Electricity Spot Prices Research Articles

Univariate vs multivariate predictability analysis of electricity consumption by LSTM approach: An empirical evaluation

Precise prediction of EC is crucial for planning, managing, and cost-effective operation of power grids, as it is a time series problem. In recent years, numerous studies have analyzed the behavior and quality of time series forecasts in different areas, including the EC. New models or variants of traditional algorithms have also been proposed, usually taking advantage of the increasing amount of data available and of today's computing power, such as LSTMs. One of the most important characteristics in the selection of a forecasting method is the number of variables that must be taken into account in the prediction of the time series since most of these variables are subject to external influences. EC is dependent on a number of external factors, such as climatic factors, economic factors, and the spot price of electricity. The EC dataset may be either univariate or multivariate. When dealing with univariate time series data, it's important to utilize specific methods that take only the historical values of the variable into consideration for accurately estimating its pattern. Prediction methods that analyze dependencies and correlations between variables to predict future values are also suitable for multivariate time series. Nevertheless, these approaches usually need more time to compute and train, and they might not even be the most appropriate way to go, because the increased complexity of the model used could outweigh any possible improvement in prediction accuracy. In this paper, a thorough comparison study was conducted to analyze the effectiveness of univariate and multivariate predictive analysis on two separate sets of EC data at hourly and daily intervals. To accomplish this, the LSTM algorithm was utilized, which has recently been widely used and recognized as the best-performing algorithm in EC forecasting studies. The comparative analysis is complemented by a comprehensive literature review, meticulously presented in a tabular format, to offer a comprehensive understanding of the univariate and multivariate forecasting methodologies and their respective outcomes. This study stands out due to its incorporation of an extensive literature review to support the experimental research, ensuring a thorough evaluation. Based on experimental studies, univariate forecasting analysis outperformed multivariate forecasting analysis for both hourly and daily interval data sets. Furthermore, the R-squared results of the univariate and multivariate predictive analyses conducted with the hourly data set are significantly higher than those of the same predictive analyses in the daily interval.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

Electricity spot price dynamics comparison in the European and Siberian price zones of Russia using a stochastic volatility model

Electricity spot price dynamics comparison in the European and Siberian price zones of Russia using a stochastic volatility model

Grid variability and value assessment of long-duration energy storage under rising photovoltaic penetration: Evidence from Japan

Using high-resolution grid power balance and market data, this work investigates the effects of rising solar photovoltaic generation on the variability of large-scale net grid load and spot prices, and conducts an analysis of the potential balancing profits of various grid-scale energy storage systems. Analysis results show the correlation between PV (photovoltaic) generation and electricity demand has been identified as a significant factor influencing spot price value. As PV penetration increases, the value of spot prices experiences a notable decline, with values declining to nearly zero when the share of hourly PV generation surpasses 70 %. The volatility of electricity spot prices has a substantial impact on utilization rates and economic profits of energy storage systems employed for grid energy balancing. Despite the lower roundtrip efficiency, reversible hydrogen storage system can offer positive benefits. The longer storage duration offers the advantage of capturing greater dispatch profits by exploiting price differences over extended periods. Considering the time-varying nature of spot prices, the rated power capacity of the storage system plays a significant role in determining its utilization rates. The larger rated charging and discharging ability of the storage system allows for more effective utilization of price fluctuations, resulting in greater profitability.

Common and idiosyncratic conditional volatility: Theory and empirical evidence from electricity prices

. We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition.

Methodology for Multi-Step Forecasting of Electricity Spot Prices Based on Neural Networks Applied to the Brazilian Energy Market

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape.

ATTnet: An explainable gated recurrent unit neural network for high frequency electricity price forecasting

The primary contribution of this study is the proposal of an explainable deep-learning neural network (ATTnet) that employs an attention mechanism to achieve accurate electricity spot price forecasting and an explainable model pipeline. The concise, single-stream network consists of a 5-head attention mechanism and gated recurrent units, which have been developed to model the temporal dependencies of the volatile market data. In addition to introducing a novel neural network architecture for volatile time series data, this study makes a substantial contribution by investigating prediction factors in two ways: temporally via the attention scores from the input sequences and globally via feature Shapely values. In real-time electricity price prediction, historical prices, temperature, hour, and zonal load are found to be the most important variables. The deep learning model was tested on real-time price profiles from eight generators within the New York Independent System Operator (NYISO) network. The proposed model achieves performance gains of 21% in MAE and 22% in MAPE over the state-of-the-art benchmark methods.

