TY - JOUR
T1 - A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning
AU - Abid, Md Shadman
AU - Apon, Hasan Jamil
AU - Hossain, Salman
AU - Ahmed, Ashik
AU - Ahshan, Razzaqul
AU - Lipu, M. S.Hossain
N1 - Funding Information:
The authors assert that they have no financial or personal connections that may have influenced their work. The network setup and load data of Case II and III were provided by Dhaka Power Distribution Company (DPDC), a subsidiary of the Ministry of Power, Energy, and Mineral Resources of Bangladesh. Each feeder's data, identity, and location are kept confidential for security concerns. However, the information can be acquired if requested via the appropriate channels at Bangladesh's Ministry of Power, Energy, Mineral Resources of Bangladesh.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of contemporary research in optimum electric vehicle (EV) charging scheduling challenges. These techniques involve multiple agents that respond to a dynamic simulation environment to strategically integrate EV charging stations (EVCSs) on microgrids by incorporating the constraints posed by stochastic trip durations. In addition, recent research works have demonstrated that planning frameworks based on multi-objective optimization (MOO) techniques are suitable for the efficient functioning of microgrids comprising renewable energy sources (RESs) and battery energy storage systems (BESSs). Even though MADRL techniques have been used to solve the optimum EV charging scheduling challenges and MOO frameworks have been developed to determine the optimal RES-BESS allocation, the potential of merging MADRL and MOO is yet to be explored. Therefore, this research provides an opportunity to determine the effectiveness of combined MOO-MADRL dynamics and their computational efficacy. In this context, this work presents a novel Multi-objective Artificial Vultures Optimization Algorithm based on Multi-agent Deep Deterministic Policy Gradient (MOAVOA-MADDPG) planning framework for allocating RESs, BESSs, and EVCSs on microgrids. The objective function is formulated to optimize the network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. Moreover, the proposed framework incorporates the sporadic nature of RES systems and intends to improve the state of charge (SOC) of the EVs present in the network. The presented approach is validated using practical weather data and EV commuting behavior on the modified IEEE 33 bus network, two practical distribution feeders in Bangladesh, and the Turkish 141 bus network. According to the findings, the MOAVOA-MADDPG framework effectively accommodated the financial, technical, and environmental considerations with improved average SOC of the vehicles. Furthermore, statistical analysis, spacing, convergence, and hyper-volume metrics are employed to compare the suggested MOAVOA-MADDPG framework with five contemporary techniques. The findings indicate that, in every metric considered, the MOAVOA-MADDPG Pareto fronts provide superior solutions.
AB - Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of contemporary research in optimum electric vehicle (EV) charging scheduling challenges. These techniques involve multiple agents that respond to a dynamic simulation environment to strategically integrate EV charging stations (EVCSs) on microgrids by incorporating the constraints posed by stochastic trip durations. In addition, recent research works have demonstrated that planning frameworks based on multi-objective optimization (MOO) techniques are suitable for the efficient functioning of microgrids comprising renewable energy sources (RESs) and battery energy storage systems (BESSs). Even though MADRL techniques have been used to solve the optimum EV charging scheduling challenges and MOO frameworks have been developed to determine the optimal RES-BESS allocation, the potential of merging MADRL and MOO is yet to be explored. Therefore, this research provides an opportunity to determine the effectiveness of combined MOO-MADRL dynamics and their computational efficacy. In this context, this work presents a novel Multi-objective Artificial Vultures Optimization Algorithm based on Multi-agent Deep Deterministic Policy Gradient (MOAVOA-MADDPG) planning framework for allocating RESs, BESSs, and EVCSs on microgrids. The objective function is formulated to optimize the network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. Moreover, the proposed framework incorporates the sporadic nature of RES systems and intends to improve the state of charge (SOC) of the EVs present in the network. The presented approach is validated using practical weather data and EV commuting behavior on the modified IEEE 33 bus network, two practical distribution feeders in Bangladesh, and the Turkish 141 bus network. According to the findings, the MOAVOA-MADDPG framework effectively accommodated the financial, technical, and environmental considerations with improved average SOC of the vehicles. Furthermore, statistical analysis, spacing, convergence, and hyper-volume metrics are employed to compare the suggested MOAVOA-MADDPG framework with five contemporary techniques. The findings indicate that, in every metric considered, the MOAVOA-MADDPG Pareto fronts provide superior solutions.
KW - Deep learning
KW - Electric vehicle
KW - Microgrid
KW - Optimization
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85173491397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173491397&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2023.122029
DO - 10.1016/j.apenergy.2023.122029
M3 - Article
AN - SCOPUS:85173491397
SN - 0306-2619
VL - 353
JO - Applied Energy
JF - Applied Energy
M1 - 122029
ER -