Future Of Wind Power Research Articles

Efficient Risk Management for Distributed Clean Energy: Principal Component based Weather Derivatives

As global efforts to achieve net-zero emissions intensify, the integration of renewable energy has brought to the critical need for effective volumetric risk hedging strategies, particularly at the local level. However, existing financial instruments based on total power output, such as wind power futures, fall short in local hedging. This study introduces Principal Component (PC) derivatives designed for the solar power sector, using multi-regional solar radiation as the underlying to overcome data handling complexities. In particular, by incorporating our previous concept of prediction error derivatives, we provide a unique solution to complex pricing to help manage cash flow volatility risks. In addition, we propose PC derivatives based on solar radiation residuals to hedge volumetric risks. Empirical analysis shows that our PC derivatives outperform existing widearea derivatives in terms of hedge effectiveness, with a 20% increase over area-specific derivatives. Using as few as three or four PC derivatives can provide comprehensive coverage across different areas, enhancing market liquidity and creating an efficient transaction framework. Our results highlight the practical benefits of this approach, including the potential to reduce transaction costs by countertrading in different regions.

A new approach to wind power futures pricing

A new approach to wind power futures pricing

Pricing analysis of wind power derivatives for renewable energy risk management

The objective of this study is to analyse the theoretical pricing of wind power derivatives, which is important for renewable energy risk management but has a problem in the pricing due to the illiquidity of the assets and to show the application of the theory to the practical implementation of the pricing. We make three contributions to the literature. First, to the best of our knowledge, we are the first to conduct a detailed econometric analysis of the wind power futures underlying, i.e., the electricity production based on windmills, resulting in strong support of seasonality and mean reversion in the logit-transformed wind power load factors. Second, after proposing a new model of wind power load factors based on the econometric findings, we analyse the theoretical prices of wind power futures and call option contracts to which the good-deal bounds pricing within an illiquid market situation is applied as well as we show the application of the theory to the practical pricing with the illiquidity. Third, our empirical pricing analysis shows that theoretical wind power futures prices derived using seasonal modelling more accurately reflect reality than those derived without seasonality compared to market observations, resulting in the importance of seasonality modelling in theoretical wind power derivatives pricing. In particular, considering that the upper and lower price boundaries represent the selling and the buying prices in the incomplete market, respectively, we show that the pricing of the short position is more affected by the seasonality than the pricing of the long position. Finally, we illustrate and discuss the practical applications of the results obtained in our study.

Pricing Wind Power Futures

AbstractWith increasing wind power (WP) penetration an extensive amount of volatile and weather dependent energy is fed into the German electricity system. To manage the volume risk of windless days and the transfer of revenue risk from wind turbine owners to investors, WP derivatives were introduced. These insurance-like securities allow the hedging of the volume risk of unstable WP production on exchanges such as NASDAQ and EEX. We present a modern and powerful methodology to model weather derivatives, with very skewed underlying assets, incorporating techniques from extreme event modelling to tune seasonal volatility. We compare transformed Gaussian and non-Gaussian CARMA(p, q) models. Our results indicate that the Gaussian CARMA(p, q) model is preferred over the non-Gaussian alternative. Out-of-sample backtesting results show good performance, with respect to benchmarks, employing smooth market price of risk (MPR) estimates based on NASDAQ weekly and monthly German WP futures prices. A seasonal MPR of a smile shape is observed, with slightly positive values in times of high volatility, for example, winter months, and negative values, in times of low volatility and production, for example, in summer months. We conclude that producers pay premiums to insure stable revenue steams, while investors pay premiums when weather risk is high.

Multivariate continuous-time modeling of wind indexes and hedging of wind risk

With the introduction of the exchange-traded German wind power futures, opportunities for German wind power producers to hedge their volumetric risk are present. We propose two continuous-time multivariate models for wind power utilization at different wind sites, and discuss the properties and estimation procedures for the models. Applying the models to wind index data for wind sites in Germany and the underlying wind index of exchange-traded wind power futures contracts, the estimation results of both models suggest that they capture key statistical features of the data. We show how these models can be used to find optimal hedging strategies using exchange-traded wind power futures for the owner of a portfolio of so-called tailor-made wind power futures. Both in-sample and out-of-sample hedging scenarios are considered, and, in both cases, significant variance reductions are achieved. Additionally, the risk premium of the German wind power futures is analysed, leading to an indication of the risk premium of tailor-made wind power futures.

