A distributed optimal power management system for microgrids with plug&amp;play capabilities

Hosseinzadeh, Mehdi; Schenato, Luca; Garone, Emanuele

doi:10.1002/adc2.65

Cited by 16 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The smart metering devices enable the operator to apply efficient dispatch with an accurate demandside model [9]. One approach is to manage the power flow through the distributed scheme [10]. This approach usually heavily relies on the communication network [11], and the issues such as packet loss are compensated in [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Management of Networked Microgrids With Real-Time Pricing by Reinforcement Learning

Cui

Jia

Guan

2024

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Coordinating the microgrids (MGs) in the distribution network is a critical task for the distribution system operator (DSO), which could be achieved by setting prices as incentive signals. The high uncertainty of loads and renewable resources motivates the DSO to adopt real-time prices. The MGs require reference price sequences for a long time horizon in advance to make generation plans. However, due to privacy concerns in practice, the MGs may not provide adequate information for the DSO to build a closed-form model. This causes challenges to the implementation of the conventional model-based methods. In this paper, the framework of the coordination system through realtime prices is proposed. In this bi-level framework, the DSO sets real-time reference price sequences as the incentive signals, based on which the MGs make the generation and charging plan. The model-free reinforcement learning (RL) is applied to optimize the pricing policy when the response behavior of the MGs is unknown to the DSO. To deal with the large action space of this problem, the reference policy is incorporated into the RL algorithm for efficiency improvement. The numerical result shows that the minimized cost obtained by the developed model-free RL algorithm is close to the model-based method while the private information is preserved.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Energy Management of Networked Microgrids With Real-Time Pricing by Reinforcement Learning

Cui

Jia

Guan

2024

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…In the secondary control layer, voltage deviation can be eliminated with the distributed controller. In the tertiary control layer with the longest time-scale, the output of the power system can be dispatched in the economical operation [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Distributed Economic Control Method for Energy Storage Unit with Random Time Delay

et al. 2023

View full text Add to dashboard Cite

The operation cost waste in the charge and discharge process cannot be ignored for islanded microgrids with energy storage units. Different from the economic dispatch methods focused on the power pricing and bid coefficient in the tertiary control layer, this paper designed a power management algorithm for the economic operation of energy storage units in the secondary control layer. The strategy is devoted to minimizing the total charge power cost of energy storage units while satisfying the constraint of power balance. Meanwhile, the maximized allowable delay bounds were derived to guarantee the communication network reliability when against random time delays. The system stability was analyzed by the Lyapunov functions. Finally, the numerical simulations on the modified IEEE 30 feeder model verify the superiority of the provided algorithm.

show abstract

“…Refs. [9,10] achieved the plug−and−play function of distributed MT with guaranteed frequency recovery and optimal tide of the island microgrid, respectively. However, neither of them considered the operational economics of the island microgrid.…”

Section: Introductionmentioning

confidence: 99%

Dual−Layer Distributed Optimal Operation Method for Island Microgrid Based on Adaptive Consensus Control and Two−Stage MATD3 Algorithm

Zhang

Zhou

et al. 2023

JMSE

View full text Add to dashboard Cite

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.

show abstract

A distributed optimal power management system for microgrids with plug&play capabilities

Cited by 16 publications

References 43 publications

Energy Management of Networked Microgrids With Real-Time Pricing by Reinforcement Learning

Energy Management of Networked Microgrids With Real-Time Pricing by Reinforcement Learning

Distributed Economic Control Method for Energy Storage Unit with Random Time Delay

Dual−Layer Distributed Optimal Operation Method for Island Microgrid Based on Adaptive Consensus Control and Two−Stage MATD3 Algorithm

Contact Info

Product

Resources

About