Energy Management Strategy of a Reconfigurable Grid-Tied Hybrid AC/DC Microgrid for Commercial Building Applications

• Published in: IEEE transactions on smart grid 2022-05, Vol.13 (3), p.1720-1738
• Main Authors: Thirugnanam, Kannan, El Moursi, Mohamed Shawky, Khadkikar, Vinod, Zeineldin, Hatem H., Hosani, Mohamed Al
• Format: Article
• Language: English
• Subjects: and reliability index; Architecture; Artificial neural network; Artificial neural networks; building electricity cost; Buildings; Commercial buildings; Converters; Costs; Distributed generation; Energy management; Energy resources; Energy sources; energy storages; hybrid ac/dc microgrid; Mathematical models; Microgrids; Multiple objective analysis; Particle swarm optimization; Reconfiguration; Reference signals; Reliability; renewable-based DGs; Stochastic processes; Switches
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2022.3141459

This paper proposes an energy management strategy (EMS) of a reconfigurable grid-tied hybrid ac/dc microgrid (HMG) architecture for commercial building (CB) applications. This HMG architecture consists of a multi-mode configuration (MMC) with renewable-based distributed generators (DGs), energy storages (ESs), and genset. The main advantage of this architecture is its capability to reconfigure its structure based on the predicted building load power (BLP) and renewable-based DGs power. However, minimizing the building electricity cost (BEC) and maximizing the reliability index (RI) of a reconfigurable grid-tied HMG architecture is a challenging task due to the stochastic behaviors of BLP and renewable-based DGs power. In this context, the BLP and renewable-based DGs behaviors are modeled using an artificial neural network approach to predict future time slot values. Then, system level models are developed for HMG energy sources and grid-tied converters/switches. Furthermore, BEC and RI models are developed based on the dynamic pricing of grid/HMG electricity cost and supplied energy. Then, an EMS is proposed for the developed reconfigurable grid-tied HMG architecture, which consists of a two-stage control strategy, i.e., stage I and stage II. Stage I control strategy minimizes BEC and maximizes RI using multi-objective particle swarm optimization (MOPSO) and MMC. Stage II control strategy generates control signals for HMG energy sources, converters, and grid-tied converters/switches based on stage I reference signals. Historical data is used to demonstrate the effectiveness of the proposed EMS for a reconfigurable grid-tied HMG architecture. Through numerical simulation studies, it is shown that the proposed EMS is capable of reducing BEC and increasing RI by concurrently enabling MMC of a reconfigurable grid-tied HMG architecture.