Electricity Spot Prices Research Articles

Electricity price spike clustering: A zero-inflated GARX approach

Sudden dramatic rises in electricity prices, known as electricity “price spikes”, are ubiquitous in electricity spot markets worldwide. These price spikes often cluster. Energy retailers in many countries, including Australia, purchase electricity in an unregulated spot market and sell it to consumers at a heavily regulated price. As a result, the occurrence and clustering of spikes in the spot electricity price is particularly hazardous for retailers, and effective modelling and forecasting of these spikes is of foremost importance for effective risk management. This paper uses a data-dependent two-regime threshold autoregression to determine the electricity price spikes and proposes a zero-inflated generalized autoregression with exogenous covariates (ZIGARX) for modelling the time series count of price spikes. This model is shown to well capture the salient features of electricity price spikes, and can provide effective rolling probability forecasts of price spike occurrence. We apply the proposed approach to analyze the spot prices in four major regions in the Australian National Electricity Market (NEM) over the period April 2016 to February 2022. Results show that price spike clustering is captured very well using our model, and the persistence in price spike occurrence remains even after controlling for the relevant exogenous covariates. We also find that, in most of our sample period, the increasing use of renewable generation has mixed effects on the occurrence of price spikes.

Regional electricity market price forecasting based on an adaptive spatial–temporal convolutional network

The accurate prediction of electricity prices has great significance for the power system and the electricity market, regional electricity prices are difficult to predict due to congestion issues in regional transmission lines. A regional electricity price prediction framework is proposed based on an adaptive spatial–temporal convolutional network. The proposed framework is expected to better explore regional electricity prices’ spatial–temporal dynamic characteristics in the electricity spot market and improve the predictive accuracy of regional electricity prices. First, different areas of the electricity market are regarded as nodes. Then, each area’s historical electricity price data are used as the corresponding node’s characteristic information and constructed into a graph. Finally, a graph containing the spatial–temporal information on electricity prices is input to the adaptive spatial–temporal prediction framework to predict the regional electricity price. Operational data from the Australian electricity market are adopted, and the prediction results from the proposed adaptive spatial–temporal prediction framework are compared with those of existing methods. The numerical example results show that the predictive accuracy of the proposed framework is better than the existing baseline and similar methods. In the twelve-step forecast example in this paper, considering the spatial dependence of the spot electricity price can improve the forecast accuracy by at least 10.3% and up to 19.8%.

Forecasting Nordic electricity spot price using deep learning networks

Forecasting Nordic electricity spot price using deep learning networks

Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network

This paper presents a rigorous investigation into the multiresolution cross-correlation and causality between European energy markets and geopolitical risk (GPR). Using daily electricity spot prices from January 2015 to January 2023, the study employs the variational mode decomposition (VMD) method to uncover the underlying patterns and reveal the interaction between European power prices and GPR. The paper further develops a VMD-assisted multi-scaled causal neural network (VMD-M-CNN) as a multivariable forecasting approach, benchmarking it against other competing models. The results demonstrate a strong dependence between electricity markets and GPR across different investment time-scale horizons. Additionally, the relationship between the two has taken different forms over the crisis period in contrast with calm periods. The proposed VMD-M-CNN approach proves to be more effective than other benchmark models, providing valuable insights for market participants and policymakers to make informed decisions. The rigorous methodology employed and the novel approach developed enhance the credibility and robustness of the study, contributing to the growing body of literature on energy markets and GPR.

A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors.

Electricity Price spirals now or later: An emerging market experience

Time series models have been employed to identify the structural breaks and the existence of volatility based on the behavior and direction of electricity prices. The models were tuned and validated with dataset consisting of the daily spot electricity prices of 13 bid areas of the Indian exchange-traded market for ten years. The Indian exchange-traded market is a diversified market divided into 13 bid areas, each consisting of states with unique trading characteristics. Hence it is vital to conduct an in-depth analysis of each to identify the problematic areas, key reasons behind it and further tame it, which has never been done before. The results revealed that the bid areas S1 and S2 depicts negative leverage effect predominantly due to the elections and transmission constraints. Whereas rest of the areas shows positive result with less price spirals due to changes in regulation, weather and new operational plants. Insights from the research encourages power market participants to further apply forecasting techniques and prepare a price risk mitigation model and reach out at a global level.

