Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy. (15th May 2022)

• Record Type: Journal Article
• Authors: Alabi, Tobi Michael; Lu, Lin; Yang, Zaiyue
• Is Part Of: Applied energy. Volume 314(2022)
• Publication Date: 2022-05-15
• Subjects: Deep learning -- Virtual power plant -- Integrated energy systems -- Zero-carbon -- Electric vehicles -- Robust-stochastic programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118997
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel deep learning model is developed for day-ahead time series forecasting. A ZCMES with carbon capture, EV flexibility, and clean energy marketer is proposed. The day-ahead optimal scheduling is solved using robust-stochastic programming. 76% self-consumption ratio and 0.85 multi-energy load cover ratio is achieved. The scheduling cost is reduced by 10.74% and 36% with the proposed approach. Abstract: The zero-carbon multi-energy systems (ZCMES) have received attention due to developed countries' promulgated carbon–neutral policy. Thus, This paper proposes a deep learning approach and optimization model for the optimal day-ahead scheduling of ZCMES virtual power plants. Technically, a carbon capture system (CCS) is introduced to harness the carbon emission associated with some equipment, consideration of electric vehicle multi-flexible potentials, followed by a clean energy marketer (CEM) strategy to ensure system reliability sustainably. For day-ahead multivariable time-series prediction, an integrated recurrent unit-bidirectional long-short term memory (GRU-BiLSTM) is developed. This is followed by an autoencoder (AE) for scenario generation and scene reduction using the fast forward reduction algorithm. A robust-stochastic modelling approach is then applied for optimal decision-making. As a case study, the proposed model is verified using accurate historical multi-energy data of a district in Arizona, the United States. The results show that the proposed model outperformed other scenarios by achieving a 76% average self-consumption ratio and 0.85 average multi-energy load cover ratio. Also, the proposed method obtains a 10.74% reduction in day-ahead scheduling cost by considering the CEM trading period and EV flexibility. Further, a 36% reduction is observed using a robust-stochastic approach, which is more robust and economical than deterministic, stochastic, and robust methods. Remarkably, it was observed that the CEM trading period restriction influenced the scheduling behaviour of ZCMES and the charging pattern of EVs. However, the integration of EV flexibility reduces dependency on the external grid and optimize the power consumption of CCS using part of cogeneration electrical output instead of total reliance on the external grid. Thus, the proposed model strengthens carbon–neutral feasibility in urban centres and serves as a reference tool for sustainable energy policymakers.