Probabilistic model for nondispatchable power resource integration with microgrid and participation in the power market. (January 2021)

• Record Type: Journal Article
• Authors: Dai, Lan; Sun, Shumin; Li, Ting; Farkoush, Saeid Gholami
• Is Part Of: Energy strategy reviews. Volume 33(2021)
• Publication Date: 2021-01
• Subjects: Participation market model -- Nondispatchable renewable energy -- Probabilistic prediction model -- Day-ahead market -- Intramarket; Energy policy -- Periodicals; 333.7905
• DOI: 10.1016/j.esr.2020.100611
• Languages: English
• ISSNs: 2211-467X

Abstract

Abstract: Participation of nondispatchable renewable energy resources (RER) such as wind turbines (WT) and photovoltaic (PV) systems is one of the main challenges of these clean energy resources. Due to the uncertain nature of these resources, predictions of their amount of power in the day-ahead market is very difficult and associated with error. This paper introduces a novel model for the participation of the mentioned resource in the day-ahead power market based on error and economic loss minimization. An intra-market (IM) model was considered to update the predicted power of these resources before the planned time horizon. Furthermore, a new probabilistic forecasting model was proposed for uncertain parameters in both the day-ahead and intramarket. The mentioned model included an integrated system of renewable energy with another distributed generation, storage device, and demand response to compensate for all resource and load economic losses. By this approach, it can be claimed that, all microgrid participants i.e., load, producer and storage, have more profits in comparison with the power supply individually. To demonstrate the efficiency of the proposed model, we considered a test case that consisted of a wind turbine, PV, fuel cell, storage unit, and demand response, by taking into account the day-ahead, intra and unbalanced market. In this case study, daily, weekly, and monthly analyses were performed. The simulation results revealed the effectiveness and performance of the proposed modelling in comparison with other participation model. Highlights: Intra-market to update of predicted power before planned time horizon. A new probabilistic forecasting model for uncertain parameters. Integrated model of renewable energy with other sources. Applying an optimal model based on k-nearest-neighbors and gradient boosting models. Unbalanced market modelling in the proposed model to achieve real bidding strategy.