scholarly journals Estimating fast mean-reverting jumps in electricity market models

2020 ◽  
Vol 24 ◽  
pp. 963-1002
Author(s):  
Thomas Deschatre ◽  
Olivier Féron ◽  
Marc Hoffmann
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Random Measure ◽  
Compound Poisson Process ◽  
Real Data ◽  
Discrete Observations ◽  
Forward Contracts ◽  
Spot Prices ◽  
A Value ◽  
Itô Semimartingale

Based on empirical evidence of fast mean-reverting spikes, electricity spot prices are often modeled X + Zβ as the sum of a continuous Itô semimartingale X and a mean-reverting compound Poisson process Ztβ=∫0t ∫ℝxe−β(t−s)p̲(ds,dt) where p̲(ds,dt) is Poisson random measure with intensity λds ⊗dt. In a first part, we investigate the estimation of (λ, β) from discrete observations and establish asymptotic efficiency in various asymptotic settings. In a second part, we discuss the use of our inference results for correcting the value of forward contracts on electricity markets in presence of spikes. We implement our method on real data in the French, German and Australian market over 2015 and 2016 and show in particular the effect of spike modelling on the valuation of certain strip options. In particular, we show that some out-of-the-money options have a significant value if we incorporate spikes in our modelling, while having a value close to 0 otherwise.

scholarly journals A STRUCTURAL RISK-NEUTRAL MODEL OF ELECTRICITY PRICES

2009 ◽  
Vol 12 (07) ◽  
pp. 925-947 ◽  
Author(s):  
RENÉ AÏD ◽  
LUCIANO CAMPI ◽  
ADRIEN NGUYEN HUU ◽  
NIZAR TOUZI
Keyword(s):  
Electricity Market ◽  
Structural Aspect ◽  
Electricity Prices ◽  
Forward Contracts ◽  
Spot Prices ◽  
Modeling Methodology ◽  
Risk Neutral ◽  
Risk Neutral Probabilities ◽  
Crude Approximation ◽  
Forward Prices

The objective of this paper is to present a model for electricity spot prices and the corresponding forward contracts, which relies on the underlying market of fuels, thus avoiding the electricity non-storability restriction. The structural aspect of our model comes from the fact that the electricity spot prices depend on the dynamics of the electricity demand at the maturity T, and on the random available capacity of each production means. Our model explains, in a stylized fact, how the prices of different fuels together with the demand combine to produce electricity prices. This modeling methodology allows one to transfer to electricity prices the risk-neutral probabilities of the market of fuels and under the hypothesis of independence between demand and outages on one hand, and prices of fuels on the other hand, it provides a regression-type relation between electricity forward prices and forward prices of fuels. Moreover, the model produces, by nature, the well-known peaks observed on electricity market data. In our model, spikes occur when the producer has to switch from one technology to the lowest cost available one. Numerical tests performed on a very crude approximation of the French electricity market using only two fuels (gas and oil) provide an illustration of the potential interest of this model.

scholarly journals A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 26-46 ◽  
Author(s):  
Radhakrishnan Angamuthu Chinnathambi ◽  
Anupam Mukherjee ◽  
Mitch Campion ◽  
Hossein Salehfar ◽  
Timothy Hansen ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Moving Average ◽  
Percentage Error ◽  
Spot Prices ◽  
Multi Stage ◽  
Proposed Model ◽  
Forecasting Method ◽  
Auto Regressive ◽  
Stage 1

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

Spatially Explicit Prediction of Wholesale Electricity Prices

2016 ◽  
Vol 40 (2) ◽  
pp. 99-140 ◽  
Author(s):  
James Wesley Burnett ◽  
Xueting Zhao
Keyword(s):  
Panel Data ◽  
Spatial Patterns ◽  
Electricity Markets ◽  
Electricity Market ◽  
Spot Prices ◽  
Spatial Panel ◽  
Out Of Sample ◽  
Geographically Distributed ◽  
Spatial Panel Data ◽  
Transmission Constraints

Transmission constraints often limit the flow of electricity in a regional transmission network leading to strong interaction effects across different geographically distributed points within the system. In modern wholesale electricity markets, these transmission constraints lead to spatial patterns within the nodal electricity spot prices. This study exploits these spatial patterns to better predict spot prices within a wholesale electricity market. More specifically, we use the latest spatial panel data econometric models to compare within-sample and out-of-sample forecasts against nonspatial panel data models. The spatial panel data approach is explained by demonstrating a simple network optimization model. We find that a dynamic, spatial panel data model provides the best predictions within a forecasting error context. Our results may suggest that the spatial autocorrelation between node prices extends beyond the current market-defined zonal boundaries, which calls into question whether the zonal boundaries accurately reflect the congestion boundaries within the system.

scholarly journals Household electricity demand, the intrinsic flexibility index and UK wholesale electricity market prices

2020 ◽  
Author(s):  
Jacopo Torriti
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Electricity Demand ◽  
Market Prices ◽  
Vast Number ◽  
Spot Prices ◽  
Time Use Survey ◽  
Wholesale Electricity Market ◽  
The Uk ◽  
Flexibility Index

AbstractDuring peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility.

scholarly journals Prosumers’ Behavior under a Regulation That Encourages Strict Self-Sufficiency. The Case of Spanish Photovoltaic Micro-Generation

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1114
Author(s):  
Pere Mir-Artigues ◽  
Pablo del Río
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Specific Form ◽  
Economic Behavior ◽  
Distribution Grid ◽  
Self Sufficiency ◽  
Regulatory Limits ◽  
Regulatory Measures ◽  
Self Production ◽  
Municipal Level

The reduction of equipment costs encourages the diffusion of photovoltaic micro-generation, however, proper regulatory measures should be implemented to facilitate self-production dissemination and to promote the emergence of new electricity markets which integrate prosumers. The specific form of these markets will depend on the level of prosumers’ self-sufficiency and the type of grid to which they will be connected. Unfortunately, Spain has been an example of resistance to micro-generation deployment. However, some things have started to change recently, albeit only to a certain extent. This article explains the key elements of the latest regulation of photovoltaic micro-generation in Spain and, through a stylized model, describes the economic behavior of prosumers in such a regulatory framework. It is concluded that this regulation only encourages prosumer plants which are strictly focused on self-sufficiency because it discourages exports and limits capacities and this regulation discourages the smart renewal of the distribution grid because it prevents prosumers from participating in the electricity market. It is recommended that the aforementioned regulatory limits be removed and pilot experiences for the market participation of prosumers be promoted by creating the appropriate technical and regulatory conditions, for example, at the municipal level.

scholarly journals Electricity Markets during the Liberalization: The Case of a European Union Country

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4317
Author(s):  
Štefan Bojnec ◽  
Alan Križaj
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Lessons Learned ◽  
Policy Implications ◽  
Daily Maximum ◽  
Energy Prices ◽  
Electricity Prices ◽  
Electricity Power

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.

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