Incorporating Data-Driven Demand-Price Uncertainty Correlations Into Microgrid Optimal Dispatch
Haifeng Qiu, Pengxiang Liu, Wei Gu, Rufeng Zhang, Shuai Lu, Hoay Beng Gooi
Abstract
A growing number of microgrid (MG) systems ipate in market trading as prosumers to improve their own operation profits, as well as the flexibility of power supply. To handle the MG dispatch problem with uncertainties in both load demands and electricity prices, this paper provides a data-driven robust optimization (RO) approach. Firstly, the correlations between the uncertainties of demands and prices are verified via the analysis of historical monitoring data from a real-world case. Based on the data-fitting for the rectangular and ellipsoidal intervals, a new data-driven demand-price uncertainty set with correlations is built to eliminate these unreasonable scenarios effectively. Secondly, a two-stage RO dispatch model is established considering multiple uncertainties of renewable-load power and prices in MGs. The bilinear objective term brings huge obstacles to the solution of the RO model, therefore a novel nested iterative algorithm is further exploited. The bilinear terms of power and price in the objective function are linearized by a binary expansion approach. Besides, to avoid the enumerations of numerous binary recourses in problem solving, the block coordinate descent (BCD) theory is adopted to co-optimize different types of variables, thereby enhancing the computational tractability. Numerical simulations indicate the validity and superiority of the developed RO model and the solution algorithm.