Multivariate Continuous-Time Modeling of Wind Indexes and Hedging of Wind Risk

With the introduction of the exchange-traded German wind power futures, opportunities for German wind power producers to hedge their volumetric risk are present. We propose two continuous-time multivariate models for the wind power utilization at different wind sites, and discuss the properties and estimation procedures for the models. Employing the models to wind index data for wind sites in Germany and the underlying wind index of exchange-traded wind power futures contracts, the estimation results of both models suggest that they capture key statistical features of the data. We argue how these models can be used to find optimal hedging strategies using exchange-traded wind power futures for the owner of a portfolio of so-called tailor-made wind power futures. Both in-sample and out-of-sample hedging scenarios are considered, and, in both cases, significant variance reductions are achieved. Additionally, the risk premium of the German wind power futures is analysed, leading to an indication of the risk premium of tailor-made wind power futures.

A New Model for Pricing Wind Power Futures

We propose a new model for the pricing of wind power futures written on the wind power production index. Our approach is based on an arithmetic multi-factor pure-jump Ornstein-Uhlenbeck setup with time-dependent coefficients. We express the wind power production index and the corresponding futures price in terms of Fourier integrals and derive the related time dynamics. We conclude the paper by an investigation of the so-called risk premium associated with our wind power model.

The risk markup of intermittent renewable supply in German electricity forward markets

Renewable energy sources such as wind or solar power currently make up a large share of the total German electricity supply as a result of the German energy transition. This paper presents an empirical analysis of how power shocks resulting from intermittent renewables affect the forecast error of the forward premium in German electricity markets. We extend the existing literature by investigating determinants of forward premiums, focusing on both wind and solar power. We find positive monthly wind shock effects on forecast errors, ie, a specific risk markup on forward prices. These findings underline the need to introduce wind power futures at the European Energy Exchange (EEX) in order to reduce the risk markup for participants in forward markets. No daily wind shock or solar shock effects are found. One explanation for this could be the almost perfect approximations of the expected spot price. This is in line with the EEX’s strategy, which does not create wind power futures for contracts with maturities of days. The wind shock effect on monthly peak-load premium is larger than that on base-load premiums. This is likely due to higher differences in marginal costs on the right-hand side of the merit order curve.

A seasonal copula mixture for hedging the clean spark spread with wind power futures

The recently introduced German wind power futures have brought the opportunity to address the problem of volume risk in wind power generation directly. In this paper, we study the hedging benefits of these instruments in the context of peak gas-fired power plants, by employing a strategy that allows trading in the day-ahead clean spark spread and wind power futures. To facilitate hedging decisions, we propose a seasonal copula mixture for the joint behavior of the day-ahead clean spark spread and the daily wind index. The model describes the data surprisingly well, both in terms of the marginals and the dependence structure, while being straightforward and easy to implement. Based on Monte Carlo simulations from the proposed model, the results indicate that significant benefits can be achieved by using wind power futures. Moreover, a comparison study shows that accounting for asymmetry, tail dependence, and seasonality in the dependence structure is especially important in the context of risk management.

A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures

ABSTRACTThe recent introduction of wind power futures written on the German wind power production index has brought with it new interesting challenges in terms of modelling and pricing. Some particularities of this product are the strong seasonal component embedded in the underlying, the fact that the wind index is bounded from both above and below and also that the futures are settled against a synthetically generated spot index. Here, we consider the non-Gaussian Ornstein–Uhlenbeck type processes proposed by Barndorff-Nielsen and Shephard in the context of modelling the wind power production index. We discuss the properties of the model and estimation of the model parameters. Further, the model allows for an analytical formula for pricing wind power futures. We provide an empirical study, where the model is calibrated to 37 years of German wind power production index that is synthetically generated assuming a constant level of installed capacity. Also, based on 1 year of observed prices for wind power futures with different delivery periods, we study the market price of risk. Generally, we find a negative risk premium whose magnitude decreases as the length of the delivery period increases. To further demonstrate the benefits of our proposed model, we address the pricing of European options written on wind power futures, which can be achieved through Fourier techniques.

