A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets

Abstract

Abstract This paper focuses on cooperative scheduling of the integrated plug-in hybrid electric vehicle fleets and wind farm system (IWPHEVS) in the day-ahead wholesale market (DWM), as well as its effects on the market outcomes and price, as a price-maker player. In this regard, a multi-objective two-stage bi-level hybrid stochastic-robust offering/bidding and scheduling strategy is developed. The upper-level problem, which is that of the IWPHEVS operator, encompasses two objectives, namely cost and emission. The cost objective is comprised of operational costs and the cost of power that is purchased in DWM. Additionally, the plug-in hybrid electric vehicles (PHEVs) are congregated into distinct fleets through k-means clustering. To inscribe PHEVs’ battery erosion, a comprehensive battery erosion model is comprehended, which is linearized by semi-integer variables. The uncertain data sets, such as vehicle fleets arrival/departure timings and their travelled miles are represented as scenarios according to their empirical distribution, which is acquired from the National household travel survey (NHTS). On the flip side, the wind power, which is a more unpredictable parameter, is designed as a robust optimization (RO) set, as it is apt to enhance the reliability issues regarding wind volatilities. The lower-level, embodies the wholesale market operator that has the objective of maximizing social welfare. Conclusively, different case studies of dump, smart and multi-objective charging are meticulously investigated to testify the potency of the proposed method. Based on the obtained findings on the proposed smart multi-objective framework, the IWPHEVS as a price-maker player, can manipulate locational marginal price as much as 4.4%, while the emissions can be curtailed by 40%.