CONVERGENCE OF WHOLESALE ELECTRICITY PRICES ACROSS EUROPEAN MARKETS: ARE WE THERE YET?

European electricity market integration has been a research focus, especially after the third electricity directive emphasizing the internal market. Electricity markets are characterized by immediate consumption with storage and transmission constraints, leading to unique price dynamics. Price convergence in European markets can lead to competitive pricing, advocating market integration. This research examines whether European wholesale spot electricity prices converge. Prior literature uses sigma and beta-convergence to test price convergence. We have examined the stochastic convergence of prices across 19 European nations from 2013 to 2021 utilizing monthly data. For this analysis, we employed LM tests incorporating trend breaks grounded on the RALS regression framework. RALS-LM tests are more powerful than linear tests when structural breaks and nonnormal errors are present. LM and RALS-LM unit root tests with two trend breaks resulted in rejection of the unit root hypothesis for all countries, suggesting relative wholesale prices are stationary. Thus, shocks to relative prices are transient, supporting price convergence at the wholesale level. This result should be considered carefully as it contradicts the results from recent literature that examines retail electricity prices using beta- sigma- and club convergence. The policy implications of this finding can provide a foundation for establishing policies that promote a harmonized and integrated European electricity market.

Optimization strategy of power purchase and sale for electricity retailers in a two-tier market

Against the backdrop of the gradual advancement of China's electricity market reform, the number of Power Trading Companies in China has been increasing year by year, and as of October 2022, the number has reached more than 10,000. As an important hub connecting the electricity market and users, electricity retailers face double risks from downstream user load fluctuations and electricity market price fluctuations. Therefore, a reasonable power purchase and sale strategy is very important for an electricity retailer. In this study, a block bidding mechanism is adopted to optimize the clearing of the medium-to long-term market and a DA-RBF neural network is established for spot electricity price forecasting model based on numerical feature similarity to improve the accuracy of electricity price forecasting. Furthermore, the model considers the differences in user demand responses and investigates the optimal power purchase and sale strategy, guided by differentiated time-of-use electricity pricing. The case study analysis demonstrated that the proposed power purchase and sale optimization strategy yields favorable results, improving profitability and enhancing the stability of the power system.

Business decision-making of power generators in competitive electricity market

This paper presents a theoretical framework for the business decision-making process of the power generators as price takers when considering the participation of energy storage. The framework assesses rational valuation, optimal sales strategies, and hedging options for power plants with and without a gross sales constraint. The valuation and optimal sales strategy problems are analyzed using a risk-neutral pricing approach, dynamic programming principles, and the trinomial tree model suitable for the regime switching model. The formulation of a price risk hedging scheme flexible and widely used over-the-counter electricity derivative, the electricity contract for difference, as a tool for hedging electricity spot price risk. The minimum variance hedge ratio and its corresponding hedging efficiency formula are derived. In the section of numerical simulations, we first use the EM algorithm to calibrate the electricity spot model based on electricity spot price data of Nord Pool. Numerical simulations are then conducted on the operational decision-making of power generators under three different forms of energy storage. The results of the simulations provide a basis for power generators to evaluate the real-time value of power plants, to select optimal real-time power sales, and to determine the optimal timing of power plant transfer and storage methods.