On the spatial hedging effectiveness of German wind power futures for wind power generators

On the spatial hedging effectiveness of German wind power futures for wind power generators

A Time-Varying Copula Mixture for Hedging the Clean Spark Spread with Wind Power Futures

The recently introduced German wind power futures have brought the opportunity to address the problem of volume risk in wind power generation directly. In this paper, we study the hedging benefits of these instruments in the context of gas-fired power plants by employing a strategy that allows trading in the spot clean spark spread and wind power futures. To facilitate hedging decisions, we propose a time-varying copula mixture for the joint behavior of the spot clean spark spread and the daily wind index. The model describes the data surprisingly well, both in terms of the marginals and the dependence structure, while being straightforward and easy to implement. Based on Monte Carlo simulations from the proposed model, the results indicate that significant benefits can be achieved by using wind power futures to hedge the spot clean spark spread. Moreover, a comparison study shows that accounting for asymmetry, tail dependence, and time variation in the dependence structure is especially important in the context of risk management.

Pricing Green Financial Products

With increasing wind power penetration more and more volatile and weather dependent energy is fed into the German electricity system. To manage the risk of windless days and transfer revenue risk from wind turbine owners to investors wind power derivatives were introduced. These insurance-like securities (ILS) allow to hedge the risk of unstable wind power production on exchanges like Nasdaq and European Energy Exchange. These products have been priced before using risk neutral pricing techniques. We present a modern and powerful methodology to model weather derivatives with very skewed underlyings incorporating techniques from extreme event modelling to tune seasonal volatility and compare transformed Gaussian and non-Gaussian CARMA(p, q) models. Our results indicate that the transformed Gaussian CARMA(p, q) model is preferred over the non-Gaussian alternative with Levy increments. Out-of-sample backtesting results show good performance wrt burn analysis employing smooth Market Price of Risk (MPR) estimates based on NASDAQ weekly and monthly German wind power futures prices and German wind power utilisation as underlying. A seasonal MPR of a smile-shape is observed, with positive values in times of high volatility, e.g. winter months, and negative values, in times of low volatility and production, e.g. in summer months. We conclude that producers pay premiums to insure stable revenue steams, while investors pay premiums when weather risk is high.

An equilibrium pricing model for wind power futures

Generation from wind power plants is intermittent and affects profits of wind power generators and conventional generators alike. Currently, generators have limited options for transferring the resulting wind-related volume risks. The European Energy Exchange (EEX) recently introduced exchange-traded wind power futures to address this market imperfection. We propose a stylized equilibrium pricing model featuring two representative agents and analyze equilibrium prices as well as the mechanics behind risk premia for wind power futures. We calibrate and simulate stochastic models for wind power generation, power prices, electricity demand, as well as other relevant sources of uncertainty and use the resulting scenarios to conduct a case study for the German market; analyzing prices, hedging effectiveness, and risk premia. Our main result suggests that wind generators are willing to pay an insurance premium to conventional generators to reduce their risks. We conduct a thorough sensitivity analysis to test the influence of model parameters and find that our results on risk premia hold for a broad range of reasonable inputs.

A Non-Gaussian Ornstein-Uhlenbeck Model for Pricing Wind Power Futures

The recent introduction of wind power futures written on the German wind power production index has brought with it new interesting challenges in terms of modeling and pricing. Some particularities of this product are the strong seasonal component embedded in the underlying, the fact that the wind index is bounded from both above and below, and also that the futures are settled against a synthetically generated spot index. Here, we consider the non-Gaussian Ornstein-Uhlenbeck type processes proposed by Barndorff-Nielsen and Shephard (2001) in the context of modeling the wind power production index. We discuss the properties of the model and estimation of the model parameters. Further, the model allows for an analytical formula for pricing wind power futures. We provide an empirical study, where the model is calibrated to 37 years of German wind power production index that is synthetically generated assuming a recent level of installed capacity. Also, based on one year of observed prices for wind power futures with different delivery periods, we study the market price of risk. Generally, we find a negative risk premium whose magnitude decreases as the length of the delivery period increases.

An Equilibrium Pricing Model for Wind Power Futures

Generation from wind power plants is intermittent and affects profits of wind power generators and conventional generators alike. Currently, generators have limited options for transferring the resulting wind-related volume risks. The European Energy Exchange (EEX) recently introduced exchange-traded wind power futures to address this market imperfection. We propose a stylized equilibrium pricing model featuring two representative agents and analyze equilibrium prices as well as the mechanics behind risk premia for wind power futures. We calibrate and simulate stochastic models for wind power generation, power prices, electricity demand, as well as other relevant sources of uncertainty and use the resulting scenarios to conduct a case study for the German market, analyzing prices, hedging effectiveness, and risk premia. Our main result suggests that wind generators are willing to pay an insurance premium to conventional generators to reduce their risks. We conduct a thorough sensitivity analysis to test the influence of model parameters and find that our results on risk premia hold for a broad range of reasonable inputs.

