A tie-line power smoothing via a novel dynamic real-time pricing mechanism in MMGs. (March 2022)

• Record Type: Journal Article
• Authors: Khavari, Farshad; Badri, Ali; Zangeneh, Ali
• Is Part Of: International journal of electrical power & energy systems. Volume 136(2022)
• Publication Date: 2022-03
• Subjects: Multi-microgrid -- Energy management system -- Peak shaving -- Valley filling -- Real-time pricing; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107671
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: This paper proposes a bi-level model for tie-line smoothing via an aggregator consider to hybrid EMS in multi-microgrids. An optimal energy management strategy based on peak shaving and valley filling is presented that fairly distributes the profit among microgrids. A novel dynamic real-time pricing mechanism based on KKT to optimize multi-microgrid payoffs, considering demand response is introduced. An agent with new duties for optimally peak shaving and valley filling management to control the trading power in the acceptable range is introduced. Abstract: In a Multi-Microgrid (MMG), the microgrids (MGs) are generally scheduled individually. Uncoordinated operations in the MMG may result in tie-line power congestion, especially in peak-load hours. Previous studies have rarely reported how to coordinate MGs operation by implementing the acquired optimal day-ahead scheduling in a competitive environment. To solve this problem, a new entity- called a microgrid aggregator has been presented to manage trading power between the MMG and the main grid. In this paper, a bi-level optimization model is proposed to control the tie-line power. On the lower level, each MG optimizes its energy trading with the main grid individually and sends the result to the upper level. On the upper level, an aggregator checks the total trading power between the MMG and the grid. In case of any violation, the aggregator activates demand response programs through a represented dynamic real-time pricing. Subsequently, a new rescheduling is implemented by the local controller (LC) in each MG to achieve maximum profit. This process is a cooperative method. Thus, the aggregated profit should be fairly distributed among the microgrids. In this paper, sharing profit between MGs is implemented based on the core concept. The performance of the proposed method is assessed by simulating various scenarios in different seasons and days, which demonstrates the effectiveness and robustness of the proposed method.