Calculation of Power Purchase Imbalance Costs for Grid Enterprises’ Agents Considering Uncertainty of Supply and Demand and Price Fluctuations in the Context of Beijing-Tianjin-Tangshan Unified Spot

Abstract The regional unified electricity spot market plays an important role in promoting the optimal allocation of power resources over a wider range and cross-regional consumption of renewable energy. This paper examines the supply and demand uncertainty and price volatility of power grid agency power purchases in the context of the Beijing-Tianjin-Tangshan unified spot purchase. It delves into the specific mechanism of unbalanced costs in agency power purchase decisions and investigates the specific channeling methods of unbalanced costs in the dual-track system. Based on the dual-track unbalanced cost under the unified spot market, a dual-track power balance model is proposed based on the typical daily load curve, and a time-based unbalanced cost calculation model is designed. Using the typical daily electricity load data from the power supply enterprise in X province as an example, the calculation and simulation of unbalanced costs are carried out. The spot electricity price fluctuates between 0.25 yuan/(kW•h) and 0.45 yuan/(kW•h) in different periods. The overall unbalanced power purchased by the power grid in the load trough stage is only 6.24% of the total monthly electricity consumption. Still, the unbalanced cost in the study period is of a deficit nature, and its average value reaches −268 million yuan. The calculation method of unbalanced costs caused by agency power purchases clearly reveals the unbalanced cost-sharing mechanism in the context of the unified spot.

Power politics: How electric grievances shape election outcomes

We argue in this study that higher electricity expenditures increase voter support for the radical right because these parties oppose costly climate mitigation policies. We use data from Sweden, which experiences extremely high demand for heating energy during winter months. The demand for electricity differs greatly between geographical regions due to large temperature differences, from 0 in the southern regions to −50 Celsius in the north. We create our independent variable of electric grievances based on this variation, which increases faster with the spot price of electricity in neighborhoods with a low disposable income relative to the average electricity bill in that area. Using this setup, we find that electric grievances are associated with increased support for the radical right. We argue that soaring electricity prices tend to hurt parties with a mainstream profile since voters associate them them with more ambitious decarbonization policies, while the radical right gain support, since they tend to oppose costly solutions. Our results suggests that the transition to renewable energy can grind to a halt when electricity prices rise higher because voters may abandon the parties who push for the green transition.

Techno-Economic Analysis of a Highly Renewable and Electrified District Heating Network Operating in the Balancing Markets

In pursuit of Finland’s carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator’s decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices significantly influence the profitability derived from balancing markets.

Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions

We consider an observation-driven location model where the unobserved location variable is modeled as a random walk process and where the error variable is from a mixture of normal distributions. The time-varying location can be extended with a stationary process to account for cyclical and/or higher order autocorrelation. The mixed normal distribution can accurately approximate many continuous error distributions. We obtain a flexible modeling framework for the robust filtering and forecasting based on time-series models with non-stationary and nonlinear features. We provide sufficient conditions for strong consistency and asymptotic normality of the maximum likelihood estimator of the parameter vector in the specified model. The asymptotic properties are valid under correct model specification and can be generalized to allow for potential misspecification of the model. A simulation study is carried out to monitor the forecast accuracy improvements when extra mixture components are added to the model. In an empirical study we show that our approach is able to outperform alternative observation-driven location models in forecast accuracy for a time-series of electricity spot prices.

Pricing evolution in the Colombian electricity market: Does regulation matter?

Electricity pricing ($/kWh) is a topic of widespread discussion, both for developing and developed countries, because it has begun a worrying upward trend that impacts the well-being of the entire population. Initially, it is expected to reflect the marginal costs of generation and to be affected mainly by the availability of primary resources for electricity generation and the interaction of agents in the market. However, regulations may lead to changes in this expected behavior, for Colombian case, several studies have highlighted the significant impact of the reliability charge (current capacity market in Colombia) on spot price formation, although this capacity market features remuneration independent of the energy market, and these revenues must reflect the costs associated with reliability. The present study conducts an impact evaluation on the time series of the electricity spot price in Colombia to establish whether the resolution that established the reliability charge (CREG071/06) significantly affected the formation of the spot market price. The results suggest that the resolution under study increased the bid of generators in the short and long term, which was accompanied by an average increase of approximately 12% in the spot price of electricity in Colombia.

