Electricity Price Volatility Research Articles

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

This study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $1.5 million from ancillary services integration and $5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.

Green hydrogen and wind synergy: Assessing economic benefits and optimal operational strategies

Volatile electricity prices have raised concerns about the economic feasibility of wind projects in Finland. This study assesses the economic viability and optimal operational strategies for integrating wind-powered green hydrogen production systems. Utilizing modeling and optimization, this research evaluates various wind farms in Western Finland over electricity market scenarios from 2019 to 2022, with forecasts extending to 2030. Key economic metrics considered include internal rate of return, future value, net present value (NPV), and the levelized cost of hydrogen (LCOH). Results indicate that integration of hydrogen production with wind farms shows economic benefits over standalone wind projects, potentially reducing LCOH to €2.0/kgH2 by 2030 in regular and low electricity price scenarios, and to as low as €0.6/kgH2 in high-price scenarios. The wind farm with the highest capacity factor achieves 47% reductions in LCOH and 22% increases in NPV, underscoring the importance of strategic site selection and operational flexibility.

Lignin extraction in chemical pulp mills: The role of flexible operation

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.

Analyzing the Impact of Volatile Electricity Prices on Solar Energy Capture Rates in Central Europe: A Comparative Study

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics.

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This 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/approach The 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. Findings The 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/value The 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.

Macroeconomic Drivers of Electricity Prices in Nigeria

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Electricity price volatility in developing economies, particularly Nigeria, presents a significant challenge to sustainable development. In this paper, we examine the macroeconomic drivers of electricity prices (ELP) in Nigeria based on annual data spanning 1980-2022 and the autoregressive distributed lag (ARDL) procedure. Our model estimates the long-run and short-run impacts of population growth (PG), economic growth (GDP), crude oil price (COPR), and electricity consumption (ELC) on electricity prices (ELP). The empirical findings reveal a positive and long-run effect of PG on ELP, indicating that rising demand from a growing population increases electricity prices. In the short-run, ELC surprisingly co-moved with ELP, which may be attributable to price-sensitive demand within specific consumer segments or periods. Furthermore, GDP and COPR exert positive effects on ELP, indicating that economic growth drives energy consumption and prices, and the cost-driven impact of fossil fuel dependence in electricity generation. These findings shed light on the complex interplay of demographic, economic, and energy market-related forces driving electricity prices in Nigeria. Therefore, the paper proposes some policy suggestions based on the empirical findings.</span></p>

Role of power-to-heat and thermal energy storage in decarbonization of district heating

The sector integration of the heat and electricity sectors promotes the flexibility of the energy system. This, together with the already almost carbon dioxide-free electricity supply in Finland, facilitates the environmental transition to a carbon-neutral heating system. In this study, pathways to decarbonize the district heating system in Åland (an autonomous region of Finland) are explored. Especially, the roles of different power-to-heat technologies, e.g., heat pumps and direct electric heating, and thermal energy storage are investigated in the decarbonization of the district heating system. The focus is on studying the impact of varying prices in electricity markets on the operation of climate-neutral district heating systems. For this, three scenarios were modeled over the five years of 2018–2022 with varying demand and electricity market prices by using the modeling software PLEXOS. The study showed that electricity prices, but also reserve market prices, have a very significant impact on the operation cost and profit of a district heating system. Overall, electrification has the strong potential to reduce emissions cost-effectively in a district heating system, but it also makes the system vulnerable to electricity price volatility. It can be concluded that thermal energy storage and reserve market participation are crucial for protecting against cost increases and reducing investment risk.

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.

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins.

Performance analysis of integrated Nuclear-Solar Energy system sharing same molten salt thermal energy storage

Advanced nuclear reactors may be deployed with integrated thermal energy storage to improve flexibility and maximize revenue. This presents opportunities for thermal integration with concentrating solar power (CSP) to generate component synergies and/or improve performance. In this study, a computational model is developed for an integrated nuclear and CSP system that both share the same molten salt thermal energy storage (TES). Optimized dispatch schedules are developed subject to various market conditions, and the ratio of the nuclear to CSP thermal output is also varied. Performance of the combined system is compared to separate nuclear and solar plants, to determine if there is an overall benefit that can be derived from sharing the TES. Given sufficient volatility in electricity prices (e.g., under CAISO market conditions), synergies of up to 8% in net revenue are observed as a result of sharing the same TES, primarily due to improved revenue from enabling operation of the turbine closer to its design point, as well as being able to better take advantage of higher electricity prices. The synergy benefits are largest when the nuclear and solar plants have similar thermal output. However, when prices are less volatile the opposite behavior can be observed and it can be preferable to operate the nuclear plant as a baseload generator.