Book Review: Wind Energy – The Facts: a guide to the technology, economics and future of wind power by The European Wind Energy Association

Wind Energy – The Facts: a guide to the technology, economics and future of wind power, by The European Wind Energy Association. London, Earthscan, 2009. ISBN: 978-1-84407-710-6

Renewable energy in power systems

Foreword . Preface . Acknowledgements . 1 Energy and Electricity . 1.1 The World Energy Scene. 1.2 The Environmental Impact of Energy Use. 1.3 Generating Electricity. 1.4 The Electrical Power System. References. 2 Features of Conventional and Renewable Generation . 2.1 Introduction. 2.2 Conventional Sources: Coal, Gas and Nuclear. 2.3 Hydroelectric Power. 2.4 Wind Power. 2.5 PV and Solar Thermal Electricity. 2.6 Tidal Power. 2.7 Wave Power. 2.8 Biomass. 2.9 Summary of Power Generation Characteristics. 2.10 Combining Sources. References. 3 Power Balance/ Frequency Control . 3.1 Introduction. 3.2 Electricity Demand. 3.3 Power Governing. 3.4 Dynamic Frequency Control of Large Systems. 3.5 Impact of Renewable Generation on Frequency Control and Reliability. 3.6 Frequency Response Services from Renewables. 3.7 Frequency Control Modelling. 3.8 Energy Storage. References. Other Useful Reading. 4 Electrical Power Generation and Conditioning . 4.1 The Conversion of Renewable Energy into Electrical Form. 4.2 The Synchronous Generator. 4.3 The Transformer. 4.4 The Asynchronous Generator. 4.5 Power Electronics. 4.6 Applications to Renewable Energy Generators. References. 5 Power System Analysis . 5.1 Introduction. 5.2 The Transmission System. 5.3 Voltage Control. 5.4 Power Flow in an Individual Section of Line. 5.5 Reactive Power Management. 5.6 Load Flow and Power System Simulation. 5.7 Faults and Protection. 5.8 Time Varying and Dynamic Simulations. 5.9 Reliability Analysis. References. 6 Renewable Energy Generation in Power Systems . 6.1 Distributed Generation. 6.2 Voltage Effects. 6.3 Thermal Limits. 6.4 Other Embedded Generation Issues. 6.5 Islanding. 6.6 Fault Ride-through. 6.7 Generator and Converter Characteristics. References. 7 Power System Economics and the Electricity Market . 7.1 Introduction. 7.2 The Costs of Electricity Generation. 7.3 Economic Optimization in Power Systems. 7.4 External Costs. 7.5 Effects of Embedded Generation. 7.6 Support Mechanisms for Renewable Energy. 7.7 Electricity Trading. References. 8 The Future - Towards a Sustainable Electricity Supply System . 8.1 Introduction. 8.2 The Future of Wind Power. 8.3 The Future of Solar Power. 8.4 The Future of Biofuels. 8.5 The Future of Hydro and Marine Power. 8.6 Distributed Generation and the Shape of Future Networks. 8.7 Conclusions. References. Appendix: Basic Electric Power Engineering Concepts . A.1 Introduction. A.2 Generators and Consumers of Energy. A.3 Why AC?. A.4 AC Waveforms. A.5 Response of Circuit Components to AC. A.6 Phasors. A.7 Phasor Addition. A.8 Rectangular Notation. A.9 Reactance and Impedance. A.10 Power in AC Circuits. A.11 Reactive Power. A.12 Complex Power. A.13 Conservation of Active and Reactive Power. A.14 Effects of Reactive Power Flow - Power Factor Correction. A.15 Three-phase AC. A.16 The Thevenin Equivalent Circuit. Reference. Index.

Future of wind power?: What lessons does the wind power market in India hold for the global market?

India represents huge opportunities for windpower and other renewables, but not the same opportunities as found in the West. Respect it as a parallel market, argues Laura Dietz of EcoSpace.