Dynamic Energy Management System for a Smart Microgrid

Abstract

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

Adaptive Asymptotic Tracking Control of Uncertain Time-Driven Switched Linear Systems

“…Controls which are uniform with respect to initial conditions take the form of feedback laws, leading to the aforementioned second family of optimization-based charging strategies, which are designed to achieve and maintain the desired operating conditions by comparing them with the actual operating conditions. In the field of smart energy and EV charging, most feedback laws are based on artificial intelligence: the authors in [14] consider a fuzzy logic-based autonomous controller for EV charging, while in [24] an evolutionary learning framework is developed for dynamic energy management of a smart microgrid, and in [25] a neurofuzzy controller is used for frequency regulation in microgrids with fuel cells. Feedback solutions are not free of challenges: the main problem, as compared to open-loop strategies, is that, since the current operating conditions must be compared with the desired operating conditions, one has to define the desired operating conditions.…”

A Review of Multi-agent Based Energy Management Systems

“…The authors have proposed the hierarchical method to manage the energy in [15]. In [16], an intelligent method has been demonstrated to manage energy dynamically in the MG. The proposed method of [16] has been defined to optimal or sub-optimal.…”

“…In [16], an intelligent method has been demonstrated to manage energy dynamically in the MG. The proposed method of [16] has been defined to optimal or sub-optimal. Besides, providing the critical loads continuously is the purpose of [16].…”

“…The proposed method of [16] has been defined to optimal or sub-optimal. Besides, providing the critical loads continuously is the purpose of [16]. In the model of [16], intelligent dynamic energy management system is responsible to send dispatchable control signals of energy.…”

“…Besides, providing the critical loads continuously is the purpose of [16]. In the model of [16], intelligent dynamic energy management system is responsible to send dispatchable control signals of energy. Moreover, forward-looking network is responsible to evaluate the dispatched control signals.…”