Energy Management of Grid-Connected Microgrids Using an Optimal Systems Approach

Abstract

Microgrids (MGs) are a growing energy industry segment and represent a paradigm shift from remote central power plants to more localized distributed generation. Controlling MGs represents a challenge mainly due to their complexity and the different properties each asset in the MG has. Various methods have been proposed to address this challenging problem of MG control. Some of these methods are considered the optimal operation of MG assets. Other works are based on a systems approach and address the scalability and simplicity of synthesizing a MG's energy management system (EMS). ε-variables based logical control strategies, which are practical methods to model control strategies in MGs, can make the control structure more scalable. However, this method is not optimal. On the other hand, Switched Model Predictive Control (S-MPC) is an advanced method utilized to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Nevertheless, its implementation is not straightforward. Therefore, to overcome these existing problems, this paper proposes a novel systems approach method called an extended optimal ε-variable method developed by combining the ε-variable based control method with the S-MPC method. This unique method has demonstrated a significant improvement in optimizing an MG's energy management and enhanced the adaptation and scalability of a control structure of the MG. Our results show that the proposed extended optimal ε-variable method: (i) reduces the operational cost of MG by nearly 35%; (ii) reduces the usage of the battery energy storage system by 42%, and (iii) enhances the practicality of photovoltaic (PV) usage by 28%. Our novel extended optimal ε-variable technique also increases the adaptation and scalability of the control structure of the MG significantly by translating the results of S-MPC to the ε-variable method.INDEX TERMS Energy management system, ε-variables, microgrids, renewable energy sources, systems approaches, switched model predictive control.

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

“…In addition, Refs. [22,23] investigated using model predictive control (MPC) to manage hybrid MG systems. In Refs.…”

“…In Refs. [24][25][26], the combination of switched MPC (S-MPC) and ε-variables, called enhanced optimal ε-variables, has been employed for improving the adaptability and scalability of the MG control. In [27], the teaching learning-based optimization (TLBO) method was applied to solve a multi-objective optimization problem, reducing costs and improving the reliability of the MG.…”

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

“…w x (k) and w u (k) are weighting coefficients reflecting the relative significance of x(k) and penalizing relatively large variations in u(k), respectively. Only the initial control input from the optimized trajectory [16] is Implemented. Figure 1a depicts the full S-MPC procedure.…”

“…S-MPC employs multiple models and switches between them, based on the system's current state. S-MPC can provide better performance and robustness than MPC, especially for complex systems with multiple modes or operating conditions [16,17]. Another novel study presented a hybrid MG model that incorporated two switched receding horizon control laws.…”

Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers

“…Microgrids (MGs) are replacing remote central station power plants with localized, distributed generation, especially in cities, towns, and campuses [1]. MGs are durable and can supply competitive services due to their ability to operate in three distinct modes: on the grid, islanded, and autonomous [2].…”