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Fresh Aggregator for Intelligent Energy Regulation in Smart Grid Framework
Md. Shirajum Munir,Do Hyeon Kim,Sun Moo Kang,Young Woo Kim,Choong Seon Hong 한국정보과학회 2021 한국정보과학회 학술발표논문집 Vol.2021 No.6
In this paper, we study the problem of energy market regulation decisions at the aggregator in a smart grid framework, in which the dynamics of the average age of information (AAoI) for regulatory status data of distributed energy resources (DERs) are considered. In particular, we capture the dynamics of the AAoI for each regulatory status of DERs by proposing a fresh aggregator scheme for an intelligent energy management system (IEMS) in a smart grid framework. Then we devise an algorithmic procedure for ensuring the regulatory status freshness as an additional feature to energy regulation aggregator. Particularly, AAoI is used as an extended input to distinct artificial intelligence (AI) models for executing several market regulation services such as load balancing, market clearance, demand-supply forecast, and so on. Finally, our experimental results demonstrate the significance of the proposed fresh aggregator with other baselines that shows the accuracy of regulation services can improve around 15.58% and gain 17.9% loss reduction for AI-based energy regulatory models.
When Edge Computing Meets Microgrid: A Deep Reinforcement Learning Approach
Munir, Md. Shirajum,Abedin, Sarder Fakhrul,Tran, Nguyen H.,Hong, Choong Seon IEEE 2019 IEEE Internet of things journal Vol.6 No.5
<P>The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks’ energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov decision process. Third, we apply a low complexity density-based spatial clustering of applications with noise to solve the first subproblem for each base station distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning. Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.</P>
Smart Agent based Dynamic Data Aggregation for Delay Sensitive Smart City Services
Md. Shirajum Munir(엠디 시라줌 무니르),Sarder Fakhrul Abedin(살더 파크룰 아베딘),Md. Golam Rabiul Alam(엠디 골람 라비울 알람),Do Hyeon Kim(김도현),Choong Seon Hong(홍충선) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.4
Smart city is the vision of modern intelligent technology toward the sustainable development of green technology and social development. Smart services e.g. smart transportation, smart health, smart home, smart grid, smart security, and IoT based applications are the key enablers of smart city, that ensure the quality life and well-being. In a bid to ensure the functionalities of those services, the IoT applications gather data from numerous IoT nodes. In such a case, it becomes more challenging to managing huge network traffic in the centralized network of smart city. Therefore, in this research, we have focused on the resolution of this problem through the introduction of of smart agent-based dynamic data aggregation (DDA) from distributed dense smart city network for city service fulfillment. In this research study, we purposed to model a peer to peer fully distributed system using distributed hash table chord protocol. We also proposed an algorithm for the IoT network and designed smart agent based IoT node searching algorithm for crowd sourcing. Finally, we simulated the result of the proposed smart agent based dynamic data aggregation model in an effort to achieve a higher performance gain for the proposed approach in respect to service fulfillment time and convergence.