Analyzing Risk Premiums in the Brazilian Power Market: A Quantitative Study

This paper conducts an empirical analysis of risk premiums in the Brazilian electricity market, a critical but understudied field. Employing two distinct methodologies—Average Forward Prices and Last Observed Forward Prices—the study calculates risk premiums between spot and forward electricity prices. Our analysis consistently identifies negative risk premiums, which serve as indicators that the market may be underestimating certain types of risk. These underestimations are potentially influenced by inherent market uncertainties, including volatile demand, unpredictable supply, and frequent regulatory shifts. Additionally, we observe a high volatility in risk premiums, signifying a dynamic and ever-changing market where expectations are continuously recalibrated. Such conditions present possible arbitrage opportunities for market actors and underline the need for policymakers to introduce measures mitigating market unpredictability. By focusing on these nuances, this paper enriches the broader discourse on risk premiums in electricity markets and underscores the necessity for further research aimed at devising effective risk management strategies.

Value stacking flexibility services in neighborhoods participating in fast frequency reserve markets

Neighborhoods are responsible for considerable amounts of the total energy demand in Europe, and increased shares of variable renewable energy sources will require energy balancing services. Local flexibility resources in neighborhoods can help provide this. However, there is a lack of insight into the economic incentives and operational consequences for property owners to adopt prosumer qualities. Using a linear program that minimizes total electricity costs, this paper evaluates annual cost savings for a Norwegian university campus when value stacking the following flexibility services: responding to electricity spot prices, grid tariffs, and provision of fast frequency reserve (FFR). Several flexibility resources are addressed in this study, including a stationary battery, electric vehicle charging stations, and a vehicle-to-grid charging station. The results found an average 6.8% yearly cost decrease by FFR participation, supporting the notion that there is a significant economic potential in applying flexible resources from prosumers in fast frequency reserve markets, without significant conflicts with other flexibility services.

A GARCH Model to Understand the Volatility of the Electricity Spot Price in Brazil

Electricity is sensitive to extreme price events and spot price volatility is an inherent characteristic of competitive electricity markets. The purpose of this article it to model the realized volatility of electricity spot price in Brazil. The Brazilian electricity industry presents unique characteristics and because of this price varies a lot in a short period. So, we developed a GARCH model using 862 weekly observations to understand the realized volatility in the four different market. We conclude that the spot price in Brazil presents high volatility that presents risk to agents. This high volatility is associated with institutional factors and the increase in the share of renewable energy in the electricity mix.

Impact of the New Electricity Remuneration Scheme on the Waste-to-Energy Recovery Activity in Portugal

The remuneration scheme for the electricity produced by Waste-to-Energy (WtE) recovery plants has changed recently in Portugal according to 2020 legislation. The new model, linking the electricity remuneration from WtE plants to the spot electricity prices, is expected to bring greater uncertainty in the waste activity, which is a novelty for the sector. In Portugal, Valorsul is the municipal waste treatment entity responsible for the recovery and treatment of municipal solid waste (MSW) produced in 19 municipalities in the Lisbon area. This paper highlights the impact of the new Portuguese electricity remuneration scheme for electricity from waste on Valorsul’s WtE plant. For this purpose, the new remuneration scheme is modeled and simulated based on electricity spot market price scenarios, which are compared with the base case scenario of the former remuneration scheme. Considering different electricity prices for the electricity produced by the WtE plant, the present study anticipates the consequences of the gate-fee of such regulatory changes. Results show that any price changes in the electricity remuneration scheme are offset by equivalent changes in the waste gate-fee. Consequently, the change in the remuneration of the electricity from the WtE plant is, in fact, neutral for the Valorsul accounts and lower revenues from the electricity generation activity of the WtE will negatively impact the gate-fee prices paid by the waste users.

Energy Price Decoupling and the Split Market Issue

Load scheduling and dispatch by merit order on electricity generation networks has used a wholesale market electricity system operator model focused on system marginal pricing, in which the spot price of electricity at any point in time is equal to the system marginal cost given by the higher value of the price, which rations demand to capacity or the operating cost of the most expensive plant on the system, which is usually a fossil fuel price. This idea has come under challenge because renewable technologies such as wind power farms or solar power farms are treated as having close to zero operating costs. The challenges, under the general heading of energy price decoupling, include suggestions for operating split markets possibly overseen by a regulator, and the prediction that marginal cost pricing should be abandoned. This review evaluates these in terms of their economic impact, relating them to the policy debates on electricity market reform.