Abstract
Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.
Highlights
From many countries’ experiences of electricity market construction, the electricity market reduces users’ electricity charges, optimizes the energy structure, and is beneficial to the operation of the power system
In [4], the authors studied single electric energy suppliers to participate in the long-term contract market and short-term retail market transactions for the user to bear the risk of electricity price fluctuations in the electricity market, to provide users with a more stable power supply price, from which to earn the difference
Risk measurement under fractal distribution, and made a fitting analysis based on the electricity price data of the electricity market in California, U.S In [21], the authors used the skewness angle of purchasing income function to analyze the value-at-risk and quantified the operational risk of retail companies
Summary
From many countries’ experiences of electricity market construction, the electricity market reduces users’ electricity charges, optimizes the energy structure, and is beneficial to the operation of the power system. In [20], the authors deduced the calculating formula of VaR risk measurement under fractal distribution, and made a fitting analysis based on the electricity price data of the electricity market in California, U.S In [21], the authors used the skewness angle of purchasing income function to analyze the value-at-risk and quantified the operational risk of retail companies. In [25], the authors took the purchase option as the object, and analyzed the optimal option contract combination strategy of the power company when the real-time price and customer demand were random variables. In [27,28], the authors simulated the reasonable allocation of power purchase risk in real-time electricity markets on the basis of the Time of Use (TOU) price, and evaluated the effectiveness of hedging contract optimization in risk mitigation by CVaR. Rationalityand of the model is analyzed and verified with an example
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