999 debate on reforming the England & Wales electricity market. Each generator is modelled as an autonomous adaptive agent capable of endogenously developing its own bidding strategies using a naive reinforcement learning algorithm. The discriminatory auction results in higher market prices than the uniform-price auction. This is because market prices are not publicly available and agents with a large market share gain a significant informational advantage in a discriminatory auction, thereby facing less competitive pressure.