Nord Pool Electricity Market Research Articles

Leveraging Pumped Storage Power Plants for Innovative Stability Enhancement of Weakly Interconnected Power Systems

The hybrid AC/DC grid, based on a significant share of renewable energy sources, is gradually becoming an essential aspect of the modern energy system. The integration of intermittent renewable generators into contemporary energy systems is accompanied by the decommissioning of power plants containing synchronous generators. Consequently, this leads to a reduction in system inertia and an increase in the risk of stability disruption. The abrupt disconnection of the primary generator or power line can result in an unanticipated mismatch between power generation and consumption. This discrepancy can trigger substantial and swiftly evolving alterations in power distribution, angular speed, load flow, and the frequency of generators. The risks of an energy system collapse can be mitigated through automation, enabling rapid adjustments to generation and load capacities, as well as power flows, in the electrical network. This article justifies the utilisation of a power control method for high-voltage power line interconnections. The technology of hydro storage power plants and measurements of voltage phasors are employed. The potential for easing power flow restrictions and realising substantial economic benefits is supported by the results obtained using simplified dynamic model of the Baltic power system and Nord Pool electricity market model.

Probabilistic electricity price forecasting based on penalized temporal fusion transformer

AbstactIn the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications.

A Pragmatic Approach to the Economic Assessment of Green Synthetic Methane Power in the Baltics

The synthesis of methane from hydrogen and carbon dioxide creates an energy resource that is suitable for long-term storage. Once this process is powered by renewable electricity, it produces a clean fuel for producing electricity and heat and supports large-scale renewable energy deployment, energy transition and climate change mitigation. This paper proposes a pragmatic approach to assessing the economic potential of synthetic methane-based power. Today, natural gas plays an important role in the Baltic region due to the existing infrastructure, which includes a transmission and distribution pipeline network, gas power plants and a large underground storage reservoir. Replacing natural gas with synthetic methane would fulfil carbon emission reduction ambitions. In this paper, we simulate electricity producers’ actions at market conditions and consider the generation portfolio in the Baltics and the interconnections with Scandinavia and Poland operating in the NORDPOOL electricity market. As a result of these calculations, we obtain the volume of the synthetic gas, the production costs, the volume of gas storage, the installed capacity of the gas power plant, and the investments required to ensure energy transition and system adequacy. These results are essential for the informed decisions made by policymakers, investors and system operators.

Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive power generation. Avoiding blackouts is crucial for economic growth, and electricity is an essential energy source for industry. Considering these facts, this study presents a detailed analysis of the forecast of hourly electricity demand by comparing novel decomposition methods with several univariate and multivariate time series models. To that end, we use the three proposed decomposition methods to divide the electricity demand time series into the following subseries: a long-run linear trend, a seasonal trend, and a stochastic trend. Next, each subseries is forecast using all conceivable combinations of univariate and multivariate time series models. Finally, the multiple forecasting models are immediately integrated to provide a final one-day-ahead electricity demand forecast. The presented modeling and forecasting technique is implemented for the Nord Pool electricity market’s hourly electricity demand. Three accuracy indicators, a statistical test, and a graphical analysis are used to assess the performance of the proposed decomposition combination forecasting technique. Hence, the forecasting results demonstrate the efficiency and precision of the proposed decomposition combination forecasting technique. In addition, the final best combination model within the proposed forecasting framework is comparatively better than the best models proposed in the literature and standard benchmark models. Finally, we suggest that the decomposition combination forecasting approach developed in this study be employed to handle additional complicated power market forecasting challenges.