The Volatility Dynamics of Prices in the European Power Markets during the COVID-19 Pandemic Period

Surging electricity demand, its limited supply, and the pandemic crisis are just some of the key factors that resulted in changes in electricity prices on the power exchanges. This topic brings about a notable economic influence on both producers and consumers. The main purpose of this paper is to explore power price volatility during the four-year period from 1 January 2018 to 31 December 2021, in 28 power exchanges in Europe, measured using daily velocity data. In addition, based on the fixed and chain base index numbers, as well as their relative merits, this paper was designed to measure the gap and convergence in trends. Considering that the price volatility varies depending on the observation period, this paper performs a comparative analysis of electricity price volatility on a daily, monthly, quarterly, and annual level for all examined countries. The obtained results indicate that electricity price volatility is higher on a daily basis. Related to this, convergent trends are demonstrated on all 28 observed markets, and there is a growing trend of hourly spot prices in the analyzed four-year period. The results of this paper also confirm a higher power price volatility during the pandemic period in 2020 and 2021 compared to the prepandemic period during 2018 and 2019.

Continuous auction mechanism model for safety electric energy market transaction

With the rapid development of microgrid in the electric power industry, the microgrid electric energy transaction has begun to be marketized, and the research on the microgrid electric energy trusted transaction has important theoretical research value and social value. The existing blockchain-based microgrid electric energy trusted transaction models mostly focus on energy management and scheduling control between microgrids when conducting electric energy transactions, and do not fully consider the bidding problems in the market-based transaction of microgrid electric energy, resulting in trading strategies are difficult to adapt to new market changes. In response to this problem, this paper proposes a reliable transaction approach for microgrid electric energy based on a continuous two-way auction mechanism. The proposed strategy accounts for the volatility of electricity prices in the microgrid trading market and employs the continuous two-way auction mechanism to evaluate the microgrid electricity trading tactics. In the microgrid electric energy transaction, the self-adaptive learning theory is applied to adjust the quotations of both parties, so that both parties can make reasonable quotations according to the market environment. By simulating experimental data, the findings indicate that the continuous two-way auction mechanism transaction strategy enables both parties to modify their quotations based on market transaction information, thereby displaying a high degree of flexibility in microgrid electricity’s market-oriented trading.

Balancing short-term costs and long-term benefits: an analysis of the impact of hydroelectric power generation on electricity prices volatility in Cameroon

This study examines the short- and long-term impacts of various factors on the volatility and price of electricity in Cameroon, including hydroelectric power generation, economic growth, energy demand, and exchange rates from 2000 to 2019. The study uses an autoregressive distributed lag model. The study found that increasing hydroelectric power generation has both positive and negative impacts on electricity prices in the short and long term. While increasing the share of hydropower in overall energy production results in increased variation of electricity costs in the short term, it leads to significant price reductions in the long run. The study also found that economic growth has a considerable positive impact on the variation of power prices, while energy demand has a negative but insignificant effect on price volatility in the short term. Further, the study indicates that measures, such as encouraging SME engagement in renewable energy production, could improve the participation of local enterprises in the power industry and reduce the volatility of electricity prices. On the other hand, the study suggests that exchange rates could have a negative impact on electricity prices in the short term, but depreciation of the local currency could lower fuel costs and improve the availability of power. Overall, the study provides insights that can inform policymakers, energy regulators, and investors in making decisions that contribute to the efficient and sustainable development of Cameroon's electricity market. The study also highlights the need to prioritize power generation to stimulate economic growth and private investment while promoting renewable energy production.

Quantifying the impact of multi-scale climate variability on electricity prices in a renewable-dominated power grid

The variability and intermittency of renewable energy sources pose several challenges for power systems operations, including energy curtailment and price volatility. In power systems with considerable renewable sources, co-variability in renewable energy supply and electricity load can intensify these outcomes. In this study, we examine the impacts of interplay between renewable resource and load co-variability and power system constraints across multiple spatial and temporal scales. We use the New York State power system as our case study, which is undergoing a major transition toward increased renewable generation. We characterize the spatiotemporal co-variability of renewable energy-generating resources and electricity load and investigate the impact of climatic variability on electricity price volatility. We use an accurate, reduced-form representation of the New York power system, which integrates additional wind and solar power resources to meet the state’s energy targets through 2030. Our study shows while diversifying renewables mitigates power variability, short-term fluctuations can still lead to inefficient energy use and substantial curtailment. Annual electricity prices varied by 9%, with price spikes fluctuating by up to 56%, while reflecting a state-wide heterogeneity. Hydropower primarily influences yearly prices, whereas solar and wind mainly affect short-term volatility. Our results underscore the importance of considering both spatiotemporal dynamics and system constraints when planning investments in renewable energy infrastructure.

