Measuring a paradox: Zero-negative electricity prices

Integrated pricing of roads and electricity enabled by wireless power transfer

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

The impact of China's electricity price deregulation on coal and power industries: Two-stage game modeling

Economical offshore wind developments depend on alternatives for cost-efficient transmission of the generated energy to connecting markets. Distance to shore, availability of an offshore power grid and scale of the wind farm may impede export through power cables. Conversion to H2 through offshore electrolysis may for certain offshore wind assets be a future option to enable energy export. Here, we analyse the cost sensitivity of offshore electrolysis for harvesting offshore wind in the North Sea using a technology-detailed multi-carrier energy system modelling framework for analysis of energy export. We include multiple investment options for electric power and hydrogen export including HVDC cables, new hydrogen pipelines, tie-in to existing pipelines and pipelines with linepacking. Existing hydropower is included in the modelling, and the effect on offshore electrolysis from increased pumping capacity in the hydropower system is analysed. Considering the lack of empirical cost data on offshore electrolysis, as well as the high uncertainty in future electricity and H2 prices, we analyse the cost sensitivity of offshore electrolysis in the North Sea by comparing costs relative to onshore electrolysis and energy prices relative to a nominal scenario. Offshore electrolysis is shown to be particularly sensitive to the electricity price, and an electricity price of 1.5 times the baseline assumption was needed to provide sufficient offshore energy for any significant offshore electrolysis investments. On the other hand, too high electricity prices would have a negative impact on offshore electrolysis because the energy is more valuable as electricity, even at the cost of increased wind power curtailment. This shows that there is a window-of-opportunity in terms of onshore electricity where offshore electrolysis can play a significant role in the production of H2. Pumped hydropower increases the maximum installed offshore electrolysis at the optimal electricity and H2 prices and makes offshore electrolysis more competitive at low electricity prices. Linepacking can make offshore electrolysis investments more robust against low H2 and high electricity prices as it allow for more variable H2 production through storing excess energy from offshore. The increased electrolysis capacity needed for variable electrolyser operation and linepacking is installed onshore due to its lower CAPEX compared to offshore installations.

PurposeThis study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors.Design/methodology/approachThe methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023.FindingsThe main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value.Originality/valueThe study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Effects of regulatory reforms in the electricity supply industry on electricity prices in developing countries

How competitive are EU electricity markets? An assessment of ETS Phase II

Data-driven modeling for long-term electricity price forecasting

Generalized maximum entropy in electrical energy price modeling for households and non-households in Portugal

Electricity price forecasting is crucial for the secure and cost-effective operation of electrical power systems. However, the uncertain and volatile nature of electricity prices makes the electricity price forecasting process more challenging. In this study, a two-stage forecasting model was proposed in order to accurately predict day-ahead electricity prices. Historical natural gas prices, electricity load forecasts, and historical electricity price values were used as the forecasting model inputs. The historical electricity and natural gas price data were decomposed in the first stage to extract more deep features. The empirical mode decomposition (EMD) algorithm was employed for the efficient decomposition process. In the second stage, the categorical boosting (CatBoost) algorithm was proposed to forecast day-ahead electricity prices accurately. To validate the effectiveness of the proposed forecasting model, a case study was conducted using the dataset from the Turkish electricity market. The proposed model results were compared with benchmark machine learning algorithms. The results of this study indicated that the proposed model outperformed the benchmark models with the lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 8.3282%, 5.2210%, 6.9675%, and 86.2256%, respectively.

Electricity price forecasting is a fundamental step for power producers in competitive electricity markets, although it is a challenging task. Participation of renewable power plants in the electricity supply chain has increased uncertainty of electricity supply, demand, and price. Probabilistic forecasting approaches are the proper tools to take into account this uncertainty. In this paper, we use the double exponential smoothing (DES) as well as triple exponential smoothing (TES) methods to forecast electricity price volatility. Regularized forecasts for volatility have been studied using the elastic net regularization method. Sample sign correlation of standardized electricity prices (standardized by volatility forecasts) is used to identify the conditional distribution of electricity price time series. Validation of the regularized volatility forecasts is demonstrated using the publicly available hourly electricity price data of Ontario. Our data analysis results show that TES forecasts of volatility outperforms DES. In addition, elastic net regularization decreases the mean square error of TES volatility forecasting from 72.95 to 59.16. The regularized probabilistic forecast of electricity demand is used to implement a decision analysis approach and to model the scheduling of power generation units in the electricity market.

Lignin extracted from black liquor in chemical pulp mills can potentially replace fossil carbon feedstocks in fuels and materials, thereby increasing the economic and environmental added values of woody biomass. However, since lignin extraction reduces the electricity generation of the mill, the added value depends on the characteristics of the electricity market in which the mill operates. In this study, a model mill is exposed to two different electricity price profiles: the low and steady prices of south-central Sweden in Year 2019; and the high and volatile prices of the same region in Year 2022. For the model mill, investments in lignin extraction designed to increase pulp production are economically viable and have low levels of sensitivity to electricity price levels and price volatility. The viability of lignin extraction without increased pulp production depends on the relationship between the electricity and lignin prices. With stable electricity prices, or steady mill operation, a rule-of-thumb holds that for lignin extraction to be viable, the lignin price (€/t) must be 1.8-times the average electricity price (€/MWh) plus 40 €/t for the supply of chemicals. With volatile electricity prices and flexible operation of the recovery boiler, the mill can shift the loss in electricity sales to low-price hours, thereby saving 15–70 % of the operational costs of lignin extraction, as compared to steady operation. This effect can be further enhanced by increasing the capacity of the lignin extraction process or extending the size of the black liquor storage tank. The proposed flexibility measures allow the market-integrated pulp mill to export lignin to replace fossil carbon supplies in other sectors, while supporting the electricity system during hours with high demand and low supply.

This paper proposes an electricity market model of Turkish electricity market for monthly and yearly electricity price forecasting in medium-term by means of supply and demand dynamics formed via a theoretical approach. The electricity market model created within this scope consists of three main components related to electricity demand, supply, and price segments along with hydro optimization submodel, which takes into account the nonlinear relation between supply and price. Electricity price is determined based on the intersection of demand curve and merit order curve that has dynamic behavior for dam-type hydrogeneration, import coal, and natural gas power plants. The paper aims to determine the range of possible electricity prices rather than a single price forecast by creating multiple scenarios based on the uncertainties in main variables affecting the electricity prices. Meanwhile, electricity generation portfolio with respect to market participants and primary energy resources as well as price forecasts can be obtained simultaneously. Ultimately, the model can identify how effective a variable of the market on the electricity price is. The developed method is validated via real data.

Characteristics of China’s coal, oil and electricity price and its regulation effect on entity economy

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.