A Hybrid Machine Learning Approach for Price Forecasting in Electricity Market with Smart Bidding Strategies and Wind Energy Influence

The price for renewable power has seen a dip in recent years with increased advanced approaches in the electricity market. For the benefit of both the producers and the consumers of electricity, a forecasting technique to predict the price for the next day is required to handle competitive market participation facilitating the prosumers. Therefore, the study’s main objective is to model a day-ahead electricity price and apply a forecasting tool based on time series analysis, promising accuracy, and minimal average forecast errors. Day-ahead real-time price data of the Nordic pool market forecast study for a period between January 2019 and March 2021 is considered for the study. The impact of wind energy production on day-ahead electricity Nord Pool electricity market for improved participation in the market is forecasted based on time series. Machine learning techniques, namely Support Vector Regression (SVR), Generalized Autoregressive Conditional Heteroskedastic (GARCH), Autoregressive Integrated Moving Average (ARIMA), Multiple Linear Regression (MLR), and hybrid of SVR-GARCH are analyzed in detail to find the best forecasting approach. The proposed forecasting technique on next-day electricity price series influenced by wind power production is validated using performance indices, namely, the Mean Absolute Percentage Error and the Root Mean Squared Error considered for checking the reliability of the proposed new model. A similar study on the Indian market was also conducted to validate the robustness of the model. The electricity price forecasting model is developed and analyzed using a machine learning technique in Python (Jupyter Notebook).

Functional Data Approach for Short-Term Electricity Demand Forecasting

In today’s liberalized electricity markets, modeling and forecasting electricity demand data are highly important for the effective management of the power system. However, electricity demand forecasting is a challenging task due to the specific features it exhibits. These features include the presence of extreme values, spikes or jumps, multiple periodicities, long trend, and bank holiday effect. In addition, the forecasts are required for a complete day as electricity demand is decided a day before the physical delivery. Therefore, this study aimed to investigate the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research. To this end, the demand time series is first treated for the extreme values. The filtered series is then divided into deterministic and stochastic components. The generalized additive modeling technique is used to model the deterministic component, whereas functional autoregressive (FAR), FAR with exogenous variable (FARX), and classical univariate AR models are used to model and forecast the stochastic component. Data from the Nord Pool electricity market are used, and the one-day-ahead out-of-sample forecast obtained for a whole year is evaluated using different forecasting accuracy measures. The results indicate that the functional modeling approach produces superior forecasting results, while FARX outperforms FAR and classical AR models. More specifically, for the NP electricity demand, FARX produces a MAPE value of 2.74, whereas 6.27 and 9.73 values of MAPE are obtained for FAR and AR models, respectively.

Estimation of a dynamic multi-level factor model with possible long-range dependence

A dynamic multi-level factor model with possible stochastic time trends is proposed. In the model, long-range dependence and short memory dynamics are allowed in global and local common factors as well as model innovations. Estimation of global and local common factors is performed on the prewhitened series, for which the prewhitening parameter is estimated semiparametrically from the cross-sectional and local average of the observable series. Employing canonical correlation analysis and a sequential least-squares algorithm on the prewhitened series, the resulting multi-level factor estimates have centered asymptotic normal distributions under certain rate conditions depending on the bandwidth and cross-section size. Asymptotic results for common components are also established. The selection of the number of global and local factors is discussed. The methodology is shown to lead to good small-sample performance via Monte Carlo simulations. The method is then applied to the Nord Pool electricity market for the analysis of price comovements among different regions within the power grid. The global factor is identified to be the system price, and fractional cointegration relationships are found between local prices and the system price, motivating a long-run equilibrium relationship. Two forecasting exercises are then discussed.

