Optimal Power Sharing in DC Microgrid Under Load and Generation Uncertainties Based on GWO Algorithm

Al-Tameemi, Zaid H.; Lie, Tek Tjing; Foo, Gilbert; Blaabjerg, Frede

doi:10.1109/isgtasia49270.2021.9715662

Cited by 3 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metaheuristic optimization approaches such as GWO, explained in [49], and HPSO-GWO [46] are investigated in this study as an alternate strategy to realize optimal PI parameters, as illustrated in Figure 3. This figure includes boost converter with its control strategy, as explained in Section 2.2, and bi-directional converters with their control approaches, as illustrated in Section 2.3, to achieve reliable and stable operation of the PV system, battery, and supercapacitor, respectively.…”

Section: Proposed Control Methodsmentioning

confidence: 99%

“…It is important to mention that improved power-sharing across paralleled DC-DC converters, improved MG voltage regulation within the specified level of 5%, reduced battery power losses, minimized settling time, and less overshoot/undershoot under diverse operating situations are significant contributions of the suggested technique. It is worth noting that this study is an extension of [49], with the new proposed approach achieving more consistent performance than [49]. For this study, a standard DC MG comprises several distributed energy resources (DERs), including a PV system and hybrid battery/supercapacitor, which are connected to a DC-bus by using boost and buck-boost converters, respectively, to support DC load in the system, as illustrated in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

et al. 2022

Self Cite

View full text Add to dashboard Cite

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency.

show abstract

Section: Proposed Control Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The key benefits of this technique are the ability to classify the MG control system into several layers to ensure high control dependability and efficient functioning in the grid-connected besides independent modes [32]. The first level of the hierarchy scheme is in charge of the preliminary power-exchange scheduling and current /voltage regulation amongst the participant converters in each MG [33]. The secondary control level, which is a higher degree of control than the primary control, deals with voltage restoration and performance enhancement.…”

Section: Hierarchical Control Strategymentioning

confidence: 99%

Optimal Coordinated Control Strategy of Clustered DC Microgrids under Load-Generation Uncertainties Based on GWO

et al. 2022

Self Cite

View full text Add to dashboard Cite

The coordination of clustered microgrids (MGs) needs to be achieved in a seamless manner to tackle generation-load mismatch among MGs. A hierarchical control strategy based on PI controllers for local and global layers has been proposed in the literature to coordinate DC MGs in a cluster. However, this control strategy may not be able to resist significant load disturbances and unexpected generated powers due to the sporadic nature of the renewable energy resources. These issues are inevitable because both layers are highly dependent on PI controllers who cannot fully overcome the abovementioned obstacles. Therefore, Grey Wolf Optimizer (GWO) is proposed to enhance the performance of the global layer by optimizing its PI controller parameters. The simulation studies were conducted using the well-established MATLAB Simulink, and the results reveal that the optimized global layer performs better than the conventional ones. It is noticed that not only accurate power-sharing and proper voltage regulation within ±1% along with fewer power losses are achieved by adopting the modified consensus algorithm for the clustered DC MGs, but also the settling time and overshoot/undershoot are reduced even with the enormous load and generation changes which indicates the effectiveness of the proposed method used in the paper.

show abstract

“…This approach has the remarkable benefit of dividing the MG control system into multiple layers, each concentrated on certain capabilities, guaranteeing outstanding control reliability and efficient operation in both grid-related and autonomous modes [7]. The primary level of the hierarchical system oversees organizing initial power exchanges and controlling current/voltage among participating converters within each MG [8]. It concentrates on maintaining basic stability while frequently ignoring inter-source communication, resulting in certain limits.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

Al-Tameemi,

Lie,

Zamora

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

show abstract

Optimal Power Sharing in DC Microgrid Under Load and Generation Uncertainties Based on GWO Algorithm

Cited by 3 publications

References 28 publications

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Optimal Coordinated Control Strategy of Clustered DC Microgrids under Load-Generation Uncertainties Based on GWO

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

Contact Info

Product

Resources

About