Machine and deep learning approaches for forecasting electricity price and energy load assessment on real datasets

This article aims to propose short-term forecasting for a combined model of the hourly electricity price and the hourly load. Where both the independent system operators and the market participants must deal with the uncertainty and volatility of the day-ahead electricity price. Therefore, an accurate prediction model has been needed for a combined short-term forecasting model for both the hourly electricity price and the hourly load in the deregulated market. To get a better prediction, real-time data for the day-ahead electricity price and the hourly load are provided from the ISO New England Control Area (ISO-NE-CA) market. This study presents the combined model based on four featured machine and deep learning algorithms: Feed Forward Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, Long Short-Term Memory, and Gated Recurrent Units. The results are obtained from two scenarios, In the first scenario, the data has been trained and tested without depending on other factors such as temperatures and calendar. In the second scenario, the data has been trained and tested depending on the previously mentioned factors. The obtained results from the four algorithms have been compared to show their performance and their efficiency in the values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE). The four featured algorithms prove their good performance with less error in the forecasting model.

A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training-length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin.

Economic viability of Li-ion batteries based on the price arbitrage in the European day-ahead markets

The accelerating increase in the power capacity of intermittent renewable energy sources has a significant impact on the operation of power systems. First, it causes a growth in electricity price volatility in energy markets. Second, it requires balancing the system to meet the demand also in windless hours at night. Li-ion batteries, as a promising energy storage technology, are considered a response to the abovementioned challenges. However, their use must generate profits to encourage potential investors. With this in mind, the objective of this paper is to examine the economic profitability of Li-ion batteries based on price arbitrage in the European day-ahead market. For this purpose, 7 years of data from 22 European markets are analysed. These data are multiplied to maintain the volatility of electricity prices within the entire lifetime of the energy storage system. The results provide for each country (i) sets of hours of electricity purchase and sale that corresponds to hours of storage charging and discharging, (ii) a number of charging/discharging cycles, (iii) potential profits of transactions in day-ahead markets, and (iv) net present values of investments for current and projected costs. The study contributes to the existing literature by pointing out the most competitive markets for Li-ion batteries, depending on economic results, market size and renewable generation in power systems. The results of NPV are below zero in each case analysed when the current capital cost is considered. However, a reduction of capital costs increases the profitability of the system, indicating Finnish and Romanian markets as the most competitive for Li-ion batteries.

An Open-Source Energy Arbitrage Model Involving Price Bands for Risk Hedging with Imperfect Price Signals

The increased uptake of variable renewable energy sources has increased electricity price volatility in many energy pool markets, providing an opportunity for storage systems to profit through energy arbitrage. Comparison between the cost or value of storage systems engaging in energy arbitrage should be performed on a levelised basis due to differences in system lifetime. Existing energy arbitrage models with bid/offer curves and imperfect forecasting are typically computationally expensive and are impractical for calculating lifetime levelised cost metrics. In this work, an open-source modular energy arbitrage model with bid and offer curve inputs was developed for a lithium-ion battery energy storage system (BESS) and pumped hydro system (PHS) to analyse lifetime levelised cost and revenue. The mixed integer linear program scheduling module included a new piece-wise linearised description of PHS charging behaviour for rapid optimisation. A one-at-a-time sensitivity analysis indicated that levelised cost and revenue were highly sensitive to discharging efficiency. In a case study based on Australia’s National Electricity Market, imperfect forecasting with no risk hedging was found to increase levelised costs by up to 24% and decrease levelised revenue by up to 50% relative to perfect price forecasting, despite 95% of prices being forecast to be within $35/MWh of the actual trading price. BESS levelised costs were more significantly correlated with consistent low risk bids (Kendall Tau-b of 0.75), since the undiscounted capital costs contribute to a larger proportion of the overall costs than in the PHS systems.

A novel probabilistic forecasting system based on quantile combination in electricity price

In the electricity market, the accuracy of electricity price forecasting is significant for real-time control; however, the complexity and volatility of electricity prices make this a challenge. Existing forecasting models focus on deterministic forecasting and rarely address the uncertainty in electricity price forecasting. Therefore, this study fills this knowledge gap by introducing a novel combined probability forecasting system (CPFS) and creatively incorporating probability density estimation based on kernel functions in a multi-objective optimization algorithm. In addition, to effectively integrate the forecasting components, the tuna optimization algorithm was enhanced to overcome the limitations of traditional multi-objective optimization algorithms. Finally, the validity of the CPFS is confirmed through two electricity price cases, considering three equally important aspects: reliability, resolution, and sharpness. From a comprehensive perspective, CPFS outperformed the most advanced benchmark by more than 5.66% and 38.93% in AIS and by more than 13.41% and 3.55% in quantile loss on the NSW and Singapore datasets, respectively. The experimental results demonstrate that the CPFS provides an effective range for electricity price fluctuations. Furthermore, given that probabilistic forecasting is essential for risk management, it offers important implications for the electricity market.