Day-Ahead Electricity Price Forecasting Based on Hybrid Regression Model

Since the deregulation of the power markets, accurate short term Electricity Price Forecasting (EPF) has become crucial in maximizing economic benefits and mitigating power market risks. Due to the challenging characteristics of electricity price, which comprise high volatility, rapid spike, and seasonality, developing robust machine learning prediction tools becomes cumbersome. This work proposes a new hybrid machine learning method for a day-ahead EPF, which involves linear regression Automatic Relevance Determination (ARD) and ensemble bagging Extra Tree Regression (ETR) models. Considering that each model of EPF has its own strengths and weaknesses, combining several models gives more accurate predictions and overcomes the limitations of an individual model. Therefore, the linear ARD model is applied because it can efficiently deal with trend and seasonality variations; on the other hand, the ensemble ETR model is employed to learn from interactions, and thus combining ARD with ETR produces robust forecasting outcomes. The effectiveness of the proposed method was validated using a data set from the Nord Pool electricity market. The proposed model is compared with other models to demonstrate its superiority using performance matrices, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiment results show that the proposed method achieves lower forecasting errors than other individual and hybrid models. Additionally, a comparative study has been performed against previous works, where forecasting measurement of the proposed method outperforms previous works’ accuracy in forecasting electricity price.

Value of bifacial photovoltaics used with highly reflective ground materials on single‐axis trackers and fixed‐tilt systems: a Danish case study

The energy produced by bifacial photovoltaic (PV) arrays can be augmented via albedo enhancements. However, the value of the additional energy must outweigh the costs for such modifications to be economically viable. In this work, the electrical performance and economic value of six 13 kWp crystalline-silicon (c-Si) PV arrays with distinct configurations are evaluated. The system designs include horizontal single axis trackers (HSAT) and 25° fixed-tilt structures, monofacial and bifacial PV panels, and low and high ground albedo. The value of the system designs is assessed using onsite electrical measurements and spot prices from the Nord Pool electricity market. We find that HSAT systems increase the annual value factor (VF) by 4% and decrease levelized cost of energy (LCOE) by 3.5 EUR/MWh relative to fixed-tilt systems. The use of bifacial panels can increase the VF by 1% and decrease LCOE by 4.0 EUR/MWh. However, a negligible VF increase and modest LCOE decrease was found in systems with bifacial panels and ground albedo enhancements. Although our results show that albedo enhancements result in lower LCOE than designs without, the uncertainty in upfront and ongoing costs of altering the ground in utility-scale PV parks makes the solution presently unadvisable.

Psychological barriers in the electric market: study applied to the Nord Pool market

PurposeThis article aims to verify if there are detectable barriers in price levels that are understood to be psychologically important (psychological barriers) in a set of hourly electricity prices. These barriers manifest themselves when the market struggles with a difficulty in crossing the barrier to a different level. Psychological barriers focus on directional price movements around regions of the barrier, thus the importance of understanding investor behavior. The authors intend to contribute empirically to the scarce literature on psychological influences in individuals trading in the energy market, hereby enhancing the knowledge concerning the behavior of investors in this market.Design/methodology/approachThe present work aims to test psychological barriers in the Nord Pool electricity market. Through a sample of hourly data on the Elspot day-ahead market, from 2013 to 2017, three groups of tests were made, following the M-values methodology: (1) uniformity tests, which clearly rejected the uniformity in hourly prices; (2) barrier tests, which included the barrier proximity and barrier hump tests, evidencing psychological barriers and (3) conditional effects tests, which allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average.FindingsUniformity tests, rejected the uniformity in hourly prices; barrier tests, included the barrier proximity and barrier hump tests, evidencing psychological barriers and conditional effects tests, allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average. Another relevant conclusion is that the period from midnight to 9 a.m. is very sensitive, since there is evidence of return and variance effects simultaneously. The implications of these results are potentially relevant, since changes on the variance are usually perceived as a proxy for risk, with changes on the return. It was also concluded that with the increase of the time span from 5 to 10 days on the conditional effects difference tests, there were significant changes on the results, the variance effect is stronger, while the return effect weakens.Research limitations/implicationsHowever, this research presents some limitations that result in representing opportunities for future research. The fact that there are reduced data available for other markets end up limiting the study of the global electricity market. Although Nord Pool is Europe's leading energy market and is seen as one of the most successful energy markets in the world, it would be interesting to do a study with more than one electricity market to make comparative considerations. Although the spot market is the main arena for energy trade, while the intraday market works as a compliment, it would be equally interesting to do a similar study for the intraday market and then compare conclusions. Moreover, in the present study, it was used standard methods in the literature on psychological barriers, but other methods could have been used–for example, those that assume that prices follow the Benford's distribution (Lu and Giles, 2010), which also present a path for future research and opportunity for confirming the robustness of the present results.Practical implicationsWhen the presence of psychological barriers is detected it means that the risk-return relationship becomes weaker around the psychological barrier (round numbers, meaning that electricity traders anchor). Identification of psychological barriers supports the claim that technical analysis strategies based on price support and resistance can be profitable. Therefore, more profitable strategies can be built by traders, but no reconciliation with the efficient market hypothesis (EMH) (provided that in inefficient markets prices should not exhibit any particular pattern). The finding of significant psychological barriers in specific hourly time intervals implies the need to address its practical implications in electricity markets, being so specific, namely, the possibility to earn extraordinarily profits exploiting this anomaly and who wins.Originality/valueThe electricity sector is a determinant sector in economic growth and a factor of development. Herein lies the importance of studying this market, which until now has not occurred in this subject, as far as it was possible to gauge. Are there barriers in the electricity market and should such a presence be taken into account? Investigating the existence of psychological barriers in the electric market becomes relevant, because knowing that investors are psychologically affected by a psychological barrier, can become a useful tool in negotiation, as it can function as another variable in the “equation” which is to trade in a complex market like this. Proving the potential presence of a psychological barrier may lead investors to believe in the idea of levels of resistance or levels of support, affecting their decision-making and price dynamics.