Development of a Joint Penalty Signal for Building Energy Flexibility in Operation with Power Grids: Analysis and Case Study

Electricity generation from renewable energy reduces greenhouse gas emissions and, in the long term, the cost of electricity in power grids. However, there is currently no strong positive correlation between greenhouse gas intensity and electricity spot prices in Germany, despite increasing renewable energy penetration. Therefore, energy flexibility programs that rely on demand response may not be fully effective in reducing carbon emissions unless the energy market aligns consistently with carbon emission factors. To address this issue, we propose a model for joint signals consisting of power grid climate gas intensity and price signals that can achieve both environmental and economic benefits for building energy flexibility applications. Next, to assess the maximum possible flexibility hours from the grid side, we explore penalty signal threshold limits with daily and biweekly aggregation. Using a case study, we analyze energy flexibility with joint signals to explore their effect on greenhouse gas emissions and building operation cost. Our results suggest that joint signals can be more effective than a single type of signal in promoting energy flexibility.

Can the Basis Lead to Arbitrage Profits on the MISO Exchange?

Previous research has shown the basis contains information about future spot prices in wholesale electricity markets. This paper examines the profitability of a trading rule that utilizes the basis on the Midcontinent Independent System Operator (MISO) exchange. 24 hourly time series regressions across each of MISO’s eight regional hubs are estimated to determine the rule’s effectiveness. 67 of the 192 total regressions yield statistically significant results. The number of significant results as well as the size of the returns tend to be highest during the afternoon through early evening hours, which calls into question the efficiency of the market during peak demand times.

Adaptive estimation of intensity in a doubly stochastic Poisson process

AbstractIn this paper, I consider a doubly stochastic Poisson process with intensity where is a continuous Itô semi‐martingale. Both processes are observed continuously over a fixed period . I propose a local polynomial estimator for the function on a given interval. Next, I propose a method to select the bandwidth in a nonasymptotic framework that leads to an oracle inequality. Considering the asymptotic , and , the accuracy of the proposed estimator over the Hölder class of order is if the degree of the chosen polynomial is greater than and it is optimal in the minimax setting. I apply those results to data on French temperature and electricity spot prices from which I infer the intensity of electricity spot spikes as a function of the temperature.

Construction of Mixed Derivatives Strategy for Wind Power Producers

Due to the inherent uncertainty of wind conditions as well as the price unpredictability in the competitive electricity market, wind power producers are exposed to the risk of concurrent fluctuations in both price and volume. Therefore, it is imperative to develop strategies to effectively stabilize their revenues, or cash flows, when trading wind power output in the electricity market. In light of this context, we present a novel endeavor to construct multivariate derivatives for mitigating the risk of fluctuating cash flows that are associated with trading wind power generation in electricity markets. Our approach involves leveraging nonparametric techniques to identify optimal payoff structures or compute the positions of derivatives with fine granularity, utilizing multiple underlying indexes including spot electricity price, area-wide wind power production index, and local wind conditions. These derivatives, referred to as mixed derivatives, offer advantages in terms of hedge effectiveness and contracting efficiency. Notably, we develop a methodology to enhance the hedge effects by modeling multivariate functions of wind speed and wind direction, incorporating periodicity constraints on wind direction via tensor product spline functions. By conducting an empirical analysis using data from Japan, we elucidate the extent to which the hedge effectiveness is improved by constructing mixed derivatives from various perspectives. Furthermore, we compare the hedge performance between high-granular (hourly) and low-granular (daily) formulations, revealing the advantages of utilizing a high-granular hedging approach.

Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case

Our research involves analyzing the latest models used for electricity price forecasting, which include both traditional inferential statistical methods and newer deep learning techniques. Through our analysis of historical data and the use of multiple weekday dummies, we have proposed an innovative solution for forecasting electricity spot prices. This solution involves breaking down the spot price series into two components: a seasonal trend component and a stochastic component. By utilizing this approach, we are able to provide highly accurate predictions for all considered time frames.

Application of the Esscher Transform to Pricing Forward Contracts on Energy Markets in a Fuzzy Environment

The paper is dedicated to modeling electricity spot prices and pricing forward contracts on energy markets. The underlying dynamics of electricity spot prices is governed by a stochastic mean reverting diffusion with jumps having mixed-exponential distribution. Application of financial mathematics and stochastic methods enabled the derivation of the analytical formula for the forward contract's price in a crisp case. Since the model parameters' incertitude is considered, their fuzzy counterparts are introduced. Utilization of fuzzy arithmetic enabled deriving an analytical expression for the futures price and proposing a modified method for decision-making under uncertainty. Finally, numerical examples are analyzed to illustrate our pricing approach and the proposed financial decision-making method.

