Using electricity options to hedge against financial risks of power producers

Pineda, Salvador; Conejo, Antonio J.

doi:10.1007/s40565-013-0018-y

Cited by 27 publications

(22 citation statements)

References 24 publications

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“…A literature review of derivatives in electricity markets [13][14][15][16] reveals that, large consumers who need a fixed price for electricity can resort to future, forward contracts or options to remove really high peak prices that could reduce their operating margins; on the other hand, retailers or aggregators can settle options to mitigate consumers load deviations or significant forecasting errors that yield economic losses.…”

Section: Financial Product Descriptionmentioning

confidence: 99%

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Malpica

Frías

2019

IEEE Trans. Power Syst.

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Abstract-This paper presents an original methodology to compute a financial product that could enhance the demand side participation in ancillary services, specially for industrial consumers. The financial product consists in an american option on the Spanish secondary reserve market for the following day, where the buyer has the right but not the obligation to offer part of its capacity to the system operator. Considering this approach, an industrial consumer would receive an economic incentive to offer its flexibility to the system without changing its production planning, paying an upfront premium. The computation of the american option is leveraged on a Monte Carlo simulation approach where the random paths are obtained from a machine learning model. The machine learning model attempts to forecast the 24-hour secondary band prices of the following day using a combination of different algorithms; the output of the model is used as a baseline to perform the Monte Carlo simulation that computes the option value. LIST OF SYMBOLSt, τ time -hour of the day d day T tenor of the american option, i.e. 24 hours i index of the random path I total number of random paths g index of basis function G total number of basis functions β r coefficient vector of ridge regression γ shrinkage parameter of ridge penalty K strike -electricity valuation α g,t coefficient of basis function g at hour t S t secondary band price prediction at hour t S t secondary band price at hour t Y t error term at hour t X t feature vector at hour t Z t logarithm of the error term at hour t W t input feature vector of the second layer at hour t V t option value at hour t V t,i option value at hour t for random path i V t+∆t,i simulated continuation value at hour t for random path i h t payoff of the option at hour t S t,i secondary band price at hour t for random path i C t continuation value at hour t C t,i estimation of the continuation value at hour t for random path i

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Section: Financial Product Descriptionmentioning

confidence: 99%

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Malpica

Frías

2019

IEEE Trans. Power Syst.

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“…Such uncertainty stems from generation availability, load fluctuation, wholesale markets, bidding behaviour, among others [68]. Since high uncertainty distorts market efficiency and prosumer welfare, a DCDS market should offer risk-hedging tools, such as forward markets [69], options [70], or stochastic clearing with risk measures [61].…”

Section: Settlement Pricing Directionsmentioning

confidence: 99%

Electricity Markets for DC Distribution Systems: Design Options

et al. 2019

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DC distribution systems (DCDSs) are a promising alternative to AC systems because they remove AC-DC conversions between renewable sources and loads. Their unique features compared to AC include low system inertia, strict power limits and power–voltage coupling. In a liberalised electricity market, merely applying an AC market design to a DCDS cannot guarantee the latter’s supply security and voltage stability; new markets must be designed to meet DC challenges. This article identifies the key design options of DCDS electricity markets. To identify these options, we develop a comprehensive design framework for local electricity markets; to our knowledge, we provide the first such analysis. Whereas previous studies focus on separate aspects of DCDS markets, we widen the scope to include the role of market architecture and investigate the arrangements of sub-markets. As an illustration, we demonstrate three promising DCDS market designs that can be defined in our framework, and provide a first assessment of their performance.

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“…As an effective approach to studying distributed system, multi-agent computational economic simulation has been widely applied in the research of electricity markets [12][13][14]. Reference [15] presented an integrative method to evaluate different wholesale market rules and the effect of market power mitigation.…”

Section: Introductionmentioning

confidence: 99%

Agent-based simulation for symmetric electricity market considering price-based demand response

Jiang

2017

J. Mod. Power Syst. Clean Energy

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With the development of electricity market mechanism and advanced metering infrastructure (AMI), demand response has become an important alternative solution to improving power system reliability and efficiency. In this paper, the agent-based modelling and simulation method is applied to explore the impact of symmetric market mechanism and demand response on electricity market. The models of market participants are established according to their behaviors. Consumers' response characteristics under time-of-use (TOU) mechanism are also taken into account. The level of clearing price and market power are analyzed and compared under symmetric and asymmetric market mechanisms. The results indicate that the symmetric mechanism could effectively lower market prices and avoid monopoly. Besides, TOU could apparently flatten the overall demand curve by enabling customers to adjust their load profiles, which also helps to reduce the price.

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Using electricity options to hedge against financial risks of power producers

Cited by 27 publications

References 24 publications

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Electricity Markets for DC Distribution Systems: Design Options

Agent-based simulation for symmetric electricity market considering price-based demand response

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