Nonlinear inverse demand curves in electricity market modeling
研究了用指数和多项式函数拟合电力市场竞价数据中的非线性需求曲线，发现其在高电价时段建模更准确，且能降低隐含市场势力，更适合透明度高的市场。
In large-scale energy market models, the price–demand relationship is usually represented by a linear function. In this paper, nonlinear demand functions are fitted to electricity market bid data; in particular, exponential and polynomial (cubic) functions are estimated from EPEX day-ahead data (i.e. Central Western European market area). The corresponding game-theoretic, large-scale electricity models were successfully solved using the Extended Mathematical Programming framework after a suitable adaptation for conjectural variations. Additionally, sufficient conditions for the existence of equilibrium solutions are tested. Numerical results show that nonlinear demand curves lead to an improved modeling especially in high price (peak) load periods and to lower levels of implied market power, which can be considered to be more realistic for markets that have strong transparency measures.