Investment in wind-based hydrogen production under economic and physical uncertainties

This paper evaluates the economic viability of a combined wind-based green-hydrogen facility from an investor’s viewpoint. The paper introduces a theoretical model and demonstrates it by example. The valuation model assumes that both the spot price of electricity and wind capacity factor evolve stochastically over time; these state variables can in principle be correlated. Besides, it explicitly considers the possibility to use curtailed wind energy for producing hydrogen. The model derives the investment project’s net present value (NPV) as a function of hydrogen price and conversion capacity. Thus, the NPV is computed for a given price and a range of capacities. The one that leads to the maximum NPV is the ‘optimal’ capacity (for the given price). Next, the authors estimate the parameters underlying the two stochastic processes from Spanish hourly data. These numerical estimates allow simulate hourly paths of both variables over the facility’s expected useful lifetime (30 years). According to the results, green hydrogen production starts becoming economically viable above 3 €/kg. Besides, it takes a hydrogen price of 4.7 €/kg to reach an optimal conversion capacity half the capacity of the wind park. The authors develop sensitivity analyses with respect to wind capacity factor, curtailment rate, and discount rate.

Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

Directional Spillover of Fossil Fuels Prices on a Hydrothermal Power Generation Market

The Colombian electricity market is based on a hydrothermal power generation market with a strong dependence on exogenous variables such as fossil fuel prices and climatology factors. Besides, the Colombian economy is characterizable by relevant mining-energy activities. Therefore, the main objective of this research was to evaluate the directional spillovers between the electricity spot prices and gas, coal, and crude oil prices and thus provide relevant information for the electricity market agents to identify the risk related to energy commodity price fluctuations. The dataset used in this research consists of monthly logarithmic returns of energy prices between September 2009 and December 2019. The main finding shows that the system's average connectedness is 13,6%. Besides, the electricity spot prices are net shock receivers of volatility, and 20% of their dynamic is related to fossil fuel price fluctuations.

Considering Forward Electricity Prices for a Hydro Power Plant Risk Analysis in the Brazilian Electricity Market

The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Beyond the Mean: Examining Electricity Spot Price Distribution in Australia

Beyond the Mean: Examining Electricity Spot Price Distribution in Australia

Operation of district heat network in electricity and balancing markets with the power-to-heat sector coupling

The stochastic nature of renewable energy resources poses a challenge in terms of balancing supply and demand in the power grid, necessitating additional sources of flexibility and balancing services. District heating networks (DHNs) equipped with power-to-heat (P2H) technologies and thermal storage can provide balancing services to the grid. Participation of DHN in a combination of diverse marketplaces to optimize revenue has been understudied. This study proposes a daily routine for the operation of a DHN in multiple energy markets, covering the day-ahead and intraday electricity markets, and various balancing markets. The emphasis is on providing balancing services by the P2H units, while considering their technical constraints of operation. The economic viability of the proposed routine is examined. Results indicate an increase in the operation profits by 1.3–9.7% compared to the total electricity sales, 0.4–3.0% of total heat sales, and 0.3–2.0% net profit in the examined DHN between 2019 and 2021. Given the complexity and uncertainty of future market development, it is critical to be aware of the most influential aspects. Sensitivity analysis indicates that fluctuations in electricity spot prices, fuel prices, and technical constraints of units significantly affect the profit.

Model-predictive control for dispatch planning of concentrating solar power plants under real-time spot electricity prices

The aim at dispatch scheduling for concentrating solar power plants is to utilize thermal storage to maximize profit from electricity generation. The dispatching plan, however, must contend with significant uncertainty in both weather forecasts (particularly solar irradiation) and, in some locations, real-time electricity prices governed by potentially volatile markets. This paper proposes a Model-Predictive Control (MPC) which employs a novel similarity-based forecast for weather variables and exploits the predictions of electricity prices provided by the market operator. A Monte-Carlo simulation is developed to evaluate true performance of the proposed MPC against two benchmarks, with perfect knowledge (PK) forecasts and day-ahead optimized using prototypical weather (DAPW). A case study is performed on a 115 MW Solar Tower plant with 8-hour storage, hypothetically located in South Australia, and operates under either a fixed-price or a real-time spot price scenario. A Monte-Carlo simulation is conducted for 150 tests for January (summer) and August (winter). The results show in fixed-price scenario MPC achieves 82.4 % of optimal profit (i.e. obtained via PK) in January and 72.4 % in August whereas that in DAPW falls to 71.5 % and 56.9 % of optimal, respectively. In the spot market scenario, MPC reaches 71.3 % and 63.6 % of optimal profit in January and August, whereas that for DAPW reaches 61.4 % and 55.1 %, respectively. In conclusion, PK forecast assumption in dispatch planning over-estimates the achievable profit by ∼ 28–40 % particularly in spot market scenario. Moreover, MPC can mitigate the influence of uncertainties on the plant economic performance by 8.5 %–15.5 % compared to DAPW benchmarks.