https://cyberleninka.org/article/n/72693.pdf

Financing Renewable Energy: Who is Financing What and Why it Matters

This paper provides a state-of-the-art summary of risk assessment in energy trading. Techniques from financial engineering are needed by electric energy companies to manage price risk. These tools are needed by suppliers, distributors, and traders in a competitive electric power marketplace. Tools that have been adapted to the specific environment of the electric power system include portfolio analysis and hedging instruments. This paper provides a comprehensive critical literature survey of what has been applied to date in the power markets and which areas continue to need additional research. One example market scenario is used throughout the paper to demonstrate the usefulness of the risk assessment methods.

Risk assessment in energy trading

This paper develops optimal bidding strategies based on hourly unit commitment in a generation company (GENCO) that participates in energy and ancillary services markets. The price-based unit commitment problem with uncertain market prices is modeled as a stochastic mixed integer linear program. The market price uncertainty is modeled using the scenario approach, Monte Carlo simulation is applied to generate scenarios, scenario reduction techniques are applied to reduce the size of the stochastic price-based unit commitment problem, and postprocessing is applied based on marginal cost of committed units to refine bidding curves. The financial risk associated with market price uncertainty is modeled using expected downside risk, which is incorporated explicitly as a constraint in the problem. Accordingly, the proposed method provides a closed-loop solution to devising specific strategies for risk-based bidding in a GENCO. Illustrative examples show the impact of market price uncertainty on GENCO's hourly commitment schedule and discuss the way GENCOs could decrease financial risks by managing expected payoffs

Risk-Constrained Bidding Strategy With Stochastic Unit Commitment

This paper addresses the self-scheduling problem of a price-taker power producer. It focuses on risk modeling, emphasizing the tradeoff existing between maximum profit and minimum risk. The paper analyzes a self-scheduling model that considers simultaneously profit and risk. This model is formulated as a mixed-integer quadratic programming problem, which is solved using commercially available software. Relevant results from a realistic case study are discussed.

/pdf/risk-constrained-self-scheduling-of-a-thermal-power-producer-gia4582boy.pdf

Risk-constrained self-scheduling of a thermal power producer

In a deregulated power industry, power producing companies bid in the hour-ahead and day-ahead power markets in an attempt to maximize their profit. For a successful bidding strategy, each power-producing company has to generate bidding curves derived from an optimal self-schedule. This self-schedule is commonly obtained from a profit-maximizing optimal power flow model based on predicted locational marginal prices (LMPs). However, at the time of self-scheduling, the predicted values of the LMPs are largely uncertain. Therefore, it is desired to produce robust self-schedules that can be used to lessen the risk resulting from exposure to fluctuating prices. In portfolio optimization theory, methods of risk management include Value-at-Risk (VaR) and conditional Value-at-Risk (CVaR). CVaR is known to be a more consistent measure of risk than VaR. In fact, whilst CVaR is the mean excess loss, the VaR provides no indication on the extent of losses that might be suffered beyond the amount indicated by this measure. This research proposes a method for robust self-scheduling based on CVaR. It will be shown that polynomial interior-point methods can be used to obtain the robust self-schedules from a second-order cone program. The obtained schedules provide a compromise solution between maximum profit and minimum risk. Simulation results on a standard IEEE bus test system will be used to demonstrate the scheduling model based on CVaR.

Robust self-scheduling under price uncertainty using conditional value-at-risk

The market risks encountered by energy asset operators can be categorized as short term/operational, intermediate term/trading, and long term/valuation in nature. This paper describes how the market risks in operations can be measured and managed using real option models and stochastic optimization techniques. It then links these results to intermediate term value at risk and related risk metrics such as cash flow, earnings, and credit risk which can be used to measure trading risks over weeks to months; and how to optimize these portfolios for risk-return relationships. Finally, it then explores the risks in longer term energy portfolio management and how these can be simulated, measured, and optimized.

http://quantlabs.net/academy/download/free_quant_instituitional_books_/[IEEE%20Transactions%20on%20Power%20Systems,%20Denton]%20Managing%20Market%20Risk%20in%20Energy.pdf

P. Skantze

Papers

Managing market risk in energy