Lithuanian electricity market price forecasting model based on univariate time series analysis

Electricity price changes can significantly affect expenses in energy intensive industries, adjust profits or losses for electricity retailers and cause problems for country’s national energy strategy implementation. Forecasting models based on statistical methods and previous variable values help to predict future values and adjust strategy according to the forecast. This paper concentrates on the Lithuanian electricity market and presents the widely used ARIMA forecasting models based on the univariate time series analysis. The Lithuanian electricity market is selected due to a lack of statistical researches based on electricity market prices in Lithuania, as well as significant future electricity market liberalization projects. Electricity price data for analysis are taken from the Nord Pool electricity market operator website. The Nord Pool represents the Northern Europe electricity market operator where Lithuania and other 14 European countries trade electricity on a daily basis. To provide a long-term electricity price outlook average monthly data from July 2012 to December 2019 are selected for analysis. Before building the ARIMA model data are tested with various statistical tests to guarantee that time series are stationary, there is no autocorrelation or structural breaks. Once the data validity is confirmed, the time series is divided into train and test sets. The train data set is used to create a fitting ARIMA model, while the test set is used to define forecasting accuracy. Created forecasts of models are compared between each other using common comparison statistics, and the most accurate models are defined. Finally, the selected model is trained on a full dataset and the electricity price forecast for the year 2020 is constructed. The created AR (1) model had the smallest error value compared to the test dataset, while the SARIMA (1,1,1) model had the best approximation statistics. By combining both models the weighted SARIMA (1,1,1) model is constructed with the features of low forecasting error and precise actual time series approximation. The final model forecast for the year 2020 shows the monthly average electricity price decrease at the beginning of the year, a significant increase at the second half of the year and a price drop at the end of the year. Forecasting results can help companies to plan their electricity production and maintenance periods to maximize income from sold energy and minimize potential losses due to planned shutdown.

