Real-time and accurate health estimation of lithium-ion batteries is necessary to ensure the safe and continuous operation of critical systems such as microgrid energy storage systems and modern electric and hybrid vehicles. Estimation of a battery’s state-of-health (SOH) requires online identification of battery model parameters such as internal resistance or battery capacity. Although many papers discuss either the estimation of these parameters in real-time or the identification of these parameter changes at different operating conditions, a unified framework for SOH estimation considering battery states and environmental conditions has not been widely accepted in the field. This paper focuses on the development of a novel methodology for online SOH estimation for lithium-ion batteries utilizing a newly proposed nonlinear model for electric circuit model (ECM) resistances with dependencies on temperature and state-of-charge (SOC).

A freshwater shortage can threaten the well-being of people and communities. With the growing global population and limited access to freshwater resources around the world, desalination has become an important technology for meeting the freshwater demand of communities. Desalination, the process of extracting freshwater from saline water, can be done in a centralized water processing plant or distributed at smaller desalination units. This paper investigates the integration of distributed desalination units within a microgrid for the dual optimization of water and energy generation by developing a distributed consensus algorithm to solve the economic dispatch problem for power and water co-generation. The simulation results validate the algorithm by finding the optimal generation capacity of each element in the microgrid while meeting the dynamic demands of power and water.

The increasing demand for energy has necessitated the expansion of Distributed Energy Resources (DERs), highlighting the critical need for an effective and reliable Energy Management System (EMS). This paper introduces a novel approach to solving the economic dispatch problem in asynchronous networked microgrid systems through a hierarchical distributed gossip consensus-based method. The proposed approach combines the strengths of gossip algorithms with hierarchical consensus strategies, specifically designed to enhance efficiency in asynchronous settings. The effectiveness of this method is validated through extensive simulations, which demonstrate faster convergence times and improved system performance compared to standard distributed gossip consensus configurations. Furthermore, the Monte-Carlo simulations showcases the scalability of the proposed approach, illustrating its suitability in real-time applications.

The fast adoption of distributed energy resources (DERs) to meet the ever-growing energy demand has created a need for an efficient and reliable energy management system (EMS). This paper proposes a weighted consensus based method for solving the economic dispatch problem in hierarchical distributed networked microgrid system. The combination of cluster size estimation and weighted consensus enable us to obtain an optimally operating energy management framework for our system. The proposed approach is validated through extensive simulations, demonstrating its effectiveness over existing methods in terms of convergence time and system performance optimization in networked microgrids.