Optimal Day-Ahead Bidding Strategy for Electricity Retailer with Inner-Outer 2-Layer Model System Based on Stochastic Mixed-Integer Optimization

摘要

Bidding in spot electricity market (EM) is a key source for electricity retailer (ER)'s power purchasing. In China for the near future, besides the real-time load and spot clearing prices uncertainties, it will be hard for a newborn ER to adjust its retail prices at will due to the strict governmental supervision. Hence, spot EM bidding decision-making is a very complicated and important issue for ER in many countries including China. In this paper, an inner-outer 2-layer model system based on stochastic mixed-integer optimization is proposed for ER's day-ahead EM bidding decision-making. This model system not only can help to make ERs more beneficial under China's EM circumstances in the near future, but also can be applied for improving their profits under many other deregulated EM circumstances (e.g., PJM and Nord Pool) if slight transformation is implemented. Different from many existing researches, we pursue optimizing both the number of blocks in ER's day-ahead piecewise staircase (energy-price) bidding curves and the bidding price of every block. Specifically, the inner layer of this system is in fact a stochastic mixed-integer optimization model, by which the bidding prices are optimized by parameterizing the number of blocks in bidding curves. The outer layer of this system implicitly possesses the characteristics of heuristic optimization in discrete space, by which the number of blocks is optimized by parameterizing bidding prices in bidding curves. Moreover, in order to maintain relatively low financial-risk brought by clearing prices and real-time load uncertainties, we introduce the conditional value at risk (CVaR) of profit in the objective function of inner layer model in addition to the expected profit. Simulations based on historical data have not only tested the scientificity and feasibility of our model system, but also verified that our model system can further improve the actual profit of ER compared to other methods.