Impact of wind penetration in electricity markets on optimal power-to-heat capacities in a local district heating system

While the share of intermittent renewable energy sources in a power sector is constantly increasing, demand response technologies are becoming a crucial part of interconnected energy systems. The district heating sector has a great potential of offering such services if power-to-heat and thermal storage technologies are implemented. This is a well-known method of utilizing low-price electricity from the power market. However, power-to-heat optimal supply capacities are rarely studied with respect to different market conditions, especially from the point of view of multi-objective optimization. This paper shows an analysis of the impact of a wind production increase in a power market on optimal power-to-heat capacities in a local district heating system. To obtain these results, a district heating optimization model was developed by using linear programming, while the power market prices reduction is analysed by using historical bidding market data and shifting of the supply curve. The district heating model was created in the open-source and free programming language called Julia. The model was tested on a case study of the Nord Pool electricity market and a numerical example of a district heating system. The main outcome of this research is to show how district heating supply technologies operate in different market conditions and how they affect optimal power-to-heat and thermal storage capacities. Heat pump capacities linearly follow wind production increase in power markets.

Optimizing power-to-H2 participation in the Nord Pool electricity market: Effects of different bidding strategies on plant operation

The operation of power-to-X systems requires measures to control the cost and sustainability of electricity purchased from spot markets. This study investigated different bidding strategies for the day-ahead market with a special focus on Sweden. A price independent order (PIO) strategy was developed assisted by forecasting electricity prices with an artificial neural network. For comparison, a price dependent order (PDO) with fixed bid price was used. The bidding strategies were used to simulate H2 production with both alkaline and proton exchange membrane electrolysers in different years and technological scenarios. Results showed that using PIO to control H2 production helped to avoid the purchase of expensive and carbon intense electricity during peak loads, but it also reduced the total number of operating hours compared to PDO. For this reason, under optimal conditions for both bidding strategies, PDO resulted in an average of 10.9% lower levelised cost of H2, and more attractive cash flows and net present values than PIO. Nevertheless, PIO showed to be a useful strategy to control costs in years with unexpected hourly price behaviour such as 2018. Furthermore, PIO could be successfully demonstrated in a practical case study to fulfil the on-demand requirement of an industrial captive customer.

Determinants of the Forward Premium in the Nord Pool Electricity Market

Nord Pool is the leading power market in Europe. It has been documented that the forward contracts traded in this market exhibit a significant forward premium, which could be a sign of market inefficiency. Efficient power markets are important, especially when there is a goal to increase the share of the power mix stemming from renewable energy sources. We therefore contribute to the understanding of this topic by examining how the forward premium in the Nord Pool market depend on several economic and physical conditions. We utilise two methods: ordinary least squares and quantile regression. The results show that the reservoir level and the basis (the difference between the forward and spot price) have a significant impact on the forward premium. The realised volatility of futures prices and the implied volatility of the stock market have strong effects on both the conditional lower and upper tails of the forward premium. We also find that, as the market has matured, the forward premium has decreased, indicating an increase in market efficiency.

A Review of Price Risk Management in PJM and Nord Pool Electricity Market

Market participants and system operators are faced with price risks caused by various factors, such as congestions or market power abuse. Therefore, the price risk management in deregulated market environment is of great importance. In this paper, various risk-hedge financial instruments in PJM and Nord Pool, such as FTRs, futures, forwards and CFDs are investigated. These risk management mechanisms can be further utilized for congestion revenue allocation, speculation or arbitrage under different market circumstances.

Can electricity market prices control power-to-heat production for peak shaving of renewable power generation? The case of Sweden

100% renewable energy systems require high penetration of variable renewable electricity (VRE) generation. This causes the net load in the system to be more variable and could cause operational problems in local power grids. Demand side management (DSM), such as fuel or energy carrier switching in response to a price signal, can provide flexibility to meet the increased variability. This study investigates the impact of VRE production on electricity prices and their potential to act as an incentive to control district heating power-to-heat (P2H) production in order to shave VRE production peaks. Also, the potential to increase P2H production flexibility with additional heat storages is studied. Electricity prices are simulated by modification of historical electricity market supply curves. A heat storage component is implemented in an existing model for district heat production. The results show that P2H production is significantly increased (up to 98%) when electricity prices are influenced by VRE production. Thermal storages further increase the P2H production by up to 46%. The increased P2H production, however, does not necessarily coincide with the peaks of VRE. Thus, in conclusion, the pricing mechanism on the Nord pool electricity market is insufficient to control P2H production for shaving VRE production peaks.

Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes

Electricity markets become more competitive due to their liberalization; therefore, electricity prices are considerably more volatile compared to other commodity prices. As the electricity is an integral part of production and economic growth processes, the electricity price may influence the stock market through affecting the real output and consequently the sum of cash flows. Hence, investors are facing electricity price risks, and need to protect their benefits. This paper investigates the impacts of electricity market variations on the Nordic stock market returns using hourly observations of electricity spot prices pairwise in aggregate market index and some sector indexes. Our sample is divided into three sub-periods according to the electricity volatility structure. A generalized long memory model is adopted to estimate the conditional mean of the studied time series, and the FIGARCH process is used to model the conditional variance. Thereafter, a VaR, c-DCC-FIGARCH, CVaR and ΔCVaR models are applied to assess electricity market exposure. Moreover, in order to evaluate the optimal portfolio, we calculated the optimal portfolio weights, the optimal hedge ratios and the hedge effectiveness index of the electricity market commodity in several sectors stock portfolios. Our results show evidence of long run dependence between electricity market returns and sectoral stock market returns, and they indicate that the tail dependence is significant and varies across sectors and over periods. Finally, the optimal weights and hedge ratios for electricity/stock portfolio holdings are sensitive to the considered sectors. Therefore, electricity market commodities can be adopted to diversify and hedge against stock market risks.

Valuation of Swing Options under a Regime-Switching Mean-Reverting Model

In this paper, we study the valuation of swing options on electricity in a model where the underlying spot price is set to be the product of a deterministic seasonal pattern and Ornstein-Uhlenbeck process with Markov-modulated parameters. Under this setting, the difficulties of pricing swing options come from the various constraints embedded in contracts, e.g., the total number of rights constraint, the refraction time constraint, the local volume constraint, and the global volume constraint. Here we propose a framework for the valuation of the swing option on the condition that all the above constraints are nontrivial. To be specific, we formulate the pricing problem as an optimal stochastic control problem, which can be solved by the trinomial forest dynamic programming approach. Besides, empirical analysis is carried out on the model. We collect historical data in Nord Pool electricity market, extract the seasonal pattern, calibrate the Ornstein-Uhlenbeck process parameters in each regime, and also get market price of risk. Finally, on the basis of calibration results, a specific numerical example concerning all typical constraints is presented to demonstrate the valuation procedure.

Studies on Several Issues of Electricity Price Forecast

The main models used in electricity price forecast have been reviewed in this paper. Several statistical models based on machine learning algorithms have been tested and compared to forecast the next-day hourly electricity price in Nordpool electricity market. Stationarity of electricity price has also been studied and it is concluded that electricity price is daily seasonal. Many features of both local region and neighboring regions from the past week have been respectively analyzed in terms of the importance to electricity price forecast. Results show that local zone, compared with neighbor zones, exerts the major influence on electricity price. Meanwhile, price is the most important category other than categories like demand, wind generation or transmission congestion rate. Hydro reserve is also an important parameter to electricity price forecast.

Revisiting the relationship between spot and futures prices in the Nord Pool electricity market

This work discusses potential pitfalls of applying linear regression models for explaining the relationship between spot and futures prices in electricity markets, in particular, the bias coming from the simultaneity problem, the effect of correlated measurement errors and the impact of seasonality on the regression results. Studying a 13-year long (1998–2010) price series of spot and futures prices at Nord Pool and employing regression models with GARCH residuals, we show that the impact of the water reservoir level on the risk premium is positive, which is to be expected, but contradicts the results of Botterud et al. (2010). We also show that after taking into account the seasonality of the water level, the storage cost theory proposed by Botterud et al. (2010) to explain the behavior of convenience yield has only limited support in the data.